Huang Chao, Zhao Jiefeng, Zhu Zhengming
Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Front Surg. 2021 Jun 16;8:681721. doi: 10.3389/fsurg.2021.681721. eCollection 2021.
The Cancer Genome Atlas (TCGA) has established a genome-wide gene expression profile, increasing our understanding of the impact of tumor heredity on clinical outcomes. The aim of this study was to construct a nomogram using data from the TCGA regarding prognosis-related genes and clinicopathological characteristics to predict the 5-years survival rate of colon cancer (CC) patients. Kaplan-Meier and Cox regression analyses were used to identify genes associated with the 5-years survival rate of CC patients. Cox regression was used to analyze the relationship between the clinicopathological features and prognostic genes and overall survival rates in patients with CC and to identify independent risk factors for the prognosis of CC patients. A nomogram for predicting the 5-years survival rate of CC patients was constructed by R software. A total of eight genes (KCNJ14, CILP2, ATP6V1G2, GABRD, RIMKLB, SIX2, PLEKHA8P1, and MPP2) related to the 5-years survival of rate CC patients were identified. Age, stage, and PLEKHA8P1 were independent risk factors for the 5-years survival rate in patients with CC. The accuracy, sensitivity and specificity of the nomogram model constructed by age, TNM staging, and PLEKHA8P1 for predicting the 5-years survival of rate CC patients were 83.3, 83.97, and 85.79%, respectively. The nomogram can correctly predict the 5-year survival rate of patients with CC, thus aiding the individualized decision-making process for patients with CC.
癌症基因组图谱（TCGA）已建立了全基因组基因表达谱，加深了我们对肿瘤遗传因素对临床结局影响的理解。本研究的目的是利用TCGA中与预后相关基因和临床病理特征的数据构建列线图，以预测结肠癌（CC）患者的5年生存率。采用Kaplan-Meier法和Cox回归分析来识别与CC患者5年生存率相关的基因。使用Cox回归分析CC患者的临床病理特征与预后基因及总生存率之间的关系，并确定CC患者预后的独立危险因素。通过R软件构建了预测CC患者5年生存率的列线图。共识别出8个与CC患者5年生存率相关的基因（KCNJ14、CILP2、ATP6V1G2、GABRD、RIMKLB、SIX2、PLEKHA8P1和MPP2）。年龄、分期和PLEKHA8P1是CC患者5年生存率的独立危险因素。由年龄、TNM分期和PLEKHA8P1构建的列线图模型预测CC患者5年生存率的准确性、敏感性和特异性分别为83.3%、83.97%和85.79%。该列线图能够正确预测CC患者的5年生存率，从而有助于CC患者的个体化决策过程。
