JP7230335B2 - Acquisition method, calculation method, evaluation device, calculation device, evaluation program, calculation program, recording medium, and evaluation system - Google Patents

Description

The present invention relates to an evaluation method, a calculation method, an evaluation device, a calculation device, an evaluation program, a calculation program, a recording medium, an evaluation system, and a terminal device for gastric cancer.

In 2003, 32,846 men and 17,711 women died from gastric cancer in Japan, which is the second highest number among all cancer deaths, the second highest among males and the first among females. It has become.

Treatment of gastric cancer has a good prognosis if the tumor is confined to the mucosa and submucosa. However, the 5-year survival rate is about 90% in patients with mucosa and submucosa without lymph node metastasis.

However, early detection is important for cure of gastric cancer because the survival rate decreases as the disease stage of gastric cancer progresses.

Here, diagnosis of gastric cancer includes pepsinogen test, X-ray test, endoscopy, tumor marker, and the like.

However, pepsinogen testing, radiography, and tumor markers do not provide definitive diagnosis. For example, the pepsinogen test is less invasive, but has a sensitivity of approximately 40-85% and a specificity of 70-85%, depending on reports. However, the pepsinogen test requires a detailed examination rate of 20%, and it is believed that many cases are overlooked. In addition, in the case of X-ray examination (indirect imaging), the sensitivity is generally 70-80%, and the specificity is 85-90%, although it differs from reports. However, there is a possibility of side effects and radiation exposure from drinking barium. At present, there are no tumor markers that are effective in diagnosing the presence of gastric cancer.

On the other hand, although endoscopic examination provides definitive diagnosis, it is a highly invasive examination, and it is not realistic to perform endoscopic examination at the screening stage. Furthermore, invasive diagnosis such as endoscopy imposes a burden on the patient, such as pain, and risks such as bleeding due to the examination.

Therefore, from the viewpoint of physical burden on the patient and cost-effectiveness, it is desirable to narrow down the subjects who are likely to develop gastric cancer and treat those subjects. Specifically, subjects are selected by a method that is less invasive and highly sensitive and specific, and subjects are narrowed down by performing gastroscopy on the selected subjects, and subjects with a definite diagnosis of gastric cancer are selected. Treatment is desirable.

By the way, it is known that the blood amino acid concentration changes due to the onset of cancer. For example, according to Sinovel (Non-Patent Document 1), for example, glutamine is mainly used as an oxidative energy source, arginine is used as a precursor of nitrogen oxides and polyamines, and methionine is used as a cancer cell by activating the ability of cancer cells to take up methionine. There are reports of increased cellular consumption. Vissels et al. (Non-Patent Document 2) and Park (Non-Patent Document 3) have reported that the amino acid composition in the plasma of colorectal cancer patients is different from that of healthy subjects.

In addition, as a prior patent, Patent Document 1 regarding a method for evaluating the state of gastric cancer using amino acid concentration has been published.

WO2009/099005

Cynober, L. ed., Metabolic and therapeutic aspects of amino acids in clinical nutrition. 2nd ed., CRC Press Vissers, Y. LJ., et.al., Plasma arginine concentration are reduced in cancer patients: evidence for arginine deficiency?, The American Journal of Clinical Nutrition, 2005. 81, p1142-1146 Park, K.G., et.al., Arginine metabolism in benign and maglinant disease of breast and colon: evidence for possible inhibition of tumor-infiltrating macrophages., Nutrition, 1991. 7, p.185-188

However, until now, there has been the problem that techniques for diagnosing gastric cancer using blood metabolites as tumor markers have not been developed, have not been put to practical use, or have insufficient accuracy.

The present invention has been made in view of the above, and an evaluation method, calculation method, evaluation device, calculation device, evaluation program, and calculation that can provide highly reliable information that can serve as a reference for understanding the state of gastric cancer. It aims at providing a program, a recording medium, an evaluation system, and a terminal device.

In order to solve the above-described problems and achieve the object, the evaluation method according to the present invention includes 32 types of metabolites (1-Me-His (1-methyl-histidine) (1-methyl-histidine) in blood to be evaluated. histidine), 3-Hydroxykynurenine (3-hydroxykynurenine), 3-Me-His (3-methyl-histidine) (3-methylhistidine), 5-HydroxyTrp (5-hydroxytryptophan), aABA (α-aminobutyric acid ), aAiBA (α-amino-iso-butylic acid) (α-aminoisobutyric acid), ADMA (asymmetric dimethylarginine) (asymmetric dimethylarginine), Aminoadipic acid (α-aminoadipic acid), bABA (β-aminobutylic acid) ( β-aminobutyric acid), bAiBA (β-amino-iso-butylic acid) (β-aminoisobutyric acid), Cadaverine, GABA (γ-aminobutylic acid) (γ-aminobutyric acid), Homoarginine, Homocitrulline (Homocitrulline), Hypotaurine, Hydroxyproline, Kinurenine, L-Cystathionine, N8-Acetylspermidine, Pipecolic acid, Putrescine ), SAH (S-Adenosylhomocysteine), Sarcosine, Serotonin, Spermidine, Spermine, Methylcysteine, Allylcysteine, Propylcysteine (propylcysteine), SDMA (symmetric dimethylarginine), N6-Acetyl-L-Lysine (N6-acetyl-L-lysine) and N-Me-bABA (N -methyl-β-aminob or a formula containing a variable into which said metabolite concentration value is substituted and said metabolite concentration value; The present invention is characterized by including an evaluation step of evaluating the gastric cancer state of the subject using the value of the formula calculated using the formula.

Further, in the evaluation method according to the present invention, in the evaluation step, the concentration value of the metabolite and 20 amino acids (Glu, Asn, His, Thr, Ala, Cit, Arg, Tyr, Val, Met, Lys, Trp, Gly, Pro, Orn, Ile, Leu, Phe, Ser and Gln), or a variable into which the amino acid concentration value is substituted. The method is characterized by using the value of the formula calculated using the formula, the concentration value of the metabolite, and the concentration value of the amino acid.

Here, various amino acids are mainly abbreviated in this specification, and their official names are as follows.
(abbreviation) (formal name)
Ala Alanine
Arg arginine
Asn Asparagine
Citrulline
Gln Glutamine
Glu-glutamic acid
Gly Glycine
His Histidine
Ile Isoleucine
Leu Leucine
Lys Lysine
Met Methionine
Orn Ornithine
Phe-phenylalanine
Pro Proline
Ser Serine
Thr Threonine
Trp Tryptophan
Tyr Tyrosine
Val Valine

Further, the evaluation method according to the present invention is characterized in that the evaluation step is executed by the control section of an information processing apparatus having a control section.

Further, the calculation method according to the present invention includes a concentration value of at least one of the 32 types of metabolites in the blood of an evaluation target, and a variable into which the concentration value of the metabolite is substituted. using a formula for evaluating the state of, a calculating step of calculating the value of the formula.

Further, in the calculation method according to the present invention, in the calculation step, the formula including a variable into which the concentration value of at least one of the 20 amino acids in the blood to be evaluated is substituted, the metabolite and the concentration value of the amino acid.

Moreover, the calculation method according to the present invention is characterized in that the calculation step is executed by the control section of an information processing apparatus having a control section.

Further, the evaluation device according to the present invention is an evaluation device comprising a control unit, wherein the control unit controls the concentration value of at least one of the 32 metabolites in blood to be evaluated, or and an evaluation means for evaluating the state of gastric cancer in the subject by using a formula including variables into which the concentration values of the metabolites are substituted and the values of the formula calculated using the concentration values of the metabolites. It is characterized by providing.

Further, the evaluation apparatus according to the present invention is communicably connected via a network to a terminal device that provides the concentration data related to the concentration value or the value of the formula, and the control unit receives the data receiving means for receiving the concentration data to be evaluated or the value of the formula; and result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device, wherein the evaluating means The method is characterized by using the density value or the value of the formula included in the density data received by the data receiving means.

Further, the calculation device according to the present invention is a calculation device comprising a control unit, wherein the control unit controls the concentration value of at least one of the 32 types of metabolites in blood to be evaluated, and and a calculation means for calculating the value of the formula using a formula for evaluating the state of gastric cancer including variables into which the concentration values of the metabolites are substituted.

Further, an evaluation program according to the present invention is an evaluation program to be executed by an information processing apparatus having a control unit, wherein the 32 types of metabolites in the blood to be evaluated are executed by the control unit. The evaluation using the concentration value of at least one of the metabolites, or the value of the expression calculated using the expression containing the variables into which the concentration values of the metabolites are substituted and the concentration values of the metabolites comprising an assessment step of assessing gastric cancer status in the subject.

Further, a calculation program according to the present invention is a calculation program to be executed in an information processing apparatus including a control unit, and is a calculation program for executing in the control unit, the 32 metabolites in the blood to be evaluated. using a formula for assessing gastric cancer status that includes a concentration value of at least one of the metabolites and a variable into which the concentration value of the metabolite is substituted, and calculating the value of the formula It is characterized by

A recording medium according to the present invention is a computer-readable recording medium recording the evaluation program or the calculation program. Specifically, the recording medium according to the present invention is a non-transitory computer-readable recording medium containing programmed instructions for causing an information processing device to execute the evaluation method or the calculation method. is characterized by

In addition, an evaluation system according to the present invention includes an evaluation device having a control unit; Alternatively, a terminal device that provides a formula including variables into which concentration values of the metabolite are substituted and a value of the formula calculated using the concentration values of the metabolite is communicably connected via a network. wherein the control unit of the terminal device includes data transmission means for transmitting the concentration data of the evaluation target or the value of the formula to the evaluation device; and a result receiving means for receiving an evaluation result regarding the state of gastric cancer for the evaluation object, wherein the control unit of the evaluation device receives the concentration data of the evaluation object transmitted from the terminal device or the formula a data receiving means for receiving a value; and a concentration value of the metabolite contained in the concentration data of the evaluation object received by the data receiving means or the value of the formula, for the evaluation object. It is characterized by comprising evaluation means for evaluating a state, and result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device.

A terminal device according to the present invention is a terminal device comprising a control unit, wherein the control unit comprises result acquisition means for acquiring an evaluation result regarding the state of gastric cancer for an evaluation target, and the evaluation result is Using the concentration value of at least one of the 32 types of metabolites in the blood of the evaluation target, or an expression containing a variable into which the concentration value of the metabolite is substituted and the concentration value of the metabolite is the result of evaluating the state of gastric cancer of the subject using the value of the formula calculated by

Further, the terminal device according to the present invention is communicably connected via a network to an evaluation device for evaluating the state of gastric cancer in the subject to be evaluated, and the control unit controls the 32 types of cancer in the blood of the subject to be evaluated. data transmission means for transmitting concentration data relating to the concentration value of at least one of the metabolites or the value of the formula to the evaluation device, wherein the result acquisition means receives the evaluation transmitted from the evaluation device receiving a result.

ADVANTAGE OF THE INVENTION According to this invention, when knowing the state of gastric cancer, it is effective in the ability to provide highly reliable information which can be used as a reference.

FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. FIG. 3 is a diagram showing an example of the overall configuration of this system. FIG. 4 is a diagram showing another example of the overall configuration of this system. FIG. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system. FIG. 6 is a diagram showing an example of information stored in the density data file 106a. FIG. 7 is a diagram showing an example of information stored in the index state information file 106b. FIG. 8 is a diagram showing an example of information stored in the specified index state information file 106c. FIG. 9 is a diagram showing an example of information stored in the formula file 106d1. FIG. 10 is a diagram showing an example of information stored in the evaluation result file 106e. FIG. 11 is a block diagram showing the configuration of the evaluation unit 102d. FIG. 12 is a block diagram showing an example of the configuration of the client device 200 of this system. FIG. 13 is a block diagram showing an example of the configuration of the database device 400 of this system.

Below, an embodiment (first embodiment) of the evaluation method and calculation method according to the present invention, an evaluation device, a calculation device, an evaluation method, a calculation method, an evaluation program, a calculation program, a recording medium, an evaluation system, and An embodiment (second embodiment) of a terminal device will be described in detail based on the drawings. In addition, this invention is not limited by these embodiments.

