Abstract
Background: Breast cancer is one of the most common malignancies, and a threat to female health all over the world. However, the molecular mechanism of breast cancer has not been fully discovered yet.
Objective: It is crucial to identify breast cancer-related genes, which could provide new biomarker for breast cancer diagnosis as well as potential treatment targets.
Methods: Here we used the minimum redundancy-maximum relevance (mRMR) method to select significant genes, then mapped the transcripts of the genes on the Protein-Protein Interaction (PPI) network and traced the shortest path between each pair of two proteins.
Results: As a result, we identified 24 breast cancer-related genes whose betweenness were over 700. The GO enrichment analysis indicated that the transcription and oxygen level are very important in breast cancer. And the pathway analysis indicated that most of these 24 genes are enriched in prostate cancer, endocrine resistance, and pathways in cancer.
Conclusion: We hope these 24 genes might be useful for diagnosis, prognosis and treatment for breast cancer.
Keywords: Breast cancer, gene expression profiles, mRMR, PPI, shortest path, microarry.
Graphical Abstract
[1]
Jacob, L.; Bleicher, L.; Kostev, K.; Kalder, M. Prevalence of depression, anxiety and their risk factors in German women with breast cancer in general and gynecological practices. J. Cancer Res. Clin. Oncol., 2016, 142(2), 447-452.
[2]
Al-Hajj, M.; Wicha, M.S.; Benito-Hernandez, A.; Morrison, S.J.; Clarke, M.F. Prospective identification of tumorigenic breast cancer cells. Proc. Natl. Acad. Sci. USA, 2003, 100(7), 3983-3988.
[3]
Glazier, A.M.; Nadeau, J.H.; Aitman, T.J. Finding genes that underlie complex traits. Science, 2002, 298(5602), 2345-2349.
[4]
Yuan, P.; Liu, D.; Deng, M.; Liu, J.; Wang, J.; Zhang, L.; Liu, Q.; Zhang, T.; Chen, Y.; Jin, G. Identification of differently expressed genes with specific SNP loci for breast cancer by the integration of SNP and gene expression profiling analyses. Pathol. Oncol. Res., 2015, 21(2), 469-475.
[5]
Nacht, M.; Ferguson, A.T.; Zhang, W.; Petroziello, J.M.; Cook, B.P.; Gao, Y.H.; Maguire, S.; Riley, D.; Coppola, G.; Landes, G.M.; Madden, S.L.; Sukumar, S. Combining serial analysis of gene expression and array technologies to identify genes differentially expressed in breast cancer. Cancer Res., 1999, 59(21), 5464-5470.
[6]
Silva, G.O.; He, X.; Parker, J.S.; Gatza, M.L.; Carey, L.A.; Hou, J.P.; Moulder, S.L.; Marcom, P.K.; Ma, J.; Rosen, J.M.; Perou, C.M. Cross-species DNA copy number analyses identifies multiple 1q21-q23 subtype-specific driver genes for breast cancer. Breast Cancer Res. Treat., 2015, 152(2), 347-356.
[7]
Erten, S.; Bebek, G.; Ewing, R.M.; Koyuturk, M. DADA: degree-aware algorithms for network-based disease gene prioritization. BioData Min., 2011, 4, 19.
[8]
Ramadan, E.; Alinsaif, S.; Hassan, M.R. Network topology measures for identifying disease-gene association in breast cancer. BMC Bioinformatics, 2016, 17(7), 274.
[9]
Liu, R.; Guo, C.X.; Zhou, H.H. Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen. Cancer Biol. Ther., 2015, 16(2), 317-324.
[10]
Srihari, S.; Kalimutho, M.; Lal, S.; Singla, J.; Patel, D.; Simpson, P.T.; Khanna, K.K.; Ragan, M.A. Understanding the functional impact of copy number alterations in breast cancer using a network modeling approach. Mol. Biosyst., 2016, 12, 963-972.
