CN111965241A - Products, uses and methods for ovarian cancer-related screening and assessment - Google Patents
Products, uses and methods for ovarian cancer-related screening and assessment Download PDFInfo
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Abstract
The invention provides a product, application and a method for Ovarian Cancer (Ovarian Cancer) related screening and evaluation, which are characterized in that body fluid samples of Ovarian Cancer patients and healthy people are mixed with nanosphere materials, metabolic molecule information in related body fluid is collected through matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS), the metabolic molecule spectrum difference of the healthy people and the Ovarian Cancer patients is distinguished through an artificial intelligence classification algorithm, and the difference is classified, so that the distinguishing effect of high accuracy, high specificity and high sensitivity is achieved, or the product becomes an Ovarian Cancer related screening and evaluation tool.
Description
Technical Field
The invention relates to the fields of biology and doctors, in particular to a product, application and a method for ovarian cancer related screening and evaluation.
Background
Ovarian Cancer (Ovarian Cancer) is a common malignancy of female reproductive organs, with the highest incidence of epithelial cancers followed by malignant germ cell tumors. Ovarian cancer death rate is the first of all gynecological tumors, and seriously threatens the life of women. Because the ovary is deeply located in the pelvic cavity, the size is small, typical symptoms are lacked, and early detection is difficult. In the operation of patients with ovarian cancer, the tumor is only limited to 30 percent of ovaries, most of the ovaries are spread to the organs of the pelvic cavity, and therefore, the early diagnosis of ovarian cancer becomes a great clinical problem. In the face of such malignant tumors, there is no efficient and noninvasive clinical way to screen ovarian cancer on a large scale. Through body fluids of healthy people and ovarian cancer patients, the inventor finds that the body fluid metabolite detection is carried out by utilizing the exclusive research and development of nano reinforced materials through a MALDI-TOF-MS mass spectrum detector, and the method can distinguish the ovarian cancer patients from non-ovarian cancer patients with high accuracy under the condition of low cost and short time or becomes a powerful tool for large-scale ovarian cancer screening.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a product, application and a method for ovarian cancer related screening and evaluation.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a product for ovarian cancer screening, early diagnosis, risk assessment, the product being an operable system, the product comprising:
(S1) the nanometer material used for selectively enhancing the resolution of the metabolic molecule of the measured body fluid, and the matrix-assisted laser desorption ionization time-of-flight mass spectrometer used for measuring the metabolic molecule signal of the body fluid are used for measuring different metabolic signals of the body fluids of patients with different sources and healthy people through a core artificial intelligence classification algorithm;
(S2), a module and a processor for calculating the ratio of metabolic molecular composition and content of healthy humans to ovarian cancer;
(S3), optionally, a module and a processor for determining whether a subject is afflicted with ovarian cancer based on the composition and content of the metabolic molecules in the bodily fluid.
In the product for screening, early diagnosis and risk assessment of ovarian cancer, the body fluid is selected from serum, plasma, saliva and urine.
In the product for screening, early diagnosis and risk assessment of ovarian cancer provided by the invention, the reagent for determining the body fluid metabolism molecule in the step (S1) is a nano gold ball material, a matrix-assisted laser desorption ionization time-of-flight mass spectrum and a corresponding matched target plate.
