Abstract
Transcription factors are recognized as the key regulators of gene expression. However, the changes in the correlation of transcription factors and their target genes between normal and tumor tissues are usually ignored. In this research, we used mRNA expression profile data from The Cancer Genome Atlas which included 5726 samples across 11 major human cancers to perform co-expression analysis by the Pearson correlation coefficients. Then, integrating 81,357 pairs of transcription factors and target genes from transcription factors databases to find out the changes in the co-expression correlation of these gene pairs from normal to tumor tissues. Based on the changes in the number of co-expressed TF-TG pairs and changes in the level of co-expression, we found the generally reduced correlation between transcription factors and their target genes in cancer. Additionally, we screened out universal and specific transcription factors-target genes pairs which may significant influence particular cancer. Then, we obtained 423 cancer cell line expression profiles from Broad Institute Cancer Cell Line Encyclopedia to verify our results. Some of these pairs like XRCC5-XRCC6 have been reported to involve in multiple cancers, while pairs like IRF1-PSMB9 without any previous articles related to tumor but involve in the biological processes of cancer, which are of great potential to be therapeutic targets. Our research may provide insights to better understand the tumor development mechanisms and find potential therapeutic targets.
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Funding
This work was partially supported by the National key research and development program (SQ2018YFC090062), Science and Technology Planning Project of Guangzhou (201704020176, 201508020040, 201510010044), and the Fundamental Research Funds for the Central Universities (2015ZZ125).
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Liang, J., Cui, Y., Meng, Y. et al. Integrated analysis of transcription factors and targets co-expression profiles reveals reduced correlation between transcription factors and target genes in cancer. Funct Integr Genomics 19, 191–204 (2019). https://doi.org/10.1007/s10142-018-0636-6
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DOI: https://doi.org/10.1007/s10142-018-0636-6