[First embodiment]
[1-1. Overview of the first embodiment]
Here, an overview of the first embodiment will be described with reference to FIG. FIG. 1 is a principle configuration diagram showing the basic principle of the first embodiment.

First, at least one of the substances ("the above 32 metabolites and the above 20 amino acids") in blood (including plasma, serum, etc.) collected from an evaluation subject (for example, an individual such as an animal or human) Concentration data on the concentration value of (blood substances including

In addition, in step S11, for example, concentration data regarding the blood substance measured by a company or the like that performs concentration value measurement may be acquired. Further, the concentration value of the blood substance is measured by measuring the concentration value of the blood substance from the blood collected from the evaluation subject, for example, by the following measurement method (A), (B), or (C). Concentration data may be obtained. Here, the unit of the concentration value of the blood substance may be, for example, molar concentration, weight concentration, or enzyme activity, and may be obtained by adding, subtracting, multiplying, or dividing these concentrations by an arbitrary constant.
(A) Plasma is separated from the blood by centrifuging the collected blood sample. All plasma samples are stored frozen at −80° C. until concentration values are measured. At the time of concentration value measurement, acetonitrile was added for deproteinization, followed by pre-column derivatization using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), followed by liquid chromatography-mass spectrometry. The concentration values are analyzed by LC/MS (see WO2003/069328, WO2005/116629). Alternatively, deproteinized plasma is analyzed for concentration value (peak area value) by LC/MS after removal of phospholipids by solid phase extraction.
(B) Separating the plasma from the blood by centrifuging the collected blood sample. All plasma samples are stored frozen at −80° C. until concentration values are measured. When measuring the concentration value, sulfosalicylic acid is added to deproteinize, and then the concentration value is analyzed with an amino acid analyzer based on the principle of the post-column derivatization method using a ninhydrin reagent.
(C) Blood cell separation is performed on the collected blood sample using membrane or MEMS technology or the principle of centrifugation to separate plasma or serum from the blood. Plasma or serum samples that are not subjected to concentration readings immediately after obtaining plasma or serum are stored frozen at -80°C until concentration readings are taken. At the time of concentration value measurement, molecules such as enzymes and aptamers that react with or bind to target blood substances are used, and concentration values are analyzed by quantifying substances and spectroscopic values that increase or decrease due to substrate recognition.

Next, using the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids contained in the concentration data acquired in step S11, the gastric cancer state of the evaluation subject is evaluated ( step S12). Data such as missing values and outliers may be removed from the density data acquired in step S11 before executing step S12. Here, evaluating the state means inspecting the current state, for example.

As described above, according to the first embodiment, in step S11, concentration data to be evaluated is acquired, and in step S12, the 32 types of metabolites and the above-mentioned Using the concentration value of at least one of the 20 types of amino acids, the gastric cancer state of the evaluation subject is evaluated (in short, information for evaluating the gastric cancer state of the evaluation subject or information for knowing the gastric cancer state of the evaluation subject to obtain reliable information that can be used as a reference). As a result, it is possible to provide highly reliable information that can serve as a reference for information for evaluating the state of gastric cancer in an evaluation subject or to know the state of gastric cancer in an evaluation subject.

Also, it may be determined that the concentration value of at least one of the 32 metabolites and the 20 amino acids reflects the gastric cancer status for the subject, and the concentration value may be determined, for example, by: It may be determined that the value after conversion reflects the state of gastric cancer for the evaluation subject. In other words, the concentration value or the converted value itself may be treated as the evaluation result regarding the gastric cancer state of the evaluation target.
The range that the density value can take is a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or the range from -10.0 to 10.0, etc.), for example, adding, subtracting, multiplying, or dividing the density value by an arbitrary value, or converting the density value by a predetermined conversion method (e.g., exponential conversion, logarithmic conversion, Angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, power transformation, etc.), or by performing a combination of these calculations on the density value. You may For example, the value of the exponential function with the concentration value as the index and the Napier number as the base (specifically, the state of gastric cancer is a predetermined state (for example, a state in which gastric cancer is likely to be present exceeding a reference value) , etc.), the natural logarithm ln (value of p/(1−p) when the natural logarithm ln (p/(1−p)) is equal to the concentration value) may be further calculated. Further, a value (specifically, the value of probability p) obtained by dividing the calculated value of the exponential function by the sum of 1 and the value may be further calculated.
Further, the density value may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the density values may be converted so that the converted value is 5.0 when the specificity is 80% and the converted value is 8.0 when the specificity is 95%.
Alternatively, for each metabolite and each amino acid, the concentration distribution may be converted to a normal distribution, and then converted to a deviation value so as to have an average of 50 and a standard deviation of 10.
Note that these conversions may be performed by gender or by age.
Note that the density value in this specification may be the density value itself, or may be a value after converting the density value.

Further, the position information about the position of the predetermined mark on the predetermined ruler visibly shown on a display device such as a monitor or on a physical medium such as paper is at least one of the 32 types of metabolites and the 20 types of amino acids. It may be generated using one density value or, if the density value is transformed, the transformed value, and it may be determined that the generated position information reflects the gastric cancer status of the evaluation subject. The predetermined ruler is for evaluating the state of gastric cancer. At least a scale corresponding to the upper and lower limits of "a portion" is shown. Further, the predetermined mark corresponds to the density value or the value after conversion, and is, for example, a circle mark or a star mark.

In addition, the concentration value of at least one of the 32 metabolites and the 20 amino acids is a predetermined value (mean ± 1SD, 2SD, 3SD, N quantile, N percentile, or clinically significant The gastric cancer status of the evaluation subject may be evaluated when it is lower than or equal to or lower than a predetermined value, or equal to or higher than or equal to a predetermined value. At that time, instead of the concentration value itself, the concentration deviation value (value obtained by normalizing the concentration distribution by gender for each metabolite and each amino acid, and then making the deviation value so that the average is 50 and the standard deviation is 10). may be used. For example, when the concentration deviation value is less than the mean value −2 SD (when the density deviation value is <30) or when the concentration deviation value is higher than the mean value +2 SD (when the density deviation value is >70), the evaluation target is determined to be gastric cancer. status may be evaluated.

Also, a variable into which the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids and the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids are substituted. The gastric cancer state of the evaluation subject may be evaluated by calculating the value of the expression using an expression including .

In addition, it may be determined that the calculated value of the formula reflects the state of gastric cancer for the evaluation subject, and the value of the formula is converted by, for example, the following methods, and the value after conversion is It may be determined to reflect the gastric cancer status for the subject being evaluated. In other words, the value of the formula or the converted value itself may be treated as the evaluation result regarding the gastric cancer state of the evaluation target.
The possible range of the value of the expression is a predetermined range (for example, the range from 0.0 to 1.0, the range from 0.0 to 10.0, the range from 0.0 to 100.0, or -10.0 to 10.0, etc.), for example, adding, subtracting, multiplying, or dividing the value of the expression by an arbitrary value, or converting the value of the expression to a predetermined conversion method (e.g., exponential conversion, logarithmic transformation, angle transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, power transformation, etc.), or by combining these calculations on the value of the expression, You may transform the value of an expression. For example, the value of the exponential function with the value of the formula as the index and the Napier number as the base (specifically, the state of gastric cancer exceeds a predetermined state (e.g., the possibility of suffering from gastric cancer exceeding the reference value is high State, etc.) further calculate the value of p / (1-p) when the natural logarithm ln (p / (1-p)) is equal to the value of the expression) when defining the probability p Alternatively, a value (specifically, the value of probability p) obtained by dividing the calculated value of the exponential function by the sum of 1 and the value may be further calculated.
Alternatively, the value of the expression may be converted so that the value after conversion under a specific condition becomes a specific value. For example, the value of the expression may be transformed so that the value after transformation is 5.0 when the specificity is 80% and the value after transformation is 8.0 when the specificity is 95%.
Alternatively, deviation values may be obtained so that the average is 50 and the standard deviation is 10.
Note that these conversions may be performed by gender or by age.
In addition, the value of the formula in this specification may be the value of the formula itself, or may be the value after converting the value of the formula.

In addition, the position information regarding the position of a predetermined mark on a predetermined ruler visibly displayed on a display device such as a monitor or a physical medium such as paper is the value of the formula or, if the value of the formula is converted, the conversion A later value may be used to generate and determine that the generated location information reflects the gastric cancer status for the subject. The predetermined ruler is for evaluating the state of gastric cancer. At least a scale corresponding to the upper and lower limits of "a portion of" is shown. Further, the predetermined mark corresponds to the value of the formula or the value after conversion, and is, for example, a circle or an asterisk.

In addition, the degree of possibility that the subject of evaluation is suffering from gastric cancer may be qualitatively evaluated. Specifically, "the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids and one or more preset threshold values" or "the 32 types of metabolites and the 20 types of a concentration value of at least one of the amino acids, an expression containing a variable into which the concentration value of at least one of the 32 metabolites and the 20 amino acids is substituted, and one or more preset Using "threshold", the subject may be classified into any one of a plurality of categories defined by at least considering the degree of possibility of suffering from gastric cancer. In addition, multiple categories include categories for classifying subjects who are likely to have gastric cancer (for example, subjects who are considered to have gastric cancer), categories for including subjects with a low degree of likelihood of having stomach cancer (e.g., subjects considered not to have stomach cancer), and for including subjects with an intermediate likelihood of having stomach cancer. A division may be included. In addition, multiple categories include a category for belonging to subjects with a high possibility of suffering from stomach cancer and a category for belonging to subjects with a low possibility of suffering from stomach cancer ( For example, a category for belonging subjects that are likely to be healthy (eg, subjects considered to be healthy) may be included. Alternatively, the density value or the value of the formula may be converted by a predetermined method, and the converted value may be used to classify the evaluation object into one of a plurality of categories.

The formula used for evaluation is not particularly limited in form, but may be of the form shown below, for example.
・Linear models such as multiple regression, linear discriminant, principal component analysis, and canonical discriminant analysis based on the least squares method ・Generalized linear models such as logistic regression and Cox regression based on the maximum likelihood method ・In addition to generalized linear models Generalized linear mixed model considering random effects such as inter-individual differences and inter-institutional differences ・ Formulas created by cluster analysis such as K-means method and hierarchical cluster analysis ・ MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as hierarchical Bayesian formulas Formulas created by class classification such as support vector machines and decision trees Formulas created by methods that do not belong to the above categories such as fractional formulas Sum of formulas in different formats An expression such as

In addition, the formula used in the evaluation is, for example, the method described in International Publication No. 2004/052191 by the applicant or the method described in International Publication No. 2006/098192 by the applicant. method can be created. The formulas obtained by these methods are suitable for evaluating the state of gastric cancer, regardless of the units of the concentration values of metabolites and/or amino acids in the concentration data as input data. can be used.

Here, in multiple regression equations, multiple logistic regression equations, canonical discriminant functions, etc., coefficients and constant terms are added to each variable, and these coefficients and constant terms are preferably real numbers, more preferably is any value within the range of the 99% confidence interval of the coefficient and constant term obtained for performing the various classifications from the data, more preferably, the value obtained for performing the various classifications from the data. Any value that falls within the range of the 95% confidence interval of the coefficient and the constant term obtained is acceptable. Also, the value of each coefficient and its confidence interval may be obtained by multiplying it by a real number, and the value of the constant term and its confidence interval may be obtained by adding, subtracting, multiplying, or dividing it by an arbitrary real constant. When using logistic regression, linear discriminant, multiple regression, etc. in evaluation, linear transformation (addition of constant, constant multiplication) and monotonically increasing (decreasing) transformation (e.g. logit transformation) change the evaluation performance. However, since it is equivalent to before conversion, the one after these conversions may be used.

