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研究生: 鍾尚庭
CHUNG, SHANG-TING
論文名稱: 乳癌和肺癌中琥珀酸脫氫酶丙型的過度表現與較差的預後相關
Succinate dehydrogenase complex subunit C (SDHC) overexpression correlates with poor prognosis in lung and breast cancers
指導教授: 張文粲
Chang, Wen-Tsan
學位類別: 碩士
Master
系所名稱: 醫學院 - 生物化學暨分子生物學研究所
Department of Biochemistry and Molecular Biology
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 70
中文關鍵詞: 檸檬酸循環琥珀酸脫氫酶生物資訊資料庫肺腺癌免疫組織染色基因沉默技術
外文關鍵詞: SDHC, lung cancer, breast cancer, poor prognosis, immunohistochemical staining, shRNA
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  • 過去二十年間,檸檬酸循環內的酵素如:烏頭酸酶、異檸檬酸脫氫酶、琥珀酸脫氫酶和延胡索酸酶基因組的多處突變可能導致癌症的發生,因為在過去他們被認為是抑制腫瘤生長的因子;我們實驗室也發表過關於琥珀酸脫氫酶乙型在肝癌中的表現量如果降低時會引起瓦氏效應導致腫瘤的惡化。然而透過多個生物資訊資料庫顯示,在檸檬酸循環中的琥珀酸脫氫酶丙型(SDHC)在一些癌症中有大量表現的情形,肺癌和乳癌尤其明顯。Cbioportal資料庫顯示在一些癌症中SDHC的DNA拷貝數,相較於一般組織有著10%以上的上升變化量。Oncomine資料庫也顯示SDHC的mRNA表現量與一般組織相比在癌症中的表現量也是大幅增加。Prognoscan資料庫則是顯示當SDHC的過度表現時,病人會有較差的預後。因此本篇研究的目的即是探究SDHC在肺癌與乳癌的不同時期的表現量與病人存活率之間的相關聯性。利用免疫組織化學染色測驗SDHC在二十一位肺腺癌患者的組織切片中之表現量。分析結果顯示在肺腺癌癌症分期晚期時,SDHC在組織內的表現量有增多的情形,與先前的三個生物資料庫的分析結果呈一致性,並且發現在前期的樣本中,出現間質轉移的癌細胞與周圍組織相比會有顯著的SDHC表現,似乎暗示著SDHC的大量表現可能會引起癌細胞的爬行能力增強,進而使癌細胞惡化,為了證實假設,利用shRNA靜默技術,降低SDHC的胞內表現量。結果顯示,當SDHC的表現下降時,細胞的生長、爬行能力和細胞週期皆受到影響,尤其是爬行能力方面,細胞傷口癒合爬行能力與追蹤爬行實驗都有顯著的變慢,暗示著SDHC與爬行能力有關。總結來說,雖然尚未解出SDHC為何在癌細胞中會大量表現,如何影響癌細胞的惡性程度也尚未明瞭,然而透過生物資訊資料庫的分析能使我們更快找到癌細胞內特殊表現的蛋白,進而作為治療之目標。

    Succinate dehydrogenase complex subunit C (SDHC) overexpression correlates with poor prognosis in lung and breast cancers

    Author: SHANG-TING CHUNG
    Advisor:Dr. Wen-Tsan Chang
    Department of Biochemistry and Molecular Biology

