Differential expression of foxo genes during embryonic development and in adult tissues of Xenopus tropicalis

Screening Potential Diagnostic Biomarkers for Age-Related Sarcopenia in the Elderly Population by WGCNA and LASSO

Background

Sarcopenia is a common chronic disease characterized by age-related decline in skeletal muscle mass and function, and the lack of diagnostic biomarkers makes community-based screening problematic.

Methods

Three gene expression profiles related with sarcopenia were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and eigengenes of a module in the merged dataset were identified by differential expression analysis and weighted gene coexpression network analysis (WGCNA), and common genes (CGs) were defined as the intersection of DEGs and eigengenes of a module. CGs were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) analysis was performed to screen the CGs for identifying the diagnostic biomarkers of sarcopenia. Based on the diagnostic biomarkers, we established a novel nomogram model of sarcopenia. At last, we validated the diagnostic biomarkers and evaluated the diagnostic performance of the nomogram model by the area under curve (AUC) value.

Results

We screened out 107 DEGs and 788 eigengenes in the turquoise module, and 72 genes were selected as CGs of sarcopenia by intersection. GO analysis showed that CGs were mainly involved in metal ion detoxification and mitochondrial structure, and KEGG analysis revealed that CGs were mainly enriched in the mineral absorption, glucagon signaling pathway, FoxO signaling pathway, insulin signaling pathway, AMPK signaling pathway, and estrogen signaling pathway. Then, six diagnostic biomarkers (ARHGAP36, FAM171A1, GPCPD1, MT1X, ZNF415, and RXRG) were identified by LASSO analysis. Finally, the validation AUC values indicated that the six diagnostic biomarkers had high diagnostic accuracy for sarcopenia.

Conclusion

We identified six diagnostic biomarkers with high diagnostic performance, providing new insights into the incidence and progression of sarcopenia in future research.

Development and Verification of a Combined Diagnostic Model for Sarcopenia with Random Forest and Artificial Neural Network

Background

Sarcopenia is a chronic disease characterized by an age-related decline in skeletal muscle mass and function, and diagnosis is challenging owing to the lack of a clear “gold standard” assessment method.

Objective

This study is aimed at combining random forest (RF) and artificial neural network (ANN) methods to screen key potential biomarkers and establish an early sarcopenia diagnostic model.

Methods

Three gene expression datasets were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the merged dataset were identified by R software and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Afterward, the STRING database was employed for interaction analysis of the differentially encoded proteins. Then, RF was used to identify key genes from the DEGs, and a sarcopenia diagnostic model was constructed by ANN. Finally, the diagnostic model was assessed using a validation dataset, while its diagnostic performance was evaluated by the area under curve (AUC) value.

Results

107 sarcopenia-related DEGs were identified, and they were mainly enriched in the FoxO and AMPK signaling pathways involved in the molecular pathogenesis of sarcopenia. Thereafter, seven key genes (MT1X, FAM171A1, ZNF415, ARHGAP36, CISD1, ETNPPL, and WISP2) were identified by the RF classifier. The proteins encoded by three of these genes (CISD1, ETNPPL, and WISP2) may be potential biomarkers for sarcopenia. Finally, a diagnostic model for sarcopenia was successfully designed by ANN, achieving an AUC of 0.999 and 0.85 in the training and testing datasets, respectively.

Conclusion

We identified several potential genetic biomarkers and successfully developed an early predictive model with high diagnostic performance for sarcopenia. Moreover, our results provide a valuable reference for the early diagnosis and screening of sarcopenia in the future.

Exercise-Induced Autophagy Suppresses Sarcopenia Through Akt/mTOR and Akt/FoxO3a Signal Pathways and AMPK-Mediated Mitochondrial Quality Control

Exercise training is one of the most effective interventional strategies for sarcopenia in aged people. Nevertheless, the underlying mechanisms are not well recognized. Increasing studies have reported abnormal regulation of autophagy in aged skeletal muscle. Our current study aims to explore the efficiency of exercise interventions, including treadmill exercise, resistance exercise, alternating exercise with treadmill running and resistance exercise, and voluntary wheel running, on 21-month-old rats with sarcopenia and to detect the underlying mechanisms. Results showed the declined mass of gastrocnemius muscle with deficient autophagy and excessive apoptosis as a result of up-regulated Atrogin-1 and MuRF1, declined Beclin1 level and LC3-II/LC3-I ratio, accumulated p62, increased Bax, and reduced Bcl-2 levels, and also exhibited a defective mitochondrial quality control due to declined PGC-1α, Mfn2, Drp1, and PINK1 levels. However, 12-week exercise interventions suppressed the decline in mass loss of skeletal muscle, accompanied by down-regulated Atrogin-1 and MuRF1, increased Beclin1 level, improved LC3-II/LC3-I ratio, declined p62 level, and reduced Bax and increased Bcl-2 level, as well as enhanced mitochondrial function due to the increased PGC-1α, Mfn2, Drp1, and PINK1 levels. Moreover, exercise interventions also down-regulated the phosphorylation of Akt, mTOR, and FoxO3a, and up-regulated phosphorylated AMPK to regulate the functional status of autophagy and mitochondrial quality control. Therefore, exercise-induced autophagy is beneficial for remedying sarcopenia by modulating Akt/mTOR and Akt/FoxO3a signal pathways and AMPK-mediated mitochondrial quality control, and resistance exercise exhibits the best interventional efficiency.