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Journal of Peking University (Health Sciences) ›› 2025, Vol. 57 ›› Issue (3): 569-577. doi: 10.19723/j.issn.1671-167X.2025.03.022

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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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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

CLC Number: 

  • R34

Figure 1

Enrichment analysis of pathways for ferroptosis-related differential genes in the KEGG database HIF-1, hypoxia-inducible factor-1; KEGG, Kyoto encyclopedia of genes and genomes."

Figure 2

Enrichment analysis of metabolic pathways for ferroptosis-related differential genes in the GO database BP, biological process; CC, cellular component; MF, molecular function; NAD(P)H, nicotinamide adenine dinucleotide phosphate-reduced; NADP, nicotinamide adenine dinucleotide phosphate; GO, gene ontology."

Figure 3

Expression levels of 14 lncRNA related to ferroptosis and the lncRNA-mRNA co-expression network The green color indicates 14 predicted lncRNA；The yellow color indicates genes related to ferroptosis. lncRNA, long non-coding RNA."

Figure 4

Correlation between the predictive model and the prognosis of patients with non-small cell lung cancer A, the overall survival time for the high-risk and low-risk groups; B, the horizontal axis represented patients arranged in increasing order of risk score, and the vertical axis represented the specific risk score values; C, the relationship between risk score and survival time; D, progression free survival in the high risk and low risk groups; E, the ROC curves for 1 year, 2 year, and 3 year survival. ROC, receiver operating characteristic curve; AUC, area under curve."

Figure 5

Prognostic impact of clinical variables on overall survival A, age≤65 years; B, age>65 years; C, male; D, female; E, stage Ⅰ-Ⅱ；F，stage Ⅲ-Ⅳ."

Table 1

Clinical characteristics of patients in the training dataset and validation dataset"

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)

Figure 6

Validation results of the training dataset and validation dataset A, B, training dataset; C, D, validation dataset. AUC, area under curve."

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