Further, a fractional expression means that the numerator of the fractional expression is represented by the sum of variables A, B, C, . is represented by Fractional expressions also include the sum of fractional expressions α, β, γ, . Fractional expressions also include divided fractional expressions. It should be noted that the variables used in the numerator and denominator may each have appropriate coefficients. Also, the variables used for the numerator and denominator may overlap. Also, each fractional expression may have an appropriate coefficient. Also, the coefficient values and constant term values of each variable may be real numbers. A certain fractional expression and a fractional expression in which the numerator variable and the denominator variable are exchanged generally reverse the positive and negative signs of the correlation with the objective variable, but the correlation is maintained. Since the evaluation performance can also be regarded as equivalent, fractional expressions include those in which the numerator and denominator variables are interchanged.

Then, when evaluating the state of gastric cancer, in addition to the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids, values related to other biological information (e.g., values listed below) are used. It may be used further. In addition, in the formula used for evaluation, in addition to the variables into which the concentration value of at least one of the 32 metabolites and the 20 amino acids is substituted, values related to other biological information (for example, It may also include one or more variables that are assigned values such as those listed.
1. Concentration values of blood metabolites other than amino acids (amino acid metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc.2. Albumin, total protein, triglyceride (neutral fat), HbA1c, glycated albumin, insulin resistance index, total cholesterol, LDL cholesterol, HDL cholesterol, amylase, total bilirubin, creatinine, estimated glomerular filtration rate (eGFR), uric acid, GOT (AST), GPT (ALT), GGTP (γ-GTP), glucose (blood sugar level), CRP (C-reactive protein), red blood cells, hemoglobin, hematocrit, MCV, MCH, MCHC, white blood cells, blood tests such as platelet count Value 3. Values obtained from image information such as ultrasonic echoes, X-rays, CT, MRI, and endoscopic images;4. Age, height, weight, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, diet information, drinking information, exercise information, stress information, sleep information, family history information, disease history information (diabetes, etc.) ) and other biomarker values

[Second embodiment]
[2-1. Overview of Second Embodiment]
Here, an overview of the second embodiment will be described with reference to FIG. FIG. 2 is a principle configuration diagram showing the basic principle of the second embodiment. In addition, in the description of the second embodiment, the description that overlaps with the above-described first embodiment may be omitted. In particular, here, when evaluating the state of gastric cancer, the case of using the formula value or its converted value is described as an example. , or a value after conversion thereof (for example, a density deviation value) may be used.

The control unit is included in previously obtained concentration data of an evaluation target (for example, an individual such as an animal or a human) regarding concentration values of at least one of the 32 types of metabolites and the 20 types of amino acids in blood. , a variable into which the concentration value of at least one of the 32 metabolites and the 20 amino acids and the concentration value of at least one of the 32 metabolites and the 20 amino acids are substituted The gastric cancer state of the evaluation target is evaluated by calculating the value of the formula using the formula stored in the storage unit in advance (step S21). This makes it possible to provide highly reliable information that can serve as a reference for understanding the state of gastric cancer.

The equation used in step S21 may be created based on the equation creating process (steps 1 to 4) described below. Here, an outline of the formula creating process will be described. It should be noted that the processing described here is merely an example, and the method of creating an expression is not limited to this.

First, the control unit controls index state information (even data with missing values, outliers, etc. has been removed in advance) stored in the storage unit in advance, including concentration data and index data relating to indices representing the state of gastric cancer. good) based on a predetermined formula creation method (for example, y = a1x1 + a2x2 + ... + anxn, y: index data, xi: concentration data, ai: constant, i = 1, 2, ..., n ) is created (step 1).

In step 1, a plurality of different formula creation methods (principal component analysis, discriminant analysis, support vector machine, multiple regression analysis, Cox regression analysis, logistic regression analysis, k-means method, cluster analysis, decision (including those related to multivariate analysis such as trees)) may be used together to create a plurality of candidate formulas. Specifically, multiple different algorithms are used for index status information, which is multivariate data composed of concentration data and index data obtained by analyzing blood obtained from a large number of healthy groups and gastric cancer groups. A plurality of groups of candidate formulas may be created concurrently using For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to generate two different candidate formulas. Alternatively, a candidate formula may be generated by converting the index state information using a candidate formula created by performing principal component analysis and performing discriminant analysis on the converted index state information. As a result, it is possible to finally create an optimal expression for evaluation.

Here, the candidate formula created using principal component analysis is a linear formula including each variable that maximizes the variance of all concentration data. Candidate formulas created using discriminant analysis are high-order formulas (including exponentials and logarithms) that contain variables that minimize the ratio of the sum of variances within each group to the variance of all concentration data. be. Also, the candidate formulas created using the support vector machine are high-order formulas (including kernel functions) containing each variable that maximizes the boundary between groups. A candidate formula created using multiple regression analysis is a high-order formula including each variable that minimizes the sum of distances from all concentration data. The candidate formula created using Cox regression analysis is a linear model including logarithmic hazard ratios, and is a linear formula including each variable and its coefficient that maximizes the likelihood of the model. A candidate formula created using logistic regression analysis is a linear model representing the logarithmic odds of probability, and is a linear formula containing each variable that maximizes the likelihood of that probability. In the k-means method, k neighbors of each concentration data are searched, and the group to which the input concentration data belongs is defined as the group to which the largest number of neighboring points belong. This is a method of selecting variables that best match the group defined as . Cluster analysis is a method of clustering (grouping) points that are closest to each other in all density data. Also, the decision tree is a method of assigning an order to variables and predicting a group of concentration data from possible patterns of variables with a higher order.

Returning to the description of the formula creation process, the control unit verifies (cross-verifies) the candidate formulas created in step 1 based on a predetermined verification method (step 2). Verification of the candidate formula is performed for each candidate formula created in step 1. In step 2, based on at least one of the bootstrap method, holdout method, N-fold method, leave-one-out method, etc., the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC ( area under the curve of the receiver characteristic curve). As a result, it is possible to create candidate formulas with high predictability or robustness that take into account index state information and evaluation conditions.

Here, the discrimination rate is the evaluation method according to the present embodiment, in which an evaluation object whose true state is negative (for example, an evaluation object not suffering from gastric cancer) is correctly evaluated as negative, and the true state is It is the proportion of positive evaluation subjects (for example, evaluation subjects suffering from gastric cancer) that are correctly evaluated as positive. In addition, the sensitivity is the ratio of correct positive evaluations of evaluation targets whose true state is positive in the evaluation method according to the present embodiment. Further, the specificity is the proportion of the evaluation targets whose true status is negative are correctly evaluated as negative in the evaluation method according to the present embodiment. The Akaike Information Criterion is a criterion that expresses how well observed data matches a statistical model in the case of regression analysis. The number of free parameters of the model) is judged to be the best model. In addition, ROC_AUC is defined as the area under the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1-specificity, sensitivity) on two-dimensional coordinates. is 1 for perfect discrimination, and the closer this value is to 1, the higher the discriminability. The predictability is the average of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate formulas. Robustness is variance of discrimination rate, sensitivity, and specificity obtained by repeating verification of candidate formulas.

Returning to the description of the formula creation process, the control unit selects a combination of concentration data included in the index state information used when creating the candidate formula by selecting variables of the candidate formula based on a predetermined variable selection method. (Step 3). In step 3, variables may be selected for each candidate formula created in step 1. This allows appropriate selection of the variables of the candidate formula. Then, using the index state information including the density data selected in step 3, step 1 is executed again. Further, in step 3, the variables of the candidate formula may be selected from the verification result in step 2 based on at least one of the stepwise method, best path method, neighborhood search method, and genetic algorithm. Note that the best path method is a method of selecting variables by sequentially reducing the variables included in the candidate formula one by one and optimizing the evaluation index given by the candidate formula.

Returning to the description of the formula creation process, the control unit repeats the above-described steps 1, 2, and 3, and selects candidate formulas to be used for evaluation from among a plurality of candidate formulas based on the verification results accumulated thereby. By selecting a formula, a formula to be used for evaluation is created (step 4). Selection of candidate formulas includes, for example, selecting the optimum candidate formula from among candidate formulas prepared by the same formula preparation method, and selecting the optimum formula from all candidate formulas.

As described above, in the formula creation process, based on the index status information, the processes related to candidate formula creation, candidate formula verification, and candidate formula variable selection are systematized in a series of flows. By doing so, it is possible to develop an optimal formula for the assessment of gastric cancer. In other words, the formulation process uses concentrations of blood substances containing at least one of the 32 metabolites and the 20 amino acids for multivariate statistical analysis to determine an optimal and robust set of variables. We combine variable selection and cross-validation to select formulas with high evaluation performance.

[2-2. Configuration of Second Embodiment]
Here, the configuration of the evaluation system according to the second embodiment (hereinafter sometimes referred to as this system) will be described with reference to FIGS. 3 to 14. FIG. Note that this system is merely an example, and the present invention is not limited to this. In particular, here, when evaluating the state of gastric cancer, the case of using the formula value or its converted value is described as an example. , or a value after conversion thereof (for example, a density deviation value) may be used.

First, the overall configuration of this system will be described with reference to FIGS. 3 and 4. FIG. FIG. 3 is a diagram showing an example of the overall configuration of this system. FIG. 4 is a diagram showing another example of the overall configuration of this system. This system, as shown in FIG. A client device 200 (corresponding to the terminal device of the present invention) that provides individual concentration data regarding concentration values of substances in the blood is communicably connected via a network 300 .

In this system, the client device 200 that provides data used for evaluation and the client device 200 that provides evaluation results may be separate devices. As shown in FIG. 4, in addition to the evaluation device 100 and the client device 200, the system includes a database device that stores index state information used when creating formulas in the evaluation device 100, formulas used for evaluation, and the like. 400 may be configured to be communicatively connected via network 300 . As a result, through the network 300, the evaluation device 100 provides the client device 200 or the database device 400, or the client device 200 or the database device 400 provides the evaluation device 100 with information that is useful for understanding the state of stomach cancer. be done. Here, the information that serves as a reference for knowing the state of gastric cancer is, for example, information on values measured for specific items related to the state of gastric cancer in organisms including humans. Information that serves as a reference for knowing the state of gastric cancer is generated by the evaluation device 100, the client device 200, or other devices (for example, various measuring devices), and is mainly stored in the database device 400.

Next, the configuration of the evaluation device 100 of this system will be described with reference to FIGS. 5 to 11. FIG. FIG. 5 is a block diagram showing an example of the configuration of the evaluation device 100 of this system, and conceptually shows only those portions of the configuration that are related to the present invention.

The evaluation device 100 includes a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in general, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. It is composed of a communication interface unit 104 communicatively connected to the network 300, a storage unit 106 storing various databases, tables, files, etc., and an input/output interface unit 108 connected to the input device 112 and the output device 114. , and these units are communicably connected via an arbitrary communication path. Here, the evaluation device 100 may be configured in the same housing as various analysis devices (for example, an amino acid analysis device, etc.). For example, the concentration value of a predetermined blood substance containing at least one of the 32 types of metabolites and the 20 types of amino acids in blood is calculated (measured), and the calculated value is output (printed or displayed on a monitor). etc.), further comprising an evaluation unit 102d described later, and outputting the results obtained by the evaluation unit 102d using the configuration. Anything is fine.

The communication interface unit 104 mediates communication between the evaluation device 100 and the network 300 (or a communication device such as a router). That is, the communication interface unit 104 has a function of communicating data with another terminal via a communication line.

The input/output interface unit 108 connects to the input device 112 and the output device 114 . Here, the output device 114 may be a monitor (including a home television), a speaker, or a printer (hereinafter, the output device 114 may be referred to as the monitor 114). The input device 112 can be a keyboard, a mouse, a microphone, or a monitor that realizes a pointing device function in cooperation with a mouse.

The storage unit 106 is storage means, and may be a memory device such as a RAM (Random Access Memory) or a ROM (Read Only Memory), a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like. The storage unit 106 stores a computer program for giving commands to the CPU to perform various processes in cooperation with an OS (Operating System). As illustrated, the storage unit 106 stores a concentration data file 106a, an index state information file 106b, a specified index state information file 106c, a formula related information database 106d, and an evaluation result file 106e.

