CN112575089A

CN112575089A - Application of gene in diagnosis of gastric cancer

Info

Publication number: CN112575089A
Application number: CN202011604631.6A
Authority: CN
Inventors: 杨承刚; 宋宏涛
Original assignee: Beijing Medintell Bioinformatic Technology Co Ltd
Current assignee: Beijing Medintell Bioinformatic Technology Co Ltd
Priority date: 2020-12-30
Filing date: 2020-12-30
Publication date: 2021-03-30

Abstract

The invention discloses an application of genes in gastric cancer diagnosis, and particularly relates to genes CLCA1, CLDN3, FAM3D and MMP 7. CLCA1, CLDN3, FAM3D and MMP7 are differentially expressed in gastric cancer tissues and tissues beside the gastric cancer, and an ROC curve shows that the kit has higher diagnostic efficacy, which suggests that the CLCA1, CLDN3, FAM3D and MMP7 can be used for effectively judging the gastric cancer.

Description

Application of gene in diagnosis of gastric cancer

Technical Field

The invention relates to the field of biomedicine, in particular to application of a gene in diagnosis of gastric cancer.

Background

In recent years, the number of malignant tumors and the number of malignant tumors dying are increasing year by year. At present, malignant tumor becomes one of the main causes of death of residents in China, and the death rate of the malignant tumor is at a higher level in the world. Stomach Cancer is one of common digestive tract tumors in China, the annual incidence rate of the latest stomach Cancer is 29/10 ten thousand, 41 ten thousand cases of new stomach Cancer are developed every year, wherein the incidence rate of stomach Cancer of male in China is listed at the 2 nd place in the malignant tumor, the incidence rate of stomach Cancer of female is listed at the 5 th place, the mortality rate of stomach Cancer of male in China is listed at the 3 rd place in the malignant tumor, the mortality rate of stomach Cancer of female is listed at the 2 nd place, and the social labor force and productivity are seriously influenced by high incidence rate and high mortality rate of stomach Cancer (Chen W, Sun K, Zheng R, et a1.Cancer initiation and mortality in China,2014[ J ]. Chin J Cancer Res,2018,30(1): 1-12). The clinical manifestations of gastric cancer lack specificity, and most patients have been found to be progressive gastric cancer (Ren W, Yu J, Zhang ZM, et al. Missed diagnosis of early gastric cancer r or high-grade intraepithelial neoplasia [ J ]. World J Gastroenterol,2013,19(13): 2092-. The high morbidity and mortality of gastric cancer pose a great threat to the medical health of residents, and therefore, work on how to prevent the occurrence of gastric cancer, improve the detection rate of early gastric cancer, improve the survival prognosis of gastric cancer patients and the like is urgently needed. Many medical workers in China have been working on the prevention and treatment of gastric cancer for many years, and the research aims to explore new effective gastric cancer markers.

The development of gastric adenocarcinoma is accompanied by the occurrence and accumulation of various molecular genetic alterations. With the development of gene research technology, the research on molecular pathogenic mechanism of gastric adenocarcinoma has made a great progress in recent years, and the research on the function of gene in gastric adenocarcinoma has important significance for personalized diagnosis of gastric adenocarcinoma, screening of high risk group and precise medical treatment.

Disclosure of Invention

The invention researches biomarkers related to the occurrence and development of gastric adenocarcinoma based on the effect of genes on the occurrence and development of gastric adenocarcinoma, thereby providing a new means for diagnosis and treatment of gastric adenocarcinoma.

The present invention provides the use of a reagent for the detection of a biomarker comprising CLDN3 in combination with FAM3D or CLCA1 in a sample for the preparation of a product for the diagnosis of gastric adenocarcinoma.

Further, the biomarker includes CLDN3, in combination with any two of FAM3D, CLCA1, MMP 7.

Further, the biomarkers include CLCA1, CLDN3, FAM3D, and MMP 7.

Further, the product comprises a reagent for detecting the level of the gene marker by a sequencing technology, a nucleic acid hybridization technology, a nucleic acid amplification technology and a protein immunity technology.

Further, the agent is selected from:

a probe that specifically recognizes the biomarker; or

Primers that specifically amplify the biomarkers; or

A binding agent that specifically binds to a protein encoded by the biomarker.

