371 results about "Differential expression" patented technology

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention provides a key node identification method for a disease marker expression regulation network, and belongs to the technical field of disease markers, and the method comprises the steps: firstly carrying out the preprocessing and quality control of original data, including batch effect removal, abnormal sample identification and the like; then identifying differential expression genes through multiple difference analysis and a pre-training model, and constructing a gene expression correlation network; and integrating multi-source regulation and control data to construct a multi-level weighted network, calculating network node features, and carrying out representation learning and module division. And based on multi-dimensional features such as network topology features, module contribution degree and biological importance, a neural network model is trained to carry out key node identification. And finally, optimizing the model through multi-layer verification such as pathway enrichment, disease gene overlapping, expression stability, time sequence change and network disturbance, and finally obtaining a verified key node set. The problem that in the prior art, the interaction relation between molecules is ignored, and consequently some key regulation and control nodes are possibly missed is solved.

The invention provides a drug relocation method and a drug relocation system. The method comprises the following steps: predicting an expression profile of a drug after cell line disturbance according to a chemical structure of the drug, dose information of the drug and an undisturbed expression profile. And calculating the differential expression profile of the gene in the cell line based on the expression profiles before and after the cell line is disturbed by the drug. And for each drug, according to the differential expression profiles of the genes, calculating an average value of the differential expression profiles of the genes after the drugs disturb different cell lines, and sorting the genes based on the average value to obtain a sorting list of the differential expression profiles of the drugs. And according to the gene characteristics of the target disease and the sorting list, calculating the enrichment score of each drug on the target disease, and according to the enrichment score, evaluating the potential efficacy of the drug on the target disease. The drug relocation method provided by the invention can be used for giving disease specific gene characteristics for drug library screening.

The invention discloses an experimental method for relation between cytokine level and tumor immune microenvironment in circulation, which comprises the following steps of: S1, collecting colorectal cancer patient data and normal data for contrast, and preferably screening a gene expression data set from a Gene Expression Omnibus (GEO) database; according to the method, a plurality of gene expression data sets from the GEO database are collected and analyzed, so that differential expression genes between colorectal cancer and normal tissues can be systematically identified, and a solid foundation is provided for subsequent research. The process not only covers batch correction, normalization and batch effect elimination of data, but also visually presents data distribution changes through principal component analysis, ensures the accuracy and reliability of analysis results, confirms that IL6 and IFN gamma are one of the most critical factors affecting the colorectal cancer immune microenvironment, and participate in formation of immune infiltration.

The invention discloses a differentiated thyroid cancer lung metastasis risk prediction model construction method and system. 4D Label-free quantitative proteomics analysis and PRM and IHC dual verification are carried out on primary focus paraffin embedded tissue samples of lung metastatic DTC patients and non-lung metastatic DTC patients, and it is found that multiple key differential expression proteins DEPs may become potential biomarkers for predicting the DTC lung metastasis risk. In addition, the sample size is enlarged, three single and combined DTC lung metastasis risk prediction models are respectively constructed based on clinical features and key DEPs expression, then internal verification is performed on the efficiency of the three fitted models, and the optimal model is taken to construct Nomogram. According to the method, help is provided for risk stratification of lung metastasis of DTC patients, and an important basis is provided for formulating clinical individualized treatment schemes and follow-up visit strategies for high-risk patients.

The invention provides a glycolipid metabolism related gene-based endometrial cancer prognosis prediction model construction method, which comprises the following steps of 1, acquiring data containing gene expression and clinical information, and preprocessing the data; 2, differential expression and prognosis gene screening; 3, constructing a prognosis model; 4, analyzing model gene enrichment; 5, evaluating the immunocompetence of the two GLRG related dangerous groups; and step 6, statistical analysis. According to the technical scheme, more accurate and reliable prognosis evaluation is provided for endometrial cancer by comprehensively analyzing multi-dimensional information such as gene expression, immune characteristics, mutation characteristics and drug sensitivity.

