乳癌在台灣及全球婦女癌症中分別占死亡原因的第一位及第二位，乳癌的死亡率亦隨著腫瘤轉移的發生而增加。在乳癌病患中，腫瘤雌激素受體為陰性(ER-negative)者有較差預後和較高轉移發生率。本研究利用跨平台巨量資料分析 (meta-analysis)方法，匯集353片具有雌激素受體檢測之原位癌及遠處轉移腫瘤的乳癌組織基因晶片，從中找出調控腫瘤轉移之目標基因及其調控路徑。首先，使用weighted gene co-expression network analysis (WGCNA)建立具有轉移特徵的基因模組以代表轉移基因組，再使用Cytoscape將轉移基因組視覺化，建構出腫瘤轉移之基因網絡圖及基因調控路徑。接著，利用人類乳癌MCF-7細胞(雌激素受體陽性)和MDA-MB-231細胞(雌激素受體陰性)驗證目標基因對於癌細胞啟始轉移之細胞移動和侵襲能力的影響。
由基因網絡分析結果顯示，雌激素受體陽性乳癌和雌激素受體陰性乳癌具有相同調控轉移之目標基因TNFα (tumor necrosis factor alpha)、PRKCA (protein kinase C alpha)、ICAM1 (intercellular adhesion molecule 1)及VWF (Von Willebrand factor)。在細胞實驗中，利用PRKCA siRNA下調PRKCA表現量後，會伴隨著ICAM1 和VWF的表現量減少，顯示PRKCA會正向調控ICAM1和VWF。此外，利用VWF siRNA降低VWF表現量後，雌激素受體陰性細胞(30.4%, 46.8%)之細胞移動和細胞侵襲能力削弱程度更勝於雌激素受體陽性細胞(8.6%, 30.4%)。另在雌激素受體陰性乳癌網絡分析中得到VWF可透過BMP4 (Bone morphogenetic protein 4)來調控其表現量。在細胞實驗中，利用BMP4 siRNA下調BMP4表現量後，雌激素受體陰性乳癌細胞會伴隨著ICAM1 和VWF的表現量減少，顯示BMP4在雌激素受體陰性乳癌細胞會正向調控ICAM1和VWF。由以上實驗結果顯示，VWF可透過TNFα-PRKCA-ICAM1-VWF路徑以及BMP4-ICAM1-VWF 路徑來影響細胞移動和細胞侵襲能力，因此，抑制VWF可能可作為帶有雌激素受體陰性反應之乳癌病患，降低腫瘤轉移的治療方法。

Breast cancer (BC) is the first and second female leading cause of death respectively in Taiwan and worldwide. Metastasis is a process that cancer cells migrate from one organ to another enhancing the mortality in BC patients. The molecular mechanism is still unclear that BC patients with estrogen receptor (ER)-negative have been observed poorer prognosis and higher incidence of metastasis than ER-positive patients. This study conducted a meta-analysis in 353 primary and metastatic breast tumor microarray datasets of ER-positive and ER-negative patients to identify the potential target genes and their regulatory pathways in the progression of metastasis. This study used weighted gene co-expression network analysis (WGCNA) and Cytoscape to explore metastasis-specific module genes, gene network and regulatory pathways. Two human BC cell lines, MCF-7 (ER-positive) cells and MDA-MB-231 (ER-negative) cells, were used to validate the regulation pattern of these target genes in the aspect of cell migration and invasion to initiate BC metastasis.
