Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34805135", "kind": "science-record", "revision": 4, "content_hash": "sha256:a803742c5e745fa6277dc8be290ec2ceb0cb6487c0c37073c2d9bcd58b35dc64", "created_at": "2026-11-01T17:36:30Z", "updated_at": "2026-11-01T22:05:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of an Immune-Related Signature Predicting Survival Risk and Immune Microenvironment in Gastric Cancer.", "doi": "10.3389/fcell.2021.687473", "pmid": "34805135", "authors": ["Shuang Dai", "Tao Liu", "Xiao-Qin Liu", "Xiao-Ying Li", "Ke Xu", "Tao Ren", "Feng Luo"], "journal": "Frontiers in cell and developmental biology", "year": 2021, "abstract": "Background: Tumor immune microenvironment plays a vital role in tumorigenesis and progression of gastric cancer (GC), but potent immune biomarkers for predicting the prognosis have not been identified yet. Methods: At first, RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) were mined to identify an immune-risk signature using least absolute shrinkage and selection operator (LASSO) regression and multivariate stepwise Cox regression analyses. Furthermore, the risk score of each sample was calculated, and GC patients were divided into high-risk group and low-risk group based on their risk scores. Subsequently, the performance of this signature, including the correlation with overall survival (OS), clinical features, immune cell infiltration, and immune response, has been tested in GC data from TCGA database and Gene Expression Omnibus (GSE84437), respectively. Results: An immune signature composed of four genes (MAGED1, ACKR3, FZD2, and CTLA4) was constructed. The single sample gene set enrichment analysis (ssGSEA) indicated that activated CD4+/CD8+ T cell, activated dendritic cell, and effector memory CD8+ T cell prominently increased in the low-risk group, showing relatively high immune scores and low stromal scores. Further GSEA analysis indicated that TGF-β, Ras, and Rap1 pathways were activated in the high-risk group, while Th17/Th1/Th2 differentiation, T cell receptor and PD-1/PD-L1 checkpoint pathways were activated in the low-risk group. Low-risk patients presented higher tumor mutation burden (TMB) and expression of HLA-related genes. The immune-associated signature showed an excellent predictive ability for 2-, 3-, and 5-year OS in GC. Conclusion: The immune-related prognosis model contributes to predicting the prognosis of GC patients and providing valuable information about their response to immunotherapy using integrated bioinformatics methods.", "claims": [{"id": "claim-method", "statement": "Lasso Cox regression analysis, a penalty regression tool, can estimate the regression coefficients by maximizing the log-likelihood function (or sum of squared residuals). We used multivariate stepwise Cox regression analysis (forward and backward) to evaluate the capabilities of immune-related signatures in predicting OS of GC patients. The predictive capacity of immune-risk signature was tested by Kaplan–Meier survival analysis using the R package “Survminer.” Time-dependent receiver operating characteristic (ROC) curves with AUC values were used to evaluate the ability of prognosis classification.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34805135", "locator": "sections[4] \"Identification of Immune-Related Genes and Development of Immune-Related Risk Signature\" paragraph 2", "quote": "Lasso Cox regression analysis, a penalty regression tool, can estimate the regression coefficients by maximizing the log-likelihood function (or sum of squared residuals)."}, {"asset": "source:pmid:34805135", "locator": "sections[4] \"Identification of Immune-Related Genes and Development of Immune-Related Risk Signature\" paragraph 2", "quote": "We used multivariate stepwise Cox regression analysis (forward and backward) to evaluate the capabilities of immune-related signatures in predicting OS of GC patients."}, {"asset": "source:pmid:34805135", "locator": "sections[4] \"Identification of Immune-Related Genes and Development of Immune-Related Risk Signature\" paragraph 2", "quote": "The predictive capacity of immune-risk signature was tested by Kaplan–Meier survival analysis using the R package “Survminer.” Time-dependent receiver operating characteristic (ROC) curves with AUC values were used to evaluate the ability of prognosis classification."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: Publicly available datasets were analyzed in this study. This data can be found here: TCGA (https://gdc.cancer.gov/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34805135", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: Publicly available datasets were analyzed in this study. This data can be found here: TCGA (https://gdc.cancer.gov/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene immune-risk signature constructed with LASSO and multivariate stepwise Cox regression using TCGA data and validated in the GEO cohort achieved 2-, 3-, and 5-year overall-survival AUC values of 0.74, 0.77, and 0.83 in the training cohort, 0.60, 0.69, and 0.77 in the TCGA testing cohort, and 0.61, 0.59, and 0.60 in the GEO cohort, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34805135", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "At first, RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) were mined to identify an immune-risk signature using least absolute shrinkage and selection operator (LASSO) regression and multivariate stepwise Cox regression analyses."}, {"asset": "source:pmid:34805135", "locator": "sections[13] \"Validation of Prognostic Value of Risk Signature\" paragraph 2", "quote": "Moreover, the AUC values of 2-, 3-, and 5-years were 0.74, 0.77, and 0.83 in the training cohort, 0.60, 0.69, and 0.77 in the TCGA testing cohort, and 0.61, 0.59, and 0.60 in the GEO cohort, respectively (Figures 3D–F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene immune-risk signature constructed with LASSO and multivariate stepwise Cox regression using TCGA data and validated in the GEO cohort achieved 2-, 3-, and 5-year overall-survival AUC values of 0.74, 0.77, and 0.83 in the training cohort, 0.60, 0.69, and 0.77 in the TCGA testing cohort, and 0.61, 0.59, and 0.60 in the GEO cohort, respectively.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34805135"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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We used multivariate stepwise Cox regression analysis (forward and backward) to evaluate the capabilities of immune-related signatures in predicting OS of GC patients. The predictive capacity of immune-risk signature was tested by Kaplan–Meier survival analysis using the R package “Survminer.” Time-dependent receiver operating characteristic (ROC) curves with AUC values were used to evaluate the ability of prognosis classification."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "chi-square"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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Methods: At first, RNA-sequencing and clinical data from The Cancer Genome Atl"], "order_basis": "paper_text_order:299", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Lasso Cox regression analysis, a penalty regression tool, can estimate the regression coefficients by maximizing the log-likelihood function (or sum of squared residuals). We used multivariate stepwise Cox regression analysis (forward and backward) to evaluate the capabilities of immune-related signatures in predicting OS of GC patients. 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