MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.
During endoderm formation, cell identity and tissue morphogenesis are tightly controlled by cell-intrinsic and cell-extrinsic factors such as biochemical and physical inputs. While the effects of biochemical factors are well studied, the physical cues that regulate cell division and differentiation are poorly understood. RNA sequencing analysis demonstrated increases of endoderm-specific gene expression in hPSCs cultured on soft substrate (Young’s modulus, 3 ± 0.45 kPa) in comparison with hard substrate (Young’s modulus, 165 ± 6.39 kPa). Further analyses revealed that multiple long noncoding RNAs (lncRNAs) were up-regulated on soft substrate; among them, LINC00458 was identified as a stiffness-dependent lncRNA specifically required for hPSC differentiation toward an early endodermal lineage. Gain- and loss-of-function experiments confirmed that LINC00458 is functionally required for hPSC endodermal lineage specification induced by soft substrates. Our study provides evidence that mechanical cues regulate the expression of LINC00458 and induce differentiation of hPSC into hepatic lineage progenitors.
Gastric cancer (GC) is the second most frequent cause of cancer-related deaths worldwide. MicroRNAs are single-stranded RNA molecules of 21–23 nucleotides that regulate target gene expression through specific base-pairing interactions between miRNA and untranslated regions of targeted mRNAs. In this study, we generated a multistep approach for the integrated analysis of miRNA and mRNA expression. First, both miRNA and mRNA expression profiling datasets in gastric cancer from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) identified 79 and 1042 differentially expressed miRNAs and mRNAs, respectively, in gastric cancer. Second, inverse correlations between miRNA and mRNA expression levels identified 3206 miRNA–mRNA pairs combined with 79 dysregulated miRNAs and their 774 target mRNAs predicted by three prediction tools, miRanda, PITA, and RNAhybrid. Additionally, miR-204, which was found to be down-regulated in gastric cancer, was ectopically over-expressed in the AGS gastric cancer cell line and all down-regulated targets were identified by RNA sequencing (RNA-seq) analysis. Over-expression of miR-204 reduced the gastric cancer cell proliferation and suppressed the expression of three targets which were validated by qRT-PCR and luciferase assays. For the first time, we identified that CKS1B, CXCL1, and GPRC5A are putative targets of miR-204 and elucidated that miR-204 acted as potential tumor suppressor and, therefore, are useful as a promising therapeutic target for gastric cancer.
Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC. Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established. Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any Huang et al. Biomarkers for LNM in EEC single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively. Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.
Globally, breast cancer is the most frequently diagnosed cancer in women, and it remains a substantial clinical challenge due to cancer relapse. The presence of a subpopulation of dormant breast cancer cells that survived chemotherapy and metastasized to distant organs may contribute to relapse. Tumor microenvironment (TME) plays a significant role as a niche in inducing cancer cells into dormancy as well as involves in the reversible epithelial-to-mesenchymal transition (EMT) into aggressive phenotype responsible for cancer-related mortality in patients. Mesenchymal stem cells (MSCs) are known to migrate to TME and interact with cancer cells via secretion of exosome- containing biomolecules, microRNA. Understanding of interaction between MSCs and cancer cells via exosomal miRNAs is important in determining the therapeutic role of MSC in treating breast cancer cells and relapse. In this study, exosomes were harvested from a medium of indirect co-culture of MCF7-luminal and MDA-MB-231-basal breast cancer cells (BCCs) subtypes with adipose MSCs. The interaction resulted in different exosomal miRNAs profiles that modulate essential signaling pathways and cell cycle arrest into dormancy via inhibition of metastasis and epithelial-to-mesenchymal transition (EMT). Overall, breast cancer cells displayed a change towards a more dormant-epithelial phenotype associated with lower rates of metastasis and higher chemoresistance. The study highlights the crucial roles of adipose MSCs in inducing dormancy and identifying miRNAs-dormancy related markers that could be used to identify the metastatic pattern, predict relapses in cancer patients and to be potential candidate targets for new targeted therapy.
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