Identification of gastric cancer subtypes based on disulfidptosis-related genes: GPC3 as a novel biomarker for prognosis prediction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39695020", "kind": "science-record", "revision": 3, "content_hash": "sha256:927db0cce95f1bd359ccd441ff70703339fc50196e5f5b862479cdb7832f62b2", "created_at": "2026-11-01T20:29:59Z", "updated_at": "2026-11-01T22:13:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of gastric cancer subtypes based on disulfidptosis-related genes: GPC3 as a novel biomarker for prognosis prediction.", "doi": "10.1007/s12672-024-01694-7", "pmid": "39695020", "authors": ["Nan Yi", "Xindong Yin", "Xiao Feng", "Ming Ren", "Chaoqun Ma"], "journal": "Discover oncology", "year": 2024, "abstract": "Gastric cancer (GC) is the fourth most common cancer type. \"Disulfidptosis,\" a distinct form of cell death, is initiated through aberrant intracellular disulfide metabolism. Here, we identified various GC subtypes based on disulfidptosis-related genes (DRGs) and constructed a risk score model to identify relevant genes to help predict patient prognosis and guide treatment. We downloaded RNA sequencing (RNA-seq) data from the TCGA-STAD database, performed a difference analysis, and combined the data with GSE84437 to successfully perform an unsupervised clustering analysis based on DRGs and differentially expressed genes (DEGs). Risk-scoring models were established by screening prognosis-related DEGs. The GC samples were segregated into high-risk (HR) and low-risk (LR) groups according to their risk scores. We then evaluated the genes screened with the model in terms of prognosis, tumor, and immune cell infiltration. The response of patients with GC to immunological therapy was assessed using tumor mutational burden, microsatellite instability, and tumor immune dysfunction and exclusion scores. Using unsupervised cluster analysis, we identified two DRG clusters and two gene clusters that differed in prognosis and tumor microenvironment. A six-gene model was developed for risk score assessment. The LR group demonstrated superior performance compared to the HR group in terms of immunity, exhibiting greater sensitivity to immunotherapy. Thereafter, we selected the model gene GPC3 for single-gene analysis and verified it by experimental validation. The results demonstrated that GPC3 can serve as a standalone biomarker with promising clinical applicability in the prognostic prediction and clinical management of GC.", "claims": [{"id": "claim-method", "statement": "Thereafter, we used Lasso–Cox regression analysis to conduct further screening of the 907 genes with prognostic value (Fig. 6a, b), and 6 key genes, namely, HEYL, CD109, GRP, DEFB1, GPC3, and GPA33, were finally identified.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39695020", "locator": "sections[23] \"Construction of the prognostic model related to disulfidptosis\" paragraph 2", "quote": "Thereafter, we used Lasso–Cox regression analysis to conduct further screening of the 907 genes with prognostic value (Fig. 6a, b), and 6 key genes, namely, HEYL, CD109, GRP, DEFB1, GPC3, and GPA33, were finally identified."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data are available in The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/) (https://www.ncbi.nlm.nih.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39695020", "locator": "sections[36] \"Data availability\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data are available in The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/) (https://www.ncbi.nlm.nih.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper developed a six-gene risk-score model for gastric cancer prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39695020", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A six-gene model was developed for risk score assessment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper developed a six-gene risk-score model for gastric cancer prognosis.", "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:39695020"], "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:GSE84437"], "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:GSE84437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39695020", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01694-7", "sha256": "sha256:d8f45b63db21f7f9ea306b749c6575b9f7441dd991c4f1f70ec8df519cab18e2", "bytes": 59182, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-024-01694-7", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11655940", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE84437", "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=GSE84437", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE84437", "version": null, "note": "Publicly available datasets were analyzed in this study. These data are available in The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/) (https://www.ncbi.nlm.nih.gov/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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"GSM4419533"], "sample_count": 483, "group_sizes": null, "group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Molecular subtypes in gastric cancer.", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE84437 not downloaded", "asset:data:geo:GSE84437 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Thereafter, we used Lasso–Cox regression analysis to conduct further screening of the 907 genes with prognostic value (Fig. 6a, b), and 6 key genes, namely, HEYL, CD109, GRP, DEFB1, 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Cell culture and transfection In this study, the human gastric mucosal cel", "nduce GPC3 overexpression, BGC823 cells were transfected with a GPC3 overexpression plasmid using Lip", "3 overexpression plasmid using Lipofectamine Transfection Reagent. The transfection efficiency was sub", "sing Lipofectamine Transfection Reagent. The transfection efficiency was subsequently assessed through"], "order_basis": "paper_text_order:3598", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["efficiency was subsequently assessed through real-time quantitative PCR. Clustering of patients with GC based on DRG"], "order_basis": "paper_text_order:4301", "id": "assay:2"}, {"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": ["Thereafter, we used Lasso–Cox regression analysis to conduct further screening of the 907 genes with prognostic value (Fig. 6a, b), and 6 key genes, namely, HEYL, CD109, GRP, DEFB1, GPC3, and GPA33, were finally identified."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}