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Predicting Functional Modules of Liver Cancer Based on Differential Network Analysis

  • Bo Hu
  • Xiao ChangEmail author
  • Xiaoping LiuEmail author
Original Research Article

Abstract

Complex diseases are generally caused by disorders of biological networks or/and mutations in multiple genes. The efficient and systematic identification of functional modules can not only supply effective diagnosis and treatment in clinic, but also benefit in further in-depth analysis of the pathological mechanism of complex diseases. In this study, we applied the method of differential network to identify functional modules between control and disease samples, which are different from most of the current approaches that focus on differential expression. In particular, we applied our approach to analyze transcriptome data of liver cancer in The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/), and we obtained two modules associated with liver cancer. One is a functional gene module that contains a set of liver cancer-related genes, and another is an lncRNA (long non-coding RNA) module that includes liver cancer-related lncRNAs. The results of survival analysis and classification show that the functional modules cannot only be used as effective modular biomarkers to identifying liver cancer, but also predict the prognosis of liver cancer. The method can identify functional modules in genes and lncRNA from liver cancer, and these modules can be used to do prognosis detection and further study in mechanism of liver cancer.

Keywords

Differential network analysis Functional modules Liver cancer LncRNA 

Notes

Funding

This work is supported by the National Natural Science Foundation of China (Grant no: 61403363) and Key Project of Natural Science of Anhui Provincial Education Department (No. KJ2016A002).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

