As a novel prognostic model for breast cancer - the identification and validation of telomere-related long noncoding RNA signatures

Sep 21, 2026

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Background Telomeres maintain chromosome integrity and influence cancer progression and cellular senescence. Breast cancer regulation by telomere-associated lncRNAs remains incompletely understood. This study aims to characterize predictive telomere-related LncRNAs (TRL) in breast cancer and clarify potential biological functions. Methods RNA-seq data, clinical data, and telomere-associated gene lists were integrated. Differential TRLs were evaluated by co-expression and univariate Cox analyses; LASSO built a 14-TRL prognostic model. Kaplan-Meier and ROC assessed prediction performance, and immune infiltration and immune drug response were inferred. Cluster analysis defined subgroups. RT-PCR validated 14 LncRNAs expression. Results A 14-TRL risk model showed prognostic discrimination, with high-risk tumors displaying reduced PD-1 sensitivity, increased macrophage infiltration, and distinct immune checkpoint and TME features. Cluster 2 presented the highest immune infiltration/checkpoint expression and the poorest survival, warranting closer attention. Conclusion The 14-TRL signature may serve as a biomarker to evaluate clinical outcomes and treatment efficacy in breast cancer patients.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusion

Introduction

Role of telomeres in cancer progression

lncRNAs and non-coding RNA context

Zhao et al. World Journal of Surgical Oncology (2024) 22:245 [https://doi.org/10.1186/s12957-024-03514-2](https://doi.org/10.1186/s12957-024-03514-2) World Journal of Surgical Oncology RESEARCH Open Access As a novel prognostic model for breast cancer, the identification and validation of telomererelated long noncoding RNA signatures Wei Zhao 1†, Beibei Li2†, Mingxiang Zhang3†, Peiyao Zhou3 and Yongyun Zhu 1,3* Abstract Background Telomeres are a critical component of chromosome integrity and are essential to the development of cancer and cellular senescence. The regulation of breast cancer by telomere-associated lncRNAs is not fully known, though. The goals of this study were to describe predictive telomere-related LncRNAs (TRL) in breast cancer and look into any possible biological roles for these RNAs. Methods We obtained RNA-seq data, pertinent clinical data, and a list of telomere-associated genes from the cancer genome atlas and telomere gene database, respectively. We subjected differential ly expressed TRLs to co-expression analysis and univariate Cox analysis to identify a prognostic TRL. Using LASSO regression analysis, we built a prognostic model with 14 TRLs. The accuracy of the model’s prognostic predictions was evaluated through the utilization of Kaplan-Meier (K-M) analysis as well as receiver operating characteristic (ROC) curve analysis. Additionally, immunological infiltration and immune drug prediction were done using this model. Patients with breast cancer were divided into two subgroups using cluster analysis, with the latter analyzed further for variations in response to immunotherapy, immune infiltration, and overall survival, and finally, the expression of 14-LncRNAs was validated by RT-PCR. Results We developed a risk model for the 14-TRL, and we used ROC curves to demonstrate how accurate the model is. The model may be a standalone prognostic predictor for patients with breast cancer, according to COX regression analysis. The immune infiltration and immunotherapy results indicated that the high-risk group had a low level of PD-1 sensitivity and a high number of macrophages infiltrating. In addition, we’ve discovered a number of smallmolecule medicines with considerable for use in treating high-risk groups. The cluster 2 subtype showed the highest immune infiltration, the highest immune checkpoint expression, and the worst prognosis among the two subtypes defined by cluster analysis, which requires more attention and treatment. Conclusion As a possible biomarker, the proposed 14-TRL signature could be utilized to evaluate clinical outcomesand treatment efficacy in breast cancer patients. †Wei Zhao, Beibei Li and Mingxiang Zhang contributed to the work equally and should be regarded as co-first authors. *Correspondence: Yongyun Zhu [zhuyongyun@shpdph.com](zhuyongyun@shpdph.com) Full list of author information is available at the end of the article © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://](http://)[ ](http://)[creativecommons.or

FAQs

What is the purpose of identifying telomere-related lncRNA signatures in breast cancer?

To develop a prognostic TRL signature and investigate possible biological roles that help predict clinical outcomes and treatment efficacy.

How was the prognostic model constructed and validated?

Differentially expressed TRLs were screened using co-expression analysis and univariate Cox analysis, then a LASSO regression model was built with 14 TRLs. Kaplan-Meier and ROC analyses evaluated prognostic accuracy, and RT-PCR validated 14 lncRNA expression.

What immune-related differences were observed between risk groups and clusters?

High-risk patients showed low PD-1 sensitivity and increased macrophage infiltration. Cluster 2 had the highest immune infiltration and immune checkpoint expression but the worst prognosis, compared with the other subgroup.

Sep 21, 2026

Uploaded by Theodore