
==== Front
World J Surg Oncol
World J Surg Oncol
World Journal of Surgical Oncology
1477-7819
BioMed Central London

3514
10.1186/s12957-024-03514-2
Research
As a novel prognostic model for breast cancer, the identification and validation of telomere-related long noncoding RNA signatures
Zhao Wei 1
Li Beibei 2
Zhang Mingxiang 3
Zhou Peiyao 3
Zhu Yongyun zhuyongyun@shpdph.com

13
1 https://ror.org/04v5gcw55 grid.440283.9 Department of Oncology, Gongli Hospital of Shanghai Pudong New Area, Shanghai, 200135 China
2 https://ror.org/04v5gcw55 grid.440283.9 Department of Laboratory, Shanghai Pudong New Area Gongli Hospital, Shanghai, 200127 China
3 https://ror.org/02hx18343 grid.440171.7 Thyroid and Breast Surgery Department, Shanghai Pudong New Area People’s Hospital, 490 Chuanhuan South Road, Chuansha New Town, Pudong New Area, Shanghai, 200000 China
11 9 2024
11 9 2024
2024
22 2458 7 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ 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://creativecommons.org/licenses/by-nc-nd/4.0/.
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 differentially 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 small-molecule 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 outcomes and treatment efficacy in breast cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-024-03514-2.

Keywords

Breast cancer
Telomere
lncRNA
Prognosis
TME
Immunotherapy
Pudong New Area Health Research General ProjectPW2023A-18 Pudong New District Health Committee Discipline Leader ProgramPWRd2023-10 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcIntroduction

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide. Being a complicated, heterogeneous disease, breast cancer (BC) is on the rise in the majority of nations and is anticipated to increase further over the next two decades [1, 2]. In recent years, thanks to molecular and gene expression research, we have experienced a true revolution in our knowledge of breast cancer [3]. This will improve patient survival with less toxicity and realize economic savings. Since the majority of oncological treatments have detrimental side effects, both short- and long-term, it is necessary to develop new techniques that can predict outcomes more accurately. Particularly, it is critical to identify the fraction of early-stage breast cancer patients who are more likely to experience recurrence and require additional treatment [4]. Therefore, we must explore prognostic biomarkers to enhance the evaluation of breast cancer prognosis.

The primary purpose of a telomere, which is a repeating region at the end of a chromosome, is to safeguard the stability and integrity of the chromosome [5]. Ordinarily, after a given number of cell divisions, the length of the telomeres gradually shortens, and when the cell reaches a senescent state, programmed death takes place [6]. However, in some circumstances, such as in cancer cells, whose highly proliferative nature leads to a rapid rate of cell division, telomere length is more likely to reduce [7]. Very short telomeres can cause an accumulation of tumor-promoting genetic changes that encourage the creation and proliferation of cancerous cells [8]. Hence, telomeres play an important role in cancer progression as well as disease prognosis.

LncRNAs are a form of non-coding RNA with more than 200 nucleotides in length and have a variety of functions in the cell, such as controlling the cell cycle, nucleic acid modification, and regulating gene expression [9]. Studies have demonstrated that abnormal expression of LncRNAs leads to the development and progression of many malignancies [10]. Many studies in recent years have revealed a connection between certain lncRNAs and telomere length shortening [11]. For example, the lncRNA TERRA affects telomere length shortening by binding to them, impacting cancer development and prognosis [12]. In addition, it was found that in renal clear cell carcinoma, a telomere-associated LncRNA prognostic model could accurately predict the prognosis of KIRC patients and guide immunotherapy and chemotherapy [13].

The aberrant expression of telomeres and LncRNA in BC has also grown in popularity as a study area. Several studies have determined that a shorter length of telomeres in breast cancer patients correlates closely with the patient’s prognosis [14]. Furthermore, studies have linked the development of breast cancer and the disease’s prognosis to the aberrant expression of particular lncRNAs [15]. The investigation of the relationship between telomere-related LncRNA and breast cancer prognosis may facilitate a more comprehensive understanding of breast cancer’s etiology and treatment.

The lncRNA-related models are significant for gastric adenocarcinomas and bladder adenocarcinomas [16, 17]. However, to date, no studies have reported on the relationship between TRL in the tumor immune microenvironment (TME) and BC prognosis. The objective of this research is to develop a predictive model that will aid clinicians in customizing potential therapy targets and medicines for breast cancer patients based on their individual features.

Materials and methods

Extracting information from data sets

BRCA mRNA expression data (TPM) and clinical information were extracted from the TCGA online database (https://portal.gdc.cancer.gov/), which included 1113 tumor and 113 normal samples. Corresponding clinicopathological data were also obtained [18].: (1) Individuals who have received a pathological diagnosis of breast cancer; (2) Patients who possess comprehensive clinicopathological data. We collected data from a total of 1006 breast cancer patients following their follow-up.

Identification of lncRNA associated with telomeres

Using the Telomere Gene Correlation Database (http://www.cancertelsys.org/telnet/) [19], we collected a total of 2093 genes that are connected to telomeres. In the first step, telomere-related long noncoding RNAs were discovered by employing Spearman correlation coefficients that were derived from the expression patterns of telomere genes and long noncoding RNAs (R2 > 0.4 and p < 0.001). In order to filter differential long noncoding RNA, the filtering parameters were set as follows: log 2(fold change) > 1 and adjust.pvalue < 0.05. Next, we examined the data matrix using the “limma” tool in the R programming language [20].