The concentration data file 106a stores concentration data on concentration values of blood substances including at least one of the 32 kinds of metabolites and the 20 kinds of amino acids in blood. FIG. 6 is a diagram showing an example of information stored in the density data file 106a. As shown in FIG. 6, the information stored in the concentration data file 106a is configured by correlating an individual number for uniquely identifying an individual (sample) to be evaluated with concentration data. Here, in FIG. 6, the density data is treated as a numerical value, ie, a continuous scale, but the density data may be a nominal scale or an ordinal scale. In the case of a nominal scale or an ordinal scale, analysis may be performed by assigning an arbitrary numerical value to each state. The concentration data may also be combined with other biometric information values (see above).

Returning to FIG. 5, the index state information file 106b stores index state information used when formulating formulas. FIG. 7 is a diagram showing an example of information stored in the index state information file 106b. The information stored in the index state information file 106b includes, as shown in FIG. 7, an individual number, index data (T) relating to indices representing the state of gastric cancer (index T1, index T2, index T3, . . . ), and concentration data are associated with each other. Here, in FIG. 7, index data and concentration data are treated as numerical values (that is, continuous scale), but index data and concentration data may be on a nominal scale or an ordinal scale. In the case of a nominal scale or an ordinal scale, analysis may be performed by assigning an arbitrary numerical value to each state. In addition, the index data is a known index that serves as a marker for the state of gastric cancer, and numerical data may be used.

Returning to FIG. 5, the designated index state information file 106c stores the index state information designated by the later-described designation section 102b. FIG. 8 is a diagram showing an example of information stored in the specified index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by mutually associating individual numbers, designated index data, and designated concentration data.

Returning to FIG. 5, the formula-related information database 106d is composed of formula files 106d1 that store formulas created by the formula creating unit 102c, which will be described later. The formula file 106d1 stores formulas used for evaluation. FIG. 9 is a diagram showing an example of information stored in the formula file 106d1. The information stored in the formula file 106d1 includes, as shown in FIG. 9, ranks, formulas (in FIG. 9, Fp (Homo, . . . ), Fp (Homo, GABA, Asn), Fk (Homo, GABA, Asn, . The character "Homo" means homoarginine.

Returning to FIG. 5, the evaluation result file 106e stores evaluation results obtained by the evaluation unit 102d, which will be described later. FIG. 10 is a diagram showing an example of information stored in the evaluation result file 106d. The information stored in the evaluation result file 106d includes an individual number for uniquely identifying an individual (sample) to be evaluated, concentration data of the individual obtained in advance, and an evaluation result regarding the state of gastric cancer (for example, as will be described later). The value of the formula calculated by the calculation unit 102d1, the value after converting the value of the formula by the conversion unit 102d2, which will be described later, the position information generated by the generation unit 102d3, which will be described later, or the classification result obtained by the classification unit 102d4, which will be described later. , etc.) and are associated with each other.

Returning to FIG. 5, the control unit 102 has an internal memory for storing a control program such as an OS, a program defining various processing procedures, required data, and the like, and performs various information processing based on these programs. to run. As illustrated, the control unit 102 is roughly divided into an acquisition unit 102a, a designation unit 102b, an expression creation unit 102c, an evaluation unit 102d, a result output unit 102e, and a transmission unit 102f. The control unit 102 removes data with missing values, removes data with many outliers, and removes data with missing values from the index state information sent from the database device 400 and the concentration data sent from the client device 200. We also perform data processing such as removing variables with a large number of

The acquisition unit 102a acquires information (specifically, concentration data, index state information, formula, etc.). For example, the acquisition unit 102a receives information (specifically, concentration data, index state information, formula, etc.) transmitted from the client device 200 or the database device 400 via the network 300, thereby acquiring information. may be performed. Note that the acquisition unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 to which the evaluation result is transmitted. Further, for example, when the evaluation device 100 includes a mechanism (including hardware and software) for reading information recorded on the recording medium, the acquisition unit 102a reads the information recorded on the recording medium (specifically, Specifically, the information may be obtained by reading concentration data, index state information, formulas, etc.) via the mechanism. The designating unit 102b designates index data and concentration data to be used in creating the formula.

The formula creating unit 102c creates a formula based on the index state information acquired by the acquiring unit 102a and the indicator state information specified by the specifying unit 102b. Note that, when formulas are stored in advance in a predetermined storage area of the storage unit 106, the formula creation unit 102c may create a formula by selecting a desired formula from the storage unit 106. FIG. Alternatively, the formula creation unit 102c may create a formula by selecting and downloading a desired formula from another computer device (for example, the database device 400) in which formulas are stored in advance.

The evaluation unit 102d is included in the formula obtained in advance (for example, the formula created by the formula creation unit 102c or the formula acquired by the acquisition unit 102a) and the individual concentration data acquired by the acquisition unit 102a. , the concentration values of at least one of the 32 metabolites and the 20 amino acids are used to calculate the value of the formula to assess gastric cancer status for the individual. In addition, the evaluation unit 102d uses the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids or a value after conversion of the concentration value (for example, a concentration deviation value) to determine whether the individual has gastric cancer. status may be evaluated.

Here, the configuration of the evaluation unit 102d will be described with reference to FIG. FIG. 11 is a block diagram showing the structure of the evaluation unit 102d, and conceptually shows only the parts of the structure related to the present invention. The evaluation unit 102d further includes a calculation unit 102d1, a conversion unit 102d2, a generation unit 102d3, and a classification unit 102d4.

The calculation unit 102d1 substitutes the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids, and the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids. Calculate the value of the expression using an expression that includes at least the variables that are Note that the evaluation unit 102d may store the value of the formula calculated by the calculation unit 102d1 as the evaluation result in a predetermined storage area of the evaluation result file 106e.

The conversion unit 102d2 converts the value of the formula calculated by the calculation unit 102d1, for example, using the conversion method described above. Note that the evaluation unit 102d may store the value converted by the conversion unit 102d2 as the evaluation result in a predetermined storage area of the evaluation result file 106e. Further, the conversion unit 102d2 may convert the concentration value of at least one of the 32 types of metabolites and the 20 types of amino acids included in the concentration data, for example, using the conversion method described above.

The generation unit 102d3 converts position information about the position of a predetermined mark on a predetermined ruler visibly displayed on a display device such as a monitor or a physical medium such as paper into the value of the formula calculated by the calculation unit 102d1 or the conversion unit 102d2. (It may be a density value or a value after conversion of the density value) after conversion by . The evaluation unit 102d may store the position information generated by the generation unit 102d3 as the evaluation result in a predetermined storage area of the evaluation result file 106e.

The classification unit 102d4 uses the value of the formula calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (concentration value or the converted value of the concentration value may be used) to classify the individual as having gastric cancer. classified into one of a plurality of categories defined by at least considering the degree of possibility of

The result output unit 102e outputs the processing results (including the evaluation results obtained by the evaluation unit 102d) of each processing unit of the control unit 102 and the like to the output device 114. FIG.

The transmission unit 102f transmits the evaluation result to the client device 200 which is the transmission source of the concentration data of the individual, and transmits the equation created by the evaluation device 100 and the evaluation result to the database device 400. FIG. Note that the transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that is the transmission source of the data used for evaluation.

Next, the configuration of the client device 200 of this system will be described with reference to FIG. FIG. 12 is a block diagram showing an example of the configuration of the client device 200 of this system, and conceptually shows only those portions of the configuration that are related to the present invention.

The client device 200 comprises a control unit 210, a ROM 220, a HD (Hard Disk) 230, a RAM 240, an input device 250, an output device 260, an input/output IF 270, and a communication IF 280. are connected so that they can communicate with each other. The client device 200 is an information processing device (for example, a known personal computer, workstation, home game device, Internet TV, PHS (Personal Handyphone System)) to which peripheral devices such as a printer, monitor, and image scanner are connected as necessary. terminals, portable terminals, mobile communication terminals, information processing terminals such as PDA (Personal Digital Assistant), etc.).

The input device 250 is a keyboard, mouse, microphone, or the like. A monitor 261, which will be described later, also cooperates with the mouse to realize the pointing device function. Output device 260 is output means for outputting information received via communication IF 280 , and includes monitor (including home television) 261 and printer 262 . In addition, the output device 260 may be provided with a speaker or the like. The input/output IF 270 connects to the input device 250 and the output device 260 .

Communication IF 280 communicably connects client device 200 and network 300 (or a communication device such as a router). In other words, the client device 200 is connected to the network 300 via a communication device such as a modem, TA (Terminal Adapter), or router, and a telephone line, or via a dedicated line. Thereby, the client device 200 can access the evaluation device 100 according to a predetermined communication protocol.

The control unit 210 has a receiving unit 211 and a transmitting unit 212 . The receiving unit 211 receives various information such as evaluation results transmitted from the evaluation device 100 via the communication IF 280 . The transmission unit 212 transmits various information such as individual concentration data to the evaluation device 100 via the communication IF 280 .

The control unit 210 may realize all or any part of the processing performed by the control unit by a CPU and a program interpreted and executed by the CPU. The ROM 220 or HD 230 stores a computer program for giving commands to the CPU in cooperation with the OS and performing various processes. The computer program is executed by being loaded into the RAM 240 and configures the control unit 210 in cooperation with the CPU. Also, the computer program may be recorded in an application program server connected to the client device 200 via any network, and the client device 200 may download all or part of it as necessary. . Also, all or any part of the processing performed by the control unit 210 may be realized by hardware such as wired logic.

Here, the control unit 210 includes an evaluation unit 210a (including a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having functions similar to those of the evaluation unit 102d provided in the evaluation apparatus 100. ). When the control unit 210 is provided with the evaluation unit 210a, the evaluation unit 210a causes the conversion unit 210a2 to convert the expression value ( The generation unit 210a3 generates position information corresponding to the value of the formula or the value after conversion (it may be the density value or the value after conversion of the density value), and the classification unit 210a4 The individual may be classified into any one of a plurality of categories using the value of the formula or the converted value (concentration value or the converted concentration value may be used).

Next, the network 300 of this system will be described with reference to FIGS. 3 and 4. FIG. The network 300 has a function of connecting the evaluation device 100, the client device 200, and the database device 400 so as to be able to communicate with each other. is. Note that the network 300 includes a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a leased line network (including both analog and digital), CATV ( Community Antenna TeleVision) network, mobile line switching network or mobile packet switching network (IMT (International Mobile Telecommunications) 2000 system, GSM (registered trademark) (Global System for Mobile Communications) system or PDC (Personal PD/CELL Digital) system, etc.), a radio calling network, a local wireless network such as Bluetooth (registered trademark), a PHS network, a satellite communication network (CS (Communication Satellite), BS (Broadcasting Satellite), or ISDB (Integrated Services Digital Broadcasting ) etc.) etc. are also acceptable.

Next, the configuration of the database device 400 of this system will be described with reference to FIG. FIG. 13 is a block diagram showing an example of the configuration of the database device 400 of this system, and conceptually shows only those portions of the configuration related to the present invention.

The database device 400 has a function of storing the evaluation device 100 or index state information used when creating formulas in the database device, formulas created by the evaluation device 100, evaluation results by the evaluation device 100, and the like. As shown in FIG. 13, the database device 400 includes a control unit 402 such as a CPU for overall control of the database device, a communication device such as a router, and a wired or wireless communication circuit such as a dedicated line. A communication interface unit 404 that communicably connects the device to the network 300 , a storage unit 406 that stores various databases, tables, files (for example, files for web pages), etc. and an output interface unit 408, and these units are communicably connected via an arbitrary communication path.

The storage unit 406 is storage means, and can use, for example, a memory device such as a RAM/ROM, a fixed disk device such as a hard disk, a flexible disk, an optical disk, or the like. The storage unit 406 stores various programs and the like used for various processes. The communication interface unit 404 mediates communication between the database device 400 and the network 300 (or a communication device such as a router). That is, the communication interface unit 404 has a function of communicating data with another terminal via a communication line. The input/output interface unit 408 connects to the input device 412 and the output device 414 . Here, the output device 414 can be a monitor (including a home television), a speaker, or a printer. Also, the input device 412 can be a keyboard, a mouse, a microphone, or a monitor that realizes a pointing device function in cooperation with a mouse.

