Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31132294", "kind": "science-record", "revision": 8, "content_hash": "sha256:722c507783d651ddd69ce8c8ff0a28272d4f427dbbb4ed22619c956460800d87", "created_at": "2026-11-01T15:40:45Z", "updated_at": "2026-11-01T22:00:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung.", "doi": "10.12659/MSM.913727", "pmid": "31132294", "authors": ["Si-Yu He", "Wen-Jing Xi", "Xin Wang", "Chao-Han Xu", "Liang Cheng", "Si-Yao Liu", "Qian-Qian Meng", "Boyan Li", "Yahui Wang", "Hong-Bo Shi", "Hong-Jiu Wang", "Zhen-Zhen Wang"], "journal": "Medical science monitor : international medical journal of experimental and clinical research", "year": 2019, "abstract": "BACKGROUND Adenocarcinoma of the lung is a type of non-small cell lung cancer (NSCLC). Clinical outcome is associated with tumor grade, stage, and subtype. This study aimed to identify RNA expression profiles, including long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA, associated with clinical outcome in adenocarcinoma of the lung using bioinformatics data. MATERIAL AND METHODS The miRNA and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and lncRNA expression profiles were downloaded from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. The independent dataset, the Gene Expression Omnibus (GEO) accession dataset, GSE81089, was used. RNA expression profiles were used to identify comprehensive prognostic RNA signatures based on patient survival time. RESULTS From 7,704 lncRNAs, 787 miRNAs, and 28,937 mRNAs of 449 patients, four joint RNA molecular signatures were identified, including RP11-909N17.2, RP11-14N7.2 (lncRNAs), MIR139 (miRNA), KLHDC8B (mRNA). The random forest (RF) classifier was used to test the prediction ability of patient survival risk and showed a good predictive accuracy of 71% and also showed a significant difference in overall survival (log-rank P=0.0002; HR, 3.54; 95% CI, 1.74-7.19). The combined RNA signature also showed good performance in the identification of patient survival in the validation and independent datasets. CONCLUSIONS This study identified four RNA sequences as a prognostic molecular signature in adenocarcinoma of the lung, which may also provide an increased understanding of the molecular mechanisms underlying the pathogenesis of this malignancy.", "claims": [{"id": "claim-method", "statement": "The R package random forest (RF) method was used to classify the patients according to the identified combined RNA signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31132294", "locator": "sections[7] \"Implementation of the random forest method and performance evaluation\" paragraph 2", "quote": "The R package random forest (RF) method was used to classify the patients according to the identified combined RNA signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data from patients with adenocarcinoma of the lung, including the microRNA (miRNA), and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database [17].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31132294", "locator": "sections[3] \"Patients cohorts with adenocarcinoma of the lung\" paragraph 2", "quote": "Data from patients with adenocarcinoma of the lung, including the microRNA (miRNA), and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database [17]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:31132294"], "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:GSE81089"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE81089"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31132294", "role": "manuscript", "format": "xml", "media_type": "application/xml", 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