Department of Gastrointestinal Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Key Laboratory of Arrhythmias of the Ministry of Education of China, Tongji University School of Medicine, Shanghai, China.
Environ Toxicol. 2024 May;39(5):2908-2926. doi: 10.1002/tox.24157. Epub 2024 Feb 1.
Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.
We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models.
Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.
The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.
结直肠癌（CRC）是一种具有显著全球健康负担的疾病，其特点是分子图谱异质性以及各种遗传和表观遗传改变。程序性细胞死亡（PCD）在 CRC 中起着关键作用，通过调节细胞消除过程来提供治疗靶标，这些过程可以抑制肿瘤生长或引发癌细胞耐药性。理解 PCD 机制与 CRC 发病机制之间的复杂相互作用至关重要。本研究旨在使用机器学习集成构建 CRC 相关的 PCD 预后签名，从而提高 CRC 预后预测的准确性。
我们从癌症基因组图谱（TCGA）和基因表达综合（GEO）数据库中检索表达数据和临床信息。确定了 15 种形式的 PCD，并编译了相应的基因集。使用 Lasso、Ridge、Enet、StepCox、survivalSVM、CoxBoost、SuperPC、plsRcox、随机生存森林（RSF）和梯度提升机等机器学习算法进行模型构建。使用六个 GEO 数据集对模型进行验证，并建立程序性细胞死亡评分（PCDS）。此外，还将该模型的有效性与 109 个基于转录组的 CRC 预后模型进行了比较。
我们的集成模型成功地识别了差异表达的 PCD 相关基因，并将 CRC 样本分为具有不同预后意义的四个亚型。与传统方法相比，最优的机器学习模型组合（RSF+Ridge）表现更优。PCDS 能够有效地将患者分为高危和低危组，生存差异显著。进一步分析揭示了与 CRC 亚型相关的免疫细胞类型和途径的预后相关性。该模型还确定了与 CRC 预后相关的关键基因和药物敏感性。
本研究强调了集成机器学习模型以增强 CRC 预后预测的潜力。开发的与 PCD 相关的预后签名有望为 CRC 的个性化和有效治疗干预提供依据。
