背景:结直肠癌(CRC)受各种环境和遗传变量的影响。在许多恶性肿瘤中已经观察到囊泡介导的转运相关基因(VMTRGs)的失调,但它们对CRC预后的影响尚不清楚.
方法:根据VMTRGs的差异表达,将CRC样本分为不同的亚型。R包被用来探索生存率的差异,免疫,不同疾病亚型之间的药物敏感性。根据亚型之间的差异表达基因(DEGs),采用回归分析建立风险评分模型并确定独立的预后因素.通过基因表达综合(GEO)数据集验证模型。免疫景观,免疫表型(IPS),计算不同风险组的肿瘤免疫功能障碍和排除(TIDE)评分。
结果:根据VMTRG确定了两种CRC亚型,这表明存活率存在显著差异,免疫细胞浸润丰度,免疫功能激活水平,和免疫检查点表达水平。Cluster2对抗肿瘤药物如尼洛替尼表现出更高的敏感性,顺铂,和奥沙利铂与Cluster1相比。DEGs主要富集在表皮发育等生物过程中,表皮细胞分化,和受体-配体活性,和胰腺分泌等信号通路。构建的13基因风险评分模型对CRC患者的预后具有良好的预测能力。此外,免疫景观的差异,IPS,并观察不同风险组的TIDE评分。
结论:本研究基于VMTRGs的差异表达,成功获得了两种具有不同生存状态和免疫水平的CRC亚型。构建了13个基因的风险模型。这些发现对CRC的预后和治疗具有重要意义。
BACKGROUND: Colorectal cancer (CRC) is impacted by various environmental and genetic variables. Dysregulation of vesicle-mediated transport-related genes (VMTRGs) has been observed in many malignancies, but their effect on prognosis in CRC remains unclear.
METHODS: CRC samples were clustered into varying subtypes per differential expression of VMTRGs. R package was utilized to explore differences in survival, immune, and drug sensitivity among different disease subtypes. According to differentially expressed genes (DEGs) between subtypes, regression analysis was employed to build a riskscore model and identify independent prognostic factors. The model was validated through a Gene Expression Omnibus (GEO) dataset. Immune landscape, immunophenoscore (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) scores for different risk groups were calculated.
RESULTS: Two subtypes of CRC were identified based on VMTRGs, which showed significant differences in survival rates, immune cell infiltration abundance, immune functional activation levels, and immune checkpoint expression levels. Cluster2 exhibited higher sensitivity to anti-tumor drugs such as Nilotinib, Cisplatin, and Oxaliplatin compared to Cluster1. DEGs were mainly enriched in biological processes such as epidermis development, epidermal cell differentiation, and receptor-ligand activity, and signaling pathways like pancreatic secretion. The constructed 13-gene riskscore model demonstrated good predictive ability for CRC patients\' prognosis. Furthermore, differences in immune landscape, IPS, and TIDE scores were observed among different risk groups.
CONCLUSIONS: This study successfully obtained two CRC subtypes with distinct survival statuses and immune levels based on differential expression of VMTRGs. A 13-gene risk model was constructed. The findings had important implications for prognosis and treatment of CRC.