医学
列线图
肿瘤科
比例危险模型
结直肠癌
内科学
基因签名
阶段(地层学)
队列
癌症
基因
基因表达
生物
生物化学
古生物学
化学
作者
Haoyu Ren,Florian Bösch,Elise Pretzsch,Sven Jacob,C. Benedikt Westphalen,Julian Walter Holch,Jens Werner,Martin K. Angele
出处
期刊:Annals of Surgery
[Ovid Technologies (Wolters Kluwer)]
日期:2022-07-26
卷期号:276 (5): 897-904
被引量:10
标识
DOI:10.1097/sla.0000000000005644
摘要
To identify a prognostic significant gene signature for predicting colorectal cancer (CRC) recurrence.Traditional prognostic risk assessment in stage II/III CRC patients remains controversial. Epithelial-mesenchymal transition is thought to be closely related to the malignant progression of tumors. Thus, it is promising to establish a prognostic model based on epithelial-mesenchymal transition-related gene (ERG) signature.We retrospectively analyzed transcriptome profiles and clinical information of 1780 stage II/III CRC patients from 15 public datasets. Coefficient variant analysis was used to select reference genes for normalizing gene expression levels. Univariate, LASSO, and multivariate Cox regression analyses were combined to develop the ERG signature predicting disease-free survival (DFS). The patients were divided into high-risk and low-risk based on the ERG signature recurrence risk score. The survival analysis was performed in different CRC cohorts.The proposed ERG signature contained 7 cancer-related ERGs and 3 reference genes. The ERG signature recurrence risk score was prognostically relevant in all cohorts ( P <0.05) and proved as an independent prognostic factor in the training cohort. In the pooled cohort, high-risk CRC patients exhibited worse DFS ( P <0.0001) and overall survival ( P =0.0058) than low-risk patients. The predictive performance of the ERG signature was superior to Oncotype DX colon cancer. An integrated decision tree and nomogram were developed to improve prognosis evaluation.The identified ERG signature is a promising and powerful biomarker predicting recurrence in CRC patients. Moreover, the presented ERG signature might help to stratify patients according to their tumor biology and contribute to personalized treatment.
科研通智能强力驱动
Strongly Powered by AbleSci AI