Thyroid transcription factor FOXE1 interacts with ETS factor ELK1 to co-regulate TERT

Bullock, Martyn; Lim, Grace; Li, Cheng; Choi, In Ho; Kochhar, Shivansh; Liddle, Chris; Zhang, Lei; Clifton‐Bligh, Roderick

doi:10.18632/oncotarget.13288

Cited by 19 publications

(12 citation statements)

References 64 publications

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“…Our results are also consistent with other studies, demonstrating that FOXE1 can interact with other factors and transactivate key genes in cancer such as SNAIL (Xu et al 2015), an E-cadherin transcriptional repressor, or TERT (telomerase reverse transcriptase), which is coregulated by FOXE1 and the ETS factor ELK1 (Bullock et al 2016). Along this line, it would be interesting to search for FOXE1 interacting partners in thyroid cancer, which may reveal unique or separate signaling pathways.…”

Section: Discussionsupporting

confidence: 92%

FOXE1 regulates migration and invasion in thyroid cancer cells and targets ZEB1

Morillo-Bernal

Fernández

Santisteban

2020

Endocrine-Related Cancer

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FOXE1 is a thyroid-specific transcription factor essential for thyroid gland development and maintenance of the differentiated state. Interestingly, a strong association has been recently described between FOXE1 expression and susceptibility to thyroid cancer, but little is known about the mechanisms underlying FOXE1-induced thyroid tumorigenesis. Here, we used a panel of human thyroid cancer-derived cell lines covering the spectrum of thyroid cancer phenotypes to examine FOXE1 expression and to test for correlations between FOXE1 expression, the allele frequency of two SNPs and a length polymorphism in or near the FOXE1 locus associated with cancer susceptibility, and the migration ability of thyroid cancer cell lines. Results showed that FOXE1 expression correlated with differentiation status according to histological sub-type, but not with SNP genotype or cell migration ability. However, loss-and-gain-of-function experiments revealed that FOXE1 modulates cell migration, suggesting a role in epithelial-to-mesenchymal transition (EMT). Our previous genome-wide expression analysis identified Zeb1, a major EMT inducer, as a putative Foxe1 target gene. Indeed, gene silencing of FOXE1 decreased ZEB1 expression, whereas its overexpression increased ZEB1 transcriptional activity. FOXE1 was found to directly interact with the ZEB1 promoter. Lastly, ZEB1 silencing decreased the ability of thyroid tumoral cells to migrate and invade, pointing to its importance in thyroid tumor mestastases. In conclusion, we have identified ZEB1 as a bona fide target of FOXE1 in thyroid cancer cells, which provides new insights into the role of FOXE1 in regulating cell migration and invasion in thyroid cancer.

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Section: Discussionsupporting

confidence: 92%

FOXE1 regulates migration and invasion in thyroid cancer cells and targets ZEB1

Morillo-Bernal

Fernández

Santisteban

2020

Endocrine-Related Cancer

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“…Moreover, we identified three important transcription factors (ELK1, ETV7 and GABPA) that may significantly influence the PTC. Bullock et al 44 claimed that FOXE1 and ELK1 established a new regulatory pathway to regulate the TCs. Jacques et al 45 found that GABPA was closely associated with human thyroid tumors.…”

Section: Discussionmentioning

confidence: 99%

Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature

You¹,

Yang²,

Sui³

et al. 2018

CMAR

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PurposePapillary thyroid carcinoma (PTC), the most frequent type of malignant thyroid tumor, lacks novel and reliable biomarkers of patients’ prognosis. In the current study, we mined The Cancer Genome Atlas (TCGA) to develop lncRNA signature of PTC.Patients and methodsThe intersection of PTC lncRNAs was obtained from the TCGA database using integrative computational method. By the univariate and multivariate Cox analysis, key lncRNAs were identified to construct the prognostic model. Then, all patients were divided into the high-risk group and low-risk group to perform the Kaplan–Meier (K–M) survival curves and time-dependent receiver operating characteristic (ROC) curve, estimating the prognostic power of the prognostic model. Functional enrichment analysis was also performed. Finally, we verified the results of the TCGA analysis by the Gene Expression Omnibus (GEO) databases and quantitative real-time PCR (qRT-PCR).ResultsAfter the comprehensive analysis, a three-lncRNA signature (PRSS3P2, KRTAP5-AS1 and PWAR5) was obtained. Interestingly, patients with low-risk scores tended to gain obviously longer survival time, and the area under the time-dependent ROC curve was 0.739. Furthermore, gene ontology (GO) and pathway analysis revealed the tumorigenic and prognostic function of the three lncRNAs. We also found three potential transcription factors to help understand the mechanisms of the PTC-specific lncRNAs. Finally, the GEO databases and qRT-PCR validation were consistent with our TCGA bioinformatics results.ConclusionWe built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.

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“…Thus, we investigated TFs and their relationship to DEGs in these tumours, including FOXC1. One prior study found that FOXC1 is strongly associated with thyroid cancers [55]. The overexpression of YY1 in differentiated thyroid cancers has also been noted [56].…”

Section: Discussionmentioning

confidence: 92%

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

Hossain

Akter

Rahman

et al. 2020

IJERPH

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Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

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Thyroid transcription factor FOXE1 interacts with ETS factor ELK1 to co-regulate TERT

Cited by 19 publications

References 64 publications

FOXE1 regulates migration and invasion in thyroid cancer cells and targets ZEB1

FOXE1 regulates migration and invasion in thyroid cancer cells and targets ZEB1

Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

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