Gene-set local hierarchical clustering (gslhc)-A gene set-based approach for characterizing bioactive compounds in terms of biological functional groups

PLoS ONE

Volumn 10, Issue 10, 2015, Pages

  Chung, Feng Hsiang ^a   Jin, Zhen Hua ^a   Hsu, Tzu Ting ^b   Hsu, Chueh Lin ^a   Liu, Hsueh Chuan ^c   Lee, Hoong Chien ^a  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

^c NATIONAL CENTER FOR THEORETICAL SCIENCES   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CYCLIN DEPENDENT KINASE INHIBITOR; DNA TOPOISOMERASE; HISTONE DEACETYLASE INHIBITOR; ANTINEOPLASTIC AGENT;

ARTICLE; CHROMATIN STRUCTURE; DATA ANALYSIS; DOWN REGULATION; ENZYME ACTIVITY; GENE EXPRESSION; GENE NUMBER; GENETIC DATABASE; INFORMATION PROCESSING; REPRODUCIBILITY; UPREGULATION; VALIDITY; WEB BROWSER; CHEMISTRY; DRUG EFFECTS; FEMALE; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; HL-60 CELL LINE; HUMAN; MALE; MCF-7 CELL LINE; TUMOR GENE;

ANTINEOPLASTIC AGENTS; DATABASES, GENETIC; FEMALE; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; GENES, NEOPLASM; HL-60 CELLS; HUMANS; MALE; MCF-7 CELLS;

EID: 84949516193     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0139889     Document Type: Article

References (65)

