Literature DB >> 27060408

IDPT: Insights into potential intrinsically disordered proteins through transcriptomic analysis of genes for prostate carcinoma epigenetic data.

Saurav Mallik1, Sagnik Sen2, Ujjwal Maulik3.   

Abstract

Involvement of intrinsically disordered proteins (IDPs) with various dreadful diseases like cancer is an interesting research topic. In order to gain novel insights into the regulation of IDPs, in this article, we perform a transcriptomic analysis of mRNAs (genes) for transcripts encoding IDPs on a human multi-omics prostate carcinoma dataset having both gene expression and methylation data. In this regard, firstly the genes that consist of both the expression and methylation data, and that are corresponding to the cancer-related prostate-tissue-specific disordered proteins of MobiDb database, are selected. We apply standard t-test for determining differentially expressed genes as well as differentially methylated genes. A network having these genes and their targeter miRNAs from Diana Tarbase v7.0 database and corresponding Transcription Factors from TRANSFAC and ITFP databases, is then built. Thereafter, we perform literature search, and KEGG pathway and Gene Ontology analyses using DAVID database. Finally, we report several significant potential gene-markers (with the corresponding IDPs) that have inverse relationship between differential expression and methylation patterns, and that are hub genes of the TF-miRNA-gene network.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Epigenetic genemarkers; Hub genes; Intrinsically disordered proteins; Multi-omics prostate carcinoma epigenetic dataset; TF-miRNA-gene network; Transcriptomic analysis of genes; t-test

Mesh:

Substances:

Year:  2016        PMID: 27060408     DOI: 10.1016/j.gene.2016.03.056

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  5 in total

1.  ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis.

Authors:  Saurav Mallik; Zhongming Zhao
Journal:  Genes (Basel)       Date:  2017-12-28       Impact factor: 4.096

2.  Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles.

Authors:  Saurav Mallik; Zhongming Zhao
Journal:  Genes (Basel)       Date:  2019-08-13       Impact factor: 4.096

3.  Identification of gene signatures from RNA-seq data using Pareto-optimal cluster algorithm.

Authors:  Saurav Mallik; Zhongming Zhao
Journal:  BMC Syst Biol       Date:  2018-12-21

4.  Detecting methylation signatures in neurodegenerative disease by density-based clustering of applications with reducing noise.

Authors:  Saurav Mallik; Zhongming Zhao
Journal:  Sci Rep       Date:  2020-12-17       Impact factor: 4.379

5.  Towards integrated oncogenic marker recognition through mutual information-based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles.

Authors:  Saurav Mallik; Zhongming Zhao
Journal:  Quant Biol       Date:  2017-11-23
  5 in total

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