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. 2019 Apr 9;39(4):BSR20182062.
doi: 10.1042/BSR20182062. Print 2019 Apr 30.

Bioinformatics analysis revealing prognostic significance of RRM2 gene in breast cancer

Wei-Xian Chen  1 , Liang-Gen Yang  2 , Ling-Yun Xu  3 , Lin Cheng  3 , Qi Qian  3 , Li Sun  3 , Yu-Lan Zhu  3
Affiliations

Bioinformatics analysis revealing prognostic significance of RRM2 gene in breast cancer

Wei-Xian Chen et al. Biosci Rep. .

Abstract

Background: Ribonucleotide reductase M2 subunit (RRM2) plays vital roles in many cellular processes such as cell proliferation, invasiveness, migration, angiogenesis, senescence, and tumorigenesis. However, the prognostic significance of RRM2 gene in breast cancer remains to be investigated. Methods:RRM2 expression was initially evaluated using the Oncomine database. The relevance between RRM2 level and clinical parameters as well as survival data in breast cancer was analyzed using the Kaplan-Meier Plotter, PrognoScan, and Breast Cancer Gene-Expression Miner (bc-GenExMiner) databases. Results:RRM2 was overexpressed in different subtypes of breast cancer patients. Estrogen receptor (ER) and progesterone receptor (PR) were negatively correlated with RRM2 expression. Conversely, the Scarff-Bloom-Richardson (SBR) grade, Nottingham prognostic index (NPI), human epidermal growth factor receptor-2 (HER-2) status, nodal status, basal-like status, and triple-negative status were positively related to RRM2 level in breast cancer samples with respect to normal tissues. Patients with increased RRM2 showed worse overall survival, relapse-free survival, distant metastasis-free survival, disease-specific survival, and disease-free survival. RRM2 also exerted positive effect on metastatic relapse event. Besides, a positive correlation between RRM2 and KIF11 genes was confirmed. Conclusion: Bioinformatics analysis revealed that RRM2 might be used as a predictive biomarker for prognosis of breast cancer. Further studies are needed to more precisely elucidate the value of RRM2 in evaluating breast cancer prognosis.

Keywords: Biomarker; Breast cancer; Prognosis; RRM2.

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Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figures

Figure 1
Figure 1. Expression of RRM2 gene in 20 types of malignant tumor and corresponding normal tissues using the Oncomine database
Red and blue represent the numbers of datasets with statistically significant (P<0.05) increased and decreased levels of RRM2 gene, respectively. Cell color is determined by the best gene rank percentile for the analyses within the cell, and the gene rank was analyzed by percentile of target genes in the top of all genes measured by each study.
Figure 2
Figure 2. Box plot comparing RRM2 expression in normal individuals and breast cancer patients derived from the Oncomine database
Analysis is shown for male breast carcinoma (A), intraductal cribriform breast adenocarcinoma (B), invasive breast carcinoma (C), invasive lobular breast carcinoma (D), invasive ductal breast carcinoma (E), ductal breast carcinoma in situ (F), invasive ductal breast carcinoma epithelia (G), and ductal breast carcinoma (H). * stands for the maximum and minimum values.
Figure 3
Figure 3. Box plot evaluating RRM2 expression among groups of patients according to different clinical parameters using the bc-GenExMiner software
Analysis is shown for age (A), SBR (B), NPI (C), ER (D), PR (E), HER-2(F), nodal status (G), basal-like status (H), and triple-negative status (I).
Figure 4
Figure 4. Survival curve and forest plot evaluating the prognostic value of RRM2
Analysis is shown for overall survival (A), relapse-free survival (B), distant metastasis-free survival (C) using the Kaplan–Meier Plotter, and forest plot of metastatic relapse event using the bc-GenExMiner database (D).
Figure 5
Figure 5. Co-expression of RRM2 gene
(A) Co-expression profile of RRM2 identified using the Oncomine database. (B) Correlation between RRM2 and KIF11 expression in breast cancer analyzed using the bc-GenExMiner software. (C) Heat map of RRM2 and KIF11 expression across PAM50 breast cancer subtypes in the TCGA database obtained from the UCSC Xena web-based tool.
See this image and copyright information in PMC

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