Examples of specific binding agents are peptides, peptidomimetics, aptamers, spiegelmers, dappin, ankyrin repeat proteins, Kunitz-type domains, antibodies, single domain antibodies and monovalent antibody fragments. As a preferred embodiment, the specific binding agent is an antibody.

Further, the sample is selected from tissue or blood.

The present invention provides a product for the diagnosis of gastric adenocarcinoma, said product comprising reagents for the detection of biomarkers comprising CLDN3 in combination with FAM3D or CLCA1 in a sample.

Further, the biomarkers include CLCA1, CLDN3, FAM3D, and MMP 7.

Further, the product comprises a kit, a chip and test paper.

Further, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit.

Further, the kit also includes instructions for assessing whether the subject is suffering from or susceptible to gastric adenocarcinoma.

Further, the product also includes reagents for processing the sample.

The invention provides the use of a biomarker comprising CLDN3, in combination with FAM3D or CLCA1, in the construction of a computational model or a system in which said computational model is embedded, for the prediction of gastric adenocarcinoma.

Further, the biomarkers include CLCA1, CLDN3, FAM3D, and MMP 7.

Further, the calculation model takes the level of the biomarker as an input variable, and carries out calculation through a bioinformatics method to output the risk probability of the disease.

The present invention provides a system for diagnosing gastric adenocarcinoma, comprising:

(1) a gastric adenocarcinoma assessment apparatus comprising a control unit and a storage unit for assessing whether a subject has gastric adenocarcinoma; and

(2) information communication terminal devices communicatively connected to each other, which provide data on the levels of the aforementioned biomarkers in a sample from a subject;

wherein the control unit of the gastric adenocarcinoma assessment apparatus comprises:

1) a data receiving unit that receives data on the level of the biomarker of the sample transmitted from the information communication terminal device;

2) a discrimination value calculation unit that calculates a discrimination value based on discrimination of the level of the biomarker in the sample received by the data reception unit and the level of the biomarker having the explanatory variable stored in the storage unit;

3) a discrimination value criterion evaluation unit that evaluates a condition of gastric adenocarcinoma in the subject based on the discrimination value calculated by the discrimination value calculation unit; and

4) an evaluation result transmitting unit that transmits the evaluation result of the subject obtained by the discrimination value reference evaluation unit to the information communication terminal device.

The present invention provides a method for identifying and assessing the effect of an agent and/or surgical and/or physical therapy on gastric adenocarcinoma, the method comprising:

(1) collecting a subject having gastric adenocarcinoma to provide a first sample;

(2) obtaining a gene expression profile from the first sample;

(3) administering to or on the subject one or more drug candidates and/or performing one or more physical or surgical treatments;

(4) providing a second sample from the subject in step (3);

(5) obtaining a gene expression profile from the second sample;

(6) comparing the gene expression profile obtained in steps (2) and (5) with a reference gene expression profile; and

(7) assessing whether the one or more drug candidates and/or treatments are effective against gastric adenocarcinoma based on the comparison in step (6);

further, the genes in step (2) and step (5) are the biomarkers described above.

The invention has the advantages and beneficial effects that:

the marker combination has extremely high association degree with gastric adenocarcinoma, has better diagnosis efficiency in both a training set and a verification set when judging the gastric adenocarcinoma, has higher accuracy, sensitivity and specificity, and can be used for early discovery of the gastric adenocarcinoma, thereby carrying out intervention treatment in the early stage of the gastric cancer and improving the life quality of patients.

Drawings

Figure 1 shows ROC plots of genes in the training set, where a: CLCA 1; b: CLDN 3; c: FAM 3D; d: MMP 7; e: CLCA1+ CLDN3+ FAM3D + MMP 7;

fig. 2 shows ROC plots of genes in the validation set, where a: CLCA 1; b: CLDN 3; c: FAM 3D; d: MMP 7; e: CLCA1+ CLDN3+ FAM3D + MMP 7.

Detailed Description

In order to screen the biomarkers for diagnosing and treating the gastric adenocarcinoma, the invention discovers the biomarkers suitable for diagnosing and treating the gastric adenocarcinoma by collecting cancer tissue samples and tissue samples beside the cancer of gastric adenocarcinoma patients, comprehensively analyzing gene expression profiles of the samples, screening genes with significant differences in levels in two groups in a training set, and further analyzing the expression condition and the diagnosis efficiency of the genes in a verification set.

