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已发表论文
端粒维持相关基因在乳腺癌中的预后及免疫学意义：机制探索
Authors Cao H, Gao F, Wang R, Xie Y, Li B
Received 9 June 2025
Accepted for publication 5 November 2025
Published 12 November 2025 Volume 2025:17 Pages 1063—1082
DOI https://doi.org/10.2147/BCTT.S545745
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Pranela Rameshwar
Hui Cao,1 Feng Gao,1 Ronghua Wang,1 Yajuan Xie,1 Bin Li2
1Department of Breast Surgery, General Hospital of Tisco (The Sixth Hospital of Shanxi Medical University, The Sixth Clinical Medical College of Shanxi Medical University), Taiyuan, People’s Republic of China; 2Department of General Surgery, General Hospital of Tisco (The Sixth Hospital of Shanxi Medical University, The Sixth Clinical Medical College of Shanxi Medical University), Taiyuan, People’s Republic of China
Correspondence: Bin Li, Email libin@163.com
Background: The elongation of telomeres endows cancer cells with the ability of replicative immortality. Nevertheless, the connection between it and breast cancer (BC) has not been clearly defined yet.
Methods: In this study, expression data from multiple BC datasets were analyzed to discover differentially expressed telomere maintenance-related genes (DE-TMRGs). Cox regression analyses were executed to determine prognostic genes, and a risk model was constructed using these genes to predict survival outcomes. Independent prognostic analysis was executed to assess independent prognostic factors. Enrichment analyses were conducted to determine pathways linked to the different risk groups. Immune infiltration levels and immune checkpoint expression were analyzed across risk categories using various algorithms. A regulatory network involving miRNAs and lncRNAs was constructed, and drug sensitivity was assessed. Mendelian randomization (MR) analysis was executed to investigate causal relationships between prognostic genes and BC. Finally, the expression of prognostic genes was further analysed at the single-cell level.
Results: A sum of 81 DE-TMRGs. Through univariate and LASSO regression analyses, five prognostic genes (WT1, TPRXL, RAD54B, JUN, and SEPHS2) were identified to construct a risk model. This model successfully distinguished between high- and low-risk categories with significant differences in survival rates. Immune profiling revealed the high-risk category had increased levels of activated T cells. Eosinophil demonstrated a high correlation with prognostic genes. Furthermore, JUN (OR = 1.1333, P = 0.0085) and RAD54B (OR = 1.0790, P = 0.0015) were identified as causal risk factors for BC. Single-cell RNA sequencing highlighted JUN’s widespread distribution across multiple cell types, suggesting its crucial role in tumor progression.
Conclusion: TM-associated risk model based on WT1, TPRXL, RAD54B, JUN, and SEPHS2 could be used to predict prognosis and treatment of BC.
Keywords: telomere maintenance, breast cancer, risk model, single cell analysis, Mendelian randomization
1Department of Breast Surgery, General Hospital of Tisco (The Sixth Hospital of Shanxi Medical University, The Sixth Clinical Medical College of Shanxi Medical University), Taiyuan, People’s Republic of China; 2Department of General Surgery, General Hospital of Tisco (The Sixth Hospital of Shanxi Medical University, The Sixth Clinical Medical College of Shanxi Medical University), Taiyuan, People’s Republic of China
Correspondence: Bin Li, Email libin@163.com
Background: The elongation of telomeres endows cancer cells with the ability of replicative immortality. Nevertheless, the connection between it and breast cancer (BC) has not been clearly defined yet.
Methods: In this study, expression data from multiple BC datasets were analyzed to discover differentially expressed telomere maintenance-related genes (DE-TMRGs). Cox regression analyses were executed to determine prognostic genes, and a risk model was constructed using these genes to predict survival outcomes. Independent prognostic analysis was executed to assess independent prognostic factors. Enrichment analyses were conducted to determine pathways linked to the different risk groups. Immune infiltration levels and immune checkpoint expression were analyzed across risk categories using various algorithms. A regulatory network involving miRNAs and lncRNAs was constructed, and drug sensitivity was assessed. Mendelian randomization (MR) analysis was executed to investigate causal relationships between prognostic genes and BC. Finally, the expression of prognostic genes was further analysed at the single-cell level.
Results: A sum of 81 DE-TMRGs. Through univariate and LASSO regression analyses, five prognostic genes (WT1, TPRXL, RAD54B, JUN, and SEPHS2) were identified to construct a risk model. This model successfully distinguished between high- and low-risk categories with significant differences in survival rates. Immune profiling revealed the high-risk category had increased levels of activated T cells. Eosinophil demonstrated a high correlation with prognostic genes. Furthermore, JUN (OR = 1.1333, P = 0.0085) and RAD54B (OR = 1.0790, P = 0.0015) were identified as causal risk factors for BC. Single-cell RNA sequencing highlighted JUN’s widespread distribution across multiple cell types, suggesting its crucial role in tumor progression.
Conclusion: TM-associated risk model based on WT1, TPRXL, RAD54B, JUN, and SEPHS2 could be used to predict prognosis and treatment of BC.
Keywords: telomere maintenance, breast cancer, risk model, single cell analysis, Mendelian randomization