Correction: Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

AbstractSignificant sex differences are observed in colon cancer, and understanding these differences is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We used both transcript-based and gene regulatory network methods to analyze RNA-Seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and found no significant sex differences except for sex-chromosome genes. We then inferred patient-specific gene regulatory networks, and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset that included 1,193 patients from five independent studies. While targeting of the drug metabolism pathway did not change the overall survival for males treated with adjuvant chemotherapy, females with greater targeting had an increase in 10-year overall survival probability, with 89% (95% CI: 78%-100%) survival compared to 61% (95% CI: 45%-82%) for women with lower targeting, respectively (p=0.034). Our network analysis uncovered patterns of transcriptional regulation that differentiate male and female colon cancer. Most importantly, targeting of the drug metabolism pathway was predictive of survival in women who received adjuvant chemotherapy. This network-based approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.

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PAGeneRN

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch055 ◽

2020 ◽

pp. 1052-1075 ◽

Cited By ~ 1

Author(s):

Dina Elsayad ◽

A. Ali ◽

Howida A. Shedeed ◽

Mohamed F. Tolba

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Network Analysis ◽

Gene Regulatory Network ◽

Gene Expression Data ◽

Regulatory Network ◽

Regulatory Networks ◽

Expression Data ◽

Gene Expression Data Analysis ◽

Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

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Identification of Hub Molecules of FUS-ALA by Bayesian Gene Regulatory Network Analysis of iPSC Model: iBRN

SSRN Electronic Journal ◽

10.2139/ssrn.3600552 ◽

2020 ◽

Author(s):

Masahiro Nogami ◽

Mitsuru Ishikawa ◽

Atsushi Doi ◽

Osamu Sano ◽

Takefumi Sone ◽

...

Keyword(s):

Network Analysis ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Gene Regulatory

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Reconstruction of Gene Regulatory Network of Colon Cancer Using Information Theoretic Approach

Confluence 2013: The Next Generation Information Technology Summit (4th International Conference) ◽

10.1049/cp.2013.2357 ◽

2013 ◽

Cited By ~ 1

Author(s):

K. Raza ◽

R. Parveen

Keyword(s):

Colon Cancer ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Theoretic Approach ◽

Information Theoretic ◽

Gene Regulatory ◽

Information Theoretic Approach

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Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

Bayesian Analysis ◽

10.1214/17-ba1087 ◽

2018 ◽

Vol 13 (4) ◽

pp. 1095-1110 ◽

Cited By ~ 1

Author(s):

Yang Ni ◽

Yuan Ji ◽

Peter Müller

Keyword(s):

Network Analysis ◽

Gene Regulatory Network ◽

Graphical Models ◽

Regulatory Network ◽

Gene Regulatory

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Integrative Gene Regulatory Network Analysis Reveals Light-Induced Regional Gene Expression Phase Shift Programs in the Mouse Suprachiasmatic Nucleus

PLoS ONE ◽

10.1371/journal.pone.0037833 ◽

2012 ◽

Vol 7 (5) ◽

pp. e37833 ◽

Cited By ~ 14

Author(s):

Haisun Zhu ◽

Rajanikanth Vadigepalli ◽

Rachel Rafferty ◽

Gregory E. Gonye ◽

David R. Weaver ◽

...

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Phase Shift ◽

Gene Regulatory Network ◽

Suprachiasmatic Nucleus ◽

Regulatory Network ◽

Gene Regulatory ◽

Regional Gene

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miRNA mediated gene regulatory network analysis of Cichorium intybus (chicory)

Agri Gene ◽

10.1016/j.aggene.2016.11.003 ◽

2017 ◽

Vol 3 ◽

pp. 37-45 ◽

Cited By ~ 3

Author(s):

Swati Srivastava ◽

Noopur Singh ◽

Gaurava Srivastava ◽

Ashok Sharma

Keyword(s):

Network Analysis ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Cichorium Intybus ◽

Gene Regulatory

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IGFBP2 is a Potential Master-Regulator Driving Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme

10.20944/preprints202010.0046.v2 ◽

2020 ◽

Author(s):

Manasa KP ◽

Darius Wlochowitz ◽

Edgar Wingender ◽

Tim Beißbarth ◽

ALEXANDER KEL

Keyword(s):

Transcription Factors ◽

Glioblastoma Multiforme ◽

Network Analysis ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Epithelial To Mesenchymal Transition ◽

Mesenchymal Transition ◽

Short Survival ◽

Tumor Invasiveness ◽

Gene Regulatory

Only two percent of Glioblastoma multiforme (GBM) patients respond to standard care and survive beyond 36 months (long-term survivors, LTS) while the majority survives less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that co-occur with aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the GO-categories “epithelial to mesenchymal transition” and “response to hypoxia”. In this paper we applied upstream analysis approach which involves state-of-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Transcription factors associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG and seven others were found enriched in regulatory regions of the dysregulated genes. Based on network analysis, we propose novel gene regulatory network regulated by five master regulators – IGFBP2, VEGFA, VEGF165, PDGFA, AEBP1 and OSMR which can potentially act as therapeutic targets for enhancing GBM prognosis. Critical analysis of this gene regulatory network gives insights on mechanism of gene regulation by IGFBP2 via several transcription factors including the key molecule of GBM tumor invasiveness and progression FRA-1. All the observations are validated in independent cohorts and their impact on overall is studied on TCGA-GBM RNA seq data.

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