TY - JOUR
T1 - Prior knowledge driven joint NMF algorithm for ceRNA co-module identification
AU - Deng, Jin
AU - Kong, Wei
AU - Wang, Shuaiqun
AU - Mou, Xiaoyang
AU - Zeng, Weiming
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (No. 61271446 and 31170952) and Natural Science Foundation of Shanghai (No. 18ZR1417200).
Publisher Copyright:
© Ivyspring International Publisher.
PY - 2018/10/3
Y1 - 2018/10/3
N2 - MRNA and lncRNA serve as a type of endogenous RNA in cell, which can competitively bind to the same miRNA through miRNA response elements (MREs), thereby regulating their respective expression levels, playing an important role in post-transcriptional regulation, and regulating the progress of tumors. The proposed competing endogenous RNA (ceRNA) hypothesis provides novel clues for the occurrence and development of tumors, but the integrative analysis methods of diverse RNA data are significantly limited. In order to find out the relationship among miRNA, mRNA and lncRNA, the previous studies only used individual dataset as seeds to search two other related data in the database to construct ceRNA network, but it was difficult to identify the synchronized effects from multiple regulatory levels. Here, we developed the joint matrix factorization method integrating prior knowledge to map the three types of RNA data of lung cancer to the common coordinate system and construct the ceRNA network corresponding to the common module. The results show that more than 90% of the modules are closely related to cancer, including lung cancer. Furthermore, the resulting ceRNA network not only accurately excavates the known correlation of the three types of RNA molecular, but also further discovers the potential biological associations of them. Our work provides support and foundation for future biological validation how competitive relationships of multiple RNAs affects the development of tumors.
AB - MRNA and lncRNA serve as a type of endogenous RNA in cell, which can competitively bind to the same miRNA through miRNA response elements (MREs), thereby regulating their respective expression levels, playing an important role in post-transcriptional regulation, and regulating the progress of tumors. The proposed competing endogenous RNA (ceRNA) hypothesis provides novel clues for the occurrence and development of tumors, but the integrative analysis methods of diverse RNA data are significantly limited. In order to find out the relationship among miRNA, mRNA and lncRNA, the previous studies only used individual dataset as seeds to search two other related data in the database to construct ceRNA network, but it was difficult to identify the synchronized effects from multiple regulatory levels. Here, we developed the joint matrix factorization method integrating prior knowledge to map the three types of RNA data of lung cancer to the common coordinate system and construct the ceRNA network corresponding to the common module. The results show that more than 90% of the modules are closely related to cancer, including lung cancer. Furthermore, the resulting ceRNA network not only accurately excavates the known correlation of the three types of RNA molecular, but also further discovers the potential biological associations of them. Our work provides support and foundation for future biological validation how competitive relationships of multiple RNAs affects the development of tumors.
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U2 - 10.7150/ijbs.27555
DO - 10.7150/ijbs.27555
M3 - Article
C2 - 30443186
AN - SCOPUS:85056665877
VL - 14
SP - 1822
EP - 1833
JO - International Journal of Biological Sciences
JF - International Journal of Biological Sciences
SN - 1449-2288
IS - 13
ER -