RRM2 as a Key Biomarker and Therapeutic Target in Letrozole-Resistant ER+ Breast Cancer: Insights from Bioinformatics and Molecular Docking

RRM2 as a Key Biomarker and Therapeutic Target in Letrozole-Resistant ER+ Breast Cancer: Insights from Bioinformatics and Molecular Docking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article RRM2 as a Key Biomarker and Therapeutic Target in Letrozole-Resistant ER+ Breast Cancer: Insights from Bioinformatics and Molecular Docking Wan-Yu Hung, Shih-Chun Huang, Shou-Tung Chen, Chi-Chen Lin, Ming-Hon Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6183643/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Letrozole is a first-line aromatase inhibitor for estrogen receptor-positive (ER+) breast cancer; however, resistance develops in 20–30% of patients, limiting therapeutic efficacy. The inability to predict treatment response before therapy initiation remains a significant challenge, as no reliable biomarkers have been established. This study aimed to identify novel prognostic biomarkers and elucidate the molecular mechanisms underlying letrozole resistance using integrative bioinformatics and molecular docking approaches. Through weighted gene coexpression network analysis, a gene module highly associated with letrozole nonresponse was identified. Seven candidate genes—BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1—were significantly overexpressed in tumors and strongly correlated with poor survival in ER + breast cancer. Among them, RRM2 emerged as the most significant prognostic marker. Molecular docking analysis demonstrated that letrozole binds to RRM2, suggesting a potential competitive interaction with aromatase that may contribute to resistance. Validation in an independent letrozole-treated cohort confirmed RRM2’s strong prognostic value. These findings provide new insights into the molecular mechanisms driving letrozole resistance and identify RRM2 as both a prognostic biomarker and a potential therapeutic target. This study advances the understanding of endocrine resistance and offers promising avenues for biomarker-driven treatment strategies in ER + breast cancer. Biological sciences/Cancer/Breast cancer Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Cancer therapy Biological sciences/Cancer/Tumour biomarkers Letrozole resistance Estrogen receptor-positive breast cancer RRM2 Molecular docking Weighted Gene Coexpression Network Analysis (WGCNA) Prognostic biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related mortality among women worldwide [ 1 ]. With over 2.3 million new cases and 685,000 deaths in 2020, it is the most commonly diagnosed malignancy globally [ 1 ]. Approximately 70–75% of breast cancer cases are estrogen receptor-positive (ER+), with many also exhibiting progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) positivity [ 2 , 3 ]. Despite advances in early detection and treatment strategies, the heterogeneous nature of breast cancer continues to pose significant challenges in achieving optimal therapeutic outcomes [ 4 ]. The genetic landscape of breast cancer exhibits substantial interpatient variability, with germline mutations in genes such as BRCA1, BRCA2, and other susceptibility genes influencing both disease risk and treatment response [ 5 , 6 ]. This genetic heterogeneity underscores the increasing emphasis on precision medicine, where therapeutic strategies are tailored to individual genetic profiles. Single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) in drug-metabolizing enzymes and target pathways can significantly impact treatment efficacy and toxicity profiles, highlighting the necessity for genetically informed treatment selection [ 7 , 8 ]. The estrogen receptor alpha (ERα) pathway plays a critical role in breast cancer progression, particularly in ER + tumors, where estrogen signaling drives tumor growth [ 9 ]. Letrozole, a third-generation aromatase inhibitor (AI), is a cornerstone therapy for postmenopausal women with ER + breast cancer [ 10 ]. By inhibiting the conversion of androgens to estrogens, letrozole effectively reduces estrogen production, thereby suppressing hormone-dependent tumor growth [ 11 ]. However, clinical observations reveal considerable variability in treatment response, with approximately 20–30% of patients experiencing primary or acquired resistance [ 12 ]. Previous studies have demonstrated that dysregulation of the ERα pathway, cell cycle regulation, and genomic stability contribute to treatment resistance in breast cancer [ 13 ]. Currently, patient selection for letrozole therapy is based primarily on ER status and clinical parameters. While these factors provide valuable information, they fail to adequately predict treatment response, leading to suboptimal clinical outcomes in a substantial proportion of patients. The inability to accurately predict treatment response before therapy initiation represents a significant clinical challenge, potentially exposing nonresponsive patients to unnecessary side effects while delaying the transition to alternative treatment options. Given the complexity of breast cancer resistance mechanisms, multiple studies have explored molecular biomarkers that predict letrozole response [ 14 ]. Gene expression profiling and pathway analyses have identified potential candidates; however, most of these studies focus on individual genes, neglecting the broader biological networks that drive treatment resistance. Furthermore, while several biomarkers have been proposed, their clinical validation and integration into practical diagnostic tools remain limited. To address these gaps, we employed Weighted Gene Coexpression Network Analysis (WGCNA), a systems biology-based approach, to analyze publicly available gene expression data from the Gene Expression Omnibus (GEO) database. WGCNA offers distinct advantages by identifying modules of highly correlated genes rather than analyzing single-gene associations, thereby improving the accuracy of biomarker discovery [ 15 ]. This network-based approach enables the identification of biologically meaningful gene signatures, which may predict letrozole response more effectively. A key focus of our study was to explore the potential role of ribonucleotide reductase regulatory subunit M2 (RRM2) in letrozole resistance. While RRM2 overexpression has been associated with tumor progression and drug resistance, its specific involvement in letrozole resistance remains unclear [ 16 ]. To investigate this, we conducted molecular docking analysis to examine the potential interaction between RRM2 and letrozole. Our results demonstrate that letrozole can bind to RRM2, suggesting a possible competitive mechanism wherein RRM2 may interfere with letrozole’s ability to inhibit aromatase. This raises the possibility that RRM2 overexpression could contribute to letrozole resistance by limiting letrozole’s availability for aromatase inhibition, potentially altering the effectiveness of endocrine therapy. Furthermore, RRM2 overexpression has been shown to increase mutation frequency, leading to the accumulation of RRM2 dimers. This process alters the composition of the ribonucleotide reductase complex, which is crucial for DNA synthesis and repair, ultimately promoting tumorigenesis and cancer progression. These findings suggest that RRM2-mediated resistance may not only involve direct competition with aromatase but also contribute to genomic instability, further complicating breast cancer treatment. By integrating clinical information with molecular data, our study seeks to develop a robust biomarker panel that can guide treatment decisions and potentially lead to more effective therapeutic strategies. Additionally, our findings provide new insights into the mechanisms of letrozole resistance, which may pave the way for novel therapeutic targets aimed at overcoming drug resistance in ER + breast cancer. Materials and methods Data Acquisition and Preprocessing The gene expression profiling datasets GSE20181 and GSE41994 were obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). The GSE20181 dataset utilized the Affymetrix Human Genome U133A Array platform (GPL96), whereas the GSE41994 dataset was based on the Illumina HumanHT-12 V4.0 expression beadchip platform (GPL16233). For the GSE20181 dataset, probe annotation was performed by using gene annotation files to map probe identification numbers (IDs) to gene symbols. In cases where multiple probe IDs corresponded to the same gene, the average expression value was calculated to ensure accurate representation of gene-level expression. This processed dataset was subsequently utilized for WGCNA to identify coexpressed gene modules. The GSE41994 dataset was used for Kaplan‒Meier survival analysis to assess the prognostic significance of the candidate genes. WGCNA WGCNA was performed on the GSE20181 dataset. Initially, the gene expression data were preprocessed and filtered to retain genes with variance above the third quartile for subsequent analysis. Sample clustering was conducted via the average linkage method, followed by sample quality assessment. Soft-thresholding analysis was performed to determine the optimal power parameter for achieving scale-free topology. Network construction utilized an unsigned network approach, with the minimum module size set to 30 genes and the module merging threshold set at 0.25. Genes were clustered into modules via the dynamic tree-cutting algorithm, and module eigengenes were calculated to evaluate module‒trait relationships. Finally, Pearson correlation analysis was used to assess the associations between modules and phenotypic traits, with the results visualized in a heatmap. Gene expression analysis Gene expression analysis was performed via the GEPIA database ( http://gepia.cancer-pku.cn/ ). The analysis parameters were set as follows: dataset selection = BRCA, p value cutoff = 0.01, log2FC (fold change) cutoff = 1, and matched TCGA normal and GTEx data, integrating TCGA tumor samples (n = 1085) and GTEx normal tissue data (n = 291). Survival analysis Survival analysis was conducted via two approaches: (1) Kaplan‒Meier survival analysis using letrozole-treated patients from the GSE41994 dataset, where patients were stratified into high-expression (n = 15) and low-expression (n = 14) groups on the basis of median gene expression; (2) validation analysis via the Kaplan‒Meier plotter online tool ( http://kmplot.com/analysis/ ). For both analyses, the log-rank test was employed to evaluate statistical significance between groups, with p < 0.05 considered statistically significant. Bioinformatics analysis and molecular docking To evaluate potential interactions between RRM2 protein and letrozole, molecular docking studies were performed using AutoDock Vina (v1.1.2) according to standard procedures [ 17 ]. MGLTools (v1.5.7) was used for docking preparation and visualization ( https://ccsb.scripps.edu/mgltools/ ). The RRM2 monomer structure (A0A7P0SBL1) was retrieved from the AlphaFold Protein Structure Database (Fig. 1 A), while letrozole (PubChem CID: 3902) was obtained from the NCBI PubChem database (Fig. 1 B). AutoDock Vina predicted the docking site of letrozole with RRM2, and the docking results were visualized using PyMOL (v2.3.2). Results Construction of Gene Coexpression Networks and Identification of Nonresponse-Associated Modules in Letrozole-Treated Breast Cancer Patients Letrozole is an AI used to treat hormone receptor-positive breast cancer, particularly in postmenopausal women. It works by reducing the production of estrogen in the body. By blocking estrogen production, letrozole helps to slow or stop the growth of breast cancer cells. However, not every patient responds to this drug. To identify key gene modules associated with the response of breast cancer patients to letrozole treatment, we performed WGCNA on the GSE20181 dataset. This dataset comprises gene expression profiles from breast cancer patients treated with letrozole (2.5 mg/day) at two timepoints: pretreatment and 10–14 days posttreatment. The cohort included 58 patients (15 nonresponders, 37 responders, and 6 nonassessable patients) (Fig. 2 A). To construct scale-free networks, we first analyzed the network topology for various soft-threshold powers. The scale independence and mean connectivity analysis revealed that a soft-threshold power of 4 was optimal for maintaining high scale-free topology model fit while ensuring adequate mean connectivity (Fig. 2 B). This threshold was used for subsequent analyses to transform the adjacency matrix. Hierarchical clustering of the topological overlap matrix (TOM) dissimilarity measure identified distinct gene modules, which are visualized in a cluster dendrogram with different colors representing individual modules (Fig. 2 C). To identify modules significantly associated with treatment response, we performed module‒trait relationship analysis (Fig. 2 D). The analysis yielded 29 modules; notably, the module‒trait correlation heatmap revealed a strong correlation between the light green module and clinical nonresponse to letrozole (R 2 = 0.46, p = 6e‒04) (Fig. 2 D). These findings suggest that the light green module identified through WGCNA is closely associated with clinical nonresponse to letrozole in breast cancer patients. This finding implies that the genes within this module may play significant roles in determining the efficacy of letrozole treatment. Functional enrichment and network analysis of light green module genes reveal cell cycle-related processes To elucidate the biological functions and molecular mechanisms associated with genes in the light green module, we