Introduction
Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. A remarkable increase in early onset colorectal cancer (EOCRC), defined as diagnosis before the age of 50 years, has been observed globally over the past decades. Recent data indicate that EOCRC presents distinct clinicopathological features, such as advanced stage at diagnosis and unique molecular signatures. Interestingly, EOCRC has been reported to be more likely to occur in the left sided colon, commonly called early onset left sided colorectal cancer (EOLCC). However, the underlying molecular mechanisms of EOLCC carcinogenesis and progression have still not been fully explored.
Concurrently, the prevalence of metabolic syndrome (MetS), including central obesity, insulin resistance, hypertension, and dyslipidemia, has increased in younger populations. As reported, MetS promotes carcinogenesis through chronic inflammation, hyperinsulinemia, and oxidative stress. Importantly, MetS is more common in patients with EOLCC. Although a hypothesis has been proposed regarding the possible link between MetS and EOLCC, the genetic and molecular mechanisms involved remain largely unexplored. Moreover, possible targeted treatment strategies for patients with both conditions remain limited.
Aims & Methods
This study aimed to investigate and elucidate key biomarkers associated with MetS-related EOLCC progression and treatment response.
The in-hospital cohort was employed to evaluate the clinical implications of the original tumor location in early-onset colorectal cancer (EOCRC). By utilizing differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA), we identified potential genes associated with MetS-related EOLCC. The potential mechanisms of MetS-related EOLCC were analyzed using functional pathway enrichment. Random forest (RF) and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to screen candidate biomarkers. The analyses of survival implications, expression patterns, and diagnostic performance were conducted to identify the key biomarkers. The responses to treatment were evaluated. Potential candidate compounds were screened, and molecular docking was performed. The expression and functional signatures were validated in single-cell RNA sequencing (scRNA-seq) datasets and experiments in vitro.
Results
The in-hospital cohort revealed the EOCRC patients had the greater proportion of EOLCC. Through the use of the edgdR package and WGCNA, we identified co-expressed genes common to both EOLCC and MetS, which are notably enriched in pathways related to stromal remodeling and metabolic regulation. By utilizing machine learning, 3 candidate biomarkers were highlighted. Moreover, only CD151 was associated to the prognosis of EOLCC and advanced stage. CD151 were found closely linked to stromal reconstruction and chemoresistance. Meanwhile, the potential compounds targeted to MetS-related EOLCC were identified by molecular docking. Subsequent scRNA-seq analysis confirmed the expression and function patterns of CD151, with especially prominent expression in tumor cells. Ultimately, the bioinformatics findings were validated by quantitative real-time PCR (qRT-PCR) and Immunohistochemical (IHC) staining of clinical samples.
Conclusion
This study investigated CD151 as the key biomarker in MetS-related EOLCC, leading to insights into prognosis, tumor biological status, and personalized treatment strategies. This key biomarker serves as a valuable reference for further exploring the development of clinical applications.
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