[11]
Chai, F.; Liang, Y.; Zhang, F.; Wang, M.; Zhong, L.; Jiang, J. Systematically identify key genes in inflammatory and non-inflammatory breast cancer. Gene, 2016, 575(2 Pt 3), 600-614.
[12]
Ma, X.; Beeghly-Fadiel, A.; Lu, W.; Shi, J.; Xiang, Y.B.; Cai, Q.; Shen, H.; Shen, C.Y.; Ren, Z.; Matsuo, K.; Khoo, U.S.; Iwasaki, M.; Long, J.; Zhang, B.; Ji, B.T.; Zheng, Y.; Wang, W.; Hu, Z.; Liu, Y.; Wu, P.E.; Shieh, Y.L.; Wang, S.; Xie, X.; Ito, H.; Kasuga, Y.; Chan, K.Y.; Iwata, H.; Tsugane, S.; Gao, Y.T.; Shu, X.O.; Moses, H.L.; Zheng, W. Pathway analyses identify TGFBR2 as potential breast cancer susceptibility gene: results from a consortium study among Asians. Cancer Epidemiol. Biomarkers Prev., 2012, 21(7), 1176-1184.
[13]
Pang, H.; Zhao, H. Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer. Cancer Inform., 2014, 13(Suppl. 4), 73-78.
[14]
Xun, L.; Mitra-Behura, S.; Alston, B.; Zong, Z.; Sun, S. Identifying DNA methylation variation patterns to obtain potential breast cancer biomarker genes. Int. J. Biomed. Data Min., 2015, 4(1), 115.
[15]
D’Alesio, C.; Punzi, S.; Cicalese, A.; Fornasari, L.; Furia, L.; Riva, L.; Carugo, A.; Curigliano, G.; Criscitiello, C.; Pruneri, G. RNAi screens identify CHD4 as an essential gene in breast cancer growth. Oncotarget, 2016, 7(49), 80901-80915.
[16]
Min, W.; Liu, J.; Luo, F.; Zhang, S. A novel two-stage method for identifying microRNA-gene regulatory modules in breast cancer. IEEE Int. Conf. Bioinform. Biomed. Washington, DC, USA, 2015, pp. 151-156.
[17]
Rafiul, H.; Ul Haq, I.; Ramadan, E.; Kamruzzaman, J.; Ahmed, A. Distinctive phenotype identification for breast cancer genotypes among hereditary breast cancer mutated genes. Curr. Bioinform., 2015, 10(1), 5-15.
[18]
Sotiriou, C.; Neo, S.Y.; McShane, L.M.; Korn, E.L.; Long, P.M.; Jazaeri, A.; Martiat, P.; Fox, S.B.; Harris, A.L.; Liu, E.T. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc. Natl. Acad. Sci. USA, 2003, 100(18), 10393-10398.
[19]
Barrenas, F.; Chavali, S.; Holme, P.; Mobini, R.; Benson, M. Network properties of complex human disease genes identified through genome-wide association studies. PLoS One, 2009, 4(11)e8090
[20]
Oti, M.; Snel, B.; Huynen, M.A.; Brunner, H.G. Predicting disease genes using protein-protein interactions. J. Med. Genet., 2006, 43(8), 691-698.
[21]
Chen, J.; Aronow, B.J.; Jegga, A.G. Disease candidate gene identification and prioritization using protein interaction networks. BMC Bioinformatics, 2009, 10, 73.
[22]
Kohler, S.; Bauer, S.; Horn, D.; Robinson, P.N. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet., 2008, 82(4), 949-958.
[23]
Navlakha, S.; Kingsford, C. The power of protein interaction networks for associating genes with diseases. Bioinformatics, 2010, 26(8), 1057-1063.
[24]
Nabieva, E.; Jim, K.; Agarwal, A.; Chazelle, B.; Singh, M. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics, 2005, 21(Suppl. 1), i302-i310.
