SLC38A1 and STX11 are mitochondria-related biomarkers associated with immune infiltration in osteoarthritis

Abstract

Background: Mitochondrial dynamics and mitophagy play crucial roles in osteoarthritis (OA); however, the specific contributions of mitochondrial dynamics-related genes (MD-RGs) and mitophagy-related genes (MP-RGs) remain unclear. This study aimed to elucidate the precise mechanisms linking these genes in the context of OA.

Methods: OA-related transcriptome datasets and single-cell RNA sequencing (scRNA-seq) dataset incorporating MD-RGs and MP-RGs were utilized in this study. Hub genes were identified through differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning. A nomogram was then constructed based on the hub genes. Enrichment and immune infiltration analyses were performed on the hub genes, and key cell types were identified based on hub gene expression. Finally, the expression of the hub genes was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results: SLC38A1 and STX11 were identified as hub genes linked to mitochondrial dynamics and mitophagy in OA. These genes enabled the construction of a reliable nomogram for predicting OA risk. Enrichment analysis revealed that the top biological processes converged on the ECM-receptor interaction, underscoring its critical role in OA pathogenesis. Immune infiltration analysis uncovered significant disparities in 10 immune cell types, including activated CD4 T cells and central memory CD4 T cells, between OA patients and healthy controls. The levels of these immune cells were strongly correlated with the expression of SLC38A1 and STX11. Additionally, endothelial cells, monocytes, and T cells emerged as key cellular players in OA. RT-qPCR validation showed that SLC38A1 was significantly downregulated in OA samples, and STX11 exhibited a similar trend, suggesting their potential roles in OA progression.

Conclusion: This study identified SLC38A1 and STX11 as key genes linked to mitochondrial dynamics and mitophagy in OA. These findings provide a theoretical basis and valuable reference for the diagnosis and treatment of OA.

Keywords: immune infiltration analysis; mitochondrial dynamics; mitophagy; osteoarthritis; single-cell analysis.

FIGURE 1

Analysis flowchart.

FIGURE 2

Single-cell sequencing analysis of osteoarthritis cartilage tissues. (A) Acquisition of highly variable genes. (B) Selection of meaningful PCs. (C) Principal component and standard deviation distribution plot. (D) UMAP plots of cartilage-associated cells colored by cluster. (E) Annotation of different cell clusters. (F) Expression levels of marker genes in various cell clusters.

FIGURE 3

Bulk RNA sequencing analysis of osteoarthritis and control cartilage tissues. (A) Volcano plot of differentially expressed genes in bulk RNA-seq. (B) Circle heatmap of differentially expressed genes. (C) Comparison of mitochondrial dynamics_single-sample gene set enrichment analysis (MD_ssGSEA) scores between the OA and control groups. (D) Comparison of mitophagy_ssGSEA (MP_ssGSEA) scores between the OA and control groups. *p < 0.05 and **p < 0.01.

FIGURE 4

WGCNA. (A) Selection of the optimal soft threshold (β) value. (B) Dendrogram of gene clusters. (C) Correlation between modules and MD-RGs and MP-RGs.

FIGURE 5

Screening of candidate genes with machine learning analysis. (A) Intersection of scRNA-seq differentially expressed genes (scRNA-seq DEGs), bulk DEGs, and key module genes. (B) GO enrichment of candidate genes. (C) KEGG pathway enrichment of candidate genes. (D) Lasso regression. (E) XGBoost analysis. (F) Intersection of candidate genes in Lasso and XGBoost analysis. (G) Violin plot of relative expression levels of candidate genes in GSE57218 and GSE117999 datasets. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

FIGURE 6

Construction of a nomogram and functional analysis of hub genes. (A) A nomogram is used to predict the risk of OA. (B) Area under the curve value of the ROC curve. (C) Model evaluation curves show that the model containing the two identified hub genes has a higher net benefit. (D) Gene set enrichment analysis of the SLC38A1 gene. (E) Gene set enrichment analysis of the STX11 gene. (F) GGI network using GeneMANIA.

FIGURE 7

Analysis of immune cell components in single-cell sequencing. (A) Cell fraction difference of 28 immune cell types between the OA and control groups. (B) Correlation analysis of all immune cells. (C) Correlation between hub genes and all immune cells. *p < 0.05, **p < 0.01, and ***p < 0.001.

FIGURE 8

Analysis of the lncRNA–miRNA–mRNA regulatory network and drugs prediction of two hub genes. (A) LncRNA–miRNA–mRNA regulatory network of SLC38A1. (B) Predicted drugs for SLC38A1 and STX11. (C) Top 10 tissues with the highest expression scores for the two hub genes.

FIGURE 9

Communication between different cell types. (A) Interaction number and strength between different cell clusters. (B) Violin plots of the expression levels of two hub genes in various cell clusters.

FIGURE 10

Detection of relative expression levels of SLC38A1 and STX11. (A) Relative expression level of SLC38A1 in OA and control tissues. (B) Relative expression level of STX11 in OA and control tissues. **p < 0.01; ns represents no statistical significance.