背景: 乳癌是女性中好發率最高且死亡率排名前四高的癌症，而乳癌細胞發生轉移是臨床治療失敗主要原因之一。而次世代定序(Next Generation Sequencing)是一種新穎且強大的高通量定序技術，利用次世代定序可以快速且全面性了解基因轉錄圖譜，因此本研究希望利用次世代定序技術全面性分析乳癌進展過程基因表現圖譜的改變，同時希望找到與調控乳癌轉移相關的基因，並且評估這些轉移相關的基因是否可做為乳癌臨床育後的生物標記。
方法: 我們蒐集三個三陰性乳癌病患組織，並且利用雷射顯微切割 (LCM)的方式準確地分離乳癌腫瘤以及癌旁正常組織，利用次世代定序全面性分析3對乳癌組織以及癌旁正常組織的基因轉錄圖譜，並且找出表現差異的基因轉錄組，利用生物途徑富集分析(Pathway Enrichment Analysis)探討這些表現差異的基因可涉及的生物途徑，並且找出參與癌細胞轉移相關的候選基因，且利用美國癌症基因圖譜資料庫(The Cancer Genome Atlas)進一步驗證這些轉移相關基因的表現量以及評估當作臨床育後或存活生物標記的可能性。
結果: 在這次的研究中，我們在乳癌中總共找到731個表現差異達4倍以上的基因。利用生物途徑富集分析發現這731個表現差異的基因，這些基因總共會顯著性的參與在19個訊號傳遞路徑上 (P <0.05)，其中有我們選擇三個可能與乳癌轉移相關的訊息路徑，包括: PI3K-Akt signaling pathway、Rap1 signaling pathway和ECM-receptor interaction，而我們所找到的表現差異基因中，總共有33個涉及在這三個訊息傳遞路徑，利用美國癌症基因圖譜資料庫進一步分析這些基因在乳癌中的表現量，結果發現大部分基因表現情況與我們的結果一致，進一步分析這些基因在乳癌中對於存活的影響，我們發現有6個基因表現量與乳癌的總存活率有顯著性相關，單變項分析結果顯示，VEGFA和HMMR高表現與乳癌較差總存活高度相關 (VEGFA: p=0.022和HMMR: p=0.01)，而BCL2和IGF1R高表現與較好的乳癌病患總存活高度相關(BCL2: p=0.007 和IGF1R: p=0.002)。另外；我們也發現在乳癌中，高表現的HMMR會有比較大的腫瘤發生，高表現的COL6A6會有腫瘤變小的情形，高表現的GNG7不會有淋巴結的侵犯。進一步多變項分析發現，在乳癌患者中，VEGFA基因表現量高和較差的總存活率(校正後存活調整風險 [AHR] 11.60，95%信賴區間 1.60-84.09，p=0.015) 以及HMMR基因表現量高和較差的總存活率(校正後存活調整風險 [AHR] 1.93，95%信賴區間 1.10-3.41，p=0.023) 都具顯著性相關。BCL2基因表現量高和較好的總存活率(校正後存活調整風險 [AHR] 0.43，95%信賴區間 0.23-0.79，p=0.007)以及IGF1R基因表現量高和較好的總存活率(校正後存活調整風險 [AHR] 0.45，95%信賴區間 0.25-0.80，p=0.006) 都具顯著性相關。COL6A6基因表現量高則和較好的無疾病存活率(校正後存活調整風險 [AHR] 0.37，95%信賴區間 0.16-0.83，p=0.016) 具顯著性相關，最後合併六個基因表現結果顯示可以更有效率的評估乳癌存活率。
結論: 我們所發現的這6個基因 (HMMR、VEGFA、BCL2、COL6A6、GNG7以及IGF1R會參與在3個癌症轉移相關路徑，這些乳癌轉移相關的基因可以提供作為侵襲性乳管癌的一個預後的生物標誌。

Background: Breast cancer is the top one leading cause of cancer and top four cancer death for females in Taiwan. Next generation sequencing (NGS) is a powerful and high-throughput technology for analyzing transcriptome profile. In this study, we like to identify differential expression genes (DEGs) in breast cancer compared to corresponding adjacent normal tissues by NGS approach. Furthermore, we will further assess whether these DEFs can be used as prognostic biomarkers for triple negative breast cancer.