performed comprehensive functional enrichment and network analyses via Metascape. This analysis aimed to identify key pathways and biological processes that might influence breast cancer prognosis. The functional enrichment analysis revealed multiple significantly enriched terms and pathways (Fig. 3 A). The most significantly enriched term was “cell cycle, mitotic” (R-HSA-69278), followed by “mitotic cell cycle process” (GO:1903047) and “retinoblastoma gene in cancer” (WP2446). Other highly enriched terms included “regulation of nuclear division” (GO:0051783), “DNA metabolic process” (GO:0006259), and “cell cycle checkpoints” (R-HSA-69620). The analysis also revealed enrichment in processes related to the meiotic cell cycle (GO:0051321), mitotic cytokinesis (GO:0000281), and the centrosome cycle (GO:0007098). Additionally, several cancer-relevant pathways were enriched, including “gastric cancer network 1” (WP2361) and “p75 NTR receptor-mediated signaling” (R-HSA-193704). Network analysis of these enriched terms revealed complex interactions and relationships between different biological processes (Fig. 3 B). The visualization demonstrated dense interconnectivity among cell cycle-related processes, DNA repair mechanisms, and cancer-associated pathways. This network analysis highlighted the central role of cell cycle regulation and its interaction with other cancer-related processes, suggesting that dysregulation of these pathways might significantly impact breast cancer prognosis. The predominance of cell cycle-related terms and their extensive network connections suggest that genes within the light green module may influence breast cancer prognosis primarily through the regulation of cell cycle processes and associated pathways. Identification and Expression Analysis of Key Candidate Genes in Breast Cancer To identify potential key genes involved in breast cancer prognosis and treatment response, we performed an integrative analysis comparing genes from the light green module with genes that were differentially expressed between nonresponders and responders in the pretreatment group. Through this integrative approach, we aimed to identify genes that were both coexpressed and differentially regulated in the context of treatment response. Venn diagram analysis revealed the intersection between the genes in the light green module (68 genes) and the genes that were differentially expressed in the pretreatment group (598 genes, P < 0.05) (Fig. 4 A). This analysis identified 11 overlapping genes (1.7% of the total), representing potential key genes involved in the treatment response of patients with breast cancer. To validate the clinical relevance of these candidate genes, we analyzed the expression levels of eleven genes (BUB1B, CENPU, GAREM, GMNN, KIF11, MCM6, MLLT11, NUSAP1, PRC1, RRM2, and TRIP13) in breast cancer tissues compared with adjacent normal tissues. All eleven genes presented different expression patterns between tumor tissues and normal tissues. Notably, BUB1B, CENPU, KIF11, NUSAP1, PRC1, RRM2, and TRIP13 were significantly overexpressed in tumor tissues, with median expression levels approximately 2–3-fold greater than those in normal tissues (P 0.05) (Fig. 4 B). These findings suggest that these eleven genes, particularly seven genes, namely, BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1, which exhibit consistent overexpression patterns, may play crucial roles in breast cancer development and progression. The differential expression patterns of these genes between tumor and normal tissues support their potential utility as prognostic biomarkers and therapeutic targets in breast cancer treatment. Prognostic Value Analysis of Key Candidate Genes in Breast Cancer Survival To evaluate the prognostic significance of the identified candidate genes, we performed survival analysis via Kaplan‒Meier plots for both overall survival (OS) and relapse-free survival (RFS) in breast cancer patients. The analysis was conducted with a 120-month follow-up threshold, with patients stratified by the median expression level of each gene. OS analysis revealed that high expression levels of several candidate genes were significantly associated with poor overall survival (Fig. 5 A). Specifically, all seven selected genes (BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1) were significantly correlated with poor survival outcomes. Similarly, the RFS analysis demonstrated that elevated expression of these genes was also associated with an increased risk of disease recurrence (Fig. 5 B). All seven selected genes were significantly correlated with shorter RFS. These survival analyses consistently demonstrated that higher expression levels of these candidate genes are associated with poorer clinical outcomes in breast cancer patients, suggesting their potential utility as prognostic biomarkers. The consistent patterns observed across both OS and RFS further strengthen their clinical significance in breast cancer progression and patient survival. Differential Prognostic Value of Candidate Genes in ER-positive and ER-negative Breast Cancer Subtypes To further investigate the prognostic significance of our candidate genes in different breast cancer subtypes, we performed stratified survival analyses on the basis of estrogen receptor (ER) status. We examined OS in both ER + and ER-negative (ER-) breast cancer patients over a 120-month follow-up period. In ER + breast cancer patients, most candidate genes were significantly associated with poor overall survival (Fig. 6 A). BUB1B, KIF11, RRM2, NUSAP1, TRIP13, and PRC1 all demonstrated strong negative correlations with survival. CENPU showed a similar trend but with lower statistical significance (P = 0.35). These results suggest that high expression of these genes is particularly harmful to survival in ER + breast cancer patients. Interestingly, the prognostic value of these genes showed distinctly different patterns in ER- breast cancer patients (Fig. 6 B). Most notably, BUB1B expression was significantly positively correlated with survival (P = 0.0045), indicating that higher BUB1B expression was associated with better outcomes in ER- patients. The other genes were not significantly associated with ER-related breast cancer: KIF11 (P = 0.082), RRM2 (P = 0.95), NUSAP1 (P = 0.075), PRC1 (P = 0.087), and TRIP13 (P = 0.23). These contrasting results between ER + and ER- breast cancers suggest that the prognostic value of these genes is highly dependent on ER status, highlighting the importance of considering molecular subtypes when evaluating potential prognostic markers in breast cancer. Impact of Candidate Genes on Relapse-Free Survival in ER + and ER- Breast Cancer Subtypes To further evaluate the clinical significance of our candidate genes, we analyzed their associations with RFS in both ER + and ER- breast cancer patients over a 120-month follow-up period. In ER + breast cancer patients, all seven candidate genes demonstrated strong negative correlations with RFS (Fig. 7 A). BUB1B, CENPU, KIF11, RRM2, NUSAP1, PRC1, and TRIP13 were associated with a significantly increased risk of disease recurrence in patients with high expression levels. The remarkably low P values and consistent hazard ratios above 1.75 indicate that these genes are robust predictors of recurrence risk in patients with ER + breast cancer. In contrast, the analysis of ER- breast cancer patients revealed markedly different patterns (Fig. 7 B). None of the candidate genes were significantly associated with RFS in ER- patients. The hazard ratios close to 1.0 and nonsignificant P values suggest that these genes may not be reliable predictors of recurrence in patients with ER-related breast cancer. These findings further emphasize the molecular subtype-specific nature of these prognostic markers, particularly highlighting their potential utility in predicting recurrence risk, especially in ER + breast cancer patients. Validation of Candidate Gene Prognostic Value in an Independent Letrozole-Treated Cohort To further validate the prognostic significance of our identified candidate genes in ER + breast cancer patients receiving endocrine therapy, we analyzed their expression patterns in an independent dataset (GSE41994) comprising patients treated with letrozole. The analysis focused on progression-free survival (PFS) outcomes stratified by high and low expression levels of the hub genes. Our analysis revealed significant associations between gene expression levels and clinical outcomes. Among the validated genes, RRM2 demonstrated the strongest prognostic value (P = 0.0002), with high expression correlating with significantly shorter PFS. Similarly, KIF11 and PRC1 showed strong prognostic significance (P = 0.001 and P = 0.002, respectively), where elevated expression levels were associated with poor clinical outcomes. TRIP13 and BUB1B also exhibited significant prognostic value (P = 0.017 and P = 0.035, respectively), with high expression correlating with reduced PFS. Although NUSAP1 showed a trend toward significance (P = 0.053), patients with high expression levels still demonstrated notably shorter PFS than did those with low expression levels (Fig. 8 ). Letrozole Targets the C-Terminal Site of RRM2 To explore potential targeting sites for RRM2 dimer formation, molecular docking studies were performed using AutoDock Vina (v1.1.2) to identify compounds with high docking scores. The docking results (Fig. 9 A) showed binding efficiencies ranging from − 8 to -6.7 kcal/mol, with values lower than − 6 kcal/mol indicating stronger binding affinity. These findings suggest that letrozole exhibits the highest binding free energy for RRM2. Among the top three docking results, all binding sites were localized at the C-terminal of RRM2 (Fig. 9 C, Supplementary Fig. S1 A and S1B). Given that protein dimerization is essential for functional enzyme complex formation, we further investigated whether letrozole influences RRM2 dimerization. The docking results were superimposed onto the RRM2 dimer to evaluate its potential effect on dimerization (Fig. 9 B). The dimeric structure of RRM2 was predicted using AlphaFold2 Multimer, confirming that letrozole does not interfere with dimerization, as its docking pose was not positioned at the center of the RRM2 dimer interface. Therefore, letrozole did not affect RRM2 dimer formation. Additionally, a detailed analysis of the best binding pose between RRM2 and letrozole was conducted using Discovery Studio. The results (Fig. 9 C) revealed that letrozole interacts with R214 and K308 of RRM2 through noncovalent interactions, including π-sigma and π-alkyl interactions. These findings suggest that letrozole strongly interacts with RRM2 through noncovalent interactions, providing insights into a potential mechanism by which letrozole exerts its function. Validation of Candidate Genes and Potential Mechanism of Letrozole Action The validation of key candidate genes in an independent letrozole-treated cohort strengthens their clinical potential as prognostic biomarkers for ER + breast cancer patients undergoing endocrine therapy. The significant association between these gene expression levels and progression-free survival (PFS) suggests their potential role in treatment response. Further investigations are required to elucidate the underlying molecular mechanisms driving these differential responses and to refine therapeutic strategies for improved patient outcomes. Expanding this research by integrating additional datasets and conducting experimental studies will provide deeper insights into the functional involvement of these candidate genes in breast cancer progression and endocrine resistance. Moreover, molecular docking studies indicate that letrozole may exert its therapeutic effect by directly interacting with RRM2, particularly at the C-terminal site, through strong noncovalent interactions with R214 and K308. The binding affinity and structural analyses suggest a potential role of RRM2-related pathways in letrozole response. While letrozole does not disrupt RRM2 dimerization, its high-affinity binding to specific residues may influence downstream regulatory mechanisms. These findings collectively highlight a novel potential mechanism of action for letrozole and suggest that RRM2 may play a crucial role in letrozole resistance, emphasizing the need for further functional validation of this interaction. Discussion Key Findings In this study, we employed WGCNA to identify and validate novel prognostic biomarkers associated with the response of breast cancer patients to letrozole. Our analysis revealed seven key genes (BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1) that consistently demonstrated significant prognostic value, particularly in ER + breast cancer patients. These genes, primarily involved in cell cycle regulation, exhibited strong associations with overall survival (OS), relapse-free survival (RFS), and progression-free survival (PFS) across multiple independent cohorts. The identification of cell cycle-related genes as predictors of letrozole response aligns with previous studies investigating resistance mechanisms in endocrine therapy. Dysregulation of cell cycle checkpoints has been implicated in therapeutic resistance in breast cancer [ 18 – 21 ]. For example, aberrant expression of cell cycle regulators has been correlated with poor endocrine therapy response, emphasizing the critical role of cell cycle progression in hormone receptor-positive breast cancer [ 22 , 23 ]. Our findings extend these observations by identifying specific cell cycle-related genes that could serve as predictive biomarkers. Among these genes, RRM2 