[25]
Jiang, M.; Chen, Y.; Zhang, Y.; Chen, L.; Zhang, N.; Huang, T.; Cai, Y.D.; Kong, X. Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein-protein interaction network. Mol. Biosyst., 2013, 9(11), 2720-2728.
[26]
Li, B.Q.; Zhang, J.; Huang, T.; Zhang, L.; Cai, Y.D. Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network. Biochimie, 2012, 94(9), 1910-1917.
[27]
Li, B.Q.; You, J.; Chen, L.; Zhang, J.; Zhang, N.; Li, H.P.; Huang, T.; Kong, X.Y.; Cai, Y.D. Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network. BioMed Res. Int., 2013, 2013267375
[28]
Barrett, T.; Suzek, T.O.; Troup, D.B.; Wilhite, S.E.; Ngau, W.C.; Ledoux, P.; Rudnev, D.; Lash, A.E.; Fujibuchi, W.; Edgar, R. NCBI GEO: mining millions of expression profiles--database and tools. Nucleic Acids Res., 2005, 33(Database issue), D562-D566.
[29]
Peng, H.; Long, F.; Ding, C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(8), 1226-1238.
[30]
Alshamlan, H.; Badr, G.; Alohali, Y. mRMR-ABC: a hybrid gene selection algorithm for cancer classification using microarray gene expression profiling. BioMed Res. Int., 2015, 2015(4), 1-15.
[31]
Franceschini, A.; Szklarczyk, D.; Frankild, S.; Kuhn, M.; Simonovic, M.; Roth, A.; Lin, J.; Minguez, P.; Bork, P.; von Mering, C.; Jensen, L.J. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res., 2013, 41(Database issue), D808-D815.
[32]
Benjamini, Y.; Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat., 2001, 29(4), 1165-1188.
[33]
Yang, X.; Zhang, J.; Huang, K. Mining the tissue-tissue gene coexpression
network for tumor microenvironment study and biomarker
prediction. BMC Genomics., 2013, 14 Suppl 5(5), S4.
[34]
Peng, Z.; Wang, H.; Shan, C. Expression of ubiquitin and cullin-1 and its clinicopathological significance in benign and malignant lesions of the lung. Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2009, 34(3), 204-209.
[35]
Chen, D.; Dou, Q.P. The ubiquitin-proteasome system as a prospective molecular target for cancer treatment and prevention. Curr. Protein Pept. Sci., 2010, 11(6), 459-470.
[36]
Matlashewski, G.; Lamb, P.; Pim, D.; Peacock, J.; Crawford, L.; Benchimol, S. Isolation and characterization of a human p53 cDNA clone: expression of the human p53 gene. The EMBO J., 1984, 3(13), 3257-3262.
[37]
Walsh, T.; Casadei, S.; Coats, K.H.; Swisher, E.; Stray, S.M.; Higgins, J.; Roach, K.C.; Mandell, J.; Lee, M.K.; Ciernikova, S.; Foretova, L.; Soucek, P.; King, M.C. Spectrum of mutations in BRCA1, BRCA2, CHEK2, and TP53 in families at high risk of breast cancer. JAMA, 2006, 295(12), 1379-1388.
[38]
Berns, E.M.; Foekens, J.A.; Vossen, R.; Look, M.P.; Devilee, P.; Henzen-Logmans, S.C.; van Staveren, I.L.; van Putten, W.L.; Inganas, M.; Meijer-van Gelder, M.E.; Cornelisse, C.; Claassen, C.J.; Portengen, H.; Bakker, B.; Klijn, J.G. Complete sequencing of TP53 predicts poor response to systemic therapy of advanced breast cancer. Cancer Res., 2000, 60(8), 2155-2162.
[39]
Patocs, A.; Zhang, L.; Xu, Y.; Weber, F.; Caldes, T.; Mutter, G.L.; Platzer, P.; Eng, C. Breast-cancer stromal cells with TP53 mutations and nodal metastases. The New . Engl. J. Med., 2007, 357(25), 2543-2551.