Methods: Using laser capture microdissection (LCM) approach, we precisely collected breast cancer tissues and adjacent normal tissues from three patients with triple negative breast cancer. The transcriptome profiles of breast cancer and corresponding adjacent normal tissue were generated using NGS approach and the DEGs were identified. We further identified metastasis-related DEGs via pathway enrichment analysis. The expression levels and clinical impacts of these metastasis-related DEGs were examined by analyzing the Cancer Genome Atlas (TCGA) database.
Results: A total of 731 DEGs (fold change RPKM >4 and <0.25) were identified in triple negative breast cancer. Our data revealed that these DEGs were significantly involved in nineteen cancer-related signaling pathways by pathway enrichment analysis. Among them, 33 DEGs were significantly enriched in three metastasis-related signal transduction pathways, including PI3K-Akt signaling pathway, Rap1 signaling pathway and ECM-receptor interaction. Univariate analysis revealed that high expression levels of VEGFA and HMMR significantly correlated with poor overall survival curve (VEGFA: p=0.022 and HMMR: p=0.01), whereas high expression levels of BCL2 and IGF1R significantly associated with better overall survival curve of breast cancer(BCL2: p=0.007 and IGF1R: p=0.002). In addition, our data indicated that a high expression levels of HMMR correlated with poor pathological stage (p <0.001) and large tumor size (p<0.001). High expression levels of COL6A6 were significantly correlated with early pathological stage (p=0.003) and small tumor size (p<0.001) and high expression levels of GNG7 well associated with absence of lymph node invasion (p=0.002). Multivariate analysis revealed that high HMMR; VEGFA expression had worse overall survival (HMMR: adjust hazard ratio [AHR] 1.93, 95%CI 1.10-3.41, p=0.023 and VEGFA: [AHR] 11.60, 95%CI 1.60-84.09, p=0.015) for breast cancer. Furthermore, high BCL2 and IGF1R expression had good overall survival (BCL2: [AHR] 0.43, 95%CI 0.23-0.79, p=0.007 and IGF1R: [AHR] 0.45, 95%CI 0.25-0.8, p=0.006). In addition, the high COL6A6 expressions were significantly correlated with better disease-free survival (DFS) ([AHR] 0.37, 95%CI 0.16-0.83, p=0.016). Combined the effects of six genes risks showed a significantly correlation with worse overall survival (OS) for breast cancer patients and the degree showed a linear trend for their impact on overall survival (p for linear trend <0.001).
Conclusions: We identified six differential expression genes, VEGFA, HMMR, BCL2, COL6A6, GNG7 and IGF1R, which significantly involved in metastasis-related signaling pathways. The six metastasis-related genes dysfunction was an effective independent prognostic biomarker for breast ductal carcinoma.

Contents
論文審定書…………………………………………………………………………i
誌謝……………………………………………………………………………...…ii
Abbreviations………………………………………………………………………iii
Abstract in Chinese ……………………………………………………………….iv-v
Abstract in English ……………………………………………………………vi-viii
Contents ………………………………………………………….……………..ix-x
1. Introduction ………………………………………………………….……1
1.1 Breast cancer……………………………………………….….………1
1.2 Breast cancer subtypes………………………………………………1
1.3 Ductal carcinoma in situ…………………………....................…..………3
1.4 Invasive ductal carcinoma……………………..……………....………4
1.5 Laser capture microdissection applications (LCM)……………………4
1.6 Transcriptome profiles of breast cancer……………...…………………4
1.7 Next-generation sequencing and breast cancer…………...………6
2. Specific Aims ………………………………………………………..………8
3. Materials and Methods ………………………………………………………10
4. Results ………………………………………………………………………..15
4.1 Patients and Samples……………………………………………...…15
4.2 RNA profiling of breast cancer…………………………………........16
4.3 Identify Differentially Expressed Genes (DEG)……………...…........ .18
4.4 Analyze the putative biological function of DEGs by Pathway Enrichment Analysis…………………..………….……………………19
4.5 Validate the expression levels of 33 gene candidates in breast cancer by analyzing TCGA database……..………………………………………21
4.6 Evaluate the clinical impacts of DEGs in breast cancer patients……22
5. Discussion………………………………………………………....................…27
5.1 Quality control for NGS data…………………………………...…………27
5.2 Vascular Endothelial Growth Factor (VEGFA)……………..……………27
5.3 Hyaluronan-mediated motility receptor (HMMR)……………..…….……29
5.4 B-cell lymphoma 2 (BCL2)……………….…………………..……...……30
5.5 Collagen Type VI Alpha 6 (COL6A6)……………………………….……31
5.6 G protein gamma 7 (GNG7)………………………....…………….………32
5.7 Insulin-like growth factor-1 receptor (IGF1R)…………………….………33
5.8 Conclusion…………………………………….…………………...………34
6. References………………………………………………………………………36
7. Tables …………………………………………….……………………..……44
8. Figures ……………………………………….…………………………………74

Aaltonen, K.E., Rosendahl, A.H., Olsson, H., Malmstrom, P., Hartman, L., and Ferno, M. (2014). Association between insulin-like growth factor-1 receptor (IGF1R) negativity and poor prognosis in a cohort of women with primary breast cancer. BMC cancer 14, 794.
