A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33718128", "kind": "science-record", "revision": 7, "content_hash": "sha256:7197b6e8c18f2d0e285046757de9c06b046220f25c7c723995ab2b16d1689424", "created_at": "2026-11-01T15:41:28Z", "updated_at": "2026-11-01T22:03:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer.", "doi": "10.3389/fonc.2021.554779", "pmid": "33718128", "authors": ["Qingchuan Chen", "Yuen Tan", "Chao Zhang", "Zhe Zhang", "Siwei Pan", "Wen An", "Huimian Xu"], "journal": "Frontiers in oncology", "year": 2021, "abstract": "BACKGROUND: Gastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.\nMETHODS: We obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.\nRESULTS: Of the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.\nCONCLUSION: This study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.", "claims": [{"id": "claim-method", "statement": "Nine genes from LASSO analysis were analyzed by multivariate Cox proportional hazards regression using the survival R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33718128", "locator": "sections[7] \"Prognostic Model Construction Using the Cox Proportional Hazards Regression Model\" paragraph 2", "quote": "Nine genes from LASSO analysis were analyzed by multivariate Cox proportional hazards regression using the survival R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data sets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33718128", "locator": "sections[22] \"Data Availability Statement\" paragraph 2", "quote": "The data sets presented in this study can be found in online repositories. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE15459"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE15459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33718128", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2021.554779", "sha256": "sha256:b978ec8bb3492e2493e0a28956419e493611e4620cc015f0fa7f3b6f42c7496e", "bytes": 46584, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2021.554779", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7947930", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15459", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15459", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15459", "version": null, "note": "The data sets presented in this study can be found in online repositories. 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IHC Staining of 105 Patients Of the IHC analysis", "ificant. IHC Staining of 105 Patients Of the IHC analysis of 105 patients with GC, we found t"], "order_basis": "paper_text_order:902", "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": ["Nine genes from LASSO analysis were analyzed by multivariate Cox proportional hazards regression using the survival R package."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}