The invention discloses a colorectal cancer prognosis model construction method based on fatty acid metabolism related genes, and belongs to the technical field of biomedicine, and the colorectal cancer prognosis model construction method comprises the following steps: S1, obtaining transcriptome data of a colorectal cancer diagnosis individual from a TCGA database; s2, performing difference analysis on fatty acid metabolism related gene expression data of the colorectal cancer tissue and the normal tissue, and screening out differentially expressed genes; s3, performing single-factor Cox regression analysis on the differentially expressed genes, and screening out genes related to colorectal cancer prognosis; according to the construction method of the colorectal cancer prognosis model based on the fatty acid metabolism related genes, provided by the invention, the tumor immunotherapy reactivity is evaluated by introducing TIDE scores, and the prior art is improved, so that the immune escape potential and the immunotherapy effect of tumor subtypes are analyzed. The prognosis of a colorectal cancer patient is effectively predicted, and a new molecular marker is provided for personalized treatment of the colorectal cancer.

The invention provides a deconvolution model construction method and device, a terminal and a medium, and the method comprises the steps: generating simulated space transcriptome data with a cell type proportion label based on single cell transcriptome data, and obtaining real space transcriptome data obtained through sampling based on a 3D concentric sphere sampling strategy, and based on a comparative learning strategy and a domain adversarial network, training an encoder and an initial prediction model by using target space transcriptome data containing differential expression genes obtained by screening from simulation data and real data, and obtaining a feature extraction model for extracting embedded information and a prediction model for predicting a cell type proportion. According to the method, deep learning is combined, target space transcriptome data is used as encoder input, encoder output is used as prediction model input, the model is trained based on domain adversarial learning and contrast learning strategies, a model with good generalization ability is obtained, and high-precision deconvolution and cell type proportion prediction of 3D space transcriptome data are realized.

The invention belongs to the technical field of biological medicine and molecular biology, and particularly relates to application of circ0005704 and related biological elements thereof in diagnosis and treatment of recurrent spontaneous abortion with unknown reasons. Researches find that differential expression and autophagy level of ULK1 in trophoblast cells of URSA patients are increased. High-throughput transcriptome sequencing is combined with bioinformatics analysis, and it is found that miR-26a-5p is down-regulated in URSA patients, is responsible for up-regulation of ULK1 and promotes autophagy of trophoblast cells, so that occurrence of URSA is increased. Through a high-throughput transcriptional set screening strategy and dual luciferase reporter gene analysis, it is found that circ0005704 enhances the expression of ULK1 through miR-26a-5p. In a word, the research of the invention shows that the circ0005704 / miR-26a-5p / ULK1 signal axis participates in the pathogenesis of URSA by adjusting the migration of the trophoblast cells, and a new target and scientific evidence are provided for the clinical treatment of URSA.

The invention discloses a method and system for evaluating the treatment effect of traditional Chinese medicine based on single cell and spatial transcriptome data, and the method comprises the following steps: respectively obtaining single cell data and spatial transcriptome data of a tissue sample before and after administration, and carrying out the preprocessing; classifying the cells and identifying cell types; the expressed ligand and receptor genes are paired to obtain ligand-receptor pairs, and the ligand-receptor pairs which are differentially expressed before and after administration are screened out; acquiring space coordinate information of a single cell, and constructing a cell interaction network and a differential gene network according to the cell type, the ligand-receptor pair and the space coordinate information; weighting processing is conducted on the cell interaction network and the differential gene network, and comprehensive indexes for evaluating the effect of the traditional Chinese medicine are obtained.The brand-new method for evaluating the disease treatment effect of the traditional Chinese medicine is provided, the method is scientific and reliable, and the treatment effect of the traditional Chinese medicine on complex diseases can be accurately reflected.

The invention discloses a rapid detection method for traumatic brain injury by fusing genetic engineering and quantum dots, and relates to the technical field of traumatic brain injury detection. The method comprises the following steps: acquiring a suspected patient biological sample, extracting biomolecule components, and comparing the biomolecule components with a healthy population biomolecule database to obtain related differential expression molecules. A specifically recognizable recombinant antibody is constructed by genetic engineering, and is connected with a quantum dot through a coupling reaction to prepare a detection probe. The probe and a biological sample are mixed and reacted, and the intensity of a fluorescence signal generated by combination is collected by a fluorescence detection device. A standardized signal value is obtained through signal processing algorithm noise reduction and feature extraction, and a preliminary detection result is determined by combining a bioinformatics database evaluation sample. And finally, according to the differential expression molecule, the standardized signal value and the preliminary result, analyzing a data change trend in a preset time window by using a multivariable statistical model, and generating a diagnosis report. According to the method, various technologies are fused, and a new path is provided for traumatic brain injury detection.