The control unit 402 has an internal memory for storing control programs such as an OS, programs defining various processing procedures, required data, and the like, and executes various information processing based on these programs. As illustrated, the control unit 402 is roughly divided into a transmission unit 402a and a reception unit 402b. The transmitting unit 402a transmits various types of information such as index state information and formulas to the evaluation apparatus 100. FIG. The receiving unit 402b receives various information such as formulas and evaluation results transmitted from the evaluation apparatus 100 .

In this description, the evaluation device 100 executes the acquisition of concentration data, the calculation of the formula values, the classification of individuals into categories, and the transmission of the evaluation results, and the client device 200 receives the evaluation results. However, if the client device 200 is provided with the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the value of the expression. and the classification of individuals into categories may be appropriately shared between the evaluation device 100 and the client device 200 .
For example, when the client device 200 receives the value of the expression from the evaluation device 100, the evaluation unit 210a converts the value of the expression with the conversion unit 210a2, and the value of the expression or the value after conversion with the generation unit 210a3. Alternatively, the classification unit 210a4 may classify the individual into one of a plurality of categories using the value of the formula or the converted value.
When the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a causes the generation unit 210a3 to generate position information corresponding to the converted value, or the classification unit 210a4 to generate location information corresponding to the converted value. Later values may be used to classify an individual into one of multiple categories.
Further, when the client device 200 receives the value of the formula or the value after conversion and the position information from the evaluation device 100, the evaluation unit 210a classifies the value of the formula or the value after conversion in the classification unit 210a4. An individual may be classified into any one of multiple categories.

[2-3. Other embodiments]
The evaluation device, the calculation device, the evaluation method, the calculation method, the evaluation program, the calculation program, the recording medium, the evaluation system, and the terminal device according to the present invention are the technologies described in the claims in addition to the second embodiment described above. Various different embodiments may be implemented within the scope of the concept.

Further, among the processes described in the second embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed manually. can also be performed automatically by known methods.

In addition, unless otherwise specified, the processing procedures, control procedures, specific names, information including parameters such as registration data and search conditions for each process, screen examples, and database configurations shown in the above documents and drawings can be changed arbitrarily.

Also, with respect to the evaluation apparatus 100, each illustrated component is functionally conceptual, and does not necessarily need to be physically configured as illustrated.

For example, the processing functions provided by the evaluation device 100, particularly the processing functions performed by the control unit 102, may be implemented in whole or in part by a CPU and a program interpreted and executed by the CPU. Alternatively, it may be implemented as hardware by wired logic. In addition, the program is recorded on a non-temporary computer-readable recording medium containing programmed instructions for causing the information processing apparatus to execute the evaluation method or calculation method according to the present invention. It is read mechanically by the device 100 . That is, the storage unit 106 such as ROM or HDD (Hard Disk Drive) stores computer programs for giving commands to the CPU in cooperation with the OS to perform various processes. This computer program is executed by being loaded into the RAM and constitutes a control section in cooperation with the CPU.

Also, this computer program may be stored in an application program server connected to the evaluation apparatus 100 via any network, and it is possible to download all or part of it as required.

Moreover, the evaluation program or calculation program according to the present invention may be stored in a non-transitory computer-readable recording medium, or may be configured as a program product. Here, the term "recording medium" includes memory cards, USB (Universal Serial Bus) memories, SD (Secure Digital) cards, flexible disks, magneto-optical disks, ROMs, EPROMs (Erasable Programmable Read Only Memory), EEPROMs (Electrically Erasable and Programmable Read Only Memory) (registered trademark), CD-ROM (Compact Disc Read Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Disk), and Blu-ray (registered trademark) Disc shall include any "portable physical medium".

A "program" is a data processing method written in any language or writing method, regardless of the format such as source code or binary code. In addition, the "program" is not necessarily limited to a single configuration, but is distributed as multiple modules or libraries, or cooperates with a separate program represented by the OS to achieve its function. Including things. It should be noted that well-known configurations and procedures can be used for the specific configuration and reading procedure for reading the recording medium in each device shown in the embodiments, the installation procedure after reading, and the like.

The various databases and the like stored in the storage unit 106 are storage means such as memory devices such as RAM and ROM, fixed disk devices such as hard disks, flexible disks, and optical disks, and are used for various processes and website provision. programs, tables, databases, and files for web pages.

The evaluation device 100 may be configured as an information processing device such as a known personal computer or workstation, or may be configured as the information processing device to which arbitrary peripheral devices are connected. Also, the evaluation device 100 may be implemented by installing software (including programs, data, etc.) that causes the information processing device to implement the evaluation method or the calculation method of the present invention.

Furthermore, the specific forms of distribution and integration of devices are not limited to those shown in the figures, and all or part of them can be functionally or physically arranged in arbitrary units according to various additions or functional loads. It can be distributed and integrated. In other words, the embodiments described above may be arbitrarily combined and implemented, or the embodiments may be selectively implemented.

Gastric cancer patients with a definite diagnosis of gastric cancer (gastric cancer group: 36 people), and healthy subjects with no history of cancer or cancer history who were matched with the gastric cancer group by sex, age and BMI (healthy group: 36 people) ), the blood metabolite concentrations were measured by the metabolite analysis method (A) described above.

３２種類の代謝物（１－Ｍｅ－Ｈｉｓ，３－Ｈｙｄｒｏｘｙｋｙｎｕｒｅｎｉｎｅ，３－Ｍｅ－Ｈｉｓ，５－ＨｙｄｒｏｘｙＴｒｐ，ａＡＢＡ，ａＡｉＢＡ，ＡＤＭＡ，Ａｍｉｎｏａｄｉｐｉｃ ａｃｉｄ，ｂＡＢＡ，ｂＡｉＢＡ，Ｃａｄａｖｅｒｉｎｅ，ＧＡＢＡ，Ｈｏｍｏａｒｇｉｎｉｎｅ，Ｈｏｍｏｃｉｔｒｕｌｌｉｎｅ，Ｈｙｐｏｔａｕｒｉｎｅ，Ｈｙｄｒｏｘｙｐｒｏｌｉｎｅ ，Ｋｉｎｕｒｅｎｉｎｅ，Ｌ－Ｃｙｓｔａｔｈｉｏｎｉｎｅ，Ｎ８－Ａｃｅｔｙｌｓｐｅｒｍｉｄｉｎｅ，Ｐｉｐｅｃｏｌｉｃ ａｃｉｄ，Ｐｕｔｒｅｓｃｉｎｅ，ＳＡＨ，Ｓａｒｃｏｓｉｎｅ，Ｓｅｒｏｔｏｎｉｎ，Ｓｐｅｒｍｉｄｉｎｅ，Ｓｐｅｒｍｉｎｅ，Ｍｅｔｈｙｌｃｙｓｔｅｉｎｅ，Ａｌｌｙｌｃｙｓｔｅｉｎｅ，Ｐｒｏｐｙｌｃｙｓｔｅｉｎｅ，ＳＤＭＡ，Ｎ６－Ａｃｅｔｙｌ－Ｌ－Ｌｙｓ，Ｎ－Ｍｅ－ｂＡＢＡ）の血漿Using intermediate concentration value (nmol/ml) or peak area value data, the discrimination ability between the gastric cancer group and the healthy group was evaluated by ROC_AUC for each metabolite. Table 1 shows ROC_AUC, which is an index for evaluating the discrimination ability of each metabolite.

Metabolites for which ROC_AUC was significant (p < 0.05) in the test when the null hypothesis was set to "ROC_AUC = 0.5" under nonparametric assumptions were 3-Hydroxykynurenine, ADMA, bABA, Kynurenine, L-Cystathionine, N8-Acetylspermidine, Pipecolic acid, Serotonin, Spermine, SDMA, N-Me-bABA. 3-Hydroxykynurenine, pipecolic acid, serotonin and spermine were significantly decreased in the gastric cancer group, and ADMA, bABA, kynurenine, L-cystathionine, N8-acetylspermidine, SDMA and N-Me-bABA were significantly increased in the gastric cancer group. Since the ROC_AUC is significant, these metabolite concentration values are considered to be useful in evaluating the state of gastric cancer in consideration of the healthy state.

The sample data obtained in Example 1 was used. A multivariate discriminant (multivariate function) for discriminating between the two groups, the gastric cancer group and the healthy group, including variables into which plasma metabolite concentration values or peak area values are substituted was determined.

A logistic regression equation was used as the multivariate discriminant. A combination of two variables to be included in the logistic regression equation, at least one of the above 32 metabolites is essential, and 20 amino acids (Glu, Asn, His, Thr, Ala, Cit, Arg, Tyr, Val, Met, Lys, Trp, Gly, Pro, Orn, Ile, Leu, Phe, Ser, Gln) and the above 32 metabolites, logistic regression with good discrimination ability between gastric cancer group and healthy group An expression search was performed.

A list of logistic regression equations in which the ROC_AUC value of the gastric cancer group and the healthy group is 0.700 or more and the number of variables is two is shown in [11.2 Variable Equation] below. These logistic regression equations are considered useful in the above evaluation due to their high ROC_AUC values. In addition, in [11.2 Variable Formulas] below, the variables and ROC_AUC values included in each formula are indicated (the same applies hereinafter).

The sample data used in Example 1 was used. A multivariate discriminant (multivariate function) for discriminating between the two groups, the gastric cancer group and the healthy group, including variables into which plasma metabolite concentration values or peak area values are substituted was obtained.

A logistic regression equation was used as the multivariate discriminant. 1 variable or 2 variables to be added to the logistic regression equation with the six amino acids Ala, Val, Leu, His, Trp, and Lys as variables and the ROC_AUC value of the gastric cancer group and the healthy group being 0.7369 were searched from the above 32 kinds of metabolites, and a search for a logistic regression formula with good ability to discriminate between the gastric cancer group and the healthy group was performed.

Metabolites added to the logistic regression equation with ROC_AUC values of the gastric cancer group and the healthy group of 0.7369 or more in the search when there is one added variable are listed in [12.1 Addition of variables] below. Indicated. In addition, in the search when there are two variables to be added, the metabolites added to the logistic regression equation in which the ROC_AUC values of the gastric cancer group and the healthy group are 0.7369 or more are added to [13.2 Addition of variables]. Indicated. These logistic regression equations are considered useful in the above evaluation due to their high ROC_AUC values.