Creation and validation of prognostic risk models

To discover lncRNAs that are linked with the overall survival of BC patients, we merged the survival data from patients in the TCGA dataset and used one-way COX regression and p < 0.05 to screen out the candidates. Next, we conducted a minimal absolute shrinkage and selection operator (LASSO) Cox analysis [21] to alleviate concerns about data overfitting and avoid false positive results. Lastly, we constructed a risk model based on telomeric lncRNA. The following is the formula that must be used in order to calculate the risk score: [Exp (lncRNA) *Coef (lncRNA)]. Next, on the basis of the risk model’s median risk score, breast cancer patients were divided into low- and high- risk categories. We used the AUC and the survival curve to validate the model’s accuracy and predictive utility.

Constructing a nomogram

We analyze the clinical data of breast cancer patients based on their age, TNM stage, and risk score. We evaluate patients’ 2-, 3-, and 5-year overall survival (OS), as well as create a nomogram that shows the model’s capability to predict survival in patients with breast cancer, using the “rms” R package [22].

Exploration of gene set enrichment

We used the “clusterProfiler” and “org” packages [23] to track the bioenrichment process of differential genes. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional annotation. Through the utilization of a gene set enrichment analysis (GSEA), the distinctions between the high-risk and low-risk groups of breast cancer were also made regarding the many biological mechanisms and signaling pathways. All instances in which the p-value was lower than 0.05 were considered to be statistically significant by our team.

Comparison of TME and immune checkpoint analysis

We carried out a Spearman correlation study on TIMER 2.0 in order to ascertain the current state of immune cell infiltration in both the high-risk and low-risk categories (http://timer.cistrome.org/) [24], which included CIBERSORT, CIBERSORT-ABS, TIMER XCELL, QUANTISEQ, MCPcounter, and EPIC. This allowed us to assess the relationship between immune cell subpopulations and risk score values. R software is used to create a bubble plot that displays the correlation assessment results. In order to obtain a more comprehensive understanding of the differences in TME between the two risk groups, the number of immune cells in both the high risk group and the low risk group was analyzed. A p-value of less than 0.05 was deemed to be statistically significant. After that, we analyzed the variations in immune function, tumor mutation burden, and response to PD-1 treatment between groups that were at high risk and those who were at low risk using the R program. The outcomes are depicted on the heat map and violin diagram. Lastly, using the “ggpubr” R program, we investigated whether or not there was a difference in immune checkpoint gene expression between the two risk groups.

Exploration of clinical treatment drugs

We applied the “pRophetic” package [25] available in the R programming language to conduct an analysis of the projected half-maximal inhibitory concentration (IC50) of breast cancer medications. This was done in order to continue the search for small molecule chemical drugs that could be used to treat high-risk breast cancer.

Subcluster analysis of 14 telomere-associated LncRNA

We used the expression of 14 telomere-related lncRNA as input information for the “ConsensusClusterPlus” software [26], which subsequently clustered 1007 breast cancer samples. Using the “Rtsne” package [27], various subgroups of breast cancer patients were subjected to principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), Kaplan-Meier survival analysis, and tumor immune microenvironment analysis. The purpose of these analyses was to compare the differences between the various subtypes of breast cancer.

Quantitation of the 14 biomarkers

Following the manufacturer’s instructions, we used the TRIzol Kit (Invitrogen) to isolate RNA from MDA-MB-436 cells, and we produced cDNA using the ReverTra Ace qPCR RT Kit (Toyobo). We then measured the relative expression levels of mRNA using a real-time fluorescent qPCR test kit (Roche) and an ABI 7900 real-time fluorescence qPCR equipment (ABI). β-actin was a gene that was used as a reference inside the cell. Primers for 14 lncRNAs are shown in the Appendix file.

Analysis based on statistics

For the purpose of comparing continuous data, the Student’s t test was utilized, whilst the 2 test was utilized for the purpose of comparing categorical variables. To compare Kaplan-Meier curves between groups, the log-rank test was utilized. The Wilcoxon rank-sum test was utilized in order to conduct an analysis of the proportional differences encountered by TME cells. We compared the overall survival (OS) of the high-risk group to that of the low-risk group by employing the Kaplan–Meier method and the log-rank test with two tails methodology. Statistical significance was determined if the p-value was less than 0.05.

Result

Telomere-associated lncRNAs in breast cancer patients

Using co-expression analysis, 3011 telomere-associated lncRNAs were discovered. After intersection of the above lncRNAs and applying the filtering criteria |Log2FC|>1 and adjut.p < 0.05 to breast cancer patients, 801 differentially expressed telomere-associated lncRNAs were identified, and the outcomes are represented by volcano and Wayne plots. (Fig. 1A, B). Subsequently, based on the filtering criterion of p < 0.05, we got a total of 27 OS-related telomere-associated lncRNAs by uni-COX regression analysis and constructed a forest plot (Fig. 1C). To prevent overfitting, we utilized LASSO analysis to isolate 14 lncRNAs associated with telomeres, and by utilizing the following risk score and those lncRNAs, we were able to create a prognostic risk model.: risk score = LINC01344*(-2.3270) + AC005486.1*(-2.0288) + AC099541.2*(-1.4454) + AL133467.1* (-1.0885) + AC061992.2*(-0.7625) + AC004816.2* (-0.6480) + LINC01087* (-0.3846)+`CRIM1-DT`*(-0.3815)2 + RSF1-IT1* 0.5781 + MIR222HG*0.8788 + U91319.1* 0.9508 + EMSLR* 0.9670 + AC000067.1* 1.0657 + LNCOG* 2.1099 (Fig. 1D, E).