The product for screening, early diagnosis and risk assessment of ovarian cancer provided by the invention further comprises one or more selected from the following groups:
a. reagents and instruments for collecting and processing a sample of a subject's bodily fluid;
b. reagents and instruments for separating and purifying serum/or plasma/or saliva/or urine;
c. reagents and instruments for separating and purifying serum/plasma/saliva/urine;
d. reagents and instruments for separating, purifying, enriching serum/plasma/saliva/urine;
e. the system comprises a database, a module and a processor for storing and processing metabolic molecular indexes of serum/plasma/saliva/urine of a subject;
f. a module and a processor for providing a decision threshold;
g. a module and a processor for comparing the metabolic molecular profile of the subject to a control to determine whether the subject is afflicted with ovarian cancer, for performing a prognostic assessment or risk assessment, condition monitoring or diagnostic assessment of the subject;
h. a module and processor for providing a diagnosis, test result report;
j. instructions or instructions for use in which one or more of the following applications and decisions are described:
(i) for ovarian cancer screening assays: determining that the subject has ovarian cancer or is at higher risk of developing ovarian cancer when the ratio between the number of all detected metabolic molecules having a molecular weight of 100-;
(ii) for judgment of health and malignancy: when the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.6 reaches or is higher than a preset threshold value, determining that the subject has ovarian cancer or is at risk of suffering from ovarian cancer;
(iii) for prognostic monitoring: when the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.35 reaches or is higher than a preset threshold value, indicating that the ovarian cancer of the subject recurs or is at risk of suffering from ovarian cancer;
(iv) ovarian carcinogenesis expectation for a subject population: when the detected molecular weight is 100-;
(v) for ovarian cancer patient condition monitoring: when the detected molecular weight is 100-;
(vi) for efficacy assessment: the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.25 is measured and calculated at different time points before and after diagnosis and treatment or in the treatment process, and when the difference of the result higher than the result obtained by the previous measurement reaches or is higher than a preset range, the treatment effect of the therapy or the drug is poor.
In the product for screening, early diagnosis and risk assessment of ovarian cancer, the preset threshold values in (i) - (iv) are determined by the optimal threshold values of 2.05-2.75, wherein the optimal threshold values are determined based on an artificial intelligence classification algorithm.
In the product for screening, early diagnosis and risk assessment of ovarian cancer, the preset threshold value in (i) - (iv) is 2.05-2.75.
In the product for screening, early diagnosis and risk assessment of ovarian cancer provided by the invention, in (vi), when the number of metabolic molecules with the signal-to-noise ratio of detected molecular signals being more than 2.25 is higher than that obtained in the previous test, the condition of the patient is shown to be developed or further worsened.
In the product for screening, early diagnosing and risk evaluating ovarian cancer provided by the invention, the ratio of the number of molecules with the signal-to-noise ratio higher than the optimal threshold value to the number of the selected detected metabolic molecules is calculated by adopting the following formula:
score ratio-number of molecules with signal to noise ratio above the optimal threshold/number of detected metabolic molecules.
The invention provides a product for screening, early diagnosis and risk assessment of ovarian cancer, which is used in a detection method comprising the following steps:
1. determining the level of a metabolic molecule in a plasma/serum/saliva/urine sample of a subject, said level of a metabolic molecule comprising: a metabolic molecule having a molecular weight of 100-;
2. calculating the ratio of the number of molecules with signal-to-noise ratio higher than the optimal threshold value to the number of detected metabolic molecules;
3. determining whether a subject is afflicted with ovarian cancer based on the level of the metabolic molecule, assessing the prognosis or risk of the subject for afflicted with ovarian cancer, monitoring the condition of the disease, or assessing the efficacy of the therapy.
In the products for screening, early diagnosis and risk assessment of ovarian cancer provided by the invention, the method further comprises one or more of the following steps:
1) collecting and processing a subject plasma/serum/saliva/urine sample;
2) separating and purifying serum/plasma/saliva/urine;
3) separating and purifying serum/plasma/saliva/urine;
4) separating, purifying, enriching serum/plasma/saliva/urine;
5) the metabolic molecule content ratio of the serum of the storage and processing object is used as an index of ovarian cancer related screening;
6) providing a judgment threshold value;
7) comparing the metabolic molecular composition content of the subject with a control to determine whether the subject is afflicted with ovarian cancer, to make a prognostic assessment or a disease monitoring or efficacy assessment of the subject;
8) providing diagnosis and detection result report;
9) monitoring other ovarian cancer indexes, and combining the detection results of the other ovarian cancer indexes with the disease condition monitoring results.
