Key Laboratory of Birth Regulation and Control Technology of National Health Commission of China, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, Shandong, China.
Institute of Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao, China.
Front Immunol. 2023 Mar 20;14:1142609. doi: 10.3389/fimmu.2023.1142609. eCollection 2023.
Colon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.
In this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.
Three clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.
In conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.
结肠癌是一种高度异质性的疾病，识别分子亚型可以深入了解肿瘤亚群中失调的途径，这可能导致个性化的治疗选择。然而，大多数预后模型都是基于单途径基因。
在这项研究中，我们旨在基于多个信号通路相关基因识别三种临床上相关的结肠癌亚型。综合多组学分析用于解释导致结肠癌侵袭性、复发和进展的生物学过程。使用 L1000 平台，采用机器学习方法识别亚型并为不同亚型提供药物指导。我们基于基因对开发了一个稳健的预后模型（MKPC 评分），并在一个内部测试集和三个外部测试集中进行了验证。通过 qPCR 提取和验证风险相关基因。
基于多个信号通路相关基因，确定了三种临床上相关的结肠癌亚型，它们具有显著不同的生存状态（对数秩检验，p<0.05）。综合多组学分析揭示了导致结肠癌侵袭性、复发和进展的生物学过程。基于基因对开发的 MKPC 评分在预测预后状态方面具有稳健性（对数秩检验，p<0.05），并且风险相关基因通过 qPCR 得到了成功验证（t 检验，p<0.05）。创建了一个用于结肠癌患者风险评分和治疗分层的易于使用的网络工具，实用的列线图可以扩展到其他癌症类型。
总之，我们的研究确定了三种临床上相关的结肠癌亚型，并基于基因对开发了一个稳健的预后模型。开发的网络工具是研究人员和临床医生在结肠癌患者风险评分和治疗分层方面的有价值的资源，实用的列线图可以扩展到其他癌症类型。
