Abstract

Objective

   Telomere maintenance mechanism significantly impacts the metastasis,
   progression, and survival of breast cancer (BC) patients. This study
   aimed to investigate the role of telomere maintenance-related genes
   (TMRGs) in BC prognosis and to construct a related prognostic model.

Methods

   Differentially expressed genes were identified from the TCGA-BC cohort,
   and functional enrichment analysis was conducted. TMRGs were sourced
   from the literature and intersected with DEGs. Candidate genes were
   selected using machine learning algorithms, including Lasso Cox, Random
   Forest, and XGBoost. Multivariate Cox regression analysis was conducted
   to construct a prognostic model and identify hub genes. Subsequent
   analyses included survival analysis, gene set enrichment analysis
   (GSEA), immune infiltration analysis, and drug sensitivity analysis of
   the hub genes. Finally, in vitro experiments were conducted to validate
   the expression of the hub genes.

Results

   A total of 1329 differentially expressed TMRGs were analyzed, with 128
   significantly associated with overall survival. Machine learning
   identified 7 prognosis-related TMRGs: MECP2, PCMT1, PFKL, PTMA, TAGLN2,
   TRMT5, and XRCC4. These genes were used to construct a prognostic
   model, with MECP2, PCMT1, PFKL, TAGLN2, and XRCC4 as harmful factors,
   while PTMA and TRMT5 were protective. The model demonstrated a
   significant prognostic value (AUC: 0.81, 0.72, 0.69 for 1-, 3-, and
   5-year, respectively). Survival analysis confirmed the prognostic
   relevance of these genes, and GSEA highlighted their roles in oxidative
   phosphorylation, glycolysis, and PI3K/AKT/mTOR signaling.

Conclusion

   The study identified 7 key TMRGs with significant prognostic value in
   BC. The constructed model effectively stratifies patient risk,
   providing a foundation for targeted therapies and personalized
   treatment strategies.

   Keywords: breast cancer, telomere, risk score, machine learning, immune
   infiltration, seven hub genes

Graphical Abstract

   [30]graphic file with name BCTT-17-225-g0001.jpg
   [31]Open in a new tab

Introduction

   Breast cancer (BC) is a malignant tumor originating from mammary
   epithelial cells and remains the most prevalent and deadly malignancy
   among women worldwide.[32]^1 From 2010 to 2019, the incidence of BC
   increased by 0.5% annually.[33]^2 In 2020, approximately 2.3 million
   cases of BC were diagnosed globally; this number is expected to exceed
   3 million cases annually by 2040.[34]^3 In China, BC patients are
   becoming younger, with most patients being diagnosed between the ages
   of 45 and 55.[35]^4 Depending on the stage and type of BC, current
   treatments include surgery, radiotherapy, chemotherapy, hormone
   therapy, and targeted therapy.[36]^5 Despite advancements in early
   detection and treatment, the prognosis of BC patients varies
   significantly. In personalized medicine, traditional prognostic
   markers, such as tumor size, histological grade, and lymph node status,
   may not provide sufficient guidance for tailoring treatment strategies
   in early-diagnosed BC patients.[37]^6^,[38]^7 This highlights the need
   for reliable prognostic biomarkers to optimize treatment.

   Telomeres are protective caps at the ends of chromosomes, playing a
   crucial role in maintaining genomic stability. With each cell division,
   telomeres shorten, and when critically shortened, they ultimately lead
   to cellular senescence or apoptosis.[39]^8 However, in cancer cells,
   mechanisms that maintain telomere length are often activated, allowing
   these cells to evade senescence and continue proliferating. This
   process is primarily mediated by two key mechanisms: telomerase and the
   alternative lengthening of telomeres (ALT) pathway. Telomerase, a
   ribonucleoprotein reverse transcriptase, adds telomeric repeats to
   chromosome ends, counteracting telomere shortening.[40]^9 It is
   reported that approximately 90% of cancers exhibit telomerase
   activity.[41]^10 In the 10% of cancers lacking telomerase expression,
   telomere length is maintained through the ALT pathway. Although the ALT
   pathway is less common, it remains significant, involving homologous
   recombination-based telomere elongation mechanisms.[42]^11 A review by
   Ricardo Leão detailed various genetic and epigenetic mechanisms leading
   to telomerase reverse transcriptase promoter (hTERT) upregulation in
   tumors and highlighted its strong potential as a biomarker.[43]^12
   Another study found that hypermethylation in specific regions of the
   hTERT promoter is a significant epigenetic marker in the development of
   BC.[44]^13 Yang et al further highlighted that therapies targeting
   hTERT and its regulatory molecules hold promise as viable strategies
   for BC treatment.[45]^14 Elsharawy et al demonstrated that NOP10, a
   factor essential for ribosome biogenesis and telomere maintenance, is
   significantly associated with aggressive BC characteristics, and its
   high expression is strongly correlated with shorter survival in BC
   patients.[46]^15

   Telomere maintenance-related genes (TMRGs) are closely associated with
   tumorigenesis and progression, including in BC.[47]^16 In-depth
   bioinformatics research into the role of TMRGs in BC holds promise for
   identifying new prognostic markers, providing clinicians with more
   references for diagnosis and treatment planning, and ultimately