487 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 discloses an autophagy biomarker related to depression, a risk assessment model and application of the autophagy biomarker. A group of autophagy related biomarkers for auxiliary diagnosis of depression are screened out through multi-dimensional screening of large sample data and a machine learning algorithm, the autophagy related biomarkers comprise the following genes: GABARAPL2, RB1CC1, FOS and ULK1, and differential expression of the autophagy related biomarkers is proved in chronic stress induced depression-like mouse cortical brain tissues. The invention also provides a risk assessment model for auxiliary diagnosis of depression, a logistic regression model is constructed by taking the biomarker as a feature, a score formula and a risk boundary value of depression risk assessment are determined, and multi-aspect assessment is carried out on the prediction performance of the model. The prediction model has good prediction accuracy and calibration degree, can efficiently and conveniently realize early diagnosis of depression, and improves the accuracy of depression treatment.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

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 discloses application of PABPC3 as a marker for evaluating prognosis of metastatic ovarian cancer, and belongs to the technical field of biological diagnosis and medicine. Specifically, differential genes in an in-situ ovarian cancer and a metastatic ovarian cancer in a database are screened, and are combined with differential genes related to ovarian cancer prognosis in a pan-cancer database, so that a gene PABPC3 with remarkable differential expression is obtained. The influence of the gene PABPC3 on the activity, migration ability and the like of ovarian cancer cells is researched through cell experiments and in-vivo experiments, and the feasibility of the marker in evaluation of ovarian cancer prognosis is proved. Meanwhile, the invention also discovers that the expression of the knock-down gene PABPC3 can inhibit the metastasis of cancer cells. Therefore, the invention provides a novel strategy for detecting and treating ovarian cancer metastasis, which is expected to overcome the defects of the traditional detection and treatment mode, and provides a new choice for clinic.

The invention discloses a method and a system for intelligently analyzing heterogeneity of endothelial cells in atherosclerosis. The method comprises the following steps: acquiring sample data of the endothelial cells in the atherosclerosis of a patient; performing feature extraction on the sample data of the endothelial cells to obtain gene expression features of the endothelial cells; dividing the endothelial cells into different subgroups according to gene expression characteristics of the endothelial cells and gene expression similarity so as to determine marker genes for distinguishing the different subgroups, and defining and representing characteristics of each subgroup by analyzing expression modes of the marker genes; gene expression differences among different endothelial cell subgroups are compared, differential expression genes of the related endothelial cell subgroups, which are significantly up-regulated or down-regulated in the atherosclerosis process, are identified, and the differential expression genes are used as key molecular markers for understanding functional changes and heterogeneity of endothelial cells. According to the method, the heterogeneity of the endothelial cells in the atherosclerosis can be accurately analyzed.

The invention relates to the technical field of machine learning, and discloses a cancer treatment effect prediction method and device, equipment and a storage medium. The method comprises the following steps: carrying out radiomics feature extraction on a CT scanning image to obtain radiomics features; performing interested pathological feature extraction based on an attention mechanism and local pathological feature extraction based on an interested pathological feature extraction result on the pathological image to obtain pathological omics features; performing differential expression gene analysis on the transcriptomics data, and performing differential expression gene screening based on a differential expression gene analysis result to obtain transcriptomics characteristics; and inputting a fusion feature obtained by fusing the radiomics feature, the pathological omics feature, the transcriptomics feature and the clinical data into a cancer treatment effect prediction model to obtain a cancer treatment effect prediction result. According to the embodiment of the invention, the cancer treatment effect is predicted by combining the radiomics characteristics, the pathomics characteristics, the transcriptomics characteristics and the clinical characteristics, and the prediction accuracy can be improved.

