甲狀腺癌是最常見的內分泌癌，乳頭狀甲狀腺癌（PTC）佔甲狀腺癌的比例最高。癌症的發病機制和進展機制是由遺傳和表觀遺傳環境多因素畸變引起的，包括PTC。在本研究中，我們通過大數據挖掘構建候選蛋白質-蛋白質相互作用網絡（PPIN）和候選基因調控網絡（GRN），然後將候選PPIN和GRN整合到候選遺傳和表觀遺傳網絡（GEN）中。我們應用系統識別方法和系統順序檢測方法，基於下一代測序（NGS）和DNA甲基化譜修剪候選GEN，以獲得真實的GEN。然後，我們通過主網絡投影（PNP）方法從真實GEN中提取核心GEN。為了研究PTC致癌過程中的致病和進展機制，將每個階段PTC的核心GEN投射到KEGG途徑，以獲得每個階段PTC的核心途徑。接下來，我們比較了PTC的兩個連續核心途徑，以研究不同的進展遺傳和表觀遺傳機制，並在PTC三個階段確定了關鍵的致癌生物標誌物。此外，在我們的結果中，我們發現這些致癌生物標誌物引起多種細胞功能障礙，包括免疫反應，增殖，細胞週期，細胞凋亡，細胞遷移，血管生成，淋巴管生成，上皮-間質轉化（EMT）和細胞分化等關鍵因素誘發致癌進展。通過這些重要的致癌生物標誌物，我們進一步選擇了5種生物標誌物，其中2種基因通過DNA甲基化在正常甲狀腺細胞進展至早期PTC和6種生物標誌物中進行了修飾，其中2種基因在早期PTC的進展中通過DNA甲基化進行了修飾。晚期PTC可以使PTC細胞作為潛在的遺傳和表觀遺傳藥物靶標進展。最後，我們利用藥物數據挖掘方法通過藥物數據庫進行藥物設計，找到5種分子藥物，用於治療各項進展中的藥物靶點。

Thyroid cancer is the most common endocrine cancer, and papillary thyroid cancer (PTC) accounts for the highest proportion of thyroid cancer. The pathogenetic and progression mechanisms of cancer are caused by genetic and epigenetic environmental multifactorial aberrations, including PTC. In this study, we constructed the candidate protein-protein interaction network (PPIN) and candidate gene regulatory network (GRN) by big data mining and then integrated candidate PPIN and GRN into candidate genetic and epigenetic network (GEN). We applied system identification method and system order detection method to prune candidate GEN based on next generation sequencing (NGS) and DNA methylation profiles to obtain real GEN. Then, we extracted core GEN from real GEN through principal network projection (PNP) method. In order to investigate the pathogenic and progression mechanisms in the carcinogenic process of PTC, core GEN of each stage PTC was projected to KEGG pathways to obtain the core pathways of each stage PTC. Next, we compared two successive core pathways of PTC to investigate different progression genetic and epigenetic mechanisms and identified the crucial carcinogenic biomarkers in three stages PTC. In addition, in our result, we found out these carcinogenic biomarkers causing multiple cellular dysfunctions including immune response, proliferation, cell cycle, apoptosis, cell migration, angiogenesis, lymphangiogenesis, epithelial-mesenchymal transition (EMT) and cell differentiation that were the key factors to induce carcinogenic progression. By these crucial carcinogenic biomarkers, we further selected 5 biomarkers which 2 genes were modified by DNA methylation in the progression of normal thyroid cells to early-stage PTC and 6 biomarkers which 2 genes were modified by DNA methylation in the progression of early-stage PTC to late-stage PTC that could make PTC cells progress as potential genetic and epigenetic drug targets. Finally, we
used drug data mining method for drug design via drug data database to find 5 molecular drugs to treat drug targets in each progressions.

