Abstract
BACKGROUND: Sarcopenia associated with systemic sclerosis (SSc) significantly compromises patient prognosis and quality of life. However, reliable diagnostic biomarkers remain lacking. This study aimed to identify molecular markers for early detection using integrative computational approaches. METHODS: An integrated analysis based on the Gene Expression Omnibus (GEO) database was performed. Crosstalk genes (CGs) were identified using least absolute shrinkage and selection operator (LASSO) regularization, ensemble decision trees, and support vector machine-based feature selection. Machine learning algorithms were employed to construct a predictive scoring model and to assess the diagnostic value of key biomarkers. Hub mRNAs were validated using quantitative polymerase chain reaction (qPCR). Immune cell infiltration profiles and functional correlations were also examined. RESULTS: Five key CGs-NOX4, STC2, NEK6, IGSF10, and EMX2-were identified as molecular links between SSc and sarcopenia. A predictive model incorporating NOX4 and NEK6 was developed, and a diagnostic threshold was established. PCR validation confirmed the differential expression of NOX4 and NEK6 in both SSc and SSc-associated sarcopenia, demonstrating high predictive accuracy. Furthermore, the combined NOX4-NEK6 model exhibited a superior area under the curve (AUC) compared to either gene alone. Immune infiltration analysis revealed significant correlations between CGs and multiple immune cell populations. CONCLUSION: This study proposes NOX4 and NEK6 as novel biomarkers, offering a non-invasive strategy for the early detection of SSc-associated sarcopenia. This study also reveals a shared immune-dysregulation node linking SSc and sarcopenia, positions these crosstalk genes as multi-disease prevention targets, and paves the way for personalized immunotherapy and rapid bench-to-bedside translation.