Molecular characterization, clinical value, and cancer-immune interactions of genes related to disulfidptosis and ferroptosis in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38787520", "kind": "science-record", "revision": 3, "content_hash": "sha256:34dcf860675d4038f77a5ebed1800001a623cf4779f4d9e55d0dce96bd711cad", "created_at": "2026-11-01T19:20:44Z", "updated_at": "2026-11-01T22:11:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Molecular characterization, clinical value, and cancer-immune interactions of genes related to disulfidptosis and ferroptosis in colorectal cancer.", "doi": "10.1007/s12672-024-01031-y", "pmid": "38787520", "authors": ["Xianqiang Liu", "Dingchang Li", "Wenxing Gao", "Peng Chen", "Hao Liu", "Yingjie Zhao", "Wen Zhao", "Guanglong Dong"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: This research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC).\nMETHODS: The data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool.\nRESULTS: SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib.\nCONCLUSION: In conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.", "claims": [{"id": "claim-method", "statement": "The RF, LASSO, and stepwise regression algorithms aided the selection of five genes to develop the SRF gene signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38787520", "locator": "sections[7] \"Development of a prognostic scoring scheme using SRF genes\" paragraph 2", "quote": "The RF, LASSO, and stepwise regression algorithms aided the selection of five genes to develop the SRF gene signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). For additional information, please feel free to contact the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38787520", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). For additional information, please feel free to contact the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38787520", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2).", "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:38787520"], "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:GSE38832"], "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:GSE38832"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38787520", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01031-y", "sha256": "sha256:8bddfad403da2b4fd844a07c5a25cb9cd85d10f0ea2329ca0a7eea2e2d986707", "bytes": 54054, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-024-01031-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11126553", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE38832", "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=GSE38832", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE38832", "version": null, "note": "The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). For additional information, please feel free to contact the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets utilized and analyzed in this study are accessible through GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). 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