The invention discloses a screening method of a protein marker combination, which is used for risk prediction of abdominal aortic aneurysm or ruptured abdominal aortic aneurysm, and optimizes the stability of a protein expression profile by constructing an initial protein expression profile; screening differential expression proteins by adopting statistical analysis, and preliminarily locking candidate proteins remarkably associated with a disease endpoint based on survival analysis or a risk regression model; performing cross validation by combining a sparse constraint algorithm and a nonlinear feature selection algorithm, and extracting a robust core marker; the independent contribution degree of each marker is evaluated through the standardized weight, and finally the optimal protein combination for disease prediction or diagnosis is determined. According to the method, six core proteins obtained through multi-algorithm cross screening can be detected in serum, multivariable logistic regression and external verification set performance both show good robustness, and an aorta protein fingerprint feature set which can be popularized and explained is formed.

The invention discloses a rape grain weight regulation gene NGAL3 based on whole genome screening and a breeding application thereof, based on the seed size and grain weight phenotypic value of a cabbage type rape germplasm resource, a candidate gene interval is positioned by using GWAS; the method comprises the following steps: selecting large-grain-weight and small-grain-weight extreme phenotypic materials, performing transcriptome sequencing in a critical period of seed development, and screening differential expression genes; and performing cross comparison on the GWAS candidate gene and the differential expression gene to obtain a key gene for jointly regulating and controlling the size and the grain weight of the seed, and performing functional verification on the key gene. The thousand seed weights of the created homozygous three mutant strains L1-sg1-1-4-5 and L2-sg1-4-8-16 are obviously increased by 46% and 29% compared with those of the wild type strains. The invention provides a new target for high-yield rape breeding, and non-transgenic high-grain-weight germplasm can be created through gene editing or beneficial allelic variation of NGAL3 is selected and enriched under the assistance of molecular markers.

The invention discloses an eccDNAcircATP6V0A1 serving as a predictive marker of cis-platinum drug resistance of hypopharyngeal cancer and an application of the eccDNAcircATP6V0A1. The nucleotide sequence of the predictive marker eccDNA is as shown in SEQ ID NO. 3. According to the invention, a hypopharyngeal carcinoma cisplatin drug-resistant cell line model FaDu / DDP is cultured for the first time, meanwhile, a differential eccDNA expression profile and a circRNA expression profile in hypopharyngeal carcinoma cells FaDu and hypopharyngeal carcinoma cisplatin drug-resistant cells FaDu / DDP are disclosed and analyzed in a combined manner, and the eccDNA circATP6V0A1 is further screened and verified as a predictive marker of hypopharyngeal carcinoma cisplatin drug resistance through combined analysis. And a new treatment strategy diagnosis and treatment thought is provided for cis-platinum chemotherapy of hypopharyngeal carcinoma patients clinically.

The invention discloses a gastric cancer biomarker composition as well as a screening method and application thereof, and belongs to the technical field of bioinformatics. The screening method of the gastric cancer biomarker composition comprises the following steps: firstly, collecting blood specimens of a gastric cancer patient and a healthy control, carrying out high-throughput plasma proteomics detection, and screening out differential expression proteins; and verifying the candidate protein markers by using a prospective queue in the British biological sample library, and finally determining 17 core proteins to obtain the gastric cancer biomarker composition. A gastric cancer risk prediction model is constructed by combining clinical risk factors and protein expression levels of gastric cancer biomarkers. The gastric cancer risk prediction model constructed by the invention can realize accurate layering of gastric cancer risks, decision curve analysis shows that the net benefit of the model is obviously superior to that of a traditional screening method, and an efficient tool is provided for early warning and personalized intervention of gastric cancer.