1. Perez-Diez A, Morgun A, Shulzhenko N. Microarrays for cancer diagnosis and classification. Adv Exp Med Biol. 2007; 593:74-85. Epub 2007/02/03. doi: 10.1007/978-0-387-39978-2-8
2. Miller JA, Oldham MC, Geschwind DH. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci. 2008; 28(6):1410-20
3. Cui X, Churchill GA. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 2003; 4(4):210
4. Zaravinos A, Lambrou GI, Boulalas I, Delakas D, Spandidos DA. Identification of common differentially expressed genes in urinary bladder cancer. PLoS One. 2011; 6(4):e18135. Epub 2011/04/13. doi: 101371/journal.pone.0018135
5. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United States of America. 2001; 98 (9):5116-21. doi: 10.1073/pnas.091062498
6. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004; 3:Article3
7. Pavlidis P. Using ANOVA for gene selection from microarray studies of the nervous system. Methods. 2003; 31(4):282-9
8. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1):25-9
9. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of largescale molecular data sets. Nucleic Acids Res. 2012; 40(Database issue):D109-14
10. Hosack DA, Dennis G Jr., Sherman BT, Lane HC, Lempicki RA. Identifying biological themes within lists of genes with EASE. Genome Biol. 2003; 4(10):R70
11. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science. 2006; 313 (5795):1929-35. doi: 10.1126/science.1132939
12. Aramadhaka LR, Prorock A, Dragulev B, Bao Y, Fox JW. Connectivity maps for biosimilar drug discovery in venoms: the case of Gila monster venom and the anti-diabetes drug Byetta(R). Toxicon: official journal of the International Society on Toxinology. 2013; 69:160-7. doi: 10.1016/j.toxicon.2013.03.018
13. Meng F, Dai E, Yu X, Zhang Y, Chen X, Liu X, et al. Constructing and characterizing a bioactive small molecule and microRNA association network for Alzheimer's disease. Journal of the Royal Society, Interface /the Royal Society. 2014; 11(92):20131057. doi: 10.1098/rsif.2013.1057
14. Chen F, Guan Q, Nie ZY, Jin LJ. Gene expression profile and functional analysis of Alzheimer's disease. American journal of Alzheimer's disease and other dementias. 2013; 28(7):693-701. doi: 101177/1533317513500838
15. Garman KS, Acharya CR, Edelman E, Grade M, Gaedcke J, Sud S, et al. A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities. Proc Natl Acad Sci U S A. 2008; 105(49):19432-7
16. Huang L, Zhao S, Frasor JM, Dai Y. An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors. PloS one. 2011; 6 (7):e22274. doi: 10.1371/journal.pone.0022274
17. Wang G, Ye Y, Yang X, Liao H, Zhao C, Liang S. Expression-based in silico screening of candidate therapeutic compounds for lung adenocarcinoma. PloS one. 2011; 6(1):e14573. doi: 10.1371/journal. pone.0014573
18. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, et al. Discovery and preclinical validation of drug indications using compendia of public gene expression data. Science translational medicine. 2011; 3(96):96ra77. doi: 10.1126/scitranslmed.3001318
19. Iskar M, Campillos M, Kuhn M, Jensen LJ, van Noort V, Bork P. Drug-induced regulation of target expression. PLoS computational biology. 2010; 6(9). doi: 10.1371/journal.pcbi.1000925
20. Pacini C, Iorio F, Goncalves E, Iskar M, Klabunde T, Bork P, et al. DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data. Bioinformatics. 2013; 29(1):132-4. doi: 10.1093/bioinformatics/bts656
21. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107(33):14621-6. doi: 10.1073/pnas.1000138107
22. Parkkinen JA, Kaski S. Probabilistic drug connectivity mapping. BMC bioinformatics. 2014; 15:113. doi: 10.1186/1471-2105-15-113
23. Nam D, Kim SY. Gene-set approach for expression pattern analysis. Brief Bioinform. 2008; 9(3):189-97
24. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003; 34(3):267-73
25. Efron B, Tibshirani R. On Testing the Significance of Sets of Genes. Ann Appl Stat. 2007; 1(1):107-29. doi: 10.1214/07-Aoas101
26. Barry WT, Nobel AB, Wright FA. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics. 2005; 21(9):1943-9
27. Breslin T, Eden P, Krogh M. Comparing functional annotation analyses with Catmap. BMC Bioinformatics. 2004; 5:193
28. Lee HK, Braynen W, Keshav K, Pavlidis P. ErmineJ: tool for functional analysis of gene expression data sets. BMC Bioinformatics. 2005; 6:269
29. Dinu I, Potter JD, Mueller T, Liu Q, Adewale AJ, Jhangri GS, et al. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics. 2007; 8:242
30. Prifti E, Zucker JD, Clement K, Henegar C. FunNet: an integrative tool for exploring transcriptional interactions. Bioinformatics. 2008; 24(22):2636-8
31. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics. 2010; 26 (12):i237-45
32. Sun CH, Kim MS, Han Y, Yi GS. COFECO: composite function annotation enriched by protein complex data. Nucleic Acids Res. 2009; 37(Web Server issue):W350-5
33. Wong DJ, Nuyten DS, Regev A, Lin M, Adler AS, Segal E, et al. Revealing targeted therapy for human cancer by gene module maps. Cancer research. 2008; 68(2):369-78. doi: 10.1158/0008-5472.CAN-07-0382
34. Lottaz C, Toedling J, Spang R. Annotation-based distance measures for patient subgroup discovery in clinical microarray studies. Bioinformatics. 2007; 23(17):2256-64. doi: 10.1093/bioinformatics/btm322
