JP2018100963A - Method for evaluating prognosis of cancer patient by in-tissue amino-acid profile, method for calculation, evaluation device, calculation device, evaluation program, calculation program, evaluation system, and terminal device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for evaluation which can provide highly reliable information useful for knowing the prognosis of the pancreatic cancer of an evaluation subject.SOLUTION: The prognosis of the pancreatic cancer of an evaluation subject is evaluated by using the concentration value of at least one of EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His, HyLys, Phe, Arg, Cit, Tyr, and Trp in a tissue with the pancreatic cancer obtained from the evaluation subject.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an evaluation method, an evaluation device, an evaluation program, an evaluation system, and a terminal device.

  In Japan, the number of deaths due to pancreatic cancer in 2014 was 16411 men and 15305 women, ranking 5th among cancer deaths among men and 4th among cancer deaths among women. The lifetime prevalence of pancreatic cancer is 2%, and the number of patients with pancreatic cancer is not high compared to other cancer types. However, the 5-year survival rate is, for example, 72.2% in the most affected colorectal cancer, and very low at 7.9% in pancreatic cancer, and improvement is required.

  In breast cancer and colorectal cancer, test methods for predicting the prognosis based on genetic profiles of surgical specimens and biopsy samples have been developed (Patent Document 1 and Non-Patent Documents 1, 2, and 3). It has been shown that prognosis can be improved by selection (Non-Patent Document 4).

  Regarding pancreatic cancer, although pathological factors such as lymphatic vessel invasion and the expression level of transporter in cancer tissue and the prognosis are related (Non-patent Documents 5 and 6), the factor and the expression level are indicated. However, no test method has been established to predict prognosis. Furthermore, a method for selecting an effective treatment method for patients stratified in this way has not been established.

  In recent years, metabolite profiles have been shown to be different from normal in cancer tissues, indicating the possibility of being a therapeutic target (Non-patent Document 7). In many cases, pathological diagnosis is performed in the treatment of cancer. Furthermore, metabolite measurement in tissue sections is possible by improving amino acid analysis technology capable of quantifying amino acids at high speed and with high sensitivity (Non-patent Document 8).

International Publication No. 2014/130617

S Paik et.al., A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.N Engl J Med. 2004 Dec 30; 351 (27): 2817-26. Yothers G et.al., Validation of the 12-gene colon cancer recurrence score in NSABP C-07 as a predictor of recurrence in patients with stage II and III colon cancer treated with fluorouracil and leucovorin (FU / LV) and FU / LV plus oxaliplatin. J Clin Oncol. 2013 Dec 20; 31 (36): 4512-9. Cullen J et. Al., A biopsy-based 17-gene Genomic Prostate Score predicts recurrence after radical prostatectomy and adverse surgical pathology in a racially diverse population of men with clinically low- and intermediate-risk prostate cancer.Eur Urol. 2014; 68 (1): 123-31. S Paik et.al., Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer.J Clin Oncol. 2006 Aug 10; 24 (23): 3726-34. G Baek et al., MCT4 Defines a Glycolytic Subtype of Pancreatic Cancer with Poor Prognosis and Unique Metabolic Dependencies.Cell Rep. 2014 Dec 24; 9 (6): 2233-49. K Kaira et al., Prognostic significance of L-type amino-acid transporter 1 expression in surgically resected pancreatic cancer.Br J Cancer. 2012 Aug 7; 107 (4): 632-8. R Blum et al., Metabolism addiction in pancreatic cancer.Cell Death Dis. 2014 Feb; 5 (2): e1065. C Okamoto et al., Determination of Amino Acids in Human Pancreas Tissue Sections Using Liquid Chromatography Tandem Mass Spectrometry., Chromatography 2016 Oct 31; 37 (3): 125-32.

  Here, if the prognosis can be predicted by the metabolite profile in the pathological tissue section, and if this prediction result can be linked to the choice of treatment, the patient can be treated more effectively within the scope of the normal treatment scheme. It is beneficial for patients because they will be able to receive it. However, there is no report so far showing the relationship between the profile of metabolites in the pathological tissue section and the prognosis.

  The present invention has been made in view of the above, and an evaluation method, an evaluation apparatus, an evaluation program, an evaluation system, which can provide highly reliable information that can be used as a reference in knowing the prognosis of pancreatic cancer in an evaluation target, And it aims at providing a terminal device.

  In order to solve the above-described problems and achieve the object, the evaluation method according to the present invention includes 26 kinds of amino acids (EtOHNH2, Gly, Ala, GABA, Ser, Pro, (Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His, HyLys, Phe, Arg, Cit, Tyr, and Trp). And an evaluation step of evaluating the prognosis of pancreatic cancer in the evaluation object.

Here, although various amino acids are mainly represented by abbreviations in the present specification, their formal names are as follows.
(Abbreviation) (official name)
Ala Alanine
Arg Arginine
Asn Asparagine
Asp Aspartic acid
Cit Circleline
EtOHNH2 Ethanolamine
GABA γ-Aminobutyric acid
Gln Glutamine
Glu Glutamic acid
Gly Glycine
His Histide
HyLys Hydroxylysine
HyPro Hydroxyproline
Ile Isolucine
Leu Leucine
Lys Lysine
Met Methionine
Orn Origine
Phe Phenylalanine
Pro Proline
Ser Serine
Tau Taurine
Thr Threoneine
Trp Tryptophan
Tyr Tyrosine
Val Valine

  In the evaluation method according to the present invention, in the evaluation method, in the evaluation step, an expression including a variable into which a concentration value of at least one of the 26 amino acids is substituted is further used. The prognosis of pancreatic cancer in the evaluation object is evaluated by calculating the value.

  The evaluation apparatus according to the present invention is an evaluation apparatus including a control unit, wherein the control unit is a concentration value of at least one of the 26 types of amino acids in pancreatic cancer tissue obtained from an evaluation target. And an evaluation means for evaluating the prognosis of pancreatic cancer in the evaluation object.

  Moreover, the evaluation method according to the present invention is an evaluation method executed in an information processing apparatus including a control unit, and the 26 types of pancreatic cancer tissues obtained from an evaluation object executed in the control unit. An evaluation step of evaluating the prognosis of pancreatic cancer in the evaluation subject using a concentration value of at least one of the amino acids of

  The evaluation program according to the present invention is an evaluation program for execution in an information processing apparatus including a control unit, and is executed in the control unit, the pancreatic cancer tissue obtained from the evaluation target in the pancreatic cancer tissue An evaluation step of evaluating the prognosis of pancreatic cancer in the evaluation object using at least one concentration value of 26 kinds of amino acids.

  A recording medium according to the present invention is a non-transitory computer-readable recording medium, and includes a programmed instruction for causing an information processing apparatus to execute the evaluation method.

  Moreover, the evaluation system according to the present invention includes an evaluation device including a control unit, and a control unit, and a concentration related to at least one concentration value of the 26 types of amino acids in the pancreatic cancer tissue obtained from the evaluation target. An evaluation system configured such that a terminal device that provides data is communicably connected via a network, wherein the control unit of the terminal device transmits the concentration data to be evaluated to the evaluation device Concentration data transmitting means, and result receiving means for receiving an evaluation result related to the prognosis of pancreatic cancer in the evaluation object transmitted from the evaluation apparatus, and the control unit of the evaluation apparatus receives from the terminal apparatus Included in the density data receiving means for receiving the transmitted density data of the evaluation target and the density data of the evaluation target received by the density data receiving means Evaluation means for evaluating the prognosis of pancreatic cancer in the evaluation object using the concentration value of at least one of the 26 kinds of amino acids, and the evaluation result obtained by the evaluation means as the terminal device And a result transmitting means for transmitting to the network.

  The terminal device according to the present invention is a terminal device including a control unit, and the control unit includes a result acquisition unit that acquires an evaluation result regarding a prognosis of pancreatic cancer in an evaluation target, and the evaluation result is It is a result of evaluating the prognosis of pancreatic cancer in the evaluation object using the concentration value of at least one of the 26 kinds of amino acids in the pancreatic cancer tissue obtained from the evaluation object.

