Prognostic and therapeutic significance of ribonucleotide reductase small subunit M2 in estrogen-negative breast cancers
Year: 2014
Type: review
Abstract: BACKGROUND: Ribonucleotide reductase (RR) is an essential enzyme involved in DNA synthesis. We hypothesized that RR subunit M2 (RRM2) might be a novel prognostic and predictive biomarker for estrogen receptor (ER)-negative breast cancers. METHODS: Individual and pooled survival analyses were conducted on six independent large-scale breast cancer microarray data sets; and findings were validated on a human breast tissue set (ZJU set). RESULTS: Gene set enrichment analysis revealed that RRM2-high breast cancers were significantly enriched for expression of gene sets that increased in proliferation, invasiveness, undifferentiation, embryonic stem/progenitor-like phenotypes, and poor patient survival (p < 0.01). Independent and pooled analyses verified that increased RRM2 mRNA levels were associated with poor patient outcome in a dose-dependent manner. The prognostic power of RRM2 mRNA was comparable to multiple gene signatures, and it was superior to TNM stage. In ER-negative breast cancers, RRM2 showed more prognostic power than that in ER-positive breast cancers. Further analysis indicated that RRM2 was a more accurate prognostic biomarker for ER-negative breast cancers than the pathoclinical indicators and uPA. A new RR inhibitor, COH29, could significantly enhance the chemosensitivity to doxorubicin in ER-negative MDA-MB-231 cells, but not in ER-positive MCF-7 cells. CONCLUSION: RRM2 appears to be a promising prognostic biomarker and therapeutic target for ER-negative breast cancer patients.
Source: BMC Cancer
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,390
Funders: National Natural Science Foundation of China, China Scholarship Council, Major State Basic Research Development Program of China, National Institutes of Health, National Key Research and Development Program of China, National Cancer Institute, Natural Science Foundation of Zhejiang Province
OpenAlex ID: W2016278165
