Age-related skeletal muscle deterioration (sarcopenia) has a significant effect on the elderly's health and quality of life, but the molecular and gene regulatory mechanisms remain largely unknown. It is necessary to identify the candidate genes related to skeletal muscle aging and prospective therapeutic targets for effective treatments. The age-line-related genes (ALRGs) and age-line-related transcripts (ALRTs) were investigated using the gene expression profiles of GSE47881 and GSE118825 from the Gene Expression Omnibus (GEO) database. The proteinprotein interaction (PPI) networks were performed to identify the key molecules with Cytoscape, and Gene Set Enrichment Analysis (GSEA) was used to clarify the potential molecular functions. Two hub molecules were finally obtained and verified with quantitative real-time PCR (qRT-PCR). The results showed that the expression of mitochondria genes involved in mitochondrial electron transport, complex assembly of the respiratory chain, tricarboxylic acid cycle, oxidative phosphorylation, and ATP synthesis were down-regulated in skeletal muscle with aging. We further identified a primary hub gene of CYCS (Cytochrome C) and a key transcription factor of ESRRA (Estrogen-related Receptor Alpha) to be associated closely with skeletal muscle aging. PCR analysis confirmed the expressions of CYCS and ESRRA in gastrocnemius muscles of mice of different ages were significantly different, and decreased gradually with age. In conclusion, the main cause of skeletal muscle aging may be the systematically reduced expression of mitochondrial functional genes. The CYCS and ESRRA may play significant roles in the progression of skeletal muscle aging and serve as potential biomarkers for future diagnosis and treatment.
To investigate the role of S100 calcium-binding protein A16 (S100A16) in hepatic lipid metabolism, S100a16 transgenic, S100a16 knockdown, and wildtype C57BL/6 mice were fed either a high-fat diet (HFD) or normal-fat diet (NFD) for 16 weeks. The results showed that for HFD-fed mice, S100a16 transgenic mice showed significantly more severe fatty liver than other HFD-fed mice, with a significant increase in serum triglyceride (TG) concentration, with more and larger lipid droplets in the liver, whereas S100a16 knockdown mice were completely opposite, with liver fat lesions and TG serological changes being the mildest; for NFD-fed mice, liver fat accumulation and serum TG concentrations were significantly lower than those fed HFD, and no significant lipid droplets were found in the liver. Further, we found that calmodulin (CaM) interacts with S100A16, a member of the AMP-activated protein kinase (AMPK) pathway. Our research found that S100A16 regulates the AMPK pathway-associated protein by interacting with CaM to regulate liver lipid synthesis. S100A16 regulates liver lipid metabolism through the CaM/CAMKK2/AMPK pathway. Overexpression of S100A16 promotes the deterioration of fatty liver induced by HFD, and low expression of S100A16 can attenuate fatty liver. K E Y W O R D Scalmodulin, fatty liver, S100 calcium-binding protein A16
Background. Glioma is the most common central nervous system (CNS) cancer with a short survival period and a poor prognosis. The S100 family gene, comprising 25 members, relates to diverse biological processes of human malignancies. Nonetheless, the significance of S100 genes in predicting the prognosis of glioma remains largely unclear. We aimed to build an S100 family-based signature for glioma prognosis. Methods. We downloaded 665 and 313 glioma patients, respectively, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database with RNAseq data and clinical information. This study established a prognostic signature based on the S100 family genes through multivariate COX and LASSO regression. The Kaplan–Meier curve was plotted to compare overall survival (OS) among groups, whereas Receiver Operating Characteristic (ROC) analysis was performed to evaluate model accuracy. A representative gene S100B was further verified by in vitro experiments. Results. An S100 family-based signature comprising 5 genes was constructed to predict the glioma that stratified TCGA-derived cases as a low- or high-risk group, whereas the significance of prognosis was verified based on CGGA-derived cases. Kaplan–Meier analysis revealed that the high-risk group was associated with the dismal prognosis. Furthermore, the S100 family-based signature was proved to be closely related to immune microenvironment. In vitro analysis showed S100B gene in the signature promoted glioblastoma (GBM) cell proliferation and migration. Conclusions. We constructed and verified a novel S100 family-based signature associated with tumor immune microenvironment (TIME), which may shed novel light on the glioma diagnosis and treatment.
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