  Further, the terminal device according to the present invention is configured to be communicably connected to an evaluation device for evaluating the prognosis of pancreatic cancer in the evaluation target via the network in the terminal device, and the control unit includes: Concentration data transmission means for transmitting concentration data relating to the concentration value of at least one of the 26 types of amino acids in the pancreatic cancer tissue obtained from the evaluation object to the evaluation device, the result acquisition means, The evaluation result transmitted from the evaluation device is received.

  Moreover, the evaluation apparatus according to the present invention can communicate with a terminal device that provides concentration data regarding at least one concentration value of the 26 types of amino acids in pancreatic cancer tissue obtained from an evaluation object via a network. A connected evaluation device including a control unit, wherein the control unit receives density data receiving means for receiving the density data to be evaluated transmitted from the terminal device, and received by the density data receiving means. Evaluation means for evaluating the prognosis of pancreatic cancer in the evaluation object using the concentration value of at least one of the 26 kinds of amino acids included in the concentration data of the evaluation object; and the evaluation means And a result transmitting means for transmitting the evaluation result obtained in (1) to the terminal device.

  According to the present invention, since the prognosis of pancreatic cancer in the evaluation object is evaluated using the concentration value of at least one of the 26 types of amino acids in the pancreatic cancer tissue obtained from the evaluation object, pancreatic cancer in the evaluation object It is possible to provide highly reliable information that can be used as a reference for knowing the prognosis.

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 illustrating an example of the overall configuration of the present system. FIG. 4 is a diagram showing another example of the overall configuration of the present system. FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 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 illustrating an example of information stored in the index state information file 106b. FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e. FIG. 11 is a block diagram illustrating a configuration of the evaluation unit 102d. FIG. 12 is a block diagram illustrating an example of the configuration of the client device 200 of the present system. FIG. 13 is a block diagram showing an example of the configuration of the database apparatus 400 of this system. FIG. 14 is a diagram showing metabolites and p-values determined to be prognostic alone by Weibull regression. FIG. 15 is a diagram showing metabolites and p-values determined to be prognostic alone by Cox regression. FIG. 16 is a diagram showing metabolites and p-values that are judged to be prognostic alone by k-means cluster analysis. FIG. 17 is a diagram showing metabolites and p-values that were determined to be prognostic alone by Weibull regression. FIG. 18 is a diagram showing metabolites and p-values that are judged to be prognostic alone by Cox regression. FIG. 19 is a diagram showing metabolites and p-values that are judged to be prognostic alone by k-means cluster analysis. FIG. 20 is a diagram showing two metabolites and p-values determined to have prognostic ability by their combination by Weibull regression. FIG. 21 is a diagram showing two metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 22 is a diagram showing two metabolites and p-values that were determined to have prognostic ability by their combination by k-means cluster analysis. FIG. 23A is a diagram showing two metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 23B is a diagram showing two metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 23C is a diagram showing two metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 24A is a diagram showing two metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 24B is a diagram showing two metabolites and p-values determined to be prognostic by their combination by Cox regression. FIG. 24C is a diagram showing two metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 24D is a diagram showing two metabolites and p-values determined to be prognostic by their combination by Cox regression. FIG. 25A is a diagram showing two metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 25B is a diagram showing two metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 26A is a diagram showing three metabolites and p-values that were determined to be prognostic by their combination by Weibull regression. FIG. 26B is a diagram showing three metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 26C is a diagram showing three metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 26D is a diagram showing three metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 27A is a diagram showing three metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 27B is a diagram showing three metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 27C is a diagram showing three metabolites and p-values that were determined to be prognostic by their combination by Cox regression. FIG. 27D is a diagram showing three metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 28A is a diagram showing three metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 28B is a diagram showing three metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 28C is a diagram showing three metabolites and p-values that were determined to be prognostic by their combination by k-means cluster analysis. FIG. 28D is a diagram showing three metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 29A is a diagram showing three metabolites and p-values that were determined to be prognostic by their combination by Weibull regression. FIG. 29B is a diagram showing three metabolites and p-values determined to have prognostic ability by their combination by Weibull regression. FIG. 29C is a diagram showing three metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 29D is a diagram showing three metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 30A is a diagram showing three metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 30B is a diagram showing three metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 30C is a diagram showing three metabolites and p-values that were determined to be prognostic by their combination by Cox regression. FIG. 30D is a diagram showing three metabolites and p-values determined to be prognostic by their combination by Cox regression. FIG. 31A is a diagram showing three metabolites and p-values that were determined to be prognostic by their combination by k-means cluster analysis. FIG. 31B is a diagram showing three types of metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 31C is a diagram showing three metabolites and p-values that were determined to be prognostic by their combination by k-means cluster analysis. FIG. 31D is a diagram showing three metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 32A is a diagram showing four metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 32B is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Weibull regression. FIG. 32C is a diagram showing four metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 32D is a diagram showing four metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 33A is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 33B is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 33C is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 33D is a diagram showing four metabolites and p-values that were determined to have prognostic ability in their combination by Cox regression. FIG. 34A is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by k-means cluster analysis. FIG. 34B is a diagram showing four metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 34C is a diagram showing four metabolites and p-values that were determined to have prognostic ability by their combination by k-means cluster analysis. FIG. 34D is a diagram showing four metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 35A is a diagram showing four metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 35B is a diagram showing four metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 35C is a diagram showing four metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 35D is a diagram showing four metabolites and p-values determined to be prognostic by their combination by Weibull regression. FIG. 36A is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 36B is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 36C is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 36D is a diagram showing four metabolites and p-values determined to have prognostic ability by their combination by Cox regression. FIG. 37A is a diagram showing four metabolites and p-values that were determined to be prognostic by their combination by k-means cluster analysis. FIG. 37B is a diagram showing four metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 37C is a diagram showing four metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 37D is a diagram showing four metabolites and p-values determined to be prognostic by their combination by k-means cluster analysis. FIG. 38 is a diagram showing the results of comparing the survival rates of the two groups classified based on the relative concentration values of the four amino acids. FIG. 39 is a diagram showing amino acids that change in common with the carcinogenic process and malignant transformation. FIG. 40 is a diagram showing the prognostic comparison results of two groups classified by F (Ser, Thr, Asn, Gln, His, Trp). FIG. 41 is a diagram showing an association between an amino acid profile and various pathological information and prognosis.

  Embodiments of an evaluation method according to the present invention (first embodiment) and embodiments of an evaluation apparatus, an evaluation method, an evaluation program, a recording medium, an evaluation system, and a terminal device according to the present invention (second embodiment) ) Will be described in detail with reference to the drawings. Note that the present invention is not limited to these embodiments.

[First Embodiment]
[1-1. Overview of 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, concentration data regarding at least one concentration value of the 26 types of amino acids in pancreatic cancer tissue obtained from an evaluation target (for example, an individual such as an animal or a human) is acquired (step S11).

In step S11, for example, density data measured by a company or the like that performs density value measurement may be acquired. In addition, a concentration value is measured from a pathological tissue section prepared on the basis of a pancreatic cancer tissue obtained from an evaluation object by, for example, the following measurement method (A), (B), (C), or (D) By doing so, density data may be acquired. Here, the unit of the concentration value may be, for example, a molar concentration, a weight concentration, or an enzyme activity, and may be obtained by adding / subtracting / dividing an arbitrary constant to / from these concentrations.
(A) The collected pathological tissue section is homogenized with a methanol solution and degreased by adding chloroform to extract an amino acid fraction. All extracted samples are stored frozen at −80 ° C. until the concentration value is measured. When measuring the concentration value, pre-column derivatization is performed using a labeling reagent (3-aminopyridyl-N-hydroxysuccinimidyl carbamate), and the concentration value is analyzed by a liquid chromatograph mass spectrometer (LC / MS). (See International Publication No. 2003/069328, International Publication No. 2005/116629). For example, AccQ • Tag (registered trademark) manufactured by Waters may be used as the precolumn derivatization reagent.
(B) The collected pathological tissue section is homogenized with a methanol solution and degreased by adding chloroform to extract an amino acid fraction. All extracted samples are stored frozen at −80 ° C. until the concentration value is measured. When measuring the concentration value, the concentration value is analyzed with an amino acid analyzer based on the post-column derivatization method. As the labeling reagent, ninhydrin or o-phthalaldehyde can be used.
(C) The collected pathological tissue section is homogenized with a methanol solution and degreased by adding chloroform to extract an amino acid fraction. All extracted samples are stored frozen at −80 ° C. until the concentration value is measured. At the time of measuring the concentration value, a molecule that reacts or binds to a target amino acid, such as an enzyme or an aptamer, is used, and the concentration value is analyzed by quantifying a substance or spectroscopic value that increases or decreases by substrate recognition.
(D) A labeling reagent (pN, N, N-trimethylammonioanilyl N′-hydroxysuccinimidyl carbamate iodide) is sprayed on the collected pathological tissue section, and an imaging image is acquired using a MALDI-mass spectrometer etc. The concentration value is analyzed by quantifying the signal intensity at the site.