The invention provides a disease marker subtype classification determination method, and belongs to the technical field of disease marker subtype classification, and the method comprises the following steps: firstly, extracting protein from a patient sample, separating through a dimensional electrophoresis technology, and obtaining a protein expression matrix through Coomassie brilliant blue dyeing and an image analysis system; differentially expressed proteins are determined, and sequence information is obtained through mass spectrum identification. And extracting network node features by using a graph neural network method, and carrying out function annotation analysis and path identification. Patient samples are grouped by using a multi-level clustering method, and feature extraction and optimization are realized by training an embedded neural network model. And inputting the extracted features into a pre-trained function enrichment analysis model, and determining the molecular pathway features of the disease subtype. And finally, the reliability of a classification result is confirmed through an immunoblotting technology and independent patient queue verification, subtype classification of the disease markers is completed, and the problem that in the prior art, the recognition degree of subtype classification of the disease markers is not high enough is solved.

The invention discloses a screening method of sicca syndrome markers based on conjunctival blotting, and relates to the technical field of biomedicine, and the screening method comprises the following steps: S1, conjunctival noninvasive sampling; s2, RNA (Ribonucleic Acid) sequencing and pathway screening; s3, machine learning modeling is carried out; s4, clinical verification. According to the invention, biomarker screening is realized, and similar biomarker screening can also be realized through tear sample analysis or other ophthalmic samples; however, the alternative schemes have the defects of relatively strong invasiveness, complex operation and the like; sampling is carried out through the conjunctival imprinting technology, invasive operation such as biopsy and blood drawing is avoided, and pain and discomfort are reduced; differential expression genes can be accurately identified, and the sensitivity and the accuracy are relatively high; through immune cell infiltration analysis, the relationship between the immune cell subpopulation and the sicca syndrome related gene is disclosed, a new perspective is provided for a disease mechanism, and a theoretical basis is provided for a future immune regulation strategy.

The invention relates to a deep feature fusion-based fabricated component surface defect multi-task detection method. The method comprises the following steps of: constructing a multi-view camera array and an illumination feedback regulation and control module at a data acquisition end; on the algorithm level, feature cross-layer propagation and information compensation are realized through an improved lightweight convolutional network and a multi-scale residual diffusion module; a dynamic feature fusion module is introduced, and a self-adaptive fusion weight is generated based on channel statistical features, so that feature sharing and differential expression are realized among different tasks; meanwhile, a defect perception attention mechanism and cross-task consistency constraint are adopted, and the problem that semantic space distribution is inconsistent in the multi-task detection process is solved; in the detection post-processing stage, a three-dimensional quantitative evaluation system based on the geometric dimension, the texture roughness and the depth volume is constructed, and unified grade evaluation of the surface defects of the component is achieved through the multi-feature fusion quality index. The problems of low detection efficiency, unstable precision, difficulty in collaborative recognition of multiple types of defects and the like in existing component delivery detection are solved.

The invention discloses a construction method of immune-related key gene prognosis indexes of metastatic melanoma and related equipment, and belongs to the field of bioinformatics, the method comprises the following steps: obtaining an immune-related differential expression gene list, and constructing a weighted gene co-expression network to divide gene modules, two gene modules with the highest correlation degree with the matrix score and the immune score are screened out, and gene intersection is taken to recognize immune related key genes; and determining immune-related key genes significantly related to the lifetime, and sequentially screening through univariate Cox regression and LASSO regression to obtain genes of which the regression coefficients are not zero so as to construct and obtain immune-related key gene prognosis indexes of metastatic melanoma. The method can solve the problems that in the prior art, when a prognosis index is constructed, an immune-related gene pair is only determined by an LASSO regression coefficient, key disease treatment genes are difficult to recognize, too many immune-related genes are contained, and clinical application convenience is low.

This disclosure provides methods for treating a subject afflicted with a tumor derived from a renal cell carcinoma. The methods comprise administering a first dose to a subject of an anti-PD-1 antibody or antigen-binding portion thereof and / or an anti-PD-L1 antibody or antigen-binding portion thereof, and administering a second dose to the subject, wherein the subject exhibited differential expression in one or more biomarker genes, e.g., CTLA-4, TIGIT, and / or PD-L2, following administration of the first dose.