Ai, J., Tan, Y., Ying, W., Hong, Y., Liu, S., Wu, M., Qian, X., and Wang, H. (2006). Proteome analysis of hepatocellular carcinoma by laser capture microdissection. Proteomics 6, 538-546.
Akent'eva, N.P., Shushanov, S.S., and Kotel'nikov, A.I. (2015). Effects of RHAMM/HMMR-Selective Peptides on Survival of Breast Cancer Cells. Bulletin of experimental biology and medicine 159, 658-661.
Alco, G., Bozdogan, A., Selamoglu, D., Pilanci, K.N., Tuzlali, S., Ordu, C., Igdem, S., Okkan, S., Dincer, M., Demir, G., et al. (2015). Clinical and histopathological factors associated with Ki-67 expression in breast cancer patients. Oncol Lett 9, 1046-1054.
Assmann, V., Gillett, C.E., Poulsom, R., Ryder, K., Hart, I.R., and Hanby, A.M. (2001). The pattern of expression of the microtubule-binding protein RHAMM/IHABP in mammary carcinoma suggests a role in the invasive behaviour of tumour cells. The Journal of pathology 195, 191-196.
Baker, H., Patel, V., Molinolo, A.A., Shillitoe, E.J., Ensley, J.F., Yoo, G.H., Meneses-Garcia, A., Myers, J.N., El-Naggar, A.K., Gutkind, J.S., et al. (2005). Proteome-wide analysis of head and neck squamous cell carcinomas using laser-capture microdissection and tandem mass spectrometry. Oral oncology 41, 183-199.
Bhushann Meka, P., Jarjapu, S., Vishwakarma, S.K., Nanchari, S.R., Cingeetham, A., Annamaneni, S., Mukta, S., Triveni, B., and Satti, V. (2016). Influence of BCL2-938 C>A promoter polymorphism and BCL2 gene expression on the progression of breast cancer. Tumour Biol 37, 6905-6912.
Chan, S., Murray, P.G., Franklyn, J.A., McCabe, C.J., and Kilby, M.D. (2004). The use of laser capture microdissection (LCM) and quantitative polymerase chain reaction to define thyroid hormone receptor expression in human 'term' placenta. Placenta 25, 758-762.
Chan, S.H., Huang, W.C., Chang, J.W., Chang, K.J., Kuo, W.H., Wang, M.Y., Lin, K.Y., Uen, Y.H., Hou, M.F., Lin, C.M., et al. (2014). MicroRNA-149 targets GIT1 to suppress integrin signaling and breast cancer metastasis. Oncogene 33, 4496-4507.
Chandrasekharappa, S.C., Guru, S.C., Manickam, P., Olufemi, S.E., Collins, F.S., Emmert-Buck, M.R., Debelenko, L.V., Zhuang, Z., Lubensky, I.A., Liotta, L.A., et al. (1997). Positional cloning of the gene for multiple endocrine neoplasia-type 1. Science 276, 404-407.
Cheang, M.C., Chia, S.K., Voduc, D., Gao, D., Leung, S., Snider, J., Watson, M., Davies, S., Bernard, P.S., Parker, J.S., et al. (2009). Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 101, 736-750.
de Groot, C.J., Steegers-Theunissen, R.P., Guzel, C., Steegers, E.A., and Luider, T.M. (2005). Peptide patterns of laser dissected human trophoblasts analyzed by matrix-assisted laser desorption/ionisation-time of flight mass spectrometry. Proteomics 5, 597-607.
Eom, Y.H., Kim, H.S., Lee, A., Song, B.J., and Chae, B.J. (2016). BCL2 as a Subtype-Specific Prognostic Marker for Breast Cancer. Journal of breast cancer 19, 252-260.