Fig. 1 The characterization of differences and the prognosis value of TRL in breast cancer: (A) A volcano plot depicting differentially expressed LncRNAs with a Log2 fold change (FC) greater than 1; (B) Telomere-associated LncRNAs and differential LncRNAs as a Venn diagram; (C) Univariate COX regression analysis for about 27 lncRNAs associated with telomeres and associated with prognosis is depicted in a forest plot; (D, E) The cvfit and lambda curves illustrate the least absolute contraction and selection operator (LASSO) regression with respect to the data

Construction and verification of the TRL prognostic model

After being randomly assigned to a test group and a control group, patients with breast cancer were subsequently divided into high-risk and low-risk groups based on their median risk scores. Then, we compared the risk score distribution, survival status, and survival profiles of patients in the training group, the test group, and the entire study population. When compared to the high-risk group, the low-risk subgroup had a much more favorable prognostic outlook for overall survival, as indicated by our researchers’ findings (Fig. 2A-D). The AUC values were 0.748, 0.711 and 0.673 at one, three and five years, respectively (Fig. 3A-D). In addition, with a score of 0.714 and excellent accuracy and specificity, the diagnostic model. We ran uni-COX and multi-factor COX regression studies to further assess the model’s efficacy. Uni-COX analysis produced an HR and 95% CI of 1.053 and 1.037–1.069, respectively (p < 0.001), while multi-factor COX analysis produced HR and CI of 1.061 and 1.044–1.079, respectively (p < 0.001). (Fig. 3E- F Finally, we extended the validation of our prognostic model in a population of patients with a range of TNM stages, and the results confirmed the model’s continued high accuracy in a variety of breast cancer patients(Fig. 3G-H).

Fig. 2 Predictions of risk models during practice, exams, and full datasets; (A) A diagram showing the spread of risk scores across practice, examination, and full datasets; (B) Annotation heat map for 14 TRL involved in prognosis; (C) Expression levels of 14-TRL, broken down by “practice,” “exam,” and “whole” sets, are shown as a heat map. (D) The survival of low- and high-risk patients in the practice, exam, and complete sets was analyzed

Fig. 3 Validation of model assessment: (A-C) The ROC curves for the practice set, the examination set, and the overall set for the previous 2, 3, and 5 years; (D) Relative probability curves for risk scores and a variety of other clinical factors; (E, F) Uni- and multi-Cox analysis of the results on 14 TRL have been used to characterize OS;

Establishment of a nomogram

That will further evaluate the prediction ability of the telomere-related LncRNA model, a nomogram was developed (Fig. 4A) based on patients’ age, TNM stage, and risk score. Overall survival rates at 2, 3, and 5 years were more accurately predicted by the model. Good agreement was found between the model and the nomogram and calibration plots (Fig. 4B).

Fig. 4 Constructing risk model nomograms and verifying their accuracy: (A) In breast cancer patients, a nomogram that predicts their overall survival at two, three, and five years; (B) A calibration curve that will be applied in order to evaluate the degree of precision that the nomogram possesses. The perfect column line diagram is represented by the diagonal line in gray with dashes

Molecular functional enrichment of the model and exploration of pathways

A comparison was made between the two risk groupings for the biological functional processes and pathways of differentially expressed genes (DEGs). Following this, GO enrichment analysis and KEGG pathway analysis were carried out. Hematopoietic cell lineage was among the immune-related pathways that were significantly enriched in KEGG analysis(Fig. 5A-B). The results of the GO analysis also showed that there was a significant enrichment in immune activities in terms of biological processes (BP), molecular functions (MF), and cellular components (CC)(Fig. 5C-D). In addition, the findings of our GSEA enrichment analysis demonstrated that groups that were at a high risk had a much higher concentration of the cell cycle and proteasome expression (Fig. 5E-F). In conclusion, the 14-lncRNA marker risk score relates mostly to breast cancer tumor metastasis, tumor immunity, and biometabolism.

Fig. 5 (A, B) According to the results of the GO analysis, a great number of immune-related biological processes were enriched. (C, D) According to the results of the KEGG analysis, a great number of immune-related pathways were significantly enriched. (E) The results of the GSEA demonstrated a considerable enrichment of cell cycle pathways in patients with high-risk breast cancer. (F) In breast cancer patients who had a low risk of the disease, the GSEA found a significant enrichment of immune-related pathways

Immunological characteristics and clinical treatment exploration in high-risk populations

Both the risk ratings and the immune cells that had infiltrated were investigated (Fig. 6A), and on various platforms, the association between immune cells and low-risk groups appeared to be tighter than expected(Fig. 6A) (e.g. Class-switched memory B cell in XCELL platform, T cell NK, T cell CD4 + central memory, T cell CD4 + in the TIMER platform, NK cell, Neutrophil and Myeloid dendritic cells in the MCPCOUNTER platform, T cell CD8 + in the CIBERSORT-ABS platform, T cell follicular helper). The high-risk grouping also had significantly higher levels of macrophage infiltration than the other subgroups (Fig. 6B), and ssGSEA (Fig. 6C) analysis revealed that immune cell function was dramatically enhanced in low-risk groups. The expression of the vast majority of immune checkpoint genes, as well as the gene mutation burden, was dramatically increased for a member of the high-risk group(Fig. 6D-E).

