Revealing the Diagnostic Potential of Three RHO GTPase-Related Genes in Osteoarthritis Based on Single-Cell RNA Sequencing and GEO Dataset Analysis

Abstract

Background

Osteoarthritis (OA) is becoming increasingly common, and RHO GTPase family members have been implicated in inflammatory processes associated with OA. We therefore examined the diagnostic utility of RHO GTPase-related genes (RGPRGs) in OA.

Methods

The OA expression datasets were collected from public repositories, whereas RGPRGs were assembled from published literature. In the training cohort, we first performed differential expression analysis to screen for differentially expressed genes (DEGs) between OA and control samples. RHO GTPase-related DEGs were obtained by intersecting the DEG list with the RGPRG set, followed by feature selection with the Boruta and XGBoost algorithms. Candidate biomarkers were further evaluated by receiver operating characteristic (ROC) analysis and cross-dataset expression validation. The selected biomarkers were used to establish and validate an artificial neural network (ANN) model, and their biological associations were examined using gene set enrichment analysis (GSEA) and immune infiltration analysis. In GSE152805, key cell populations were identified and subsequently characterized by pseudotime, cell–cell communication, pathway activity, and metabolic analyses.

Results

Analysis yielded 801 DEGs, 30 candidate genes, 14 candidate biomarkers, and 3 final biomarkers. The ANN achieved an area under the curve (AUC) of 0.940 in the training set and 0.833 in the validation set. Following Benjamini–Hochberg adjustment, VAV1, STMN2, and RND1 were associated with 34, 7, and 31 significant KEGG pathways, respectively, and five pathways were common to all three (adjusted p < 0.05 and |NES| > 1). STMN2 also showed a significant positive association with natural killer (NK) cells (correlation coefficient (cor) = 0.604, p < 0.01). Fibroblasts were selected as the key cell population and displayed strong communication with endothelial cells. Along the fibroblast pseudotime trajectory, RND1 expression decreased initially, increased subsequently, and declined again at later stages. Single-cell Reactome analysis further indicated functional differences among fibroblast subpopulations involving cell interactions, immune signaling, transcriptional regulation, and metabolic processes.

Conclusion

VAV1, STMN2, and RND1 emerged as reliable diagnostic biomarkers for OA and may provide molecular candidates for early detection and therapeutic investigation. The findings also extend current insight into RHO GTPase-related processes in OA and support further evaluation of biomarker-based diagnostic strategies.