The product for screening, early diagnosis and risk assessment of ovarian cancer also comprises a kit, a device, a system and a combination thereof for detecting other ovarian cancer indexes.
In the product for screening, early diagnosis and risk assessment of ovarian cancer provided by the invention, the index is selected from the following indexes: metabolic molecules of serum/plasma/saliva/urine of a patient.
In the products for screening, early diagnosis and risk assessment of ovarian cancer provided by the invention, the basic condition of the patient is selected from the following: the number of molecules whose metabolic molecular signal-to-noise ratio is above an optimal threshold.
In the product for screening, early diagnosis and risk assessment of ovarian cancer, the clinical manifestations of the tumor are selected from one or more of the following: fully healthy/abnormal polycystic ovary/diagnosed ovarian cancer/ovarian cancer cure/ovarian cancer recurrence.
In the product for screening, early diagnosis and risk assessment of ovarian cancer, the specific physicochemical inspection indexes comprise tumor biochemical indexes and medical image data, wherein the tumor biochemical indexes are selected from one or more of the following indexes: and serum CA19-9, CA125, CA72-4, etc.
Use of an apparatus, module and processor for the manufacture of a product for screening, early diagnosis, prognosis evaluation, risk evaluation, condition monitoring and efficacy evaluation of ovarian cancer in a subject, the product being a device, system and combination thereof, the apparatus, module and processor comprising:
A. reagents, instruments, modules and processors for determining the levels of serum/plasma/saliva/urine metabolic molecules for determining the composition and content of metabolic molecules;
B. optionally, a module or processor for calculating that the signal-to-noise ratio of the metabolic molecule is above a certain threshold;
C. optionally, a module or processor for determining whether the subject is afflicted with ovarian cancer, for performing a prognostic assessment or risk assessment of afflicted ovarian cancer, for monitoring the condition of the subject, or for evaluating the efficacy of the treatment based on the ratio of the signal-to-noise ratios.
The instrument, the module and the processor are applied to preparing products for screening, early diagnosis, prognosis evaluation, risk evaluation, disease monitoring and curative effect evaluation of ovarian cancer of a subject, wherein the instrument, the module and the processor are used for determining blood metabolites, and after the blood metabolites are combined with a nanogold ball material, matrix-assisted laser desorption ionization time-of-flight mass spectrometry is used for measuring signal-to-noise ratio data of the metabolites in a sample, and then a disease performance judgment and evaluation result is given based on an artificial intelligence algorithm.
Compared with the prior art, the invention has the advantages that: the invention develops a new method for diagnosing and typing ovarian cancer based on a small amount or trace (less than 5 mL) of body fluid samples (including plasma, serum, urine, saliva and the like). The accuracy (sensitivity and specificity) of the method for diagnosing ovarian cancer is higher than that of the current clinical gold standard, the early ovarian cancer which is difficult to judge in the aspect of imaging can be identified, and the method has prompting significance for benign and malignant tumors and pathogenic gene mutation carried by the tumors. In addition, compared with the existing method for detecting CT and biochemical indexes, the mass spectrum technology adopted in the invention has the advantages of shorter time consumption, no harm to human bodies, more convenient detection and greatly reduced cost.
Drawings
FIG. 1 is a schematic view of a sample collection device.
FIG. 2 is a schematic view of a sample storage device.
FIG. 3 is a schematic view of a sample preparation device.
FIG. 4 is a schematic diagram of a mass spectrometry data acquisition device.
FIG. 5 is a schematic diagram of the data generated by the mass spectrometric data acquisition apparatus.
FIG. 6 is a schematic diagram of a classification analysis of mass spectral data using an artificial intelligence algorithm.
FIG. 7 is a schematic diagram of the biological signal pathway for identifying relevant metabolite molecules that can be used for basic classification and studying the association of the metabolite molecules.
FIG. 8 is a flow chart of an algorithm for diagnosing, typing, and predicting which disease-causing mutations a tumor carries.
