OVOL1 inhibits breast cancer cell invasion by enhancing the degradation of TGF-β type I receptor

Fan, Chuannan; Wang, Qian; Zon, Gerard van der; Ren, Jiang; Agaser, Cedrick; Slieker, Roderick C.; Iyengar, Prasanna Vasudevan; Mei, Hailiang; Dijke, Peter ten

doi:10.1038/s41392-022-00944-w

Cited by 17 publications

(17 citation statements)

References 77 publications

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“…According to Guo et al [ 48 ] ZNF106 enjoyed great prognostic significance in tumors with considerably lower expression. OVOL1, a key mediator of epithelial lineage determination and mesenchymal–epithelial transition (MET), has been proved to be inversely correlated with the epithelial-mesenchymal transition (EMT) signature and serve as a great prognostic indicator for BRCA patients [ 49 ]. In addition, CBX2 encodes a component of the multiprotein complex, which is required to maintain the transcriptionally repressive state of many genes throughout development via chromatin remodeling and modification of histones.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer

Zheng

Lin

et al. 2023

BMC Med Genomics

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Background Breast cancer (BRCA) is the most common malignancy with high morbidity and mortality in women, and transcription factor (TF) is closely related to the occurrence and development of BRCA. This study was designed to identify a prognostic gene signature based on TF family to reveal immune characteristics and prognostic survival of BRCA. Methods In this study, RNA-sequence with corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and GSE42568. Prognostic differentially expressed transcription factor family genes (TFDEGs) were screened to construct a risk score model, after which BRCA patients were stratified into low-risk and high-risk groups based on their corresponding risk scores. Kaplan–Meier (KM) analysis was applied to evaluate the prognostic implication of risk score model, and a nomogram model was developed and validated with the TCGA and GSE20685. Furthermore, the GSEA revealed pathological processes and signaling pathways enriched in the low-risk and high-risk groups. Finally, analyses regarding levels of immune infiltration, immune checkpoints and chemotactic factors were all completed to investigate the correlation between the risk score and tumor immune microenvironment (TIME). Results A prognostic 9-gene signature based on TFDEGs was selected to establish a risk score model. According to KM analyses, high-risk group witnessed a significantly worse overall survival (OS) than low-risk group in both TCGA-BRCA and GSE20685. Furthermore, the nomogram model proved great possibility in predicting the OS of BRCA patients. As indicted in GSEA analysis, tumor-associated pathological processes and pathways were relatively enriched in high-risk group, and the risk score was negatively correlated with ESTIMATE score, infiltration levels of CD4+ and CD8+T cells, as well as expression levels of immune checkpoints and chemotactic factors. Conclusions The prognostic model based on TFDEGs could distinguish as a novel biomarker for predicting prognosis of BRCA patients; in addition, it may also be utilized to identify potential benefit population from immunotherapy in different TIME and predict potential drug targets.

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Section: Discussionmentioning

confidence: 99%

Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer

Zheng

Lin

et al. 2023

BMC Med Genomics

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“…A total of 73 competing circRNA–miRNA–mRNA triplets were identified, within which several key regulators were found, including miR-143 [lipid droplet formation, ( 41 )], miR-125b [mammary inflammatory response ( 61 )], SLC family members [cell cycle ( 62 )] and OVOL1 [mammary epithelial–mesenchymal transition, ( 63 )]. The most connected regulators in the ceRNA networks were novel_circ_0006589, oar_miR_432, and PRADC1 .…”

Section: Discussionmentioning

confidence: 99%

Non-coding transcriptomic profiles in the sheep mammary gland during different lactation periods

Chen

et al. 2022

Front. Vet. Sci.

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Sheep milk production is a dynamic and multifactorial trait regulated by diverse biological mechanisms. To improve the quality and production of sheep milk, it is necessary to understand the underlying non-coding transcriptomic mechanisms. In this study, ribonucleic acid-sequencing (RNA-seq) was used to profile the expression of microRNAs (miRNAs) and circular RNAs (circRNAs) in the sheep mammary gland at three key lactation time points (perinatal period, PP; early lactation, EL; and peak lactation, PL). A total of 2,369 novel circRNAs and 272 miRNAs were profiled, of which 348, 373, and 36 differentially expressed (DE) circRNAs and 30, 34, and 7 DE miRNAs were detected in the comparison of EL vs. PP, PL vs. PP, and PL vs. EL, respectively. A series of bioinformatics analyses including functional enrichment, machine learning prediction, and competing endogenous RNA (ceRNA) network analyses were conducted to identify subsets of the potential candidate miRNAs (e.g., oar_miR_148a, oar_miR_362, and oar_miR_432) and circRNAs (e.g., novel_circ_0011066, novel_circ_0010460, and novel_circ_0006589) involved in sheep mammary gland development. Taken together, this study offers a window into the dynamics of non-coding transcriptomes that occur during sheep lactation and may provide further insights into miRNA and circRNA that influence sheep mammary gland development.

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“…MCF10A-M1 and MCF10A-M2 cells were provided by F. Miller (Barbara Ann Karmanos Cancer Institute, Detroit, USA). All the cell lines were cultured as described previously ( 73 ). Recombinant TGF-β3 was a gift from A. P. Hinck (University of Pittsburgh).…”

Section: Methodsmentioning

confidence: 99%

“…Production of lentivirus was described elsewhere ( 73 ). Cells stably expressing the indicated constructs were selected by adding the corresponding antibiotics to the culture medium after 2 days postinfection.…”

Section: Methodsmentioning

confidence: 99%

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The lncRNA LETS1 promotes TGF-β–induced EMT and cancer cell migration by transcriptionally activating a TβR1-stabilizing mechanism

et al. 2023

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Transforming growth factor–β (TGF-β) signaling is a critical driver of epithelial-to-mesenchymal transition (EMT) and cancer progression. In SMAD-dependent TGF-β signaling, activation of the TGF-β receptor complex stimulates the phosphorylation of the intracellular receptor-associated SMADs (SMAD2 and SMAD3), which translocate to the nucleus to promote target gene expression. SMAD7 inhibits signaling through the pathway by promoting the polyubiquitination of the TGF-β type I receptor (TβRI). We identified an unannotated nuclear long noncoding RNA (lncRNA) that we designated LETS1 (lncRNA enforcing TGF-β signaling 1) that was not only increased but also perpetuated by TGF-β signaling. Loss of LETS1 attenuated TGF-β–induced EMT and migration in breast and lung cancer cells in vitro and extravasation of the cells in a zebrafish xenograft model. LETS1 potentiated TGF-β–SMAD signaling by stabilizing cell surface TβRI, thereby forming a positive feedback loop. Specifically, LETS1 inhibited TβRI polyubiquitination by binding to nuclear factor of activated T cells (NFAT5) and inducing the expression of the gene encoding the orphan nuclear receptor 4A1 (NR4A1), a component of a destruction complex for SMAD7. Overall, our findings characterize LETS1 as an EMT-promoting lncRNA that potentiates signaling through TGF-β receptor complexes.

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OVOL1 inhibits breast cancer cell invasion by enhancing the degradation of TGF-β type I receptor

Cited by 17 publications

References 77 publications

Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer

Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer

Non-coding transcriptomic profiles in the sheep mammary gland during different lactation periods

The lncRNA LETS1 promotes TGF-β–induced EMT and cancer cell migration by transcriptionally activating a TβR1-stabilizing mechanism

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