The results of the gene network analysis showed four target genes to regulate the progression of metastasis in ER-positive and ER-negative breast tumors, including TNFα (tumor necrosis factor alpha), PRKCA (protein kinase C alpha), ICAM1 (intercellular adhesion molecule 1) and VWF (Von Willebrand factor). With the treatment of siPRKCA, this study demonstrated that the reduction of PRKCA attenuated ICAM1 and VWF expression in both ER-positive and ER-negative BC cells. Moreover, the attenuation of VWF by siVWF obviously decreased cell migration and invasion in ER-negative (30.4%, 46.8%) more than in ER-positive BC cells (8.6%, 30.4%). The further results of the ER-negative BC network analysis found that VWF was also regulated by BMP4 (Bone morphogenetic protein 4) in BMP4-ICAM1-VWF pathway. With the treatment of siBMP4, this study indicated that the reduction of BMP4 attenuated ICAM1 and VWF expression in ER-negative BC cells only. This study found TNFα-PRKCA-ICAM1-VWF and BMP4-ICAM1-VWF eventually contributed to cell migration and invasion, and VWF attenuation would be a new therapeutic way to retard metastasis particularly in ER-negative BC patients.

摘要 2
Abstract 4
Contents 7
Lists of Tables 9
Lists of Figures 9
Chapter 1 Introduction 11
Chapter 2 Paper review 13
2.1 Breast cancer 13
2.2 Classification of breast cancer 13
2.3 Biomarker of breast cancer 14
2.4 Tumor metastasis in breast carcinoma 16
2.5 Microarray analysis in breast cancer 18
2.6 Weighted gene co-expression network analysis (WGCNA) and Cytoscape for gene-network analysis 20
Chapter 3 Aim of this study 23
Chapter 4 Material and methods 25
4.1 Data collections 25
4.2 Preprocessing and normalization of microarray data 27
4.2.1 Normalization of microarray data 27
4.2.2 Processing of Affymetrix GeneChip 28
4.2.3 Processing of Agilent Microarray 28
4.2.4 Processing of Illumina Bead Chip 29
4.2.5 Cross-Platform Normalization and Differentially expressed genes analysis 30
4.3 Weighted Gene Co-expression Network Analysis (WGCNA) 32
4.4 Reconstruction of Gene Expression Network 34
4.5 Cell culture 35
4.6 Total RNA extraction 36
4.7 Reverse transcription polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR) 36
4.8 Transfection of small interfering RNA 38
4.9 Wound healing migration assay 39
4.10 Cell migration assay 40
4.11 Cell invasion assay 41
4.12 Statistical analysis 41
Chapter 5 Results 43
5.1 Ontological network of metastatic module genes in BC 44
5.2 Ontological networks of metastatic ER-positive module genes and ER-negative module genes in BC 47
5.3 Gene regulatory network of ER-positive/negative and metastasis module genes 49
5.4 Sub-networks of three modules and regulatory pathway 53
5.5 Determination of gene expression of PRKCA, ICAM1 and VWF 57
5.6 Capability of migration and invasion in MCF-7 cells and MDA-MB-231 cells 58
5.7 Capability of migration and invasion in MCF-7 cells and MDA-MB-231 cells after inhibition of VWF 62
5.8 Validation of metastatic pathway via PRKCA siRNA transfection in MCF-7 cells and MDA-MB-231 cells 70
5.9 Regulation of VWF via BMP4-ICAM1-VWF pathway in MDA-MB-231 cells 80
Chapter 6 Discussion 84
6.1 Metastatic BC modules concluded by the regulation of immune system, cell cycle, and epithelial to mesenchymal transition 85
6.2 Metastatic progression of BC underwent TNFα-PRKCA-ICAM1-VWF and BMP4-ICAM1-VWF pathway 86
6.3 VWF promoted cell migration and invasion 90
Chapter 7 Conclusion 92
Chapter 8 References 93

1. Breast Cancer Facts & Figures 2013-2014. American Cancer Society.
2. McGuire, W.L., Breast cancer prognostic factors: evaluation guidelines. J Natl Cancer Inst, 1991. 83(3): p. 154-5.
3. Perou, C.M., et al., Molecular portraits of human breast tumours. Nature, 2000. 406(6797): p. 747-52.
4. Reis-Filho, J.S. and L. Pusztai, Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet, 2011. 378(9805): p. 1812-23.
5. Reis-Filho, J.S. and L. Pusztai, Gene expression profiling in breast cancer: classification, prognostication, and prediction. The Lancet. 378(9805): p. 1812-1823.