demonstrated the strongest prognostic value in the validation cohort (P = 0.0002), consistent with prior findings linking RRM2 overexpression to cancer progression and drug resistance. Previous studies have shown that RRM2 overexpression promotes breast cancer cell proliferation and metastasis, and its elevated levels are correlated with poor prognosis in multiple cancer types [ 24 , 25 ]. Similarly, KIF11 and PRC1, which also showed strong prognostic significance (P = 0.001 and P = 0.002, respectively), have been implicated in cell division and chromosomal stability [ 26 , 27 ]. A particularly noteworthy finding is the differential prognostic value of these genes between ER + and ER- breast cancer subtypes. While high expression consistently predicts poor outcomes in ER + patients, its prognostic significance is either absent or reversed in ER- patients. This molecular subtype-specific pattern supports the increasing recognition that breast cancer is a heterogeneous disease, necessitating personalized therapeutic approaches. Similar subtype-specific biomarker patterns have been observed in previous studies investigating endocrine therapy response [ 28 ]. Our functional enrichment analysis revealed significant enrichment in cell cycle-related processes, particularly mitotic regulation and DNA replication, which aligns with previous findings that cell cycle dysregulation is a key mechanism of endocrine therapy resistance [ 29 , 30 ]. The strong correlation between the light green module and clinical nonresponse to letrozole (R² = 0.46, P = 6e-04) suggests that these cell cycle-related genes may serve as both prognostic markers and potential therapeutic targets. Additionally, the integration of WGCNA with differential expression analysis provided a robust approach for identifying clinically relevant biomarkers. While previous studies have often focused on individual genes, our network-based approach captured the complex interactions between genes and their collective impact on treatment response. This methodology aligns with recent trends in systems biology-based biomarker discovery, which have successfully been applied in other cancer types [ 31 , 32 ]. Collectively, these findings significantly enhance our understanding of letrozole resistance mechanisms and offer potential biomarkers for predicting treatment outcomes. Potential Mechanism of Letrozole Resistance Due to the heterogeneous nature of breast cancer, the development of drug resistance remains a significant challenge, leading to reduced treatment efficacy. Currently, there is no direct evidence proving that RRM2 interacts with letrozole to mediate drug resistance. However, our molecular docking results demonstrate that letrozole can bind to RRM2. Based on this finding, we propose that RRM2 competes with aromatase for binding to letrozole, thereby preventing letrozole from effectively inhibiting aromatase. Furthermore, RRM2 overexpression has been shown to increase mutation frequency, leading to the accumulation of RRM2 dimers. This process alters the composition of the ribonucleotide reductase complex, subsequently promoting tumorigenesis and cancer progression [ 33 , 34 ]. These findings suggest a novel potential mechanism of action for RRM2 in letrozole resistance, emphasizing the need for further functional validation and exploration of targeted therapeutic strategies aimed at overcoming this resistance mechanism. Clinical Implications, Limitations, and Future Directions The clinical implications of our findings are significant in advancing personalized medicine for breast cancer treatment. The identified seven-gene panel could serve as a pretreatment screening tool to identify patients likely to respond to letrozole therapy. Such a tool could enable more personalized treatment strategies, sparing nonresponsive patients from ineffective therapy and directing them toward alternative treatments earlier. The strong prognostic value demonstrated in ER + breast cancer patients suggests specific clinical relevance for this subgroup, where treatment decisions often present significant challenges. Moreover, the identification of cell cycle-related genes as key predictors of treatment response opens new therapeutic possibilities. The strong association between these genes and cell cycle regulation suggests that they may serve as potential therapeutic targets for overcoming letrozole resistance. For example, combining letrozole with cell cycle inhibitors could improve treatment outcomes in patients with high expression of these marker genes. This approach aligns with emerging trends in combination therapy strategies, which have shown promise in overcoming endocrine resistance [ 35 ]. However, several limitations should be considered when interpreting these findings. Although our results were validated across multiple datasets, including the independent GSE41994 cohort, prospective clinical trials are needed to confirm the predictive value of these biomarkers in real-world clinical settings. Additionally, the development of clinically applicable testing methods is necessary for practical implementation. Further functional studies are required to elucidate the precise mechanisms by which these genes influence treatment response. Understanding these mechanisms may lead to the development of more effective therapeutic strategies. Future research should also investigate potential therapeutic strategies targeting these pathways to overcome resistance, possibly through novel drug combinations or alternative treatment approaches. In conclusion, while our findings represent a significant step toward improved patient stratification in letrozole therapy, additional validation and mechanistic studies are required before clinical implementation. The potential role of RRM2 in letrozole resistance, as suggested by our molecular docking analysis, provides a novel avenue for future research, with potential implications for targeted treatment strategies in ER + breast cancer. Conclusion In this study, we identified and validated novel prognostic biomarkers associated with letrozole resistance in estrogen receptor-positive (ER+) breast cancer using WGCNA and molecular docking analysis. We discovered a gene module associated with treatment nonresponse, highlighting seven key genes (BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1) that demonstrated consistent prognostic significance across multiple datasets, particularly in ER + breast cancer patients. The differential prognostic value between ER + and ER- subtypes underscores the importance of molecular subtype-specific treatment strategies in breast cancer management. Additionally, molecular docking analysis confirmed that letrozole directly binds to RRM2, suggesting a potential competitive interaction with aromatase, which may contribute to letrozole resistance. RRM2 overexpression has been associated with increased mutation frequency and ribonucleotide reductase complex alterations, further promoting tumor progression. These findings provide new insights into letrozole resistance mechanisms, linking cell cycle dysregulation and RRM2-mediated resistance to endocrine therapy failure. Strong validation in an independent letrozole-treated cohort supports the clinical relevance of these biomarkers. Our findings highlight RRM2 as a key mediator of letrozole resistance, offering a potential predictive marker and therapeutic target. Future research should focus on prospective validation and functional studies to further elucidate the mechanistic role of these genes in letrozole resistance, paving the way for precision medicine approaches in ER + breast cancer treatment. Declarations Ethics approval and consent to participate All data used in this study were obtained from publicly available datasets (GSE20181 and GSE41994) in the Gene Expression Omnibus (GEO) database. These datasets were originally approved by the respective ethics committees of the primary studies. Since this research involves secondary analysis of de-identified, publicly available data, no additional ethical approval or participant consent was required. Consent for publication Consent for publication is not applicable, as this manuscript does not include any individual person’s data in any form (e.g., identifiable images or personal details). Availability of data and materials The data that support the findings of this study are available from the corresponding authors upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Author Contributions Conceptualization: Wan-Yu Hung, Chi-Chen Lin and Ming-Hon Hou; methodology: Chi-Chen Lin and Ming-Hon Hou; validation: Wan-Yu Hung; formal analysis: Shou-Tung Chen; investigation: Chi-Chen Lin and Wan-Yu Hung; resources: Shou-Tung Chen; data curation: Ming-Hon Hou, Shih-Chun Huang and Wan-Yu Hung; writing—original draft preparation:Wan-Yu Hung; writing—review and editing: Wan-Yu Hung, Chi-Chen Lin and Ming-Hon Hou. All the authors have read and agreed to the published version of the manuscript. Acknowledgments Not applicable. References Arnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne M. et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast 66 ,15-23(2022). Walter, V., Fischer, C., Deutsch, T.M., Ersing, C., Nees, J., Schütz, F. et al. Estrogen, progesterone, and human epidermal growth factor receptor 2 discordance between primary and metastatic breast cancer. Breast Cancer Res. Treat. 183(1) ,137-144(2020). Ciruelos Gil, E.M. Targeting the PI3K/AKT/mTOR pathway in estrogen receptor-positive breast cancer. Cancer Treat. Rev. 40(7) ,862-871(2014). Tuasha, N., Petros, B. Heterogeneity of Tumors in Breast Cancer: Implications and Prospects for Prognosis and Therapeutics. Scientifica (Cairo) . 2020 ,4736091;10.1155/2020/4736091(2020). Welcsh, P.L., King, M.C. BRCA1 and BRCA2 and the genetics of breast and ovarian cancer. Hum. Mol. Genet. 10(7) ,705-713(2001). Li, Y.Y., Jones, S.J. Drug repositioning for personalized medicine. Genome Med. 4(3) ,27. 10.1186/gm326(2012). He, Y., Hoskins, J.M., McLeod, H.L. Copy number variants in pharmacogenetic genes. Trends Mol. Med. 17(5) , 244-251(2011). Her, L., Zhu, H.J. Carboxylesterase 1 and Precision Pharmacotherapy: Pharmacogenetics and Nongenetic Regulators. Drug Metab. Dispos. 48(3), 230-244(2020). Miziak, P., Baran, M., Błaszczak, E., Przybyszewska-Podstawka, A., Kałafut, J., Smok-Kalwat, J. et al. Estrogen Receptor Signaling in Breast Cancer. Cancers (Basel) . 15(19) ,4689. 10.3390/cancers15194689(2023). Koeberle, D., Thuerlimann, B. Letrozole as upfront endocrine therapy for postmenopausal women with hormone-sensitive breast cancer: BIG 1-98. Breast Cancer Res . Treat. 105 Suppl 1(Suppl 1) ,55-66(2007). Bhatnagar, A.S. The discovery and mechanism of action of letrozole. Breast Cancer Res . Treat. 105 Suppl 1(Suppl 1) ,7-17(2007). Sim, S.H., Yang, H.N., Jeon, S.Y., Lee, K.S., Park, I.H. Mutation analysis using cell-free DNA for endocrine therapy in patients with HR+ metastatic breast cancer. Sci . Rep. 11(1) ,55-66(2021). Need, E.F., Selth, L.A., Harris, T.J., Birrell, S.N., Tilley, W.D., Buchanan G. Research resource: interplay between the genomic and transcriptional networks of androgen receptor and estrogen receptor α in luminal breast cancer cells. Mol . Endocrinol. 26(11) ,1941-1952(2012). Mosly, D., Turnbull, A., Sims, A., Ward, C., Langdon, S. Predictive markers of endocrine response in breast cancer. World J . Exp . Med. 8(1) ,1-7(2018). van Dam, S., Võsa, U., van der Graaf, A., Franke, L., de Magalhães, J.P. Gene coexpression analysis for functional classification and gene-disease predictions. Brief Bioinform. 19(4) ,575-592(2018). Kholodenko, B.N. Drug Resistance Resulting from Kinase Dimerization Is Rationalized by Thermodynamic Factors Describing Allosteric Inhibitor Effects. Cell Rep. 12(11) ,1939-49(2015). Forli, S., Huey, R., Pique, M.E., Sanner, M.F., Goodsell, D.S., Olson, A.J. et al. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat . Protoc . 11 ,905-919(2016). Hanker, A.B., Sudhan, D.R., Arteaga, C.L. Overcoming Endocrine Resistance in Breast Cancer. Cancer Cell. 37(4) ,496-513(2020). Stewart, Z.A., Westfall, M.D., Pietenpol, J.A. Cell-cycle dysregulation and anticancer therapy. Trends Pharmacol . Sci. 24(3) ,139-145(2003). Thu, K.L., Soria-Bretones, I., Mak, T.W., Cescon, D.W. Targeting the cell cycle in breast cancer: toward the next phase. Cell Cycle . 17(15) ,1871-1885(2018). Visconti, R., Della Monica, R., Grieco, D. Cell cycle checkpoint in cancer: a therapeutically targetable double-edged sword. J . Exp . Clin . Cancer Res. 35(1) ,153(2016). McNeil, C.M., Sergio, C.M., Anderson, LR, Inman, C.K., Eggleton, S.A., Murphy, N.C. et al. c-Myc overexpression and endocrine resistance in breast cancer. J . Steroid Biochem . Mol . Biol. 102(1-5) ,147-155(2006). Petrossian. K., Kanaya. N., Lo. C., Hsu. P.Y., Nguyen. D., Yang. L. et al. ERα-mediated cell cycle progression is an important requisite for CDK4/6 inhibitor response in HR+ breast cancer. Oncotarget . 9(45) ,27736-27751(2018). Zhuang, S., Li, L., Zang, Y., Li, G., Wang, F. RRM2 elicits the metastatic potential of breast cancer cells by regulating cell invasion, migration and VEGF expression via the PI3K/AKT signaling. Oncol . Lett. 19(4) ,3349-3355(2020). Zhou, Z., Song, Q., Yang, Y., Wang, L., Wu, Z. Comprehensive Landscape of RRM2 with Immune Infiltration in Pan-Cancer. Cancers (Basel) . 