Extracting a few functionally reproducible biomarkers to build robust subnetwork-based classifiers for the diagnosis of cancer

Gene

Volumn 526, Issue 2, 2013, Pages 232-238

  Zhang, Lin ^a   Li, Shan ^a   Hao, Chunxiang ^a   Hong, Guini ^a   Zou, Jinfeng ^a   Zhang, Yuannv ^b   Li, Pengfei ^b   Guo, Zheng ^a,b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b HARBIN MEDICAL UNIVERSITY   (China)

Author keywords

Cancer; Diagnosis; Gene expression profiling; Protein interaction networks; Reproducibility of biomarkers

Indexed keywords

BIOLOGICAL MARKER;

ARTICLE; CANCER DIAGNOSIS; DIFFERENTIALLY EXPRESSED GENE; DOWN REGULATION; GENE; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; REPRODUCIBILITY; SCORING SYSTEM; UPREGULATION;

CANCER; DE; DIAGNOSIS; DIFFERENTIALLY EXPRESSED; FALSE DISCOVERY RATE; FDR; GENE EXPRESSION PROFILING; PERCENTAGE OF OVERLAP; PERCENTAGE OF OVERLAP IN THE PPI NETWORK; PERCENTAGE OF OVERLAPPING DEREGULATIONS; PO; POD; PON; PPI; PROTEIN INTERACTION NETWORKS; PROTEIN-PROTEIN INTERACTION; RECURSIVE FEATURE ELIMINATION; REPRODUCIBILITY OF BIOMARKERS; RFE; SAM; SIGNIFICANCE ANALYSIS OF MICROARRAY; SUPPORT VECTOR MACHINE; SVM;

CASE-CONTROL STUDIES; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; NEOPLASMS; PROTEIN INTERACTION MAPPING; PROTEIN INTERACTION MAPS; REPRODUCIBILITY OF RESULTS; TUMOR MARKERS, BIOLOGICAL;

EID: 84880710519     PISSN: 03781119     EISSN: 18790038     Source Type: Journal    
DOI: 10.1016/j.gene.2013.05.011     Document Type: Article

References (54)

