Transforming growth factor-beta1 (TGF-beta1) can be tumor-suppressive through the activation of the Smad-mediated signaling pathway. TGF-beta1 can also enhance tumor progression by stimulating epithelial-to-mesenchymal transition (EMT) through additional pathways. EMT is characterized by the acquisition of a fibroblast-like cell morphology, dissolution of tight junctions, disruption of adherence junctions, and formation of actin stress fibers. There is evidence linking the activation of mitogen-activated protein kinase pathways to the induction of TGF-beta1-mediated EMT. However, the role of Erk in the induction of TGF-beta1-mediated EMT remains unclear. TGF-beta1 treatment of normal murine mammary gland (NMuMG) epithelial cells resulted in increased gene expression of Ras, Raf, MEK1/2, and Erk1/2, as shown by microarray analysis and real-time polymerase chain reaction. Upon 24 and 48 hours of treatment with TGF-beta1, NMuMG and mouse cortical tubule (MCT) epithelial cells underwent EMT as shown by changes in cell morphology, delocalization of zonula occludens-1 and E-cadherin from cell-cell junctions, and formation of actin stress fibers. TGF-beta1 treatment also resulted in increased levels of phosphorylated Erk and Erk kinase activity. Treatment with an MEK inhibitor, U0126, inhibited increased Erk phosphorylation and kinase activity, and blocked TGF-beta1-induced EMT in both cell lines. These data show that TGF-beta1 induces the activation of the Erk signaling pathway, which is required for TGF-beta1-mediated EMT in vitro.
Background Transforming growth factor beta (TGF-β) plays an essential role in a wide array of cellular processes. The most well studied TGF-β response in normal epithelial cells is growth inhibition. In some cell types, TGF-β induces an epithelial to mesenchymal transition (EMT). NMuMG is a nontransformed mouse mammary gland epithelial cell line that exhibits both a growth inhibitory response and an EMT response to TGF-β, rendering NMuMG cells a good model system for studying these TGF-β effects. Method A National Institutes of Aging mouse 15,000 cDNA microarray was used to profile the gene expression of NMuMG cells treated with TGF-β1 for 1, 6, or 24 hours. Data analyses were performed using GenePixPro and GeneSpring software. Selected microarray results were verified by northern analyses. Results Of the 15,000 genes examined by microarray, 939 were upregulated or downregulated by TGF-β. This represents approximately 10% of the genes examined, minus redundancy. Seven genes previously not known to be regulated by TGF-β at the transcriptional level (Akt and RhoB) or not at all (IQGAP1, mCalpain, actinin α3, Ikki, PP2A-PR53), were identified and their regulation by TGF-β verified by northern blotting. Cell cycle pathway examination demonstrated downregulation of cyclin D 2 , c- myc , Id2, p107, E2F5, cyclin A, cyclin B, and cyclin H. Examination of cell adhesion-related genes revealed upregulation of c-Jun, α-actinin, actin, myosin light chain, p120cas catenin (Catns), α-integrin, integrin β5, fibronectin, IQGAP1, and mCalpain. Conclusion Using a cDNA microarray to examine TGF-β-regulated gene expression in NMuMG cells, we have shown regulation of multiple genes that play important roles in cell cycle control and EMT. In addition, we have identified several novel TGF-β-regulated genes that may mediate previously unknown TGF-β functions.
Background: Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.Methods: Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways.Results: PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3-and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer. Conclusions:In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'
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