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 is applicable to the technical field of molecular biology, and provides a macrophage related gene KCNA3 and application thereof. The invention discloses a macrophage related gene KCNA3 for the first time, and the gene is identified as a macrophage related differential expression gene of a patient with type 2 diabetes mellitus with carotid atherosclerosis, and shows high expression in the patient with type 2 diabetes mellitus with carotid atherosclerosis. Through analysis of a public database transcriptome chip and high-throughput sequencing data and further verification of CIBERSORTX and WGCNA algorithms, close association between the KCNA3 gene and the macrophages is confirmed. Based on differentiated expression of the KCNA3 gene and correlation of the KCNA3 gene and macrophages, the gene can be used as a potential treatment target for further treating patients with the diabetes mellitus with the carotid atherosclerotic plaque, so that a new mechanism participating in disease progression of the diabetes mellitus with the carotid atherosclerotic plaque is disclosed.

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 discloses application of HOXD1 in diagnosis, prognosis and treatment of liver cancer, and particularly relates to application of a reagent for detecting expression of a biomarker HOXD1 in preparation of a product for diagnosing a liver cancer object. The invention discloses application of a reagent for detecting expression of a biomarker HOXD1 in preparation of a product for prognosis of a liver cancer object. The invention discloses an application of HOXD1 as a target in screening medicines for treating liver cancer. The HOXD1 expression inhibitor is applied to preparation of a medicine for treating liver cancer. Differential expression of the HOXD1 in hepatocellular carcinoma tissues and normal hepatocellular carcinoma tissues is clear through research, high expression of the HOXD1 is closely related to the survival reduction period of clinical hepatocellular carcinoma patients, specific knock-down of the HOXD1 can obviously reduce the proliferation capacity of the hepatocellular carcinoma, and a new diagnosis / prognosis marker and a treatment target are provided for early discovery, early treatment and prognosis of the hepatocellular carcinoma. And a new treatment strategy and direction are provided.

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 for improving synthesis of sakuranetin from yarrowia lipolytica based on transcriptome analysis and application of the method, and belongs to the technical field of gene engineering. Target genes are analyzed and screened through transcriptome sequencing so as to find a method capable of improving sakuranetin synthesis, and the method specifically comprises the steps that an Illumina HiseqTM platform is used for conducting comprehensive transcriptome analysis on an original strain in the logarithmic phase by applying an RNA-seq technology, then transcriptome data are deeply analyzed through KEGG pathway analysis, and the sakuranetin synthesis can be improved. Therefore, the gene showing significant differential expression in the key path of sakuranetin synthesis is identified. And finally, carrying out free expression and integrated expression on the screened key target gene, and verifying the influence on the sakuranetin yield. Finally, a recombinant yarrowia lipolytica strain which is integrated with the YALI0C01859g gene and has high sakuranetin yield is obtained, and the titer of sakuranetin is increased to 344.0 mg / L.

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 provides a drug recommendation method for drug effect prediction and / or personalized medication guidance. The drug recommendation method is based on a multi-dimensional differential expression map reference database. According to the invention, the association between the gene and a drug action mechanism or a disease phenotype is inferred through multi-dimensional information of the gene and functional modules including a GO function, a signal channel, transcriptional regulation, protein-protein interaction and the like; and meanwhile, a model integrated statistical method is benefited, and more accurate drug effect prediction and personalized medication guidance are finally realized compared with similar work. The method has a wide application prospect.

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 belongs to the technical field of single cell data analysis, and particularly relates to a single cell pseudo-trajectory identification method based on a gene co-expression network. Comprising the following steps: S1, constructing a single cell transcription data matrix of behavior gene names and cell names; s2, preprocessing the single-cell transcription data matrix, constructing a cell expression matrix, and performing characteristic gene screening on the cell expression matrix to obtain a single-cell gene expression matrix; s3, constructing a gene co-expression network; s4, constructing a gene rank differential expression network in combination with the single-cell gene expression matrix and the gene co-expression network; and S5, carrying out integrated clustering on the gene rank differential expression network, fusing by using a similarity network to obtain a final clustering result, determining an initial cluster, distributing pseudo time for cells in each cluster, and deducing a cell pseudo trajectory. According to the method, the differentiation track of the cell can be identified, and compared with an original gene expression matrix, a more stable identification result can be obtained by utilizing a relative expression sequence.

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.