High RRM2 expression has poor prognosis in specific types of breast cancer.

Breast cancer has become the leading cause of global cancer in 2020, accounting for 11.7% of all new cases of malignant tumors. Among female patients with malignant tumors, the incidence rate is 24.5%, and the mortality rate is 15.5%, both ranking first among cancers [ 1 ]. Aibonucleotide reductase (RR) can catalyze the conversion of nucleoside diphosphate to deoxynucleoside diphosphate, which is the rate-limiting enzyme for DNA synthesis and repair, and plays an important role in cell division, proliferation and differentiation. RR is composed of ribonucleotide reductase M1 (RRM1), ribonucleotide reductase M2(RRM2), and the newly discovered p53R2 subunit. The activity of the holoenzyme is determined by the expression of the RRM2 protein [ 2 , 3 ]. Studies have shown that gene mutations and gene disorders are the main reasons leading to the occurrence and development of many cancers [ 4 ]. The differential expression of RRM2 is closely related to the occurrence, development, and prognosis of pancreatic cancer, gastric cancer, non-small cell lung cancer, liver cancer, malignant endometriosis, and other tumors [ 5 – 10 ]. Although targeted therapies, immunotherapy, chemotherapy, and radiotherapy are effective in the treatment of breast cancer, morbidity and mortality are still increasing. Therefore, it is very important to find new biomarkers and therapeutic targets that affect the survival and prognosis of breast cancer. To evaluate the role of RRM2 as a prognostic biomarker in breast cancer, we analyzed the relationship between the expression level of RRM2 in the METABRIC database and the overall survival and DMFS of patients with breast cancer. First, 1350 breast cancer samples were downloaded and sorted, and then SPSS statistical software was used to analyze the relationship between the expression level of RRM2 and various clinicopathological characteristics. The level of RRM2 mRNA and protein expression in breast cancer tissues and cell lines was then analyzed using the BCIP database, qRT-PCR, western blotting. Univariate and multivariate Cox regression analysis obtained independent prognostic factors that affect the overall survival of breast cancer. The Kaplan-Meier (KM) method was used to explore the relationship between high expression of RRM2 and overall survival and DMFS of breast cancer, and to study the effect of RRM2 expression on overall survival and DMFS in specific types of breast cancer. Finally, we defined pathways associated with the high expression of RRM2 and breast cancer progression through GSEA analysis.

To study the relationship between RRM2 expression and clinicopathological characteristics, we downloaded breast cancer patient data from the METABRIC database, which contained 2509 breast cancer samples. After censored cases were removed, a total of 1350 breast cancer patient samples were obtained. Among the patients of breast cancer, there were 630 individuals aged <60 years and 719 individuals aged ≥60 years; 1228 individuals with tumor stage 1/2 and 120 individuals with tumor stage 3/4; 605 individuals with a tumor size ≤2cm, 684 individuals with a tumor size ≤5cm and 60 individuals with a tumor size >5cm; 113 individuals with Grade 1, 532 individuals with Grade 2 and 704 individuals with Grade 3; 312 individuals were premenopausal, and 1,037 individuals were postmenopausal. In total, 134 individuals had Basal-like subtype, 156 individuals had Claudin-low subtype, 137 individuals had Her-2 enriched subtype, 498 individuals had Luminal A, 331 individuals had Luminal B, 93 individuals had Normal-like breast cancer; 630 individuals with lymph node metastasis, and 719 individuals without lymph node metastasis. Overall, 313 individuals had ER negative breast cancer, and 1,036 individuals had ER positive; 647 individuals had PR negative and 702 individuals had PR positive; 1,181 individuals with Her-2 negative, and 168 individuals had Her-2 positive breast cancer. The statistical