Fournier, M.V., and Martin, K.J. (2006). Transcriptome profiling in clinical breast cancer: from 3D culture models to prognostic signatures. J Cell Physiol 209, 625-630.
Geho, D.H., Bandle, R.W., Clair, T., and Liotta, L.A. (2005). Physiological mechanisms of tumor-cell invasion and migration. Physiology 20, 194-200.
Goldhirsch, A., Wood, W.C., Coates, A.S., Gelber, R.D., Thurlimann, B., and Senn, H.J. (2011). Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22, 1736-1747.
Hartmann, S., Bergmann, M., Bohle, R.M., Weidner, W., and Steger, K. (2006). Genetic imprinting during impaired spermatogenesis. Molecular human reproduction 12, 407-411.
Hartmann, S., Szaumkessel, M., Salaverria, I., Simon, R., Sauter, G., Kiwerska, K., Gawecki, W., Bodnar, M., Marszalek, A., Richter, J., et al. (2012). Loss of protein expression and recurrent DNA hypermethylation of the GNG7 gene in squamous cell carcinoma of the head and neck. Journal of applied genetics 53, 167-174.
Heo, W.I., Park, K.Y., Jin, T., Lee, M.K., Kim, M., Choi, E.H., Kim, H.S., Bae, J.M., Moon, N.J., and Seo, S.J. (2017). Identification of novel candidate variants including COL6A6 polymorphisms in early-onset atopic dermatitis using whole-exome sequencing. BMC medical genetics 18, 8.
Hill, J.J., Tremblay, T.L., Pen, A., Li, J., Robotham, A.C., Lenferink, A.E., Wang, E., O'Connor-McCourt, M., and Kelly, J.F. (2011). Identification of vascular breast tumor markers by laser capture microdissection and label-free LC-MS. J Proteome Res 10, 2479-2493.
Hu, M., Yao, J., Carroll, D.K., Weremowicz, S., Chen, H., Carrasco, D., Richardson, A., Violette, S., Nikolskaya, T., Nikolsky, Y., et al. (2008). Regulation of in situ to invasive breast carcinoma transition. Cancer cell 13, 394-406.
Hu, Y., Qiu, Y., Yague, E., Ji, W., Liu, J., and Zhang, J. (2016). miRNA-205 targets VEGFA and FGF2 and regulates resistance to chemotherapeutics in breast cancer. Cell death & disease 7, e2291.
Huang, G., and Li, M. (2015). The role of EphB4 and IGF-IR expression in breast cancer cells. International journal of clinical and experimental pathology 8, 5997-6004.
Huber-Keener, K.J., Liu, X., Wang, Z., Wang, Y., Freeman, W., Wu, S., Planas-Silva, M.D., Ren, X., Cheng, Y., Zhang, Y., et al. (2012). Differential gene expression in tamoxifen-resistant breast cancer cells revealed by a new analytical model of RNA-Seq data. PLoS One 7, e41333.
Huber, M.C., Mall, R., Braselmann, H., Feuchtinger, A., Molatore, S., Lindner, K., Walch, A., Gross, E., Schmitt, M., Falkenberg, N., et al. (2016). uPAR enhances malignant potential of triple-negative breast cancer by directly interacting with uPA and IGF1R. BMC cancer 16, 615.
Hwang, K.T., Han, W., Kim, J., Moon, H.G., Oh, S., Song, Y.S., Kim, Y.A., Chang, M.S., and Noh, D.Y. (2017). Prognostic Influence of BCL2 on Molecular Subtypes of Breast Cancer. Journal of breast cancer 20, 54-64.
Ignatiadis, M., and Sotiriou, C. (2013). Luminal breast cancer: from biology to treatment. Nat Rev Clin Oncol 10, 494-506.
Iyer, M.K., Chinnaiyan, A.M., and Maher, C.A. (2011). ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics 27, 2903-2904.
Jayaraman, S., Doucet, M., and Kominsky, S.L. (2017). Down-regulation of CITED2 attenuates breast tumor growth, vessel formation and TGF-beta-induced expression of VEGFA. Oncotarget 8, 6169-6178.
Jin, L., Majerus, J., Oliveira, A., Inwards, C.Y., Nascimento, A.G., Burgart, L.J., and Lloyd, R.V. (2003). Detection of fusion gene transcripts in fresh-frozen and formalin-fixed paraffin-embedded tissue sections of soft-tissue sarcomas after laser capture microdissection and rt-PCR. Diagnostic molecular pathology : the American journal of surgical pathology, part B 12, 224-230.
