CHST3, PGBD5, and SLIT2 can be identified as potential genes for the diagnosis and treatment of osteoporosis and sarcopenia

Abstract

Osteoporosis and sarcopenia are common diseases in the older. This study aims to use transcriptomics and explore common diagnostic genes of osteoporosis and sarcopenia and predict potentially effective treatment drugs. Three datasets for osteoporosis and sarcopenia were downloaded from the GEO database, and transcriptome sequencing was performed on clinical samples. A total of 23 differentially expressed genes (DEGs) were selected using the LIMMA, WGCNA, and the DEseq2 package. Three machine learning methods were employed to determine the final common diagnostic genes for the diseases. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of genes. Single-gene enrichment analysis (GSEA), immune infiltration abundance calculation, and related metabolic analysis were used to study the pathogenesis of the two diseases. Finally, the CMap database was used to predict potential therapeutic drugs for the diseases, and further validation was conducted through RT-PCR and WB. Three genes for the diseases CHST3, PGBD5, and SLIT2 were identified, showing good predictive performance in both internal and external validations. GSEA analysis revealed that genes were enriched primarily in pathways related to cell cycle regulation, fatty acid metabolism, DNA replication, and carbohydrate synthesis. CHST3 and SLIT2 were involved in the immune response, but PGBD5 seemed unrelated to the immune response. Potential therapeutic drugs were predicted, and the RT-PCR, WB results further validated our hypotheses. CHST3, PGBD5, and SLIT2 can serve as potential genes for the diagnosis and treatment of osteoporosis and sarcopenia; furthermore, these results provide new clues for further experimental research and treatment.

Keywords: Diagnostic genes; Osteoporosis; Pathogenesis; Sarcopenia; Transcriptomics.

Fig. 1

Flowchart of the experiment. OP osteoporosis, SA sarcopenia, GSE gene expression composite series, LIMMA linear modelling of microarray data, WGCNA weighted gene co-expression network analysis, DEGs differentially expressed genes, LASSO least absolute shrinkage and selection operator, SVM-RFE support vector machine-recursive feature elimination, GSEA gene set enrichment analysis, RT-PCR real-time polymerase chain reaction.

Fig. 2

Employed ‘Limma’ for screening DEGs. (A,B) Heatmap and volcano plot of GSE35958. (C,D) Heatmap and volcano plot of GSE1428. (E) Intersection of DEGs in GSE35958 and GSE1428.

Fig. 3

Utilised ‘WGCNA’ to identify module genes. (A) Cluster analysis of highly connected genes in key modules of GSE35956. (B) Correlation and P-values of modules with features in GSE35956. (C) Cluster analysis of highly connected genes in key modules of GSE38718. (D) Correlation and P-values of modules with features in GSE38718. (E) Intersection of module genes in the two datasets.

Fig. 4

Screening of clinical samples for DEGs and GO analysis. (A) Volcano plot of DEGs in clinical samples; (B) Intersection of all DEGs; (C) Perform GO analysis on the 23 hub genes.

Fig. 5

Employing three machine learning methods to screen for common diagnostic genes between OP and SA. (A) LASSO regression model to identify the optimal diagnostic genes. (B) Employing SVM-RFE algorithm to screen for the top 8 genes associated with the disease. (C) Utilizing the random forest algorithm to determine the top 3 shared diagnostic genes. (D) Intersection of the results from the three algorithms to identify the final set of common diagnostic genes. OP osteoporosis, SA muscle atrophy, LASSO least absolute shrinkage and selection operator, SVM-RFE support vector machine – recursive feature elimination, RF random forest.

Fig. 6

Validation of the three disease codiagnostic genes. (A–C) ROC curves of the three genes in the training cohort; (D–F) ROC curves when using GSE7158 as the validation cohort; (G–I) ROC curves when using GSE52699 as the validation cohort.

Fig. 7

Analysis of single-gene GSEA. (A,C,E) GSEA analysis of the three diagnostic genes in the OP samples. (B,D,F) Analysis of the three diagnostic genes in the SA samples. GSEA gene set enrichment analysis.

Fig. 8

Immunological correlation analysis. (A,F) Show the proportions of immune cells in OP samples and SA samples, respectively; (B,G) Display the differences in different immune cells between OP samples and SA samples; (C–E) In OP samples, the correlation between the expressions of CHST3, PGBD5, SLIT2, and immune cells; (H–J) In SA samples, the correlation between the expressions of shared diagnostic genes and immune cells.

Fig. 9

Therapeutic drug prediction and chemical molecular formulas. (A) Heatmap of relevant genes; (B) PU-H71; (C) Scandenin; (D) BMS-345541.

Fig. 10

RT-PCR and WB of human samples. (A) Expression levels of CHST3 in OP and SA samples; (B) Expression levels of PGBD5; (C) Expression levels of SLIT2; (D) Western blot.

Fig. 11

PPI network and investigation of related metabolic pathways. (A) PPI network; (B) Disease-associated metabolites; (C) Enrichment analysis of related metabolic pathways.