Identification of subtype-specific genes signature by WGCNA for prognostic prediction in diffuse type gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32915770", "kind": "science-record", "revision": 5, "content_hash": "sha256:1a8f948a115dc8b3db44541504795cd8f0e19077c603a215703fb4dd6bc7cd7a", "created_at": "2026-11-01T15:41:10Z", "updated_at": "2026-11-01T21:23:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of subtype-specific genes signature by WGCNA for prognostic prediction in diffuse type gastric cancer.", "doi": "10.18632/aging.103743", "pmid": "32915770", "authors": ["Qi Zhou", "Li-Qiang Zhou", "Shi-Hao Li", "Yi-Wu Yuan", "Li Liu", "Jin-Liang Wang", "Deng-Zhong Wu", "You Wu", "Lin Xin"], "journal": "Aging", "year": 2020, "abstract": "BACKGROUND: Gastric cancer is a common malignancy and had poor response to treatment due to its strong heterogeneity. This study aimed to identify essential genes associated with diffuse type gastric cancer and construct a powerful prognostic model.\nRESULTS: We conducted a weighted gene co-expression network analysis (WGCN) using transcripts per million (TPM) expression data from The Cancer Genome Atlas (TCGA) to find out the module related with diffuse type gastric cancer. Combining Least Absolute Shrinkage and Selection Operator (LASSO) with multi-cox regression, the 10 specific genes risk score model of diffuse type gastric cancer was established. The concordance index (0.97), the area under the respective ROC curves (AUCs) (1-years: 0.98; 3-years: 1; 5-years: 1) and survival difference of high- and low risk groups (p=2.84e-10) of this model in TCGA dataset were obtained. The moderate predicting performance was observed in the independent cohort of GSE15459 and GSE62254. The results of the gene set enrichment analysis (GSEA) using high-and low risk group as phenotype indicated differential expression of tumor-related pathways.\nCONCLUSION: Thus, we constructed a reliable prognostic model for diffuse type gastric cancer, which should be beneficial for clinical therapeutic decision-making.", "claims": [{"id": "claim-method", "statement": "To find modules of highly correlated with diffuse type GC, WGCNA was performed using the WGCNA R package [22] and carried out on top 50% most variable genes (9752 genes).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32915770", "locator": "sections[11] \"Weighted Gene co-expression network construction\" paragraph 2", "quote": "To find modules of highly correlated with diffuse type GC, WGCNA was performed using the WGCNA R package [22] and carried out on top 50% most variable genes (9752 genes)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TCGA-STAD RNA-seq data and clinical data of the 375 GC samples were downloaded by using the “TCGAbiolinks” package in R [20], which was used as a training set for prognostic prediction of the multi-gene signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32915770", "locator": "sections[10] \"Data collection and processing\" paragraph 3", "quote": "The TCGA-STAD RNA-seq data and clinical data of the 375 GC samples were downloaded by using the “TCGAbiolinks” package in R [20], which was used as a training set for prognostic prediction of the multi-gene signature."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:32915770"], "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. 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:32915770", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.103743", "sha256": 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