2020
DOI: 10.3390/cancers13010028
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High Risk α-HPV E6 Impairs Translesion Synthesis by Blocking POLη Induction

Abstract: High risk genus α human papillomaviruses (α-HPVs) express two versatile oncogenes (α-HPV E6 and E7) that cause cervical cancer (CaCx) by degrading tumor suppressor proteins (p53 and RB). α-HPV E7 also promotes replication stress and alters DNA damage responses (DDR). The translesion synthesis pathway (TLS) mitigates DNA damage by preventing replication stress from causing replication fork collapse. Computational analysis of gene expression in CaCx transcriptomic datasets identified a frequent increased express… Show more

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Cited by 8 publications
(37 citation statements)
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“…We have previously reported that the expression of TLS genes is often increased in cervical cancers (Wendel et al, 2021). To understand the extent that this increased expression occurred more or less frequently than in other cancers, we compared TLS gene expression across 16 tumor types found in the TCGA database(Cancer Genome Atlas Research Network et al, 2013).…”
Section: Resultsmentioning
confidence: 99%
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“…We have previously reported that the expression of TLS genes is often increased in cervical cancers (Wendel et al, 2021). To understand the extent that this increased expression occurred more or less frequently than in other cancers, we compared TLS gene expression across 16 tumor types found in the TCGA database(Cancer Genome Atlas Research Network et al, 2013).…”
Section: Resultsmentioning
confidence: 99%
“…However, the extent that cells respond to HPV16 E7-associated replication stress by increasing TLS activity is unclear. We have shown that TLS gene expression is often elevated in cervical cancers (Wendel et al, 2021). Given that TLS responds to replication stress and that replication stress is common in tumors, it is not surprising that these genes are highly expressed in cervical cancer.…”
mentioning
confidence: 93%
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“…To determine the expression pattern of NFX1-123-regulated genes across cervical cancer development, we analyzed a previously described dataset [ 23 ] by merging transcriptomic analyses from five datasets in the National Center for Biotechnology Information Gene Expression Omnibus or NCBI GEO (GSE145976) that contained transcriptomic data linked with disease progression information [ 26 ]. This combined dataset has transcriptomic data for 262 cervical tissues including: tissue with no evidence of disease (NED, n = 89), premalignant lesions (CIN1 = 89, CIN2 = 22, CIN3 = 47), and cervical cancers (Stage 1 = 25, Stage 2 = 44, Stage 3 = 11).…”
Section: Resultsmentioning
confidence: 99%
“…Least absolute shrinkage and selection operator (LASSO) and ranked stability selection was used to determine promising candidates correlating with cervical cancer disease progression from a pool of nineteen NFX1-123 responsive genes (TGM1, FBN2, HSPB1P1, PPL, SLPI, FOXA2, BNIPL, CCNB1IP1, IMPA2, RPS29, KRT16, RAET1G, LCE2B, LCE1B, ALDH3B2, NOTCH1, SPRR2G, CEBPD, LOR). The disease stage dependent gene expression data for LASSO variable selection stem from GSE145976 [ 26 ]. The LASSO computation was conducted using the GLMnet package in R-software.…”
Section: Methodsmentioning
confidence: 99%