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...

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Bibliographic Details
Published inJournal of integrative bioinformatics Vol. 18; no. 4; pp. 284 - 96
Main Authors Qazi, Sahar, Raza, Khalid
Format Journal Article
LanguageEnglish
Published Germany De Gruyter 18.11.2021
Subjects
Antigens, Neoplasm
deep neural network
Epigenesis, Genetic
Humans
MD simulation
network-based epigenetics
ovarian cancer
Ovarian Neoplasms - genetics
POTEE
template-based modeling
MD simulation
POTEE
network-based epigenetics
ovarian cancer
deep neural network
template-based modeling
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Summary: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.
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ISSN:1613-4516
1613-4516
DOI:10.1515/jib-2021-0028