Ferroptosis-related long non-coding RNA to predict the clinical outcome of non-small cell lung cancer after radiotherapy
10.19723/j.issn.1671-167X.2025.03.022
- VernacularTitle: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
- Author:
Qiushi XU
1
;
Tong LIU
;
Junjie WANG
Author Information
1. 北京大学医学部医学技术研究院,北京 100191
- Publication Type:Journal Article
- Keywords:
Ferroptosis;
Long non-coding RNA;
Radiotherapy;
Non-small cell lung cancer
- From:
Journal of Peking University(Health Sciences)
2025;57(3):569-577
- CountryChina
- Language:Chinese
-
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 transcrip-tome 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 signa-ling,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 as-sociated 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 vali-dated 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 path-ways,including ferroptosis itself,glutathione metabolism,lipid signaling,and processes involving oxida-tive 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 po-tential of the model to serve as a valuable tool for predicting prognosis for NSCLC patients undergoing ra-diotherapy.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 fer-roptosis-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 high-lights the importance of personalized medicine approaches in oncology,paving the way for more precise and effective treatment strategies.