  Next, using the concentration value of at least one of the 26 types of amino acids included in the concentration data acquired in step S11, the prognosis of pancreatic cancer in the evaluation target (for example, life prognosis, functional prognosis, and no recurrence) Survival period etc.) is evaluated (step S12). Note that before executing step S12, data such as missing values and outliers may be removed from the density data acquired in step S11. In addition, “evaluating the prognosis of pancreatic cancer in the evaluation target” means, for example, predicting the survival period (for example, the number of days of survival) of the evaluation target after pancreatic cancer resection surgery, and the prognosis of pancreatic cancer in the evaluation target. Determining whether it is good or bad, evaluating the recurrence-free survival period in the evaluation object, predicting the 5-year survival rate of pancreatic cancer in the evaluation object, and the like. When the concentration values of the 26 types of amino acids are obtained in step S11, for example, in step S12, the concentration value of each amino acid is divided by the sum of the concentration values of the 26 types of amino acids and obtained by the division. The evaluation may be performed using the obtained value (relative concentration value).

  As described above, according to the first embodiment, the concentration data to be evaluated is acquired in step S11, and in step S12, the concentration data among the 26 types of amino acids included in the concentration data to be evaluated acquired in step S11. The prognosis of pancreatic cancer in the evaluation object is evaluated using at least one concentration value (may be the above-described relative concentration value) (in short, information for evaluating the prognosis of pancreatic cancer in the evaluation object or pancreatic cancer in the evaluation object) To obtain reliable information that can be helpful in determining the prognosis of Thereby, it is possible to accurately evaluate the prognosis of pancreatic cancer in the evaluation object using the amino acid concentration in the pancreatic cancer tissue (in short, information for evaluating the prognosis of pancreatic cancer in the evaluation object or the pancreas in the evaluation object) Reliable information that can be helpful in determining the prognosis of cancer can be provided).

Further, at least one concentration value (or the above-mentioned relative concentration value) of the 26 kinds of amino acids reflects the prognosis of pancreatic cancer in the evaluation object, or serves as an index of prognosis of pancreatic cancer in the evaluation object. Further, the concentration value (or the above-mentioned relative concentration value) may be converted by, for example, the following method, and the converted value reflects the prognosis of pancreatic cancer in the evaluation target Alternatively, it may be determined to be an index of prognosis of pancreatic cancer in the evaluation target. In other words, the concentration value or the converted value itself may be treated as an evaluation result regarding the prognosis of pancreatic cancer in the evaluation target.
The possible range of the density value is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or from -10.0 For example, an arbitrary value is added / subtracted / multiplied / divided from / to the density value, or the density value is converted into a predetermined conversion method (for example, exponential conversion, logarithmic conversion, Conversion by angle conversion, square root conversion, probit conversion, reciprocal conversion, Box-Cox conversion, power conversion, etc., or by combining these calculations for the density value, the density value is converted. May be. For example, the value of an exponential function with the concentration value as the index and the Napier number as the base (specifically, the natural logarithm ln (p / (1-p)) when defining the probability p that the prognosis of pancreatic cancer is poor (P / (1-p) value in the case where is equal to the density value), or a value obtained by dividing the calculated exponential function value by the sum of 1 and the value (specifically, The value of probability p) 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.
Further, for each metabolite and each amino acid, the concentration distribution may be converted into a normal distribution and then converted into a deviation value so that the average is 50 and the standard deviation is 10.
These conversions may be performed by gender or age.
The density value in the present specification may be the density value itself or a value after the density value is converted.

  In addition, position information regarding the position of a predetermined mark on a predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper is obtained as a concentration value of at least one of the 26 types of amino acids (described above). (It may be a relative concentration value) or when the concentration value is converted, it is generated using the converted value, and the generated positional information reflects the prognosis of the pancreatic cancer in the evaluation object or the pancreatic cancer in the evaluation object It may be determined that the index is a prognosis. The predetermined scale is for evaluating the prognosis of pancreatic cancer. For example, the scale is a scale with a scale, and the “concentration value or range after conversion or the range , A scale corresponding to the upper limit value and the lower limit value in “part of” is shown at least. 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, at least one concentration value (may be the above-described relative concentration value) of the 26 kinds of amino acids is a predetermined value (mean value ± 1SD, 2SD, 3SD, N quantile, N percentile, or clinical significance. The prognosis of pancreatic cancer in the evaluation subject may be evaluated when it is lower than or lower than a predetermined value or higher than a predetermined value or higher than a predetermined value. In this case, instead of the concentration value itself, a concentration deviation value (a value obtained by normalizing the concentration distribution for each gender for each amino acid and then converting the concentration distribution to an average of 50 and a standard deviation of 10) may be used. . For example, when the concentration deviation value is less than the average value −2SD (when the concentration deviation value <30) or when the concentration deviation value is higher than the average value + 2SD (when the concentration deviation value> 70), the pancreatic cancer in the evaluation target Prognosis may be assessed.

  Further, at least one concentration value (may be the above-mentioned relative concentration value) of the 26 types of amino acids and at least one concentration value (may be the above-mentioned relative concentration value) of the 26 types of amino acids are substituted. The prognosis of pancreatic cancer in the evaluation target may be evaluated by calculating the value of the expression using an expression including the variable.

Further, it may be determined that the value of the calculated formula reflects the prognosis of pancreatic cancer in the evaluation target or is an index of the prognosis of pancreatic cancer in the evaluation target. It is possible to determine that the converted value reflects the prognosis of pancreatic cancer in the evaluation object or becomes an index of prognosis of pancreatic cancer in the evaluation object after conversion by the listed method or the like. In other words, the value of the formula or the converted value itself may be treated as an evaluation result regarding the prognosis of pancreatic cancer in the evaluation target.
A possible range of the value of the expression is a predetermined range (for example, a range from 0.0 to 1.0, a range from 0.0 to 10.0, a range from 0.0 to 100.0, or -10.0 In order to fit within the range from 1 to 10.0, etc., for example, an arbitrary value is added / subtracted / divided / divided from / to the value of the expression, or the value of the expression is converted into a predetermined conversion method (for example, exponential conversion, Logarithmic transformation, angular transformation, square root transformation, probit transformation, reciprocal transformation, Box-Cox transformation, or power transformation), or by combining these calculations on the value of the expression, The value of the expression may be converted. For example, the value of the exponential function with the value of the expression as the index and the Napier number as the base (specifically, the natural logarithm ln (p / (1-p) when defining the probability p that the prognosis of pancreatic cancer is poor) ) Is equal to the value of the formula, the value of p / (1-p)) may be further calculated, and the value of the calculated exponential function divided by the sum of 1 and the value ( Specifically, the value of probability p) may be further calculated.
Further, the value of the expression may be converted so that the value after conversion under a specific condition becomes a specific value.
Further, the deviation value may be converted to an average of 50 and a standard deviation of 10.
These conversions may be performed by gender or age.
Note that the value of the expression in this specification may be the value of the expression itself, or may be a value after converting the value of the expression.

  In addition, if the position information on the position of a predetermined mark on a predetermined ruler that is visible on a display device such as a monitor or a physical medium such as paper is converted into an expression value or the value of the expression The position information generated using the later values may be determined to reflect the prognosis of the pancreatic cancer in the evaluation object or to be an index of the prognosis of the pancreatic cancer in the evaluation object. The predetermined ruler is for evaluating the prognosis of pancreatic cancer. For example, the ruler is a ruler with a scale, and “the range of the value of the formula or the value after conversion, or the That is, at least a scale corresponding to the upper limit value and the lower limit value in “part of range” is shown. The predetermined mark corresponds to the value of the expression or the value after conversion, and is, for example, a circle mark or a star mark.

