TCF7L1 Modulates Colorectal Cancer Growth by Inhibiting Expression of the Tumor-Suppressor Gene EPHB3

Murphy, Matthew; Chatterjee, Satabdi; Jain, Sidharth; Katari, Manpreet S.; DasGupta, Ramanuj

doi:10.1038/srep28299

Cited by 45 publications

(39 citation statements)

References 49 publications

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“…6a ). For instance, the protein product of the non-CGC gene TCF7L1 directly mediates the Wnt signaling activity of CTNNB1 53 , 54 , which is listed in the CGC; the non-CGC gene ERRFI1 encodes a protein that inhibits activation of EGFR 55 (listed in the CGC); and transcriptional activity of POLR2A (not in CGC) is mediated by MED12, which is part of the transcriptional mediator complex 56 , 57 and the CGC ( Fig. 6a ).…”

Section: Resultsmentioning

confidence: 99%

Identification of cancer driver genes based on nucleotide context

et al. 2020

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Cancer genomes contain large numbers of somatic mutations, but few of these mutations drive tumor development. Current approaches identify driver genes based on mutational recurrence, or they approximate the functional consequences of nonsynonymous mutations using bioinformatic scores. While passenger mutations are enriched in characteristic nucleotide contexts, driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified based on mutations in unusual nucleotide contexts.

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Section: Resultsmentioning

confidence: 99%

Identification of cancer driver genes based on nucleotide context

et al. 2020

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“…Meanwhile, inhibition of FOXM1 is shown to have the capacity of promoting the senescence of HCC cells ( 41 ). TCF7L1 , a member of the T cell factor/lymphoid enhancer factor family, is reported to modulate colorectal cancer growth via inhibiting the tumor suppressor EPH Receptor B3 ( 42 ). As another member of the family, TCF7L2 is revealed to be associated with the susceptibility of HCC in patients with liver cirrhosis ( 43 ).…”

Section: Discussionmentioning

confidence: 99%

Key signaling pathways, genes and transcription factors associated with hepatocellular carcinoma

et al. 2018

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The purpose of the present study was to investigate the underlying molecular mechanism of hepatocellular carcinoma (HCC) using bioinformatics approaches. The microarray dataset GSE64041 was downloaded from the Gene Expression Omnibus database, which included 60 tumor liver samples and 60 matched control samples. Differentially expressed genes (DEGs) between HCC and control groups were identified. Then functional enrichment analyses, protein-protein interaction (PPI) network, sub-network and integrated transcription factor (TF)-microRNA (miRNA)-target network analyses were performed for these DEGs. A total of 378 DEGs were obtained, including 101 upregulated and 277 downregulated DEGs. In addition, functional enrichment analysis for DEGs in the sub-network revealed ‘cell division’ and ‘cell cycle’ as key Gene Ontology (GO) terms and pathways. Topoisomerase (DNA) IIα (TOP2A) and integrin subunit α2 (ITGA2) were hub nodes in the PPI network. TOP2A, cyclin dependent kinase 1 (CDK1) and polo like kinase 1 (PLK1) were revealed to be hub nodes in the sub-network. Finally, 4 TFs including forkhead box M1 (FOXM1), E2F transcription factor 4 (E2F4), SIN3 transcription regulator family member A (SIN3A) and transcription factor 7 like 1 (TCF7L1) were obtained through integrated network analysis. TOP2A, ITGA2, PLK1 and CDK1 may be key genes involved in HCC development. ‘Cell division’ and ‘cell cycle’ were indicated to act as key GO terms and Kyoto Encyclopedia of Genes and Genomes pathways in HCC. In addition, FOXM1, TCF7L1, E2F4 and SIN3A were revealed to be key TFs associated with HCC.

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“…For example, the SOX10 transcription factor was found to be essential in only skin cells where it plays a major role in the production and function of melanocytes (Harris et al, 2010;Nonaka et al, 2008). CTNNB1 and TCF7L2 are essential only in colorectal cancer cell lines, where activation of the Wnt pathway results in accumulation of B-catenin that interacts with and acts as a coactivator for TCF7L2 that in turn activates downstream genes responsible for colorectal cancer cell survival as well as resistance to chemo-radiotherapy (Albuquerque and Pebre Pereira, 2018; Emons et al, 2017;Murphy et al, 2016). ER+ breast cancer cell lines specifically depend on transcription factors FOXA1 and GATA3, which are overexpressed in ER+ breast carcinomas (Albergaria et al, 2009;Davis et al, 2016).…”

Section: Context-specific Essential Genes Define Lineage Relationshipsmentioning

confidence: 99%

Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

Dede

Hart

2020

Preprint

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It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR 5 screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a ~20% false negative rate, beyond library-specific false negatives previously described. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses 10 across tissues suggest only a small number of lineage-specific essential genes and that these genes are highly enriched for transcription factors that define pathways of tissue differentiation.In addition, we show that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. Together these observations strongly suggest that functional buffering masks single knockout phenotypes for a 15 substantial number of genes, describing a major blind spot in CRISPR-based mammalian functional genomics approaches. 65 negatives) and confounding off-target effects (false positives) (Boutros and Ahringer, 2008;Echeverri et al., 2006;Hart et al., 2014).More recently, adaptation of the bacterial CRISPR-Cas9 system to mammalian cells enabled genome-scale approaches to define human essential genes. Studies using this technology 70 revealed that mammalian cells have more essential genes than RNAi screens were able to detect and that, at the same false discovery rate, CRISPR screens generated 3-4 times more essential genes (Hart et al., 2014). Moreover, multiple groups revealed lists of ~2000 highly concordant human essential genes, and comparison of CRISPR technology to orthogonal techniques such as random insertion of gene traps also showed consistent results (Blomen et al., 2015;

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TCF7L1 Modulates Colorectal Cancer Growth by Inhibiting Expression of the Tumor-Suppressor Gene EPHB3

Cited by 45 publications

References 49 publications

Identification of cancer driver genes based on nucleotide context

Identification of cancer driver genes based on nucleotide context

Key signaling pathways, genes and transcription factors associated with hepatocellular carcinoma

Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

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