Jones, M.B., Krutzsch, H., Shu, H., Zhao, Y., Liotta, L.A., Kohn, E.C., and Petricoin, E.F., 3rd (2002). Proteomic analysis and identification of new biomarkers and therapeutic targets for invasive ovarian cancer. Proteomics 2, 76-84.
Kaserer, K., Knezevic, V., Pichlhofer, B., Scheuba, C., Passler, C., Worth, J., Niederle, B., and Krizman, D. (2002). Construction of cDNA libraries from microdissected benign and malignant thyroid tissue. Laboratory investigation; a journal of technical methods and pathology 82, 1707-1714.
Lee, J.R., Baxter, T.M., Yamaguchi, H., Wang, T.C., Goldenring, J.R., and Anderson, M.G. (2003). Differential protein analysis of spasomolytic polypeptide expressing metaplasia using laser capture microdissection and two-dimensional difference gel electrophoresis. Applied immunohistochemistry & molecular morphology : AIMM 11, 188-193.
Leethanakul, C., Patel, V., Gillespie, J., Shillitoe, E., Kellman, R.M., Ensley, J.F., Limwongse, V., Emmert-Buck, M.R., Krizman, D.B., and Gutkind, J.S. (2000). Gene expression profiles in squamous cell carcinomas of the oral cavity: use of laser capture microdissection for the construction and analysis of stage-specific cDNA libraries. Oral oncology 36, 474-483.
Li, C., Hong, Y., Tan, Y.X., Zhou, H., Ai, J.H., Li, S.J., Zhang, L., Xia, Q.C., Wu, J.R., Wang, H.Y., et al. (2004). Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry. Molecular & cellular proteomics : MCP 3, 399-409.
Liu, L., Tong, Q., Liu, S., Cui, J., Zhang, Q., Sun, W., and Yang, S. (2016a). ZEB1 Upregulates VEGF Expression and Stimulates Angiogenesis in Breast Cancer. PLoS One 11, e0148774.
Liu, W., Ma, J., Cheng, Y., Zhang, H., Luo, W., and Zhang, H. (2016b). HMMR antisense RNA 1, a novel long noncoding RNA, regulates the progression of basal-like breast cancer cells. Breast cancer 8, 223-229.
Martinet, W., Abbeloos, V., Van Acker, N., De Meyer, G.R., Herman, A.G., and Kockx, M.M. (2004). Western blot analysis of a limited number of cells: a valuable adjunct to proteome analysis of paraffin wax-embedded, alcohol-fixed tissue after laser capture microdissection. The Journal of pathology 202, 382-388.
Mehlen, P., and Puisieux, A. (2006). Metastasis: a question of life or death. Nat Rev Cancer 6, 449-458.
Melle, C., Bogumil, R., Ernst, G., Schimmel, B., Bleul, A., and von Eggeling, F. (2006). Detection and identification of heat shock protein 10 as a biomarker in colorectal cancer by protein profiling. Proteomics 6, 2600-2608.
Min, K.W., Kim, D.H., Do, S.I., Pyo, J.S., Chae, S.W., Sohn, J.H., Kim, K., Lee, H.J., Kim, D.H., Oh, S., et al. (2016). High Ki67/BCL2 index is associated with worse outcome in early stage breast cancer. Postgraduate medical journal 92, 707-714.
Monteiro, J., and Fodde, R. (2010). Cancer stemness and metastasis: therapeutic consequences and perspectives. European journal of cancer 46, 1198-1203.
Muller, K.E., Marotti, J.D., de Abreu, F.B., Peterson, J.D., Miller, T.W., Chamberlin, M.D., Tsongalis, G.J., and Tafe, L.J. (2016). Targeted next-generation sequencing detects a high frequency of potentially actionable mutations in metastatic breast cancers. Experimental and molecular pathology 100, 421-425.
Nakazono, M., Qiu, F., Borsuk, L.A., and Schnable, P.S. (2003). Laser-capture microdissection, a tool for the global analysis of gene expression in specific plant cell types: identification of genes expressed differentially in epidermal cells or vascular tissues of maize. The Plant cell 15, 583-596.
Nakhlis, F., and Morrow, M. (2003). Ductal carcinoma in situ. The Surgical clinics of North America 83, 821-839.