14(12) ,2938(2022). Meißner, L., Niese, L., Schüring, I., Mitra, A., Diez, S. Human kinesin-5 KIF11 drives the helical motion of anti-parallel and parallel microtubules around each other. EMBO J. 43(7) ,1244-1256(2024). Li, X.H., Ju, J.Q., Pan, Z.N., Wang, H.H., Wa,n X., Pan, M.H. et al. PRC1 is a critical regulator for anaphase spindle midzone assembly and cytokinesis in mouse oocyte meiosis. FEBS J. 288(9) ,3055-3067(2021). Jeong, Y., Bae, S.Y., You, D., Jung, S.P., Choi, H.J., Kim, I. et al. EGFR is a Therapeutic Target in Hormone Receptor-Positive Breast Cancer. Cell Physiol . Biochem. 53(5) ,805-819(2019). Glaviano, A., Wander, S.A., Baird, R.D., Yap, K.C., Lam, H.Y., Toi, M. et al. Mechanisms of sensitivity and resistance to CDK4/CDK6 inhibitors in hormone receptor-positive breast cancer treatment. Drug Resist . Updat. 76 ,101103.10.1016/j.drup.2024.101103(2024). Miller, T.W. Endocrine resistance: what do we know?. Am . Soc . Clin . Oncol . Educ . Book. 10.14694/EdBook_AM.2013.33.e37(2013). Sadat Kalaki, N., Ahmadzadeh, M., Najafi, M., Mobasheri, M., Ajdarkosh, H., Karbalaie Niya, M.H. Systems biology approach to identify biomarkers and therapeutic targets for colorectal cancer. Biochem . Biophys . Rep. 37 ,101633. 10.1016/j.bbrep.2023.101633(2024). Sheng, K.L., Kang, L., Pridham, K.J., Dunkenberger, L.E., Sheng, Z., Varghese, R.T. An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression. Sci . Rep. 10(1),21246(2020). Xu, X., Page, J.L., Surtees, J.A., Liu, H., Lagedrost, S., Lu Y. et al. Broad overexpression of ribonucleotide reductase genes in mice specifically induces lung neoplasms. Cancer Res. 68(8) ,2652-2660(2008). Chabes, A., Thelander, L. Controlled protein degradation regulates ribonucleotide reductase activity in proliferating mammalian cells during the normal cell cycle and in response to DNA damage and replication blocks. J . Biol . Chem. 275(23) ,17747-53(2000). Yuan, Y., Lee, J.S., Yost, S.E., Frankel, P.H., Ruel, C., Egelston, C.A. et al. Phase I/II trial of palbociclib, pembrolizumab and letrozole in patients with hormone receptor-positive metastatic breast cancer. Eur. J. Cancer. 154 ,11-20(2021). Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 15 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers invited by journal 19 Mar, 2025 Editor assigned by journal 19 Mar, 2025 Editor invited by journal 19 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 08 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6183643","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":434551121,"identity":"bfdbf9ea-727f-49ae-abd6-637364fafd8d","order_by":0,"name":"Wan-Yu Hung","email":"","orcid":"","institution":"National Chung-Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Wan-Yu","middleName":"","lastName":"Hung","suffix":""},{"id":434551124,"identity":"338b8a3f-9069-45d3-8a8f-4bb02d995bb0","order_by":1,"name":"Shih-Chun Huang","email":"","orcid":"","institution":"National Chung-Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Shih-Chun","middleName":"","lastName":"Huang","suffix":""},{"id":434551127,"identity":"7b7f9a09-db7c-42c5-b44e-b54fed704b90","order_by":2,"name":"Shou-Tung Chen","email":"","orcid":"","institution":"Changhua Christian Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shou-Tung","middleName":"","lastName":"Chen","suffix":""},{"id":434551130,"identity":"cc42d374-2959-4cd4-8d91-a9844fa07369","order_by":3,"name":"Chi-Chen Lin","email":"","orcid":"","institution":"National Chung Hsing University","correspondingAuthor":false,"prefix":"","firstName":"Chi-Chen","middleName":"","lastName":"Lin","suffix":""},{"id":434551133,"identity":"30ea9f55-a469-4bca-b84b-403892a59661","order_by":4,"name":"Ming-Hon Hou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYDACCR4Ghg8MDAlgNtFaGGcgtBgQp4WZhyQturN7Dz623WGXZ3CA+eBtHoY/iQ2EtJjdOZdsnHsmudjgAFuyNQ+DARFabuSYSee2HUjccIDHTBqoJZc4LZZgLfzfSNDCCLGFjWgtxoa9bcmJMw+zGVvOMTCuJ0aL4YOfbXaJfcebH954UyFnTEgHEmAGEcTE5CgYBaNgFIwCwgAAOw46KhFH630AAAAASUVORK5CYII=","orcid":"","institution":"National Chung-Hsing University","correspondingAuthor":true,"prefix":"","firstName":"Ming-Hon","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2025-03-08 10:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6183643/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6183643/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79575329,"identity":"ae3d77f4-0ee3-4288-b801-db17dd60b5cf","added_by":"auto","created_at":"2025-03-31 11:14:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":305137,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Structural overview of human RRM2 features. The RRM2 structure was obtained from AlphaFold and AlphaFold Multimer. (Upper) Structure of the RRM2 monomer. (Lower) Structure of the RRM2 dimer. (B) The chemical structure of Letrozole. The RRM2 structure is shown in a cartoon representation, with the N-terminal in orange, the C-terminal in pink, and other regions in green.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/979449aad4bf86cd833e4f7f.jpg"},{"id":79573429,"identity":"8b12ac2e-6ef3-4bc3-b385-9fc9c278058a","added_by":"auto","created_at":"2025-03-31 11:06:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":309355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWGCNA construction and identification of key modules associated with breast cancer treatment response. \u003c/strong\u003e(A) Distribution of patient samples in the GSE20181 dataset. The table shows the number of patients classified as nonresponders, responders, and nonassessable cases at different letrozole treatment timepoints (pretreatment and 2.5 mg/day for 10–14 days).\u003cstrong\u003e \u003c/strong\u003e(B) Determination of soft-thresholding power for WGCNA. Left panel: Analysis of the scale-free fit index for various soft-thresholding powers (β). Right panel: Analysis of mean connectivity for various soft-thresholding powers. Power 4 was selected as the optimal soft-thresholding power for subsequent analyses.\u003cstrong\u003e \u003c/strong\u003e(C) Hierarchical clustering dendrogram of genes on the basis of topological overlap. Each color represents a distinct module of coexpressed genes. The colored bars beneath the dendrogram indicate module assignment for each gene in the network.\u003cstrong\u003e \u003c/strong\u003e(D) Module‒trait relationship analysis showing the correlation between identified modules and treatment response. Each row corresponds to a module eigengene (ME), and the column represents the treatment response trait. The color scale ranges from -1 (blue) to 1 (red), indicating the strength and direction of correlation. The numbersin each cell represent the correlation coefficient, with the corresponding p values in parentheses.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/b016e65eee28cc17a6085b51.jpg"},{"id":79573436,"identity":"8566be81-6097-472f-876b-56a1809e39ed","added_by":"auto","created_at":"2025-03-31 11:06:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":251135,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment and network analysis of genes in the light green module. \u003c/strong\u003e(A) Bar plot showing significantly enriched terms and pathways identified via Metascape analysis. The x-axis represents the -log10(P) value, indicating statistical significance. The terms included Gene Ontology (GO) biological processes, Reactome (R-HSA) pathways, and WikiPathways (WP). The top 20 most significantly enriched terms are shown. (B) Network visualization of enriched terms via Metascape. Each node represents an enriched term, and edges represent similarities between terms. Node colors correspond to different functional clusters as indicated.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/427be202ee00b13fb9651cb8.jpg"},{"id":79575332,"identity":"fa00fcf1-00a9-4cfa-855f-0e1ae5fcb9c6","added_by":"auto","created_at":"2025-03-31 11:14:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":205480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and expression analysis of key genes in breast cancer. \u003c/strong\u003e(A) Venn diagram showing the overlap between genes in the light green module (68 genes, yellow circles) and genes differentially expressed between nonresponders and responders in the pretreatment group (598 genes, blue circles, paired t test: P\u0026lt;0.05). The overlapping region identified 11 genes (1.7%) common to both sets. (B) Box plots showing the transcription levels of eleven candidate genes (BUB1B, CENPU, GAREM, GMNN, KIF11, MCM6, MLLT11, NUSAP1, PRC1, RRM2, and TRIP13) in breast cancer tissues compared with adjacent normal tissues. The red boxes represent tumor samples, and the gray boxes represent normal tissue samples. The y-axis shows log2-transformed expression values. The horizontal line within each box represents the median, boxes representthe interquartile range, and whiskers extend to the minimum and maximum values, excluding outliers. Individual points represent outliers. *P \u0026lt; 0.05 indicates statistically significant differences in gene expression levels between tumor and normal tissues.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/594454139b0f2898d0dc02df.jpg"},{"id":79573448,"identity":"902812f5-5326-4543-991b-4e53fa91c90a","added_by":"auto","created_at":"2025-03-31 11:06:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":222063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eK‒M survival analysis of key candidate genes in breast cancer patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis of seven candidate genes (BUB1B, CENPU, KIF11, NUSAP1, TRIP13, ASF1, and RRM2) in breast cancer patients at the 120-month follow-up. Patients were stratified into high (red line) and low (black line) expression groups on the basis of median expression values. (A) OS, (B) RFS. For each gene, the number of patients at risk at different time points is shown below the corresponding plot. The hazard ratio (HR) and P value are indicated for each analysis. The x-axis represents time in months, and the y-axis representsthe probability of survival. The mRNA probes used are indicated.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/bba6de06e0b537c87fa0e677.jpg"},{"id":79578004,"identity":"7a6a9a21-f47f-422e-8f5b-2d2db404c3d1","added_by":"auto","created_at":"2025-03-31 11:30:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":227917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential prognostic value of candidate genes in ER-positive and ER-negative breast cancer patients. \u003c/strong\u003eK‒M survival analysis of seven candidate genes (BUB1B, CENPU, KIF11, RRM2, NUSAP1, PRC1, and TRIP13) stratified by ER status. (A) OS analysis of ER+ breast cancer patients. (B) OS analysis of ER- breast cancer patients. Patients were stratified into high (red line) and low (black line) expression groups on the basis of the median expression value of each gene. The analysis was performed with a 120-month follow-up period. The numbersbelow each plot indicate the number of patients at risk at the corresponding time points. HR and log-rank P values are shown for each analysis. The x-axis represents time in months, and the y-axis representsthe probability of survival. Statistical significance was set at P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/ad0c4b58553aad1e467925ed.jpg"},{"id":79573443,"identity":"19d5e94f-df2c-4b6e-beb9-ad7866fe648b","added_by":"auto","created_at":"2025-03-31 11:06:25","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":223546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of candidate gene expression and survival analysis in ER-positive and ER-negative breast cancer cohorts. \u003c/strong\u003eK‒M survival curves showing the relationship between candidate gene expression levels and RFS in patients with (A) ER+ breast cancer or (B) ER- breast cancer. Patients were stratified into high (red line) and low (black line) expression groups on the basis of the median expression value of each gene. HR and log-rank P values are shown for each analysis. The x-axis represents time in months, and the y-axis representsthe probability of survival. Statistical significance was set at P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/58299abe428dd020ccc76a59.jpg"},{"id":79576589,"identity":"405a19a2-57c8-4e6b-91d6-3037b07bb40a","added_by":"auto","created_at":"2025-03-31 11:22:26","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":335270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of candidate gene expression and survival analysis in letrozole-treated breast cancer patients via the GSE41994 dataset. \u003c/strong\u003eK‒M curves showing PFS analysis of six candidate genes (BUB1B, KIF11, RRM2, NUSAP1, PRC1, and TRIP13) in breast cancer patients treated with letrozole. Patients were stratified into high (n = 15, red lines) and low (n = 14, blue lines) expression groups on the basis of median expression values. The mRNA probes used are indicated. The survival probability is displayed on the y-axis, and the time in months is displayed on the x-axis. n, sample size. P values are shown as indicated. P values \u0026lt; 0.05 were considered statistically significant.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/f2be581514be1cf3939a73da.jpg"},{"id":79575339,"identity":"6aa7fe55-9d63-42bc-9d5a-dcbf79f79781","added_by":"auto","created_at":"2025-03-31 11:14:25","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":771009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDocking results of the RRM2–Letrozole complex. \u003c/strong\u003e(A) Predicted binding scores of Letrozole at various binding sites on RRM2. (B) Superimposition of the docking results onto the RRM2 dimer. (C) The best binding affinity of detailed interactions with RRM2 and Letrozole were analyzed using Discovery Studio. (Right) Interactions are represented by orange and green dashed lines. The structure is shown in a cartoon representation, with ligand-binding residues labeled and displayed as sticks.