Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33195688", "kind": "science-record", "revision": 8, "content_hash": "sha256:39e6b23ecaf90270e8de82580914d3e468da45c02353d9850414093a4a35ac8f", "created_at": "2026-11-01T15:41:16Z", "updated_at": "2026-11-01T22:45:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma.", "doi": "10.1155/2020/1836542", "pmid": "33195688", "authors": ["Yingqing Zhang", "Xiaoping Zhang", "Xiaodong Lv", "Ming Zhang", "Xixi Gao", "Jialiang Liu", "Yufen Xu", "Zhixian Fang", "Wenyu Chen"], "journal": "BioMed research international", "year": 2020, "abstract": "BACKGROUND: Prognosis is a main factor affecting the survival of patients with lung adenocarcinoma (LUAD), yet no robust prognostic model of high effectiveness has been developed. This study is aimed at constructing a stable and practicable gene signature-based model via bioinformatics methods for predicting the prognosis of LUAD sufferers.\nMETHODS: The mRNA expression data were accessed from the TCGA-LUAD dataset, and paired clinical information was collected from the GDC website. R package \"edgeR\" was employed to select the differentially expressed genes (DEGs), which were then used for the construction of a gene signature-based model via univariate COX, Lasso, and multivariate COX regression analyses. Kaplan-Meier and ROC survival analyses were conducted to comprehensively evaluate the performance of the model in predicting LUAD prognosis, and an independent dataset GSE26939 was accessed for further validation.\nRESULTS: Totally, 1,655 DEGs were obtained, and a 7-gene signature-based risk score was developed and formulated as risk_score = 0.000245∗NTSR1 + (7.13E - 05)∗RHOV + 0.000505∗KLK8 + (7.01E - 05)∗TNS4 + 0.000288∗C1QTNF6 + 0.00044∗IVL + 0.000161∗B4GALNT2. Kaplan-Meier survival curves revealed that the survival rate of patients in the high-risk group was lower in both the TCGA-LUAD dataset and GSE26939 relative to that of patients in the low-risk group. The relationship between the risk score and clinical characteristics was further investigated, finding that the model was effective in prognosis prediction in the patients with different age (age > 65, age < 65) and TNM stage (N0&N1, T1&T2, and tumor stage I/II). In sum, our study provides a robust predictive model for LUAD prognosis, which boosts the clinical research on LUAD and helps to explore the mechanism underlying the occurrence and progression of LUAD.", "claims": [{"id": "claim-data-access", "statement": "All the data in my manuscript is available.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33195688", "locator": "sections[16] \"Data Availability\" paragraph 2", "quote": "All the data in my manuscript is available."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "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:33195688"], "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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