Nguyen, D.X., and Massague, J. (2007). Genetic determinants of cancer metastasis. Nature reviews Genetics 8, 341-352.
Niida, A., Smith, A.D., Imoto, S., Aburatani, H., Zhang, M.Q., and Akiyama, T. (2009). Gene set-based module discovery in the breast cancer transcriptome. BMC Bioinformatics 10, 71.
Obiakor, H., Sehgal, D., Dasso, J.F., Bonner, R.F., Malekafzali, A., and Mage, R.G. (2002). A comparison of hydraulic and laser capture microdissection methods for collection of single B cells, PCR, and sequencing of antibody VDJ. Analytical biochemistry 306, 55-62.
Ochnik, A.M., and Baxter, R.C. (2016). Combination therapy approaches to target insulin-like growth factor receptor signaling in breast cancer. Endocrine-related cancer 23, R513-R536.
Ornstein, D.K., Gillespie, J.W., Paweletz, C.P., Duray, P.H., Herring, J., Vocke, C.D., Topalian, S.L., Bostwick, D.G., Linehan, W.M., Petricoin, E.F., 3rd, et al. (2000). Proteomic analysis of laser capture microdissected human prostate cancer and in vitro prostate cell lines. Electrophoresis 21, 2235-2242.
Pagedar, N.A., Wang, W., Chen, D.H., Davis, R.R., Lopez, I., Wright, C.G., and Alagramam, K.N. (2006). Gene expression analysis of distinct populations of cells isolated from mouse and human inner ear FFPE tissue using laser capture microdissection--a technical report based on preliminary findings. Brain research 1091, 289-299.
Paweletz, C.P., Liotta, L.A., and Petricoin, E.F., 3rd (2001). New technologies for biomarker analysis of prostate cancer progression: Laser capture microdissection and tissue proteomics. Urology 57, 160-163.
Perou, C.M., Sorlie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Rees, C.A., Pollack, J.R., Ross, D.T., Johnsen, H., Akslen, L.A., et al. (2000). Molecular portraits of human breast tumours. Nature 406, 747-752.
Pimentel, F., Bonilla, P., Ravishankar, Y.G., Contag, A., Gopal, N., LaCour, S., Lee, T., and Niemz, A. (2014). Technology in MicroRNA Profiling: Circulating MicroRNAs as Noninvasive Cancer Biomarkers in Breast Cancer. J Lab Autom.
Pinto, L.S., de Aguiar, F.C., Jr., Kowalski, L.P., Graner, E., and Lopes, M.A. (2010). FAS and ErbB2 expression in early local recurrent oral cancer. Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology 39, 176-181.
Porter, P.L. (2009). Global trends in breast cancer incidence and mortality. Salud publica de Mexico 51 Suppl 2, s141-146.
Poznanovic, S., Wozny, W., Schwall, G.P., Sastri, C., Hunzinger, C., Stegmann, W., Schrattenholz, A., Buchner, A., Gangnus, R., Burgemeister, R., et al. (2005). Differential radioactive proteomic analysis of microdissected renal cell carcinoma tissue by 54 cm isoelectric focusing in serial immobilized pH gradient gels. J Proteome Res 4, 2117-2125.
Rothe, F., Laes, J.F., Lambrechts, D., Smeets, D., Vincent, D., Maetens, M., Fumagalli, D., Michiels, S., Drisis, S., Moerman, C., et al. (2014). Plasma circulating tumor DNA as an alternative to metastatic biopsies for mutational analysis in breast cancer. Ann Oncol 25, 1959-1965.
Roy, S., Khanna, S., Kuhn, D.E., Rink, C., Williams, W.T., Zweier, J.L., and Sen, C.K. (2006). Transcriptome analysis of the ischemia-reperfused remodeling myocardium: temporal changes in inflammation and extracellular matrix. Physiological genomics 25, 364-374.
Sanger, F., Air, G.M., Barrell, B.G., Brown, N.L., Coulson, A.R., Fiddes, C.A., Hutchison, C.A., Slocombe, P.M., and Smith, M. (1977). Nucleotide sequence of bacteriophage phi X174 DNA. Nature 265, 687-695.
Shibata, K., Tanaka, S., Shiraishi, T., Kitano, S., and Mori, M. (1999). G-protein gamma 7 is down-regulated in cancers and associated with p 27kip1-induced growth arrest. Cancer Res 59, 1096-1101.
