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26-09-2017

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

Authors: Yajun Liu, Junying Zhang, Aimin Li, Yuanyuan Zhang, Yaoyao Li, Xiguo Yuan, Zhongzhen He, Zhaowen Liu, Shouheng Tuo

Published in: Cluster Computing | Special Issue 1/2019

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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.

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Title: Identification of PIWI-interacting RNA modules by weighted correlation network analysis
Authors: Yajun Liu
Junying Zhang
Aimin Li
Yuanyuan Zhang
Yaoyao Li
Xiguo Yuan
Zhongzhen He
Zhaowen Liu
Shouheng Tuo
Publication date: 26-09-2017
Publisher: Springer US
Published in: Cluster Computing / Issue Special Issue 1/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1194-8

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