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北京大学学报（医学版） ›› 2025, Vol. 57 ›› Issue (3): 569-577. doi: 10.19723/j.issn.1671-167X.2025.03.022

• 论著 • 上一篇    下一篇

铁死亡相关长链非编码核糖核酸预测放射治疗后非小细胞肺癌患者的临床结局

许秋实1,*, 刘彤2,*, 王俊杰3,*()   

  1. 1. 北京大学医学部医学技术研究院, 北京 100191
    2. 北京大学第三医院医学创新研究院基础医学研究中心, 北京 100191
    3. 北京大学第三医院放射肿瘤科, 北京 100191
  • 收稿日期:2022-05-28 出版日期:2025-06-18 发布日期:2025-06-13
  • 通讯作者: 王俊杰
  • 作者简介:

    * These authors contributed equally to this work

  • 基金资助:
    北京市自然科学基金(7202228); 国家自然科学基金(82073335); 国家自然科学基金(82073057); 北京大学临床医学+X(PKU2020LCXQ024)

Ferroptosis-related long non-coding RNA to predict the clinical outcome of non-small cell lung cancer after radiotherapy

Qiushi XU1, Tong LIU2, Junjie WANG3,*()   

  1. 1. Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
    2. Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China
    3. Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China
  • Received:2022-05-28 Online:2025-06-18 Published:2025-06-13
  • Contact: Junjie WANG
  • Supported by:
    the Beijing Natural Science Foundation(7202228); National Natural Science Foundation of China(82073335); National Natural Science Foundation of China(82073057); Clinical Medicine plus X Project of Peking University(PKU2020LCXQ024)

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摘要：

目的: 构建基于铁死亡的相关长链非编码核糖核酸(long non-coding RNA, lncRNA)模型，预测放射治疗(放疗)后非小细胞肺癌患者的预后。方法: 从癌症基因组图谱数据库(the cancer genome atlas，TCGA)下载标准化原发瘤和正常组织转录组数据，以及相应的临床信息数据，进行单变量和多变量Cox回归模型分析，构建与铁死亡相关的lncRNA高、低风险组预测模型，使用数据包预测患者的生存率和无进展生存期，验证模型在高、低风险组中的差异。结果: 铁死亡相关的差异性表达基因主要富集于铁死亡、谷胱甘肽代谢、脂质和动脉粥样硬化信号通路及氧化应激、活性氧的代谢过程；用14个与铁死亡相关的lncRNA构建一个预后模型，数据分析表明铁死亡相关的lncRNA可以独立预测放疗后非小细胞肺癌患者的预后；以年龄、性别、分期作为临床病理学变量，可预测出放疗后非小细胞肺癌高风险组预后较差。结论: 风险模型能够独立预测放疗后非小细胞肺癌患者的预后，可为铁死亡相关lncRNA在放疗后非小细胞肺癌中预后预测提供依据，并为非小细胞肺癌患者放疗联合铁死亡治疗提供临床治疗指导。

关键词: 铁死亡, 长链非编码核糖核酸, 放射治疗, 非小细胞肺癌

Abstract:

Objective: To construct a long non-coding RNA (lncRNA) model based on ferroptosis and predict the prognosis of non-small cell lung cancer (NSCLC) patients after radiotherapy, to develop a comprehensive framework that integrates genomic data with clinical outcomes, and to identify lncRNA associated with ferroptosis and evaluate their predictive power for patient survival and progression-free survival following radiotherapy. Methods: This study commenced by acquiring standardized transcriptome data from primary tumors and normal tissues, along with corresponding clinical information, from the cancer genome atlas (TCGA) database. This dataset provided a robust foundation for identifying differentially expressed genes (DEGs) related to ferroptosis. These analyses helped pinpoint specific pathways and biological processes involved in ferroptosis, such as glutathione metabolism, lipid signaling, oxidative stress, and reactive oxygen species (ROS) metabolism. Subsequently, univariate and multivariate Cox regression analyses were conducted to construct a predictive model based on lncRNA associated with ferroptosis. The goal was to differentiate between the high-risk and low-risk groups of NSCLC patients who had undergone radiotherapy. By incorporating these lncRNA into the model, we aimed to provide a more accurate prediction of patient outcomes. The performance of the model was validated by comparing the survival rates and progression-free survival between the high-risk and low-risk groups. Additionally, differences in gene expression patterns and pathway activities between these two groups were examined to further validate the model's effectiveness. Results: Our analysis revealed that the differentially expressed genes related to ferroptosis were significantly enriched in several key pathways, including ferroptosis itself, glutathione metabolism, lipid signaling, and processes involving oxidative stress and ROS metabolism. Based on these findings, we constructed a prognostic model using 14 lncRNA that showed strong associations with ferroptosis. Further data analysis demonstrated that these lncRNA could independently predict the prognosis of NSCLC patients after radiotherapy. Specifically, age, stage, and gender were used as clinical pathological variables, and the results indicated that the high-risk group of NSCLC patients had a poorer prognosis following radiotherapy. This finding underscores the potential of the model to serve as a valuable tool for predicting prognosis for NSCLC patients undergoing radiotherapy. Conclusion: The risk model developed in this study can independently predict the prognosis of NSCLC patients after radiotherapy. This model provides a solid basis for understanding the role of ferroptosis-related lncRNA in the prognosis of NSCLC patients following radiotherapy. Furthermore, it offers clinical guidance for combining radiotherapy with ferroptosis-targeted treatments, potentially improving therapeutic outcomes for NSCLC patients. The integration of genomic and clinical data in this study highlights the importance of personalized medicine approaches in oncology, paving the way for more precise and effective treatment strategies.

Key words: Ferroptosis, Long non-coding RNA, Radiotherapy, Non-small cell lung cancer

中图分类号: 

  • R34

图1

KEGG数据库中铁死亡相关差异基因通路的富集分析"

图2

GO数据库中铁死亡相关差异基因代谢的富集分析"

图3

14个与铁死亡相关的lncRNA表达水平和lncRNA-mRNA共表达网络"

图4

预测模型与非小细胞肺癌患者预后的相关性"

图5

不同临床变量对总生存期的预测"

表1

训练数据集和验证数据集患者临床特征"

Items Total number of datasets (n=175) Training dataset (n=88) Validation dataset (n=87)
Age/years, n (%)
    ≤65 90 (51.43) 45 (51.14) 45 (51.72)
    ＞65 85 (48.57) 43 (48.86) 42 (48.28)
Gender, n (%)
    Male 84 (48.00) 43 (48.86) 41 (47.13)
    Female 91 (52.00) 45 (51.14) 46 (52.87)
Stage, n (%)
    Ⅰ+Ⅱ 103 (58.86) 49 (55.68) 54 (62.07)
    Ⅲ+Ⅳ 69 (39.43) 37 (42.05) 22 (25.29)
    Unknown 3 (1.71) 2 (2.27) 1 (1.15)
T, n (%)
    T1+T2 133 (76.00) 69 (78.41) 64 (73.56)
    T3+T4 40 (22.86) 18 (20.45) 22 (25.29)
    TX+unknown 2 (1.14) 1 (1.14) 1 (1.15)
M, n (%)
    M0 122 (69.71) 61 (69.32) 61 (70.11)
    M1 10 (5.71) 3 (3.41) 7 (8.05)
    MX+unknown 42 (24.00) 24 (27.27) 19 (21.84)
N, n (%)
    N0 82 (46.86) 40 (45.45) 42 (48.28)
    N1+N2 86 (49.14) 44 (5.00) 42 (48.28)
    N3 3 (1.71) 2 (2.27) 1 (1.15)
    NX+unknown 4 (2.29) 2 (2.27) 2 (2.30)

图6

训练数据集与验证数据集的验证结果"

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