Abstract
Cerebral palsy (CP) is a prevalent motor disorder originating from early brain injury or malformation, with significant variability in its clinical presentation and etiology. Early diagnosis and personalized therapeutic interventions are hindered by the lack of reliable biomarkers. This study aims to identify potential biomarkers for cerebral palsy and develop predictive models to enhance early diagnosis and prognosis. We conducted a comprehensive bioinformatics analysis of gene expression profiles in muscle samples from CP patients to identify candidate biomarkers. Six key genes (CKMT2, TNNT2, MYH4, MYH1, GOT1, and LPL) were validated in an independent cohort, and potential biological pathways and molecular networks involved in CP pathogenesis were analyzed. The importance of processes such as functional regulation, energy metabolism, and cell signaling pathways in the muscles of CP patients was emphasized. Predictive models of muscle sample biomarkers related to CP were developed and visualized. Calibration curves and receiver operating characteristic analysis demonstrated that the predictive models exhibit high sensitivity and specificity in distinguishing individuals at risk of CP. The identified biomarkers and developed prediction models offer significant potential for early diagnosis and personalized management of CP. Future research should focus on validating these biomarkers in larger cohorts and integrating them into clinical practice to improve outcomes for individuals with CP.