6. Spano, D., et al., Molecular networks that regulate cancer metastasis. Seminars in Cancer Biology, 2012. 22(3): p. 234-249.
7. Howlader N, et al., SEER Cancer Statistics Review, 1975-2011. National Cancer Institute.
8. Zhang, X.H.-F., et al., Metastasis Dormancy in Estrogen Receptor–Positive Breast Cancer. Clinical Cancer Research, 2013. 19(23): p. 6389-6397.
9. Tobin, N.P., et al., Molecular subtype and tumor characteristics of breast cancer metastases as assessed by gene expression significantly influence patient post-relapse survival. Annals of Oncology, 2015. 26(1): p. 81-88.
10. BREEN, L., L. O'DRISCOLL, and M. CLYNES, Gene Expression Microarray Technology: Some Applications in Lung Cancer Research. Cancer Genomics - Proteomics, 2006. 3(3-4): p. 197-202.
11. Lapointe, J., et al., Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proceedings of the National Academy of Sciences of the United States of America, 2004. 101(3): p. 811-816.
12. Loging, W.T., et al., Identifying Potential Tumor Markers and Antigens by Database Mining and Rapid Expression Screening. Genome Research, 2000. 10(9): p. 1393-1402.
13. Missiaglia, E., et al., Analysis of gene expression in cancer cell lines identifies candidate markers for pancreatic tumorigenesis and metastasis. International Journal of Cancer, 2004. 112(1): p. 100-112.
14. Dan, S., et al., An Integrated Database of Chemosensitivity to 55 Anticancer Drugs and Gene Expression Profiles of 39 Human Cancer Cell Lines. Cancer Research, 2002. 62(4): p. 1139-1147.
15. J. Ferlay, I.S., R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D.M. Parkin, D. Forman, F. Bray, Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International Journal of Cancer, 2014.
16. Ferlay, J., et al., Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012. European Journal of Cancer, 2013. 49: p. 1374-1403.
17. 2011年癌症登記報告. Health Promotion Administration, Ministry of Health and Welfare, 2014.
18. Ministry of Health and Welfare, Taiwan, 2014.
19. Weigelt, B., F.C. Geyer, and J.S. Reis-Filho, Histological types of breast cancer: How special are they? Molecular Oncology, 2010. 4(3): p. 192-208.
20. Organization, W.H., Tumours of the Breast and Female Genital Organs. Oxford University Press., 2003.
21. Elston, C.W. and I.O. Ellis, Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. C. W. Elston & I. O. Ellis. Histopathology 1991; 19; 403–410. Histopathology, 2002. 41(3a): p. 151-151.
22. Prat, A. and C.M. Perou, Deconstructing the molecular portraits of breast cancer. Molecular Oncology, 2011. 5(1): p. 5-23.
23. Burstein, H.J., et al., Adjuvant Endocrine Therapy for Women With Hormone Receptor–Positive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline Focused Update. Journal of Clinical Oncology, 2014. 32(21): p. 2255-2269.
24. Targeted therapy for breast cancer. American Cancer Society.
25. Fisher, B., et al., Relative worth of estrogen or progesterone receptor and pathologic characteristics of differentiation as indicators of prognosis in node negative breast cancer patients: findings from National Surgical Adjuvant Breast and Bowel Project Protocol B-06. Journal of Clinical Oncology, 1988. 6(7): p. 1076-87.
26. Yu, K.-D., et al., Hazard of Breast Cancer-Specific Mortality among Women with Estrogen Receptor-Positive Breast Cancer after Five Years from Diagnosis: Implication for Extended Endocrine Therapy. The Journal of Clinical Endocrinology & Metabolism, 2012. 97(12): p. E2201-E2209.