INDUSTRIAL APPLICABILITY As described above, the present invention can be widely implemented in many industrial fields, particularly in the fields of pharmaceuticals, foods, medical care, etc. In particular, it is possible to predict the progress of gastric cancer, predict disease risk, and analyze proteome and metabolome. It is extremely useful in the field of bioinformatics, such as

100 evaluation device (including calculation device)
102 control unit 102a acquisition unit 102b designation unit 102c formula creation unit 102d evaluation unit 102d1 calculation unit 102d2 conversion unit 102d3 generation unit 102d4 classification unit 102e result output unit 102f transmission unit 104 communication interface unit 106 storage unit 106a concentration data file 106b index state information File 106c Specified index state information file 106d Formula related information database 106d1 Formula file 106e Evaluation result file 108 Input/output interface unit 112 Input device 114 Output device 200 Client device (terminal device (information communication terminal device))
300 network 400 database device

[11.2 Variable Expressions]
1-Me-His, N-Me-bABA, 1.0000; Kynurenine, N-Me-bABA, 1.0000; His, N-Me-bABA, 0.9992; Val, N-Me-bABA, 0.9992; bABA, 0.9992; Aminoadipic acid, N-Me-bABA, 0.9992; Homoarginine, N-Me-bABA, 0.9992; Homocitrulline, N-Me-bABA, 0.9992; -bABA, 0.9985; GABA, N-Me-bABA, 0.9985; Hypotaurine, N-Me-bABA, 0.9985; SDMA, N-Me-bABA, 0.9985; Glu, N-Me-bABA, 0.9977; Asn, N-Me-bABA, 0.9977; Tyr, N-Me-bABA, 0.9977; Lys, N-Me-bABA, 0.9977; -bABA, 0.9977; 5-HydroxyTrp, N-Me-bABA, 0.9977; bAiBA, N-Me-bABA, 0.9977; N8-Acetylspermidine, N-Me-bABA, 0.9977; Methylcysteine, N-Me-bABA, 0.9977; Allylcysteine, N-Me-bABA, 0.9977; Propylcysteine, N-Me-bABA, 0.9977; Gly, N-Me-bABA, 0.9969; Thr, N-Me-bABA, 0.9969; Ala, N-Me-bABA, 0.9969; Cit, N-Me-bABA, 0.9969; Arg, N-Me-bABA, 0.9969; ,N-Me-bABA, 0.9969; Met, N-Me-bABA, 0.9969; Phe, N-Me-bABA, 0.9969; 3-Hydroxykynurenine, N-Me-bABA, 0.9969; ;aAiBA,N-Me-bABA,0.9969;bABA,N-Me-bA BA, 0.9969; Hydroxyproline, N-Me-bABA, 0.9969; L-Cystathionine, N-Me-bABA, 0.9969; Pipecolic acid, N-Me-bABA, 0.9969; SAH, N-Me-bABA, 0.9969; -Me-bABA, 0.9969; Spermidine, N-Me-bABA, 0.9969; Spermine, N-Me-bABA, 0.9969; Trp, N-Me-bABA, 0.9961; ADMA, N-Me-bABA, 0.9961; -Me-bABA, 0.9954; Orn, N-Me-bABA, 0.9954; 3-Hydroxykynurenine, bABA, 0.9244; bABA, L-Cystathionine, 0.9174; His, bABA, 0.9136; bABA, Pipecolic acid, 0.9043; bABA, Serotonin, 0.9035; Glu, L-Cystathionine, 0.9005; bABA, Spermine, 0.8981; Pro, bABA, 0.8935; 0.8912; aABA, bABA, 0.8904; 5-HydroxyTrp, bABA, 0.8897; bABA, Homocitrulline, 0.8897; bABA, Methylcysteine, 0.8897; bABA, Allylcysteine, 0.8889; bABA, 0.8858; Met, bABA, 0.8850; ADMA, bABA, 0.8850; Aminoadipic acid, bABA, 0.8850; bABA, Spermidine, 0.8843; bABA, Hypotaurine, 0.8827; bABA, Propylcysteine, 0.8827; Trp, bABA, 0.8819; bABA, N8-Acetylspermidine, 0.8819; Gly, bABA, 0.8812; Ala, bABA, 0.8796; Tyr, bABA, 0.8789; Phe, bABA, 0.8789; bABA, bAiBA, 0.8789; Ser, bABA, 0.8781; Ile, bABA, 0.8781; Homoarginine, 0.8781; Cit, bABA, 0.8773; bABA, Hydroxyproline, 0.8773; bABA, Putrescine, 0.8773; bABA, Sarcosine, 0.8773; Val, bABA, 0.8742; aAiBA, bABA, 0.8742; bABA, Cadaverine, 0.8742; Trp, Serotonin, 0.8665; Glu, 3-Hydroxykynurenine, 0.8619; Serotonin, 0.8596; Kynurenine, Serotonin, 0.8596; Ala, Serotonin, 0.8580; ADMA, Serotonin, 0.8565; Spermine, 0.8549; Serotonin, Methylcystein, 0.8549; Pro, Serotonin, 0.8534; Cadaverine, Serotonin, 0.8526; Hypotaurine, Serotonin, 0.8526; Serotonin, 0.8511; 5-HydroxyTrp, Serotonin, 0.8511; 1-Me-His, Serotonin, 0.8503; Serotonin, Spermidine, 0.8503; ine, Serotonin, 0.8495; SAH, Serotonin, 0.8495; Ile, Serotonin, 0.8488; Aminoadipic acid, Serotonin, 0.8480; Homoarginine, Serotonin, 0.8480; -Lys, 0.8472; Gln, Serotonin, 0.8465; Val, Serotonin, 0.8465; Orn, Serotonin, 0.8465; Leu, Serotonin, 0.8465; Pipecolic acid, Serotonin, 0.8457; L-Cystathionine, 0.8434; Met, Serotonin, 0.8434; Sarcosine, Serotonin, 0.8426; Tyr, Serotonin, 0.8403; Asn, Serotonin, 0.8395; Serotonin, 0.8349; bAiBA, Serotonin, 0.8349; Hydroxyproline, Serotonin, 0.8341; His, 3-Hydroxykynurenine, 0.8333; 3-Hydroxykynurenine, SAH, 0.8326; 0.8302; Trp, L-Cystathionine, 0.8295; L-Cystathionine, Spermine, 0.8287; Trp, 3-Hydroxykynurenine, 0.8248; 3-Me-His, Serotonin, 0.8241; , 0.8148; L-Cystathionine, SDMA, 0.8148; Glu, Kynurenine, 0.8140; 1-Me-His, L-Cystathionine, 0.8125; 3-Hydroxykynurenine, Methylcysteine, 0.8117; Gly, L-Cystathionine, 0.8110; 5-HydroxyTrp, L-Cystathionine, 0.8102; SAH, SDMA, 0.8094; Cystathionine, 0.8056; 3-Hydroxykynurenine, Pipecolic acid, 0.8025; L-Cystathionine, Methylcysteine, 0.8025; 3-Hydroxykynurenine, Propylcysteine, 0.8002; , Spermine, 0.7971; L-Cystathionine, Allylcysteine, 0.7971; 3-Hydroxykynurenine, Allylcysteine, 0.7963; Ser, 3-Hydroxykynurenine, 0.7955; 0.7940; 1-Me-His,3-Hydroxykynurenine, 0.7940; L-Cystathionine, Pipecolic acid, 0.7940; Glu, N8-Acetylspermidine, 0.7924; Leu, L-Cystathionine, 0.7924; -Cystathionine, 0.7917; L-Cystathionine, Propylcysteine, 0.7917; Pro, L-Cystathionine, 0.7909; Ala, L-Cystathionine, 0.7901; L-Cystathionine, Spermidine, 0.7901; , 0.7886; Glu, Homocitrullin e, 0.7878; Asn, L-Cystathionine, 0.7878; 3-Hydroxykynurenine, Hydroxyproline, 0.7878; Thr, L-Cystathionine, 0.7870; Cit, 3-Hydroxykynurenine, 0.7870; 0.7870; Glu, 1-Me-His, 0.7863; Met, L-Cystathionine, 0.7863; L-Cystathionine, N8-Acetylspermidine, 0.7863; Ser, L-Cystathionine, 0.7855; His, L-Cystathionine, 0.7847; Glu, N6-Acetyl-L-Lys, 0.7840; Arg, L-Cystathionine, 0.7840; 3-Hydroxykynurenine, N8-Acetylspermidine, 0.7840; N6-Acetyl-L-Lys, 0.7840; Glu, Spermidine, 0.7832; 3-Hydroxykynurenine, Aminoadipic acid, 0.7832; 3-Hydroxykynurenine, bAiBA, 0.7832; Hydroxyproline, L-Cystathionine, 0.7832; -Hydroxykynurenine, Homoarginine, 0.7824; aAiBA, L-Cystathionine, 0.7824; Thr, 3-Hydroxykynurenine, 0.7816; Ile, L-Cystathionine, 0.7816; , Cadaverine, 0.7809; 3-Hydroxykynurenine, Spermidine, 0.7809; Aminoadipic acid, L-Cystathionine, 0.7809; Kynurenine, L-Cystathionine, 0.7809; Phe, L-Cystathionine, 0.7801; L-Cystathionine, Putrescine, 0.7801; Ala, 3-Hydroxykynurenine, 0.7793; , SDMA, 0.7793; Gln, L-Cystathionine, 0.7785; Orn, 3-Hydroxykynurenine, 0.7785; Orn, L-Cystathionine, 0.7785; 3-Hydroxykynurenine, 3-Me-His, 0.7778; 3-Hydroxykynurenine, aABA, 0.7778; L-Cystathionine, Sarcosine, 0.7778; Trp, Spermine, 0.7770; 3-Hydroxykynurenine, Kynurenine, 0.7770; Val, 3-Hydroxykynurenine, 0.7762; Leu, 3-Hydroxykynurenine, 0.7762; Homoarginine, L-Cystathionine, 0.7762; Gln, 3-Hydroxykynurenine, 0.7747; -Hydroxykynurenine, N6-Acetyl-L-Lys, 0.7747; Asn, 3-Hydroxykynurenine, 0.7739; 3-Hydroxykynurenine, ADMA, 0.7739; His, ADMA, 0.7724; Arg, 3-Hydroxykynurenine, 0.7724; ,3-Hydroxykynurenine,0.7716;3-Hydroxykynurenine,GABA,0 .7716; Glu, Cadaverine, 0.7708; Phe, 3-Hydroxykynurenine, 0.7708; 3-Hydroxykynurenine, Hypotaurine, 0.7708; His, Homocitrulline, 0.7701; Glu,5-HydroxyTrp,0.7662;Glu,aAiBA,0.7662;Glu,aABA,0.7639;Glu,Hypotaurine,0.7639;His,3-Me-His,0.7639;His,N6-Acetyl-L-Lys,0.7639; Glu, GABA, 0.7623; Gly, SDMA, 0.7623; Glu, SAH, 0.7600; Glu, Homoarginine, 0.7585; Methylcystein, SDMA, 0.7569; SDMA, 0.7554; 5-HydroxyTrp, SDMA, 0.7554; Glu, Propylcysteine, 0.7546; Ser, SDMA, 0.7546; Glu, Sarcosine, 0.7531; His, Kynurenine, 0.7523; SDMA, 0.7515; 1-Me-His, SDMA, 0.7515; N8-Acetylspermidine, Spermine, 0.7515; Allylcysteine, SDMA, 0.7515; Spermine, SDMA, 0.7508; Thr, SDMA, 0.7469; Trp, Methylcystein, 0.7469; Trp, 3-Me-His, 0.7446; Trp, N8-Acetylspermidine, 0.7446; Val, SDMA, 0.7431; ,0.7431 Propylcysteine, SDMA, 0.7431; SDMA, N6-Acetyl-L-Lys, 0.7431; Met, SDMA, 0.7423; ADMA, SDMA, 0.7423; Hypotaurine, SDMA, 0.7423; Lys, Aminoadipic acid, 0.7377; Lys, N6-Acetyl-L-Lys, 0.7377; Trp, Homocitrulline, 0.7377; Trp, Sarcosine, 0.7369; Trp, Cadaverine, 0.7361; Tyr, SDMA, 0.7346; Cadaverine, SDMA, 0.7346; Gly, N8-Acetylspermidine, 0.7338; His, 5-HydroxyTrp, 0.7338; Trp, Allylcysteine, 0.7338; N8-Acetylspermidine, SAH, 0.7338; Trp, ADMA, 0.7330; aABA, SDMA, 0.7323; N8-Acetylspermidine, Methylcystein, 0.7323; Trp, Hydroxyproline, 0.7315; -Acetylspermidine, 0.7307; His, Methylcystein, 0.7307; Trp, 1-Me-His, 0.7307; 3-Me-His, SDMA, 0.7299; GABA, SDMA, 0.7299; Asn, SDMA, 0.7292; Pipecolic acid, SDMA, 0.7292; Ala, SDMA, 0.7284; Arg, SDMA, 0.7284; Ile, SDMA, 0.7284; Trp, SAH, 0.7284; citrulline, SDMA, 0.7276; Gln, SDMA, 0.7269; Trp, Propylcysteine, 0.7269; aAiBA, SDMA, 0.7269; Phe, SDMA, 0.7261; -L-Lys, 0.7253; Homoarginine, SDMA, 0.7245; Lys, ADMA, 0.7238; His, Hydroxyproline, 0.7230; Trp, bAiBA, 0.7230; Cit, SDMA, 0.7215; Trp, Putrescine, 0.7215; Kynurenine, Spermine, 0.7199; Trp, aABA, 0.7168; Cadaverine, Pipecolic acid, 0.7168; Spermine, 0.7145; Lys, N8-Acetylspermidine, 0.7137; 1-Me-His, N8-Acetylspermidine, 0.7137; His, Propylcysteine, 0.7130; Pipecolic acid, N6-Acetyl-L-Lys, 0.7130; ,N8-Acetylspermidine, 0.7114; N8-Acetylspermidine, N6-Acetyl-L-Lys, 0.7114; Lys, Spermine, 0.7099; His, Pipecolic acid, 0.7076; Homocitrulline, N8-Acetylspermidine, 0.7076; 0.7068; N8-Acetylspermidine, Pipecolic acid, 0.7068; His, bAiBA, 0.7060; Spermine, Propylcysteine, 0.7060; Lys, 3-Me-His, 0.7052; ;ADMA, N8-Acetylspermidine, 0.7029; His, Sarcosine, 0.7022; Thr, N8-Acetylspermidine, 0.7014; Lys, Kynurenine, 0.7014; 5-HydroxyTrp, Kynurenine, 0.7014;