Detailed Description
The technical solution adopted by the present invention will be further explained with reference to the schematic drawings.
The invention needs to obtain a high-accuracy and high-sensitivity prediction classification model which is based on a large number of real biological samples and can be used for repeated detection application, and the specific method comprises the following steps:
1. mixing a biological sample (blood plasma, blood serum, urine, saliva or the like) with a nano material for selectively enhancing the resolution of measuring the metabolism molecules of the body fluid, wherein the nano material comprises but is not limited to silicon and silicon compounds, gold, silver, platinum and other noble metals;
2. preparing the mixed sample on a target plate of a mass spectrum;
3. obtaining data (with accuracy of 0.001-0.1 Dalton) by mass spectrometer, wherein the obtained data is relative number value of metabolic molecules above 100 Dalton, and we select data of all metabolic molecules with 100-3000 Dalton and signal-to-noise ratio greater than 2.05;
4. grouping and classifying the obtained data according to requirements, wherein the classification mode is as follows: diseased Vs healthy, metastatic Vs non-metastatic, types of gene mutations, etc.;
5. the data is classified using a machine learning algorithm, typically trained using 70% of the data taken randomly, tested using 30% of the data, and the model accuracy is cross-validated, where the purpose of data training and validation is: a high accuracy, high sensitivity predictive classification model based on a large number of real biological samples is obtained that can be used for repeated detection applications.
FIG. 1 represents the collection of serum or plasma or saliva or urine from a test person using a sample collection device; FIG. 2 is a schematic representation of the storage of collected samples using a sample storage device, the storage of collected samples at-80 ℃ and the transport to the corresponding detection laboratory; FIG. 3 is a schematic diagram of a sample preparation apparatus for mixing a sample with prepared nanomaterial to prepare the sample before processing; FIG. 4 is a schematic view of a mass spectrometry data acquisition device to obtain mass spectrometry data of a sample; FIG. 5 is a schematic diagram of the data generated by the mass spectrometric data acquisition device, which is a graph of the data generated by the mass spectrometric data acquisition device; FIG. 6 is a schematic diagram of a classification analysis of mass spectrometry data using an artificial intelligence algorithm; FIG. 7 is a schematic diagram of the biological signal pathway for identifying relevant metabolite molecules that can be used for basic classification and studying the association of the metabolite molecules. Fig. 1-7 are experimental and analytical routes for relevant screening and assessment of ovarian cancer.
The invention utilizes MALDI-TOF-MS (Matrix Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry) based on a new material to detect a large number of clinically confirmed ovarian cancer patients and plasma of non-ovarian cancer control population at high flux, and uses the obtained Mass data for machine learning.
The technology of the invention adopts laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) based on nanometer results (including but not limited to quantum dots, nanowires, nanospheres, nano hollow spheres, nano triangular shapes and the like) of nanometer materials (including but not limited to silicon and silicon compounds, gold, silver, platinum and other precious metals), detects metabolites (substances with molecular weight below 3000 daltons) of serum samples of a large number of diagnosed ovarian cancer patients, and develops a set of brand-new algorithm for diagnosing and typing ovarian cancer and predicting which pathogenic gene mutation is carried by tumor by learning big data by using a machine, wherein the overall flow chart of the algorithm is shown in FIG. 8, and the specific content of the algorithm is as follows:
1) converting the raw data derived from the mass spectrometer (i.e. the aforementioned "relative number value of metabolic molecules above 100 daltons, we select 100-;
2) repeating the grouping of the data set processed in the step 1) for 10 times, wherein 30% of the groups are used as a test data set and 70% of the groups are used as a model training data set. A layered sampling method is adopted during grouping, so that the distribution of positive and negative samples in each group of data is consistent with the distribution of positive and negative samples in the total samples;
3) denoising and smoothing the data set processed in the step 2);
4) carrying out baseline calibration operation on the data set processed in the step 3);
5) carrying out standardization operation on the data set processed in the step 4) to obtain an integral mean value and variance parameters of the training set data;
6) aligning the data set processed in the step 5);
7) and repeating five-fold cross validation for the model training set data in the step 6) for 5 times, wherein the data are divided into a validation data set of 20% and a training data set of 80%. A layered sampling method is adopted during grouping, so that the distribution of positive and negative samples in each group of data is consistent with the distribution of positive and negative samples in the total samples;
8) training the data in the step 7) by using a classifier, outputting the average verification precision of the classifier model, and selecting the hyper-parameter of the classifier model when the verification precision is highest. The classifier model specifically adopts a Support Vector Machine (SVM), the SVM is a machine learning model utilizing hyperplane segmentation data, the SVM is generally applied to a two-classification problem under a supervised learning situation, a learning strategy is interval maximization, and SVM solution can be converted into a convex quadratic programming problem. The linear SVM is proposed by cortex and Vapnik in 1995, has great advantages in solving the problem of high-dimensional small sample classification, and can be popularized and applied to other machine learning problems such as function fitting and the like.
