[1]张伟国,陆秀宏,黄钢,等.整合分析构建基于增强子的非小细胞肺癌预后风险模型[J].中国医学物理学杂志,2025,42(1):112-121.
　ZHANG Weiguo,LU Xiuhong,et al.Integrative analysis reveals enhancer-based prognostic risk prediction model for non-small cell lung cancer[J].Chinese Journal of Medical Physics,2025,42(1):112-121.
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整合分析构建基于增强子的非小细胞肺癌预后风险模型()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第1期
页码:
112-121
栏目:
医学生物信息
出版日期:
2025-01-19

文章信息/Info

Title:
Integrative analysis reveals enhancer-based prognostic risk prediction model for non-small cell lung cancer
作者:
张伟国1; 2; 陆秀宏2; 黄钢2; 靳明明2; 程云章1
1.上海理工大学健康科学与工程学院， 上海 200093； 2.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
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
关键词:
非小细胞肺癌; 增强子; 甲基化; 加权基因共表达网络分析; 预后
Keywords:
Keywords: non-small cell lung cancer enhancer methylation weighted gene co-expression network analysis prognosis
文献标志码:
A
摘要:
目的：通过整合DNA甲基组数据和转录组数据构建基于增强子的非小细胞肺癌预后风险模型。方法：使用加权基因共表达网络分析（WGCNA）从甲基化差异位点的增强子中识别非小细胞肺癌相关基因。并基于转录组数据通过LASSO-Cox回归算法构建并验证预后风险模型。结果：基于非小细胞肺癌的DNA甲基组数据分析获得了19 784个差异甲基化位点，并对其分布模式进行了表征，其中包括6 089个差异甲基化增强子位点。WGCNA从这6 089个位点筛选出79个和非小细胞肺癌高度相关的增强子位点。基于转录组数据通过LASSO-Cox回归对79个增强子位点靶基因分析，构建10个基因的预后风险模型。在训练集和验证集中分析3年、5年和10年时间依赖的受试者工作特征曲线下面积（AUC）来评估预后风险模型。结果显示，训练集和验证集中的3年、5年和10年AUC均大于0.7。最后构建预测非小细胞肺癌患者3、5、10年生存情况的列线图。结论：本研究为理解增强子在非小细胞肺癌中的作用提供了新的见解，并具有通过指导个性化治疗决策来改善患者预后的潜力。
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.

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备注/Memo

备注/Memo:
【收稿日期】2024-09-20 【基金项目】国家自然科学基金（82127807）；上海市分子影像学重点实验室建设项目（18DZ2260400） 【作者简介】张伟国，博士研究生，研究方向：生物医学，E-mail: zhangwg@sumhs.edu.cn 【通信作者】程云章，教授，研究方向：生物医学，E-mail: cyz2008@usst.edu.cn
更新日期/Last Update: 2025-01-19