  Moreover, you may evaluate qualitatively the prognosis of the pancreatic cancer in an evaluation object. Specifically, “at least one concentration value (or the above-mentioned relative concentration value) of the 26 types of amino acids and one or more preset threshold values” or “of the 26 types of amino acids Using at least one density value (may be the above-described relative density value), an expression including a variable to which the density value (or the above-described relative density value may be substituted), and one or more preset threshold values ” Thus, the evaluation target may be classified into any one of a plurality of categories defined in consideration of at least the prognosis level of pancreatic cancer. In addition, a plurality of categories include categories for assigning subjects with good prognosis of pancreatic cancer (for example, subjects considered to have a long postoperative survival period or a high 5-year survival rate), and prognosis of pancreatic cancer. Categories for assigning poor subjects (eg, subjects that have a short postoperative survival period or subjects with a low 5-year survival rate) and subjects with a moderate prognosis for pancreatic cancer (eg, postoperative survival time) May be included as a category for subjects who are considered to have moderate or five-year survival rates. In addition, the plurality of categories may include a category for assigning a subject having a good prognosis for pancreatic cancer and a category for assigning a subject having a poor prognosis for pancreatic cancer. Alternatively, the density value or the expression value may be converted by a predetermined method, and the evaluation target may be classified into any one of a plurality of categories using the converted value.

In addition, the form used for the evaluation is not particularly limited, but for example, the following form may be used.
・ Linear models such as multiple regression, linear discriminant, principal component analysis, canonical discriminant analysis based on least square method ・ Generalized linear model such as logistic regression based on maximum likelihood method, Cox regression ・ In addition to generalized linear model Generalized linear mixed models that take into account random effects such as inter-individual differences, inter-facility differences, formulas created by cluster analysis such as K-means method, hierarchical cluster analysis, MCMC (Markov chain Monte Carlo method), Bayesian network, Formulas created based on Bayesian statistics such as Hierarchical Bayes method, formulas created by class classification such as support vector machines and decision trees, formulas created by methods not belonging to the above categories such as fractional formulas, sums of formulas of different formats Formula as shown in

  In addition, the formula used in the evaluation is described in, for example, the method described in International Publication No. 2004/052191 which is an international application by the present applicant or International Publication No. 2006/098192 which is an international application by the present applicant. You may create by the method. Any formula obtained by these methods can be suitably used to evaluate the prognosis of pancreatic cancer regardless of the unit of the amino acid concentration value in the concentration data as input data. .

  Here, in the multiple regression equation, multiple logistic regression equation, canonical discriminant function, etc., a coefficient and a constant term are added to each variable. The coefficient and the constant term are preferably real numbers, and more preferably May be any value belonging to the range of the 99% confidence interval of the coefficient and constant term obtained for performing the various classifications from the data, and more preferably, the value obtained for performing the various classifications from the data. Any value may be used as long as it falls within the 95% confidence interval of the obtained coefficient and constant term. Further, 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 / subtracting an arbitrary real constant thereto. When logistic regression, linear discriminant, multiple regression, etc. are used for evaluation, linear conversion (addition of constants, multiple of constants) and monotonic increase (decrease) conversion (for example, logit conversion) change evaluation performance. However, since it is equivalent to before conversion, the one after these conversions may be used.

  Further, the fractional expression means that the numerator of the fractional expression is represented by the sum of the variables A, B, C,... And / or the denominator of the fractional expression is the sum of the variables a, b, c,. It is represented by In addition, the fractional expression includes a sum of fractional expressions α, β, γ,. The fractional expression also includes a divided fractional expression. Note that each variable used in the numerator and denominator may have an appropriate coefficient. The variables used for the numerator and denominator may overlap. Further, an appropriate coefficient may be attached to each fractional expression. Further, the value of the coefficient of each variable and the value of the constant term may be real numbers. In some fractional expressions and those in which the numerator and denominator variables are interchanged, the sign of the correlation with the target variable is generally reversed, but their correlation is maintained. Since the evaluation performance can be regarded as equivalent, the fractional expression includes one in which the numerator variable and the denominator variable are interchanged.

Then, when evaluating the prognosis of pancreatic cancer, in addition to the concentration value of at least one of the 26 types of amino acids, other values related to biological information (for example, the values listed below) may be further used. . In addition to the variable into which the concentration value of at least one of the 26 types of amino acids is substituted, other biological information values (for example, the values listed below) are substituted into the formula used for evaluation. One or more variables may be further included.
1. 1. Concentration values of blood metabolites other than amino acids (amino acid metabolites, sugars, lipids, etc.), proteins, peptides, minerals, hormones, etc. 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 glucose level), CRP (C-reactive protein), red blood cell, hemoglobin, hematocrit, MCV, MCH, MCHC, white blood cell, platelet count, etc. Value 3. 3. Value obtained from image information such as ultrasonic echo, X-ray, CT, MRI, endoscopic image, etc. Age, height, weight, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, gender, smoking information, meal information, drinking information, exercise information, stress information, sleep information, family history information, disease history information (diabetes, etc.) ) Values related to biomarkers such as: 5. Information obtained from histopathological examinations such as cancer mass, invasive growth mode, lymphatic vessel invasion, venous invasion, intrapancreatic nerve invasion, and main pancreatic duct progression

[Second Embodiment]
[2-1. Outline 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 the description of the second embodiment, the description overlapping the first embodiment described above may be omitted. In particular, here, the case of using the value of the formula or the value after the conversion when evaluating the prognosis of pancreatic cancer is described as an example. For example, the concentration of at least one of the 26 types of amino acids is described. A value, a relative density value, or a value after conversion (for example, a density deviation value) may be used.

  The control unit includes the 26 types of concentration data included in the concentration data of an evaluation target (for example, an individual such as an animal or a human) acquired in advance regarding the concentration value of at least one of the 26 types of amino acids in the pancreatic cancer tissue. Prognosis of pancreatic cancer in the evaluation target by calculating the value of the expression using the expression stored in the storage unit in advance including the concentration value of at least one of the amino acids and the variable into which the concentration value is substituted Is evaluated (step S21). When concentration data including the concentration values of the 26 types of amino acids is used in step S21, the control unit, for example, divides the concentration value of each amino acid by the sum of the concentration values of the 26 types of amino acids, You may perform the said evaluation using the value (relative density value) obtained by the said division.

  Note that the formula used in step S21 may be created based on formula creation processing (steps 1 to 4) described below. Here, an overview of the formula creation process will be described. Note 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 can select a candidate formula (based on a predetermined formula creation method from index state information stored in advance in the storage unit (data having missing values, outliers, etc. may be removed in advance)). For example, y = a1x1 + a2x2 +... + Anxn, y: index data, xi: density data, ai: constant, i = 1, 2,..., N) are created (step 1). The index state information includes concentration data (may be the above-described relative concentration value) and index data related to the survival period after excision of pancreatic cancer (for example, data related to the number of days of survival).

  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, determination are made from index state information. A plurality of candidate expressions may be created using a combination of multivariate analysis such as trees). Specifically, a plurality of different algorithms are used for index state information that is multivariate data composed of concentration data and index data obtained by analyzing cancer tissues obtained from a large number of pancreatic cancer groups. Multiple groups of candidate formulas may be created concurrently. For example, discriminant analysis and logistic regression analysis may be performed simultaneously using different algorithms to create two different candidate formulas. Alternatively, the candidate formulas may be created by converting index state information using candidate formulas created by performing principal component analysis and performing discriminant analysis on the converted index status information. As a result, it is possible to finally create an optimum 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 density data. Candidate formulas created using discriminant analysis are high-order formulas (including exponents and logarithms) that contain variables that minimize the ratio of the sum of variances within each group to the variance of all concentration data. is there. The candidate formula created using the support vector machine is a high-order formula (including a kernel function) including variables that maximize the boundary between groups. Moreover, the candidate formula created using the multiple regression analysis is a high-order formula including each variable that minimizes the sum of the distances from all density data. The candidate formula created using Cox regression analysis is a linear model including a log hazard ratio, and is a linear expression including each variable and its coefficient that maximize the likelihood of the model. The candidate formula created using logistic regression analysis is a linear model that represents log odds of probability, and is a linear formula that includes each variable that maximizes the likelihood of the probability. In the k-means method, k neighborhoods of each density data are searched, and the largest number of groups to which neighboring points belong is defined as the group to which the data belongs, and the group to which the input density data belongs. This is a method for selecting a variable that best matches the group defined as. Cluster analysis is a technique for clustering (grouping) points that are closest to each other in all density data. Further, the decision tree is a technique for predicting a group of density data from patterns that can be taken by variables with higher ranks by adding ranks to the variables.