27. Yang, X., et al., Role of EHD2 in migration and invasion of human breast cancer cells. Tumor Biology, 2015: p. 1-10.
28. Crowe JP, G.N., Hubay CA, et al., Estrogen receptor determination and long term survival of patients with carcinoma of the breast. . Surg Gynecol Obstet. , 1991. 173(4): p. 273-278.
29. Weigelt, B., J.L. Peterse, and L.J. van't Veer, Breast cancer metastasis: markers and models. Nat Rev Cancer, 2005. 5(8): p. 591-602.
30. Kennecke, H., et al., Metastatic Behavior of Breast Cancer Subtypes. Journal of Clinical Oncology, 2010. 28(20): p. 3271-3277.
31. Cardoso, F., et al., Locally recurrent or metastatic breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology, 2012. 23(suppl 7): p. vii11-vii19.
32. Yokota, J., Tumor progression and metastasis. Carcinogenesis, 2000. 21(3): p. 497-503.
33. Hanahan, D. and R.A. Weinberg, The Hallmarks of Cancer. Cell, 2000. 100(1): p. 57-70.
34. Naslavsky, N. and S. Caplan, C-terminal EH-domain-containing proteins: consensus for a role in endocytic trafficking, EH? Journal of Cell Science, 2005. 118(18): p. 4093-4101.
35. Richardson, C., et al., Rad51 overexpression promotes alternative double-strand break repair pathways and genome instability. Oncogene, 0000. 23(2): p. 546-553.
36. Chang, A.-M., et al., STC1 expression is associated with tumor growth and metastasis in breast cancer. Clinical & Experimental Metastasis, 2014: p. 1-13.
37. Gupta, R.A., et al., Long noncoding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature, 2010. 464(7291): p. 1071-1076.
38. Wiegmans, A.P., et al., Rad51 supports triple negative breast cancer metastasis. 2014. 2014.
39. Sørensen, K., et al., Long non-coding RNA HOTAIR is an independent prognostic marker of metastasis in estrogen receptor-positive primary breast cancer. Breast Cancer Research and Treatment, 2013. 142(3): p. 529-536.
40. Gökmen-Polar, Y., et al., Prognostic Impact of HOTAIR Expression is Restricted to ER-Negative Breast Cancers. Scientific Reports, 2015. 5: p. 8765.
41. Dumitrescu, R.G. and I. Cotarla, Understanding breast cancer risk - where do we stand in 2005? Journal of Cellular and Molecular Medicine, 2005. 9(1): p. 208-221.
42. Steffen, J., et al., Germline mutations 657del5 of the NBS1 gene contribute significantly to the incidence of breast cancer in Central Poland. International Journal of Cancer, 2006. 119(2): p. 472-475.
43. King, M.-C., et al., Breast and Ovarian Cancer Risks Due to Inherited Mutations in BRCA1 and BRCA2. Science, 2003. 302(5645): p. 643-646.
44. Honrado, E., et al., Pathology and gene expression of hereditary breast tumors associated with BRCA1, BRCA2 and CHEK2 gene mutations. Oncogene, 2006. 25(43): p. 5837-5845.
45. van 't Veer, L.J., et al., Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002. 415(6871): p. 530-536.
46. Chang, H.Y., et al., Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proceedings of the National Academy of Sciences of the United States of America, 2005. 102(10): p. 3738-3743.
47. Wang, Y., et al., Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. The Lancet. 365(9460): p. 671-679.
48. Langfelder, P. and S. Horvath, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008. 9: p. 559-559.
49. Horvath, S., et al., Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proceedings of the National Academy of Sciences, 2006. 103(46): p. 17402-17407.
50. Miller, J., et al., Genes and pathways underlying regional and cell type changes in Alzheimer's disease. Genome Medicine, 2013. 5(5): p. 48.