analysis revealed correlations between RRM2 expression and clinicopathological parameters. Our study showed that the expression of RRM2 significantly correlated with age (P<0.001), tumor size (P = 0.039), grade (P<0.001), menopausal status (P<0.001), molecular typing (P<0.001), ER (P<0.001), PR (P<0.001), and Her-2 status (P<0.001), there was no significant correlation between the number of positive lymph nodes (P = 0.194) and tumor stage (P = 0.087) ( Table 1 ). Through the BCIP database, we analyzed the expression level of RRM2 in 4 data sets (METABRIC, TCGA Agilent, TCGA RNA-seq, GSE5364 ), and the results showed that RRM2 expression in breast cancer tissues was significantly up-regulated compared to adjacent normal tissues ( Fig 1 ). In the breast cancer cell lines MCF-7, MDA-MB-231, MDA-MB-453, BT-474, the relative expression level of RRM2 mRNA was significantly up-regulated, compared to the breast epithelial cell line MCF-10A ( Fig 1 ). We then detected the relative mRNA expression of RRM2 in 45 breast cancers and their paired adjacent normal tissues by qRT-PCR. The qRT-PCR results showed that the relative expression of RRM2 mRNA was higher in breast cancer tissues than in adjacent normal tissues ( Fig 2A ). Next, we detected the protein expression of RRM2 by western blotting. The protein expression of RRM2 was significantly increased in breast cancer tissues compared to adjacent normal tissues. We also detected the level of protein expression of RRM2 in breast cancer cell lines, and the same trend was obtained ( Fig 2B and 2C ). (A) METABRIC; (B) TCGA Agilent; (C) TCGA RNA-seq; (D) GSE5364 ; (E) MDA-MB-453; (F) MDA-MB-231; (G) MCF-7; and (H) BT474. (A) The relative expression level of RRM2 mRNA was higher in breast cancer tissues than in adjacent normal tissues. (B) The protein expression of RRM2 was higher in breast cancer tissues and breast cancer cell lines. (C) Quantitative results of protein expression levels obtained by Image J. As shown in Fig 3 , using R software, the boxplots indicate that the expression of RRM2 was higher in the <60-year-old subgroup (P = 1.09e-08). The expression of RRM2 was higher in the Basal and Her-2 types, and lower in the Luminal A and Luminal B types (P = 3.46e-79). The expression of RRM2 was positively correlated with the grade of breast cancer grade (P = 1.57e-32). RRM2 expression was higher in the premenopausal subgroup (P = 2.44e-09); in the Her-2 positive patients (P = 1.11e-11); and in hormone receptor negative (ER-, PR-negative) subtypes (P = 1.03e-7; P = 4.68e-21). However, we observed that there was no significant correlation between RRM2 expression and tumor stage (P = 0.159), tumor size (P = 0.0141) or the number of positive lymph nodes (P = 0.361). (A) Age; (B) Molecular subtype; (C) Grade; (D) Menopausal state; (E) Tumor stage; (F) Tumor size; (G) Lymph Node; (H) Her-2 status; (I) ER status; (J) PR status. To obtain independent prognostic factors that influence the OS of breast cancer, we performed a univariate and multivariate Cox regression analysis. In the univariate Cox regression analysis, RRM2, the number of positive lymph nodes, ER, PR, Her-2, Grade, Tumor size, Tumor stage have significant correlations with OS (P<0.001), ( Fig 4 ). All the above factors with significant differences were selected for multivariate Cox regression analysis. Only RRM2 expression, the number of positive lymph nodes, ER, Her-2, tumor size, tumor stage are significantly correlated with OS (P<0.05), and could be used as independent prognostic factors for predicting OS of breast cancer patients ( Fig 4 ). RRM2, the number of positive lymph nodes, ER status, Her-2 status, Tumor size, and Tumor stage were significantly correlated with overall survival (P<0.05). (A): Univariate Cox analysis. (B): Multivariate Cox analysis. The longest follow-up time for patients was 351 months, and the median follow-up time was 116 months. OS was significantly different across breast cancer subtypes. The Luminal A subtype had the best prognosis, while Her-2 