35. Khan SA, Faisal A, Mpindi JP, Parkkinen JA, Kalliokoski T, Poso A, et al. Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs. BMC bioinformatics. 2012; 13:112. doi: 10.1186/1471-2105-13-112
36. Ben-Dor A, Chor B, Karp R, Yakhini Z. Discovering local structure in gene expression data: the orderpreserving submatrix problem. J Comput Biol. 2003; 10(3-4):373-84
37. Tanay A, Sharan R, Shamir R. Discovering statistically significant biclusters in gene expression data. Bioinformatics. 2002; 18 Suppl 1:S136-44
38. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, 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):15545-50
39. Li QL, Chen TJ, Wang YL, Bryant SH. PubChem as a public resource for drug discovery. Drug Discovery Today. 2010; 15(23-24):1052-7. doi: 10.1016/j.drudis.2010.10.003
40. Manning CD, Raghavan P, Schutze H. Introduction to Information Retrieval: Cambridge University Press
41. Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, et al. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res. 2012; 40(Database issue):D1128-36
42. Hollander M, Wolfe D. Nonparametric Statistical Methods 2ed. New York: Wiley; 1999
43. Lamb J, Ramaswamy S, Ford HL, Contreras B, Martinez RV, Kittrell FS, et al. A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell. 2003; 114(3):323-34
44. Li Q, Cheng T, Wang Y, Bryant SH. PubChem as a public resource for drug discovery. Drug Discov Today. 2010; 15(23-24):1052-7
45. Richon VM. Cancer biology: mechanism of antitumour action of vorinostat (suberoylanilide hydroxamic acid), a novel histone deacetylase inhibitor. Br J Cancer. 2006; 95(S1):S2-S6
46. Gottlicher M, Minucci S, Zhu P, Kramer OH, Schimpf A, Giavara S, et al. Valproic acid defines a novel class of HDAC inhibitors inducing differentiation of transformed cells. EMBO J. 2001; 20(24):6969-78
47. Kemp MG, Ghosh M, Liu G, Leffak M. The histone deacetylase inhibitor trichostatin A alters the pattern of DNA replication origin activity in human cells. Nucleic Acids Res. 2005; 33(1):325-36
48. Keen JC, Yan L, Mack KM, Pettit C, Smith D, Sharma D, et al. A novel histone deacetylase inhibitor, scriptaid, enhances expression of functional estrogen receptor alpha (ER) in ER negative human breast cancer cells in combination with 5-Aza 2'-deoxycytidine. Breast Cancer Res Treat. 2003; 81(3):177-86
49. Balakin KV, Ivanenkov YA, Kiselyov AS, Tkachenko SE. Histone deacetylase inhibitors in cancer therapy: latest developments, trends and medicinal chemistry perspective. Anticancer Agents Med Chem. 2007; 7(5):576-92. Epub 2007/09/28
50. Yadav B, Pemovska T, Szwajda A, Kulesskiy E, Kontro M, Karjalainen R, et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific reports. 2014; 4:5193. doi: 10.1038/srep05193
51. Wood ER, Kuyper L, Petrov KG, Hunter RN, Harris PA, Lackey K. Discovery and in vitro evaluation of potent TrkA kinase inhibitors: oxindole and aza-oxindoles. Bioorg Med Chem Lett. 2004; 14(4):953-7
52. Lahusen T, De Siervi A, Kunick C, Senderowicz AM. Alsterpaullone, a novel cyclin-dependent kinase inhibitor, induces apoptosis by activation of caspase-9 due to perturbation in mitochondrial membrane potential. Mol Carcinog. 2003; 36(4):183-94. Epub 2003/04/02. doi: 10.1002/mc.10114
53. Keller HU, Zimmermann A, Niggli V. Diacylglycerols and the protein kinase inhibitor H-7 suppress cell polarity and locomotion of Walker 256 carcinosarcoma cells. Int J Cancer. 1989; 44(5):934-9
54. Tan C, Tasaka H, Yu KP, Murphy ML, Karnofsky DA. Daunomycin, an antitumor antibiotic, in the treatment of neoplastic disease. Clinical evaluation with special reference to childhood leukemia. Cancer. 1967; 20(3):333-53
55. Rose MG. Hematology: Azacitidine improves survival in myelodysplastic syndromes. Nat Rev Clin Oncol. 2009; 6(9):502-3
56. Ko MW, Tamhankar MA, Volpe NJ, Porter D, McGrath C, Galetta SL. Acute promyelocytic leukemic involvement of the optic nerves following mitoxantrone treatment for multiple sclerosis. J Neurol Sci. 2008; 273(1-2):144-7
57. Kim JY, Lee SG, Chung JY, Kim YJ, Park JE, Koh H, et al. Ellipticine induces apoptosis in human endometrial cancer cells: the potential involvement of reactive oxygen species and mitogen-Activated protein kinases. Toxicology. 2011; 289(2-3):91-102
58. Ulukan H, Swaan PW. Camptothecins: a review of their chemotherapeutic potential. Drugs. 2002; 62 (14):2039-57
59. Rubin BK, Tamaoki J. Antibiotics as anti-inflammatory and immunomodulatory agents. Basel; Boston: Birkhäuser; 2005. xiii, 273
60. Sanders WE Jr. Antibiotics during anesthesia and surgery. Int Anesthesiol Clin. 1968; 6(1):211-8
61. Smith TJ, Blackman SA, Foster SJ. Autolysins of Bacillus subtilis: multiple enzymes with multiple functions. Microbiology. 2000; 146 (Pt 2):249-62
62. Holtje JV. From growth to autolysis: the murein hydrolases in Escherichia coli. Arch Microbiol. 1995; 164(4):243-54
63. Garcia P, Gonzalez MP, Garcia E, Lopez R, Garcia JL. LytB, a novel pneumococcal murein hydrolase essential for cell separation. Mol Microbiol. 1999; 31(4):1275-81
64. Damian D, Gorfine M. Statistical concerns about the GSEA procedure. Nature Genetics. 2004; 36 (7):663-. doi: 10.1038/Ng0704-663a
65. Mootha VK, Daly MJ, Patterson N, Hirschhorn JN, Groop LC, Altshuler D. Statistical concerns about the GSEA procedure-Reply. Nature Genetics. 2004; 36(7):663-. doi: 10.1038/Ng0704-663b