Gu Changhao, Jin Lulu, Lv Xiaoyan, Wang Cheng, Wen Congle, Su Xiuxiu
Cangnan Hospital of Traditional Chinese Medicine, Wenzhou, 325800, China.
Cangnan Branch of Zhejiang Provincial Hospital of Chinese Medicine, Wenzhou, 325800, China.
Discov Oncol. 2024 Oct 9;15(1):535. doi: 10.1007/s12672-024-01421-2.
Mitotic processes play a pivotal role in tumor progression and immune responses. However, the correlation between mitosis-related genes, clinical outcomes, and the tumor microenvironment (TME) in colon cancer remains unclear. This study aims to develop a prognostic and therapeutic significance model for colon cancer based on mitosis-related genes.
RNA expression profiles and clinical data of 453 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Mitosis-related genes were selected from the MsigDb database. The gene model was constructed using differential analysis, univariate and multivariate Cox regression, and Lasso regression analyses. The predictive model was validated using data from the GSE17536, GSE17537, and GSE39582 datasets. Predictive accuracy was evaluated via Receiver Operating Characteristic (ROC) curves, while nomograms were developed by integrating clinical and pathological features. Gene set enrichment analysis explored biological processes and pathways linked to the model. TME was assessed using ESTIMATE, and the proportion and function of immune cells were analyzed through CIBERSORT. Drug sensitivity analysis was conducted using the CTRP database.
A predictive model based on 17 mitosis-related genes (KIFC1, CCNF, EME1, CDC25C, ORC1, CCNJL, ANKRD53, MEIS2, FZD3, TPD52L1, MAPK3, CDKN2A, EDN3, NPM2, PSRC1, INHBA, BIRC5) was created. The model exhibited robust predictive performance across both training and validation cohorts. Nomograms for predicting 3-, 5-, and 7-year survival rates in colon cancer (COAD) patients were generated. The model's correlation with immune cell infiltration and function was highlighted.
The mitosis-related gene model serves as a valuable indicator for predicting survival outcomes in colon cancer patients.
有丝分裂过程在肿瘤进展和免疫反应中起关键作用。然而，结肠癌中与有丝分裂相关基因、临床结局和肿瘤微环境（TME）之间的相关性仍不清楚。本研究旨在基于有丝分裂相关基因开发一种用于结肠癌的预后和治疗意义模型。
从癌症基因组图谱（TCGA）下载了453例结肠癌患者的RNA表达谱和临床数据。从MsigDb数据库中选择与有丝分裂相关的基因。使用差异分析、单变量和多变量Cox回归以及Lasso回归分析构建基因模型。使用来自GSE17536、GSE17537和GSE39582数据集的数据验证预测模型。通过受试者工作特征（ROC）曲线评估预测准确性，同时通过整合临床和病理特征绘制列线图。基因集富集分析探索与该模型相关的生物学过程和途径。使用ESTIMATE评估TME，并通过CIBERSORT分析免疫细胞的比例和功能。使用CTRP数据库进行药物敏感性分析。
创建了一个基于17个与有丝分裂相关基因（KIFC1、CCNF、EME1、CDC25C、ORC1、CCNJL、ANKRD53、MEIS2、FZD3、TPD52L1、MAPK3、CDKN2A、EDN3、NPM2、PSRC1、INHBA、BIRC5）的预测模型。该模型在训练和验证队列中均表现出强大的预测性能。生成了用于预测结肠癌（COAD）患者3年、5年和7年生存率的列线图。突出了该模型与免疫细胞浸润和功能的相关性。
与有丝分裂相关的基因模型是预测结肠癌患者生存结局的有价值指标。