In the present invention, the term "biomarker" means a compound, preferably a gene, which is differentially present (i.e. increased or decreased) in a biological sample from a subject or a group of subjects having a first phenotype (e.g. having a disease) compared to a biological sample from a subject or a group of subjects having a second phenotype (e.g. no disease). The term "biomarker" generally refers to the presence/concentration/amount of one gene or the presence/concentration/amount of two or more genes.

Biomarkers can be differentially present at any level, but are typically present at levels that are increased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more; or generally at a level that is reduced by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e., absent).

Preferably, the biomarkers are differentially present at levels of statistical significance (i.e., p-value less than 0.05 and/or q-value less than 0.10, as determined using the Welch's T-Test or the Wilcoxon rank-sum Test).

In a specific embodiment of the invention, the biomarker comprises CLCA1, CLDN3, FAM3D and/or MMP 7.

In the present invention, CLCA1 (gene ID: 1179) includes the CLCA1 gene and its encoded protein and homologs, mutations and isoforms. The term encompasses full-length, unprocessed CLCA1, as well as any form of CLCA1 that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of CLCA 1.

CLDN3 (gene ID: 1365) includes the CLDN3 gene and its encoded proteins and homologues, mutations, and isoforms. The term encompasses full-length, unprocessed CLDN3, as well as any form of CLDN3 that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of CLDN 3.

FAM3D (Gene ID: 131177) includes the FAM3D gene and its encoded protein and homologs, mutations, and isoforms. The term encompasses full-length, unprocessed FAM3D, as well as any form of FAM3D that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of FAM 3D.

MMP7 (Gene ID: 4316) includes the human MMP7 gene and its encoded protein and homologs, mutations, and isoforms. The term encompasses full-length, unprocessed MMP7, as well as any form of MMP7 that results from processing in the cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of MMP 7.

In the present invention, any suitable method may be used to analyze a biological sample to determine the level of the biomarker in the sample. These methods include, but are not limited to: nucleic acid sequencing, nucleic acid hybridization, nucleic acid amplification technology and protein immunization technology.

Illustrative, non-limiting examples of the nucleic acid sequencing methods of the present invention include, but are not limited to, chain terminator (Sanger) sequencing and dye terminator sequencing. One of ordinary skill in the art will recognize that RNA is typically reverse transcribed into DNA prior to sequencing because it is less stable in cells and more susceptible to nuclease attack in experiments.

Another illustrative, non-limiting example of a nucleic acid sequencing method of the present invention includes next generation sequencing (deep sequencing/high throughput sequencing), a high throughput sequencing technique that is a unimolecular cluster-based sequencing-by-synthesis technique based on proprietary reversible termination chemical reaction principles. Random fragments of genome DNA are attached to an optically transparent glass surface during sequencing, hundreds of millions of clusters are formed on the glass surface after the DNA fragments are extended and subjected to bridge amplification, each cluster is a monomolecular cluster with thousands of identical templates, and then four kinds of special deoxyribonucleotides with fluorescent groups are utilized to sequence the template DNA to be detected by a reversible edge-to-edge synthesis sequencing technology.

Methods of nucleic acid hybridization in the present invention include, but are not limited to, In Situ Hybridization (ISH), microarrays, and Southern or Northern blots. In Situ Hybridization (ISH) is a hybridization of specific DNA or RNA sequences in a tissue section or section using a labeled complementary DNA or RNA strand as a probe (in situ) or in the entire tissue if the tissue is small enough (whole tissue embedded ISH). DNA ISH can be used to determine the structure of chromosomes. RNA ISH is used to measure and locate mRNA and other transcripts (e.g., ncRNA) within tissue sections or whole tissue embedding. Sample cells and tissues are typically treated to fix the target transcript in situ and to increase probe access. The probe is hybridized to the target sequence at high temperature, and then excess probe is washed away. The localization and quantification of base-labeled probes in tissues labeled with radiation, fluorescence or antigens is performed using autoradiography, fluorescence microscopy or immunohistochemistry, respectively. ISH can also use two or more probes labeled with radioactive or other non-radioactive labels to detect two or more transcripts simultaneously.