    Summary
    In the past two decades the enzymes in the TCAB cycle, aconitase, isocitrate dehydrogenase 1(IDH1), Succinate dehydrogenase (SDH) and Fumarase (FH) has multiple mutation, lead to certain types of cancer, and they were usually considered as tumor suppressors, our lab also established SDHB decreased in human hepatocellular carcinoma accelerates tumor malignancy by inducing the Warburg effect.However, the multiple mega databases shown that succinate dehydrogenase complex subunit C (SDHC) is overexpression in certain types of cancer, especially in lung and breast cancers. Cbioportal database showed DNA copy number of SDHC is increased 10% up in certain cancers than normal tissue. Oncomine database showa significant increase in its mRNA when compared with normal tissue The Prognoscan database also establish the SDHC overexpression correlates with poor survival. The purpose of the present study was to investigate the role of SDHC expression in tumor grade and survival rate in patients with lung and breast cancers. SDHC expression was assayed in 25 lung cancer tissues by immunohistochemical staining (IHC). Multivariate and univariate analyses were performed to determine the association between SDHC expression and prognosis. Although the mechanism how SDHC overexpression effects the tumor is unknown. It seems SDHC overexpression is an important factor in lung adenocarcinoma and breast invasive adenocarcinoma prognosis. SDHC overexpression can be an interesting potential novel biomarker for lung and breast cancers.
    Keyword: SDHC, lung cancer, breast cancer, poor prognosis, immunohistochemical staining,shRNA.
    Introduction
    Succinate dehydrogenase (SDH), also known as succinate-coenzyme Q reductase (SQR), is composed of four subunits (SDHA, SDHB, SDHC, SDHD) and four A complex of cofactors (SDHAF1, SDHAF2, SDHA3, SDHAF4) and embedded in the inner membrane of the mitochondria. The four subunits that make up the succinate dehydrogenase are translated by genes located in the nucleus. SDHA (also known as flavoprotein) has a covalently bonded flavin adenine dinucleotide (FAD). Coenzyme and succinate binding sites; SDHB (also known as iron-sulphur protein) contains three iron-sulfur clusters [2Fe-2S], [4Fe-4S], and [3Fe-4S], and participates in Qualitative binding, oxidation and electron transport chains; SDHC (also known as cybL) and SDHD (also known as cybS) are hydrophobic proteins responsible for binding to the mitochondrial inner membrane and providing binding sites for ubiquinone. Succinate dehydrogenase is involved in both citric acid and electron transport chains oxidizing fumarate to succinate in the citric acid cycle while in the respiratory chain Coenzyme Q, in turn, transmits electrons along with the reduction of ubiquinone .
    The Cbioportal database provides a platform for the collection of more than 20 cancer gene studies.Centrally analyzed nucleic acid microarray results, including data on somatic mutations, changes in the number of DNA copies, miRNA expression, and DNA methylation. Here I analyzed the changes in the number of DNA copies of SDHC in general tissues and cancer tissues. To observe the changes in SDHC mRNA between cancer and general tissues, I used the online analysis website Oncomine database) to collect more than 700 data from more than eight studies. Millions of microarray analysis results also make the results more trustworthy.The Human protein atlas database uses more than 24,000 antibodies for immunohistochemical staining of forty-four organs in humans, but in general organ tissues and cancer tissues and organs. A comparison of staining ,I collected in this database is the performance of SDHC in general organizations. Finally, for the association between SDHC performance in cancer and patient outcome, I used the PrognoScan database for analysis. PrognoScan brings together nucleic acid microarray analysis from Gene Expression Omnibus, Array express and other independent laboratories to analyze more than forty cancers with sufficient patient and medical data to more efficiently find new cancer treatment target.

    Material and Methods
    Through the analysis of the bioinformatics database, we found that the succinate dehydrogenase-type protein in succinate dehydrogenase has a large number of manifestations in lung cancer, and we have previously found succinate dehydrogenase type B in our laboratory. Excessive performance in liver cancer may reduce the degree of dysfunction , and the results of a large number of succinate dehydrogenase-type C proteins are associated with poor prognosis in patients. Therefore, this study on the relationship between lung cancer and succinate dehydrogenase, to explore whether succinate dehydrogenase affects the malignant degree of lung cancer, first use immunohistochemical staining to confirm succinate dehydrogenase type C protein in patients The performance of the sample, using RNA interference technology to inhibit its performance, and observe the growth rate, crawling ability of lung cancer cells and glycolytic capacity, to explore the relationship between SDHC and lung cancer prognosis.

    Results and Discussion
    The stable inhibition of SDHC may lead to cell migration, growth, and cell cycle. Unlike other SDH subunits, SDHC may play a role in helping cancer cells grow and crawl, leading to lung glands. The prognosis of cancer patients is worse, which suggests that SDHC may be an indicator or target for lung adenocarcinoma surveillance. Through analysis from the database, from DNA to RNA to protein level, it is proved that SDHC is associated with increased lung cancer growth and tumor growth. This research model can be used as a target and prognosis for cancer treatment in the future. Indicators to develop possible treatments.

