IntroductionOsteoarthritis (OA) is a persistent joint ailment distinguished primarily by the deterioration of articular cartilage, the formation of osteophytes, and the occurrence of synovitis1. This disease is particularly prevalent among older adults, and it has a profound effect on their overall quality of life2. The impact of OA on daily functioning and well-being can lead to both physical and emotional distress, highlighting the significance of effective management and treatment strategies in this population.The synovial membrane, which is situated within the inner layer of the joint capsule, is made up of loose connective tissue. It exhibits a pale red hue and possesses a smooth surface that is both thin and flexible. A vital role of the synovial membrane is to produce and regulate synovial fluid, which serves to minimize friction between the bones during movement while simultaneously providing essential nourishment to the joint3. This lubrication is critical for maintaining joint function and mobility.Synovitis, one of the key pathological features of OA, has a complex role in the disease’s progression. It contributes to various processes that worsen the condition, such as inciting inflammatory responses, heightening cartilage degradation, promoting the formation of new blood vessels (angiogenesis), and inducing pain4. Given its significant involvement in the pathogenesis of OA, synovitis is considered a potential therapeutic target in OA, although its precise role in disease progression is still under investigation.Ferroptosis represents a specialized form of iron-dependent programmed cell death characterized by lipid peroxidation. It is distinguished by a remarkable increase in the levels of intracellular iron and an escalation in lipid peroxidation5. These changes are critical to the ferroptotic process, setting it apart from other cell death mechanisms. The unique characteristics of ferroptosis manifest in its morphological features, underlying mechanisms of action, and the specific molecular pathways involved, which differ significantly from those observed in apoptosis, necrosis, and other cell death modalities6.The significance of ferroptosis extends beyond mere cell death; it plays a pivotal role in the pathophysiology of a wide range of diseases. Conditions such as cancer, neurological disorders, ischemia–reperfusion injury, renal injury, and hematological diseases have been linked to the dysregulation of this form of cell death. As researchers further explore the implications of ferroptosis within these contexts, it becomes increasingly evident that understanding its mechanisms may offer new insights into disease prevention, progression, and potential treatment strategies7.In the context of joint diseases, ferroptosis has attracted growing attention. Recent bioinformatics studies in rheumatoid arthritis (RA) have identified ferroptosis-related genes such as GABARAPL1, DUSP1, JUN, and MAPK8 as potential diagnostic biomarkers and therapeutic targets through regulation of ferroptosis pathways8. Similarly, cuproptosis—another form of programmed cell death linked to mitochondrial metabolism—has been shown to be closely associated with immune infiltration in RA, with integrated bioinformatics and single-cell RNA sequencing analyses identifying cuproptosis-related diagnostic models9. These studies highlight the importance of programmed cell death pathways in joint inflammatory diseases. However, similar systematic investigations focusing specifically on ferroptosis-related genes in OA synovitis remain scarce. Several studies have reported that iron accumulation, oxidative stress, and lipid peroxidation are elevated in OA joint tissues, creating a microenvironment conducive to ferroptosis. Yao et al. (2023) demonstrated that ferroptosis of chondrocytes contributes to cartilage degradation in OA. Nevertheless, the specific ferroptosis-related genes (FRGs) involved in OA synovitis and their relationship with the synovial immune microenvironment have not been systematically investigated10.Furthermore, ferroptosis is also significant in the pathogenesis of osteoarthritis synovitis, particularly in the regulatory death of chondrocytes and synovial cells. Studies have shown that knee synovitis (KS) is an important pathological change in the progression of knee osteoarthritis (KOA)11. The inflammatory microenvironment induced by KS is prone to trigger ferroptosis, which in turn can participate in regulating the synovial inflammatory response through the activation of non-specific immunity, lipoxygenases, and epoxy products, significantly influencing the occurrence, development, treatment, and prognosis of KOA.The rationale for investigating ferroptosis-related genes in OA synovitis is grounded in several converging lines of evidence: (1) Iron deposition