Dataset Information

Systematic Identification of Multi Omics-based Biomarkers in KEAP1 Mutated TCGA Lung Adenocarcinoma.

ABSTRACT: Mutations in KEAP1 and/or NRF2 genes have been identified across many cancers and the dysregulation of the NRF2 pathway due to these mutations leads to drug and radioresistance in several cancers. Identification of biomarkers associated with these mutations allows the researchers and clinicians to identify the personalized medicine and quicker diagnosis. In this current study, we carried out an integrated, multi-omics, multi-database analysis of exome, transcriptomics data's of KEAP1 mutated TCGA- Lung adenocarcinoma (LUAD) patients against non-mutated counterparts. Finally, we discovered the gene signature associated with KEAP1 mutations, prognostic genes which were highly correlated with the upregulation of the NRF2 pathway in the KEAP1 mutated LUAD patients. Our finding might be useful to identify the early diagnosis of KEAP1 mutated LUAD patients.

SUBMITTER: Namani A

PROVIDER: S-EPMC6909944 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Systematic Identification of Multi Omics-based Biomarkers in KEAP1 Mutated TCGA Lung Adenocarcinoma.

Namani Akhileshwar A Zheng Zhaohong Z Wang Xiu Jun XJ Tang Xiuwen X

Journal of Cancer 20191101 27

Mutations in KEAP1 and/or NRF2 genes have been identified across many cancers and the dysregulation of the NRF2 pathway due to these mutations leads to drug and radioresistance in several cancers. Identification of biomarkers associated with these mutations allows the researchers and clinicians to identify the personalized medicine and quicker diagnosis. In this current study, we carried out an integrated, multi-omics, multi-database analysis of exome, transcriptomics data's of ...[more]

PMID: 31839815

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Project description:Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Dataset Information

Systematic Identification of Multi Omics-based Biomarkers in KEAP1 Mutated TCGA Lung Adenocarcinoma.

Publications

Systematic Identification of Multi Omics-based Biomarkers in <i>KEAP1</i> Mutated TCGA Lung Adenocarcinoma.

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