The idea of SVM classification is as follows: a hyperplane is found to segment the data (a straight line in the two-dimensional case) and then the segmentation of the data is maximized using the support vectors. The mathematical model is as follows:
simultaneously, the following requirements are met: y isi(wT·xi+b)-1≥0，i＝1，2，…，n
Where the sample points of the data set T are hyperplanes. If the dataset T is linearly separable, then a maximum spaced separation hyperplane exists and is unique that can completely separate the samples in the dataset.
9) Putting the whole model training data set into the classifier model with the highest precision and the hyperparameter in the step 8) for training to obtain a final classifier model;
10) denoising and smoothing the model test set data obtained in the step 2);
11) carrying out baseline removal work on the data set processed in the step 10);
12) carrying out standardization operation on the data set processed in the step 11) by using the standardization parameters of the model training set data obtained in the step 5);
13) aligning the data set processed in the step 12);
14) putting the data set processed in the step 13) into the final classifier model in the step 9) for testing;
15) obtaining a predicted value of each sample in the test data set through the processing of the classifier in the step 14);
16) and comparing the predicted value obtained in the step 15) with the sample gold standard to obtain the average test error of the classifier model.
Steps 1) to 16) are detailed for the "high accuracy, high sensitivity predictive classification models based on large numbers of real biological samples that can be used for repeated detection applications" obtaining method.
Example 1: serum metabolic molecule time-of-flight mass spectrometry detection for screening/screening of ovarian cancer
The method comprises the following steps of determining the content of 100-3000 Da metabolic molecules in a control serum sample and an ovarian cancer patient serum sample by adopting a nanogold ball material and a method, wherein the detection results are as follows:
1. by observing the metabolic molecular spectrogram of 100-3000 Da of serum samples of 500 patients in total, the number of the metabolic molecules with the signal-to-noise ratio of more than 2.75 can be found to be obviously different between ovarian cancer patients and controls.
2. Comparing the metabolic molecular maps of 500 healthy human serum samples of 100-3000 Da, and simultaneously carrying out comparison analysis on the number of metabolic molecules with the signal-to-noise ratio of a test subject being more than 2.75. The result shows that the number of the metabolic molecules with the signal-to-noise ratio of more than 2.75 in the metabolic molecules of 100-3000 Da is compared with the total number of the detected metabolic molecules to serve as an ovarian cancer screening index, and the corresponding sensitivity is up to 91.2% under the condition that the specificity is controlled to be 98.5%. The index is highly accurate as a diagnosis index by referring to the judgment standard of the puncture tissue pathology.
3. Numerically, the difference between the metabolic molecules with the signal-to-noise ratio of greater than 2.75 in the 100-3000 Da metabolic molecules of ovarian cancer patients and the control samples is very significant.