and oxidative stress are well-documented features of OA synovial tissue, creating a microenvironment conducive to ferroptosis; (2) Ferroptotic cell death releases damage-associated molecular patterns (DAMPs), including HMGB1 and lipid peroxidation products, which activate innate immune responses and sustain chronic inflammation in the synovium; The synovial immune microenvironment in OA is characterized by infiltration of macrophages, T cells, and other immune populations whose activation states are influenced by the metabolic milieu, including iron and lipid metabolism; Aminoacyl-tRNA synthetases, including CARS, have emerged as metabolic sensors linking translational control to immune modulation and cell death sensitivity. Therefore, identifying ferroptosis-related key genes such as CARS in OA synovitis may reveal novel molecular connections between metabolic dysregulation, cell death, and immune activation.In recent years, the rise of bioinformatics analysis has led to a significant increase in the use of machine learning techniques within medical research, especially for pinpointing factors that affect the advancement of osteoarthritis (OA)12,13. For example, research has indicated that machine learning models are effective in forecasting the progression rate, disease severity, and treatment responses in knee osteoarthritis14. Furthermore, in the realm of imaging analysis, these models are capable of thoroughly investigating pathological changes present in MRI and X-ray images15. Machine learning is also utilized to predict outcomes reported by patients, evaluate treatment efficacy, and support early screening along with the anticipation of treatment responses, which aids in initiating timely interventions and tailored therapies16,17,18. Such applications not only improve the accuracy of diagnoses and the effectiveness of treatments but also contribute to better patient prognoses. Nonetheless, studies that apply machine learning methodologies to uncover ferroptosis-related genes (FRGs) that are crucial for the onset and development of OA synovitis are still scarce. This study aims to explore potential ferroptosis-related gene signatures associated with OA synovitis using machine learning methods, pinpoint gene modules strongly linked to OA, and explore the function of CARS in OA synovitis along with its connection to immune cell infiltration19.This study conducts an in-depth examination of the expression patterns of FRGs in OA synovitis, employing machine learning techniques. It identifies gene modules that show significant correlations with OA and explores the role of CARS in OA synovitis in relation to immune cell infiltration. By utilizing a range of advanced bioinformatics and machine learning methods, this research not only underscores the importance of FRGs in the pathogenesis of OA but also reveals particular expression trends of CARS linked to immune responses in OA. The findings are expected to provide innovative insights for the diagnosis and treatment of OA. Furthermore, we demonstrate the effectiveness of machine learning methods in identifying and evaluating genes associated with the disease.MethodData collection and preprocessingThe gene expression profile data pertaining to OA synovial tissue utilized in this research, along with that from normal tissue, were sourced from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/)20. By employing “osteoarthritis” as the search keyword in the GEO database, sequencing data associated with OA synovial tissue (GSE55235, GSE55457, GSE46750) and their corresponding platform information (GPL96, GPL10558) were collected. Among these datasets, GSE55235 includes sequencing information from 10 OA cases and 10 normal synovial tissue cases. Similarly, GSE55457 presents sequencing data from another 10 OA cases and 10 normal synovial tissue samples. GSE46750 contains sequencing data from 12 OA cases matched with 12 normal synovial tissue cases. These three datasets were combined to create a robust dataset for further analysis. The identification of ferroptosis-related genes stemmed from a detailed study carried out by Ze-Xian Liu et al., which examined the irregularities and roles of ferroptosis in cancer, thereby identifying 24 ferroptosis-related genes (FRGs) for further investigation21.Gene differential analysisThe analysis of differentially expressed messenger RNA (mRNA) was conducted utilizing the limma package within R software. To determine the criteria for mRNA differential expression, the study established a threshold defined by an adjusted P value of less than 0.05 coupled with a log (fold change) greater than 1.3 or less than -1.3. The |logFC|> 1.3 threshold was adopted to filter biologically meaningful DEGs and reduce noise for downstream machine learning, with an FDR-adjusted p-value 1, adjusted p