1. Auffray C. Protein subnetwork markers improve prediction of cancer outcome. Mol. Syst. Biol. 2007, 3:141.
2. Bader G.D., et al. BIND - the Biomolecular Interaction Network Database. Nucleic Acids Res. 2001, 29(1):242-245.
3. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 1995, 57(1):289-300.
4. Berchuck A., et al. Microarray analysis of early stage serous ovarian cancers shows profiles predictive of favorable outcome. Clin. Cancer Res. 2009, 15(7):2448-2455.
5. Chakraborty G., Jain S., Kundu G.C. Osteopontin promotes vascular endothelial growth factor-dependent breast tumor growth and angiogenesis via autocrine and paracrine mechanisms. Cancer Res. 2008, 68(1):152-161.
6. Chuang H.Y., et al. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 2007, 3:140.
7. Dao P., et al. Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics 2011, 27(13):i205-i213.
8. Diehn M., et al. SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 2003, 31(1):219-223.
9. Dittrich M.T., et al. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 2008, 24(13):i223-i231.
10. Ein-Dor L., et al. Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics 2005, 21(2):171-178.
11. Ein-Dor L., Zuk O., Domany E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl. Acad. Sci. U. S. A. 2006, 103(15):5923-5928.
12. Ferreira J.A., Zwinderman A. Approximate sample size calculations with microarray data: an illustration. Stat. Appl. Genet. Mol. Biol. 2006, 5. (p. Article25).
13. Finak G., et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat. Med. 2008, 14(5):518-527.
14. Furey T.S., et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16(10):906-914.
15. Gong X., et al. Extracting consistent knowledge from highly inconsistent cancer gene data sources. BMC Bioinformatics 2010, 11:76.
16. Gong X., et al. Evaluating the consistency of differential expression of microRNA detected in human cancers. Mol. Cancer Ther. 2011, 10(5):752-760.
17. Guo Z., et al. Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network. Bioinformatics 2007, 23(16):2121-2128.
18. Guyon I., et al. Gene selection for cancer classification using support vector machines. Mach. Learn. 2002, 46(1-3):389-422.
19. Hahn A., et al. Confirmation of human protein interaction data by human expression data. BMC Bioinformatics 2005, 6:112.
20. Hart G.T., Ramani A.K., Marcotte E.M. How complete are current yeast and human protein-interaction networks?. Genome Biol. 2006, 7(11):120.
21. Hermjakob H., et al. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004, 32:D452-D455. (Database issue).
22. Huang E., et al. Gene expression predictors of breast cancer outcomes. Lancet 2003, 361(9369):1590-1596.
23. Ideker T., et al. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 2002, 18(Suppl. 1):S233-S240.
24. Joshi-Tope G., et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005, 33:D428-D432. (Database issue).
25. Kanehisa M., et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006, 34:D354-D357. (Database issue).
26. Klebanov L., Jordan C., Yakovlev A. A new type of stochastic dependence revealed in gene expression data. Stat. Appl. Genet. Mol. Biol. 2006, 5. (p. Article7).
27. Lage K., et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat. Biotechnol. 2007, 25(3):309-316.
28. Lee E., et al. Inferring pathway activity toward precise disease classification. PLoS Comput. Biol. 2008, 4(11):e1000217.
29. Leeflang M.M., et al. Systematic reviews of diagnostic test accuracy. Ann. Intern. Med. 2008, 149(12):889-897.
30. Leek J.T., et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 2010, 11(10):733-739.
31. Maslov S., Sneppen K. Specificity and stability in topology of protein networks. Science 2002, 296(5569):910-913.
32. Mewes H.W., et al. MIPS: analysis and annotation of genome information in 2007. Nucleic Acids Res. 2008, 36:D196-D201. Database issue.
33. Mi Z., et al. Module-based prediction approach for robust inter-study predictions in microarray data. Bioinformatics 2010, 26(20):2586-2593.
34. Ostman A., Hellberg C., Bohmer F.D. Protein-tyrosine phosphatases and cancer. Nat. Rev. Cancer 2006, 6(4):307-320.
35. Peri S., et al. Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 2004, 32:D497-D501. (Database issue).
36. Qiu X., et al. Assessing stability of gene selection in microarray data analysis. BMC Bioinformatics 2006, 7:50.
37. Ransohoff D.F. Bias as a threat to the validity of cancer molecular-marker research. Nat. Rev. Cancer 2005, 5(2):142-149.
38. Rhodes D.R., et al. Probabilistic model of the human protein-protein interaction network. Nat. Biotechnol. 2005, 23(8):951-959.
39. Salwinski L., et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004, 32:D449-D451. (Database issue).
40. Sharan R., Ulitsky I., Shamir R. Network-based prediction of protein function. Mol. Syst. Biol. 2007, 3:88.
41. Shi L., et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 2006, 24(9):1151-1161.
42. Su J., Yoon B.J., Dougherty E.R. Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network. BMC Bioinformatics 2010, 11(Suppl. 6):S8.
43. Subramanian A., et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 2005, 102(43):15545-15550.
44. Tamayo P., et al. Metagene projection for cross-platform, cross-species characterization of global transcriptional states. Proc. Natl. Acad. Sci. U. S. A. 2007, 104(14):5959-5964.
45. Tusher V.G., Tibshirani R., Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 2001, 98(9):5116-5121.
46. Ulitsky I., Shamir R. Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 2009, 25(9):1158-1164.
47. van Vliet M.H., et al. Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability. BMC Genomics 2008, 9:375.
48. Wang J., et al. GO-function: deriving biologically relevant functions from statistically significant functions. Brief. Bioinform. 2011, 13(2):216-227.
49. Xu M., et al. An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer. BMC Genomics 2008, 9(Suppl. 1):S12.
50. Yao C., et al. Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis. BMC Syst. Biol. 2010, 4:151.
51. Zanzoni A., et al. MINT: a Molecular INTeraction database. FEBS Lett. 2002, 513(1):135-140.
52. Zhang M., et al. Apparently low reproducibility of true differential expression discoveries in microarray studies. Bioinformatics 2008, 24(18):2057-2063.
53. Zhang M., et al. Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes. Bioinformatics 2009, 25(13):1662-1668.
54. Zhu J., et al. Viewing cancer genes from co-evolving gene modules. Bioinformatics 2010, 26(7):919-924.