Fig. 6 Investigating the use of immunotherapy and immune infiltration in patients who are suffering from BC. (A) The immunocell correlation analysis is displayed as a bubble chart; (B-C) Immune infiltrating cells and function vary between high risk and low risk groups, revealing these differences; (D, E) An examination of immune checkpoints as well as the mutational burden of tumors in two risk subgroups( **p < 0.01, *p < 0.05 )

To further assess the immunotherapy response in the high-risk group, we verified the immunotherapy response in the prognostic model using TCIA data. Immunosuppressive drugs CTLA4 and PD1 were shown to be less effective in the high-risk group. (Fig. 7A). In order to continue the search for small molecule medicines with high sensitivity in groups at high risk, the “pRophetic” package was used to study medication sensitivity. We found that patients who were at a higher risk were more sensitive to Dactinomycin 、Dactolisib 、Afatinib 、Pictilisib、Pevonedistat、Obatoclax Mesylate 、Mitoxantrone 、and Gemcitabine (Fig. 7B). This may have significant ramifications for the treatment of breast cancer patients at high risk.

Fig. 7 (A) Comparing the responses of two risk subgroups to the immunotherapeutic agents PD1 and CTAL4; (B) Evaluation of how susceptible patients are to various medications

Immunological profiling of different breast cancer subtypes

We divided breast cancer patients into categories C1 and C2 based on the expression of telomere-associated LncRNA (Fig. 8A). The results of the T-SNE showed that the distribution of the two risk categories and the two clusters was consistent with previous findings(Fig. 8B-C). Our principal component analysis revealed that the distribution of the two risk categories and the two subtypes had a different cumulative tendency. This was supported by the findings of our research(Fig. 8D-E). The Sankey diagram illustrates the average distribution of clusters 1 and 2 inside the two subgroups that are being talked about(Fig. 8F). Immune score, stromal score, and degree of immune infiltration were all considerably elevated in subtype C2 compared to subtype C1 (Fig. 8G-I). In addition, in cluster 2 practically all immunological checkpoints, including TNFRSF8, PD-1, and CTLA-4, were more active (Fig. 8J). Survival research revealed that cluster 2 had a longer survival rate than cluster 1 (Fig. 8K). This seems to indicate that cluster 2 requires additional attention with treatment to optimize patient prognosis.

Fig. 8 TME of the two subtypes of breast cancer and the associated disparities in prognosis: (A) Breast cancer can be further broken down into two distinct subgroups; (B-E) Examination of the 2 subtypes of the t-SNE and the principal component analysis; (F) The link between two subtypes, as well as high and low risk, as depicted in a Sankey diagram; (G, H) Variations in the immune microenvironment scores exhibited by the various subtypes; (I) The subtype immune cell association study presented as a heat map; (J) Histogram illustrating variances in immune checkpoint gene expression among subtypes; (K) Survival analysis curves for patients with distinct subtype of breast cancer; (**p < 01.0, *p < 05)

QRT-PCR

RT-qPCR was used to determine the expression levels of telomere-associated lncRNA in 30 pairs of normal and tumor cells in order to confirm their expression levels. Seven LncRNAs associated with telomeres were highly increased in tumor tissues, but the remaining seven LNCRNAs were dramatically downregulated (Fig. 9). The outcomes were compatible with the study of TGCA BRCA data.

Fig. 9 RT-PCR validation of the expression of fourteen TRL in BC.( *p < 0.05, **p < 0.01)

Discussion

Breast cancer incidence, death, and recurrence rates are all on the rise around the world, making breast cancer risks for women an increasingly pressing issue. Patients with breast cancer now have longer OS than in the past, thanks to significant efforts made by medical professionals and researchers, as well as the combination of early detection and therapy [28]. Drug resistance, early recurrence, and distant metastases are still frequent, which presents difficulties for patients with breast cancer and challenges for clinicians [29–31]. Patients with the same molecular subtype of breast cancer often experience varying outcomes even with the same treatment [32, 33]. As a result, it was critical to conduct more studies as soon as possible in order to identify novel prognostic biomarkers, a prognosis prediction model for breast cancer, and, ultimately, to offer direction for tailored treatment.

Research has demonstrated that LncRNA plays a role in regulating both normal mammary gland development and the progression of cancer [34, 35]. Studies have increasingly established the use of aberrant lncRNA as indicators, prognosticators, and therapy targets for breast cancer treatment [36–38]. Several studies have looked at the link between the lncRNA signature and the survival rate of breast cancer patients. These studies have confirmed that lncRNA is much better at diagnosing and predicting outcomes than protein-coding genes [39]. Several lncRNAs, such as MALAT1, were discovered to negatively connect with breast cancer progression, metastatic capability, and breast cancer patients’ overall survival [40]. Studies have identified HOTAIR, AK024118, XIST, H19 U79277, BC040204, AK000974, LINC00324, PTPRGAS1, and SNHG17 as aberrantly expressed and survival predictors for breast cancer patients [41]. Furthermore, studies have demonstrated the significant role of telomeres in the cancer development process [42]. Several studies have indicated that changed telomere length can affect the aggressiveness of breast tumor cells, hence influencing the prognosis and treatment response of breast cancer patients [43, 44]. In breast cancer, there are limited investigations on telomeres and lncRNAs connected with telomeres. Hence, telomere-associated lncRNAs have a promising future for predicting patient survival and guiding customized treatment.

In this article, we attempted to analyze clinical follow-up data and lncRNA data from TCGA to screen breast cancer prognostic factors and construct a new prognosis prediction model so as to provide potential targets for treatment and optimize the prognostic assessment system of BC. This study aimed to pinpoint prognostic-significant lncRNAs and gather 802 telomere-related lncRNAs that varied in expression across the body. 14 telomere-associated lncRNAs were identified using single Cox regression and LASSO regression analyses, and these lncRNAs will be used to construct risk models. These lncRNAs include CRIM1-DT, which is a prognostic model for lung adenocarcinoma, “MIR222HG,” which is a key prognostic factor for LuminalA breast cancer, and LINC01087, which controls the aggressive behavior of breast cancer cells by means of the miR-335-5p/Rock1 pathway. In-depth research into these telomere-associated lncRNAs may lead to the discovery of new therapy targets for BC. On the basis of the median risk score, we classified patients into two distinct groups: those who were at a high risk, and those who were at a low risk during the course of the study. We determined that the risk model was accurate in its ability to forecast breast cancer patients’ prognosis using ROC curves and a nomogram. We determined that the model had a high sensitivity to survival prediction following validations.