[40]
Spizzo, R.; Nicoloso, M.S.; Lupini, L.; Lu, Y.; Fogarty, J.; Rossi, S.; Zagatti, B.; Fabbri, M.; Veronese, A.; Liu, X.; Davuluri, R.; Croce, C.M.; Mills, G.; Negrini, M.; Calin, G.A. miR-145 participates with TP53 in a death-promoting regulatory loop and targets estrogen receptor-alpha in human breast cancer cells. Cell Death Differ., 2010, 17(2), 246-254.
[41]
Vleugel, M.M.; Greijer, A.E.; Bos, R.; van der Wall, E.; van Diest, P.J. c-Jun activation is associated with proliferation and angiogenesis in invasive breast cancer. Hum. Pathol., 2006, 37(6), 668-674.
[42]
Cui, X.; Kim, H.J.; Kuiatse, I.; Kim, H.; Brown, P.H.; Lee, A.V. Epidermal growth factor induces insulin receptor substrate-2 in breast cancer cells via c-Jun NH(2)-terminal kinase/activator protein-1 signaling to regulate cell migration. Cancer Res., 2006, 66(10), 5304-5313.
[43]
Langer, S.; Singer, C.F.; Hudelist, G.; Dampier, B.; Kaserer, K.; Vinatzer, U.; Pehamberger, H.; Zielinski, C.; Kubista, E.; Schreibner, M. Jun and Fos family protein expression in human breast cancer: correlation of protein expression and clinicopathological parameters. Eur. J. Gynaecol. Oncol., 2006, 27(4), 345-352.
[44]
Bu, X.; Avraham, H.K.; Li, X.; Lim, B.; Jiang, S.; Fu, Y.; Pestell, R.G.; Avraham, S. Mayven induces c-Jun expression and cyclin D1 activation in breast cancer cells. Oncogene, 2005, 24(14), 2398-2409.
[45]
Ju, X.; Katiyar, S.; Wang, C.; Liu, M.; Jiao, X.; Li, S.; Zhou, J.; Turner, J.; Lisanti, M.P.; Russell, R.G.; Mueller, S.C.; Ojeifo, J.; Chen, W.S.; Hay, N.; Pestell, R.G. Akt1 governs breast cancer progression in vivo. Proc. Natl. Acad. Sci. USA, 2007, 104(18), 7438-7443.
[46]
Liu, H.; Radisky, D.C.; Nelson, C.M.; Zhang, H.; Fata, J.E.; Roth, R.A.; Bissell, M.J. Mechanism of Akt1 inhibition of breast cancer cell invasion reveals a protumorigenic role for TSC2. Proc. Natl. Acad. Sci. USA, 2006, 103(11), 4134-4139.
[47]
Chin, Y.R.; Toker, A. The actin-bundling protein palladin is an Akt1-specific substrate that regulates breast cancer cell migration. Mol. Cell, 2010, 38(3), 333-344.
[48]
Liang, K.; Lu, Y.; Li, X.; Zeng, X.; Glazer, R.I.; Mills, G.B.; Fan, Z. Differential roles of phosphoinositide-dependent protein kinase-1 and akt1 expression and phosphorylation in breast cancer cell resistance to Paclitaxel, Doxorubicin, and gemcitabine. Mol. Pharmacol., 2006, 70(3), 1045-1052.
[49]
Meisner, H.; Daga, A.; Buxton, J.; Fernandez, B.; Chawla, A.; Banerjee, U.; Czech, M.P. Interactions of Drosophila Cbl with epidermal growth factor receptors and role of Cbl in R7 photoreceptor cell development. Mol. Cell. Biol., 1997, 17(4), 2217-2225.
[50]
Truitt, L.; Freywald, T.; DeCoteau, J.; Sharfe, N.; Freywald, A. The EphB6 receptor cooperates with c-Cbl to regulate the behavior of breast cancer cells. Cancer Res., 2010, 70(3), 1141-1153.