Southern and Northern blots were used to detect specific DNA or RNA sequences, respectively. DNA or RNA extracted from the sample is fragmented, separated by electrophoresis on a matrix gel, and then transferred to a membrane filter. The filter-bound DNA or RNA is hybridized to a labeled probe complementary to the sequence of interest. Detecting the hybridization probes bound to the filter. A variation of this procedure is a reverse Northern blot, in which the substrate nucleic acid immobilized to the membrane is a collection of isolated DNA fragments and the probe is RNA extracted from the tissue and labeled.

The nucleic acid amplification method of the present invention is selected from the group consisting of Polymerase Chain Reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), Transcription Mediated Amplification (TMA), Ligase Chain Reaction (LCR), Strand Displacement Amplification (SDA), and Nucleic Acid Sequence Based Amplification (NASBA). Among them, PCR requires reverse transcription of RNA into DNA before amplification (RT-PCR), TMA and NASBA to directly amplify RNA.

Generally, PCR uses multiple cycles of denaturation, annealing of primer pairs to opposite strands, and primer extension to exponentially increase the copy number of a target nucleic acid sequence; RT-PCR Reverse Transcriptase (RT) is used to prepare complementary DNA (cDNA) from mRNA, and the cDNA is then amplified by PCR to produce multiple copies of the DNA; TMA autocatalytically synthesizes multiple copies of a target nucleic acid sequence under substantially constant conditions of temperature, ionic strength and pH, wherein multiple RNA copies of the target sequence autocatalytically generate additional copies, TMA optionally including the use of blocking, partial, terminating and other modifying moieties to improve the sensitivity and accuracy of the TMA process; LCR with target nucleic acid adjacent region hybridization of two sets of complementary DNA oligonucleotides. The DNA oligonucleotides are covalently linked by DNA ligase in repeated cycles of heat denaturation, hybridization, and ligation to produce a detectable double-stranded ligated oligonucleotide product; the SDA uses multiple cycles of the following steps: primer sequence pairs anneal to opposite strands of the target sequence, primer extension in the presence of dNTP α S to produce double-stranded hemiphosphorothioated (phosphorothioated) primer extension products, endonuclease-mediated nicking of the hemimodified restriction enzyme recognition site, and polymerase-mediated extension from the 3' end of the nick to displace the existing strand and produce a strand for the next round of primer annealing, nicking and strand displacement, thereby causing geometric amplification of the products.

The protein immunization methods of the invention include sandwich immunoassays, such as sandwich ELISA, in which the detection of a biomarker is performed using two antibodies that recognize different epitopes on the biomarker; radioimmunoassay (RIA), direct, indirect or contrast enzyme-linked immunosorbent assay (ELISA), Enzyme Immunoassay (EIA), Fluorescence Immunoassay (FIA), western blot, immunoprecipitation, and any particle-based immunoassay (e.g., using gold, silver or latex particles, magnetic particles, or quantum dots). The immunization can be carried out, for example, in the form of microtiter plates or strips.

The terms "sample" and "sample" are used interchangeably herein to refer to a composition obtained or derived from a subject (e.g., an individual of interest) that comprises cells and/or other molecular entities to be characterized and/or identified based on, for example, physical, biochemical, chemical, and/or physiological characteristics. For example, the phrase "disease sample" or variants thereof refers to any sample obtained from a subject of interest that is expected or known to contain the cells and/or molecular entities to be characterized. Samples include, but are not limited to, tissue samples (e.g., tumor tissue samples), primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymph, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tumor lysates, and tissue culture fluids, tissue extracts such as homogenized tissue, tumor tissue, cell extracts, and combinations thereof.

As a preferred embodiment, the sample is selected from blood, serum, plasma.

In another preferred embodiment, the sample is selected from the group consisting of tissues.

The invention provides a product for diagnosing gastric adenocarcinoma, which comprises a reagent for detecting the biomarker in a sample; and instructions for using the kit to assess whether a subject is suffering from or susceptible to gastric adenocarcinoma can be included.

The most reliable results are possible when processing samples in a laboratory environment. For example, a sample may be taken from a subject in a doctor's office and then sent to a hospital or commercial medical laboratory for further testing. However, in many cases, it may be desirable to provide immediate results at the clinician's office or to allow the subject to perform the test at home. In some cases, the need for testing that is portable, prepackaged, disposable, ready to use by the subject without assistance or guidance, etc., is more important than a high degree of accuracy. In many cases, especially in the case of physician visits, it may be sufficient to perform a preliminary test, even a test with reduced sensitivity and/or specificity. Thus, assays provided in product form can involve detecting and measuring relatively small amounts of biomarkers to reduce the complexity and cost of the assay.