The GSEA results revealed an enrichment in cell cycle and metabolic pathways in the high-risk group. Examining the differences between low- and high-risk patients’ immunological microenvironments, immune checkpoints, and immune status using the ssGSEA method, We found that the group with a low risk of developing the disease had higher levels of immune infiltration and responded better to treatment with PD-1 and CTLA-4. In addition, we investigated the sensitivity of small molecule immunologic drugs to these two groups, presenting new immunotherapeutic ideas and choices for breast cancer patients at high risk. We split the expression of telomere-associated lncRNA clustering into two groups. According to Kaplan-Meier analysis, the C2 subtype had a better prognosis than the C1 subtype. The results of immune checkpoints showed that PD1, PD-L1 was more active in cluster 2, and cluster 2 also had a higher TME score, which suggests that cluster 2 may be more effective against this type of immune checkpoint inhibitor. Finally, we evaluated the expression levels of these 14 telomere-associated LncRNAs in breast cancer cells, and the expression trends were basically consistent with the bioinformatics analysis predictions. The results of this study support the hypothesis that novel biomarkers may play a significant role in breast cancer progression. However, it is yet unknown whether the additional LncRNAs have the same function in breast cancer cells and more research is required. Our research is critical in order to develop a novel strategy for breast cancer prognostic evaluation and treatment.

Conclusion

Overall, we developed a TRL-based model to predict prognosis and guide tailored immunotherapy in BRCA patients. Targeting these lncRNAs could lead to systemic therapeutic failure and novel immunotherapy routes. As a result, molecular pathways involving TRLs, telomeres, and BRCA should be investigated.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.

Author contributions

ZW, ZYY and LBB conceived the idea and conceptualised the study. ZW and ZMX collected the data. ZMX, ZYY and ZPY analysed the data. ZW, ZYY and LBB drafted the manuscript, then LBB and ZPY reviewed the manuscript. All authors read and approved the final draft.

Funding

Pudong New Area Health Research General Project (No. PW2023A-18).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval

In accordance with the Human Medical Ethics Committee of the Shanghai Pudong New Area People’s Hospital, this research was granted the green light. This study was conducted in accordance with the declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Wei Zhao, Beibei Li and Mingxiang Zhang contributed to the work equally and should be regarded as co-first authors.
==== Refs
References