positive and basal-like subtypes had the worst prognosis (P<0.05) ( Fig 5 ). In different tumor stages, the OS of the patients was significantly different (P<0.05). Stage 1 breast cancer patients had the best OS and stage 4 the worst ( Fig 5 ). To evaluate the diagnostic value of RRM2, we used the KM method for analysis. The 1-, 3-, and 5-year AUC values were 0.703, 0.694, and 0.654, respectively. The ROC curve is shown in Fig 5 . In all samples (n = 1350), the high RRM2 expression group had the worse OS (P<0.05) ( Fig 5 ). (A): Breast cancer patients with High-RRM2 expression have poor overall survival compared to those with Low-RRM2 expression. (P<0.05). (B): The ROC curve shows the diagnostic value of RRM2 for patients with breast cancer. (C): Overall survival analysis showed that patients with the Luminal A subtype of breast cancer have the best prognosis, and the Basal-like subtype has the worst prognosis in 1,350 breast cancer patients. (D): The high expression of RRM2 has poor overall survival in stage 1 and stage 2 breast cancer patients. (P<0.05). To further evaluate the impact of RRM2 on breast cancer patients, we evaluated the relationship between RRM2 expression levels and survival of breast cancer patients in different molecular subtypes. We found that the RRM2 high-expression group had poor OS in patients with the Luminal A subtype and the Normal-like subtype breast cancer patients (P<0.05). The expression of RRM2 did not has a significant correlation with the Basal-like subtype, the Luminal B subtype, Claudin low subtype, or Her-2 enriched subtype ( Fig 6 ). We found that in Luminal A and Normal like breast cancers, the High RRM2 expression group had poor overall survival. (A) Normal-like; (B) Claudin-low; (C) Her-2 enriched; (D) Luminal A; (E) Luminal B; (F) Basal-like. The OS of patients with breast cancer in different stages was analyzed and the results showed that the high expression of RRM2 was associated with poor OS in patients with stage1 and stage2 breast cancer(P<0.001). In stage3 breast cancer patients, the expression level of RRM2 was not significantly correlated with the OS of the patients (P = 0.065) ( Fig 7 ). In the METABRIC database, due to the few patients in stage 0 and stage 4, these were not evaluated. Our study found that the RRM2 high expression group had a poor prognosis among stage1 and stage2 breast cancer patients. However, there was no significant difference in stage3. (A) stage 1; (B) stage 2; (C) stage 3. To evaluate the effects of RRM2 expression on DMFS in breast cancer patients, we performed KM online survival analysis. Among all the breast cancer patients, the RRM2 high expression group had worse DMFS (P < 0.05). We further evaluated the effects of RRM2 expression on DMFS in patients with different subtypes of breast cancer, and the results showed that in Luminal A breast cancer patients, high expression of RRM2 had worse DMFS (P < 0.05). However, the expression of RRM2 was not significantly correlated with the Basal-like subtype, the Luminal B subtype, basal like subtype, or the Her-2 enriched subtype, as shown in Fig 8 ). (A) All breast cancer patients, (B) Luminal A, (C) Luminal B, (D) Her-2 enriched, and (E) Basal-like. The analysis of the RRM2 KEGG pathway ex by GSEA software showed that the high RRM2 expression group was mainly enriched in the ERBB signaling pathway, the JAK-STAT signaling pathway, the mTOR signaling pathway, the P53 signaling pathway, the VEGF signaling pathway, the WNT signaling pathway ( Fig 9 ). These pathways play an important role in the development of a variety of malignant tumors.

Highly expressed RRM2 is associated with poor OS and DMFS of breast cancer patients. Furthermore, the high expression of RRM2 was associated with poor OS in breast cancer patients characterized by specific molecular subtypes (Luminal A subtype and Normal-like subtype) and specific stages (stage 1 and stage 2). RRM2 can be used as a biomarker, and is associated with the survival and prognosis of breast cancer patients.

