Hou Yingdong, Xia Hubin, Xu Chenshan, Yu Yuhua, Ji Chenghao, Ruan Wenli, Kong Wencheng, Zhou Yifeng, Zhang Xiaofeng
Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, Zhejiang Province, 310000, China.
Key Laboratory of Integrated Traditional Chinese and Western Medicine for Biliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, Zhejiang Province, 310000, China.
Funct Integr Genomics. 2025 Jun 9;25(1):122. doi: 10.1007/s10142-025-01634-w.
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.
新出现的证据表明，早发性左侧结直肠癌（EOLCC）与代谢综合征（MetS）之间可能存在病理关联。然而，其潜在的遗传和分子机制仍未得到充分阐明。本研究旨在识别和表征与MetS相关的EOLCC的进展和治疗反应相关的关键生物标志物。利用一个住院队列来评估原发性肿瘤位置在早发性结直肠癌（EOCRC）中的临床意义。采用差异表达基因（DEG）和加权基因共表达网络分析（WGCNA）来识别可能与MetS相关的EOLCC相关的基因。进行功能富集分析以探索潜在机制。使用随机森林（RF）和支持向量机递归特征消除（SVM-RFE）算法筛选候选生物标志物。分析生存相关性、表达谱和诊断性能以识别关键生物标志物。评估治疗反应，并通过分子对接鉴定潜在的治疗化合物。使用单细胞RNA测序（scRNA-seq）数据和体外实验来验证基因表达和功能特征。住院队列显示EOCRC患者中EOLCC的比例更高。使用edgeR软件包和WGCNA，我们识别出EOLCC和MetS共有的共表达基因，这些基因在与基质重塑和代谢调节相关的途径中显著富集。机器学习算法突出了三个候选生物标志物。其中，只有CD151与预后和疾病晚期相关。CD151与基质重塑和化疗耐药性密切相关。此外，通过分子对接鉴定了针对MetS相关的EOLCC的潜在治疗化合物。scRNA-seq分析证实了CD151的表达和功能模式，特别是在肿瘤细胞中。通过定量实时PCR（qRT-PCR）、蛋白质印迹和免疫组织化学（IHC）染色进一步验证了生物信息学结果。本研究将CD151确定为MetS相关的EOLCC中的关键生物标志物，为预后、肿瘤生物学和个性化治疗策略提供了有价值的见解。CD151可为未来针对该疾病亚型的研究和临床应用提供参考。
