An anoikis-related lncRNA signature may predict the prognosis, immune infiltration, and drug sensitivity in esophageal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38803953", "kind": "science-record", "revision": 4, "content_hash": "sha256:c2910abbbf296ad6d355f0c6340ec93d26be5244fcf43755db25b8675ecd4d6f", "created_at": "2026-11-01T19:20:45Z", "updated_at": "2026-11-01T22:54:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An anoikis-related lncRNA signature may predict the prognosis, immune infiltration, and drug sensitivity in esophageal cancer.", "doi": "10.1016/j.heliyon.2024.e31202", "pmid": "38803953", "authors": ["Fan Feng", "Yuxin Chu", "Yi Yao", "Bin Xu", "Qibin Song"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Esophageal cancer (EC) is a prevalent malignancy with heterogeneous outcomes. This study explores the significance of anoikis-related long non-coding RNAs (lncRNAs) in EC, aiming to unravel their molecular roles and clinical implications.\nMethods: Transcriptome and clinical data were obtained from TCGA database for EC samples. We identified anoikis-related genes and lncRNAs by Pearson correlation analysis. The risk score model hinged on prognostic lncRNAs filtered from multiple steps. Risk scores were calculated using the derived formula, and categorized patients into low- and high-risk groups. Model robustness was assessed through Kaplan-Meier (KM) survival analysis and Receiver Operating Characteristic (ROC) curve, with clinical utility achieved via a constructed nomogram. We also explored the interplay between the risk score and immune cell infiltration, and investigated drug sensitivity.\nResults: We identified 2365 anoikis-related lncRNAs through co-expression analysis, including 1415 significant lncRNAs differentially expressed between normal and tumor samples. A risk score model was constructed from ten prognostic lncRNAs. The risk score model effectively stratified patients based on the median score, and its predictive capacity was validated through KM survival, ROC curve analyses, and the external GSE53622 dataset. The nomogram provided a practical tool for individualized prognosis evaluation. We unveiled significant correlations between specific immune cell subsets and the risk score. Eosinophils and common lymphoid progenitors exhibited positive associations, while endothelial cells and myeloid dendritic cells showed negative correlations. Drug sensitivity analysis revealed potential sensitive drugs for EC treatment that aligned with the risk subgroups.\nConclusion: This study established an anoikis-related lncRNAs risk score model that may predict the prognosis, immune infiltration, and drug sensitivity in EC, in hope of facilitating tailored patient management.", "claims": [{"id": "claim-method", "statement": "Gene expression disparities between the two groups were examined using the Mann-Whitney U test. To gauge survival discrepancies among subgroups, we used Kaplan-Meier method with log-rank test. Correlation analysis entailed the Pearson and Spearman correlation test. A multivariate Cox regression model was employed to calculate the coefficients of prognostic lncRNAs, contributing to the construction of a risk score model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38803953", "locator": "sections[9] \"Statistical analysis\" paragraph 2", "quote": "Gene expression disparities between the two groups were examined using the Mann-Whitney U test. To gauge survival discrepancies among subgroups, we used Kaplan-Meier method with log-rank test. Correlation analysis entailed the Pearson and Spearman correlation test. A multivariate Cox regression model was employed to calculate the coefficients of prognostic lncRNAs, contributing to the construction of a risk score model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available in the public, openly accessible TCGA and GEO database. All the data analysis results in this study are included in the manuscript and supplementary files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38803953", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "Data are available in the public, openly accessible TCGA and GEO database. All the data analysis results in this study are included in the manuscript and supplementary files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk score model effectively stratified patients based on the median score, and its predictive capacity was validated through KM survival, ROC curve analyses, and the external GSE53622 dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38803953", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The risk score model effectively stratified patients based on the median score, and its predictive capacity was validated through KM survival, ROC curve analyses, and the external GSE53622 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The risk score model effectively stratified patients based on the median score, and its predictive capacity was validated through KM survival, ROC curve analyses, and the external GSE53622 dataset.", "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:38803953"], "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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