Identification of survival-associated biomarkers based on three datasets by bioinformatics analysis in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37584004", "kind": "science-record", "revision": 3, "content_hash": "sha256:f9c6a73799bb16536372ae4b659fb76d6a096b307dc97a86142383abdfb4e94d", "created_at": "2026-11-01T19:02:22Z", "updated_at": "2026-11-01T22:10:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of survival-associated biomarkers based on three datasets by bioinformatics analysis in gastric cancer.", "doi": "10.12998/wjcc.v11.i20.4763", "pmid": "37584004", "authors": ["Long-Kuan Yin", "Hua-Yan Yuan", "Jian-Jun Liu", "Xiu-Lian Xu", "Wei Wang", "Xiang-Yu Bai", "Pan Wang"], "journal": "World journal of clinical cases", "year": 2023, "abstract": "BACKGROUND: Gastric cancer (GC) is one of the most common malignant tumors with poor prognosis in terms of advanced stage. However, the survival-associated biomarkers for GC remains unclear.\nAIM: To investigate the potential biomarkers of the prognosis of patients with GC, so as to provide new methods and strategies for the treatment of GC.\nMETHODS: RNA sequencing data from The Cancer Genome Atlas (TCGA) database of STAD tumors, and microarray data from Gene Expression Omnibus (GEO) database (GSE19826, GSE79973 and GSE29998) were obtained. The differentially expressed genes (DEGs) between GC patients and health people were picked out using R software (x64 4.1.3). The intersections were underwent between the above obtained co-expression of differential genes (co-DEGs) and the DEGs of GC from Gene Expression Profiling Interactive Analysis database, and Gene Ontology (GO) analysis, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), Protein-protein Interaction (PPI) analysis and Kaplan-Meier Plotter survival analysis were performed on these DEGs. Using Immunohistochemistry (IHC) database of Human Protein Atlas (HPA), we verified the candidate Hub genes.\nRESULTS: With DEGs analysis, there were 334 co-DEGs, including 133 up-regulated genes and 201 down-regulated genes. GO enrichment analysis showed that the co-DEGs were involved in biological process, cell composition and molecular function pathways. KEGG enrichment analysis suggested the co-DEGs pathways were mainly enriched in ECM-receptor interaction, protein digestion and absorption pathways, etc. GSEA pathway analysis showed that co-DEGs mainly concentrated in cell cycle progression, mitotic cell cycle and cell cycle pathways, etc. PPI analysis showed 84 nodes and 654 edges for the co-DEGs. The survival analysis illustrated 11 Hub genes with notable significance for prognosis of patients were screened. Furtherly, using IHC database of HPA, we confirmed the above candidate Hub genes, and 10 Hub genes that associated with prognosis of GC were identified, namely BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN.\nCONCLUSION: The 10 Hub genes may be the potential biomarkers for predicting the prognosis of GC, which can provide new strategies and methods for the diagnosis and treatment of GC.", "claims": [{"id": "claim-method", "statement": "Gene Ontology (GO) analysis, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), Protein-protein Interaction (PPI) analysis and Kaplan-Meier Plotter survival analysis were performed on these DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37584004", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Gene Ontology (GO) analysis, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), Protein-protein Interaction (PPI) analysis and Kaplan-Meier Plotter survival analysis were performed on these DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data supporting the results of this study are available from GEO database (GSE19826, GSE79973 and GSE29998).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37584004", "locator": "sections[32] \"Data sharing statement\" paragraph 2", "quote": "The data supporting the results of this study are available from GEO database (GSE19826, GSE79973 and GSE29998)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The signal pathways of the involvement of the co-expression of differential genes in GC were screened out, and the 10 Hub genes, including BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN, were associated with prognosis of GC and identified as the potential prognostic biomarkers of GC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37584004", "locator": "sections[27] \"Research results\" paragraph 2", "quote": "The signal pathways of the involvement of the co-expression of differential genes in GC were screened out, and the 10 Hub genes, including BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN, were associated with prognosis of GC and identified as the potential prognostic biomarkers of GC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The signal pathways of the involvement of the co-expression of differential genes in GC were screened out, and the 10 Hub genes, including BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN, were associated with prognosis of GC and identified as the potential prognostic biomarkers of GC.", "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:37584004"], "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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METHODS: RNA sequencing data from The Cancer Genome Atlas (TCGA) dat", "ped interactive web server for analyzing the RNA sequencing expression data from the TCGA and the GTEx p"], "order_basis": "paper_text_order:426", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["analysis were performed on these DEGs. Using Immunohistochemistry (IHC) database of Human Protein Atlas (HPA),", "d on these DEGs. Using Immunohistochemistry (IHC) database of Human Protein Atlas (HPA), we v", "of patients were screened. 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