MiRNA Synergistic Network Construction and Enrichment Analysis for Common Target Genes in Small-cell Lung Cancer

  • Zhang, Tie-Feng (Department of Respiratory Medicine, Shanghai Dachang Hospital) ;
  • Cheng, Ke-Wen (Department of Respiratory Medicine, Shanghai Renhe Hospital) ;
  • Shi, Wei-Yin (Department of Respiratory Medicine, Shanghai Dachang Hospital) ;
  • Zhang, Jin-Tao (Department of Emergency Medicine, Shanghai Dachang Hospital) ;
  • Liu, Ke-Di (Department of Respiratory Medicine, Shanghai Dachang Hospital) ;
  • Xu, Shu-Guang (Department of Respiratory Medicine, Shanghai Dachang Hospital) ;
  • Chen, Ji-Quan (Department of Respiratory Medicine, Shanghai Changzheng Hospital)
  • Published : 2012.12.31


Background: Small-cell lung cancer (also known as SCLC) is an aggressive form and untreated patients generally die within about 3 months. To obtain further insight into mechanism underlying malignancy with this cancer, an miRNA synergistic regulatory network was constructed and analyzed in the present study. Method: A miRNA microarray dataset was downloaded from the NCBI GEO database (GSE27435). A total of 546 miRNAs were identified to be expressed in SCLC cells. Then a miRNA synergistic network was constructed, and the included miRNAs mapped to the network. Topology analysis was also performed to analyze the properties of the synergistic network. Consequently, we could identified constitutive modules. Further, common target genes of each module were identified with CFinder. Finally, enrichment analysis was performed for target genes. Results: In this study, a miRNA synergistic network with 464 miRNAs and 2981 edges was constructed. According to the topology analysis, the topological properties between the networks constructed by LC related miRNAs and LC unrelated miRNAs were significantly different. Moreover, a module cilque0 could be identified in our network using CFinder. The module included three miRNAs (hsa-let-7c, hsa-let-7b and hsa-let-7d). In addition, several genes were found which were predicted to be common targets of cilque0. The enrichment analysis demonstrated that these target genes were enriched in MAPK signaling pathways. Conclusions: Although limitations exist in the current data, the results uncovered here are important for understanding the key roles of miRNAs in SCLC. However, further validation is required since our results were based on microarray data derived from a small sample size.


Small cell lung cancer;mechanism;miRNA synergistic network


  1. Navarro A, Marrades RM, Vinolas N, et al (2009). MicroRNAs expressed during lung cancer development are expressed in human pseudoglandular lung embryogenesis. Oncology, 76, 162-9.
  2. Ranade AR, Cherba D, Sridhar S, et al (2010). MicroRNA 92a-2*: a biomarker predictive for chemoresistance and prognostic for survival in patients with small cell lung cancer. J Thorac Oncol, 5, 1273-8.
  3. Roush S, Slack FJ (2008). The let-7 family of microRNAs. Tr cell biol , 18, 505.
  4. Xiong F, Wu C, Chang J, et al (2011). Genetic variation in an miRNA-1827 binding site in MYCL1 alters susceptibility to small-cell lung cancer. Cancer Res, 71, 5175-81.
  5. Xu J, Li CX, Li YS, et al (2011). MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features. Nucleic Acids Res, 39, 825-36.
  6. Xue M, Cao X, Zhong Y, et al (2012). Insulin-like growth factor-1 receptor (IGF-1R) kinase inhibitors in cancer therapy: advances and perspectives. Curr Pharm Des, 18, 2901-13.
  7. Cui Q, Yu Z, Purisima EO, Wang E (2006). Principles of microRNA regulation of a human cellular signaling network. Mol Syst Biol, 2, 46.
  8. Ding C, Li R, Peng J, et al (2012). A polymorphism at the miR-502 binding site in the 3' untranslated region of the SET8 gene is associated with the outcome of small-cell lung cancer. Exp Ther Med, 3, 689-92.
  9. Enright AJ, John B, Gaul U, et al (2003). MicroRNA targets in Drosophila. Genome Biol, 5, R1.
  10. Enright AJ, John B, Gaul U, et al (2004). MicroRNA targets in Drosophila. Genome Biol , 5, 1.
  11. Garzon R, Calin GA, Croce CM (2009). MicroRNAs in cancer. Ann Rev Med , 60, 167-79.
  12. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ (2008). miRBase: tools for microRNA genomics. Nucleic Acids Res, 36, D154-8.
  13. Guo L, Liu Y, Bai Y, et al (2010). Gene expression profiling of drugresistant small cell lung cancer cells by combining microRNA and cDNA expression analysis. Eur J Cancer, 46, 1692-702.
  14. Huang da W, Sherman BT, Lempicki RA (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 37, 1-13.
  15. Jiang Q, Wang Y, Hao Y, et al (2009). miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res, 37, D98-104.
  16. Johnson CD, Esquela-Kerscher A, Stefani G, et al (2007). The let-7 microRNA represses cell proliferation pathways in human cells. Cancer Res, 67, 7713-22.
  17. Kanehisa M (2002). The KEGG database. Novartis Found Symp, 247, 91-101; discussion -3, 19-28, 244-52.
  18. Killcoyne S, Carter GW, Smith J, Boyle J (2009). Cytoscape: a community-based framework for network modeling. Methods Mol Biol, 563, 219-39.
  19. Krek A, Grun D, Poy MN, et al (2005). Combinatorial microRNA target predictions. Nat Genet, 37, 495-500.
  20. Lewis BP, Burge CB, Bartel DP (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets, Cell, 120, 15-20.
  21. Li G, Qian T, Li G, et al (2012). Sodium valproate inhibits MDAMB-231 breast cancer cell migration by upregulating NM23H1 expression. Genet Mol Res, 11, 77-86.
  22. Lu M, Zhang Q, Deng M, et al (2008). An analysis of human microRNA and disease associations. PloS one, 3, e3420.
  23. Manning G, Whyte DB, Martinez R, et al (2002). The protein kinase complement of the human genome. Sci Signal, 298, 1912.
  24. Nasu K, Hirakawa T, Okamoto M, et al (2011). Advanced small cell carcinoma of the uterine cervix treated by neoadjuvant chemotherapy with irinotecan and cisplatin followed by radical surgery. Rare Tumors, 3, e6.
  25. Nau MM, Brooks BJ, Battey J, et al (1985). L-myc, a new mycrelated gene amplified and expressed in human small cell lung cancer. Nature, 318, 69-73.
  26. Adamcsek B, Palla G, Farkas IJ, et al (2006). CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics, 22, 1021-3.
  27. Agirre X, Jimenez-Velasco A, San Jose-Eneriz E, et al (2008). Down-regulation of hsa-miR-10a in chronic myeloid leukemia CD34+ cells increases USF2-mediated cell growth. Mol Cancer Res, 6, 1830-40.
  28. Anglicheau D, Muthukumar T, Suthanthiran M (2010). MicroRNAs: small RNAs with big effects. Transplantation, 90, 105.
  29. Ashburner M, Ball CA, Blake JA, et al (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25, 25-9.
  30. Assenov Y, Ramirez F, Schelhorn SE, et al (2008). Computing topological parameters of biological networks. Bioinformatics, 24, 282-4.
  31. Boyerinas B, Park SM, Hau A, et al (2010). The role of let-7 in cell differentiation and cancer. Endocr Relat Cancer, 17, F19-36.

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