51. Chou, W.-C., et al., Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer. BMC Genomics, 2014. 15(1): p. 1-12.
52. Angela P Presson, N.K.Y., Lora Bagryanova, Vei Mah, Mohammad Alavi, Erin L Maresh, Ayyappan K Rajasekaran, Lee Goodglick, David Chia and Steve Horvath, Protein expression based multimarker analysis of breast cancer samples. BMC Cancer 2011. 11(230).
53. Cline, M.S., et al., Integration of biological networks and gene expression data using Cytoscape. Nat. Protocols, 2007. 2(10): p. 2366-2382.
54. Bindea, G., et al., ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 2009. 25(8): p. 1091-1093.
55. Bindea, G., J. Galon, and B. Mlecnik, CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics, 2013. 29(5): p. 661-663.
56. Garnett, M.J., et al., Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature, 2012. 483(7391): p. 570-575.
57. Ciofani, M., et al., A Validated Regulatory Network for Th17 Cell Specification. Cell. 151(2): p. 289-303.
58. Jan Budczies*, C.D., Berit M Müller, Scarlet F Brockmöller, Frederick Klauschen, Balazs Györffy, Manfred Dietel, Christiane Richter-Ehrenstein, Ulrike Marten, Reza M Salek, Julian L Griffin, Mika Hilvo, Matej Orešič, Gert Wohlgemuth and Oliver Fiehn, Remodeling of central metabolism in invasive breast cancer compared to normal breast tissue – a GC-TOFMS based metabolomics study. BMC Genomics, 2012. 13( 334).
59. Husi, H., et al., Proteome-Based Systems Biology Analysis of the Diabetic Mouse Aorta Reveals Major Changes in Fatty Acid Biosynthesis as Potential Hallmark in Diabetes Mellitus–Associated Vascular Disease. Circulation: Cardiovascular Genetics, 2014. 7(2): p. 161-170.
60. Hampel, H., et al., Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat Rev Drug Discov, 2010. 9: p. 560 - 574.
61. Heider, A. and R. Alt, virtualArray: a R/bioconductor package to merge raw data from different microarray platforms. BMC Bioinformatics, 2013. 14(1): p. 75.
62. Rudy, J. and F. Valafar, Empirical comparison of cross-platform normalization methods for gene expression data. BMC Bioinformatics, 2011. 12(1): p. 467.
63. Walker, W.L., et al., Empirical Bayes accomodation of batch-effects in microarray data using identical replicate reference samples: application to RNA expression profiling of blood from Duchenne muscular dystrophy patients. BMC Genomics, 2008. 9: p. 494-494.
64. Scheller, J., et al., The pro- and anti-inflammatory properties of the cytokine interleukin-6. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, 2011. 1813(5): p. 878-888.
65. Bradley, J.R., TNF-mediated inflammatory disease. The Journal of Pathology, 2008. 214(2): p. 149-160.
66. Hanahan, D. and Robert A. Weinberg, Hallmarks of Cancer: The Next Generation. Cell, 2011. 144(5): p. 646-674.
67. Bidwell, B.N., et al., Silencing of Irf7 pathways in breast cancer cells promotes bone metastasis through immune escape. Nat Med, 2012. 18(8): p. 1224-1231.
68. Kitamura, T., B.-Z. Qian, and J.W. Pollard, Immune cell promotion of metastasis. Nat Rev Immunol, 2015. 15(2): p. 73-86.
69. Sceneay, J., et al., Primary Tumor Hypoxia Recruits CD11b+/Ly6Cmed/Ly6G+ Immune Suppressor Cells and Compromises NK Cell Cytotoxicity in the Premetastatic Niche. Cancer Research, 2012. 72(16): p. 3906-3911.
70. Chow, A.Y., Cell Cycle Control by Oncogenes and Tumor Suppressors: Driving the Transformation of Normal Cells into Cancerous Cells. Nature Education, 2010. 3(9)(7).
