Dataset Information

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma.

ABSTRACT: Background:A hypoxia microenvironment plays a role in the initiation and progression of many cancer types, but its involvement in lung adenocarcinoma is still unclear. This study aimed to explore the potential correlation between hypoxia and lung adenocarcinoma and establish the hypoxia-associated gene signature in lung adenocarcinoma. Methods:Lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The genes to be included in the hypoxia-associated signature were selected by performing univariate Cox regression analysis and lasso regression analysis. Then, the gene signature was verified by performing a survival analysis and constructing the multiple receiver operating characteristic (ROC) curve. The CIBERSORT tool was then used to investigate the potential correlation between the gene signature and immune cells. Moreover, a nomogram was constructed and evaluated by calculating the C-index. Results:Four genes (XPNPEP1, ANGPTL4, SLC2A1, and PFKP) were included in the final signature. The results showed that patients in the high-risk group showed worse survival than those in the low-risk group. Moreover, we found two types of immune cells (memory activated CD4+ T cell and M0 macrophages) which showed a significant infiltration in the tissues of the high-risk group patients. Conclusion:The hypoxia-associated gene signature established and validated in this study could be used as a potential prognostic factor in lung adenocarcinoma and may guide the immunotherapy choice.

SUBMITTER: Mo Z

PROVIDER: S-EPMC7324800 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma.

Mo Zhuomao Z Yu Ling L Cao Zhirui Z Hu Hao H Luo Shaoju S Zhang Shijun S

Frontiers in genetics 20200623

<h4>Background</h4>A hypoxia microenvironment plays a role in the initiation and progression of many cancer types, but its involvement in lung adenocarcinoma is still unclear. This study aimed to explore the potential correlation between hypoxia and lung adenocarcinoma and establish the hypoxia-associated gene signature in lung adenocarcinoma.<h4>Methods</h4>Lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The genes to ...[more]

PMID: 32655624

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Project description:Background:Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient's outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods:The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results:A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

Dataset Information

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma.

Publications

Identification of a Hypoxia-Associated Signature for Lung Adenocarcinoma.

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