Any form of sample assay capable of detecting a sample biomarker described herein may be used. Typically, the assay will quantify the biomarkers in the sample to an extent, for example whether their concentration or amount is above or below a predetermined threshold. Such kits may take the form of test strips, dipsticks, cartridges, chip-based or bead-based arrays, multi-well plates, or a series of containers, and the like. One or more reagents are provided to detect the presence and/or concentration and/or amount of a selected sample biomarker. The sample from the subject may be dispensed directly into the assay or indirectly from a stored or previously obtained sample. The presence or absence of a biomarker above or below a predetermined threshold may be indicated, for example, by chromogenic, fluorogenic, electrochemiluminescent or other output (e.g., in an Enzyme Immunoassay (EIA), such as an enzyme-linked immunoassay (ELISA)).

In one embodiment, the product may comprise a solid substrate such as a chip, slide, array, or the like, having reagents capable of detecting and/or quantifying one or more sample biomarkers immobilized at predetermined locations on the substrate. As an illustrative example, the chip may be provided with reagents immobilized at discrete predetermined locations for detecting and quantifying the presence and/or concentration and/or amount of a biomarker in a sample. As described above, a reduced or increased level of the biomarker is found in a sample of a subject suffering from gastric adenocarcinoma. The chip may be configured such that a detectable output (e.g. a colour change) is provided only when the concentration of one or more of these biomarkers exceeds a threshold value selected or differentiated between the concentration and/or amount of the biomarker indicative of a control subject and the concentration and/or amount of the biomarker indicative of a patient suffering from or susceptible to gastric adenocarcinoma. Thus, the presence of a detectable output (such as a color change) immediately indicates that a significantly reduced level of biomarker is contained in the sample, indicating that the subject is suffering from or susceptible to gastric adenocarcinoma.

In the present invention, biomarkers may be determined individually, or in one embodiment of the invention, they may be determined simultaneously, for example using a chip or bead-based array technology. The concentration of the biomarkers is then interpreted independently, for example using individual retention of each marker, or a combination thereof.

As the skilled artisan will appreciate, the step of associating a marker level with a certain likelihood or risk may be implemented and realized in different ways. Preferably, the measured concentrations of the protein and one or more other markers are mathematically combined and the combined value is correlated with the underlying diagnostic problem. The determination of marker values may be combined by any suitable prior art mathematical method.

Preferably, the mathematical algorithm applied in the marker combination is a logarithmic function. Preferably, the result of applying such a mathematical algorithm or such a logarithmic function is a single value. Such values can be readily correlated with, for example, an individual's risk for gastric adenocarcinoma or with other diagnostic uses of interest that are helpful in assessing patients with gastric adenocarcinoma, based on underlying diagnostic questions. In a preferred manner, such a logarithmic function is obtained as follows: a) classifying individuals into groups, e.g., normal humans, individuals at risk of gastric adenocarcinoma, patients with gastric adenocarcinoma, etc., b) identifying markers that differ significantly between these groups by univariate analysis, c) log regression analysis to assess independent difference values of the markers that can be used to assess these different groups, and d) constructing a log function to combine the independent difference values. In this type of analysis, the markers are no longer independent, but represent a combination of markers.

The logarithmic function used to correlate marker combinations with disease preferably employs algorithms developed and obtained by applying statistical methods. For example, suitable statistical methods are Discriminant Analysis (DA) (i.e., linear, quadratic, regular DA), Kernel methods (i.e., SVM), nonparametric methods (i.e., k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest methods, boosting/bagging methods), generalized linear models (i.e., logistic regression), principal component-based methods (i.e., SIMCA), generalized additive models, fuzzy logic-based methods, neural network-and genetic algorithm-based methods. The skilled person will not have problems in selecting a suitable statistical method to evaluate the marker combinations of the invention and thereby obtain a suitable mathematical algorithm. In one embodiment, the statistical method used to obtain the mathematical algorithm used in assessing gastric adenocarcinoma is selected from DA (i.e., linear, quadratic, rule-discriminant analysis), Kernel method (i.e., SVM), non-parametric method (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based method (i.e., logistic regression, CART, random forest method, boosting method), or generalized linear model (i.e., logarithmic regression).

