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Identification of potential regulatory mutations using multi-omics analysis and haplotyping of lung adenocarcinoma
Sarun Sereewattanawoot1, Ayako Suzuki2, Masahide Seki1
1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Chiba, Japan.
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.
Insights
This study identifies regulatory mutations in lung adenocarcinoma that alter gene transcription and impact patient prognosis. These findings shed light on the functional role of non-coding mutations in cancer development.
Area of Science:
- Genomics
- Cancer Biology
- Molecular Oncology
Background:
- The functional significance of mutations in cancer regulatory regions is largely unknown.
- Understanding these mutations is crucial for identifying novel therapeutic targets in lung adenocarcinoma.
Purpose of the Study:
- To identify and analyze regulatory mutations with transcriptional consequences in lung adenocarcinoma.
- To investigate the link between these mutations, gene expression, and patient outcomes.
Main Methods:
- Phasing of regulatory mutations with downstream coding SNPs.
- Analysis of ChIP-Seq and RNA-Seq data to assess allele-specific effects.
- Identification of SNVs impacting transcription factor binding sites.
Main Results:
- 137 potential regulatory mutations affecting 146 transcripts were identified.
- 84 SNVs were found to create or disrupt transcription factor binding sites.
- 31 mutations were present in clinical samples and associated with patient prognosis.
Conclusions:
- Regulatory mutations can significantly impact gene transcription in lung adenocarcinoma.
- These findings highlight the clinical relevance of non-coding mutations in cancer prognosis.
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