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Identification of a three-gene expression signature and construction of a prognostic nomogram predicting overall survival in lung adenocarcinoma based on TCGA and GEO databases.
| Content Provider | Europe PMC |
|---|---|
| Author | Zhou, Yuwei Gao, Shenhu Yang, Rong Du, Chengli Wang, Yanli Wu, Yihe |
| Copyright Year | 2022 |
| Abstract | BackgroundLung adenocarcinoma (LUAD) is the major cause of cancer mortality. Traditional prognostic factors have limited importance after including other parameters. Thus, developing a more credible prognostic model combined with genes and clinical parameters is necessary.MethodsThe messenger RNA (mRNA) expression and clinical information from The Cancer Genome Atlas (TCGA)-LUAD datasets and microarray data from three Gene Expression Omnibus (GEO) databases were obtained. We identified differentially-expressed genes (DEGs) between lung tumor and normal tissues through integrated analysis of the three GEO datasets. Univariate and multivariate Cox regression analyses were conducted to select survival-associated DEGs and to establish a prognostic gene signature which was associated with overall survival (OS). The expression of gene proteins was assessed in 180 LUAD tissue microarrays (TMAs) by immunohistochemistry (IHC). We verified its predictive performance with a Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, and Harrell’s concordance index (C-index) and validated it in external GEO databases. Multivariate Cox regression analysis was performed to identify the significant prognostic indicators in LUAD. Furthermore, we established a prognostic nomogram based on TCGA-LUAD dataset.ResultsA three-gene signature was constructed to predict the OS of LUAD patients. The KM analysis, ROC curve, and C-index present a good predictive ability of the gene signature in TCGA dataset [P<0.0001; C-index 0.6375; 95% confidence interval (CI): 0.5632–0.7118; area under the ROC curve (AUC) 0.674] and the external GEO datasets (P=0.05, 0.004, and 0.04, respectively). Univariate and multivariate Cox regression analyses also verified that LUAD patients with low-risk scores had a decreased risk of death compared to those with a high-risk score in TCGA database [hazard ratio (HR) =0.3898; 95% CI: 0.1938–0.7842; P<0.05]. Finally, we constructed a nomogram integrating the gene signature and clinicopathological parameters (P<0.0001; C-index 0.762; 95% CI: 0.714–0.845; AUC 0.8136). Compared with conventional staging, a nomogram can effectively improve prognosis prediction.ConclusionsThe nomogram is closely associated to the OS of LUAD patients. This consequence may be beneficial to individualized treatment and clinical decision-making. |
| ISSN | 22186751 |
| Journal | Translational Lung Cancer Research |
| Volume Number | 11 |
| PubMed Central reference number | PMC9359966 |
| Issue Number | 7 |
| PubMed reference number | 35958325 |
| e-ISSN | 22264477 |
| DOI | 10.21037/tlcr-22-444 |
| Language | English |
| Publisher | AME Publishing Company |
| Publisher Date | 2022-07-01 |
| Access Restriction | Open |
| Rights License | Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0. 2022 Translational Lung Cancer Research. All rights reserved. |
| Subject Keyword | Gene Expression Omnibus (GEO) The Cancer Genome Atlas (TCGA) overall survival (OS) lung adenocarcinoma (LUAD) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Oncology |