    中文摘要 I SUMMARY II 致謝 V 目錄 VI 第一章 緒論 1 1-1 檸檬酸循環與細胞生長代謝 1 1-2 琥珀酸脫氫酶 (SUCCINATE DEHYDROGENASE,SDH) 1 1-3 琥珀酸脫氫酶在癌症的研究 2 1-4 生物資訊資料庫的應用 3 1-5 肺癌介紹 4 1-6 研究目的 4 第二章 實驗材料及方法 6 2-1 實驗材料 6 2-2 實驗方法 17 第三章 實驗結果 25 3-1 分析SDHC表現量在正常組織及腫瘤細胞之改變情形 25 3-2 利用核酸干擾(RNAI)技術建立穩定默化SDHC之細胞株 26 3-3 分析SDHC缺失對肺癌細胞生長與群落形成能力之影響 26 3-4 觀察SDHC缺失對於肺癌細胞爬行能力之影響 27 3-5 分析穩定抑制SDHC蛋白表現對肺癌細胞糖解作用代謝之影響 27 3-6 分析穩定抑制SDHC後對肺癌細胞能量代謝相關蛋白質表現之影響 28 3-7 分析SDHC在肺腺癌檢體中之表現情形 28 第四章 討論 29 4-1 總結 29 第五章 參考文獻 31 第六章 實驗結果圖表 35 附表一 35 表一、SDH酵素與FH酵素發生突變造成之病灶 36 附表二 37 表二、不同癌症分期之肺腺癌組織切片(N=21) 37 附表三 38 表三、利用IMAGE J影像分析軟體計算組織染色得分 40 圖ㄧ、檸檬酸循環之酵素以及其中間產物 41 圖二、利用THE HUMAN PROTEIN ATLAS DATABASE分析參與檸檬酸循環之酵素胞內位置以及免疫螢光染色 42 圖三、利用THE HUMAN PROTEIN ATLAS DATABASE分析SDHC在正常人體組織內之RNA表達 43 圖四、利用CBIOPROTAL DATABASE分析SDHC在多種癌症中DNA複製數目改變情形 44 圖五、利用資料庫(ONCOMINE DATABASE)分析癌症中SDHC之RNA表現情形 45 圖六、利用資料庫(ONCOMINE DATABASE)分析肺腺癌中SDHC之RNA表現情形 46 圖七、利用資料庫(ONCOMINE DATABASE)分析乳癌中SDHC之RNA表現情形 47 圖八、利用資料庫(PROGNOSCAN DATABASE)分析癌症中SDHC之表現情形 48 圖九、利用資料庫(PROGNOSCAN DATABASE)分析乳癌中SDHC表現情形 49 圖十、利用資料庫(PROGNOSCAN DATABASE)分析肺癌中SDHC之表現情形 50 圖十一、分析實驗室常用六種癌細胞株琥珀酸脫氫酶丙型(SDHC)蛋白表現情形 51 圖十二、利用免疫組織染色偵測肺腺癌病患檢體中琥珀酸脫氫酶丙型(SDHC)蛋白表現情形 52 圖十三、利用資料庫(ONCOMINE DATABASE)分析SDHC表現與肺腺癌分期之關係 53 圖十四、SDHC表現與肺腺癌分期之關係 54 圖十五、SDHC表現與肺腺癌腫瘤轉移分期(M0,M1)之關係 55 圖十六、觀察穩定抑制SDHC之A549細胞株型態及其SDHC蛋白表現量 56 圖十七、分析穩定抑制SDHC之A549細胞株其生長速度與形成群落能力 57 圖十八、分析穩定抑制SDHC之A549細胞株其生長速度與形成群落能力 58 圖十九A、分析穩定抑制SDHC之A549細胞株其爬行能力 59 圖十九B、分析穩定抑制SDHC之A549細胞株其爬行能力 59 圖二十、分析穩定抑制SDHC之A549細胞株其糖解作用代謝情況 61 圖二十一、分析穩定抑制SDHC之A549細胞株其生長代謝相關蛋白表現量 62 圖二十二、觀察穩定抑制SDHC之H1299細胞株型態及其SDHC蛋白表現量 63 圖二十三、分析穩定抑制SDHC之H1299細胞株其生長速度與形成群落能力 64 圖二十四、分析穩定抑制SDHC之H1299細胞株其生長速度與形成群落能力 65 圖二十五A、分析穩定抑制SDHC之H1299細胞株其爬行能力 66 圖二十五B、分析穩定抑制SDHC之H1299細胞株其爬行能力 66 圖二十六、分析穩定抑制SDHC之H1299細胞株其糖解作用代謝情況 68 圖二十七、分析穩定抑制SDHC之H1299細胞株其生長代謝相關蛋白表現量 69 補充資料 70