4. Meanwhile, the threshold value of 2.75 is used as a criterion for judging whether the subject has ovarian cancer or is at risk of having ovarian cancer, and the results are found in the following table 1:
TABLE 1
In the samples, the detection rate of ovarian cancer can reach about 91.2 percent;
the results show that the matrix-assisted laser desorption ionization time-of-flight mass spectrometry can be effectively used for screening ovarian cancer with high sensitivity and high specificity, and compared with the existing standard, the method and the index have higher accuracy. Furthermore, the methods and indicators of the present invention may be used in combination with existing criteria to further improve the detection rate of ovarian cancer.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A product for ovarian cancer screening, early diagnosis, risk assessment, the product being an operable system, the product comprising:
(S1) the nanometer material used for selectively enhancing the resolution of the metabolic molecule of the measured body fluid, and the matrix-assisted laser desorption ionization time-of-flight mass spectrometer used for measuring the metabolic molecule signal of the body fluid are used for measuring different metabolic signals of the body fluids of patients with different sources and healthy people through a core artificial intelligence classification algorithm;
(S2), a module and processor for calculating the ratio of metabolic molecular composition and content of healthy humans to ovarian cancer patients;
(S3), optionally, a module and a processor for determining whether a subject is afflicted with ovarian cancer based on the composition and content of the metabolic molecules in the bodily fluid, and for assessing the risk of acquiring a tumor in the subject;
(S4), optionally, the product further comprises:
j. instructions or instructions for use in which one or more of the following applications and decisions are described:
(i) for ovarian cancer screening assays: determining that the subject has ovarian cancer or is at higher risk of developing ovarian cancer when the ratio between the number of all detected metabolic molecules having a molecular weight of 100-;
(ii) for judgment of health and malignancy: when the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.6 reaches or is higher than a preset threshold value, determining that the subject has ovarian cancer or is at risk of suffering from ovarian cancer;
(iii) for prognostic monitoring: when the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.35 reaches or is higher than a preset threshold value, indicating that the ovarian cancer of the subject recurs or is at risk of suffering from ovarian cancer;
(iv) ovarian carcinogenesis expectation for a subject population: when the ratio of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.15 reaches or is higher than a preset threshold value, indicating that the possibility of ovarian cancer of the subject is higher than that of other subjects;
(v) for ovarian cancer patient condition monitoring: when the detected molecular weight is 100-;
(vi) for efficacy assessment: measuring and calculating the proportion of the number of all detected metabolic molecules with the molecular weight of 100-3000 daltons and the signal-to-noise ratio of more than 2.25 at different time points before and after diagnosis and treatment or in the treatment process, and when the difference of the result higher than the result obtained by the previous measurement reaches or is higher than a preset range, indicating that the treatment effect of the therapy or the medicament is poor;
the body fluid is selected from serum, plasma, saliva, urine;
the reagent for measuring the body fluid metabolism molecule in (S1) is a nanogold ball material.
2. The product of claim 1, further comprising one or more selected from the group consisting of:
a. reagents and instruments for collecting and processing a sample of a subject's bodily fluid;
b. reagents and instruments for separating and purifying serum/plasma/saliva/urine;
c. reagents and instruments for separating and purifying serum/plasma/saliva/urine;
d. reagents and instruments for separating, purifying, enriching serum/plasma/saliva/urine;
e. the system comprises a database, a module and a processor for storing and processing metabolic molecular indexes of serum/plasma/saliva/urine of a subject;
f. a module and a processor for providing a decision threshold;
g. a module and a processor for comparing the metabolic molecular profile of the subject to a control to determine whether the subject is afflicted with ovarian cancer, for performing a prognostic assessment or risk assessment, condition monitoring or diagnostic assessment of the subject;
h. a module and a processor for providing diagnosis and detection result report.
3. The product of claim 1, wherein the predetermined threshold values in (i) - (iv) are determined by an optimal threshold value of 2.05-2.75, wherein the optimal threshold value is determined based on an artificial intelligence classification algorithm.