The invention relates to the technical field of biology, and provides a genetic marker for children nephrotic syndrome genetic risk assessment. The invention relates to genetic detection and application of hormone sensitive nephrotic syndrome (pSSNS) of children. Nine risk sites, including new sites of 1q23.1, 1p36.13, 5p13.2, 10q21.3, 10q24.1 and the like, highly related to diseases are found by integrating whole genome association research (GWAS) data, combining Meta analysis and a conjugate false discovery rate (conjFDR) method and taking genetic information of IgA nephropathy as assistance. Research results show that genes near the loci have differential expression in pSSNS and IgAN patients, which prompts that the genes play an important role in the occurrence and development of diseases. The invention provides a molecular detection method based on the risk site, which can be used for risk assessment, auxiliary diagnosis and prognosis prediction of children's nephropathy. Meanwhile, the invention provides potential application values of the loci and related genes thereof in individualized medication and targeted therapy.

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on deep learning, and relates to the technical field of biological information analys.The method comprises the steps that rice multi-modal stress response data is obtained and preprocessed, and preprocessed gene expression data is obtained; carrying out differential expression gene screening and co-expression network analysis on the preprocessed gene expression data, extracting multi-modal features, and fusing the multi-modal features to generate a multi-modal input feature matrix; constructing a double-layer deep learning model, training the double-layer deep learning model by using the multi-modal input feature matrix, and respectively outputting a regulation and control relationship matrix of transcription factors and target genes and a regulation and control relationship matrix of transcription factors and target pathways; and according to an output result, calculating a comprehensive score of each transcription factor through a multi-dimensional scoring system, and screening out the stress high-photosynthetic-efficiency transcription factor according to a predetermined screening standard.

The invention discloses application of MNDA in diagnosis of multiple sclerosis. According to the application, the gene MNDA presenting significant differential expression in multiple sclerosis is screened out by analyzing database data, and verification is further carried out by sequencing data and collecting clinical samples, so that the MNDA presents significant up-regulation in multiple sclerosis patients and has relatively high diagnosis efficiency; a new direction is provided for diagnosing and treating multiple sclerosis, and the application prospect is wide.

The invention discloses a method for screening a biomarker for early non-invasive Alzheimer's disease screening based on machine learning. The method comprises the following steps: acquiring blood-derived cell free RNA (Ribonucleic Acid) sequencing (cfRNA-seq) and brain-derived single cell transcriptome sequencing (scRNA-seq) data of an Alzheimer's disease (AD) patient and an age-matched contrast; and based on the standardized data, screening out genes presenting the same expression mode in the two types of data as the biomarker for non-invasive Alzheimer's disease screening. According to the method, 34 characteristic genes which are jointly and differentially expressed in cfRNA and scRNA data sets are found, and the AD diagnosis classifier is successfully constructed by using the 34 characteristic genes. A diagnosis classifier can accurately predict AD patients and effectively distinguish AD early-stage patients, and meanwhile, the risk of an individual suffering from AD is evaluated. Results show that the key marker genes can be applied to early AD non-invasive screening and prevent disease progression; meanwhile, AD patients with different disease progresses can be distinguished, and support is provided for personalized treatment schemes of the AD patients.

The circulating snoRNA biomarker comprises the following six specific snoRNAs: SNORA7B, SNORD41, SNORA74A, SNORA79B, SNORD83A and SNORD94, the differential expression of the circulating snoRNA biomarker in plasma is remarkable, and the circulating snoRNA biomarker is suitable for early screening, diagnosis and prognosis evaluation of gastric cancer. The circulating snoRNA is applied to a detection reagent for gastric cancer diagnosis, and has the advantages of non-invasiveness and convenience in detection.