    [1] Romano, A. H. and T. Conway (1996). "Evolution of carbohydrate metabolic pathways." Res Microbiol 147(6-7): 448-455.
    [2] Horecker, B. L. (2002). "The pentose phosphate pathway." J Biol Chem 277(50): 47965-47971.
    [3] Cecchini, G., et al. (2002). "Succinate dehydrogenase and fumarate reductase from Escherichia coli." Biochim Biophys Acta 1553(1-2): 140-157.
    [4] Rhodes, D. R., et al. (2004). ONCOMINE: a cancer microarray database and integrated data-mining platform.Neoplasia 6(1): 1-6.
    [5] Kim JW1, Dang CV. (2005) Multifaceted roles of glycolytic enzymes.Trends Biochem Sci.30(3):142-50.
    [6] Ganti, A. K. and J. L. Mulshine (2005). Lung cancer screening: Panacea or pipe dream? Annals of Oncology 16(SUPPL. 2): ii215-ii219.
    [7] Pollard, P., et al. (2005). Evidence of increased microvessel density and activation of the hypoxia pathway in tumours from the hereditary leiomyomatosis and renal cell cancer syndrome. J Pathol 205(1): 41-49.
    [8] Reisch, A. S. and O. Elpeleg (2007). "Biochemical assays for mitochondrial activity: assays of TCA cycle enzymes and PDHc." Methods Cell Biol 80: 199-222.
    [9] Koivunen, P., et al. (2007). "Inhibition of hypoxia-inducible factor (HIF) hydroxylases by citric acid cycle intermediates: possible links between cell metabolism and stabilization of HIF." J Biol Chem 282(7): 4524-4532.
    [10] Herbst, R. S., et al. (2008). "Lung cancer." N Engl J Med 359(13): 1367-1380.
    [11] Cooper, W. A., et al. (2009). "Expression and prognostic significance of cyclin B1 and cyclin A in non-small cell lung cancer." Histopathology 55(1): 28-36.
    [12] Mizuno, H., et al. (2009). "PrognoScan: a new database for meta-analysis of the prognostic value of genes." BMC Med Genomics 2: 18.
    [13] Bardella, C., et al. (2011). "SDH mutations in cancer." Biochim Biophys Acta 1807(11): 1432-1443.
    [14] Raimundo, N., et al. (2011). "Revisiting the TCA cycle: signaling to tumor formation." Trends Mol Med 17(11): 641-649.
    [15] Cerami, E., et al. (2012). "The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data." Cancer Discov 2(5): 401-404.
    [16] Couraud, S., et al. (2012). "Lung cancer in never smokers--a review." Eur J Cancer 48(9): 1299-1311.
    [17] McCleland, M. L., et al. (2012). "An Integrated Genomic Screen Identifies LDHB as an Essential Gene for Triple-Negative Breast Cancer." 72(22): 5812-5823.
    [18] Howington, J. A., et al. (2013). "Treatment of stage I and II non-small cell lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines." Chest 143(5 Suppl): e278S-e313S.
    [19] Ramnath, N., et al. (2013). "Treatment of stage III non-small cell lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines." Chest 143(5 Suppl): e314S-e340S.
    [20] Hoekstra, A. S. and J. P. Bayley (2013). "The role of complex II in disease." Biochim Biophys Acta 1827(5): 543-551.
    [21] Bumgarner, R. (2013). "DNA microarrays: Types, Applications and their future." Curr Protoc Mol Biol 0 22: Unit-22.21.
    [22] Gao, J., et al. (2013). "Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal." Sci Signal 6(269): pl1.
    [23] Walters, S., et al. (2013). "Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: a population-based study, 2004–2007." Thorax 68(6): 551-564.
    [24] Robert, J. (2013). "[Biology of cancer metastasis]." Bull Cancer 100(4): 333-342.
    [25] Akram, M. (2014). "Citric acid cycle and role of its intermediates in metabolism." Cell Biochem Biophys 68(3): 475-478.
    [26] Van Vranken, J. G., et al. (2014). "SDHAF4 promotes mitochondrial succinate dehydrogenase activity and prevents neurodegeneration." Cell Metab 20(2): 241-252.
    [27] Mills, E. and L. A. O'Neill (2014). "Succinate: a metabolic signal in inflammation." Trends Cell Biol 24(5): 313-320.
    [28] Gallo, M., et al. (2015). "Lactic dehydrogenase and cancer: an overview." Front Biosci (Landmark Ed) 20: 1234-1249.
    [29] Uhlen, M., et al. (2015). "Proteomics. Tissue-based map of the human proteome." Science 347(6220): 1260419.
    [30] Lu, J., et al. (2015). "The Warburg effect in tumor progression: mitochondrial oxidative metabolism as an anti-metastasis mechanism." Cancer Lett 356(2 Pt A): 156-164.
    [31] Santamaria, P. G., et al. (2017). "EMT: Present and future in clinical oncology." Mol Oncol 11(7): 718-738.
    [32] Gill, A. J. (2018). "Succinate dehydrogenase (SDH)-deficient neoplasia." Histopathology 72(1): 106-116.
    [33] Siegel, R. L., et al. (2018). "Cancer statistics, 2018." CA Cancer J Clin 68(1): 7-30.
    [34] Tseng, P. L., et al. (2018). "Decreased succinate dehydrogenase B in human hepatocellular carcinoma accelerates tumor malignancy by inducing the Warburg effect." Sci Rep 8(1): 3081.
    [35] DeWaal, D., et al. (2018). "Hexokinase-2 depletion inhibits glycolysis and induces oxidative phosphorylation in hepatocellular carcinoma and sensitizes to metformin." Nature Communications 9(1): 446.
    [36] Anderson, N. M., et al. (2018). "The emerging role and targetability of the TCA cycle in cancer metabolism." Protein Cell 9(2): 216-237.

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