Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38849442", "kind": "science-record", "revision": 3, "content_hash": "sha256:eb04a86a931661123ee1c73e95333713436b25b0367bb0c5cdffaf8fe00310b8", "created_at": "2026-11-01T19:20:47Z", "updated_at": "2026-11-01T22:12:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.", "doi": "10.1038/s41598-024-63949-1", "pmid": "38849442", "authors": ["Yang Pan", "Xuanhong Jin", "Haoting Xu", "Jiandong Hong", "Feng Li", "Taobo Luo", "Jian Zeng"], "journal": "Scientific reports", "year": 2024, "abstract": "Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan-Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis.", "claims": [{"id": "claim-method", "statement": "The optimal selection was the Enet model with α = 0.2.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38849442", "locator": "sections[4] \"Prognostic model construction\" paragraph 13", "quote": "The optimal selection was the Enet model with α = 0.2."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The public datasets were obtained from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/). GEO Accession Numbers: GSE31210, GSE30219, and GSE50081.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38849442", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "The public datasets were obtained from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/). GEO Accession Numbers: GSE31210, GSE30219, and GSE50081."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38849442", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups.", "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:38849442"], "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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