[12.1 Addition of variables]
N-Me-bABA, 0.9992; bABA, 0.8997; Serotonin, 0.8711; L-Cystathionine, 0.8372; 3-Hydroxykynurenine, 0.8349; Lys, 0.7940; 3-Me-His, 0.7878; Sarcosine, 0.7693; Allylcysteine, 0.7670; ADMA, 0.7654; 1-Me-His, 0.7508; aAiBA, 0.7492; 5-HydroxyTrp, 0.7446; Homoarginine, 0.7431; N8-Acetylspermidine, 0.7431;

[13.2 Addition of variables]
1-Me-His, N-Me-bABA, 1.0000; aABA, N-Me-bABA, 1.0000; Aminoadipic acid, N-Me-bABA, 1.0000; GABA, N-Me-bABA, 1.0000; -bABA, 1.0000; Homocitrulline, N-Me-bABA, 1.0000; Hypotaurine, N-Me-bABA, 1.0000; Hydroxyproline, N-Me-bABA, 1.0000; Kynurenine, N-Me-bABA, 1.0000; Me-bABA, 1.0000; SAH, N-Me-bABA, 1.0000; Sarcosine, N-Me-bABA, 1.0000; Spermidine, N-Me-bABA, 1.0000; Methylcysteine, N-Me-bABA, 1.0000; Me-bABA, 1.0000; Propylcysteine, N-Me-bABA, 1.0000; SDMA, N-Me-bABA, 1.0000; 3-Hydroxykynurenine, N-Me-bABA, 0.9992; 0.9992; 5-HydroxyTrp, N-Me-bABA, 0.9992; aAiBA, N-Me-bABA, 0.9992; ADMA, N-Me-bABA, 0.9992; bABA, 0.9992; Cadaverine, N-Me-bABA, 0.9992; L-Cystathionine, N-Me-bABA, 0.9992; N8-Acetylspermidine, N-Me-bABA, 0.9992; N-Me-bABA, 0.9992; Spermine, N-Me-bABA, 0.9992; N6-Acetyl-L-Lys, N-Me-bABA, 0.9992; 3-Hydroxykynurenine, bABA, 0.9483; bABA, L-Cystathionine, 0.9468; bABA, Allylcysteine, 0.9259; aABA, bABA, 0.9213; bABA, Serotonin, 0.9198; ABA, Kynurenine, 0.9151; bABA, SDMA, 0.9144; bABA, Pipecolic acid, 0.9128; bABA, Methylcystein, 0.9128; 1-Me-His, bABA, 0.9090; -Acetyl-L-Lys, 0.9082; 5-HydroxyTrp, bABA, 0.9074; Aminoadipic acid, bABA, 0.9066; 3-Me-His, bABA, 0.9059; bABA, Putrescine, 0.9035; bABA, Sarcosine, 0.9035; bABA, Propylcysteine, 0.9035; L-Cystathionine, Serotonin, 0.9035; bABA, bAiBA, 0.9020; bABA, Spermidine, 0.9012; ADMA, bABA, 0.9005; bABA, Cadaverine, 0.8997; bABA, Hydroxyproline, 0.8997; bABA, Homoarginine, 0.8989; Kynurenine, Serotonin, 0.8943; Serotonin, Allylcysteine, 0.8912; Homocitrulline, Serotonin, 0.8904; Serotonin, Methylcysteine, 0.8881; 0.8858; bAiBA, Serotonin, 0.8835; ADMA, Serotonin, 0.8819; Hydroxyproline, Serotonin, 0.8812; Sarc osine, Serotonin, 0.8812; 3-Me-His, Serotonin, 0.8804; N8-Acetylspermidine, Serotonin, 0.8796; Aminoadipic acid, Serotonin, 0.8789; Homoarginine, Serotonin, 0.8789; -Me-His, Serotonin, 0.8750; 3-Hydroxykynurenine, L-Cystathionine, 0.8742; aAiBA, Serotonin, 0.8735; Putrescine, Serotonin, 0.8735; 0.8719; 5-HydroxyTrp, Serotonin, 0.8719; Cadaverine, Serotonin, 0.8711; SAH, Serotonin, 0.8711; Hypotaurine, Serotonin, 0.8696; Serotonin, Spermidine, 0.8688; HydroxyTrp, L-Cystathionine, 0.8650; 3-Hydroxykynurenine, Allylcysteine, 0.8642; 3-Hydroxykynurenine, Methylcysteine, 0.8619; 3-Hydroxykynurenine, 5-HydroxyTrp, 0.8611; Cystathionine, 0.8580; 3-Hydroxykynurenine, aAiBA, 0.8573; 3-Hydroxykynurenine, Spermine, 0.8526; SAH, SDMA, 0.8519; 1-Me-His, 3-Hydroxykynurenine, 0.8511; Cystathion ine, 0.8480; 3-Hydroxykynurenine, Propylcysteine, 0.8472; 3-Hydroxykynurenine, aABA, 0.8465; Allylcysteine, SDMA, 0.8457; 3-Hydroxykynurenine, Aminoadipic acid, 0.8449; 3-Hydroxykynurenine, Pipecolic acid, 0.8426; 3-Hydroxykynurenine, Putrescine, 0.8426; 3-Me-His, L-Cystathionine, 0.8418; Homoarginine, L-Cystathionine, 0.8418; Cystathionine, 0.8410; L-Cystathionine, Propylcysteine, 0.8410; L-Cystathionine, N6-Acetyl-L-Lys, 0.8410; 3-Hydroxykynurenine, Sarcosine, 0.8403; L-Cystathionine, 0.8395; L-Cystathionine, N8-Acetylspermidine, 0.8395; 3-Hydroxykynurenine, Homoarginine, 0.8387; aABA, L-Cystathionine, 0.8387; aAiBA, L-Cystathionine, 0.8387; -Cystathionine, Spermidine, 0.8387; Methylcystein, SDMA, 0.8387; 3-Hydroxykynurenine, GABA, 0.8380; 3-Hydroxykynurenine, Spermidine, 0.8380; daverine, 0.8372; Cadaverine, L-Cystathionine, 0.8372; GABA, L-Cystathionine, 0.8372; L-Cystathionine, Sarcosine, 0.8372; 3-Hydroxykynurenine, N8-Acetylspermidine, 0.8364; , Putrescine, 0.8364; 3-Hydroxykynurenine, bAiBA, 0.8356; Hydroxyproline, L-Cystathionine, 0.8349; 3-Hydroxykynurenine, Hydroxyproline, 0.8341; 3-Hydroxykynurenine, Hypotaurine, 0.8318; Homocitrulline, Spermine, 0.8310; Spermine, Methylcystein, 0.8287; Kynurenine, Spermine, 0.8272; Allylcysteine, N6-Acetyl-L-Lys, 0.8256; 3-Me-His, Spermine, 0.8202; Sarcosine, Spermine, 0.8202; Sarcosine, SDMA, 0.8202; Hydroxyproline, SDMA, 0.8194; Hypotaurine, SDMA, 0.8187; Spermine, Allylcysteine, 0.8179; 1-Me-His, 3-Me-His, 0.8148; Propylcysteine, SDMA, 0.8148; 5-HydroxyTrp, SDMA, 0.8140; aAiBA, SDMA, 0.8140; bAiBA, Spermine, 0.8133; roxyproline, Spermine, 0.8133; Putrescine, SDMA, 0.8125; Spermidine, Spermine, 0.8125; 1-Me-His, SDMA, 0.8117; N8-Acetylspermidine, Spermine, 0.8117; 0.8110; ADMA, Spermine, 0.8110; Aminoadipic acid, Spermine, 0.8110; bAiBA, SDMA, 0.8110; Spermidine, SDMA, 0.8110; L-Lys, 0.8094; Kynurenine, Allylcysteine, 0.8094; SDMA, N6-Acetyl-L-Lys, 0.8094; aABA, SDMA, 0.8086; Aminoadipic acid, Methylcysteine, 0.8086; Homocitrulline, SDMA, 0.8071; Homocitrulline, Hypotaurine, 0.8063; Homocitrulline, Sarcosine, 0.8063; Pipecolic acid, Allylcysteine, 0.8063; His, Homocitrulline, 0.8056; 3-Me-His, SDMA, 0.8056; ADMA, Allylcysteine, 0.8056; Homoarginine, SDMA, 0.8056; N8-Acetylspermidine, SDMA, 08056; Lys, 0.8048; aAiBA, N6-Acetyl-L-Lys, 0.8040; Homocitrulline, Allylcysteine, 0.8040; Cadaverine, SDMA, 0.8032; GA BA, Spermine, 0.8032; Pipecolic acid, N6-Acetyl-L-Lys, 0.8032; Homocitrulline, Hydroxyproline, 0.8025; 5-HydroxyTrp, N6-Acetyl-L-Lys, 0.8017; , aAiBA, 0.8009; Hypotaurine, Methylcystein, 0.8009; Pipecolic acid, SDMA, 0.8009; Sarcosine, N6-Acetyl-L-Lys, 0.8009; 3-Me-His, Homocitrulline, 0.8002; 5-HydroxyTrp, Homocitrulline, 0.8002; Spermidine, N6-Acetyl-L-Lys, 0.8002; Aminoadipic acid, Homocitrulline, 0.7994; bAiBA, N6-Acetyl-L-Lys, 0.7994; Homocitrulline, Propylcysteine, 0.7986; Hydroxyproline, N6-Acetyl-L-Lys, 0.7986; aAiBA, Spermine, 0.7978; 3-Me-His, SAH, 0.7971; -His, Spermine, 0.7963; aABA, N6-Acetyl-L-Lys, 0.7963; Homoarginine, Spermine, 0.7963; N8-Acetylspermidine, Methylcystein, 0.7963; Pipecolic acid, Spermine, 0.7963; 3-Me-His, bAiBA, 0.7955; aABA, Spermine, 0.7955; Aminoadipic acid, N6-Acetyl-L-Lys, 0.7955; Pipecolic acid, Methylcystein, 0.7955; 3-Me-His, Aminoadipic acid, 0.7948; -His, Allylcysteine, 0 GABA, N6-Acetyl-L-Lys, 0.7948; bAiBA, Homocitrulline, 0.7940; SAH, Spermine, 0.7940; Sarcosine, Methylcystein, 0.7940; 3-Me-His, Pipecolic acid, 0.7932; Putrescine, Methylcystein, 0.7932; 3-Me-His, Hydroxyproline, 0.7924; bAiBA, Sarcosine, 0.7924; Spermidine, Methylcystein, 0.7924; 3-Me-His, Propylcysteine, 0.7917; GABA, Homocitrulline, 0.7917; Homoarginine, Homocitrulline, 0.7917; Homoarginine, N6-Acetyl-L-Lys, 0.7917; 3-Me-His, aABA, 0.7909; Spermidine, 0.7909; Aminoadipic acid, Allylcysteine, 0.7909; Hypotaurine, Spermine, 0.7909; Hydroxyproline, Methylcysteine, 0.7909; Putrescine, N6-Acetyl-L-Lys, 0.7909; 5-HydroxyTrp, Spermine, 0.7901; 5-HydroxyTrp, Methylcystein, 0.7901; aABA, Methylcystein, 0.7901; Homocitrulline, Pipecolic acid, 0.7901; Methylcystein, 0.7894; Cadaverine, Spermine, 0.7894; Homoarginine, Methylcy stein, 0.7894; 3-Me-His, Homoarginine, 0.7886; aAiBA, Sarcosine, 0.7886; ADMA, Homocitrulline, 0.7886; bAiBA, Methylcysteine, 0.7886; aABA, Homocitrulline, 0.7870; bAiBA, Hydroxyproline, 0.7870; 3-Me-His, GABA, 0.7863; aAiBA, Homocitrulline, 0.7863; Cadaverine, Homocitrulline, 0.7863; ADMA, N6-Acetyl-L-Lys, 0.7855; Cadaverine, N6-Acetyl-L-Lys, 0.7855; Hypotaurine, Sarcosine, 0.7855; Kynurenine, SAH, 0.7855; Hypotaurine, Hydroxyproline, 0.7847; N8-Acetylspermidine, 0.7840; Methylcysteine, Allylcysteine, 0.7840; N8-Acetylspermidine, N6-Acetyl-L-Lys, 0.7832; 1-Me-His, ADMA, 0.7824; 0.7824; Kynurenine, Spermidine, 0.7824; Kynurenine, N6-Acetyl-L-Lys, 0.7816; Sarcosine, Spermidine, 0.7816; aABA, Aminoadipic acid, 0.7809; ,0.7809;3-Me-His,N8-Acetyls permidine, 0.7801; ADMA, Sarcosine, 0.7801; Aminoadipic acid, Sarcosine, 0.7801; 3-Me-His, ADMA, 0.7793; Aminoadipic acid, Hydroxyproline, 0.7793; -His, Sarcosine, 0.7778; 5-HydroxyTrp, Kynurenine, 0.7778; aAiBA, Kynurenine, 0.7778; Pipecolic acid, Sarcosine, 0.7778; -His, Allylcysteine, 0.7762; ADMA, Hydroxyproline, 0.7762; bAiBA, Hypotaurine, 0.7762; 1-Me-His, Hydroxyproline, 0.7755; Allylcysteine, Propylcysteine, 0.7755; 5-HydroxyTrp, ADMA, 0.7739; aABA, ADMA, 0.7739; ADMA, Hypotaurine, 0.7739; Aminoadipic acid, Propylcysteine, 0.7739; Hypotaurine, Propylcysteine, 0.7724; Homoarginine, Sarcosine, 0.7724; Hydroxyproline, Allylcysteine, 0.7724; Sarcosine, Propylcysteine, 0.7724; 5-HydroxyTrp, Hydroxyproline, 0.7716; GABA, Sarcosine, 0.7716; renine, 0.7708; aAiBA, Hydroxyproline, 0.7701; ADMA, spermidine, 0.7701; aminoadipic acid, SAH, 0.7701; N8-Acetylspermidine, sarcosine, 0.7701; Kynurenine