[51]
Vennin, C.; Spruyt, N.; Dahmani, F.; Julien, S.; Bertucci, F.; Finetti, P.; Chassat, T.; Bourette, R.P.; Le Bourhis, X.; Adriaenssens, E. H19 non coding RNA-derived miR-675 enhances tumorigenesis and metastasis of breast cancer cells by downregulating c-Cbl and Cbl-b. Oncotarget, 2015, 6(30), 29209-29223.
[52]
Wang, Y.; Chen, L.; Wu, Z.; Wang, M.; Jin, F.; Wang, N.; Hu, X.; Liu, Z.; Zhang, C.Y.; Zen, K.; Chen, J.; Liang, H.; Zhang, Y.; Chen, X. miR-124-3p functions as a tumor suppressor in breast cancer by targeting CBL. BMC Cancer, 2016, 16(1), 826.
[53]
Nicholson, S.; Richard, J.; Sainsbury, C.; Halcrow, P.; Kelly, P.; Angus, B.; Wright, C.; Henry, J.; Farndon, J.R.; Harris, A.L. Epidermal Growth Factor Receptor (EGFr); results of a 6 year follow-up study in operable breast cancer with emphasis on the node negative subgroup. Br. Cancer, 1991, 63(1), 146-150.
[54]
Cao, X.X.; Xu, J.D.; Liu, X.L.; Xu, J.W.; Wang, W.J.; Li, Q.Q.; Chen, Q.; Xu, Z.D.; Liu, X.P. RACK1: a superior independent predictor for poor clinical outcome in breast cancer. Int. J. Cancer, 2010, 127(5), 1172-1179.
[55]
Cao, X.X.; Xu, J.D.; Xu, J.W.; Liu, X.L.; Cheng, Y.Y.; Li, Q.Q.; Xu, Z.D.; Liu, X.P. RACK1 promotes breast carcinoma migration/metastasis via activation of the RhoA/Rho kinase pathway. Breast Cancer Res. Treat., 2011, 126(3), 555-563.
[56]
Kawai, H.; Li, H.; Avraham, S.; Jiang, S.; Avraham, H.K. Overexpression of histone deacetylase HDAC1 modulates breast cancer progression by negative regulation of estrogen receptor alpha. Int. J. Cancer, 2003, 107(3), 353-358.
[57]
Wu, M.Y.; Fu, J.; Xiao, X.; Wu, J.; Wu, R.C. MiR-34a regulates therapy resistance by targeting HDAC1 and HDAC7 in breast cancer. Cancer Lett., 2014, 354(2), 311-319.
[58]
Arabsolghar, R.; Azimi, T.; Rasti, M. Mutant p53 binds to estrogen receptor negative promoter via DNMT1 and HDAC1 in MDA-MB-468 breast cancer cells. Mol. Biol. Rep., 2013, 40(3), 2617-2625.
[59]
Contino, F.; Mazzarella, C.; Ferro, A.; Lo Presti, M.; Roz, E.; Lupo, C.; Perconti, G.; Giallongo, A.; Feo, S. Negative transcriptional control of ERBB2 gene by MBP-1 and HDAC1: diagnostic implications in breast cancer. BMC Cancer, 2013, 13, 81.
[60]
Graham, T.R.; Yacoub, R.; Taliaferro-Smith, L.; Osunkoya, A.O.; Odero-Marah, V.A.; Liu, T.; Kimbro, K.S.; Sharma, D.; O’Regan, R.M. Reciprocal regulation of ZEB1 and AR in triple negative breast cancer cells. Breast Cancer Res. Treat., 2010, 123(1), 139-147.
[61]
Chen, W.; Wang, W.; Zhu, B.; Guo, H.; Sun, Y.; Ming, J.; Shen, N.; Li, Z.; Wang, Z.; Liu, L.; Cai, B.; Duan, J.; Li, J.; Liu, C.; Zhong, R.; Hu, W.; Huang, T.; Miao, X. A functional variant rs1820453 in YAP1 and breast cancer risk in Chinese population. PLoS One, 2013, 8(11)e79056
[62]
Farnie, G.; Clarke, R.B. Mammary stem cells and breast cancer--role of Notch signalling. Stem Cell Rev., 2007, 3(2), 169-175.
