Integrative analysis reveals enhancer-based prognostic risk prediction model for non-small cell lung cancer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2025年第1期
- Page:
- 112-121
- Research Field:
- 医学生物信息
- Publishing date:
Info
- Title:
- Integrative analysis reveals enhancer-based prognostic risk prediction model for non-small cell lung cancer
- Author(s):
- ZHANG Weiguo1; 2; LU Xiuhong2; HUANG Gang2; JIN Mingming2; CHENG Yunzhang1
- 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Key Laboratory of Molecular Imaging, Jiading District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- Keywords:
- Keywords: non-small cell lung cancer enhancer methylation weighted gene co-expression network analysis prognosis
- PACS:
- -
- DOI:
- -
- 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, 19 784 DMPs were obtained and their distribution patterns were characterized, including 6 089 DMPs of enhancers. WGCNA analysis screened 79 highly correlated DMPs of enhancer with NSCLC from the 6 089 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.
Last Update: 2025-01-19