Machine Learning-Based Identification of Molecular Signatures in PTOA Cell Subtypes via Single-Cell Transcriptomics

Objectives Osteoarthritis (OA) is the most prevalent joint disorder, whereas post-traumatic osteoarthritis (PTOA) denotes a form of arthritis that arises secondary to acute joint injury. Methods As no curative therapy for PTOA currently exists, prevention and early intervention constitute critical avenues of investigation. Among the various injuries predisposing to PTOA, anterior cruciate ligament (ACL) rupture is among the most prototypical. Our study systematically analyzed dynamic RNA sequencing data derived from murine knee joint tissues after ACL rupture. Granulocyte subpopulations were systematically classified and annotated, revealing subgroups related to PTOA progression. Furthermore, co-expressed gene modules positively correlated with OA granulocytes were identified and selected via high-dimensional weighted gene co-expression network analysis (hdWGCNA). Utilizing machine learning (ML)-based methodologies, a predictive model was constructed and validated through nomogram models, calibration curves, and decision curves. The prognostic utility of characteristic genes in these OA granulocyte subtypes was also investigated. Additionally, immune infiltration analysis (IIA) was carried out to visualize immune cell infiltration and explore the relationships between key genes and immune cells, and qRT-PCR was performed for the results. Results In summary, this study identified the distinctive molecular and biological characteristics of granulocyte subtypes and applied ML algorithms to predict diagnostic biomarkers specific to PTOA. Conclusions These findings hold promise for improving targeted predictive capabilities for the disease, with the ultimate goal of interrupting the vicious cycle of inflammation and mechanical abnormalities prior to the onset of irreversible joint damage.