[63]
Rizzo, P.; Miao, H.; D’Souza, G.; Osipo, C.; Song, L.L.; Yun, J.; Zhao, H.; Mascarenhas, J.; Wyatt, D.; Antico, G.; Hao, L.; Yao, K.; Rajan, P.; Hicks, C.; Siziopikou, K.; Selvaggi, S.; Bashir, A.; Bhandari, D.; Marchese, A.; Lendahl, U.; Qin, J.Z.; Tonetti, D.A.; Albain, K.; Nickoloff, B.J.; Miele, L. Cross-talk between notch and the estrogen receptor in breast cancer suggests novel therapeutic approaches. Cancer Res., 2008, 68(13), 5226-5235.
[64]
Sethi, N.; Dai, X.; Winter, C.G.; Kang, Y. Tumor-derived JAGGED1 promotes osteolytic bone metastasis of breast cancer by engaging notch signaling in bone cells. Cancer Cell, 2011, 19(2), 192-205.
[65]
Robinson, D.R.; Kalyana-Sundaram, S.; Wu, Y.M.; Shankar, S.; Cao, X.; Ateeq, B.; Asangani, I.A.; Iyer, M.; Maher, C.A.; Grasso, C.S.; Lonigro, R.J.; Quist, M.; Siddiqui, J.; Mehra, R.; Jing, X.; Giordano, T.J.; Sabel, M.S.; Kleer, C.G.; Palanisamy, N.; Natrajan, R.; Lambros, M.B.; Reis-Filho, J.S.; Kumar-Sinha, C.; Chinnaiyan, A.M. Functionally recurrent rearrangements of the MAST kinase and Notch gene families in breast cancer. Nat. Med., 2011, 17(12), 1646-1651.
[66]
Yin, X.; Wolford, C.C.; Chang, Y.S.; McConoughey, S.J.; Ramsey, S.A.; Aderem, A.; Hai, T. ATF3, an adaptive-response gene, enhances TGFbeta signaling and cancer-initiating cell features in breast cancer cells. J. Cell Sci., 2010, 123(Pt 20), 3558-3565.
[67]
Wolford, C.C.; McConoughey, S.J.; Jalgaonkar, S.P.; Leon, M.; Merchant, A.S.; Dominick, J.L.; Yin, X.; Chang, Y.; Zmuda, E.J.; O’Toole, S.A.; Millar, E.K.; Roller, S.L.; Shapiro, C.L.; Ostrowski, M.C.; Sutherland, R.L.; Hai, T. Transcription factor ATF3 links host adaptive response to breast cancer metastasis. The J. Clin. Invest., 2013, 123(7), 2893-2906.
[68]
Puvirajesinghe, T.M.; Bertucci, F.; Jain, A.; Scerbo, P.; Belotti, E.; Audebert, S.; Sebbagh, M.; Lopez, M. Identification of
p62/SQSTM1 as a component of non-canonical Wnt VANGL2-
JNK signalling in breast cancer. 2016, 7, 10318.
[69]
Xu, L.Z.; Li, S.S.; Zhou, W.; Kang, Z.J.; Zhang, Q.X.; Kamran, M.; Xu, J.; Liang, D.P.; Wang, C.L.; Hou, Z.J.; Wan, X.B.; Wang, H.J.; Lam, E.W.; Zhao, Z.W.; Liu, Q. p62/SQSTM1 enhances breast cancer stem-like properties by stabilizing MYC mRNA. Oncogene, 2017, 36(3), 304-317.
[70]
Hockel, M.; Vaupel, P. Tumor hypoxia: definitions and current clinical, biologic, and molecular aspects. J. Natl. Cancer Inst., 2001, 93(4), 266-276.
