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Myocardial fibrosis (MF) can cause heart remodeling and it is an independent risk factor for malignant arrhythmias, sudden cardiac death, and other malignant cardiovascular events. It is often characterized by myocardial interstitial collagen deposition and hyperproliferation of cardiac fibroblasts (CFs). The transforming growth factor-β1 (TGF-β1) is the most influential profibrogenic factor. Resveratrol (RSV) is an active polyphenol substance that inhibits myocardial fibrosis. The mechanism of RSV-mediated inhibition of the proliferation of CFs at the microRNA level is not fully understood. We used TGF-β1 to induce CFs proliferation to simulate the pathogenesis of myocardial fibrosis. Neonatal rat CFs were treated with TGF-β1 in the presence or absence of resveratrol. Cell proliferation was measured using the CCK-8 and EdU assay. Collagen secretion was measured using hydroxyproline kit. Further, qPCR analysis was performed to determine microRNA levels after TGF-β1 or resveratrol treatment. To identify the target gene for miR-17, miR-17 was overexpressed or silenced, and the mRNA and protein levels of Smad7 were assessed. The effects of miR-17 silencing or Smad7 overexpression on cell proliferation and collagen secretion were also examined. Resveratrol treatment significantly decreased the TGF-β1-induced CF proliferation and collagen secretion. Resveratrol also decreased the levels of miR-17, miR-34a, and miR-181a in TGF-β1-treated CFs. Overexpression of miR-17 decreased the Smad7 mRNA and protein levels while silencing miR-17 increased them. Additionally, silencing miR-17 or overexpressing Smad7 decreased the TGF-β1-induced CFs proliferation and collagen secretion. In conclusion, resveratrol inhibits TGF-β1-induced CFs proliferation and collagen secretion. This inhibitory effect of resveratrol is orchestrated by the downregulation of miR-17 and the regulation of Smad7. [ABSTRACT FROM AUTHOR]

Background: Since miRNAs can play important roles in different cancer types, how to discover cancer related miRNAs is an important issue. In general, the miRNAs with differential expression is the focus of attention. However, some important cancer related miRNAs are not excavated by differential expression analysis. We take this type of miRNAs as 'dark matters' (DM-miRNA). It is our great interests to develop an algorithm to discover DM-miRNAs. Results: An effective method was developed to find DM-miRNAs. This method is mainly for mining potential DM-miRNAs by building basic miRNA-mRNA network (BMMN) and miRNA-lncRNA network (BMLN). The results indicate that miRNA-mRNA and miRNA-lncRNA interactions can be used as novel cancer biomarkers. Conclusions: The BMMN and BMLN can excavate the non-differentially expressed miRNAs which play an important role in the cancer. What's more, the edge biomarkers (miRNA-mRNA and miRNA-lncRNA interactions) contain more information than the node biomarkers. It will contribute to developing novel therapeutic candidates in cancers. [ABSTRACT FROM AUTHOR]