Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38903804", "kind": "science-record", "revision": 3, "content_hash": "sha256:68dbf0acbd141b7bbf45e6e3a2066db8022945a8013c836124fee16e2598adfd", "created_at": "2026-11-01T20:28:58Z", "updated_at": "2026-11-01T22:12:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis.", "doi": "10.3389/fmed.2024.1382004", "pmid": "38903804", "authors": ["Bingqing Xia", "Ping Zeng", "Yuling Xue", "Qian Li", "Jianhui Xie", "Jiamin Xu", "Wenzhen Wu", "Xiaobo Yang"], "journal": "Frontiers in medicine", "year": 2024, "abstract": "Background: Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.\nMethods: The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.\nResults: A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that \"ECM-receptor interaction,\" \"AGE-RAGE signaling pathway in diabetic complications,\" \"aging,\" and \"cellular response to copper ion\" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.\nConclusion: \"ECM-receptor interaction,\" \"AGE-RAGE signaling pathway in diabetic complications,\" \"aging,\" and \"cellular response to copper ion\" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.", "claims": [{"id": "claim-method", "statement": "The GC and T2D datasets were subjected to WGCNA through the WGCNA (24) package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38903804", "locator": "sections[5] \"Weighted gene co-expression network analysis\" paragraph 2", "quote": "The GC and T2D datasets were subjected to WGCNA through the WGCNA (24) package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38903804", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38903804", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.", "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:38903804"], "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:GSE164416"], "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:GSE164416"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38903804", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2024.1382004", "sha256": 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