[71]
Sonveaux, P.; Vegran, F.; Schroeder, T.; Wergin, M.C.; Verrax, J.; Rabbani, Z.N.; De Saedeleer, C.J.; Kennedy, K.M.; Diepart, C.; Jordan, B.F.; Kelley, M.J.; Gallez, B.; Wahl, M.L.; Feron, O.; Dewhirst, M.W. Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. The J. Clin. Invest., 2008, 118(12), 3930-3942.
[72]
Chao, K.S.; Bosch, W.R.; Mutic, S.; Lewis, J.S.; Dehdashti, F.; Mintun, M.A.; Dempsey, J.F.; Perez, C.A.; Purdy, J.A.; Welch, M.J. A novel approach to overcome hypoxic tumor resistance: cu-ATSM-guided intensity-modulated radiation therapy. Int. J. Radiat. Oncol. Biol. Phys., 2001, 49(4), 1171-1182.
[73]
Teicher, B.A.; Lazo, J.S.; Sartorelli, A.C. Classification of antineoplastic agents by their selective toxicities toward oxygenated and hypoxic tumor cells. Cancer Res., 1981, 41(1), 73-81.
[74]
Bataineh, Z.M.; Habbal, O. Immunoreactivity of ubiquitin in human prostate gland. Neuroendocrinol. Lett., 2006, 27(4), 517.
[75]
Clarke, R.; Tyson, J.J.; Dixon, J.M. Endocrine resistance in breast cancer--an overview and update. Mol. Cell. Endocrinol., 2015, 418(Pt 3), 220-234.
[76]
Loeser, A.A. A new therapy for prevention of post-operative recurrences in genital and breast cancer; a six-years study of prophylactic thyroid treatment. Br. Med. J., 1954, 2(4901), 1380-1383.
[77]
Luo, M.; Guan, J.L. Focal adhesion kinase: a prominent determinant in breast cancer initiation, progression and metastasis. Cancer Lett., 2010, 289(2), 127-139.
[78]
Arbach, H.; Viglasky, V.; Lefeu, F.; Guinebretiere, J.M.; Ramirez, V.; Bride, N.; Boualaga, N.; Bauchet, T.; Peyrat, J.P.; Mathieu, M.C.; Mourah, S.; Podgorniak, M.P.; Seignerin, J.M.; Takada, K.; Joab, I. Epstein-Barr virus (EBV) genome and expression in breast cancer tissue: effect of EBV infection of breast cancer cells on resistance to paclitaxel (Taxol). J. Virol., 2006, 80(2), 845-853.
[79]
Theocharis, A.D.; Skandalis, S.S.; Neill, T.; Multhaupt, H.A.; Hubo, M.; Frey, H.; Gopal, S.; Gomes, A.; Afratis, N.; Lim, H.C.; Couchman, J.R.; Filmus, J.; Sanderson, R.D.; Schaefer, L.; Iozzo, R.V.; Karamanos, N.K. Insights into the key roles of proteoglycans in breast cancer biology and translational medicine. Biochim. et Biophys. Acta, 2015, 1855(2), 276-300.
[80]
Yeo, W.; Chan, P.K.; Chan, H.L.; Mo, F.K.; Johnson, P.J. Hepatitis B virus reactivation during cytotoxic chemotherapy-enhanced viral replication precedes overt hepatitis. J. Med. Virol., 2001, 65(3), 473-477.
[81]
Yeo, W.; Chan, P.K.; Hui, P.; Ho, W.M.; Lam, K.C.; Kwan, W.H.; Zhong, S.; Johnson, P.J. Hepatitis B virus reactivation in breast cancer patients receiving cytotoxic chemotherapy: a prospective study. J. Med. Virol., 2003, 70(4), 553-561.
[82]
Elloul, S.; Kedrin, D.; Knoblauch, N.W.; Beck, A.H.; Toker, A. The adherens junction protein afadin is an AKT substrate that regulates breast cancer cell migration. Mol. Cancer Res., 2014, 12(3), 464-476.
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