Dataset Information

Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines.

ABSTRACT: The functional relevancy of mutations occurring in the regulatory regions in cancers remains mostly elusive. Here, we identified and analyzed regulatory mutations having transcriptional consequences in lung adenocarcinoma-derived cell lines. We phased the mutations in the regulatory regions to the downstream heterozygous SNPs in the coding regions and examined whether the ChIP-Seq variant tags of the regulatory SNVs and the RNA-Seq variant tags of their target transcripts showed biased frequency between the mutant and reference alleles. We identified 137 potential regulatory mutations affecting the transcriptional regulation of 146 RefSeq transcripts with at least 84 SNVs that create and/or disrupt potential transcription factor binding sites. For example, in the regulatory region of NFATC1 gene, a novel and active binding site for the ETS transcription factor family was created. Further examination revealed that 31 of these disruptions were presented in clinical lung adenocarcinoma samples and were associated with prognosis of patients.

SUBMITTER: Sereewattanawoot S

PROVIDER: S-EPMC5862974 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines.

Sereewattanawoot Sarun S Suzuki Ayako A Seki Masahide M Sakamoto Yoshitaka Y Kohno Takashi T Sugano Sumio S Tsuchihara Katsuya K Suzuki Yutaka Y

Scientific reports 20180321 1

The functional relevancy of mutations occurring in the regulatory regions in cancers remains mostly elusive. Here, we identified and analyzed regulatory mutations having transcriptional consequences in lung adenocarcinoma-derived cell lines. We phased the mutations in the regulatory regions to the downstream heterozygous SNPs in the coding regions and examined whether the ChIP-Seq variant tags of the regulatory SNVs and the RNA-Seq variant tags of their target transcripts showed biased frequency ...[more]

PMID: 29563587

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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

Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines.

Publications

Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma cell lines.

Similar Datasets

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