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In silico approach to understand epigenetics of POTEE in ovarian cancer
Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog m...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709732/ https://www.ncbi.nlm.nih.gov/pubmed/34788504 http://dx.doi.org/10.1515/jib-2021-0028 |
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author | Qazi, Sahar Raza, Khalid |
author_facet | Qazi, Sahar Raza, Khalid |
author_sort | Qazi, Sahar |
collection | PubMed |
description | Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog. |
format | Online Article Text |
id | pubmed-8709732 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
spelling | pubmed-87097322022-01-20 In silico approach to understand epigenetics of POTEE in ovarian cancer Qazi, Sahar Raza, Khalid J Integr Bioinform Article Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog. De Gruyter 2021-11-18 /pmc/articles/PMC8709732/ /pubmed/34788504 http://dx.doi.org/10.1515/jib-2021-0028 Text en © 2021 Sahar Qazi et al., published by De Gruyter, Berlin/Boston https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. |
spellingShingle | Article Qazi, Sahar Raza, Khalid In silico approach to understand epigenetics of POTEE in ovarian cancer |
title |
In silico approach to understand epigenetics of POTEE in ovarian cancer |
title_full |
In silico approach to understand epigenetics of POTEE in ovarian cancer |
title_fullStr |
In silico approach to understand epigenetics of POTEE in ovarian cancer |
title_full_unstemmed |
In silico approach to understand epigenetics of POTEE in ovarian cancer |
title_short |
In silico approach to understand epigenetics of POTEE in ovarian cancer |
title_sort | in silico approach to understand epigenetics of potee in ovarian cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709732/ https://www.ncbi.nlm.nih.gov/pubmed/34788504 http://dx.doi.org/10.1515/jib-2021-0028 |
work_keys_str_mv | AT qazisahar insilicoapproachtounderstandepigeneticsofpoteeinovariancancer AT razakhalid insilicoapproachtounderstandepigeneticsofpoteeinovariancancer |