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Cluster Computing

, Volume 22, Supplement 1, pp 707–717 | Cite as

Identification of PIWI-interacting RNA modules by weighted correlation network analysis

  • Yajun Liu
  • Junying ZhangEmail author
  • Aimin Li
  • Yuanyuan Zhang
  • Yaoyao Li
  • Xiguo Yuan
  • Zhongzhen He
  • Zhaowen Liu
  • Shouheng Tuo
Article

Abstract

PIWI-interacting RNAs (piRNAs) are the largest class of small noncoding RNAs in animal cells; they cooperate with PIWI proteins to safeguard the genome. Although several studies have established that piRNAs can be biomarkers of cancer, it is still difficult to elucidate the exact function of piRNAs. In addition, researchers need to further investigate the interplay between piRNAs and piRNA groups in tumorigenesis. To identify cancer-associated piRNA-modules, we performed a weighted gene coexpression network analysis (WGCNA) on piRNA expression data from 11 types of cancer. Thereafter, genes associated with hub piRNAs in modules were predicted, and functional analysis of these genes was used to interpret the relation between piRNA and cancer. The results indicated that these piRNA modules have significant associations with cancer. A module with a high correlation coefficient (cor: −0.83, p value: 1.86E−128) was found; it was especially relevant to head and neck squamous cell carcinoma. Moreover, we found that hub piRNAs in modules may contribute to metastasis. This finding advances the understanding of piRNA function and its association with human cancer.

Keywords

WGCNA PiRNA module Cancer 

Notes

Acknowledgements

This work was supported by the Natural Science Foundation of China under Grants 61571341, 61201312 and 91530113, Research Fund for the Doctoral Program of Higher Education of China (No. 2013 0203110017), the Fundamental Research Funds for the Central Universities of China (Nos BDY171416, JBZ170301, JB140306 and 20101164977), the Natural Science Foundation of Shaanxi Province in China (2015JM6275, 2017JM6024).

Author contributions Yajun Liu and Junying Zhang participated in the design of the study and drafted the manuscript. Yajun Liu conducted the data analysis. Aimin Li, Yuanyuan Zhang, and Yaoyao Li proposed numerous constructive ideas and revised the manuscript in detail. In addition, Xiguo Yuan, Zhongzhen He, Zhaowen Liu, and Shouheng Tuo contributed some insights into this work.

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflicts of interest with respect to the publication of this paper.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Yajun Liu
    • 1
    • 2
  • Junying Zhang
    • 1
    Email author
  • Aimin Li
    • 2
  • Yuanyuan Zhang
    • 3
  • Yaoyao Li
    • 1
  • Xiguo Yuan
    • 1
  • Zhongzhen He
    • 1
  • Zhaowen Liu
    • 1
  • Shouheng Tuo
    • 1
  1. 1.School of Computer Science and TechnologyXidian UniversityXi’anPeople’s Republic of China
  2. 2.Xi’an University of TechnologyXi’anPeople’s Republic of China
  3. 3.Qingdao University of Science and TechnologyQingdaoPeople’s Republic of China

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