  Returning to the description of the formula creation process, the control unit verifies (mutually verifies) the candidate formula created in step 1 based on a predetermined verification method (step 2). Candidate expressions are verified for each candidate expression created in step 1. In Step 2, the discrimination rate, sensitivity, specificity, information criterion, ROC_AUC (candidate formula of candidate formulas are determined based on at least one of the bootstrap method, holdout method, N-fold method, leave one-out method, and the like. It may be verified with respect to at least one of the area under the receiver characteristic curve). Thereby, a candidate formula having high predictability or robustness in consideration of the index state information and the evaluation conditions can be created.

  Here, the discrimination rate is an evaluation method according to the present embodiment. An evaluation object whose true state is negative (for example, an evaluation object with good prognosis) is correctly evaluated as negative, and the true state is positive. This is the rate at which subjects (for example, subjects with poor prognosis) are correctly evaluated as positive. Sensitivity is the rate at which an evaluation object whose true state is positive is correctly evaluated as positive in the evaluation method according to the present embodiment. Further, the specificity is a rate at which an evaluation object whose true state is negative is correctly evaluated as negative in the evaluation method according to the present embodiment. The Akaike Information Criterion is a standard that indicates how much the observed data matches the statistical model in the case of regression analysis, etc., and is “−2 × (maximum log likelihood of statistical model) + 2 × (statistics). The model having the smallest value defined by “the number of free parameters of the model)” is determined to be the best. ROC_AUC is defined as the area under the curve of the receiver characteristic curve (ROC), which is a curve created by plotting (x, y) = (1-specificity, sensitivity) on two-dimensional coordinates, and ROC_AUC The value of 1 is 1 in complete discrimination, and the closer this value is to 1, the higher the discriminability. The predictability is an average of the discrimination rate, sensitivity, and specificity obtained by repeating the verification of candidate formulas. Robustness is the 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 density data included in the index state information used when creating a candidate formula by selecting a variable of the candidate formula based on a predetermined variable selection method. (Step 3). In step 3, the selection of variables may be performed for each candidate formula created in step 1. Thereby, the variable of a candidate formula can be selected appropriately. Then, Step 1 is executed again using the index state information including the density data selected in Step 3. In step 3, the candidate expression variable may be selected based on at least one of the stepwise method, the best path method, the neighborhood search method, and the genetic algorithm from the verification result in step 2. 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 repeatedly executes the above-described step 1, step 2, and step 3, and based on the verification results accumulated thereby, candidates to be used for evaluation from a plurality of candidate formulas By selecting an expression, an expression used for evaluation is created (step 4). The selection of candidate formulas includes, for example, selecting an optimal formula from candidate formulas created by the same formula creation method and selecting an optimal formula from all candidate formulas.

  As described above, in the formula creation process, processing related to creation of candidate formulas, verification of candidate formulas and selection of variables of candidate formulas is organized (systemized) in a series of flows based on index state information. By executing it, it is possible to create an optimal formula for prognostic evaluation of pancreatic cancer. In other words, in the formula creation process, at least one concentration of the 26 types of amino acids is used for multivariate statistical analysis, and a variable selection method and cross-validation are combined to select an optimal and robust variable set. To extract a formula with high evaluation performance.

[2-2. Configuration of Second Embodiment]
Here, the configuration of an evaluation system according to the second embodiment (hereinafter may be referred to as the present system) will be described with reference to FIGS. 3 to 14. This system is merely an example, and the present invention is not limited to this. In particular, here, the case of using the value of the formula or the value after the conversion when evaluating the prognosis of pancreatic cancer is described as an example. For example, the concentration of at least one of the 26 types of amino acids is described. A value, a relative density value, or a value after conversion (for example, a density deviation value) may be used.

  First, the overall configuration of this system will be described with reference to FIG. 3 and FIG. FIG. 3 is a diagram showing an example of the overall configuration of the present system. FIG. 4 is a diagram showing another example of the overall configuration of the present system. As shown in FIG. 3, the present system includes an evaluation apparatus 100 that evaluates the prognosis of pancreatic cancer in an individual to be evaluated, and an individual related to the concentration value of at least one of the 26 types of amino acids in the pancreatic cancer tissue. A client device 200 (corresponding to a terminal device of the present invention) that provides density data is configured to be communicably connected via a network 300. In the present system, the client apparatus 200 that is a provider of data used for evaluation and the client apparatus 200 that is a provider of evaluation results may be different. As shown in FIG. 4, this system stores a database device that stores index state information used when creating formulas used in evaluation, formulas used during evaluation, and the like in addition to the evaluation device 100 and the client device 200. 400 may be configured to be communicably connected via the network 300.

  Next, the configuration of the evaluation apparatus 100 of the present system will be described with reference to FIGS. FIG. 5 is a block diagram showing an example of the configuration of the evaluation apparatus 100 of the present system, and conceptually shows only the portion related to the present invention in the configuration.

  The evaluation device 100 includes a control unit 102 such as a CPU (Central Processing Unit) that controls the evaluation device in an integrated manner, a communication device such as a router, and a wired or wireless communication line such as a dedicated line. The communication interface unit 104 that is communicably connected to the network 300, the storage unit 106 that stores various databases, tables, and files, and the input / output interface unit 108 that is connected to the input device 112 and the output device 114 are configured. These units are communicably connected via an arbitrary communication path. Here, the evaluation apparatus 100 may be configured in the same housing as various analysis apparatuses (for example, an amino acid analysis apparatus). For example, a small analysis having a configuration (hardware and software) that calculates (measures) a concentration value of at least one of the 26 types of amino acids in a tissue and outputs the calculated value (printing, monitor display, etc.) The apparatus may further include an evaluation unit 102d to be described later, and output a result obtained by the evaluation unit 102d using the above configuration.

  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 other terminals via a communication line.

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

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

  The concentration data file 106a stores concentration data relating to the concentration value of at least one of the 26 types of amino acids in the pancreatic cancer tissue. 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 density data file 106a is configured by associating an individual number for uniquely identifying an individual (sample) to be evaluated with density data. Here, in FIG. 6, the density data is handled as a numerical value, that is, a continuous scale, but the density data may be a nominal scale or an order scale. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state. Moreover, you may combine the value regarding the biometric information mentioned above other than an amino acid with density | concentration data.

  Returning to FIG. 5, the index state information file 106b stores the index state information used when creating the formula. FIG. 7 is a diagram illustrating an example of information stored in the index state information file 106b. As shown in FIG. 7, the information stored in the index state information file 106b correlates an individual number, index data (for example, day data, etc.) related to the survival period after pancreatic cancer resection surgery, and concentration data. Configured. Here, in FIG. 7, the index data and the density data are handled as numerical values (that is, continuous scales), but the index data and the density data may be nominal scales or order scales. In the case of a nominal scale or an order scale, analysis may be performed by giving an arbitrary numerical value to each state.

  Returning to FIG. 5, the designated index state information file 106c stores the index state information designated by the designation unit 102b described later. FIG. 8 is a diagram illustrating an example of information stored in the designated index state information file 106c. As shown in FIG. 8, the information stored in the designated index state information file 106c is configured by associating an individual number, designated index data, and designated density data with each other.

  Returning to FIG. 5, the formula-related information database 106d is configured by a formula file 106d1 that stores formulas created by a formula creation unit 102c described later. The expression file 106d1 stores expressions used for evaluation. FIG. 9 is a diagram illustrating an example of information stored in the expression file 106d1. As shown in FIG. 9, the information stored in the expression file 106d1 includes a rank, an expression (in FIG. 9, Fp (His,...), Fp (His, EtOHNH2, GABA), Fk (His, EtOHNH2, GABA,...)), A threshold corresponding to each formula creation method, and a verification result of each formula (for example, the value of each formula) are associated with each other.

