Bta-miR-181d and Bta-miR-196a mediated proliferation, differentiation, and apoptosis in Bovine Myogenic Cells.

Skeletal muscle is an important component of livestock and poultry organisms. The proliferation and differentiation of myoblasts are highly coordinated processes, which rely on the regulation of miRNA. MiRNAs are widely present in organisms and play roles in various biological processes, including cell proliferation, differentiation, and apoptosis. MiR-181d and miR-196a, identified as tumor suppressors, have been found to be involved in cell proliferation, apoptosis, directed differentiation, and cancer cell invasion. However, their role in beef cattle skeletal muscle metabolism remains unclear. In this study, we discovered that overexpression of bta-miR-181d and bta-miR-196a in Qinchuan cattle myoblasts inhibited proliferation and apoptosis while promoting myogenic differentiation through EDU staining, flow cytometry analysis, immunofluorescence staining, and Western blotting. RNA-seq analysis of differential gene expression revealed that after overexpression of bta-miR-181d and bta-miR-196a, the differentially expressed genes were mainly enriched in the PI3K-Akt and MAPK signaling pathways. Furthermore, the phosphorylation levels of key proteins p-AKT in the PI3K signaling pathway and p-MAPK in the MAPK signaling pathway were significantly decreased after overexpression of bta-miR-181d and bta-miR-196a. Overall, this study provides preliminary evidence that bta-miR-181d and bta-miR-196a may regulate proliferation, apoptosis, and differentiation processes in Qinchuan cattle myoblasts by affecting the phosphorylation status of key proteins in PI3K-Akt and MAPK-ERK signaling pathways.

In this research, we explored the functions of two specific microRNAs, bta-miR-181d and bta-miR-196a, in the muscle cells of Qinchuan cattle. These tiny molecules are known to play crucial roles in various cellular processes. Our findings reveal that these microRNAs significantly influence cell growth, death, and differentiation in muscle cells through their interactions with the AKT and MAPK signaling pathways. This study not only expands our understanding of the miR-181 and miR-196 families but also sheds light on the broader role of microRNAs in the development and growth of skeletal muscles. These insights could have important implications for animal husbandry and the study of muscle biology.

Based on the Cancer Genome Atlas Database Development of a prognostic model of RNA binding protein in stomach adenocarcinoma.

The purpose of this study was to identify potential RNA binding proteins associated with the survival of gastric adenocarcinoma, as well as the corresponding biological characteristics and signaling pathways of these RNA binding proteins. RNA sequencing and clinical data were obtained from the cancer genome map (N = 32, T = 375) and the comprehensive gene expression database (GSE84437, N = 433). The samples in The Cancer Genome Atlas were randomly divided into a development group and a test group. A total of 1495 RNA binding protein related genes were extracted. Using nonparametric tests to analyze the difference of RNA binding protein related genes, 296 differential RNA binding proteins were obtained, 166 were up-regulated and 130 were down regulated. Twenty prognosis-related RNA binding proteins were screened using Cox regression, including 14 high-risk genes (hazard ratio > 1.0) and 6 low-risk genes (hazard ratio < 1.0). Seven RNA binding protein related genes were screened from the final prognostic model and used to construct a new prognostic model. Using the development group and test group, the model was verified with survival analysis, receiver operating characteristics curves and prognosis analysis curves. A prediction nomogram was finally developed and showed good prediction performance.

Bioinformatics role of the WGCNA analysis and co-expression network identifies of prognostic marker in lung cancer.

Lung cancer is the most talked about cancer in the world. It is also one of the cancers that currently has a high mortality rate. The aim of our research is to find more effective therapeutic targets and prognostic markers for human lung cancer. First, we download gene expression data from the GEO database. We performed weighted co-expression network analysis on the selected genes, we then constructed scale-free networks and topological overlap matrices, and performed correlation modular analysis with the cancer group. We screened the 200 genes with the highest correlation in the cyan module for functional enrichment analysis and protein interaction network construction, found that most of them focused on cell division, tumor necrosis factor-mediated signaling pathways, cellular redox homeostasis, reactive oxygen species biosynthesis, and other processes, and were related to the cell cycle, apoptosis, HIF-1 signaling pathway, p53 signaling pathway, NF-κB signaling pathway, and several cancer disease pathways are involved. Finally, we used the GEPIA website data to perform survival analysis on some of the genes with GS > 0.6 in the cyan module. CBX3, AHCY, MRPL12, TPGB, TUBG1, KIF11, LRRC59, MRPL17, TMEM106B, ZWINT, TRIP13, and HMMR was identified as an important prognostic factor for lung cancer patients. In summary, we identified 12 mRNAs associated with lung cancer prognosis. Our study contributes to a deeper understanding of the molecular mechanisms of lung cancer and provides new insights into drug use and prognosis.