Version 1
: Received: 18 January 2023 / Approved: 19 January 2023 / Online: 19 January 2023 (02:10:02 CET)
How to cite:
Labani, M.; Beheshti, A.; Argha, A.; Alinejad, H. An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer. Preprints2023, 2023010343. https://doi.org/10.20944/preprints202301.0343.v1
Labani, M.; Beheshti, A.; Argha, A.; Alinejad, H. An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer. Preprints 2023, 2023010343. https://doi.org/10.20944/preprints202301.0343.v1
Labani, M.; Beheshti, A.; Argha, A.; Alinejad, H. An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer. Preprints2023, 2023010343. https://doi.org/10.20944/preprints202301.0343.v1
APA Style
Labani, M., Beheshti, A., Argha, A., & Alinejad, H. (2023). An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer. Preprints. https://doi.org/10.20944/preprints202301.0343.v1
Chicago/Turabian Style
Labani, M., Ahmadreza Argha and Hamid Alinejad. 2023 "An Integrative Analysis of Chromatin Interactions, Gene Expression and Genomic Variants Identifies 30 Likely Regulatory Variants in Prostate Cancer" Preprints. https://doi.org/10.20944/preprints202301.0343.v1
Abstract
Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies showed that genomic alterations represent the most common mechanism for molecular alterations that cause the development and progression of PC. Great efforts have been done to identify common protein-coding genetic variations; however, the impact of non-coding variations including regulatory genetic variants is not still well understood. To gain an understanding of the functional impact of genetic variants, particularly, regulatory variants in PC, we developed an integrative pipeline (AGV) that used whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 of them significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants may affect 131 coding and non-coding genes. Interestingly, our study showed the 131 protein-coding genes are involved in disease-related pathways including Reactome and MSigDB in which for most of them targeted treatment options are currently available. Together, our results provide a comprehensive map of genomic variants in PC and revealed their potential contribution to prostate cancer progression and development.
Keywords
Prostate cancer; Somatic point mutations; Copy number variation; Regulatory variant, Hi-C; Per-sonalized medicine; Biomedical machine learning
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