1. Howell A Anderson AS Clarke RB Duffy SW Gareth Evans D Garcia-Closas M Risk determination and Prevention of breast Cancer Breast Cancer Research: BCR 2014 16 5 446 10.1186/s13058-014-0446-2 25467785
Howell A, Anderson AS, Clarke RB, Duffy SW, Gareth Evans D, Garcia-Closas M, et al. Risk determination and prevention of breast cancer. Breast Cancer Research: BCR. 2014;16(5):446. 10.1186/s13058-014-0446-225467785 10.1186/s13058-014-0446-2
2. Coughlin SS Social determinants of breast Cancer Risk, Stage, and Survival Breast Cancer Res Treat 2019 177 3 537 48 10.1007/s10549-019-05340-7 31270761
Coughlin SS. Social determinants of breast cancer risk, stage, and survival. Breast Cancer Res Treat. 2019;177(3):537–48. 10.1007/s10549-019-05340-731270761 10.1007/s10549-019-05340-7
3. Gambardella G, Viscido G, Tumaini B, Isacchi A, Bosotti R, di Bernardo D. A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response. Nat Commun. 2022;13(1). 10.1038/s41467-022-29358-6
4. Greenlee H DuPont-Reyes MJ Balneaves LG Carlson LE Cohen MR Deng G Clinical practice guidelines on the evidence-based use of integrative therapies during and after breast Cancer Treatment Cancer J Clin 2017 67 3 194 232 10.3322/caac.21397
Greenlee H, DuPont-Reyes MJ, Balneaves LG, Carlson LE, Cohen MR, Deng G, et al. Clinical practice guidelines on the evidence-based use of integrative therapies during and after breast cancer treatment. Cancer J Clin. 2017;67(3):194–232. 10.3322/caac.2139710.3322/caac.21397
5. Shay JW Telomeres and aging Curr Opin Cell Biol 2018 52 June 1 7 10.1016/j.ceb.2017.12.001 29253739
Shay JW. Telomeres and aging. Curr Opin Cell Biol. 2018;52(June):1–7. 10.1016/j.ceb.2017.12.00129253739 10.1016/j.ceb.2017.12.001
6. Bejarano L, Bosso G, Louzame J, Serrano R et al. Elena Gómez-Casero, Jorge Martínez-Torrecuadrada. Multiple cancer pathways regulate telomere protection. EMBO Molecular Medicine. 2019;11(7):e10292. 10.15252/emmm.201910292
7. De Vitis, Marco F Berardinelli Telomere length maintenance in Cancer: at the crossroad between Telomerase and Alternative Lengthening of telomeres (ALT) Int J Mol Sci 2018 19 2 606 10.3390/ijms19020606 29463031
De Vitis, Marco F, Berardinelli, and Antonella Sgura. Telomere length maintenance in cancer: at the crossroad between telomerase and alternative lengthening of telomeres (ALT). Int J Mol Sci. 2018;19(2):606. 10.3390/ijms1902060629463031 10.3390/ijms19020606
8. Graham M Kim Telomeres and telomerase in prostate Cancer Development and Therapy Nat Rev Urol 2017 14 10 607 19 10.1038/nrurol.2017.104 28675175
Graham M, Kim, and Alan Meeker. Telomeres and telomerase in prostate cancer development and therapy. Nat Rev Urol. 2017;14(10):607–19. 10.1038/nrurol.2017.10428675175 10.1038/nrurol.2017.104
9. Bridges M Catherine AC Daulagala LNCcation: LncRNA localization and function J Cell Biol 2021 220 2 e202009045 10.1083/jcb.202009045 33464299
Bridges M, Catherine AC, Daulagala, and Antonis Kourtidis. LNCcation: LncRNA localization and function. J Cell Biol. 2021;220(2):e202009045. 10.1083/jcb.20200904533464299 10.1083/jcb.202009045
10. Bhan A Soleimani M Mandal SS Long noncoding RNA and Cancer: a New Paradigm Cancer Res 2017 77 15 3965 81 10.1158/0008-5472.CAN-16-2634 28701486
Bhan A, Soleimani M, Mandal SS. Long noncoding RNA and cancer: a new paradigm. Cancer Res. 2017;77(15):3965–81. 10.1158/0008-5472.CAN-16-263428701486 10.1158/0008-5472.CAN-16-2634
11. Silva B Arora R Bione S Azzalin CM TERRA Transcription Destabilizes Telomere Integrity to Initiate Break-Induced replication in human ALT cells Nat Commun 2021 12 1 3760 10.1038/s41467-021-24097-6 34145295
Silva B, Arora R, Bione S, Azzalin CM. TERRA transcription destabilizes telomere integrity to initiate break-induced replication in human ALT cells. Nat Commun. 2021;12(1):3760. 10.1038/s41467-021-24097-634145295 10.1038/s41467-021-24097-6
12. Oliva-Rico D, and Herrera La. Regulated expression of the LncRNA TERRA and its impact on telomere biology. Mech Ageing Dev. 2017;167. 10.1016/j.mad.2017.09.001
13. Li X Li Y Yu X Identification and validation of stemness-related lncRNA prognostic signature for breast Cancer J Translational Med 2020 18 1 331 10.1186/s12967-020-02497-4
Li X, Li Y, Yu X, and Feng Jin. Identification and validation of stemness-related lncRNA prognostic signature for breast cancer. J Translational Med. 2020;18(1):331. 10.1186/s12967-020-02497-410.1186/s12967-020-02497-4
14. Herbert BS Wright WE Shay JW Telomerase and breast Cancer Breast Cancer Research: BCR 2001 3 3 146 49 10.1186/bcr288 11305948
Herbert BS, Wright WE, Shay JW. Telomerase and breast cancer. Breast Cancer Research: BCR. 2001;3(3):146–49. 10.1186/bcr28811305948 10.1186/bcr288
15. Abdi E LncRNA polymorphisms and breast Cancer risk Pathol Res Pract 2022 229 153729 10.1016/j.prp.2021.153729 34952422
Abdi E, Saeid Latifi-Navid, and Hamid Latifi-Navid. LncRNA polymorphisms and breast cancer risk. Pathol Res Pract. 2022;229:153729. 10.1016/j.prp.2021.15372934952422 10.1016/j.prp.2021.153729
16. Cai Y, Wu S, Jia Y, Pan X, Li C. Potential key markers for predicting the prognosis of gastric adenocarcinoma based on the expression of ferroptosis-related LncRNA. J Immunol Res. 2022;2022:1249290. 10.1155/2022/1249290
