The Impact of Radioresistant-Related Telomere Genes on Prognosis and Immune Infiltration in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD), a common subtype of non-small cell lung cancer (NSCLC), has a high incidence and mortality rate. Despite significant progress in early detection and treatment, the overall survival (OS) rate of LUAD patients remains unsatisfactory. This has driven researchers to explore novel biomarkers and therapeutic targets. This study, published in Cancer Cell International and conducted by a research team from Shanghai Pulmonary Hospital and Tongji University School of Medicine, investigates the role of radioresistant-related telomere genes (RRTGs) in LUAD prognosis and immune infiltration.

Background

Telomeres, repetitive DNA sequences located at the ends of chromosomes, play a vital role in maintaining chromosomal stability. Proper maintenance of telomere length is crucial for cell survival, and its disruption is linked to various diseases, particularly cancer. In lung cancer treatment, radiotherapy is a key approach, inducing DNA damage in cancer cells to block cell division and trigger apoptosis. However, radioresistance often undermines the efficacy of radiotherapy, posing significant clinical challenges.

Recent studies have shown that telomere dynamics and telomerase activity may influence cancer cell responses to radiotherapy. Additionally, immune cells in the tumor microenvironment (TME) are crucial for cancer progression and treatment outcomes. Therefore, elucidating how radiotherapy-related telomere genes impact immune infiltration and LUAD prognosis could enhance understanding of LUAD pathogenesis and inform new therapeutic strategies.

Methods

This study employed comprehensive bioinformatics analyses, integrating in-house sequencing data with publicly available datasets (TCGA and GEO). Key steps included:

Data Acquisition and Processing

• RNA expression data were extracted from TCGA-LUAD and GEO (GSE72094) datasets, with batch effects corrected using the “combat” algorithm.
• Telomere-related genes were identified from the TelNet database and intersected with differentially expressed genes to select 44 RRTGs associated with radiation and telomeres.

Model Development and Validation

• Univariate Cox regression analysis identified prognostically significant genes, followed by Lasso and multivariate Cox regression analyses, yielding three key genes: ARRB1, PLK1, and DSG2.
• TCGA patients were split into training and testing cohorts to develop a three-gene RRTGs risk score. Kaplan-Meier survival analysis and ROC curves were used to evaluate the model’s predictive performance.
• A nomogram combining clinical features (e.g., age, gender, tumor stage) with the RRTGs risk score provided individualized survival predictions.

Experimental Validation

• Western blot, qRT-PCR, and immunohistochemistry were used to assess ARRB1, PLK1, and DSG2 expression in LUAD cell lines (A549 and A549/X) and tumor tissues.

Functional and Pathway Analysis

• GO and KEGG enrichment analyses explored the roles of the 44 RRTGs in biological processes such as cell division and DNA repair.
• GSEA analysis examined molecular mechanism differences between high- and low-risk groups.

Immune Infiltration and Therapy Response Evaluation

• Immune infiltration status was analyzed using ESTIMATE and CIBERSORT algorithms, and its association with RRTGs risk scores was assessed.
• Relationships between RRTGs scores, tumor mutation burden (TMB), immune checkpoint genes (ICGs), and cancer stem cell (CSC) indices were evaluated.
• TIDE analysis predicted immunotherapy responses, and drug sensitivity was examined in high- and low-risk groups.

Results

Genetic and Transcriptomic Variations of RRTGs

Among the 44 RRTGs identified, CENPF, MKI67, and DSG2 showed high mutation frequencies. PIAS3 exhibited the highest amplification rate, while GAMT and FANCA experienced significant losses in copy number variations (CNVs).

Predictive Power of the Risk Model

The three-gene RRTGs risk score effectively distinguished high-risk from low-risk patients. Kaplan-Meier analysis revealed significantly lower OS in the high-risk group. A nomogram integrating clinical features demonstrated robust predictive accuracy.

Differences in the Immune Microenvironment

High-risk patients exhibited lower immune and stromal scores, suggesting that RRTGs may influence tumor progression by regulating the TME. Differential expression of immune checkpoint genes (e.g., PDCD1, CD274) and HLA-related genes was observed between the high- and low-risk groups.

Experimental Validation

ARRB1 and PLK1 were highly expressed in radioresistant NSCLC tissues, while DSG2 expression showed no significant difference. These findings align with the model predictions, further supporting ARRB1 and PLK1 as potential biomarkers.

Mechanistic Insights

GSEA analysis showed distinct enrichment patterns in the high- and low-risk groups. The high-risk group exhibited enrichment in immune-related processes (e.g., antigen processing, MHC II complex) and metabolism-related pathways. Conversely, the low-risk group was enriched in pathways related to cell cycle regulation and genomic stability (e.g., DNA replication, p53 signaling).

Significance

This study highlights the critical role of RRTGs in LUAD prognosis and immune infiltration. The three-gene risk score model (ARRB1, PLK1, DSG2) offers a valuable tool for individualized prognosis and therapeutic decision-making. Additionally, the findings provide theoretical support for targeting telomere genes to enhance radiotherapy sensitivity and reduce cancer-related toxicity.

While limitations such as dataset heterogeneity and retrospective analysis were acknowledged, rigorous quality control and standardized analytical pipelines were employed to ensure reliability. Future validation using independent cohorts and experimental models will further strengthen these findings and advance precision medicine for LUAD.

Conclusion

This comprehensive analysis of RRTGs in LUAD developed a novel risk model incorporating ARRB1, PLK1, and DSG2 as key components. These genes were found to influence tumor immune microenvironments, clinical characteristics, therapeutic responses, and outcomes. The study emphasizes the potential of RRTGs as therapeutic targets and their clinical implications in LUAD management.