TOX3 inhibits cancer cell migration and invasion via transcriptional regulation of SNAI1 and SNAI2 in clear cell renal cell carcinoma

Jiang, Bo; Chen, Wei; Qin, Haixiang; Diao, Wenli; Li, Binghua; Cao, Wenmin; Zhang, Zhenxing; Qi, Wei; Gao, Jie; Chen, Mengxia; Zhao, Xiaozhi; Guo, Hongqian

doi:10.1016/j.canlet.2019.02.020

Cited by 22 publications

(26 citation statements)

References 37 publications

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“…Next, we performed a gene ontology analysis focusing on cancer-related genes in which RNAPII occupancy on the TSS, as well as its RNA levels, was increased (figure 4f; electronic supplementary material, table S3). A large number of genes that function as tumour and growth suppressors, such as PHACTR4 and ARID4A [42,43], as well as genes that correspond to chemotherapy/radiotherapy response genes, such as SNAI1 and SNX1 [44,45], were overexpressed. However, a large number of genes considered as oncogenes or that promote tumour growth and/or metastasis, such as SKI, EGR4, SNAI1 and CEMIP2 [45][46][47][48], were also overexpressed in response to TPL (figure 4f; electronic supplementary material, table S2).…”

Section: Promoter Occupancy and Elongating Rnapii Are Maintained In Genes Upregulated In Response To Tplmentioning

confidence: 99%

Disruption of TFIIH activities generates a stress gene expression response and reveals possible new targets against cancer

et al. 2020

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Disruption of the enzymatic activities of the transcription factor TFIIH by the small molecules Triptolide (TPL) or THZ1 could be used against cancer. Here, we used the MCF10A-ErSrc oncogenesis model to compare the effect of TFIIH inhibitors between transformed cells and their progenitors. We report that tumour cells exhibited highly increased sensitivity to TPL or THZ1 and that the combination of both had a synergic effect. TPL affects the interaction between XPB and p52, causing a reduction in the levels of XPB, p52 and p8, but not other TFIIH subunits. RNA-Seq and RNAPII-ChIP-Seq experiments showed that although the levels of many transcripts were reduced, the levels of a significant number were increased after TPL treatment, with maintained or increased RNAPII promoter occupancy. A significant number of these genes encode for factors that have been related to tumour growth and metastasis, suggesting that transformed cells might rapidly develop resistance to TPL/THZ inhibitors. Some of these genes were also overexpressed in response to THZ1, of which depletion enhances the toxicity of TPL, and are possible new targets against cancer.

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Section: Promoter Occupancy and Elongating Rnapii Are Maintained In Genes Upregulated In Response To Tplmentioning

confidence: 99%

Disruption of TFIIH activities generates a stress gene expression response and reveals possible new targets against cancer

et al. 2020

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“…Du et al found that transactivation of SNAI2 and c-MET could promote colorectal cancer metastasis [40]. SNAI2 also participates in regulation of the initiation and metastasis of breast cancer cells [41]. MYL9 (myosin light chain 9) can predict malignant progression and poor biochemical recurrence-free survival of PCa [42].…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes and their clinical value for predicting the development of prostate cancer from benign prostate hyperplasia by bioinformatic analysis

Chen

José

et al. 2021

Preprint

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BackgroundProstate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in elderly males. The two diseases have some commonalities: both are growth depend on hormone and respond to antiandrogen therapy. Some studies have shown that genetic factors are responsible for the occurrences of both diseases. There may be a correlation between BPH and PCa. MethodsThe GEO database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were utilized to find pathways in which the DEGs were enriched. The STRING database can provide a protein–protein interaction (PPI) network, and Cytoscape software can find hub genes in PPI network. GEPIA can be used to analyze expression and survival data for hub genes. R software was used to progress regression analysis, decision curve analysis and built nomograph. UALCAN and The Human Protein Atlas was utilized to test the results. Finally, we made clinical and cell experiments to verify the results.ResultsSixty DEGs, consisting of 15 up-regulated and 45 down-regulated genes, were found based on the GEO database. Using Cytoscape, we found 7 hub gene in the PPI network. The hub gene expression was tested on TCGA database. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Meanwhile, all hub genes exclude CXCR4 has diagnostic value in predicting PCa and their mutations are risk factors leading to PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect the disease-free survival (DFS). The same results reflected in different database. In addition, we also chose three hub gene, MYC, MYL9, and SNAI2, to validate their functions in clinical specimens and cells.ConclusionThese identified hub genes can help us to understand the process and mechanism by which BPH develops into PCa and provide achievable targets for predicting which BPH patients may later develop PCa.

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“…Inhibition of SNAI1 via miR-211-5p suppresses metastatic behavior in RCC [56]. The TOX High Mobility Group Box Family Member 3 (TOX3) protein is a repressor for inhibition of SNAIL proteins encoded by SNAI1 and SNAI2 in clear cell RCC [64]. Therefore, molecular SNAI1 tend to promote cell migration and invasion.…”

Section: Discussionmentioning

confidence: 99%

Identification of the Potential Prognostic Markers from the miRNA-lncRNA-mRNA Interactions for Metastatic Renal Cancer via Next-Generation Sequencing and Bioinformatics

et al. 2020

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The survival rate in patients with metastatic renal cell carcinoma (RCC) is low. In addition, metastatic RCC resists traditional treatment. Therefore, identification of novel biomarkers, signaling pathways, and therapeutic targets is an important issue. The aim of the present study is to identify novel prognostic markers from the miRNA-mediated network for the regulation of metastasis of RCC. To address this issue, the RNA of human RCC cell lines, 786-O and ACHN, derived from primary and metastatic sites, respectively, were collected and subjected to RNA sequencing and small RNA sequencing. The bioinformatic analysis revealed that the pathways of the genes with different expressions were related to tumor progression, and identified miRNA and miRNA-long non-coding RNA (lncRNA) interactions, and mRNA. The results revealed that the expressions of seven miRNAs were associated with the overall survival rate of patients with RCC. Furthermore, the expressions of two lncRNA and three protein-coding genes (mRNA) were significantly associated with the increased or decreased disease-free survival rate. Although the detailed regulatory mechanism between miRNAs and targeted genes was not fully understood, our findings present novel prognostic markers and novel insight on miRNA-mediated pathways for metastatic RCC.

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TOX3 inhibits cancer cell migration and invasion via transcriptional regulation of SNAI1 and SNAI2 in clear cell renal cell carcinoma

Cited by 22 publications

References 37 publications

Disruption of TFIIH activities generates a stress gene expression response and reveals possible new targets against cancer

Disruption of TFIIH activities generates a stress gene expression response and reveals possible new targets against cancer

Identification of hub genes and their clinical value for predicting the development of prostate cancer from benign prostate hyperplasia by bioinformatic analysis

Identification of the Potential Prognostic Markers from the miRNA-lncRNA-mRNA Interactions for Metastatic Renal Cancer via Next-Generation Sequencing and Bioinformatics

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