The invention discloses a method for improving the application efficiency of frozen semen of sheep, and relates to the field of animal reproduction and breeding science, and the method comprises the following steps: collecting semen, freezing half of the semen, then respectively carrying out exosome extraction and identification experiments, then carrying out pandorah sequencing on small non-coding RNA (Ribonucleic Acid), and finally obtaining the application efficiency of the frozen semen of the sheep. The small non-coding RNA in fresh semen and frozen semen is qualitatively and quantitatively analyzed, differential expression microRNAs are screened out, corresponding target genes and signal channels are analyzed, and the small non-coding RNA and the signal channels related to semen cryopreservation are screened out. Finally, the differentially expressed small non-coding RNAs are added into frozen semen in a manner of in-vitro chemical synthesis and the like, or are added in an auxiliary manner during in-vitro artificial insemination, or are delivered through lipidosome, so that the phenotype of an embryo after insemination is influenced, the conception rate of in-vitro artificial insemination is increased, the problem in practical production is favorably solved, and the economic benefit is increased. And economic values are created for enterprises.

Alzheimer's disease, the most common cause of dementia in older individuals, is a debilitating neurodegenerative disease for which there is currently no cure. In the past, AD could only be definitively diagnosed by brain biopsy or upon autopsy after a patient died. These methods, which demonstrate the presence of the characteristic plaque and tangle lesions in the brain, are still considered the gold standard for the pathological diagnoses of AD. However, in the clinical setting brain biopsy is rarely performed and diagnosis depends on a battery of neurological, psychometric and biochemical tests, including the measurement of biochemical markers such as the ApoE and tau proteins or the beta-amyloid peptide in cerebrospinal fluid and blood. The present invention discloses and describes panels of makers that are differentially expressed in the disease state relative to their expression in the normal state and, in particular, identifies and describes panels of makers associated with neurocognitive disorders. Such biomarker panel might have considerable value in triaging patients with early memory disorders to yet more specific but more invasive and costly approaches such as molecular markers in CSF and on PET imaging in clinical trials and possibly in clinical practice.

The invention discloses a method for promoting renal clear cell carcinoma metastasis by VSIG4 through epithelial-mesenchymal transition, which comprises the following steps: S1, clinical data analysis of ccRCC patients: S1.1, data collection: collecting clinical data of ccRCC patients, including two groups of primary metastatic-free tumor patients and metastatic patients, and observing whether there is statistical difference in expression of VSIG4 on renal carcinoma pathological tissues of the two groups of patients; s1.2, immunohistochemical staining and scoring: performing VSIG4 immunohistochemical staining and scoring on pathological section tissues of the ccRCC patients, grouping according to whether the VSIG4 is highly expressed, and observing whether the patients have disease imaging progression and whether the total lifetime is different; and S1.3, expression difference research: determining the effect of the VSIG4 in invasion and metastasis of the kidney cancer according to the expression difference of the VSIG4 among the kidney cancer, the para-carcinoma and the cancerous pseudoenvelop. According to the application disclosed by the invention, the VSIG4 promotes the metastasis of the renal clear cell carcinoma through epithelial-mesenchymal transition, and a potential means is provided for treating metastatic renal carcinoma by taking the VSIG4 as a target.

The invention discloses a method for screening potential treatment targets of acrolein-induced retinal injury, and relates to the field of acrolein toxicity prediction. According to the method disclosed by the invention, a retinal injury related differential expression gene and an acrolein related target spot are obtained, an intersection is taken, protein interaction relationship network analysis is carried out, and a potential mechanism of acrolein-induced retinal injury is predicted. GO functional clustering analysis results show that acrolein is mainly subjected to biological processes such as apoptosis signal channel regulation and epithelial cell development, and KEGG key signal channel enrichment analysis results show that acrolein may induce retinal injury through IL-17 signal channels, human cytomegalovirus infection and cell senescence cell signal channels. The predicted key genes comprise CXCR4, EDN1, ITGA6 and PECAM1, and a new target is provided for subsequent prevention and treatment of acrolein-induced retinal injury.

The invention discloses an application of a reagent for detecting AP3S2 in preparation of a diagnostic reagent for neoadjuvant immunochemotherapy resistance of gastric cancer in a local progression stage, which comprises the following steps: screening differential expression proteins in neoadjuvant immunochemotherapy resistance tissues of gastric cancer in the local progression stage through proteomics; a new marker is provided for diagnosis of the local progression stage gastric cancer immunochemotherapy drug resistance, and a basis is also provided for development and future development of a local progression stage gastric cancer immunochemotherapy drug resistance diagnosis method.