The area under the receiver operating curve (AUC) is an indicator of the performance or accuracy of a diagnostic procedure. The accuracy of a diagnostic method is best described by its Receiver Operating Characteristics (ROC). ROC plots are line graphs of all sensitivity/specificity pairs derived from continuously varying decision thresholds across the entire data range observed.

The clinical performance of a laboratory test depends on its diagnostic accuracy, or the ability to correctly classify a subject into a clinically relevant subgroup. Diagnostic accuracy measures the ability to correctly discriminate between two different conditions of the subject under investigation. Such conditions are, for example, health and disease or disease progression versus no disease progression.

In each case, the ROC line graph depicts the overlap between the two distributions by plotting sensitivity versus 1-specificity for the entire range of decision thresholds. On the y-axis is the sensitivity, or true positive score [ defined as (number of true positive test results)/(number of true positives + number of false negative test results) ]. This is also referred to as a positive for the presence of a disease or condition. It is calculated from the affected subgroups only. On the x-axis is the false positive score, or 1-specificity [ defined as (number of false positive results)/(number of true negatives + number of false positive results) ]. It is an indicator of specificity and is calculated entirely from unaffected subgroups. Because the true and false positive scores are calculated completely separately using test results from two different subgroups, the ROC line graph is independent of the prevalence of disease in the sample. Each point on the ROC line graph represents a sensitivity/1-specificity pair corresponding to a particular decision threshold. One test with perfect discrimination (no overlap of the two result distributions) has a ROC line graph that passes through the upper left corner where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity). A theoretical line graph for an undifferentiated test (the results of the two groups are equally distributed) is a 45 ° diagonal from the lower left to the upper right. Most line graphs fall between these two extremes. (if the ROC line graph falls well below the 45 ° diagonal, this is easily corrected by reversing the criteria for "positive" from "greater to" less than "or vice versa.) qualitatively, the closer the line graph is to the upper left corner, the higher the overall accuracy of the test.

One convenient goal to quantify the diagnostic accuracy of a laboratory test is to express its performance by a single numerical value. The most common global metric is the area under the ROC curve (AUC). Conventionally, this area is always ≧ 0.5 (if not, the decision rule can be reversed to do so). The range of values was between 1.0 (test values that perfectly separated the two groups) and 0.5 (no significant distribution difference between the test values of the two groups). The area depends not only on a particular part of the line graph, such as the point closest to the diagonal or the sensitivity at 90% specificity, but also on the entire line graph. This is a quantitative, descriptive representation of how the ROC plot is close to perfect (area 1.0).

Overall assay sensitivity will depend on the specificity required to carry out the methods disclosed herein. In certain preferred settings, a specificity of 75% may be sufficient, and statistical methods and resulting algorithms may be based on this specificity requirement. In a preferred embodiment, the method for assessing an individual at risk for gastric adenocarcinoma is based on specificity of 80%, 85%, or further preferably 90% or 95%.

Evaluation of the efficacy of pharmaceutical and/or physical and/or surgical treatments

Gene expression profiling is an ideal choice for identifying and assessing the effects of potential agents and/or new physical and/or surgical treatments on gastric adenocarcinoma. By the methods described herein before, samples can be taken from a subject one or more times before and after treatment. Treatment may include administering one or more agents to or to the subject at one or more doses, and/or performing one or more physical and/or surgical treatments to or on the subject. Administration of the agent can be carried out in a number of different ways, including but not limited to injection, oral administration, patch or ointment application.

The biomarker expression profiles obtained from the samples may be compared to each other and/or to the biomarker expression profile of a normal subject. The comparison may indicate the efficacy of the pharmaceutical agent and/or physical therapy and/or surgical therapy by a change in the expression profile of the biomarker in the sample of the subject.

The term "subject" means any animal, also human and non-human animals. The term "non-human animal" includes all vertebrates, e.g., mammals, such as non-human primates (particularly higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, and any domestic or pet animal; and non-mammals, such as chickens, amphibians, reptiles, and the like. In a preferred embodiment, the subject is a human.

The technical solutions of the present invention are further illustrated by the following specific examples, which do not represent limitations to the scope of the present invention. Insubstantial modifications and adaptations of the present invention by others of the concepts fall within the scope of the invention.