71. Hartwell, L. and M. Kastan, Cell cycle control and cancer. Science, 1994. 266(5192): p. 1821-1828.
72. Takeshita, F., et al., Systemic Delivery of Synthetic MicroRNA-16 Inhibits the Growth of Metastatic Prostate Tumors via Downregulation of Multiple Cell-cycle Genes. Mol Ther, 2009. 18(1): p. 181-187.
73. Bonnet, M.-E., et al., Systemic delivery of sticky siRNAs targeting the cell cycle for lung tumor metastasis inhibition. Journal of Controlled Release, 2013. 170(2): p. 183-190.
74. Sarah Heerboth, G.H., Meghan Leary, Mckenna Longacre, Shannon Byler, Karolina Lapinska, Amber Willbanks and Sibaji Sarkar, EMT and tumor metastasis. Clinical and Translational Medicine, 2015. 4(6).
75. Chaffer, C.L. and R.A. Weinberg, A Perspective on Cancer Cell Metastasis. Science, 2011. 331(6024): p. 1559-1564.
76. Jeanine Pignatelli, D.A.T., Ronald P. Schmidt, and Christopher E. Turner, Hic-5 promotes invadopodia formation and invasion during TGF-β–induced epithelial–mesenchymal transition. J Cell Biol, 2012. 197(3): p. 421-437.
77. Wajant, H., The Role of TNF in Cancer, in Death Receptors and Cognate Ligands in Cancer, H. Kalthoff, Editor. 2009, Springer Berlin Heidelberg. p. 1-15.
78. Bates, R.C. and A.M. Mercurio, Tumor Necrosis Factor-α Stimulates the Epithelial-to-Mesenchymal Transition of Human Colonic Organoids. Molecular Biology of the Cell, 2003. 14(5): p. 1790-1800.
79. Michalaki, V., et al., Serum levels of IL-6 and TNF-[alpha] correlate with clinicopathological features and patient survival in patients with prostate cancer. Br J Cancer, 2004. 90(12): p. 2312-2316.
80. Scott, K.A., et al., An Anti-Tumor Necrosis Factor-α Antibody Inhibits the Development of Experimental Skin Tumors. Molecular Cancer Therapeutics, 2003. 2(5): p. 445-451.
81. Nishizuka, Y., Protein kinase C and lipid signaling for sustained cellular responses. The FASEB Journal, 1995. 9(7): p. 484-96.
82. Liu, W.S. and C.A. Heckman, The Sevenfold Way of PKC Regulation. Cellular Signalling, 1998. 10(8): p. 529-542.
83. Gry Kalstad Lønne, L.C., Iris Omanovic Zahirovic, Göran Landberg, Karin Jirström and Christer Larsson, PKCα expression is a marker for breast cancer aggressiveness. Molecular Cancer 2010. 9: p. 76.
84. Ways, D.K., et al., MCF-7 breast cancer cells transfected with protein kinase C-alpha exhibit altered expression of other protein kinase C isoforms and display a more aggressive neoplastic phenotype. The Journal of Clinical Investigation, 1995. 95(4): p. 1906-1915.
85. Hubbard, A.K. and R. Rothlein, Intercellular adhesion molecule-1 (ICAM-1) expression and cell signaling cascades. Free Radical Biology and Medicine, 2000. 28(9): p. 1379-1386.
86. Buitrago, D., et al., Intercellular Adhesion Molecule-1 (ICAM-1) is Upregulated in Aggressive Papillary Thyroid Carcinoma. Annals of Surgical Oncology, 2012. 19(3): p. 973-980.
87. Maruo, Y., et al., ICAM-1 expression and the soluble ICAM-1 level for evaluating the metastatic potential of gastric cancer. International Journal of Cancer, 2002. 100(4): p. 486-490.
88. Terraube, V., I. Marx, and C.V. Denis, Role of von Willebrand factor in tumor metastasis. Thrombosis Research. 120: p. S64-S70.
