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. 2016 Nov 1;17(4):463–471. doi: 10.3233/CBM-160663

Screening for the optimal gene and functional gene sets related to breast cancer using differential co-expression and differential expression analysis

Lei Wang a,*, Hong Ma b, Lixia Zhu c, Liping Ma a, Lanting Cao d, Hui Wei e, Jumei Xu e
PMCID: PMC13020522  PMID: 27802197

Abstract

OBJECTIVE:

To investigate novel gene sets related to breast cancer (BC) using differential co-expression and differential expression (DECODE).

METHODS:

T statistics was used to quantify the degree of DE of each gene, and then Z was adopted to quantify the correlation difference between expression levels of two genes. Two optimal thresholds for defining substantial change in DE and DC were selected for each gene using chi-square maximization, and the corresponding gene was defined as the optimal gene. Based on the optimal thresholds, genes were categorized into four partitions with either high or low DC and DE characteristics. Finally, we evaluated the functional relevance of a gene partition with high DE and high DC, and the gene set with best association was considered as the optimal functional gene set.

RESULTS:

The optimal thresholds for DC and DE were respective 2.254 and 1.616, and the optimal gene was UBE2Q2L. Based on the optimal thresholds, genes were divided into four partitions including HDE-HDC (875 genes), HED-LDC (8038 genes), LDE-HDC (678 genes), and LDE-LDC (10516 genes). The best associated gene set was ``fatty acid catabolic process'' with 34 HDC and HDE partitions. Among these partitions, UBE2Q2L attained the highest minimum FI gain of 18.973.

CONCLUSION:

UBE2Q2L and fatty acid catabolic process might be potentially useful signatures in diagnostic purposes for BC.

Keywords: Breast cancer, differential co-expression and differential expression, functional information