  Returning to FIG. 5, the evaluation result file 106e stores the evaluation result obtained by the evaluation unit 102d described later. FIG. 10 is a diagram illustrating an example of information stored in the evaluation result file 106e. Information stored in the evaluation result file 106e includes an individual number for uniquely identifying an individual (sample) to be evaluated, concentration data of the individual acquired in advance, and an evaluation result regarding the prognosis of pancreatic cancer (for example, 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 described later, the position information generated by the generation unit 102d3 described later, or the classification obtained by the classification unit 102d4 described later Results) and the like.

  Returning to FIG. 5, the control unit 102 has an internal memory for storing a control program such as an OS, a program that defines various processing procedures, and necessary data, and various information processing based on these programs. Execute. As shown in the figure, the control unit 102 is roughly divided into a reception unit 102a, a specification 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 has data with missing values from the index state information sent from the database device 400 and the density data sent from the client device 200. Data processing such as removal of many variables is also performed.

  The receiving unit 102a may receive information (specifically, concentration data, index state information, formulas, etc.) transmitted from the client device 200 or the database device 400 via the network 300 or the like. The receiving unit 102a may receive data used for evaluation transmitted from a client device 200 different from the client device 200 that is the transmission destination of the evaluation result. The designating unit 102b designates index data and density data that are targets for creating an expression.

  The formula creating unit 102c creates a formula based on the index status information received by the receiving unit 102a and the index status information specified by the specifying unit 102b. Note that if the formula is stored in a predetermined storage area of the storage unit 106 in advance, the formula creation unit 102 c may create the formula by selecting a desired formula from the storage unit 106. 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) that stores the formula in advance.

  The evaluation unit 102d is included in a formula obtained in advance (for example, a formula created by the formula creation unit 102c or a formula received by the reception unit 102a) and the concentration data of the individual received by the reception unit 102a. The prognosis of pancreatic cancer in an individual is evaluated by calculating the value of the equation using the concentration value of at least one of the 26 types of amino acids. Note that the evaluation unit 102d evaluates the prognosis of pancreatic cancer in the individual using at least one concentration value or relative concentration value of the 26 types of amino acids or a value after conversion thereof (for example, concentration deviation value). May be.

  Here, the configuration of the evaluation unit 102d will be described with reference to FIG. FIG. 11 is a block diagram showing a configuration of the evaluation unit 102d, and conceptually shows only a portion 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 calculates the value of the expression using an expression including at least one concentration value (or the above-described relative concentration value) of the 26 types of amino acids and a variable into which the concentration value is substituted. . Note that the evaluation unit 102d may store the value of the expression calculated by the calculation unit 102d1 as an evaluation result in a predetermined storage area of the evaluation result file 106e.

  The conversion unit 102d2 converts the value of the expression calculated by the calculation unit 102d1 using, for example, the conversion method described above. Note that the evaluation unit 102d may store the value after the conversion by the conversion unit 102d2 as an evaluation result in a predetermined storage area of the evaluation result file 106e. Further, the conversion unit 102d2 may convert the density value or the relative density value included in the density data, for example, by the conversion method described above.

  The generation unit 102d3 uses the value of the expression calculated by the calculation unit 102d1 or the conversion unit 102d2 for the position information related to the position of the predetermined mark on the predetermined ruler that is visibly displayed on a display device such as a monitor or a physical medium such as paper. It is generated using the value after conversion (can be a density value or a relative density value, or a value after these conversions). The evaluation unit 102d may store the position information generated by the generation unit 102d3 as an evaluation result in a predetermined storage area of the evaluation result file 106e.

  The classification unit 102d4 uses the value of the expression calculated by the calculation unit 102d1 or the value after conversion by the conversion unit 102d2 (which may be a concentration value, a relative concentration value, or a value after conversion thereof) to identify an individual as pancreatic cancer Is classified into one of a plurality of categories defined in consideration of at least the degree of prognosis.

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

  The transmission unit 102 f transmits the evaluation result to the client device 200 that is the transmission source of the individual concentration data, or transmits the formula or evaluation result created by the evaluation device 100 to the database device 400. Note that the transmission unit 102f may transmit the evaluation result to a client device 200 different from the client device 200 that is a transmission source of 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 apparatus 200 of the present system, and conceptually shows only the portion related to the present invention in the configuration.

  The client device 200 includes a control unit 210, a ROM 220, an 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. These units are connected via an arbitrary communication path. Are connected to communicate. 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)) connected to peripheral devices such as a printer, a monitor, and an image scanner as necessary. It may be based on a terminal, a portable terminal, a mobile communication terminal, an information processing terminal such as a PDA (Personal Digital Assistant), or the like.

  The input device 250 is a keyboard, a mouse, a microphone, or the like. A monitor 261, which will be described later, also realizes a pointing device function in cooperation with the mouse. The output device 260 is an output unit that outputs information received via the communication IF 280, and includes a monitor (including a home television) 261 and a printer 262. In addition, the output device 260 may be provided with a speaker or the like. The input / output IF 270 is connected to the input device 250 and the output device 260.

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

  The control unit 210 includes a reception unit 211 and a transmission unit 212. The receiving unit 211 receives various types of information such as an evaluation result transmitted from the evaluation device 100 via the communication IF 280. The transmission unit 212 transmits various types of information such as individual concentration data to the evaluation apparatus 100 via the communication IF 280.

  The control unit 210 may be realized by a CPU and a program that is interpreted and executed by the CPU and all or any part of the processing performed by the control unit. The ROM 220 or the HD 230 stores computer programs for giving instructions 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 constitutes the control unit 210 in cooperation with the CPU. Further, the computer program may be recorded in an application program server connected to the client apparatus 200 via an arbitrary network, and the client apparatus 200 may download all or a part thereof as necessary. . In addition, 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 (a calculation unit 210a1, a conversion unit 210a2, a generation unit 210a3, and a classification unit 210a4) having the same functions as those of the evaluation unit 102d provided in the evaluation apparatus 100. ) May be provided. And when the evaluation part 210a is provided in the control part 210, the evaluation part 210a is based on the information contained in the evaluation result transmitted from the evaluation apparatus 100, and the value of a formula (in the conversion part 210a2) ( The position value corresponding to the value of the expression or the value after conversion (the value may be the density value or the relative density value or the value after conversion) may be generated by the generation unit 210a3. Or the classification unit 210a4 classifies the individual into one of a plurality of divisions using the value of the expression or the value after conversion (which may be a density value, a relative density value, or a value after conversion). Or you may.

  Next, the network 300 of this system will be described with reference to FIGS. The network 300 has a function of connecting the evaluation apparatus 100, the client apparatus 200, and the database apparatus 400 so that they can communicate with each other. For example, the Internet, an intranet, a LAN (Local Area Network) (including both wired and wireless), and the like. It is. The network 300 includes a VAN (Value-Added Network), a personal computer communication network, a public telephone network (including both analog and digital), a dedicated line network (including both analog and digital), CATV ( Community Antenna TeleVision (PDM), mobile circuit switching network or mobile packet switching network (International Mobile Telecommunication (IMT) 2000 system, Global System for Mobile Communications (PDC) PPD (PDC)) System), a wireless paging network, a local wireless network such as Bluetooth (registered trademark), a PH An S network, a satellite communication network (including CS (Communication Satellite), BS (Broadcasting Satellite), ISDB (Integrated Services Digital Broadcasting), etc.) may be used.

  Next, the configuration of the database apparatus 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 apparatus 400 of this system, and conceptually shows only the portion related to the present invention in the configuration.

  The database apparatus 400 has a function of storing index state information used when creating an expression in the evaluation apparatus 100 or the database apparatus, an expression created in the evaluation apparatus 100, an evaluation result in the evaluation apparatus 100, and the like. As shown in FIG. 13, the database apparatus 400 includes a control unit 402 such as a CPU that controls the database apparatus in an integrated manner, a communication apparatus such as a router, and a wired or wireless communication circuit such as a dedicated line. A communication interface unit 404 that connects the apparatus to the network 300 to be communicable, a storage unit 406 that stores various databases, tables, and files (for example, files for Web pages), and an input unit that connects to the input unit 412 and the output unit 414. The output interface unit 408 is configured to be communicable via an arbitrary communication path.

  The storage unit 406 is a storage unit, and 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 can be used. The storage unit 406 stores various programs 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 other terminals via a communication line. The input / output interface unit 408 is connected to the input device 412 and the output device 414. Here, in addition to a monitor (including a home television), a speaker or a printer can be used as the output device 414. In addition to the keyboard, mouse, and microphone, the input device 412 can be a monitor that realizes a pointing device function in cooperation with the mouse.

