AUTHOR=Ding Yongfeng , Zhong Tingting , Wang Min , Xiang Xueping , Ren Guoping , Jia Zhongjuan , Lin Qinghui , Liu Qian , Dong Jingwen , Li Linrong , Li Xiawei , Jiang Haiping , Zhu Lijun , Li Haoran , Shen Dejun , Teng Lisong , Li Chen , Shao Jimin TITLE=Integrative Analysis Reveals Across-Cancer Expression Patterns and Clinical Relevance of Ribonucleotide Reductase in Human Cancers JOURNAL=Frontiers in Oncology VOLUME=9 YEAR=2019 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.00956 DOI=10.3389/fonc.2019.00956 ISSN=2234-943X ABSTRACT=

Mining cancer-omics databases deepens our understanding of cancer biology and can lead to potential breakthroughs in cancer treatment. Here, we propose an integrative analytical approach to reveal across-cancer expression patterns and identify potential clinical impacts for genes of interest from five representative public databases. Using ribonucleotide reductase (RR), a key enzyme in DNA synthesis and cancer-therapeutic targeting, as an example, we characterized the mRNA expression profiles and inter-component associations of three RR subunit genes and assess their differing pathological and prognostic significance across over 30-types of cancers and their related subtypes. Findings were validated by immunohistochemistry with clinical tissue samples (n = 211) collected from multiple cancer centers in China and with clinical follow-up. Underlying mechanisms were further explored and discussed using co-expression gene network analyses. This framework represents a simple, efficient, accurate, and comprehensive approach for cancer-omics resource analysis and underlines the necessity to separate the tumors by their histological or pathological subtypes during the clinical evaluation of molecular biomarkers.