References

  • [1]. Stewart B. and Wild C., World cancer report, 2014 international agency for research on cancer, World Health Organization (2014). [Google Scholar]
  • [2]. Dirksen S.R. and Epstein D.R., Efficacy of an insomnia intervention on fatigue, mood and quality of life in breast cancer survivors, Journal of Advanced Nursing 61 (2008), 664-675. [DOI] [PubMed] [Google Scholar]
  • [3]. Ferguson R.J., McDonald B.C., Rocque M.A., Furstenberg C.T., Horrigan S., Ahles T.A. and Saykin A.J., Development of CBT for chemotherapy - related cognitive change: results of a waitlist control trial, Psycho - Oncology 21 (2012), 176-186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4]. Hutchinson L., Breast cancer: Challenges, controversies, breakthroughs, Nature Reviews Clinical Oncology 7 (2010), 669-670. [DOI] [PubMed] [Google Scholar]
  • [5]. Douglass S., Ali S., Meeson A.P., Browell D. and Kirby J.A., The role of FOXP3 in the development and metastatic spread of breast cancer, Cancer and Metastasis Reviews 31 (2012), 843-854. [DOI] [PubMed] [Google Scholar]
  • [6]. Moelans C.B., van der Groep P., Hoefnagel L.D., van de Vijver M.J., Wesseling P., Wesseling J., van der Wall E. and van Diest P.J., Genomic evolution from primary breast carcinoma to distant metastasis: Few copy number changes of breast cancer related genes, Cancer Letters 344 (2014), 138-146. [DOI] [PubMed] [Google Scholar]
  • [7]. Francis R.E., Myatt S.S., Krol J., Hartman J., Peck B., McGovern U.B., Wang J., Guest S.K., Filipovic A. and Gojis O., FoxM1 is a downstream target and marker of HER2 overexpression in breast cancer, International Journal of Oncology 35 (2009), 57-68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [8]. Park Y.-Y., Jung S.Y., Jennings N.B., Rodriguez-Aguayo C., Peng G., Lee S.-R., Kim S.B., Kim K., Leem S.-H. and Lin S.-Y., FOXM1 mediates Dox resistance in breast cancer by enhancing DNA repair, Carcinogenesis 33 (2012), 1843-1853. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9]. Kwok J.M.-M., Peck B., Monteiro L.J., Schwenen H.D., Millour J., Coombes R.C., Myatt S.S. and Lam E.W.-F., FOXM1 confers acquired cisplatin resistance in breast cancer cells, Molecular Cancer Research 8 (2010), 24-34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10]. Chang A.C., Doherty J., Huschtscha L.I., Redvers R., Restall C., Reddel R.R. and Anderson R.L., STC1 expression is associated with tumor growth and metastasis in breast cancer, Clinical & Experimental Metastasis 32 (2015), 15-27. [DOI] [PubMed] [Google Scholar]
  • [11]. McCormick K.P., Willmann M.R. and Meyers B.C., Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments, Silence 2 (2011), 2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12]. Kulkarni H., Göring H.H., Diego V., Cole S., Walder K.R., Collier G.R., Blangero J. and Carless M.A., Association of differential gene expression with imatinib mesylate and omacetaxine mepesuccinate toxicity in lymphoblastoid cell lines, BMC Medical Genomics 5 (2012), 1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13]. Lucas S.M. and Heath E.I., Current challenges in development of differentially expressed and prognostic prostate cancer biomarkers, Prostate Cancer 2012 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14]. Choi C., Choi J., Park Y., Lee Y., Song S., Sung C., Song T., Kim M., Kim T. and Lee J., Identification of differentially expressed genes according to chemosensitivity in advanced ovarian serous adenocarcinomas: Expression of GRIA2 predicts better survival, British Journal of Cancer 107 (2012), 91-99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15]. Macgregor P.F. and Squire J.A., Application of microarrays to the analysis of gene expression in cancer, Clinical Chemistry 48 (2002), 1170-1177. [PubMed] [Google Scholar]
  • [16]. Do J.H. and Choi D., Clustering approaches to identifying gene expression patterns from DNA microarray data, Molecules and Cells 25 (2008), 279. [PubMed] [Google Scholar]
  • [17]. Segal E., Shapira M., Regev A., Pe'er D., Botstein D., Koller D. and Friedman N., Module networks: Identifying regulatory modules and their condition-specific regulators from gene expression data, Nature Genetics 34 (2003), 166-176. [DOI] [PubMed] [Google Scholar]
  • [18]. Mootha V.K., Lindgren C.M., Eriksson K.-F., Subramanian A., Sihag S., Lehar J., Puigserver P., Carlsson E., Ridderstråle M. and Laurila E., PGC-1α -responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nature Genetics 34 (2003), 267-273. [DOI] [PubMed] [Google Scholar]
  • [19]. Minguez P. and Dopazo J., Assessing the biological significance of gene expression signatures and co-expression modules by studying their network properties, PloS One 6 (2011), e17474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20]. Ackermann M. and Strimmer K., A general modular framework for gene set enrichment analysis, BMC Bioinformatics 10 (2009), 47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21]. Anglani R., Creanza T.M., Liuzzi V.C., Piepoli A., Panza A., Andriulli A. and Ancona N., Loss of connectivity in cancer co-expression networks, PloS One 9 (2014), e87075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22]. Cuadros M., Cano C., Lopez F., Lopez-Castro R. and Concha A., Expression profiling of breast tumors based on human epidermal growth factor receptor 2 status defines migration-related genes, Pathobiology 80 (2012), 32-40. [DOI] [PubMed] [Google Scholar]
