Integrative analysis reveals enhancer-based prognostic risk prediction model for non-small cell lung cancer
10.3969/j.issn.1005-202X.2025.01.015
- VernacularTitle:整合分析构建基于增强子的非小细胞肺癌预后风险模型
- Author:
Weiguo ZHANG
1
;
Xiuhong LU
;
Gang HUANG
;
Mingming JIN
;
Yunzhang CHENG
Author Information
1. 上海理工大学健康科学与工程学院,上海200093;上海健康医学院附属嘉定中心医院上海市分子影像学重点实验室,上海201318
- Publication Type:Journal Article
- Keywords:
non-small cell lung cancer;
enhancer;
methylation;
weighted gene co-expression network analysis;
prognosis
- From:
Chinese Journal of Medical Physics
2025;42(1):112-121
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct an enhancer-based prognostic risk prediction model for non-small cell lung cancer (NSCLC) by integrating DNA methylome data and transcriptome data. Methods The weighted gene co-expression network analysis (WGCNA) was used to identify NSCLC related genes from the differentially methylated positions (DMPs) of enhancers. Based on the transcriptome data,the prognostic risk prediction model was constructed using LASSO-Cox regression algorithm. Results Through the analysis on DNA methylome data of NSCLC,19784 DMPs were obtained and their distribution patterns were characterized,including 6089 DMPs of enhancers. WGCNA analysis screened 79 highly correlated DMPs of enhancer with NSCLC from the 6089 DMPs. After analyzing the target genes of 79 DMPs with LASSO-Cox regression based on the transcriptome data,10 genes were used to construct a prognostic risk prediction model. The prognostic risk prediction model was evaluated by calculating the areas under the curve (AUC) of 3-,5-,and 10-year time-dependent receiver operating characteristic (ROC) curves in training set and validation set;and the results showed that the 3-,5-,and 10-year AUC in training set and validation set were all higher than 0.7. Finally,a nomogram was constructed to predict the 3-,5-,and 10-year survival probabilities of NSCLC. Conclusion This study provides new insights into the role of enhancers in NSCLC and has the potential to improve the prognosis by guiding personalized treatment decisions.
