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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2019.00956

Integrative analysis reveals across-cancer expression patterns and clinical relevance of ribonucleotide reductase in human cancers

 Yongfeng Ding1, Tingting Zhong1, Min Wang1,  Xueping Xiang1, Guoping Reng1,  Zhongjuan Jia1, Qinghui Lin1,  Qian Liu1,  Jingwen Dong1, Linrong Li2, Xiawei Li2,  Haiping Jiang1,  Lijun Zhu1,  Haoran Li3,  dejun shen4,  Lisong Teng1,  Chen Li1 and  Jimin Shao1*
  • 1School of Medicine, Zhejiang University, China
  • 2Second Affiliated Hospital, School of Medicine, Zhejiang University, China
  • 3Kymera Therapeutics, United States
  • 4Downey Medical Center, United States

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.

Keywords: Cancer-omics, integrative analysis, ribonucleotide reductase, Expression characteristics, Clinical relevance, gene networks

Received: 24 Jul 2019; Accepted: 10 Sep 2019.

Copyright: © 2019 Ding, Zhong, Wang, Xiang, Reng, Jia, Lin, Liu, Dong, Li, Li, Jiang, Zhu, Li, shen, Teng, Li and Shao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Jimin Shao, School of Medicine, Zhejiang University, Hangzhou, China, shaojimin@zju.edu.cn