World Journal of Urology

, Volume 36, Issue 2, pp 241–247 | Cite as

Identification of key genes and construction of microRNA–mRNA regulatory networks in bladder smooth muscle cell response to mechanical stimuli using microarray expression profiles and bioinformatics analysis

  • Liao Peng
  • De-Yi LuoEmail author
Original Article



To identify keys genes and elucidate miRNA–mRNA regulatory networks in Bladder smooth muscle cell (BSMC) response to mechanical stimuli.


Human BSMCs, seeded on a silicone membrane, were subjected to mechanical stretch or without stretch. Microarray was used to analyze the differential expression of mRNAs and miRNAs between human BSMCs under mechanical stretch and control static control group. Differentially expressed genes(DEGs) and miRNAs (DEMs) in these two groups were identified. Subsequently, differentially expressed DEGs were conducted with functional analysis, and then PPI network was constructed. Finally, miRNA–mRNA regulatory network was visualized using Cytoscape.


1639 significant DEGs and three DEMs were identified between the stretch group and control static group. The PPI network of DEGs was constructed by STRING, which was composed of 1459 nodes and 1481 edges, including 188 upregulated genes and 255 downregulated genes. Moreover, 36 genes in the PPI network were identified as hub genes in BSMCs response to mechanical stretch, e.g. CCNH, CPSF2, TSNAX, ARPC5 and PSMD3 genes. Subsequently, 39 clusters were selected from PPI network using MCODE, and it was shown that the most significant cluster consisted of 14 nodes and 91 edges. Besides, miR-503HG was the most significantly downregulated miRNA and was predicted to target five upregulated genes, including SMAD7, CCND3, WIPI2, NYNRIN and PVRL1.


Our data mining and integration help reveal the mechanotransduction mechanism of BSMCs’ response to mechanical stimulation and contribute to the early diagnosis of bladder outlet obstruction (BOO) as well as the improvement of pathogenesis of BOO treatment.


Bladder smooth muscle cell Mechanical stimulation Microarray expression profiles Bioinformatics analysis 


Authors’ contribution

LP, D-YL: Protocol/project development. D-YL: Data collection or management. LP, D-YL: Data analysis. D-YL: Manuscript writing/editing.


This study was funded by Grant No. 81770673, No. 31170907, No. 31370951, and No. 81470927 from the National Natural Science Foundation of China, Grant No. JH2015017 from Application-oriented Foundation of Committee Organization Department of Sichuan Provincial Party and 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

345_2017_2132_MOESM1_ESM.docx (922 kb)
Supplementary material 1 (DOCX 922 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Urology, Institute of UrologyWest China Hospital, Sichuan UniversityChengduPeople’s Republic of China

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