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. 2025 May 29:16:1574113.
doi: 10.3389/fimmu.2025.1574113. eCollection 2025.

Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer

Jie Lv #  1 , Chun-Yang Yu #  2 , Yao-Zu Xiong  1 , Ting-Ting Dai  1 , Xiao-Chu Hu  1 , Peng Pan  1 , Shun Yue  3 , Chang-Hua Yu  1
Affiliations

Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer

Jie Lv et al. Front Immunol. .

Abstract

Introduction: A significant challenge in treating non-small cell lung cancer (NSCLC) is its inherent resistance to radiation therapy, leading to poor patient prognosis. This study aimed to identify key genes influencing radiotherapy resistance in NSCLC through comprehensive bioinformatics analysis.

Methods: A total of 103 common genes were identified, enriched in critical biological pathways such as coagulation, complement activation, growth factor activity, and cytokine signaling. Using advanced machine learning techniques like SVM-RFE, LASSO regression, and random forest algorithms, four pivotal genes-TGFBI, FAS, PTK6, and FA2H-were identified.

Results: TGFBI showed the strongest correlation with NSCLC prognosis as indicated by a diagnostic nomogram. Additionally, significant differences in immune cell infiltration, particularly involving naive B cells and M0 macrophages, were noted between high-risk and low-risk patients.

Discussion: The study suggests that targeting pathways regulating macrophage polarization or enhancing naive B cell activation could play a crucial role in addressing radiotherapy resistance. The findings highlight the potential therapeutic targets, thereby advancing the understanding of the molecular mechanisms underlying radiotherapy resistance in NSCLC, with implications for improving patient management and outcomes.

Keywords: NSCLC; bioinformatic analysis; bioinformatics analysis; immunological biomarkers; radiotherapy resistance.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Analysis flowchart.
Figure 2
Figure 2
Screening of radiotherapy tolerance-related genes. (A). Intersection of differentially expressed genes from two datasets. (B). GO/KEGG enrichment analysis.
Figure 3
Figure 3
Construction of diagnostic nomograms. (A) Key gene diagnostic nomogram. (B) Calibration curve of the diagnostic nomogram. (C) ROC curve of FA2H. (D) ROC curve of FAS. (E) ROC curve of PTK6. (F) ROC curve of TGFB1.
Figure 4
Figure 4
Construction of the prognostic model. (A) Variable path diagram of LASSO regression. (B) LASSO regression cross-validation graph. (C) Training set risk curve. (D) Training set survival status. (E) Heatmap of prognostic-related gene expression levels of the training group. (F) Survival analysis of the test group. (G) ROC curve of the training group.
Figure 5
Figure 5
Immune infiltration analysis. (A) Proportion of immune cells in different samples. (B) Box plot of immune cell infiltration levels between high and low-risk group. (C) Box plot of immune cell infiltration levels. (D) Box plot of the expression levels of prognostic-related genes in high and low-risk group. The asterisk represents the P value: ****p<0.0001, ***p<0.001, **p<0.01, *p<0.05. ns, no significance.
Figure 6
Figure 6
GSVA analysis of high and low-risk groups. (A) The top 20 pathways. (B) Analysis of tumor mutation burden. (C) Expression levels of immune checkpoints. The asterisk represents the P value: ****p<0.0001, ***p<0.001, **p<0.01, *p<0.05.
See this image and copyright information in PMC

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