The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) is a public database of breast cancer. We downloaded clinical data from breast cancer patients and the Z-score value of mRNA expression through the cBioportal website ( http://www.cbioportal.org/ ) [ 11 – 13 ]. After filtering, 1350 breast cancer samples were used for subsequent analysis. The Breast cancer integrative platform (BCIP) is a website that enables the analysis and visualization profiles of breast cancer patients. The data for this platform derives from public databases, including METABRIC, The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) datasets. We obtained the mRNA expression of RRM2 in breast cancer tissues and adjacent tissues through BCIP. Breast cancer tissues and adjacent tissues were collected between January 2018 and August 2020 from 45 patients, who underwent surgery at the Department of Thyroid and Breast Surgery, Hubei No. 3 People’s Hospital of Jianghan University, immediately stored in liquid nitrogen and stored at -80°C. The present study was approved by the Ethics Committee of The Hubei No.3 People’s Hospital of Jianghan University. The human mammary epithelial cell line MCF-10A, breast cancer cell lines MCF-7, BT474, MDA-MB-453, and MDA-MB-231 were obtained from the Culture Collection of the Chinese Academy of Sciences (Shanghai, China), and were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Thermo Fisher Scientific; USA) supplemented with 10% fetal bovine serum (FBS; Gibco; USA), and penicillin/streptomycin at 37°C in a humidified incubator with 5% CO2 incubator. The cell lines required for this experiment were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences. Total RNA was extracted from breast epithelial cell line MCF-10A and breast cancer cell line MCF-7, BT474, MDA-MB-453, MDA-MB-231.Using the Bio-Rad Real-time fluorescent quantitative PCR instrument, GAPDH as an internal reference, adopt two-step PCR reaction program, conditions are predenaturation at 95°C for 10 min; denaturation at 95°C for 15 seconds, annealing/extension at 60°C for 60 seconds, 42 cycles. By analyzing and deriving the Ct value of each sample, calculating the 2-△△Ct value, and finally determining the relative expression level of mRNA [ 14 ]. Primer sequence: GAPDH (Forward 5’-ATGGCACCGTCAAGGCTG-3’ and Reverse 5’-AGCATCGCCCCACTTGATTT-3’ ); RRM2 (Forward 5’-CTGGCTCAAGAAACGAGGACT-3’ and Reverse 5’-ACATCAGGCAAGCAAAATCACA-3’ ). A total of 100 mg of tissue was finely cut and placed in a test tube, and an appropriate amount of pre-cooled PBS was added; the samples were centrifuged for 3 min. Next, the extraction reagent (Sangon Biotech, One Step Animal Tissue Active Protein Extraction Kit) was added to the test tube, and the sample was homogenized on ice and incubated for 20 minutes. The sample was then centrifuged at 12,000 rpm for 10 minutes, and the supernatant containing the total tissue protein was removed. The protein concentration was determined using the BCA protein detection kit (Tiangen Biotechnology, Beijing, China). The protein was denatured at 100°C for 10 minutes and the samples were loaded and separated by 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE). The protein was then transferred to a polyvinylidene fluoride (PVDF) membrane (Millipore, Bedford, MA, USA). The membrane was blocked with TBST containing 5% skim milk for 1 hour and then incubated with the specific primary antibody overnight at 4°C with shaking. The membrane was washed three times with TBST for 10 minutes each time and incubated with the secondary antibody for 1 hour at room temperature. Protein detection was carried out using ECL substrate (Pierce) and signal intensities were quantified by Image J. Antibodies (Abcam) were used at the following dilutions: RRM2 (1:3000) and GAPDH (1:5000). Secondary antibodies used were HRP goat anti-mouse and HRP goat anti-rabbit IgG antibodies (Proteintech). The KM-plot database simultaneously integrates gene expression and clinical data. Survival curves were calculated using the "survival" package. The online KM-plot database can be used to identify the correlation of individual RRM2 mRNA expression in survival analysis, including examination of overall survival (OS), recurrence-free survival (RFS), distant metastasis-free survival (DMFS), and postprogression survival period (PPS). We performed GSEA analysis to study the molecular mechanism of RRM2 on breast cancer [ 15 ]. The GSEA 4.0.3 software was downloaded from Broad Institute ( http://www.gsea-msigdb.org/gsea/index.jsp ). The median value of RRM2 expression was used to classify patients into high expression groups and low expression groups, where FDR<0.05; The number of permutations: 1000 times; and R software was used to analyze and draw a GSEA enrichment analysis chart. All statistical analyses were performed with R (R version 4.0.3) [ 16 ] and SPSS (version 22) statistical software. The median expression of mRNA was used to classify RRM2 as high or low expression [ 17 , 18 ]. The relationship between the expression level of RRM2 and clinicopathological characteristics were statistically analyzed by chi-square test or Fisher exact test. The KM method with the log-rank test was used to analyze the OS of patients. The ROC curve was used to evaluate the diagnostic value of RRM2 gene expression, and the area under curve (AUC) represents the diagnostic value. Univariate cox analysis was used to screen potential prognostic factors, and multivariate Cox analysis was used to screen independent prognostic factors that affect the OS of breast cancer patients.

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