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/643ffef3e2cff2991f95e1ed.jpg"},{"id":79578978,"identity":"53baad0a-f5b5-468b-b54b-48c8d7b36e9a","added_by":"auto","created_at":"2025-03-31 11:38:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4287764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/14be5a4b-fd50-4f25-b8b4-55931bc7e1cc.pdf"},{"id":79573433,"identity":"07ff59bc-380a-4ed9-9bdb-3a9e32deedc3","added_by":"auto","created_at":"2025-03-31 11:06:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":358403,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6183643/v1/50d9c879f0ff3214722148e9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"RRM2 as a Key Biomarker and Therapeutic Target in Letrozole-Resistant ER+ Breast Cancer: Insights from Bioinformatics and Molecular Docking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related mortality among women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With over 2.3\u0026nbsp;million new cases and 685,000 deaths in 2020, it is the most commonly diagnosed malignancy globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 70\u0026ndash;75% of breast cancer cases are estrogen receptor-positive (ER+), with many also exhibiting progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) positivity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite advances in early detection and treatment strategies, the heterogeneous nature of breast cancer continues to pose significant challenges in achieving optimal therapeutic outcomes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe genetic landscape of breast cancer exhibits substantial interpatient variability, with germline mutations in genes such as BRCA1, BRCA2, and other susceptibility genes influencing both disease risk and treatment response [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This genetic heterogeneity underscores the increasing emphasis on precision medicine, where therapeutic strategies are tailored to individual genetic profiles. Single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) in drug-metabolizing enzymes and target pathways can significantly impact treatment efficacy and toxicity profiles, highlighting the necessity for genetically informed treatment selection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe estrogen receptor alpha (ERα) pathway plays a critical role in breast cancer progression, particularly in ER\u0026thinsp;+\u0026thinsp;tumors, where estrogen signaling drives tumor growth [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Letrozole, a third-generation aromatase inhibitor (AI), is a cornerstone therapy for postmenopausal women with ER\u0026thinsp;+\u0026thinsp;breast cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By inhibiting the conversion of androgens to estrogens, letrozole effectively reduces estrogen production, thereby suppressing hormone-dependent tumor growth [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, clinical observations reveal considerable variability in treatment response, with approximately 20\u0026ndash;30% of patients experiencing primary or acquired resistance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies have demonstrated that dysregulation of the ERα pathway, cell cycle regulation, and genomic stability contribute to treatment resistance in breast cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, patient selection for letrozole therapy is based primarily on ER status and clinical parameters. While these factors provide valuable information, they fail to adequately predict treatment response, leading to suboptimal clinical outcomes in a substantial proportion of patients. The inability to accurately predict treatment response before therapy initiation represents a significant clinical challenge, potentially exposing nonresponsive patients to unnecessary side effects while delaying the transition to alternative treatment options.\u003c/p\u003e \u003cp\u003eGiven the complexity of breast cancer resistance mechanisms, multiple studies have explored molecular biomarkers that predict letrozole response [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Gene expression profiling and pathway analyses have identified potential candidates; however, most of these studies focus on individual genes, neglecting the broader biological networks that drive treatment resistance. Furthermore, while several biomarkers have been proposed, their clinical validation and integration into practical diagnostic tools remain limited.\u003c/p\u003e \u003cp\u003eTo address these gaps, we employed Weighted Gene Coexpression Network Analysis (WGCNA), a systems biology-based approach, to analyze publicly available gene expression data from the Gene Expression Omnibus (GEO) database. WGCNA offers distinct advantages by identifying modules of highly correlated genes rather than analyzing single-gene associations, thereby improving the accuracy of biomarker discovery [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This network-based approach enables the identification of biologically meaningful gene signatures, which may predict letrozole response more effectively.\u003c/p\u003e \u003cp\u003eA key focus of our study was to explore the potential role of ribonucleotide reductase regulatory subunit M2 (RRM2) in letrozole resistance. While RRM2 overexpression has been associated with tumor progression and drug resistance, its specific involvement in letrozole resistance remains unclear [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To investigate this, we conducted molecular docking analysis to examine the potential interaction between RRM2 and letrozole. Our results demonstrate that letrozole can bind to RRM2, suggesting a possible competitive mechanism wherein RRM2 may interfere with letrozole\u0026rsquo;s ability to inhibit aromatase. This raises the possibility that RRM2 overexpression could contribute to letrozole resistance by limiting letrozole\u0026rsquo;s availability for aromatase inhibition, potentially altering the effectiveness of endocrine therapy.\u003c/p\u003e \u003cp\u003eFurthermore, RRM2 overexpression has been shown to increase mutation frequency, leading to the accumulation of RRM2 dimers. This process alters the composition of the ribonucleotide reductase complex, which is crucial for DNA synthesis and repair, ultimately promoting tumorigenesis and cancer progression. These findings suggest that RRM2-mediated resistance may not only involve direct competition with aromatase but also contribute to genomic instability, further complicating breast cancer treatment.\u003c/p\u003e \u003cp\u003eBy integrating clinical information with molecular data, our study seeks to develop a robust biomarker panel that can guide treatment decisions and potentially lead to more effective therapeutic strategies. Additionally, our findings provide new insights into the mechanisms of letrozole resistance, which may pave the way for novel therapeutic targets aimed at overcoming drug resistance in ER\u0026thinsp;+\u0026thinsp;breast cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eThe gene expression profiling datasets GSE20181 and GSE41994 were obtained from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE20181 dataset utilized the Affymetrix Human Genome U133A Array platform (GPL96), whereas the GSE41994 dataset was based on the Illumina HumanHT-12 V4.0 expression beadchip platform (GPL16233). For the GSE20181 dataset, probe annotation was performed by using gene annotation files to map probe identification numbers (IDs) to gene symbols. In cases where multiple probe IDs corresponded to the same gene, the average expression value was calculated to ensure accurate representation of gene-level expression. This processed dataset was subsequently utilized for WGCNA to identify coexpressed gene modules. The GSE41994 dataset was used for Kaplan‒Meier survival analysis to assess the prognostic significance of the candidate genes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWGCNA\u003c/h3\u003e\n\u003cp\u003eWGCNA was performed on the GSE20181 dataset. Initially, the gene expression data were preprocessed and filtered to retain genes with variance above the third quartile for subsequent analysis. Sample clustering was conducted via the average linkage method, followed by sample quality assessment. Soft-thresholding analysis was performed to determine the optimal power parameter for achieving scale-free topology. Network construction utilized an unsigned network approach, with the minimum module size set to 30 genes and the module merging threshold set at 0.25. Genes were clustered into modules via the dynamic tree-cutting algorithm, and module eigengenes were calculated to evaluate module‒trait relationships. Finally, Pearson correlation analysis was used to assess the associations between modules and phenotypic traits, with the results visualized in a heatmap.\u003c/p\u003e\n\u003ch3\u003eGene expression analysis\u003c/h3\u003e\n\u003cp\u003eGene expression analysis was performed via the GEPIA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The analysis parameters were set as follows: dataset selection\u0026thinsp;=\u0026thinsp;BRCA, p value cutoff\u0026thinsp;=\u0026thinsp;0.01, log2FC (fold change) cutoff\u0026thinsp;=\u0026thinsp;1, and matched TCGA normal and GTEx data, integrating TCGA tumor samples (n\u0026thinsp;=\u0026thinsp;1085) and GTEx normal tissue data (n\u0026thinsp;=\u0026thinsp;291).\u003c/p\u003e\n\u003ch3\u003eSurvival analysis\u003c/h3\u003e\n\u003cp\u003eSurvival analysis was conducted via two approaches: (1) Kaplan‒Meier survival analysis using letrozole-treated patients from the GSE41994 dataset, where patients were stratified into high-expression (n\u0026thinsp;=\u0026thinsp;15) and low-expression (n\u0026thinsp;=\u0026thinsp;14) groups on the basis of median gene expression; (2) validation analysis via the Kaplan‒Meier plotter online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/analysis/\u003c/span\u003e\u003cspan address=\"http://kmplot.com/analysis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For both analyses, the log-rank test was employed to evaluate statistical significance between groups, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eBioinformatics analysis and molecular docking\u003c/h3\u003e\n\u003cp\u003eTo evaluate potential interactions between RRM2 protein and letrozole, molecular docking studies were performed using AutoDock Vina (v1.1.2) according to standard procedures [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. MGLTools (v1.5.7) was used for docking preparation and visualization (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccsb.scripps.edu/mgltools/\u003c/span\u003e\u003cspan address=\"https://ccsb.scripps.edu/mgltools/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The RRM2 monomer structure (A0A7P0SBL1) was retrieved from the AlphaFold Protein Structure Database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), while letrozole (PubChem CID: 3902) was obtained from the NCBI PubChem database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). AutoDock Vina predicted the docking site of letrozole with RRM2, and the docking results were visualized using PyMOL (v2.3.2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Gene Coexpression Networks and Identification of Nonresponse-Associated Modules in Letrozole-Treated Breast Cancer Patients\u003c/h2\u003e \u003cp\u003eLetrozole is an AI used to treat hormone receptor-positive breast cancer, particularly in postmenopausal women. It works by reducing the production of estrogen in the body. By blocking estrogen production, letrozole helps to slow or stop the growth of breast cancer cells. However, not every patient responds to this drug. To identify key gene modules associated with the response of breast cancer patients to letrozole treatment, we performed WGCNA on the GSE20181 dataset. This dataset comprises gene expression profiles from breast cancer patients treated with letrozole (2.5 mg/day) at two timepoints: pretreatment and 10\u0026ndash;14 days posttreatment. The cohort included 58 patients (15 nonresponders, 37 responders, and 6 nonassessable patients) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo construct scale-free networks, we first analyzed the network topology for various soft-threshold powers. The scale independence and mean connectivity analysis revealed that a soft-threshold power of 4 was optimal for maintaining high scale-free topology model fit while ensuring adequate mean connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). This threshold was used for subsequent analyses to transform the adjacency matrix. Hierarchical clustering of the topological overlap matrix (TOM) dissimilarity measure identified distinct gene modules, which are visualized in a cluster dendrogram with different colors representing individual modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo identify modules significantly associated with treatment response, we performed module‒trait relationship analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The analysis yielded 29 modules; notably, the module‒trait correlation heatmap revealed a strong correlation between the light green module and clinical nonresponse to letrozole (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;=\u0026thinsp;6e‒04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These findings suggest that the light green module identified through WGCNA is closely associated with clinical nonresponse to letrozole in breast cancer patients. This finding implies that the genes within this module may play significant roles in determining the efficacy of letrozole treatment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional enrichment and network analysis of light green module genes reveal cell cycle-related processes\u003c/h3\u003e\n\u003cp\u003eTo elucidate the biological functions and molecular mechanisms associated with genes in the light green module, we performed comprehensive functional enrichment and network analyses via Metascape. This analysis aimed to identify key pathways and biological processes that might influence breast cancer prognosis.