A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32613949", "kind": "science-record", "revision": 7, "content_hash": "sha256:b442ff2c202bdcb19a2b04977aa1c5f9dcf0e587753a35b8f17589951ba9b495", "created_at": "2026-11-01T15:41:05Z", "updated_at": "2026-11-01T22:01:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma.", "doi": "10.12659/MSM.924269", "pmid": "32613949", "authors": ["Xuan Zou", "Zhihuang Hu", "Changjing Huang", "Jianhua Chang"], "journal": "Medical science monitor : international medical journal of experimental and clinical research", "year": 2020, "abstract": "BACKGROUND Lung adenocarcinoma is the most life-threatening malignancy with high incidence and poor long-term survival. The crucial role of tumor immunity reveals the significance of exploring immune-related prognostic predictors in lung adenocarcinoma. MATERIAL AND METHODS Immune-related genes were screened out applying the ESTIMATE algorithm. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset was trained for the construction of Cox proportional hazard model. Univariate Cox and Lasso regression analysis was conducted to reduce the overfitting of model. Nomogram integrated the signature and clinicopathological characteristics was established for prognosis prediction of LUAD. The GSE30219, GSE41271, and GSE42127 datasets were analyzed for external validation. LUAD patients were separated into low-risk and high-risk subgroups based on the optimum cutoff threshold of calculated risk score. The predictive value of the signature was evaluated using Kaplan-Meier survival analysis, Harrell's C-index, and receiver operating characteristic (ROC) curve analysis and calibration curve. Clinical- and immune-correlation of the signature was further performed. Gene Set Enrichment Analysis (GSEA) was performed for functional exploration. RESULTS An immune-related signature containing 7 genes was identified. The signature exhibited reliable performance in the prediction of overall survival for LUAD with the C-index being 0.72. The areas under the curve (AUCs) of the model in 1-year risk prediction were 0.781, 0.797, 0.659, and 0.822 for TCGA-LUAD, GSE30219, GSE41271, and GSE42127 datasets, respectively. In all datasets, the signature proved to an independent risk factor for LUAD. Correlation analyses and GSEA further revealed the close relationship between the predictive biomarker and tumor immunity. CONCLUSIONS A Cox proportional hazard model consisting of 7 genes was identified for prognostic prediction of LUAD. The signature was highly correlated with immunity and deserves further exploration.", "claims": [{"id": "claim-method", "statement": "The TCGA-LUAD dataset was used as the training set for the development of Cox regression model predicting survival in LUAD patients, and 3 GEO datasets (GSE30219, GSE41271, and GSE42127) were explored for external validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32613949", "locator": "sections[5] \"Building and validation of the prognosis predictive model\" paragraph 2", "quote": "The TCGA-LUAD dataset was used as the training set for the development of Cox regression model predicting survival in LUAD patients, and 3 GEO datasets (GSE30219, GSE41271, and GSE42127) were explored for external validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Supplementary Tables 1 and 2 available from the corresponding author on request", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32613949", "locator": "sections[19] \"Supplementary Data\" paragraph 2", "quote": "Supplementary Tables 1 and 2 available from the corresponding author on request"}], "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:32613949"], "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": "blocked", "blocked_on": "method:group_comparison_mann_whitney", "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": ["step:run_analysis blocked on method:group_comparison_mann_whitney"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32613949", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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