Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer
Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucl...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-12-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.1071352/full |
| _version_ | 1811178092637454336 |
|---|---|
| author | Vigneshwaran G. Qurratulain Annie Hasan Rahul Kumar Avinash Eranki |
| author_facet | Vigneshwaran G. Qurratulain Annie Hasan Rahul Kumar Avinash Eranki |
| author_sort | Vigneshwaran G. |
| collection | DOAJ |
| description | Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%–29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity. |
| first_indexed | 2024-04-11T06:13:39Z |
| format | Article |
| id | doaj.art-f1bf314b5a1e4404b6d670e7c4365516 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-11T06:13:39Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-f1bf314b5a1e4404b6d670e7c43655162022-12-22T04:41:09ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-12-011310.3389/fgene.2022.10713521071352Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancerVigneshwaran G.0Qurratulain Annie Hasan1Rahul Kumar2Avinash Eranki3Department of Biomedical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, IndiaDepartment of Genetics and Molecular Medicine, Kamineni Hospitals, Hyderabad, Telangana, IndiaDepartment of Biotechnology, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, IndiaDepartment of Biomedical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, IndiaTriple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%–29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity.https://www.frontiersin.org/articles/10.3389/fgene.2022.1071352/fulloncogenomicscomputational biologyinsilicosingle nucleotide polymorphismtriple negative breast cancer |
| spellingShingle | Vigneshwaran G. Qurratulain Annie Hasan Rahul Kumar Avinash Eranki Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer Frontiers in Genetics oncogenomics computational biology insilico single nucleotide polymorphism triple negative breast cancer |
| title | Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer |
| title_full | Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer |
| title_fullStr | Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer |
| title_full_unstemmed | Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer |
| title_short | Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer |
| title_sort | analysis of single nucleotide polymorphisms in genes associated with triple negative breast cancer |
| topic | oncogenomics computational biology insilico single nucleotide polymorphism triple negative breast cancer |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.1071352/full |
| work_keys_str_mv | AT vigneshwarang analysisofsinglenucleotidepolymorphismsingenesassociatedwithtriplenegativebreastcancer AT qurratulainanniehasan analysisofsinglenucleotidepolymorphismsingenesassociatedwithtriplenegativebreastcancer AT rahulkumar analysisofsinglenucleotidepolymorphismsingenesassociatedwithtriplenegativebreastcancer AT avinasheranki analysisofsinglenucleotidepolymorphismsingenesassociatedwithtriplenegativebreastcancer |