\u003c/p\u003e \u003cp\u003eThe functional enrichment analysis revealed multiple significantly enriched terms and pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The most significantly enriched term was \u0026ldquo;cell cycle, mitotic\u0026rdquo; (R-HSA-69278), followed by \u0026ldquo;mitotic cell cycle process\u0026rdquo; (GO:1903047) and \u0026ldquo;retinoblastoma gene in cancer\u0026rdquo; (WP2446). Other highly enriched terms included \u0026ldquo;regulation of nuclear division\u0026rdquo; (GO:0051783), \u0026ldquo;DNA metabolic process\u0026rdquo; (GO:0006259), and \u0026ldquo;cell cycle checkpoints\u0026rdquo; (R-HSA-69620). The analysis also revealed enrichment in processes related to the meiotic cell cycle (GO:0051321), mitotic cytokinesis (GO:0000281), and the centrosome cycle (GO:0007098). Additionally, several cancer-relevant pathways were enriched, including \u0026ldquo;gastric cancer network 1\u0026rdquo; (WP2361) and \u0026ldquo;p75 NTR receptor-mediated signaling\u0026rdquo; (R-HSA-193704).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNetwork analysis of these enriched terms revealed complex interactions and relationships between different biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The visualization demonstrated dense interconnectivity among cell cycle-related processes, DNA repair mechanisms, and cancer-associated pathways. This network analysis highlighted the central role of cell cycle regulation and its interaction with other cancer-related processes, suggesting that dysregulation of these pathways might significantly impact breast cancer prognosis. The predominance of cell cycle-related terms and their extensive network connections suggest that genes within the light green module may influence breast cancer prognosis primarily through the regulation of cell cycle processes and associated pathways.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and Expression Analysis of Key Candidate Genes in Breast Cancer\u003c/h2\u003e \u003cp\u003eTo identify potential key genes involved in breast cancer prognosis and treatment response, we performed an integrative analysis comparing genes from the light green module with genes that were differentially expressed between nonresponders and responders in the pretreatment group. Through this integrative approach, we aimed to identify genes that were both coexpressed and differentially regulated in the context of treatment response. Venn diagram analysis revealed the intersection between the genes in the light green module (68 genes) and the genes that were differentially expressed in the pretreatment group (598 genes, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This analysis identified 11 overlapping genes (1.7% of the total), representing potential key genes involved in the treatment response of patients with breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the clinical relevance of these candidate genes, we analyzed the expression levels of eleven genes (BUB1B, CENPU, GAREM, GMNN, KIF11, MCM6, MLLT11, NUSAP1, PRC1, RRM2, and TRIP13) in breast cancer tissues compared with adjacent normal tissues. All eleven genes presented different expression patterns between tumor tissues and normal tissues. Notably, BUB1B, CENPU, KIF11, NUSAP1, PRC1, RRM2, and TRIP13 were significantly overexpressed in tumor tissues, with median expression levels approximately 2\u0026ndash;3-fold greater than those in normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). MCM6, MLLT11, and GMNN presented moderate but not significant increases in expression, whereas GAREM presented distinct expression patterns in tumor samples compared with normal tissues (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThese findings suggest that these eleven genes, particularly seven genes, namely, BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1, which exhibit consistent overexpression patterns, may play crucial roles in breast cancer development and progression. The differential expression patterns of these genes between tumor and normal tissues support their potential utility as prognostic biomarkers and therapeutic targets in breast cancer treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Value Analysis of Key Candidate Genes in Breast Cancer Survival\u003c/h2\u003e \u003cp\u003eTo evaluate the prognostic significance of the identified candidate genes, we performed survival analysis via Kaplan‒Meier plots for both overall survival (OS) and relapse-free survival (RFS) in breast cancer patients. The analysis was conducted with a 120-month follow-up threshold, with patients stratified by the median expression level of each gene.\u003c/p\u003e \u003cp\u003eOS analysis revealed that high expression levels of several candidate genes were significantly associated with poor overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Specifically, all seven selected genes (BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1) were significantly correlated with poor survival outcomes. Similarly, the RFS analysis demonstrated that elevated expression of these genes was also associated with an increased risk of disease recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). All seven selected genes were significantly correlated with shorter RFS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese survival analyses consistently demonstrated that higher expression levels of these candidate genes are associated with poorer clinical outcomes in breast cancer patients, suggesting their potential utility as prognostic biomarkers. The consistent patterns observed across both OS and RFS further strengthen their clinical significance in breast cancer progression and patient survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Prognostic Value of Candidate Genes in ER-positive and ER-negative Breast Cancer Subtypes\u003c/h2\u003e \u003cp\u003eTo further investigate the prognostic significance of our candidate genes in different breast cancer subtypes, we performed stratified survival analyses on the basis of estrogen receptor (ER) status. We examined OS in both ER\u0026thinsp;+\u0026thinsp;and ER-negative (ER-) breast cancer patients over a 120-month follow-up period. In ER\u0026thinsp;+\u0026thinsp;breast cancer patients, most candidate genes were significantly associated with poor overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). BUB1B, KIF11, RRM2, NUSAP1, TRIP13, and PRC1 all demonstrated strong negative correlations with survival. CENPU showed a similar trend but with lower statistical significance (P\u0026thinsp;=\u0026thinsp;0.35). These results suggest that high expression of these genes is particularly harmful to survival in ER\u0026thinsp;+\u0026thinsp;breast cancer patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, the prognostic value of these genes showed distinctly different patterns in ER- breast cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Most notably, BUB1B expression was significantly positively correlated with survival (P\u0026thinsp;=\u0026thinsp;0.0045), indicating that higher BUB1B expression was associated with better outcomes in ER- patients. The other genes were not significantly associated with ER-related breast cancer: KIF11 (P\u0026thinsp;=\u0026thinsp;0.082), RRM2 (P\u0026thinsp;=\u0026thinsp;0.95), NUSAP1 (P\u0026thinsp;=\u0026thinsp;0.075), PRC1 (P\u0026thinsp;=\u0026thinsp;0.087), and TRIP13 (P\u0026thinsp;=\u0026thinsp;0.23). These contrasting results between ER\u0026thinsp;+\u0026thinsp;and ER- breast cancers suggest that the prognostic value of these genes is highly dependent on ER status, highlighting the importance of considering molecular subtypes when evaluating potential prognostic markers in breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Candidate Genes on Relapse-Free Survival in ER\u0026thinsp;+\u0026thinsp;and ER- Breast Cancer Subtypes\u003c/h2\u003e \u003cp\u003eTo further evaluate the clinical significance of our candidate genes, we analyzed their associations with RFS in both ER\u0026thinsp;+\u0026thinsp;and ER- breast cancer patients over a 120-month follow-up period. In ER\u0026thinsp;+\u0026thinsp;breast cancer patients, all seven candidate genes demonstrated strong negative correlations with RFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). BUB1B, CENPU, KIF11, RRM2, NUSAP1, PRC1, and TRIP13 were associated with a significantly increased risk of disease recurrence in patients with high expression levels. The remarkably low P values and consistent hazard ratios above 1.75 indicate that these genes are robust predictors of recurrence risk in patients with ER\u0026thinsp;+\u0026thinsp;breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the analysis of ER- breast cancer patients revealed markedly different patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). None of the candidate genes were significantly associated with RFS in ER- patients. The hazard ratios close to 1.0 and nonsignificant P values suggest that these genes may not be reliable predictors of recurrence in patients with ER-related breast cancer. These findings further emphasize the molecular subtype-specific nature of these prognostic markers, particularly highlighting their potential utility in predicting recurrence risk, especially in ER\u0026thinsp;+\u0026thinsp;breast cancer patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of Candidate Gene Prognostic Value in an Independent Letrozole-Treated Cohort\u003c/h2\u003e \u003cp\u003eTo further validate the prognostic significance of our identified candidate genes in ER\u0026thinsp;+\u0026thinsp;breast cancer patients receiving endocrine therapy, we analyzed their expression patterns in an independent dataset (GSE41994) comprising patients treated with letrozole. The analysis focused on progression-free survival (PFS) outcomes stratified by high and low expression levels of the hub genes.\u003c/p\u003e \u003cp\u003eOur analysis revealed significant associations between gene expression levels and clinical outcomes. Among the validated genes, RRM2 demonstrated the strongest prognostic value (P\u0026thinsp;=\u0026thinsp;0.0002), with high expression correlating with significantly shorter PFS. Similarly, KIF11 and PRC1 showed strong prognostic significance (P\u0026thinsp;=\u0026thinsp;0.001 and P\u0026thinsp;=\u0026thinsp;0.002, respectively), where elevated expression levels were associated with poor clinical outcomes. TRIP13 and BUB1B also exhibited significant prognostic value (P\u0026thinsp;=\u0026thinsp;0.017 and P\u0026thinsp;=\u0026thinsp;0.035, respectively), with high expression correlating with reduced PFS. Although NUSAP1 showed a trend toward significance (P\u0026thinsp;=\u0026thinsp;0.053), patients with high expression levels still demonstrated notably shorter PFS than did those with low expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLetrozole Targets the C-Terminal Site of RRM2\u003c/h2\u003e \u003cp\u003eTo explore potential targeting sites for RRM2 dimer formation, molecular docking studies were performed using AutoDock Vina (v1.1.2) to identify compounds with high docking scores. The docking results (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) showed binding efficiencies ranging from \u0026minus;\u0026thinsp;8 to -6.7 kcal/mol, with values lower than \u0026minus;\u0026thinsp;6 kcal/mol indicating stronger binding affinity. These findings suggest that letrozole exhibits the highest binding free energy for RRM2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the top three docking results, all binding sites were localized at the C-terminal of RRM2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA and S1B). Given that protein dimerization is essential for functional enzyme complex formation, we further investigated whether letrozole influences RRM2 dimerization. The docking results were superimposed onto the RRM2 dimer to evaluate its potential effect on dimerization (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). The dimeric structure of RRM2 was predicted using AlphaFold2 Multimer, confirming that letrozole does not interfere with dimerization, as its docking pose was not positioned at the center of the RRM2 dimer interface. Therefore, letrozole did not affect RRM2 dimer formation. Additionally, a detailed analysis of the best binding pose between RRM2 and letrozole was conducted using Discovery Studio. The results (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC) revealed that letrozole interacts with R214 and K308 of RRM2 through noncovalent interactions, including π-sigma and π-alkyl interactions. These findings suggest that letrozole strongly interacts with RRM2 through noncovalent interactions, providing insights into a potential mechanism by which letrozole exerts its function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation of Candidate Genes and Potential Mechanism of Letrozole Action\u003c/h2\u003e \u003cp\u003eThe validation of key candidate genes in an independent letrozole-treated cohort strengthens their clinical potential as prognostic biomarkers for ER\u0026thinsp;+\u0026thinsp;breast cancer patients undergoing endocrine therapy. The significant association between these gene expression levels and progression-free survival (PFS) suggests their potential role in treatment response. Further investigations are required to elucidate the underlying molecular mechanisms driving these differential responses and to refine therapeutic strategies for improved patient outcomes. Expanding this research by integrating additional datasets and conducting experimental studies will provide deeper insights into the functional involvement of these candidate genes in breast cancer progression and endocrine resistance.