Contents
摘要…………………………………………………………………………………….i
Abstract…………………………………………………………………………….....ii
Chapter 1 Introduction………………………….…………………………………....1
Chapter 2 Results…………………………………………….…………………….....5
2.1 Constructing and investigating core signaling pathways from genome-wide GENs and core GENs in each stage thyroid cancer cells………………………….5
2.2 Investigating progression mechanisms by differential core pathway from normal stage to early-stage thyroid cancer cells……………………………………………7
2.3 Investigating progression mechanisms of differential core pathways from early-stage to late-stage thyroid cancer cells……………………………………………10
Chapter 3 Discussion……………………………………………....………..………14
3.1 The carcinogenic progression mechanism from normal thyroid cells to early-stage papillary thyroid cancer cells……………………………………………….14
3.2 The carcinogenic progression mechanism between early-stage papillary thyroid cancer cells and late-stage papillary thyroid cancer cells…………………………19
3.3 The overview of genetic and epigenetic progression mechanism from normal to late-stage papillary thyroid cancer cells…………………………………………..25
3.4 The multiple-molecules drug design for papillary thyroid cancer by drug data mining…………………………………………………………………………….28
Chapter 4 Conclusion…………………….….……………………………………...31
Chapter 5 Materials and Methods…………………….…………………...……….33
5.1 Constructing candidate genome-wide GEN by big data mining……………...34
5.2 Constructing real PPINs and real GRNs in real GENs at different stages of PTC cells……………………………………………………………………………….35
5.3 Parameter estimation of the systematic models of candidate GENs via system identification method and system order detection…………………………...…...39
5.4 Applying the PNP method to extract core GENs in the real GENs………......45
Tables………………………………………………………………………………...51
Table 1. The number of nodes and edges in candidate GENs and identified GENs of PTC each stage…………………………………………………………….......51
Table 2. The pathway enrichment analysis of proteins by applying the DAVID in the real GWGEN of normal thyroid cells……………………………………...…52
Table 3. The pathway enrichment analysis of proteins by applying the DAVID in the GWGEN of early-stage papillary thyroid cancer cells……….…………...….53
Table 4. The pathway enrichment analysis of proteins by applying the DAVID in the GWGEN of late-stage papillary thyroid cancer cells……………………...…54
Table 5. The number of overlap proteins and genes in the core pathways of normal to early-stage papillary thyroid cancer and early to late-stage papillary thyroid cancer…………………………………………………………………………….55
Table 6. Multiple molecule drugs and the corresponding target genes for carcinogenic progression from normal thyroid cells to early-stage papillary thyroid cancer cells…………………………………………………………………....….56
Table 7. Multiple molecule drugs and the corresponding target genes for carcinogenic progression from early-stage papillary thyroid cancer cells to late-stage papillary thyroid cancer cells……………………………………...……….57
Figures…………………………………………………………………………….....58
Figure 1. Flowchart of using systems biology method to construct GENs, core GENs and core signaling pathways of carcinogenic progression mechanism in each stage of PTC and development of multiple drugs by potential genetic and epigenetic biomarkers………………………………………………………………………..58
Figure 2. The genetic and epigenetic network (GEN) of normal stage of thyroid cells……………………………………………………………………………….60
Figure 3. The genetic and epigenetic network (GEN) of early-stage of thyroid cancer cells…………………………………………....………………………….61
Figure 4. The genetic and epigenetic network (GEN) of late-stage of thyroid cancer cells……………………………………………………………………………….62
Figure 5. The core genetic and epigenetic network (GEN) of normal stage of thyroid cells……………………………………………………………………………….63
Figure 6. The core genetic and epigenetic network (GEN) of early-stage of thyroid cancer cells……………………………………………………………………….64
Figure 7. The core genetic and epigenetic network (GEN) of late-stage of thyroid cancer cells……………………………………………………………………….65
Figure 8. The core signaling pathways are obtained by projecting core GENs to KEGG pathways to investigate the carcinogenic progression mechanism from normal thyroid cells to early-stage papillary thyroid cancer cells…………………66
Figure 9. The core signaling pathways are obtained by projecting core GENs to KEGG pathways to investigate the carcinogenic progression mechanism from early-stage thyroid cancer cells to late-stage papillary thyroid cancer cells………68
Figure 10. The overview of carcinogenic progression mechanism from normal to late-stage papillary thyroid cancer cells…………………………………………..70
References………………………………………………………………………..….71

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