Example 1 screening of gastric cancer differentially expressed Gene

1. Data source

Chip data and clinical information of the GSE54129 dataset were downloaded from GEO as a training set, and the sample size was paracarcinoma-21: 111. The RNA-seq data and clinical information of gastric adenocarcinoma were downloaded from the TCGA database as a validation set, and the remaining sample size was paracarcinoma, i.e., cancer: 32:371, after removing the samples lacking survival information.

2. Data pre-processing

Joint processing, trimming and quality control are carried out on raw data by using fastp software, software default parameters are used for analysis, and high-quality sequencing data are output for subsequent analysis. Linker processing utilizes fastp software to default paired-end sequence automatic detection mode. The analyzed clean data was aligned to the human reference genome, version grch38.d1.vd1, using the Voom method, using ICGC software.

The GEO data was normalized by the RMA method, and annotated by a Platform file, and a plurality of probes corresponded to the same gene, and the average value was taken as the expression level of the gene.

3. Differential expression analysis

Differential expression analysis was performed using the "limma" package in the R software, with the screening criteria for differential genes being adj. P value<0.05，|log₂FC|>1。

4. Results

The analysis results showed that there were 1815 differentially expressed genes in the GEO, 830 up-regulated differentially expressed genes, and 985 down-regulated differentially expressed genes. There were 3057 differentially expressed genes, 1245 up-regulated differentially expressed genes and 1812 down-regulated differentially expressed genes in TCGA.

The genes with differential expression and consistent expression trend in the two databases are screened, and the genes comprise 123 genes which are consistently up-regulated and 185 genes which are consistently down-regulated.

The expression of the differentially expressed genes CLCA1, CLDN3, FAM3D and MMP7 in GEO and TCGA cancer tissues is shown in Table 1 and Table 2, respectively, wherein Log₂FC indicates paracarcinoma vs.

TABLE 1 expression of genes in training set

Gene	log₂FC	AveExpr	t	P.Value	adj.P.Val
						CLDN3	1.615397	7.386595	4.26398	3.79E-05	9.40E-05
MMP7	1.65305	8.003668	3.636287	0.000395	0.000826
						FAM3D	-1.08772	7.855388	-3.27018	0.00137	0.002603
CLCA1	-1.58488	5.663667	-2.89519	0.004433	0.00766

TABLE 2 expression of genes in the validation set

Gene	log₂FC	AveExpr	t	P.Value	adj.P.Val
						CLDN3	3.465181	5.741616	6.668396	8.53E-11	8.84E-10
MMP7	3.394976	4.325664	6.206499	1.35E-09	1.12E-08
						CLCA1	-3.44005	-1.56964	-4.56667	6.59E-06	2.57E-05
FAM3D	-1.46901	4.402179	-2.8012	0.005336	0.011103

Example 2 diagnostic Performance validation

Receiver Operating Curves (ROCs) were plotted using the R package "pROC" (version 1.15.0), AUC values, sensitivity and specificity were analyzed, and the diagnostic efficacy of the markers alone or in combination was judged.

In judging the diagnostic efficacy of an individual index, the expression level (log) of a gene is used as it is₂Expression amount), and selecting a level corresponding to the point with the maximum johnson index as the cutoff value, namely, the optimal division threshold is determined by the point with the maximum johnson index.

When the diagnosis efficiency of the index combination is judged, firstly, the genes are subjected to logistic regression, wherein independent variables are corresponding indexes, dependent variables are diseased conditions, the probability of whether each individual suffers from cancer can be calculated through a fitted regression curve, and different probability division threshold values are determined to obtain a prediction result. The optimal probability partition threshold is determined by the point at which the john index is maximum. According to the determined probability division threshold values, the sensitivity, specificity, positive predicted values, negative predicted values and the like of each combined detection scheme in the training set and the verification set can be calculated.

The diagnostic potency of CLCA1, CLDN3, FAM3D, MMP7 in the training and validation sets, alone or in combination, is shown in tables 3 and 4 and fig. 1-2, the diagnostic potency of the combination of CLCA1, CLDN3, FAM3D, MMP7 showed higher in both the training and validation sets, AUC values of 0.980 and 0.971, respectively, and sensitivity and specificity in the training set of 0.919, 1.000, respectively. The sensitivity and specificity of the validation set were 0.903, 0.938, respectively.

