Korean J Physiol Pharmacol.  2025 May;29(3):359-372. 10.4196/kjpp.24.322.

Identification of telomere-related diagnostic markers in osteoarthritis based on bioinformatics analysis and machine learning

• Xu S
• Ye J
• Cai X

• KMID: 2567193
• DOI: http://doi.org/10.4196/kjpp.24.322

Abstract

Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues. Finally, potential drugs targeting candidate genes were predicted. Three telomererelated genes, PGD, SLC7A5, and TKT, have been identified as biomarkers for OA diagnosis and were confirmed through ROC diagnostic tests. The immune infiltration of mast cells, neutrophils, common lymphoid precursors, and eosinophils associated with PGD, SLC7A5, and TKT was reduced. Recognizing telomere-related genes PGD, SLC7A5, and TKT as potential diagnostic biomarkers for OA is significant, as it offers valuable insights into the role of telomere-related genes in OA. This discovery also provides valuable information for the diagnosis and treatment of OA.

Keyword

Biomarkers; Immune system; Osteoarthritis; Telomere shortening

Figure

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  Fig. 1 Identification and PPI analysis of DETMRGs. (A) DEGs volcano plot, yellow nodes indicate upregulated DEGs, bule nodes indicate downregulated DEGs, and grey nodes indicate no significant. (B) Heatmap of 20 DEGs, the left part represents normal samples, the right part represents OA samples, yellow represents upregulation and bule represents downregulation. (C) UpSetR plots depicts the number of unique and shared genes between DEGs and TMRGs. (D) Interaction map of 25 DETMRGs PPI network. The node size indicates the clustering coefficient; a larger node represents a larger clustering coefficient. The node color indicates the degree; a higher degree represents a greater connection. PPI, protein and protein interaction; DETMRGs, differentially expressed telomere related genes; DEGs, differentially expressed genes; OA, osteoarthritis; TMRGs, telomere related genes.

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  Fig. 2 DETMRG functional enrichment analysis. (A) Correlation matrix of 34 DETMRGs. (B) CeRNA network of DETMRGs, brown represents mRNA, pink represents miRNA and purple represents lncRNA. (C) KEGG enrichment analysis of DETMRGs. The bubble plots depict the seven most significant pathway enrichment, the bubble size represents the number of DETMRGs, a larger circle indicates a greater number of DETMRGs. The color represents the p-value, a redder color indicates a smaller p-value. (D) GO enrichment analysis of DETMRGs, yellow bar indicates biological process, blue bar indicates cellular component and green bar indicates molecular function. DETMRGs, differentially expressed telomere related genes; ceRNA, competing endogenous RNA; KEGG, Kyoto encyclopedia of genes and genomes; GO, gene ontology.

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  Fig. 3 Screening of feature genes from DETMRGs for OA diagnosis. (A) Coefficient distribution map for logarithmic (λ) sequences in LASSO regression model. (B) Coefficient spectrum of LASSO Cox analysis. (C) Feature gene expression validation by SVM-RFE selection. (D) RandomForest residual distribution map. The X-axis represents the number of trees, and the Y-axis represents the error rate. (E) RandomForest feature importance map of DETMRGs. (F) Venn diagram shows 3 common genes shared by LASSO, SVM-RFE, and RandomForest methods. DETMRGs, differentially expressed telomere related genes; OA, osteoarthritis; LASSO, least absolute shrinkage and selection operator; SVM-RFE, support vector machine-recursive feature elimination.

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  Fig. 4 Diagnostic value and expression pattern of the feature genes. (A) Receiver operating characteristic (ROC) curve of PGD, SLC7A5, and TKT for OA diagnosis in training set GSE51588. (B) Expression of PGD, SLC7A5, and TKT between OA cases and normal control in training set GSE51588. (C) ROC curve of PGD, SLC7A5, and TKT for OA diagnosis in validation set GSE12021+GSE55457. (D) Expression of PGD, SLC7A5, and TKT between OA cases and normal control in validation set GSE12021+GSE55457. (E) Nomogram based on PGD, SLC7A5, and TKT. (F) ROC curve of PGD, SLC7A5, TKT and diagnostic model. (G) DCA curve of PGD, SLC7A5, TKT and diagnostic model. A further the end point from the grey line, a higher accuracy. (H) Calibration curve, the X-axis represents nomogram-predicted probability of disease risk, and the Y-axis the proportion of represents actual disease, a closer line to the ideal dashed a more reliable result. OA, osteoarthritis; DCA, decision curve analysis. ***p < 0.001, ****p < 0.0001.

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  Fig. 5 Immune cells infiltration in osteoarthritis (OA) by CIBERSORT algorithm. (A) Relative abundance of 22 immune cells infiltration in OA and normal samples. (B) Comparison of 22 immune cells infiltration between OA and normal control, green represents normal samples and orange represent OA samples. (C) Correlation matrix of 20 immune cells, red represents positive correlation and bule represents negative correlation. (D) Correlation of PGD, SLC7A5, and TKT to immune cells infiltration in OA, red represents positive correlation and green represents negative correlation. ns, not significant. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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  Fig. 6 Gene set enrichment analysis (GSEA) of feature genes and prediction of potential drug target. (A) GSEA of TKT for top 5 pathways. (B) GSEA of SLC7A5 for top 5 pathways. (C) GSEA of PGD for top 5 pathways. (D) Prediction of potential drugs for PGD and SLC7A5. Ellipses represent genes, V-shaped represents drugs, and darker purple indicates higher interaction scores.

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  Fig. 7 Validation of RT-qPCR in normal (CP-H107) and OA (402OAK-05a) human chondrocyte cells. Comparative mRNA expression levels of PGD, SLC7A5, and TKT in CP-H107 and 402OAK-05a cell lines (n = 3). OA, osteoarthritis. *p < 0.05.

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