\u003c/p\u003e \u003cp\u003eMoreover, molecular docking studies indicate that letrozole may exert its therapeutic effect by directly interacting with RRM2, particularly at the C-terminal site, through strong noncovalent interactions with R214 and K308. The binding affinity and structural analyses suggest a potential role of RRM2-related pathways in letrozole response. While letrozole does not disrupt RRM2 dimerization, its high-affinity binding to specific residues may influence downstream regulatory mechanisms. These findings collectively highlight a novel potential mechanism of action for letrozole and suggest that RRM2 may play a crucial role in letrozole resistance, emphasizing the need for further functional validation of this interaction.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eKey Findings\u003c/h2\u003e \u003cp\u003eIn this study, we employed WGCNA to identify and validate novel prognostic biomarkers associated with the response of breast cancer patients to letrozole. Our analysis revealed seven key genes (BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1) that consistently demonstrated significant prognostic value, particularly in ER\u0026thinsp;+\u0026thinsp;breast cancer patients. These genes, primarily involved in cell cycle regulation, exhibited strong associations with overall survival (OS), relapse-free survival (RFS), and progression-free survival (PFS) across multiple independent cohorts.\u003c/p\u003e \u003cp\u003eThe identification of cell cycle-related genes as predictors of letrozole response aligns with previous studies investigating resistance mechanisms in endocrine therapy. Dysregulation of cell cycle checkpoints has been implicated in therapeutic resistance in breast cancer [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For example, aberrant expression of cell cycle regulators has been correlated with poor endocrine therapy response, emphasizing the critical role of cell cycle progression in hormone receptor-positive breast cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our findings extend these observations by identifying specific cell cycle-related genes that could serve as predictive biomarkers.\u003c/p\u003e \u003cp\u003eAmong these genes, RRM2 demonstrated the strongest prognostic value in the validation cohort (P\u0026thinsp;=\u0026thinsp;0.0002), consistent with prior findings linking RRM2 overexpression to cancer progression and drug resistance. Previous studies have shown that RRM2 overexpression promotes breast cancer cell proliferation and metastasis, and its elevated levels are correlated with poor prognosis in multiple cancer types [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, KIF11 and PRC1, which also showed strong prognostic significance (P\u0026thinsp;=\u0026thinsp;0.001 and P\u0026thinsp;=\u0026thinsp;0.002, respectively), have been implicated in cell division and chromosomal stability [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA particularly noteworthy finding is the differential prognostic value of these genes between ER\u0026thinsp;+\u0026thinsp;and ER- breast cancer subtypes. While high expression consistently predicts poor outcomes in ER\u0026thinsp;+\u0026thinsp;patients, its prognostic significance is either absent or reversed in ER- patients. This molecular subtype-specific pattern supports the increasing recognition that breast cancer is a heterogeneous disease, necessitating personalized therapeutic approaches. Similar subtype-specific biomarker patterns have been observed in previous studies investigating endocrine therapy response [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur functional enrichment analysis revealed significant enrichment in cell cycle-related processes, particularly mitotic regulation and DNA replication, which aligns with previous findings that cell cycle dysregulation is a key mechanism of endocrine therapy resistance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The strong correlation between the light green module and clinical nonresponse to letrozole (R\u0026sup2; = 0.46, P\u0026thinsp;=\u0026thinsp;6e-04) suggests that these cell cycle-related genes may serve as both prognostic markers and potential therapeutic targets.\u003c/p\u003e \u003cp\u003eAdditionally, the integration of WGCNA with differential expression analysis provided a robust approach for identifying clinically relevant biomarkers. While previous studies have often focused on individual genes, our network-based approach captured the complex interactions between genes and their collective impact on treatment response. This methodology aligns with recent trends in systems biology-based biomarker discovery, which have successfully been applied in other cancer types [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Collectively, these findings significantly enhance our understanding of letrozole resistance mechanisms and offer potential biomarkers for predicting treatment outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePotential Mechanism of Letrozole Resistance\u003c/h2\u003e \u003cp\u003eDue to the heterogeneous nature of breast cancer, the development of drug resistance remains a significant challenge, leading to reduced treatment efficacy. Currently, there is no direct evidence proving that RRM2 interacts with letrozole to mediate drug resistance. However, our molecular docking results demonstrate that letrozole can bind to RRM2. Based on this finding, we propose that RRM2 competes with aromatase for binding to letrozole, thereby preventing letrozole from effectively inhibiting aromatase.\u003c/p\u003e \u003cp\u003eFurthermore, RRM2 overexpression has been shown to increase mutation frequency, leading to the accumulation of RRM2 dimers. This process alters the composition of the ribonucleotide reductase complex, subsequently promoting tumorigenesis and cancer progression [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings suggest a novel potential mechanism of action for RRM2 in letrozole resistance, emphasizing the need for further functional validation and exploration of targeted therapeutic strategies aimed at overcoming this resistance mechanism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications, Limitations, and Future Directions\u003c/h2\u003e \u003cp\u003eThe clinical implications of our findings are significant in advancing personalized medicine for breast cancer treatment. The identified seven-gene panel could serve as a pretreatment screening tool to identify patients likely to respond to letrozole therapy. Such a tool could enable more personalized treatment strategies, sparing nonresponsive patients from ineffective therapy and directing them toward alternative treatments earlier. The strong prognostic value demonstrated in ER\u0026thinsp;+\u0026thinsp;breast cancer patients suggests specific clinical relevance for this subgroup, where treatment decisions often present significant challenges.\u003c/p\u003e \u003cp\u003eMoreover, the identification of cell cycle-related genes as key predictors of treatment response opens new therapeutic possibilities. The strong association between these genes and cell cycle regulation suggests that they may serve as potential therapeutic targets for overcoming letrozole resistance. For example, combining letrozole with cell cycle inhibitors could improve treatment outcomes in patients with high expression of these marker genes. This approach aligns with emerging trends in combination therapy strategies, which have shown promise in overcoming endocrine resistance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered when interpreting these findings. Although our results were validated across multiple datasets, including the independent GSE41994 cohort, prospective clinical trials are needed to confirm the predictive value of these biomarkers in real-world clinical settings. Additionally, the development of clinically applicable testing methods is necessary for practical implementation.\u003c/p\u003e \u003cp\u003eFurther functional studies are required to elucidate the precise mechanisms by which these genes influence treatment response. Understanding these mechanisms may lead to the development of more effective therapeutic strategies. Future research should also investigate potential therapeutic strategies targeting these pathways to overcome resistance, possibly through novel drug combinations or alternative treatment approaches.\u003c/p\u003e \u003cp\u003eIn conclusion, while our findings represent a significant step toward improved patient stratification in letrozole therapy, additional validation and mechanistic studies are required before clinical implementation. The potential role of RRM2 in letrozole resistance, as suggested by our molecular docking analysis, provides a novel avenue for future research, with potential implications for targeted treatment strategies in ER\u0026thinsp;+\u0026thinsp;breast cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we identified and validated novel prognostic biomarkers associated with letrozole resistance in estrogen receptor-positive (ER+) breast cancer using WGCNA and molecular docking analysis. We discovered a gene module associated with treatment nonresponse, highlighting seven key genes (BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1) that demonstrated consistent prognostic significance across multiple datasets, particularly in ER\u0026thinsp;+\u0026thinsp;breast cancer patients. The differential prognostic value between ER\u0026thinsp;+\u0026thinsp;and ER- subtypes underscores the importance of molecular subtype-specific treatment strategies in breast cancer management.\u003c/p\u003e \u003cp\u003eAdditionally, molecular docking analysis confirmed that letrozole directly binds to RRM2, suggesting a potential competitive interaction with aromatase, which may contribute to letrozole resistance. RRM2 overexpression has been associated with increased mutation frequency and ribonucleotide reductase complex alterations, further promoting tumor progression. These findings provide new insights into letrozole resistance mechanisms, linking cell cycle dysregulation and RRM2-mediated resistance to endocrine therapy failure.\u003c/p\u003e \u003cp\u003eStrong validation in an independent letrozole-treated cohort supports the clinical relevance of these biomarkers. Our findings highlight RRM2 as a key mediator of letrozole resistance, offering a potential predictive marker and therapeutic target. Future research should focus on prospective validation and functional studies to further elucidate the mechanistic role of these genes in letrozole resistance, paving the way for precision medicine approaches in ER\u0026thinsp;+\u0026thinsp;breast cancer treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study were obtained from publicly available datasets (GSE20181 and GSE41994) in the Gene Expression Omnibus (GEO) database. These datasets were originally approved by the respective ethics committees of the primary studies. Since this research involves secondary analysis of de-identified, publicly available data, no additional ethical approval or participant consent was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication is not applicable, as this manuscript does not include any individual person’s data in any form (e.g., identifiable images or personal details).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Wan-Yu Hung, Chi-Chen Lin and Ming-Hon Hou; methodology: Chi-Chen Lin and Ming-Hon Hou; validation: Wan-Yu Hung; formal analysis: Shou-Tung Chen; investigation: Chi-Chen Lin and Wan-Yu Hung; resources: Shou-Tung Chen; data curation: Ming-Hon Hou, Shih-Chun Huang and Wan-Yu Hung; writing—original draft preparation:Wan-Yu Hung; writing—review and editing: Wan-Yu Hung, Chi-Chen Lin and Ming-Hon Hou. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne M. et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. \u003cem\u003eBreast\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e,15-23(2022). \u003c/li\u003e\n\u003cli\u003eWalter, V., Fischer, C., Deutsch, T.M., Ersing, C., Nees, J., Sch\u0026uuml;tz, F. et al. Estrogen, progesterone, and human epidermal growth factor receptor 2 discordance between primary and metastatic breast cancer. \u003cem\u003eBreast Cancer Res. Treat.\u003c/em\u003e \u003cstrong\u003e183(1)\u003c/strong\u003e,137-144(2020).\u003c/li\u003e\n\u003cli\u003eCiruelos Gil, E.M. Targeting the PI3K/AKT/mTOR pathway in estrogen receptor-positive breast cancer. \u003cem\u003eCancer Treat. Rev. \u003c/em\u003e\u003cstrong\u003e40(7)\u003c/strong\u003e,862-871(2014).\u003c/li\u003e\n\u003cli\u003eTuasha, N., Petros, B. Heterogeneity of Tumors in Breast Cancer: Implications and Prospects for Prognosis and Therapeutics. \u003cem\u003eScientifica (Cairo)\u003c/em\u003e.\u003cstrong\u003e 2020\u003c/strong\u003e,4736091;10.1155/2020/4736091(2020).\u003c/li\u003e\n\u003cli\u003eWelcsh, P.L., King, M.C. BRCA1 and BRCA2 and the genetics of breast and ovarian cancer.