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Transcriptional network modulated by the prognostic signature transcription factors and their long noncoding RNA partners in primary prostate cancer

Mei Jiang, Yihang Cheng, Dan Wang and 5 more

EBioMedicine · 2021

doi:10.1016/j.ebiom.2020.103150 (opens in a new tab)

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Abstract

Background: Transcriptional regulators are seminal players in the onset and progression of prostate cancer. However, clarification of their underlying regulatory circuits and mechanisms demands considerable effort. Methods: Integrated analyses were performed on genomic, transcriptomic, and clinicopathological profiles of primary prostate cancer and transcription factor-binding profiles, which included estimating transcription factor activity, identifying transcription factors of prognostic values, and discovering cis- and trans-regulations by long noncoding RNAs. Interactions between transcription factors and long noncoding RNAs were validated by RNA immunoprecipitation quantitative PCR. RNA interference assays were performed to explore roles of the selected transcription regulators. Findings: Sixteen transcription factors, namely, ETS1, ARID4B, KLF12, GMEB1, HBP1, MXI1, MYC, MAX, PGR, BCL11A, AR, KLF4, SRF, HIF1A, EHF, and ATOH1, were jointly identified as a prognostic signature. Candidate long noncoding RNAs interplaying with the prognostic signature constituent transcription factors were further discovered. Their interactions were randomly checked, and many of them were experimentally proved. Transcription regulation by MYC and its long noncoding RNA partner AL590617.2 was further validated on their candidate targets. Moreover, the regulatory network governed by the transcription factors and their interacting long noncoding RNA partners is illustrated and stored in our LNCTRN database (https://navy.shinyapps.io/lnctrn). Interpretation: The prognostic signature constituent transcription factors and their interacting long noncoding RNAs may represent promising biomarkers and/or therapeutic targets for prostate cancer. Furthermore, the computational framework proposed in the present study can be utilized to explore critical transcriptional regulators in other types of cancer. Funding: This work was supported by National Natural Science Foundation of China and Fudan University.

Abstract licence: CC BY-NC

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