89. L.M. Röhsig1, D.C.D., S.D. Stefani1, C.G. Castro Jr.1, I. Roisenberg2 and G. Schwartsmann, von Willebrand factor antigen levels in plasma of patients with malignant breast disease. Braz J Med Biol Res, 2001. 34(9): p. 1125-1129.
90. Xia Yang*, H.-j.S., Zhi-rong Li, Hao Zhang, Wei-jun Yang, Bing Ni and Yu-zhang Wu, Gastric cancer-associated enhancement of von Willebrand factor is regulated by vascular endothelial growth factor and related to disease severity. BMC Cancer, 2015. 15: p. 80.
91. Ferro, T., et al., Protein kinase C-α mediates endothelial barrier dysfunction induced by TNF-α. Vol. 278. 2000. L1107-L1117.
92. Arnott, C.H., et al., Tumour necrosis factor-alpha mediates tumour promotion via a PKC alpha- and AP-1-dependent pathway. Oncogene, 2002. 21(31): p. 4728-4738.
93. Min, J.-K., et al., TNF-Related Activation-Induced Cytokine Enhances Leukocyte Adhesiveness: Induction of ICAM-1 and VCAM-1 via TNF Receptor-Associated Factor and Protein Kinase C-Dependent NF-κB Activation in Endothelial Cells. The Journal of Immunology, 2005. 175(1): p. 531-540.
94. Zhang, P., et al., Melanoma upregulates ICAM-1 expression on endothelial cells through engagement of tumor CD44 with endothelial E-selectin and activation of a PKCα–p38–SP-1 pathway. The FASEB Journal, 2014. 28(11): p. 4591-4609.
95. Lorenzi, O., et al., Protein kinase C-δ mediates von Willebrand factor secretion from endothelial cells in response to vascular endothelial growth factor (VEGF) but not histamine. Journal of Thrombosis and Haemostasis, 2008. 6(11): p. 1962-1969.
96. Havemann, B.F.P.F.C.L.M.G.K.L.W.M.-L.Z.A.H.-P.E.M.K., Endothelial-like cells derived from human CD14 positive monocytes. Differentiation 2000. 65: p. 287-300.
97. Alarmo, E.-L. and A. Kallioniemi, Bone morphogenetic proteins in breast cancer: dual role in tumourigenesis? Endocrine-Related Cancer, 2010. 17(2): p. R123-R139.
98. Alarmo, E.-L., et al., Bone morphogenetic protein 4 expression in multiple normal and tumor tissues reveals its importance beyond development. Mod Pathol, 2013. 26(1): p. 10-21.
99. Guo, D., J. Huang, and J. Gong, Bone morphogenetic protein 4 (BMP4) is required for migration and invasion of breast cancer. Molecular and Cellular Biochemistry, 2012. 363(1-2): p. 179-190.
100. Sorescu, G.P., et al., Bone Morphogenic Protein 4 Produced in Endothelial Cells by Oscillatory Shear Stress Stimulates an Inflammatory Response. Journal of Biological Chemistry, 2003. 278(33): p. 31128-31135.
101. Csiszar, A., et al., Differential proinflammatory and prooxidant effects of bone morphogenetic protein-4 in coronary and pulmonary arterial endothelial cells. Vol. 295. 2008. H569-H577.
102. Banerjee, S., S.K. Dhara, and M. Bacanamwo, Endoglin is a novel endothelial cell specification gene. Stem Cell Research, 2012. 8(1): p. 85-96.
103. Elzarrad, K., et al., Early incorporated endothelial cells as origin of metastatic tumor vasculogenesis. Clinical & Experimental Metastasis, 2009. 26(6): p. 589-598.
104. Liu G, R.Y., Effect of von Willebrand factor on the biological characteristics of colorectal cancer cells. Chinese Journal of Gastrointestinal Surgery, 2010. 13(8): p. 616-619.