Tan Shanyue, Gui Weiwei, Wang Sumeng, Sun Chongqi, Xu Xian, Liu Lingxiang
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
J Gastrointest Oncol. 2021 Aug;12(4):1590-1600. doi: 10.21037/jgo-21-376.
To construct a model that could effectively predict the prognosis of colorectal cancer (CRC) by searching for methylated-differentially expressed genes (MDEGs).
We identified MDEGs through four databases from Gene Expression Omnibus (GEO) and annotated their functions via bioinformatics analysis. Subsequently, after adjusting for gender, age, and grading, multivariate Cox hazard analysis was utilized to select MDEGs interrelated with the prognosis of CRC, and LASSO analysis was utilized to fit the prediction model in the training set. Furthermore, another independent dataset was harnessed to verify the effectiveness of the model in predicting prognosis.
In total, 252 hypomethylated and up-regulated genes and 132 hypermethylated and down-regulated genes were identified, 27 of which were correlated with the prognosis of CRC, and a 10-gene prognostic model was established after LASSO analysis. The overall survival rate could be effectively grouped into different risks by the median score of this model in the training set [risk ratio (HR) =2.27, confidence interval (95% CI), 1.69-3.13, P=8.15×10], and the validity of its effect in predicting prognosis in CRC was verified in the validation dataset (HR =1.75, 95% CI, 1.15-2.70, P=9.32×10).
Our model could effectively predict the overall survival rate of patients with CRC and provides potential application guidelines for its clinically personalized treatment.
通过搜索甲基化差异表达基因（MDEGs）构建一个能够有效预测结直肠癌（CRC）预后的模型。
我们通过来自基因表达综合数据库（GEO）的四个数据库鉴定MDEGs，并通过生物信息学分析注释其功能。随后，在调整性别、年龄和分级后，采用多变量Cox风险分析选择与CRC预后相关的MDEGs，并利用LASSO分析在训练集中拟合预测模型。此外，利用另一个独立数据集验证该模型预测预后的有效性。
共鉴定出252个低甲基化上调基因和132个高甲基化下调基因，其中27个与CRC预后相关，经LASSO分析后建立了一个10基因预后模型。在训练集中，通过该模型的中位数评分可将总生存率有效分为不同风险组[风险比（HR）=2.27，置信区间（95%CI），1.69 - 3.13，P = 8.15×10]，并在验证数据集中验证了其在预测CRC预后方面的有效性（HR = 1.75，95%CI，1.15 - 2.70，P = 9.32×10）。
我们的模型能够有效预测CRC患者的总生存率，并为其临床个性化治疗提供潜在的应用指导。