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

In this description, the evaluation apparatus 100 executes from the reception of the concentration data to the calculation of the value of the expression, the classification into the individual categories, and the transmission of the evaluation result, and the client apparatus 200 receives the evaluation result. In the case where the client device 200 includes the evaluation unit 210a, it is sufficient for the evaluation device 100 to calculate the value of the expression. For example, conversion of the value of the expression, position information The generation and the classification into individual sections may be appropriately shared by the evaluation apparatus 100 and the client apparatus 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 in the conversion unit 210a2, or the value of the expression or the value after conversion in the generation unit 210a3. Or the classification unit 210a4 may classify the individual into one of a plurality of categories using the value of the expression or the value after conversion.
Further, when the client device 200 receives the converted value from the evaluation device 100, the evaluation unit 210a generates position information corresponding to the converted value in the generation unit 210a3, or converts it in the classification unit 210a4. An individual may be classified into any one of a plurality of divisions using a later value.
When the client device 200 receives the value of the expression or the value after conversion and the position information from the evaluation device 100, the evaluation unit 210a uses the value of the expression or the value after conversion in the classification unit 210a4. The individual may be classified into any one of a plurality of sections.

[2-3. Other Embodiments]
The evaluation device, the evaluation method, the evaluation program, the evaluation system, and the terminal device according to the present invention are not limited to the second embodiment described above, but various different embodiments within the scope of the technical idea described in the claims. May be implemented.

  In addition, among the processes described in the second embodiment, all or part of the processes described as being performed automatically can be performed manually, or the processes described as being performed manually All or a part of the above can be automatically performed by a known method.

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

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

  For example, all or some of the processing functions provided in the evaluation apparatus 100, particularly the processing functions performed by the control unit 102, may be realized by the CPU and a program interpreted and executed by the CPU. Alternatively, it may be realized as hardware by wired logic. The program is recorded on a non-transitory computer-readable recording medium including programmed instructions for causing the information processing apparatus to execute the evaluation method according to the present invention, and is stored in the evaluation apparatus 100 as necessary. Read mechanically. That is, in the storage unit 106 such as a ROM or an HDD (Hard Disk Drive), a computer program for giving instructions to the CPU in cooperation with the OS and performing various processes is recorded. This computer program is executed by being loaded into the RAM, and constitutes a control unit in cooperation with the CPU.

  The computer program may be stored in an application program server connected to the evaluation apparatus 100 via an arbitrary network, and may be downloaded in whole or in part as necessary.

  In addition, the evaluation program according to the present invention may be stored in a computer-readable recording medium that is not temporary, or may be configured as a program product. Here, the “recording medium” refers to a memory card, USB (Universal Serial Bus) memory, SD (Secure Digital) card, flexible disk, magneto-optical disk, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electric Erasable and Programmable Read Only Memory (registered trademark), CD-ROM (Compact Disc Read Only Memory), MO (Magneto-Optical disk), DVD (Digital Versatile Disc), and DVD (Digital Versatile Disc) Any “portable physical media It is intended to include.

  The “program” is a data processing method described in an arbitrary language or description method, and may be in any form such as source code or binary code. Note that the “program” is not necessarily limited to a single configuration, and functions are achieved in cooperation with a separate configuration such as a plurality of modules and libraries or a separate program represented by the OS. Including things. In addition, a well-known structure and procedure can be used about the specific structure and reading procedure for reading a recording medium in each apparatus shown to embodiment, the installation procedure after reading, etc.

  Various databases and the like stored in the storage unit 106 are storage devices such as a memory device such as a RAM and a ROM, a fixed disk device such as a hard disk, a flexible disk, and an optical disk. Programs, tables, databases, web page files, and the like.

  Further, the evaluation apparatus 100 may be configured as an information processing apparatus such as a known personal computer or workstation, or may be configured as the information processing apparatus to which an arbitrary peripheral device is connected. The evaluation apparatus 100 may be realized by installing software (including a program or data) that causes the information processing apparatus to realize the evaluation method of the present invention.

  Furthermore, the specific form of distribution / integration of the devices is not limited to that shown in the figure, and all or a part of them may be functionally or physically in arbitrary units according to various additions or according to functional loads. It can be configured to be distributed and integrated. That is, the above-described embodiments may be arbitrarily combined and may be selectively implemented.

  Using the pathological tissue sections of 130 patients undergoing excision of tubular adenocarcinoma of the pancreas, 26 types of tissue metabolites (intracorporeal amino acids: EtOHNH2, Gly, Ala, GABA) by the amino acid analysis method (A) described above. , Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His, HyLys, Phe, Arg, Cit, Tyr, Trp) concentration values (pmol) / Mg tissue). In addition, the survival period after resection surgery was observed.

  The prognostic ability of each metabolite alone was evaluated by Weibull regression, Cox regression, and k-means cluster analysis using the concentration values and survival data of 26 types of metabolites for all patients.

  In the case of Weibull regression and Cox regression, the model was estimated with the concentration value of each metabolite alone, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, it was discriminated into two groups based on the concentration value of each metabolite alone, and the difference in survival curve between the two groups was tested by a log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIGS. 14 to 16 show metabolites and p-values determined to have prognostic ability in each analysis.

  The relative concentration value of each metabolite (= “the concentration value of each metabolite” / “the sum of the concentration values of the 26 metabolites”) was calculated from the concentration values of the 26 metabolites measured in Example 1. .

  The prognostic ability of each metabolite alone was evaluated by Weibull regression, Cox regression, and k-means cluster analysis using the relative concentration values and survival data of 26 metabolites for all patients.

  In the case of Weibull regression and Cox regression, the model was estimated by the relative concentration value of each metabolite alone, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, the metabolites were distinguished into two groups based on the relative concentration value of each metabolite alone, and the difference in survival curves between the two groups was tested by a log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIGS. 17 to 19 show metabolites and p-values determined to have prognostic ability in each analysis.

  Using the concentration values of 26 kinds of metabolites measured in Example 1 and survival time data, the prognostic ability of the combination of each two metabolites was evaluated by Weibull regression, Cox regression, and k-means cluster analysis.

  In the case of Weibull regression and Cox regression, the model was estimated with the concentration values of the combination of each of the two metabolites, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, two groups were discriminated based on the concentration value of the combination of each of the two metabolites, and the difference in the survival curve between the two groups was tested by a log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIG. 20 to FIG. 22 show the combinations and p-values of two kinds of metabolites determined to have prognostic ability in each analysis.

  Using the relative concentration values of 26 kinds of metabolites calculated in Example 2 and survival time data, the prognostic ability of each combination of two metabolites was evaluated by Weibull regression, Cox regression, and k-means cluster analysis. .

  In the case of Weibull regression and Cox regression, the model was estimated based on the relative concentration values of the combinations of the two metabolites, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, two groups were discriminated based on the relative concentration values of the two metabolite combinations, and the difference in survival curves between the two groups was tested by log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIG. 23 to FIG. 25 show combinations of two metabolites judged to have prognostic ability in each analysis and p values.

  Using the concentration values of 26 kinds of metabolites measured in Example 1 and survival time data, the prognostic ability of each of the three metabolite combinations was evaluated by Weibull regression, Cox regression, and k-means cluster analysis.

  In the case of Weibull regression and Cox regression, the model was estimated from the concentration values of the combinations of the three metabolites, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, two groups were discriminated by the concentration value of the combination of each of the three metabolites, and the difference in survival curves between the two groups was tested by a log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIG. 26 to FIG. 28 show 200 combinations in ascending order of the p-value among the three metabolite combinations determined to have prognostic ability in each analysis.

  Using the relative concentration values of 26 types of metabolites calculated in Example 2 and survival data, the prognostic ability of each of the three metabolite combinations was evaluated by Weibull regression, Cox regression, and k-means cluster analysis. .

  In the case of Weibull regression and Cox regression, the model was estimated based on the relative concentration values of the combinations of the three metabolites, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, two groups were discriminated based on the relative concentration values of the combinations of the three metabolites, and the difference in survival curves between the two groups was tested by the log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIG. 29 to FIG. 31 show 200 combinations in ascending order of the p-value among the three metabolite combinations determined to have prognostic ability in each analysis.