17. Xu Z Chen H Sun J Mao W Chen S Multi-omics Analysis identifies a LncRNA-Related prognostic signature to predict bladder Cancer recurrence Bioengineered 2021 12 2 11108 25 10.1080/21655979.2021.2000122 34738881
Xu Z, Chen H, Sun J, Mao W, Chen S, and Ming Chen. Multi-omics analysis identifies a LncRNA-related prognostic signature to predict bladder cancer recurrence. Bioengineered. 2021;12(2):11108–25. 10.1080/21655979.2021.200012234738881 10.1080/21655979.2021.2000122
18. Wang Z Jensen MA A practical guide to the Cancer Genome Atlas (TCGA) Methods Mol Biology (Clifton N J) 2016 1418 111 41 10.1007/978-1-4939-3578-9_6
Wang Z, Jensen MA, and Jean Claude Zenklusen. A practical guide to the cancer genome atlas (TCGA). Methods Mol Biology (Clifton N J). 2016;1418:111–41. 10.1007/978-1-4939-3578-9_610.1007/978-1-4939-3578-9_6
19. Braun DM Chung I Kepper N Deeg KI Rippe K TelNet - a database for human and yeast genes involved in Telomere maintenance BMC Genet 2018 19 1 32 10.1186/s12863-018-0617-8 29776332
Braun DM, Chung I, Kepper N, Deeg KI, Rippe K. TelNet - a database for human and yeast genes involved in telomere maintenance. BMC Genet. 2018;19(1):32. 10.1186/s12863-018-0617-829776332 10.1186/s12863-018-0617-8
20. Ritchie ME Phipson B Wu D Charity YH Law W Wei Shi Limma Powers Differential expression analyses for RNA-Sequencing and microarray studies Nucleic Acids Res 2015 43 7 e47 10.1093/nar/gkv007 25605792
Ritchie ME, Phipson B, Wu D, Charity YH, Law W, Wei, Shi, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. 10.1093/nar/gkv00725605792 10.1093/nar/gkv007
21. Tibshirani R The Lasso Method for Variable Selection in the Cox Model Stat Med 1997 16 4 385 95 10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co;2-3 9044528
Tibshirani R. The Lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95. 10.1002/(sici)1097-0258(19970228)16:4%3C385::aid-sim380%3E3.0.co;2-39044528 10.1002/(sici)1097-0258(19970228)16:4<385::aid-sim380>3.0.co;2-3
22. Zhang J-A Zhou X-Y Huang D Luan C Gu H Ju M Development of an Immune-related gene signature for prognosis in Melanoma Front Oncol 2020 10 602555 10.3389/fonc.2020.602555 33585219
Zhang J-A, Zhou X-Y, Huang D, Luan C, Gu H, Ju M, et al. Development of an immune-related gene signature for prognosis in melanoma. Front Oncol. 2020;10:602555. 10.3389/fonc.2020.60255533585219 10.3389/fonc.2020.602555
23. Yu G Wang L-G Han Y Qing-Yu H ClusterProfiler: an R Package for comparing Biological themes among Gene Clusters OMICS 2012 16 5 284 87 10.1089/omi.2011.0118 22455463
Yu G, Wang L-G, Han Y, Qing-Yu H. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–87. 10.1089/omi.2011.011822455463 10.1089/omi.2011.0118
24. Feng Z Chen Y Cai C Tan J Liu P Chen Y Pan-cancer and single-cell analysis reveals CENPL as a Cancer Prognosis and Immune Infiltration-Related Biomarker Front Immunol 2022 13 916594 10.3389/fimmu.2022.916594 35844598
Feng Z, Chen Y, Cai C, Tan J, Liu P, Chen Y, et al. Pan-cancer and single-cell analysis reveals CENPL as a cancer prognosis and immune infiltration-related biomarker. Front Immunol. 2022;13:916594. 10.3389/fimmu.2022.91659435844598 10.3389/fimmu.2022.916594
25. Wu Z, Lu Z, Li L, Ma M, Wu FLR, et al. Identification and validation of ferroptosis-related LncRNA signatures as a novel prognostic model for colon cancer. Front Immunol. 2022;12. 10.3389/fimmu.2021.783362. https://www.frontiersin.org/articles/
26. Wilkerson MD Neil Hayes D ConsensusClusterPlus: a Class Discovery Tool with confidence assessments and item Tracking Bioinf (Oxford England) 2010 26 12 1572 73 10.1093/bioinformatics/btq170
Wilkerson MD, Neil Hayes D. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinf (Oxford England). 2010;26(12):1572–73. 10.1093/bioinformatics/btq17010.1093/bioinformatics/btq170
27. Pezzotti N Lelieveldt BPF Maaten LVD Hollt T Eisemann E Vilanova A Approximated and user Steerable TSNE for Progressive Visual Analytics IEEE Trans Vis Comput Graph 2017 23 7 1739 52 10.1109/TVCG.2016.2570755 28113434
Pezzotti N, Lelieveldt BPF, Maaten LVD, Hollt T, Eisemann E, Vilanova A. Approximated and user steerable TSNE for progressive visual analytics. IEEE Trans Vis Comput Graph. 2017;23(7):1739–52. 10.1109/TVCG.2016.257075528113434 10.1109/TVCG.2016.2570755
28. Pashayan N Antoniou AC Ivanus U Esserman LJ Douglas F Easton D French Personalized early detection and Prevention of breast Cancer: ENVISION Consensus Statement Nat Rev Clin Oncol 2020 17 11 687 705 10.1038/s41571-020-0388-9 32555420
Pashayan N, Antoniou AC, Ivanus U, Esserman LJ, Douglas F, Easton D, French, et al. Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Nat Rev Clin Oncol. 2020;17(11):687–705. 10.1038/s41571-020-0388-932555420 10.1038/s41571-020-0388-9
29. McArthur H Breast Cancer brain metastasis: an Ongoing Clinical Challenge and Opportunity for Innovation Oncol (Williston Park N Y) 2016 30 10 934 35
McArthur H. Breast cancer brain metastasis: an ongoing clinical challenge and opportunity for innovation. Oncol (Williston Park N Y). 2016;30(10):934–35.
30. Bray F Ferlay J Soerjomataram I Siegel RL Lindsey A Torre 2018 Global Cancer Statistics 2018: GLOBOCAN Estimates of 10.3322/caac.21492
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Lindsey A, Torre, and Ahmedin Jemal. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2018;68(6):394–424. 10.3322/caac.2149210.3322/caac.21492