\u003cem\u003e Hum. Mol. Genet.\u003c/em\u003e \u003cstrong\u003e10(7)\u003c/strong\u003e,705-713(2001).\u003c/li\u003e\n\u003cli\u003eLi, Y.Y., Jones, S.J. Drug repositioning for personalized medicine. \u003cem\u003eGenome Med.\u003c/em\u003e \u003cstrong\u003e4(3)\u003c/strong\u003e,27. 10.1186/gm326(2012).\u003c/li\u003e\n\u003cli\u003eHe, Y., Hoskins, J.M., McLeod, H.L. Copy number variants in pharmacogenetic genes. \u003cem\u003eTrends Mol. Med.\u003c/em\u003e \u003cstrong\u003e17(5)\u003c/strong\u003e, 244-251(2011).\u003c/li\u003e\n\u003cli\u003eHer, L., Zhu, H.J. Carboxylesterase 1 and Precision Pharmacotherapy: Pharmacogenetics and Nongenetic Regulators. \u003cem\u003eDrug Metab. Dispos.\u003c/em\u003e\u003cstrong\u003e48(3),\u003c/strong\u003e230-244(2020).\u003c/li\u003e\n\u003cli\u003eMiziak, P., Baran, M., Błaszczak, E., Przybyszewska-Podstawka, A., Kałafut, J., Smok-Kalwat, J. et al. Estrogen Receptor Signaling in Breast Cancer. \u003cem\u003eCancers (Basel)\u003c/em\u003e. \u003cstrong\u003e15(19)\u003c/strong\u003e,4689. 10.3390/cancers15194689(2023).\u003c/li\u003e\n\u003cli\u003eKoeberle, D., Thuerlimann, B. Letrozole as upfront endocrine therapy for postmenopausal women with hormone-sensitive breast cancer: BIG 1-98. \u003cem\u003eBreast Cancer Res\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Treat.\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e \u003cstrong\u003eSuppl 1(Suppl 1)\u003c/strong\u003e,55-66(2007).\u003c/li\u003e\n\u003cli\u003eBhatnagar, A.S. The discovery and mechanism of action of letrozole. \u003cem\u003eBreast Cancer Res\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Treat.\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e \u003cstrong\u003eSuppl 1(Suppl 1)\u003c/strong\u003e,7-17(2007).\u003c/li\u003e\n\u003cli\u003eSim, S.H., Yang, H.N., Jeon, S.Y., Lee, K.S., Park, I.H. Mutation analysis using cell-free DNA for endocrine therapy in patients with HR+ metastatic breast cancer. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep.\u003c/em\u003e \u003cstrong\u003e11(1)\u003c/strong\u003e,55-66(2021).\u003c/li\u003e\n\u003cli\u003eNeed, E.F., Selth, L.A., Harris, T.J., Birrell, S.N., Tilley, W.D., Buchanan G. Research resource: interplay between the genomic and transcriptional networks of androgen receptor and estrogen receptor \u0026alpha; in luminal breast cancer cells. \u003cem\u003eMol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Endocrinol.\u003c/em\u003e \u003cstrong\u003e26(11)\u003c/strong\u003e,1941-1952(2012).\u003c/li\u003e\n\u003cli\u003eMosly, D., Turnbull, A., Sims, A., Ward, C., Langdon, S. Predictive markers of endocrine response in breast cancer. \u003cem\u003eWorld J\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Exp\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Med.\u003c/em\u003e \u003cstrong\u003e8(1)\u003c/strong\u003e,1-7(2018).\u003c/li\u003e\n\u003cli\u003evan Dam, S., V\u0026otilde;sa, U., van der Graaf, A., Franke, L., de Magalh\u0026atilde;es, J.P. Gene coexpression analysis for functional classification and gene-disease predictions. \u003cem\u003eBrief Bioinform.\u003c/em\u003e\u003cstrong\u003e19(4)\u003c/strong\u003e,575-592(2018).\u003c/li\u003e\n\u003cli\u003eKholodenko, B.N. Drug Resistance Resulting from Kinase Dimerization Is Rationalized by Thermodynamic Factors Describing Allosteric Inhibitor Effects. \u003cem\u003eCell Rep.\u003c/em\u003e \u003cstrong\u003e12(11)\u003c/strong\u003e,1939-49(2015).\u003c/li\u003e\n\u003cli\u003eForli, S., Huey, R., Pique, M.E., Sanner, M.F., Goodsell, D.S., Olson, A.J. et al. Computational protein\u0026ndash;ligand docking and virtual drug screening with the AutoDock suite. \u003cem\u003eNat\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Protoc\u003c/em\u003e.\u003cstrong\u003e11\u003c/strong\u003e,905-919(2016).\u003c/li\u003e\n\u003cli\u003eHanker, A.B., Sudhan, D.R., Arteaga, C.L. Overcoming Endocrine Resistance in Breast Cancer. \u003cem\u003eCancer Cell.\u003c/em\u003e \u003cstrong\u003e37(4)\u003c/strong\u003e,496-513(2020).\u003c/li\u003e\n\u003cli\u003eStewart, Z.A., Westfall, M.D., Pietenpol, J.A. Cell-cycle dysregulation and anticancer therapy. \u003cem\u003eTrends Pharmacol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Sci.\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cstrong\u003e24(3)\u003c/strong\u003e,139-145(2003).\u003c/li\u003e\n\u003cli\u003eThu, K.L., Soria-Bretones, I., Mak, T.W., Cescon, D.W. Targeting the cell cycle in breast cancer: toward the next phase. \u003cem\u003eCell Cycle\u003c/em\u003e. \u003cstrong\u003e17(15)\u003c/strong\u003e,1871-1885(2018).\u003c/li\u003e\n\u003cli\u003eVisconti, R., Della Monica, R., Grieco, D. Cell cycle checkpoint in cancer: a therapeutically targetable double-edged sword. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Exp\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Clin\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Cancer Res.\u003c/em\u003e \u003cstrong\u003e35(1)\u003c/strong\u003e,153(2016).\u003c/li\u003e\n\u003cli\u003eMcNeil, C.M., Sergio, C.M., Anderson, LR, Inman, C.K., Eggleton, S.A., Murphy, N.C. et al. c-Myc overexpression and endocrine resistance in breast cancer. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Steroid Biochem\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Mol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Biol.\u003c/em\u003e\u003cstrong\u003e 102(1-5)\u003c/strong\u003e,147-155(2006).\u003c/li\u003e\n\u003cli\u003ePetrossian. K., Kanaya. N., Lo. C., Hsu. P.Y., Nguyen. D., Yang. L. et al. ER\u0026alpha;-mediated cell cycle progression is an important requisite for CDK4/6 inhibitor response in HR+ breast cancer. \u003cem\u003eOncotarget\u003c/em\u003e. \u003cstrong\u003e9(45)\u003c/strong\u003e,27736-27751(2018).\u003c/li\u003e\n\u003cli\u003eZhuang, S., Li, L., Zang, Y., Li, G., Wang, F. RRM2 elicits the metastatic potential of breast cancer cells by regulating cell invasion, migration and VEGF expression via the PI3K/AKT signaling. \u003cem\u003eOncol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Lett.\u003c/em\u003e \u003cstrong\u003e19(4)\u003c/strong\u003e,3349-3355(2020).\u003c/li\u003e\n\u003cli\u003eZhou, Z., Song, Q., Yang, Y., Wang, L., Wu, Z. Comprehensive Landscape of RRM2 with Immune Infiltration in Pan-Cancer. \u003cem\u003eCancers (Basel)\u003c/em\u003e. \u003cstrong\u003e14(12)\u003c/strong\u003e,2938(2022).\u003c/li\u003e\n\u003cli\u003eMei\u0026szlig;ner, L., Niese, L., Sch\u0026uuml;ring, I., Mitra, A., Diez, S. Human kinesin-5 KIF11 drives the helical motion of anti-parallel and parallel microtubules around each other. \u003cem\u003eEMBO J.\u003c/em\u003e\u003cstrong\u003e 43(7)\u003c/strong\u003e,1244-1256(2024).\u003c/li\u003e\n\u003cli\u003eLi, X.H., Ju, J.Q., Pan, Z.N., Wang, H.H., Wa,n X., Pan, M.H. et al. PRC1 is a critical regulator for anaphase spindle midzone assembly and cytokinesis in mouse oocyte meiosis. \u003cem\u003eFEBS J.\u003c/em\u003e \u003cstrong\u003e288(9)\u003c/strong\u003e,3055-3067(2021).\u003c/li\u003e\n\u003cli\u003eJeong, Y., Bae, S.Y., You, D., Jung, S.P., Choi, H.J., Kim, I. et al. EGFR is a Therapeutic Target in Hormone Receptor-Positive Breast Cancer. \u003cem\u003eCell Physiol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Biochem.\u003c/em\u003e\u003cstrong\u003e 53(5)\u003c/strong\u003e,805-819(2019).\u003c/li\u003e\n\u003cli\u003eGlaviano, A., Wander, S.A., Baird, R.D., Yap, K.C., Lam, H.Y., Toi, M. et al. Mechanisms of sensitivity and resistance to CDK4/CDK6 inhibitors in hormone receptor-positive breast cancer treatment.\u003cem\u003e Drug Resist\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Updat.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e,101103.10.1016/j.drup.2024.101103(2024).\u003c/li\u003e\n\u003cli\u003eMiller, T.W. Endocrine resistance: what do we know?. \u003cem\u003eAm\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Soc\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Clin\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Oncol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Educ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Book.\u003c/em\u003e 10.14694/EdBook_AM.2013.33.e37(2013).\u003c/li\u003e\n\u003cli\u003eSadat Kalaki, N., Ahmadzadeh, M., Najafi, M., Mobasheri, M., Ajdarkosh, H., Karbalaie Niya, M.H. Systems biology approach to identify biomarkers and therapeutic targets for colorectal cancer. \u003cem\u003eBiochem\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Biophys\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep. \u003c/em\u003e\u003cstrong\u003e37\u003c/strong\u003e,101633. 10.1016/j.bbrep.2023.101633(2024).\u003c/li\u003e\n\u003cli\u003eSheng, K.L., Kang, L., Pridham, K.J., Dunkenberger, L.E., Sheng, Z., Varghese, R.T. An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep.\u003c/em\u003e 10(1),21246(2020).\u003c/li\u003e\n\u003cli\u003eXu, X., Page, J.L., Surtees, J.A., Liu, H., Lagedrost, S., Lu Y. et al. Broad overexpression of ribonucleotide reductase genes in mice specifically induces lung neoplasms. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cstrong\u003e68(8)\u003c/strong\u003e,2652-2660(2008).\u003c/li\u003e\n\u003cli\u003eChabes, A., Thelander, L. Controlled protein degradation regulates ribonucleotide reductase activity in proliferating mammalian cells during the normal cell cycle and in response to DNA damage and replication blocks.\u003cem\u003e J\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Biol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Chem.\u003c/em\u003e \u003cstrong\u003e275(23)\u003c/strong\u003e,17747-53(2000).\u003c/li\u003e\n\u003cli\u003eYuan, Y., Lee, J.S., Yost, S.E., Frankel, P.H., Ruel, C., Egelston, C.A. et al. Phase I/II trial of palbociclib, pembrolizumab and letrozole in patients with hormone receptor-positive metastatic breast cancer. \u003cem\u003eEur. J. Cancer.\u003c/em\u003e \u003cstrong\u003e154\u003c/strong\u003e,11-20(2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Letrozole resistance, Estrogen receptor-positive breast cancer, RRM2, Molecular docking, Weighted Gene Coexpression Network Analysis (WGCNA), Prognostic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6183643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6183643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLetrozole is a first-line aromatase inhibitor for estrogen receptor-positive (ER+) breast cancer; however, resistance develops in 20\u0026ndash;30% of patients, limiting therapeutic efficacy. The inability to predict treatment response before therapy initiation remains a significant challenge, as no reliable biomarkers have been established. This study aimed to identify novel prognostic biomarkers and elucidate the molecular mechanisms underlying letrozole resistance using integrative bioinformatics and molecular docking approaches.\u003c/p\u003e \u003cp\u003eThrough weighted gene coexpression network analysis, a gene module highly associated with letrozole nonresponse was identified. Seven candidate genes\u0026mdash;BUB1B, CENPU, KIF11, RRM2, NUSAP1, TRIP13, and PRC1\u0026mdash;were significantly overexpressed in tumors and strongly correlated with poor survival in ER\u0026thinsp;+\u0026thinsp;breast cancer. Among them, RRM2 emerged as the most significant prognostic marker. Molecular docking analysis demonstrated that letrozole binds to RRM2, suggesting a potential competitive interaction with aromatase that may contribute to resistance. Validation in an independent letrozole-treated cohort confirmed RRM2\u0026rsquo;s strong prognostic value.\u003c/p\u003e \u003cp\u003eThese findings provide new insights into the molecular mechanisms driving letrozole resistance and identify RRM2 as both a prognostic biomarker and a potential therapeutic target. This study advances the understanding of endocrine resistance and offers promising avenues for biomarker-driven treatment strategies in ER\u0026thinsp;+\u0026thinsp;breast cancer.\u003c/p\u003e","manuscriptTitle":"RRM2 as a Key Biomarker and Therapeutic Target in Letrozole-Resistant ER+ Breast Cancer: Insights from Bioinformatics and Molecular Docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 11:06:19","doi":"10.21203/rs.3.rs-6183643/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T08:31:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T12:48:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T02:34:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16900492090407254309993971526871955557","date":"2025-04-14T01:41:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9421609787762048877932496405503181209","date":"2025-03-27T01:48:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-19T13:09:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T13:08:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-19T06:10:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-17T12:43:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-08T10:43:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ac10c0c-b9f2-4216-ac63-d2c831f19acb","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46271811,"name":"Biological sciences/Cancer/Breast cancer"},{"id":46271812,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":46271813,"name":"Biological sciences/Cancer/Cancer therapy"},{"id":46271814,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2026-04-13T01:24:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 11:06:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6183643","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6183643","identity":"rs-6183643","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}