No abstract

Accumulating studies have suggested that microRNAs (miRNAs) play vital roles in the pathogenesis of osteoarthritis (OA). Nevertheless, the specific function of miR-128-3p in OA remains unknown. In this study, we demonstrated that miR-128-3p was decreased and ZEB1 was increased in OA. Additionally, miR-128-3p expression was negatively correlated with ZEB1. miR-128-3p overexpression or ZEB1 silencing attenuated extracellular matrix degradation and cell apoptosis, and increased the proliferation of IL-1β-activated CHON-001 cells. Furthermore, ZEB1 was directly targeted by miR-128-3p. In addition, ZEB1 upregulation restored the effects of miR-128-3p overexpression on OA progression. Overall, our findings suggested that miR-128-3p might regulate the development of OA via targeting ZEB1.

This study is aimed at screening prognostic biomarkers in cholangiocarcinoma (CHOL) based on competitive endogenous RNA (ceRNA) regulatory network analysis. Microarray data for lncRNAs, mRNA, and miRNAs were downloaded from the GEO and TCGA databases. Differentially expressed RNAs (DERs) were identified in CHOL and normal liver tissue samples. WGCNA was used to identify disease-related gene modules. By integrating the information from the starBase and DIANA-LncBasev2 databases, we constructed a ceRNA network. Survival analysis was performed, and a prognostic gene-based prognostic score (PS) model was generated. The correlation between gene expression and immune cell infiltration or immune-related feature genes was analyzed using TIMER. Finally, real-time quantitative PCR (RT-qPCR) was used to verify the expression of the 10 DERs with independent prognosis. A large cohort of DERs was identified in the CHOL and control samples. The ceRNA network consisted of 6 lncRNAs, 2 miRNAs, 90 mRNAs, and 98 nodes. Ten genes were identified as prognosis-related genes, and a ten-gene signature PS model was constructed, which exhibited a good prognosis predictive ability for risk assessment of CHOL patients (AUC value = 0.975). Four genes, ELF4, AGXT, ABCG2, and LDHD, were associated with immune cell infiltration and closely correlated with immune-related feature genes (CD14, CD163, CD33, etc.) in CHOL. Additionally, the consistency rate of the RT-qPCR results and bioinformatics analysis was 80%, implying a relatively high reliability of the bioinformatic analysis results. Our findings suggest that the ten-signature gene PS model has significant prognostic predictive value for patients with CHOL. These four immune-related DERs are involved in the progression of CHOL and may be useful prognostic biomarkers for CHOLs.

A visible-light-induced photocatalyst-free three-component radical tandem cyclization of N-propargylamine and N-allylbromodifluoroacetamides with the insertion of sulfur dioxide has been developed. Diverse difluoroamidosulfonylated quinolines are obtained in moderate to good yields....

Background: Apoptosis is a type of cell death, which can produce abundant mediators to modify the tumor microenvironment. However, relationships between apoptosis, immunosuppression, and immunotherapy resistance of gastric cancer (GC) remain unclear.Methods: Gene expression data and matching clinical information were extracted from TCGA-STAD, GSE84437, GSE34942, GSE15459, GSE57303, ACRG/GSE62254, GSE29272, GSE26253, and IMvigor210 datasets. A consensus clustering analysis based on six apoptosis-related genes (ARGs) was performed to determine the molecular subtypes, and then an apoptosisScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Estimate R package was utilized to calculate the tumor microenvironment condition. Kaplan-Meier analysis and ROC curves were performed to further confirm the apoptosisScore efficacy.Results: Based on six ARGs, two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, an apoptosisScore was built to quantify the apoptosis index of each GC patient. Next, we investigated the correlations between the clinical characteristics and apoptosisScore using logistic regression. Multivariate Cox analysis shows that low apoptosisScore was an independent predictor of poor overall survival in TCGA and ACRG datasets, and was associated with the higher pathological stage. Meanwhile, low apoptosisScore was associated with higher immune cell, higher ESTIMATEScore, higher immuneScore, higher stromalScore, higher immune checkpoint, and lower tumorpurity, which was consistent with the “immunity tidal model theory”. Importantly, low apoptosisScore was sensitive to immunotherapy. In addition, GSEA indicated that several gene ontology and Kyoto Encyclopedia of Genes and Genomes items associated with apoptosis, several immune-related pathways, and JAK–STAT signal pathway were considerably enriched in the low apoptosisScore phenotype pathway.Conclusion: Our findings propose that low apoptosisScore is a prognostic biomarker, correlated with immune infiltrates, and sensitivity to immunotherapy in GC.