31. Burton R The global challenge of reducing breast Cancer mortality Oncologist 2013 18 3 5 10.1634/theoncologist.18-S2-3 24334476
Burton R, and Robin Bell. The global challenge of reducing breast cancer mortality. Oncologist. 2013;18:3–5. 10.1634/theoncologist.18-S2-324334476 10.1634/theoncologist.18-S2-3
32. Bergen ES Tichy C Berghoff AS Rudas M Dubsky P Bago-Horvath Z Prognostic impact of breast Cancer subtypes in Elderly patients Breast Cancer Res Treat 2016 157 1 91 9 10.1007/s10549-016-3787-y 27107570
Bergen ES, Tichy C, Berghoff AS, Rudas M, Dubsky P, Bago-Horvath Z, et al. Prognostic impact of breast cancer subtypes in elderly patients. Breast Cancer Res Treat. 2016;157(1):91–9. 10.1007/s10549-016-3787-y27107570 10.1007/s10549-016-3787-y
33. Yang Y Im S-A Keam B Lee K-H Kim T-Y Prognostic impact of AJCC Response Criteria for Neoadjuvant Chemotherapy in Stage II/III breast Cancer patients: breast Cancer subtype analyses BMC Cancer 2016 16 515 10.1186/s12885-016-2500-1 27444430
Yang Y, Im S-A, Keam B, Lee K-H, Kim T-Y, Koung Jin Suh, et al. Prognostic impact of AJCC response criteria for neoadjuvant chemotherapy in stage II/III breast cancer patients: breast cancer subtype analyses. BMC Cancer. 2016;16:515. 10.1186/s12885-016-2500-127444430 10.1186/s12885-016-2500-1
34. Hansji H Leung EY Baguley BC Graeme J Finlay Askarian-Amiri ME Keeping abreast with long non-coding RNAs in mammary gland development and breast Cancer Front Genet 2014 5 379 10.3389/fgene.2014.00379 25400658
Hansji H, Leung EY, Baguley BC, Graeme J, Finlay, Askarian-Amiri ME. Keeping abreast with long non-coding RNAs in mammary gland development and breast cancer. Front Genet. 2014;5:379. 10.3389/fgene.2014.0037925400658 10.3389/fgene.2014.00379
35. Cheetham SW Gruhl F Mattick JS Dinger ME Long noncoding RNAs and the Genetics of Cancer Br J Cancer 2013 108 12 2419 25 10.1038/bjc.2013.233 23660942
Cheetham SW, Gruhl F, Mattick JS, Dinger ME. Long noncoding RNAs and the genetics of cancer. Br J Cancer. 2013;108(12):2419–25. 10.1038/bjc.2013.23323660942 10.1038/bjc.2013.233
36. Meng J Li P Zhang Q Yang Z Fu S A four-long non-coding RNA signature in Predicting breast Cancer Survival J Experimental Clin Cancer Research: CR 2014 33 1 84 10.1186/s13046-014-0084-7
Meng J, Li P, Zhang Q, Yang Z, Fu S. A four-long non-coding RNA signature in predicting breast cancer survival. J Experimental Clin Cancer Research: CR. 2014;33(1):84. 10.1186/s13046-014-0084-710.1186/s13046-014-0084-7
37. Arun G Spector DL MALAT1 long non-coding RNA and breast Cancer RNA Biol 2019 16 6 860 63 10.1080/15476286.2019.1592072 30874469
Arun G, Spector DL. MALAT1 long non-coding RNA and breast cancer. RNA Biol. 2019;16(6):860–63. 10.1080/15476286.2019.159207230874469 10.1080/15476286.2019.1592072
38. Soudyab M Iranpour M The role of long non-coding RNAs in breast Cancer Arch Iran Med 2016 19 7 508 17 27362246
Soudyab M, Iranpour M, and Soudeh Ghafouri-Fard. The role of long non-coding RNAs in breast cancer. Arch Iran Med. 2016;19(7):508–17.27362246
39. Kosir MA Jia H Ju D Challenging paradigms: long non-coding RNAs in breast ductal carcinoma in situ (DCIS) Front Genet 2013 4 50 10.3389/fgene.2013.00050 23577021
Kosir MA, Jia H, Ju D, and Leonard Lipovich. Challenging paradigms: long non-coding RNAs in breast ductal carcinoma in situ (DCIS). Front Genet. 2013;4:50. 10.3389/fgene.2013.0005023577021 10.3389/fgene.2013.00050
40. Kim J, Hl P, Bj K, Yao F, Han Z, Wang Y, et al. Long noncoding RNA MALAT1 suppresses breast cancer metastasis. Nat Genet. 2018;50(12). 10.1038/s41588-018-0252-3
41. Zhao W Luo J Comprehensive characterization of Cancer Subtype Associated Long non-coding RNAs and their clinical implications Sci Rep 2014 4 6591 10.1038/srep06591 25307233
Zhao W, Luo J, and Shunchang Jiao. Comprehensive characterization of cancer subtype associated long non-coding RNAs and their clinical implications. Sci Rep. 2014;4:6591. 10.1038/srep0659125307233 10.1038/srep06591
42. McNally EJ Paz J Luncsford Long telomeres and Cancer Risk: the price of Cellular Immortality J Clin Investig 2019 129 9 3474 81 10.1172/JCI120851 31380804
McNally EJ, Paz J, Luncsford, and Mary Armanios. Long telomeres and cancer risk: the price of cellular immortality. J Clin Investig. 2019;129(9):3474–81. 10.1172/JCI12085131380804 10.1172/JCI120851
43. Ceja-Rangel HA Sánchez-Suárez P Castellanos-Juárez E Peñaroja-Flores R Diego J Arenas-Aranda P Gariglio Shorter telomeres and high telomerase activity correlate with a highly aggressive phenotype in breast Cancer cell lines Tumor Biology: J Int Soc Oncodevelopmental Biology Med 2016 37 9 11917 26 10.1007/s13277-016-5045-7
Ceja-Rangel HA, Sánchez-Suárez P, Castellanos-Juárez E, Peñaroja-Flores R, Diego J, Arenas-Aranda P, Gariglio, et al. Shorter telomeres and high telomerase activity correlate with a highly aggressive phenotype in breast cancer cell lines. Tumor Biology: J Int Soc Oncodevelopmental Biology Med. 2016;37(9):11917–26. 10.1007/s13277-016-5045-710.1007/s13277-016-5045-7
44. Ennour-Idrissi K, Maunsell E. and Caroline Diorio. Telomere length and breast cancer prognosis: a systematic review. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology. 2017;26(1):3–10. 10.1158/1055-9965.EPI-16-0343