  Using the concentration values of 26 kinds of metabolites measured in Example 1 and survival time data, the prognostic ability of each of the four metabolite combinations was evaluated by Weibull regression, Cox regression, and k-means cluster analysis.

  In the case of Weibull regression and Cox regression, the model was estimated from the concentration values of the combinations of the four metabolites, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, two groups were discriminated based on the concentration value of the combination of each of the four metabolites, and the difference in survival curves between the two groups was tested by a log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIG. 32 to FIG. 34 show 200 combinations in ascending order of the p-value among the four types of metabolites determined to have prognostic ability in each analysis.

  Using the relative concentration values of 26 types of metabolites calculated in Example 2 and survival time data, the prognostic ability of each of the four metabolite combinations was evaluated by Weibull regression, Cox regression, and k-means cluster analysis. .

  In the case of Weibull regression and Cox regression, the model was estimated by the relative concentration values of the combinations of the four metabolites, and the p value of the entire model was calculated. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability. In the case of k-means cluster analysis, two groups were discriminated based on the relative concentration values of the combination of each of the four metabolites, and the difference in survival curves between the two groups was tested by a log rank test. And when p value was smaller than 0.05, it was judged that there was prognosis prediction ability.

  FIG. 35 to FIG. 37 show 200 combinations in ascending order of the p-value among the four metabolite combinations determined to have prognostic ability in each analysis.

  Using the combination of four amino acids determined to have prognostic ability in Example 8 (GABA, Pro, Arg, Tyr), two groups were discriminated by k-means cluster analysis. And the survival time between two groups was compared by the Kaplan-Meier method. As a result, it was confirmed that the survival time in Group 2 was significantly long (FIG. 38).

  Using the concentration values of the 26 types of metabolites measured in Example 1 and the relative concentration values of the 26 types of metabolites calculated in Example 2, 6 amino acids that change in common with the carcinogenic process and malignancy ( Ser, Thr, Asn, Gln, His, Trp) were extracted (FIG. 39). In FIG. 39, IPMA is an abbreviation for intraductal papillary-mucinous adenoma, IPMC is an intraductal papillary-mucinous adenoma, and abbreviated intraductal papillary-mucinous adenoma Abbreviation of well-differentiated tubular adenocarcinoma, Tub2 is a moderately differentiated tubular adenocarcinoma, and Por is a poorly differentiated adenocarcinoma. Por is a poorly differentiated adenocarcinoma. ANA is an anaplastic cancer (ana It is an abbreviation of lastic carcinoma). Cox proportional hazard analysis was performed with these six amino acid combinations. Based on the analysis results, the median was divided into two groups, and analysis was performed by the Kaplan-Meier method. As a result, it was revealed that the prognosis was significantly different between the two groups (FIG. 40). In addition, the relationship between the six amino acid profiles and the existing prognostic factors and the prognosis was examined by Cox proportional hazard analysis. As a result, the six amino acid profiles were found to be independent prognostic factors (FIG. 41).

  As described above, the present invention can be widely implemented in many industrial fields, particularly pharmaceuticals, foods, and medical fields, and is particularly useful in the bioinformatics field for predicting the prognosis of pancreatic cancer. .

DESCRIPTION OF SYMBOLS 100 Evaluation apparatus 102 Control part 102a Receiving part 102b Specification part 102c Formula creation part 102d Evaluation part 102d1 Calculation part 102d2 Conversion part 102d3 Generation part 102d4 Classification part 102e Result output part 102f Transmission part 104 Communication interface part 106 Storage part 106a Concentration data file 106b Index state information file 106c Designated index state information file 106d Expression related information database 106d1 Expression 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

Claims (11)

EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His in pancreatic cancer tissue obtained from the evaluation object , HyLys, Phe, Arg, Cit, Tyr, and Trp, or an equation including a variable to which the concentration value is substituted and a value of the equation calculated using the concentration value An evaluation step of evaluating the prognosis of pancreatic cancer in the evaluation subject,
Evaluation method characterized by The evaluation step is executed in the control unit of the information processing apparatus including the control unit;
The evaluation method according to claim 1, wherein: EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His in pancreatic cancer tissue obtained from the evaluation object , HyLys, Phe, Arg, Cit, Tyr and Trp, and a formula for evaluating the prognosis of pancreatic cancer, including a variable to which the concentration value is substituted, Including a calculating step for calculating a value;
A calculation method characterized by The calculation step is executed in the control unit of the information processing apparatus including the control unit;
The calculation method according to claim 3. An evaluation device including a control unit,
The controller is
EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His in pancreatic cancer tissue obtained from the evaluation object , HyLys, Phe, Arg, Cit, Tyr, and Trp, or an equation including a variable to which the concentration value is substituted and a value of the equation calculated using the concentration value An evaluation means for evaluating the prognosis of pancreatic cancer in the evaluation target,
An evaluation apparatus characterized by. The terminal device that provides the density data related to the density value or the value of the formula is connected to be communicable via a network,
The controller is
Data receiving means for receiving the concentration data of the evaluation object or the value of the expression transmitted from the terminal device;
A result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device;
Further comprising
The evaluation means uses the density value or the value of the formula included in the density data received by the data receiving means;
The evaluation apparatus according to claim 5. A calculation device including a control unit,
The controller is
EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His in pancreatic cancer tissue obtained from the evaluation object , HyLys, Phe, Arg, Cit, Tyr and Trp, and a formula for evaluating the prognosis of pancreatic cancer, including a variable to which the concentration value is substituted, Provided with a calculation means for calculating a value;
A calculation device characterized by the above. An evaluation program for execution in an information processing apparatus provided with a control unit,
For executing in the control unit,
EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His in pancreatic cancer tissue obtained from the evaluation object , HyLys, Phe, Arg, Cit, Tyr, and Trp, or an equation including a variable to which the concentration value is substituted and a value of the equation calculated using the concentration value An evaluation step for evaluating the prognosis of pancreatic cancer in the evaluation subject,
An evaluation program characterized by A calculation program for execution in an information processing apparatus including a control unit,
For executing in the control unit,
EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys, Glu, Met, His in pancreatic cancer tissue obtained from the evaluation object , HyLys, Phe, Arg, Cit, Tyr and Trp, and a formula for evaluating the prognosis of pancreatic cancer, including a variable to which the concentration value is substituted, Including a calculation step for calculating a value,
A calculation program characterized by An evaluation apparatus including a control unit, and a control unit including EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn in a pancreatic cancer tissue obtained from an evaluation target , Orn, Asp, Gln, Lys, Glu, Met, His, HyLys, concentration data regarding at least one concentration value of Phe, Arg, Cit, Tyr and Trp, or a variable into which the concentration value is substituted An evaluation system configured by connecting a terminal device that provides a value of the formula calculated using the formula and the concentration value via a network so as to be communicable,
The control unit of the terminal device is
Data transmission means for transmitting the concentration data of the evaluation object or the value of the equation to the evaluation device;
A result receiving means for receiving an evaluation result relating to a prognosis of pancreatic cancer in the evaluation target transmitted from the evaluation device;
With
The control unit of the evaluation apparatus includes:
Data receiving means for receiving the concentration data of the evaluation object or the value of the expression transmitted from the terminal device;
Evaluation means for evaluating the prognosis of pancreatic cancer in the evaluation object using the concentration value or the value of the formula included in the concentration data of the evaluation object received by the data receiving means;
A result transmitting means for transmitting the evaluation result obtained by the evaluating means to the terminal device;
Having
An evaluation system characterized by A terminal device comprising a control unit,
The controller is
A result acquisition means for acquiring an evaluation result relating to a prognosis of pancreatic cancer in an evaluation target;
The evaluation results are EtOHNH2, Gly, Ala, GABA, Ser, Pro, Val, Thr, Tau, HyPro, Ile, Leu, Asn, Orn, Asp, Gln, Lys in pancreatic cancer tissue obtained from the evaluation object , Glu, Met, His, HyLys, Phe, Arg, Cit, Tyr, and Trp, or calculated using an equation including a variable to which the concentration value is substituted and the concentration value Using the value of the formula, the result of evaluating the prognosis of pancreatic cancer in the evaluation target,
A terminal device characterized by the above.

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