4. The product of claim 1, wherein (vi) the number of metabolic molecules whose signal-to-noise ratio of the detected molecular signal is greater than 2.25 is greater than that obtained in the previous test, indicating that the patient's condition has progressed or has further deteriorated.
5. The product of claim 4, wherein the calculation of the ratio of the number of molecules with a signal-to-noise ratio above the optimal threshold to the number of selected detected metabolic molecules is performed using the formula:
score ratio-number of molecules with signal to noise ratio above the optimal threshold/number of detected metabolic molecules.
6. The product of claim 1, wherein the product is used in a test method comprising the steps of:
1. determining the level of a metabolic molecule in a plasma/serum/saliva/urine sample of a subject, said level of a metabolic molecule comprising: a metabolic molecule having a molecular weight of 100-;
2. calculating the ratio of the number of molecules with signal-to-noise ratio higher than the optimal threshold value to the number of detected metabolic molecules;
3. determining whether a subject is afflicted with ovarian cancer based on the level of the metabolic molecule, assessing the prognosis or risk of the subject for afflicted with ovarian cancer, monitoring the condition of the disease, or assessing the efficacy of the therapy.
7. The product of claim 6, wherein the method further comprises one or more of the following:
1) collecting and processing a subject plasma/serum/saliva/urine sample;
2) separating and purifying serum/plasma/saliva/urine;
3) separating and purifying serum/plasma/saliva/urine;
4) separating, purifying, enriching serum/plasma/saliva/urine;
5) storing and processing the metabolic molecule content ratio of the serum/plasma/saliva/urine of the object as an index of ovarian cancer related screening;
6) providing a judgment threshold value;
7) comparing the metabolic molecular composition content of the subject with a control to determine whether the subject is afflicted with ovarian cancer, to make a prognostic assessment or a disease monitoring or efficacy assessment of the subject;
8) providing diagnosis and detection result report;
9) monitoring other ovarian cancer indexes, and combining the detection results of the other ovarian cancer indexes with the disease condition monitoring results.
8. The product of claim 1, further comprising kits, devices, systems, and combinations thereof for detecting other indicators of ovarian cancer.
9. The product of claim 8, wherein the indicator is selected from the group consisting of: metabolic molecules of serum/plasma/saliva/urine of the patient;
the patient's basic condition is selected from the following: the number of molecules whose metabolic molecular signal-to-noise ratio is above an optimal threshold;
the clinical presentation of the tumor is selected from one or more of the following: fully healthy/abnormal polycystic ovary/diagnosed ovarian cancer/ovarian cancer cure/ovarian cancer recurrence;
the specific physicochemical examination index comprises a tumor biochemical index and a medical image index, wherein the tumor biochemical index is selected from one or more of the following indexes: serum CA19-9, CA125, CA 72-4.
10. Use of an apparatus, module and processor for the manufacture of a product for ovarian cancer screening, early diagnosis, prognosis evaluation, risk evaluation, condition monitoring and efficacy evaluation in a subject, wherein the product is a device, system and combination thereof, the apparatus, module and processor comprising:
A. reagents, instruments, modules and processors for determining the levels of serum/plasma/saliva/urine metabolic molecules for determining the composition and content of metabolic molecules;
B. optionally, a module or processor for calculating that the signal-to-noise ratio of the metabolic molecule is above a certain threshold;
C. optionally, a module or processor for determining whether the subject is afflicted with ovarian cancer, for performing a prognostic assessment or risk assessment, for disease monitoring or for efficacy assessment of the afflicted subject based on the ratio of the signal to noise ratios;
and (A) after the blood metabolite is combined with the nano gold ball material, matrix-assisted laser desorption ionization time-of-flight mass spectrometry is used for determining the blood metabolite, the signal-to-noise ratio data of the sample metabolite is measured, and then a disease performance judgment and evaluation result is given based on an artificial intelligence algorithm.
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| CN114414656A (en) * | 2022-01-26 | 2022-04-29 | 上海交通大学 | Serum metabolism fingerprint-based autoimmune disease model construction method |
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