TABLE 3 AUC values of genes in training set

Gene	AUC
		CLCA1	0.701415701
CLDN3	0.774774775
		FAM3D	0.718146718
MMP7	0.765765766
		CLDN3+FAM3D	0.908622909
CLCA1+CLDN3	0.903903904
		CLDN3+MMP7	0.858858859
FAM3D+MMP7	0.839124839
		CLCA1+MMP7	0.77992278
CLCA1+FAM3D	0.73959674
		CLDN3+FAM3D+MMP7	0.965250965
CLCA1+CLDN3+FAM3D	0.947232947
		CLCA1+CLDN3+MMP7	0.922350922
CLCA1+FAM3D+MMP7	0.834405834
		CLCA1+CLDN3+FAM3D+MMP7	0.98026598

TABLE 4 AUC values of genes in the validation set

Gene	AUC
		CLCA1	0.67831873
CLDN3	0.74797844
		FAM3D	0.71807615
MMP7	0.79110512
		CLDN3+FAM3D	0.83995957
CLCA1+CLDN3	0.91762129
		CLDN3+MMP7	0.85470013
FAM3D+MMP7	0.84518194
		CLCA1+MMP7	0.84728774
CLCA1+FAM3D	0.7076314
		CLDN3+FAM3D+MMP7	0.93707884
CLCA1+CLDN3+FAM3D	0.93303571
		CLCA1+CLDN3+MMP7	0.95341981
CLCA1+FAM3D+MMP7	0.86666105
		CLCA1+CLDN3+FAM3D+MMP7	0.97094003

It can be seen from the results of this example that not any combination of several markers has better diagnostic effect on gastric cancer than a single marker, but a specific combination of markers has better diagnostic efficacy.

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims

1. Use of a reagent for detecting a biomarker in a sample for the manufacture of a product for the diagnosis of gastric adenocarcinoma, wherein the biomarker comprises CLDN3 in combination with FAM3D or CLCA 1;

preferably, the biomarker comprises CLDN3, in combination with any two of FAM3D, CLCA1, MMP 7;

preferably, the biomarkers include CLCA1, CLDN3, FAM3D and MMP 7.

2. The use according to claim 1, wherein the product comprises reagents for detecting levels of gene markers by sequencing techniques, nucleic acid hybridization techniques, nucleic acid amplification techniques, protein immunization techniques.

3. Use according to claim 2, wherein said agent is selected from:

a probe that specifically recognizes the biomarker; or

Primers that specifically amplify the biomarkers; or

A binding agent that specifically binds to a protein encoded by the biomarker.

4. The use according to any one of claims 1 to 3, wherein the sample is selected from the group consisting of tissue, blood.

5. A product for the diagnosis of gastric adenocarcinoma, comprising reagents for the detection of biomarkers comprising CLDN3 in combination with FAM3D or CLCA1 in a sample;

preferably, the biomarkers include CLCA1, CLDN3, FAM3D and MMP 7.

6. The product of claim 5, wherein the product comprises a kit, chip, dipstick;

preferably, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit;

preferably, the kit further comprises instructions for assessing whether the subject is suffering from or susceptible to gastric adenocarcinoma.

7. A product according to claim 5 or 6, further comprising reagents for processing the sample.

8. Use of a biomarker comprising CLDN3 in combination with FAM3D or CLCA1 in the construction of a computational model or a system embedding said computational model for predicting gastric adenocarcinoma;

preferably, the biomarkers include CLCA1, CLDN3, FAM3D, and MMP 7;

preferably, the calculation model takes the level of the biomarker as an input variable, and outputs the risk probability of the disease by performing an operation through a bioinformatics method.

9. A system, comprising:

(2) information communication terminal devices communicatively connected to each other, which provide data on the level of the biomarker recited in claim 1 in a sample from a subject;

10. A method for identifying and assessing the effect of a pharmaceutical agent and/or a surgical and/or physical treatment on gastric adenocarcinoma, comprising:

(2) obtaining a gene expression profile from the first sample;

(4) providing a second sample from the subject in step (3);

(5) obtaining a gene expression profile from the second sample;

preferably, the genes in step (2) and step (5) are the biomarkers described in claim 1.