Sohr, S., and Engeland, K. (2008). RHAMM is differentially expressed in the cell cycle and downregulated by the tumor suppressor p53. Cell cycle 7, 3448-3460.
Sotiriou, C., and Piccart, M.J. (2007). Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer 7, 545-553.
Stephens, P.J., Tarpey, P.S., Davies, H., Van Loo, P., Greenman, C., Wedge, D.C., Nik-Zainal, S., Martin, S., Varela, I., Bignell, G.R., et al. (2012). The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400-404.
Tan, Y.O., Han, S., Lu, Y.S., Yip, C.H., Sunpaweravong, P., Jeong, J., Caguioa, P.B., Aggarwal, S., Yeoh, E.M., and Moon, H. (2010). The prevalence and assessment of ErbB2-positive breast cancer in Asia: a literature survey. Cancer 116, 5348-5357.
Tung, N., Battelli, C., Allen, B., Kaldate, R., Bhatnagar, S., Bowles, K., Timms, K., Garber, J.E., Herold, C., Ellisen, L., et al. (2015). Frequency of mutations in individuals with breast cancer referred for BRCA1 and BRCA2 testing using next-generation sequencing with a 25-gene panel. Cancer 121, 25-33.
Turner, N.C., and Reis-Filho, J.S. (2006). Basal-like breast cancer and the BRCA1 phenotype. Oncogene 25, 5846-5853.
Uji, K., Naoi, Y., Kagara, N., Shimoda, M., Shimomura, A., Maruyama, N., Shimazu, K., Kim, S.J., and Noguchi, S. (2014). Significance of TP53 mutations determined by next-generation "deep" sequencing in prognosis of estrogen receptor-positive breast cancer. Cancer Lett 342, 19-26.
Upson, J.J., Stoyanova, R., Cooper, H.S., Patriotis, C., Ross, E.A., Boman, B., Clapper, M.L., Knudson, A.G., and Bellacosa, A. (2004). Optimized procedures for microarray analysis of histological specimens processed by laser capture microdissection. J Cell Physiol 201, 366-373.
Wang, C., Thor, A.D., Moore, D.H., 2nd, Zhao, Y., Kerschmann, R., Stern, R., Watson, P.H., and Turley, E.A. (1998). The overexpression of RHAMM, a hyaluronan-binding protein that regulates ras signaling, correlates with overexpression of mitogen-activated protein kinase and is a significant parameter in breast cancer progression. Clin Cancer Res 4, 567-576.
Wang, R., Tian, S., Wang, H.B., Chu, D.P., Cao, J.L., Xia, H.F., and Ma, X. (2014). MiR-185 is involved in human breast carcinogenesis by targeting Vegfa. FEBS letters 588, 4438-4447.
Wong, M.H., Saam, J.R., Stappenbeck, T.S., Rexer, C.H., and Gordon, J.I. (2000). Genetic mosaic analysis based on Cre recombinase and navigated laser capture microdissection. Proceedings of the National Academy of Sciences of the United States of America 97, 12601-12606.
Wu, J., Yuan, P., Mao, Q., Lu, P., Xie, T., Yang, H., and Wang, C. (2016). miR-613 inhibits proliferation and invasion of breast cancer cell via VEGFA. Biochemical and biophysical research communications 478, 274-278.
Wulfkuhle, J.D., Sgroi, D.C., Krutzsch, H., McLean, K., McGarvey, K., Knowlton, M., Chen, S., Shu, H., Sahin, A., Kurek, R., et al. (2002). Proteomics of human breast ductal carcinoma in situ. Cancer Res 62, 6740-6749.
Yao, F., Yu, F., Gong, L., Taube, D., Rao, D.D., and MacKenzie, R.G. (2005). Microarray analysis of fluoro-gold labeled rat dopamine neurons harvested by laser capture microdissection. Journal of neuroscience methods 143, 95-106.
Youlden, D.R., Cramb, S.M., Yip, C.H., and Baade, P.D. (2014). Incidence and mortality of female breast cancer in the Asia-Pacific region. Cancer biology & medicine 11, 101-115.
Zhang, H., Chen, Y., Fan, B., Wang, W., and Zhu, W. (2015). Overexpression of VEGF183 promotes murine breast cancer cell proliferation in vitro and induces dilated intratumoral microvessels. Tumour Biol 36, 3871-3880.