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Despite its prominence, the mechanisms through which the tumor suppressor p53 regulates most genes remain unclear. Recently, the regulatory factor X 7 (RFX7) emerged as a suppressor of lymphoid neoplasms, but its regulation and target genes mediating tumor suppression remain unknown. Here, we identify a novel p53-RFX7 signaling axis. Integrative analysis of the RFX7 DNA binding landscape and the RFX7-regulated transcriptome in three distinct cell systems reveals that RFX7 directly controls multiple established tumor suppressors, including PDCD4, PIK3IP1, MXD4, and PNRC1, across cell types and is the missing link for their activation in response to p53 and stress. RFX7 target gene expression correlates with cell differentiation and better prognosis in numerous cancer types. Interestingly, we find that RFX7 sensitizes cells to Doxorubicin by promoting apoptosis. Together, our work establishes RFX7's role as a ubiquitous regulator of cell growth and fate determination and a key node in the p53 transcriptional program., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The transcription factor p53 is the best-known tumor suppressor, but its sibling p63 is a master regulator of epidermis development and a key oncogenic driver in squamous cell carcinomas (SCC). Despite multiple gene expression studies becoming available, the limited overlap of reported p63-dependent genes has made it difficult to decipher the p63 gene regulatory network. Particularly, analyses of p63 response elements differed substantially among the studies. To address this intricate data situation, we provide an integrated resource that enables assessing the p63-dependent regulation of any human gene of interest. We use a novel iterative de novo motif search approach in conjunction with extensive ChIP-seq data to achieve a precise global distinction between p53-and p63-binding sites, recognition motifs, and potential co-factors. We integrate these data with enhancer:gene associations to predict p63 target genes and identify those that are commonly de-regulated in SCC representing candidates for prognosis and therapeutic interventions., Competing Interests: KR, HK, AS, SM, SH, MF No competing interests declared, (© 2020, Riege et al.)