Aminoadipic acid, Hypotaurine, 0.7693; Aminoadipic acid, Pipecolic acid, 0.7693; bAiBA, Kynurenine, 0.7693; Hypotaurine, Spermidine, 0.7693; Hydroxyproline, Spermidine, 0.7677; Hydroxyproline, Propylcysteine, 0.7677; Kynurenine, Putrescine, 0.7677; Kynurenine, Propylcysteine, 0.7677; ADMA, Putrescine, 0.7670; Hydroxyproline, 0.7670; Spermidine, Propylcysteine, 0.7670; aAiBA, Aminoadipic acid, 0.7662; ADMA, GABA, 0.7662; Aminoadipic acid, Putrescine, 0.7662; Allylcysteine, 0.7662; 5-HydroxyTrp, Pipecolic acid, 0.7654; Aminoadipic acid, N8-Acetylspermidine, 0.7654; GABA, Hydroxyproline, 0.7654; 1-Me-His, N8-Acetylspermidine, 0.7647; Spermidine, 0.7647; Hydroxyproline, N8-Acetylspermidine, 0.7647; Pipecolic acid, Spermidine, 0.7647; Putrescine, Spermidine dine, 0.7647; ADMA, Homoarginine, 0.7639; ADMA, Pipecolic acid, 0.7639; Aminoadipic acid, Homoarginine, 0.7639; aABA, Sarcosine, 0.7631; , Hypotaurine, 0.7631; Hydroxyproline, Pipecolic acid, 0.7631; Kynurenine, N8-Acetylspermidine, 0.7631; Kynurenine, Pipecolic acid, 0.7631; Aminoadipic acid, GABA, 0.7616; bAiBA, Propylcysteine, 0.7616; Hypotaurine, Putrescine, 0.7616; Kynurenine, Sarcosine, 0.7616; Spermidine, 0.7608; Hypotaurine, N8-Acetylspermidine, 0.7608; Hydroxyproline, Putrescine, 0.7608; Putrescine, Allylcysteine, 0.7608; 5-HydroxyTrp, Hypotaurine, 0.7600; 5-HydroxyTrp, Sarcosine, 0.7593; aAiBA, Allylcysteine, 0.7593; N8-Acetylspermidine, Pipecolic acid, 0.7593; 1-Me- His, Hypotaurine, 0.7585; aABA, Hydroxyproline, 0.7585; a AiBA, Spermidine, 0.7585; Hypotaurine, SAH, 0.7585; 0.7569; 5-HydroxyTrp, Aminoadipic acid, 0.7562; aAiBA, Propylcysteine, 0.7562; Cadaverine, Kynurenine, 0.7562; GABA, Kynurenine, 0.7562; 1-Me-His, aAiBA, 0.7546; 1-Me-His, Spermidine, 0.7546; 5-HydroxyTrp, aABA, 0.7546; aABA, N8-Acetylspermidine, 0.7546; aABA, Propylcysteine, 0.7546; Homoarginine, Kynurenine, 0.7546; N8-Acetylspermidine, Propylcysteine, 0.7546; Putrescine, Propylcysteine, 0.7546; aAiBA, Pipecolic acid, 0.7539; Cadaverine, Pipecolic acid, 0.7539; Homoarginine, 0.7531; bAiBA, Cadaverine, 0.7531; bAiBA, Homoarginine, 0.7531; 1-Me-His, aABA, 0.7523; 5-HydroxyTrp, aAiBA, 0.7523; His,Pipecolic acid,0.7515;5-HydroxyTrp,Spermidine,0.7515;aAiB A, Putrescine, 0.7515; bAiBA, N8-Acetylspermidine, 0.7515; Cadaverine, Propylcysteine, 0.7515; 1-Me-His, SAH, 0.7508; 5-HydroxyTrp, Propylcysteine, 0.7508; Pipecolic acid, SAH, 0.7508; 1-Me-His, GABA, 0.7500; aAiBA, N8-Acetylspermidine, 0.7492; bAiBA, GABA, 0.7492; GABA, Pipecolic acid, 0.7485; aAiBA, Homoarginine, 0.7477; bAiBA, SAH, 0.7477; SAH, Propylcysteine, 0.7477; 1-Me-His, Putrescine, 0.7469; aABA, aAiBA, 0.7461; 5-HydroxyTrp, N8-Acetylspermidine, 0.7438; aABA, Cadaverine, 0.7438; aABA, Spermidine, 0.7438; Cadaverine, N8-Acetylspermidine, 0.7438; 0.7438; 1-Me-His, Cadaverine, 0.7431; 5-HydroxyTrp, Homoarginine, 0.7431; aABA, Putrescine, 0.7431; 5-HydroxyTrp, SAH, 0.7423; Pipecolic acid, 0.7415; aAiBA, SAH, 0.7415; Cadaverine, Putrescine, 0. 7415; Homoarginine, Putrescine, 0.7415; Cadaverine, GABA, 0.7400; GABA, Homoarginine, 0.7400; 5-HydroxyTrp, GABA, 0.7392; Cadaverine, Homoarginine, 0.7392;

Claims (11)

A formula containing at least two metabolite concentration values of 1-Me-His and 3-Hydroxykynurenine or L-Cystationin e in the blood to be evaluated, or a variable into which the concentration values of the two metabolites are substituted, and comprising an obtaining step of obtaining information for evaluating the possibility of suffering from gastric cancer for the subject, using the values of the formula calculated using the concentration values of the two metabolites;
A method of acquisition characterized by the acquiring step being executed by the control unit of an information processing device comprising a control unit;
The acquisition method according to claim 1, characterized by: Gastric cancer comprising at least two metabolite concentration values of 1-Me-His and 3-Hydroxykynurenine or L-Cystationin e in the blood to be evaluated, and a variable into which the concentration values of the two metabolites are substituted using a formula to assess the likelihood of being afflicted, comprising a calculating step of calculating the value of said formula;
A calculation method characterized by the calculating step is executed by the control unit of an information processing device comprising a control unit;
The calculation method according to claim 3, characterized by: An evaluation device comprising a control unit,
The control unit
A formula containing at least two metabolite concentration values of 1-Me-His and 3-Hydroxykynurenine or L-Cystationin e in the blood to be evaluated, or a variable into which the concentration values of the two metabolites are substituted, and evaluation means for evaluating the possibility of suffering from gastric cancer with respect to the evaluation subject using the values of the formula calculated using the concentration values of the two metabolites;
An evaluation device characterized by: communicatively connected via a network to a terminal device that provides concentration data relating to the concentration value or the value of the formula;
The control unit
data receiving means for receiving the concentration data of the evaluation object or the value of the formula transmitted from the terminal device;
result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device;
further comprising
The evaluation means uses the concentration value or the formula value included in the concentration data received by the data reception means;
The evaluation device according to claim 5, characterized by: A computing device comprising a control unit,
The control unit
Gastric cancer comprising at least two metabolite concentration values of 1-Me-His and 3-Hydroxykynurenine or L-Cystationin e in the blood to be evaluated, and a variable into which the concentration values of the two metabolites are substituted using a formula for assessing the likelihood of being afflicted, calculating means for calculating the value of said formula;
A computing device characterized by: An evaluation program to be executed in an information processing device comprising a control unit,
for execution in the control unit,
A formula containing at least two metabolite concentration values of 1-Me-His and 3-Hydroxykynurenine or L-Cystationin e in the blood to be evaluated, or a variable into which the concentration values of the two metabolites are substituted, and an evaluation step of evaluating the possibility of the subject being afflicted with gastric cancer using the value of the formula calculated using the concentration values of the two metabolites;
A rating program characterized by: A calculation program to be executed in an information processing device comprising a control unit,
for execution in the control unit,
Gastric cancer comprising at least two metabolite concentration values of 1-Me-His and 3-Hydroxykynurenine or L-Cystationin e in the blood to be evaluated, and a variable into which the concentration values of the two metabolites are substituted using the formula for assessing the likelihood of being afflicted and calculating the value of said formula;
A calculation program characterized by: A computer-readable recording medium recording the program according to claim 8 or 9. an evaluation device comprising a controller, and concentration data relating to concentration values of at least two metabolites, 1-Me-His and 3-Hydroxykynurenine or L- Cystathionine, in the blood to be evaluated, or the aforementioned A terminal device that provides a formula including variables into which the concentration values of two metabolites are substituted and a value of the formula calculated using the concentration values of the two metabolites is communicably connected via a network. An evaluation system configured by
The control unit of the terminal device,
data transmission means for transmitting the concentration data to be evaluated or the value of the expression to the evaluation device;
result receiving means for receiving an evaluation result regarding the possibility of suffering from gastric cancer with respect to the subject of evaluation, which is transmitted from the evaluation device;
with
The control unit of the evaluation device,
data receiving means for receiving the concentration data of the evaluation object or the value of the formula transmitted from the terminal device;
Using the concentration values of the two metabolites or the value of the formula included in the concentration data of the subject of evaluation received by the data receiving means, the possibility of suffering from gastric cancer is determined for the subject of evaluation. an evaluation means to evaluate;
result transmission means for transmitting the evaluation result obtained by the evaluation means to the terminal device;
to provide
A rating system characterized by:

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