Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types

Nature Communications

Volumn 5, Issue , 2014, Pages

  Yang, Yang ^a   Han, Leng ^a   Yuan, Yuan ^a,b   Li, Jun ^a   Hei, Nainan ^a   Liang, Han ^a,b  

^a UNIVERSITY OF TEXAS   (United States)

^b BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA; TUMOR MARKER;

COMPARATIVE STUDY; GENETICS; HUMAN; METABOLISM; NEOPLASMS; PROGNOSIS;

BIOMARKERS, TUMOR; HUMANS; MICRORNAS; NEOPLASMS; PROGNOSIS;

EID: 84893867792     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms4231     Document Type: Article

References (42)

1. Sotiriou, C. et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl Cancer Inst. 98, 262-272 (2006). (Pubitemid 43264710)
2. Bullinger, L. et al. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. New Engl. J. Med. 350, 1605-1616 (2004). (Pubitemid 38501163)
3. Spentzos, D. et al. Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J. Clin. Oncol. 22, 4700-4710 (2004).
4. Zhao, H. et al. Gene expression profiling predicts survival in conventional renal cell carcinoma. PLoS Med. 3, e13 (2006).
5. Adler, A. S. & Chang, H. Y. From description to causality: mechanisms of gene expression signatures in cancer. Cell Cycle 5, 1148-1151 (2006). (Pubitemid 43845372)
6. Abba, M. C., Lacunza, E., Butti, M. & Aldaz, C. M. Breast cancer biomarker discovery in the functional genomic age: a systematic review of 42 gene expression signatures. Biomark. Insights 5, 103-118 (2010).
7. Buyse, M. et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J. Natl Cancer Inst. 98, 1183-1192 (2006). (Pubitemid 44390989)
8. Chakravarti, A. et al. The prognostic significance of phosphatidylinositol 3-kinase pathway activation in human gliomas. J. Clin. Oncol. 22, 1926-1933 (2004). (Pubitemid 41095185)
9. van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999-2009 (2002). (Pubitemid 35461656)
10. Langer, C. et al. Prognostic importance of MN1 transcript levels, and biologic insights from MN1-associated gene and microRNA expression signatures in cytogenetically normal acute myeloid leukemia: a cancer and leukemia group B study. J. Clin. Oncol. 27, 3198-3204 (2009).
11. Oscier, D. G. et al. Multivariate analysis of prognostic factors in CLL: clinical stage, IGVH gene mutational status, and loss or mutation of the p53 gene are independent prognostic factors. Blood 100, 1177-1184 (2002). (Pubitemid 34864269)
12. Furlong, L. I. Human diseases through the lens of network biology. Trends Genet. 29, 150-159 (2013).
13. Barabasi, A. L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113 (2004). (Pubitemid 38160277)
14. Cai, J. J., Borenstein, E. & Petrov, D. A. Broker Genes in Human Disease. Genome Biol. Evol. 2, 815-825 (2010).
15. Zhao, W. et al. Weighted gene coexpression network analysis: state of the art. J. Biopharm. Stat. 20, 281-300 (2010).
16. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061-1068 (2008).
17. The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609-615 (2011).
18. The Cancer Genome Atlas Research Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61-70 (2012).
19. The Cancer Genome Atlas Research Network. Integrative analysis of genomic and molecular alterations in clear cell renal cell carcinoma. Nature 499, 43-49 (2013).
20. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
21. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).
22. Goh, K. I. et al. The human disease network. Proc. Natl Acad. Sci. USA. 104, 8685-8690 (2007).
23. Jeong, H., Mason, S. P., Barabasi, A. L. & Oltvai, Z. N. Lethality and centrality in protein networks. Nature 411, 41-42 (2001). (Pubitemid 32428180)
24. Liang, H. & Li, W. H. Gene essentiality, gene duplicability and protein connectivity in human and mouse. Trends Genet. 23, 375-378 (2007). (Pubitemid 47088007)
25. Yu, H., Kim, P. M., Sprecher, E., Trifonov, V. & Gerstein, M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 3, e59 (2007).
26. Yu, H., Greenbaum, D., Xin, Lu, H., Zhu, X. & Gerstein, M. Genomic analysis of essentiality within protein networks. Trends Genet. 20, 227-231 (2004). (Pubitemid 38625679)
27. Sun, J. & Zhao, Z. A comparative study of cancer proteins in the human protein-protein interaction network. BMC Genomics 11(Suppl 3): S5 (2010).
28. Han, J. D. et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88-93 (2004).
29. Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249-255 (2003). (Pubitemid 37248749)
30. Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215-233 (2009).
31. Chen, K. & Rajewsky, N. The evolution of gene regulation by transcription factors and microRNAs. Nat. Rev. Genet. 8, 93-103 (2007). (Pubitemid 46122839)
32. Lewis, B. P., Burge, C. B. & Bartel, D. P. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20 (2005). (Pubitemid 40094598)
33. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29 (2000). (Pubitemid 30257031)
34. Yu, H. & Gerstein, M. Genomic analysis of the hierarchical structure of regulatory networks. Proc. Natl Acad. Sci. USA 103, 14724-14731 (2006). (Pubitemid 44527797)
35. Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834-838 (2005). (Pubitemid 40839735)
36. Calin, G. A. & Croce, C. M. MicroRNA-cancer connection: The beginning of a new tale. Cancer Res. 66, 7390-7394 (2006). (Pubitemid 44289189)
37. Hadad, S. M. et al. Histological evaluation of AMPK signalling in primary breast cancer. BMC Cancer 9, 307 (2009).
38. Hwang, J. T. et al. Resveratrol induces apoptosis in chemoresistant cancer cells via modulation of AMPK signaling pathway. Ann. N.Y. Acad. Sci. 1095, 441-448 (2007).
39. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719-720 (2008). (Pubitemid 351321220)
40. Grimson, A. et al. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol. Cell 27, 91-105 (2007). (Pubitemid 46991392)
41. Brunet, J. P., Tamayo, P., Golub, T. R. & Mesirov, J. P. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA 101, 4164-4169 (2004). (Pubitemid 38405900)
42. Alexa, A., Rahnenfuhrer, J. & Lengauer, T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600-1607 (2006). (Pubitemid 43985293)