Bioinformatics and systems biology approaches to identify potential common pathogeneses for sarcopenia and osteoarthritis

Abstract

Sarcopenia, a geriatric syndrome characterized by progressive loss of muscle mass and strength, and osteoarthritis, a common degenerative joint disease, are both prevalent in elderly individuals. However, the relationship and molecular mechanisms underlying these two diseases have not been fully elucidated. In this study, we screened microarray data from the Gene Expression Omnibus to identify associations between sarcopenia and osteoarthritis. We employed multiple statistical methods and bioinformatics tools to analyze the shared DEGs (differentially expressed genes). Additionally, we identified 8 hub genes through functional enrichment analysis, protein-protein interaction analysis, transcription factor-gene interaction network analysis, and TF-miRNA coregulatory network analysis. We also discovered potential shared pathways between the two diseases, such as transcriptional misregulation in cancer, the FOXO signalling pathway, and endometrial cancer. Furthermore, based on common DEGs, we found that strophanthidin may be an optimal drug for treating sarcopenia and osteoarthritis, as indicated by the Drug Signatures database. Immune infiltration analysis was also performed on the sarcopenia and osteoarthritis datasets. Finally, receiver operating characteristic (ROC) curves were plotted to verify the reliability of our results. Our findings provide a theoretical foundation for future research on the potential common pathogenesis and molecular mechanisms of sarcopenia and osteoarthritis.

Keywords: bioinformatics; disease markers; hub genes; osteoarthritis; pathogenesis; sarcopenia.

Figure 1

Schematic illustration of the overall workflow of this study.

Figure 2

Expression characteristics of DEGs in sarcopenia patients and OA patients. (A) Sarcopenia-Heatmap and (B) Sarcopenia-Volcano plot (C) OA-Heatmap (D) OA-Volcano plot (A,B) present the DEGs identified between patients with sarcopenia and healthy controls (|logFC| >0.585 was defined as the screening criterion to obtain DEGs in sarcopenia). (C,D) The DEGs identified between OA patients and healthy controls (|logFC| >1) were defined as the screening criterion for obtaining DEGs for OA. Blue indicates low expression values, and red indicates high expression values.

Figure 3

Identification of DEGs shared between sarcopenia and OA patients. Venn diagrams show two datasets containing 7 shared upregulated DEGs and 25 shared downregulated DEGs.

Figure 4

GO functional enrichment analysis of genes shared between sarcopenia patients and OA patients. (A) Bar plot. (B) Bubble plot. (C) Circle chart. The results are shown as-log10 (p value).

Figure 5

Functional enrichment analysis of KEGG pathways revealed genes shared between sarcopenia patients and OAs. (A) Barplot. (B) Bubble plot. The results are shown as the-log10 (p-value).

Figure 6

The PPI network of DEGs was shared between OA and sarcopenia patients.

Figure 7

Venn diagram showing 7 algorithms screening for 8 overlapping hub genes.

Figure 8

Hub genes and their coexpressed genes were analyzed via GeneMANIA.

Figure 9

The DEG-TF (A) and hub gene-TF (B) regulatory interaction networks. Herein, the square nodes are TFs, and the gene symbols that interact with TFs are represented as circle nodes. DEGs, differentially expressed genes; TF, transcription factor.

Figure 10

The DEG–miRNA (A) and hub gene–miRNA (B) regulatory interaction networks. Herein, the square nodes indicate miRNAs, and the gene symbols indicate interactions with miRNAs in a circular shape. DEGs, differentially expressed genes; miRNAs, microRNAs.

Figure 11

Gene–disease association networks represent diseases associated with hub genes. Diseases are represented by square nodes, and genes are represented by round nodes.

Figure 12

List of the top 10 drugs recommended for patients with OA and sarcopenia.

Figure 13

Validation of the diagnostic shared hub genes in the sarcopenia patient (GSE1428) dataset and the OA (GSE5523) dataset. (A) BTG2, (B) CDKNIA, (C) CEBPB, (D) DDIT4, (E) FOXO3, (F) NFKBIA, (G) ZBTB16, (H) ZFP36.

Figure 14

Infiltration analysis and correlation analysis of immune cells in the sarcopenia group and healthy controls. (A) Heatmap of immune cell subpopulations in the sarcopenia cohort. (B) Violin plot of immune cell subpopulations in the sarcopenia dataset. (C) Correlations of immune cell subpopulations with shared key genes. p < 0.05 indicated a significant difference.

Figure 15

Infiltration analysis and correlation analysis of immune cells in the OA group and healthy controls. (A) Heatmap of immune cell subpopulations in the OA dataset. (B) Violin plot of immune cell subpopulations in the OA dataset. (C) Correlations of immune cell subpopulations with shared key genes. p < 0.05 indicated a significant difference.