  • [23]. Clarke C., Madden S.F., Doolan P., Aherne S.T., Joyce H., O'Driscoll L., Gallagher W.M., Hennessy B.T., Moriarty M. and Crown J., Correlating transcriptional networks to breast cancer survival: A large-scale coexpression analysis, Carcinogenesis 34 (2013), 2300-2308. [DOI] [PubMed] [Google Scholar]
  • [24]. Irizarry R.A., Hobbs B., Collin F., Beazer-Barclay Y.D., Antonellis K.J., Scherf U. and Speed T.P., Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics 4 (2003), 249-264. [DOI] [PubMed] [Google Scholar]
  • [25]. Bolstad B.M., Irizarry R.A., Åstrand M. and Speed T.P., A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics 19 (2003), 185-193. [DOI] [PubMed] [Google Scholar]
  • [26]. Pepper S.D., Saunders E.K., Edwards L.E., Wilson C.L. and Miller C.J., The utility of MAS5 expression summary and detection call algorithms, BMC Bioinformatics 8 (2007), 1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27]. Lazar C., Meganck S., Taminau J., Steenhoff D., Coletta A., Molter C., Weiss-Solís D.Y., Duque R., Bersini H. and Nowé A., Batch effect removal methods for microarray gene expression data integration: A survey, Briefings in Bioinformatics (2012), bbs037. [DOI] [PubMed] [Google Scholar]
  • [28]. Taminau J., Taminau M.J., Meganck S. and BiocGenerics S., Package `inSilicoMerging', March, 2013.
  • [29]. Bockmayr M., Klauschen F., Györffy B., Denkert C. and Budczies J., New network topology approaches reveal differential correlation patterns in breast cancer, BMC Systems Biology 7 (2013), 78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30]. McHugh M.L., The chi-square test of independence, Biochem Med (Zagreb) 23 (2013), 143-149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31]. Kanehisa M., Goto S., Sato Y., Furumichi M. and Tanabe M., KEGG for integration and interpretation of large-scale molecular data sets, Nucleic Acids Research (2011), gkr988. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32]. Croft D., O'Kelly G., Wu G., Haw R., Gillespie M., Matthews L., Caudy M., Garapati P., Gopinath G. and Jassal B., Reactome: A database of reactions, pathways and biological processes, Nucleic Acids Research (2010), gkq1018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33]. Fisher R.A., On the interpretation of χ 2 from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85 (1922), 87-94. [Google Scholar]
  • [34]. Benjamini Y. and Hochberg Y., Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B (Methodological) (1995), 289-300. [Google Scholar]
  • [35]. Zhu S., Yao F., Li W.-H., Wan J.-N., Zhang Y.-M., Tang Z., Khan S., Wang C.-H. and Sun S.-R., PKC?-dependent activation of the ubiquitin proteasome system is responsible for high glucose-induced human breast cancer MCF-7 cell proliferation, migration and invasion, Asian Pacific Journal of Cancer Prevention: APJCP 14 (2012), 5687-5692. [DOI] [PubMed] [Google Scholar]
  • [36]. Bassermann F. and Pagano M., Dissecting the role of ubiquitylation in the DNA damage response checkpoint in G2, Cell Death & Differentiation 17 (2010), 78-85. [DOI] [PubMed] [Google Scholar]
  • [37]. Graner E., Tang D., Rossi S., Baron A., Migita T., Weinstein L.J., Lechpammer M., Huesken D., Zimmermann J. and Signoretti S., The isopeptidase USP2a regulates the stability of fatty acid synthase in prostate cancer, Cancer Cell 5 (2004), 253-261. [DOI] [PubMed] [Google Scholar]
  • [38]. Porta R., Blancafort A., Casòliva G., Casas M., Dorca J., Buxo M., Viñas G., Oliveras G. and Puig T., Fatty acid synthase expression is strongly related to menopause in early-stage breast cancer patients, Menopause 21 (2014), 188-191. [DOI] [PubMed] [Google Scholar]
  • [39]. Kuhajda F.P., Fatty-acid synthase and human cancer: New perspectives on its role in tumor biology, Nutrition 16 (2000), 202-208. [DOI] [PubMed] [Google Scholar]
  • [40]. Kuhajda F.P., Fatty acid synthase and cancer: new application of an old pathway, Cancer Research 66 (2006), 5977-5980. [DOI] [PubMed] [Google Scholar]
  • [41]. van Wijk S.J. and Timmers H.M., The family of ubiquitin-conjugating enzymes (E2s): Deciding between life and death of proteins, The FASEB Journal 24 (2010), 981-993. [DOI] [PubMed] [Google Scholar]
  • [42]. Maeda H., Miyajima N., Kano S., Tsukiyama T., Okumura F., Fukuda S. and Hatakeyama S., Ubiquitin-conjugating enzyme UBE2Q2 suppresses cell proliferation and is down-regulated in recurrent head and neck cancer, Molecular Cancer Research 7 (2009), 1553-1562. [DOI] [PubMed] [Google Scholar]
  • [43]. Monabati A. and Bordbar M.-R., Expression of UBE2Q2, a putative member of the ubiquitin-conjugating enzyme family in pediatric acute lymphoblastic leukemia, Archives of Iranian Medicine 15 (2012), 352. [PubMed] [Google Scholar]
  • [44]. Nikseresht M., Seghatoleslam A., Monabati A., Talei A., Ghalati F.B. and Owji A.A., Overexpression of the novel human gene, UBE2Q2, in breast cancer, Cancer Genetics and Cytogenetics 197 (2010), 101e106. [DOI] [PubMed] [Google Scholar]
  • [45]. Cox-Limpens K.E., Vles J.S., van den Hove D.L., Zimmermann L.J. and Gavilanes A.W., Fetal asphyctic preconditioning alters the transcriptional response to perinatal asphyxia, BMC Neuroscience 15 (2014), 67. [DOI] [PMC free article] [PubMed] [Google Scholar]

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