Abstract
Metabolic reprogramming is a hallmark of cancer. In cancer cells, transcription factors (TFs) govern metabolic reprogramming through abnormally increasing or decreasing the transcription rate of metabolic enzymes, which provides cancer cells growth advantages and concurrently leads to the altered metabolic phenotypes observed in many cancers. Consequently, targeting TFs that govern metabolic reprogramming can be highly effective for novel cancer therapeutics. In this paper, we present TFmeta, a machine learning approach to uncover TFs that govern reprogramming of cancer metabolism. Our approach achieves the state-of-the-art performance in reconstructing relations between TFs and their target genes on public benchmark datasets. Leveraging TF binding profiles inferred from genome-wide ChIP-seq experiments and 150 RNA-seq samples from 75 paired cancerous and non-cancerous human lung tissues, our approach predicted 19 key TFs that may be the major regulators of the gene expression changes of metabolic enzymes of the central metabolic pathway glycolysis, which may underlie the dysregulation of glycolysis in non-small-cell lung cancer patients.
Original language | English |
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Article number | 8781833 |
Pages (from-to) | 1528-1536 |
Number of pages | 9 |
Journal | IEEE Journal of Biomedical and Health Informatics |
Volume | 24 |
Issue number | 5 |
DOIs | |
State | Published - May 2020 |
Bibliographical note
Funding Information:Manuscript received November 20, 2018; revised June 21, 2019 and July 8, 2019; accepted July 23, 2019. Date of publication July 30, 2019; date of current version May 6, 2020. This work was supported in part by the National Institutes of Health under Awards 1P01CA163223-01A1 and 1U24DK097215-01A1, and in part by the Redox Metabolism Shared Resource(s) of the University of Kentucky Markey Cancer Center under NCI Grant P30CA177558. (Corresponding author: Yi Zhang.) Y. Zhang, X. Zhang, and J. Liu are with the Department of Computer Science, University of Kentucky, Lexington, KY 40506 USA (e-mail:, yi.zhang@uky.edu; xiaofei.zhang@uky.edu; liuj@cs.uky.edu).
Publisher Copyright:
© 2013 IEEE.
Keywords
- Machine learning
- gene regulatory network
- lung cancer
- metabolic reprogramming
- transcription factor
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics
- Electrical and Electronic Engineering
- Health Information Management