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References

  1. 1.
    Vousden KH, Lu X (2002) Live or let die: the cell’s response to p53. Nat Rev Cancer 2(8):594–604CrossRefGoogle Scholar
  2. 2.
    Futreal PA, Coin L, Marshall M et al (2004) A census of human cancer genes. Nat Rev Cancer 4(3):177–183CrossRefGoogle Scholar
  3. 3.
    Ng EK, Chong WW, Jin H et al (2009) Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening. Gastroenterology 136(5):1375–1381Google Scholar
  4. 4.
    Liu ZP, Wang Y, Zhang XS et al (2011) Detecting and analyzing differentially activated pathways in brain regions of Alzheimer’s disease patients. Mol Biosyst 7(5):1441–1452CrossRefGoogle Scholar
  5. 5.
    Liu X, Tang WH, Zhao XM et al (2010) A network approach to predict pathogenic genes for Fusarium graminearum. PLoS One 5(10):e13021CrossRefGoogle Scholar
  6. 6.
    Liu X, Chang X, Liu R et al (2017) Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. PLoS Comput Biol 13(7):e1005633CrossRefGoogle Scholar
  7. 7.
    Liu X, Wang Y, Ji H et al (2016) Personalized characterization of disease using sample-specific networks[J]. Nucleic Acids Res 44(22):e164CrossRefGoogle Scholar
  8. 8.
    Liu X, Liu ZP, Zhao XM et al (2012) Identifying disease genes and module biomarkers by differential interactions. J Am Med Inform Assoc 19:241–248CrossRefGoogle Scholar
  9. 9.
    Liu X, Chang X (2016) Identifying module biomarkers from gastric cancer by differential correlation network. OncoTargets Ther 2016(9):5701–5711CrossRefGoogle Scholar
  10. 10.
    Gov E, Arga KY (2017) Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer. Sci Rep 7(1):4996CrossRefGoogle Scholar
  11. 11.
    Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9(1):559CrossRefGoogle Scholar
  12. 12.
    Gill R, Datta S, Datta S (2010) A statistical framework for differential network analysis from microarray data. BMC Bioinform 11(1):95CrossRefGoogle Scholar
  13. 13.
    Piñero J, Bravo À, Queralt-Rosinach N et al (2017) DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 45:D833–D839 (Database issue)CrossRefGoogle Scholar
  14. 14.
    Cui T, Zhang L, Huang Y et al (2018) MNDR v2.0: an updated resource of ncRNA-disease associations in mammals. Nucleic Acids Res 46:D371–D374 (Database issue)CrossRefGoogle Scholar
  15. 15.
    Miao YR, Liu W, Zhang Q et al (2018) lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs. Nucleic Acids Res 46:D276–D280 (Database issue)CrossRefGoogle Scholar
  16. 16.
    R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  17. 17.
    Chawla NV, Bowyer KW, Hall LO et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357CrossRefGoogle Scholar
  18. 18.
    Khageh SH, Kolterer S, Steiner M et al (2017) Camptothecin and its analog SN-38, the active metabolite of irinotecan, inhibit binding of the transcriptional regulator and oncoprotein FUBP1 to its DNA target sequence FUSE. Biochem Pharmacol 146:53–62CrossRefGoogle Scholar
  19. 19.
    Duan J, Xu B, Ma X et al (2017) Upregulation of far upstream element-binding protein 1 (FUBP1) promotes tumor proliferation and tumorigenesis of clear cell renal cell carcinoma. PLoS One 12(1):e0169852CrossRefGoogle Scholar
  20. 20.
    Liu C, Shi X, Wang L et al (2014) SUZ12 is involved in progression of non-small cell lung cancer by promoting cell proliferation and metastasis. Tumor Biol 35(6):6073–6082CrossRefGoogle Scholar
  21. 21.
    Wang X, Lu X, Geng Z et al (2017) LncRNA PTCSC3/miR-574-5p governs cell proliferation and migration of papillary thyroid carcinoma via Wnt/β-catenin signaling. J Cell Biochem 118(12):4745CrossRefGoogle Scholar
  22. 22.
    Xia S, Ji R, Zhan W (2017) Long noncoding RNA papillary thyroid carcinoma susceptibility candidate 3 (PTCSC3) inhibits proliferation and invasion of glioma cells by suppressing the Wnt/β-catenin signaling pathway. BMC Neurol 17(1):30CrossRefGoogle Scholar
  23. 23.
    Wang L, Chen Z, An L et al. (2016) Analysis of long non-coding RNA expression profiles in non-small cell lung cancer. Cell Physiol Biochem 38(6):2389CrossRefGoogle Scholar
  24. 24.
    Bian Y, Chang X, Liao Y et al (2016) Promotion of epithelial-mesenchymal transition by Frizzled2 is involved in the metastasis of endometrial cancer. Oncol Rep 36(2):803–810CrossRefGoogle Scholar
  25. 25.
    Zins K, Schäfer R, Paulus P et al (2016) Frizzled2 signaling regulates growth of high-risk neuroblastomas by interfering with β-catenin-dependent and β-catenin-independent signaling pathways. Oncotarget 7(29):46187CrossRefGoogle Scholar
  26. 26.
    Li C, Cai S, Wang X et al (2015) Identification and characterization of ANO9 in stage II and III colorectal carcinoma. Oncotarget 6(30):29324PubMedPubMedCentralGoogle Scholar
  27. 27.
    Jun I, Park HS, Piao H et al (2017) ANO9/TMEM16J promotes tumourigenesis via EGFR and is a novel therapeutic target for pancreatic cancer. Br J Cancer 117(12):1798CrossRefGoogle Scholar
  28. 28.
    Binothman N, Hachim IY, Lebrun JJ et al (2017) CPSF6 is a clinically relevant breast cancer vulnerability target: role of CPSF6 in breast cancer. EBioMedicine 21:65–78CrossRefGoogle Scholar
  29. 29.
    Lee YH, Liu X, Qiu F et al (2015) Correction: HP1β is a biomarker for breast cancer prognosis and PARP inhibitor therapy. PLoS One 10(3):e0121207CrossRefGoogle Scholar
  30. 30.
    Lu M, Shi B, Wang J et al (2010) TAM: A method for enrichment and depletion analysis of a microRNA category in a list of microRNAs. BMC Bioinform 11(1):419CrossRefGoogle Scholar
  31. 31.
    Li K, Sun D, Gou Q et al (2018) Long non-coding RNA linc00460 promotes epithelial-mesenchymal transition and cell migration in lung cancer cells. Cancer Lett 420:80–90CrossRefGoogle Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsShandong University at WeihaiWeihaiChina
  2. 2.School of Statistics and Applied MathematicsAnhui University of Finance & EconomicsBengbuChina

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