Integrated muti-omics data and machine learning reveal CD151 as a key biomarker inducing chemoresistance in metabolic syndrome-related early-onset left-sided colorectal cancer

Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (MetS). However, the underlying genetic and molecular mechanisms remain insufficiently elucidated. This study aimed to identify and characterize key biomarkers associated with the progression and treatment response of MetS-related EOLCC. An in-hospital cohort was utilized to assess the clinical implications of primary tumor location in early-onset colorectal cancer (EOCRC). Differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA) were employed to identify genes potentially associated with MetS-related EOLCC. Functional enrichment analyses were conducted to explore the underlying mechanisms. Candidate biomarkers were screened using random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Survival relevance, expression profiles, and diagnostic performance were analyzed to identify key biomarkers. Treatment responses were evaluated, and potential therapeutic compounds were identified through molecular docking. Single-cell RNA sequencing (scRNA-seq) data and in vitro experiments were used to validate gene expression and functional characteristics. The in-hospital cohort revealed a higher proportion of EOLCC among EOCRC patients. Using the edgeR package and WGCNA, we identified coexpressed genes common to both EOLCC and MetS, significantly enriched in pathways associated with stromal remodeling and metabolic regulation. Machine learning algorithms highlighted three candidate biomarkers. Among them, only CD151 was associated with prognosis and advanced disease stage. CD151 was strongly correlated with stromal remodeling and chemoresistance. Additionally, potential therapeutic compounds targeting MetS-related EOLCC were identified via molecular docking. scRNA-seq analysis confirmed the expression and functional patterns of CD151, particularly in tumor cells. The bioinformatics results were further validated through quantitative real-time PCR (qRT-PCR), western blotting, and immunohistochemical (IHC) staining. This study identified CD151 as a key biomarker in MetS-related EOLCC, offering valuable insights into prognosis, tumor biology, and personalized treatment strategies. CD151 may serve as a reference for future research and clinical applications targeting this disease subtype.