三陰性乳腺癌（TNBC）是一種更為異質的乳腺癌亞型。三陰性乳腺癌（TNBC）的病因涉及各種生物信號級聯和遺傳，表觀遺傳和微環境的多因素畸變。 TNBC迫切需要新的治療方法，因為目前只有手術和化療是有效的方式。然而，為TNBC和非TNBC設計多分子藥物是一項挑戰。它需要很好地理解由級聯信號傳導途徑，遺傳和表觀遺傳調控，靶標和藥物特徵以及藥物 - 靶標相互作用引發的分子機制。本研究在系統生物學方法和深度學習方法方面，提出了一系列針對TNBC和非TNBC的系統藥物設計策略。對於系統生物學方法，我們通過大數據挖掘技術構建候選全基因組遺傳和表觀遺傳網絡（GWGEN），並通過系統識別和模型順序選擇方法，通過相應的微陣列數據識別TNBC和非TNBC的真實GWGEN。 TNBC和非TNBC的核心GWGEN由其相應的GWGEN構建，並且由KEGG途徑表示以獲得TNBC和非TNBC的核心信號傳導途徑，將其進行比較以發現導致多種細胞功能障礙（包括細胞增殖）的重要致癌生物標誌物，自噬，免疫應答，細胞凋亡，轉移，血管生成，上皮 - 間質轉化（EMT）和細胞分化。借助基於藥物 - 目標數據庫特徵向量訓練的深度神經網絡的藥物 - 靶標相互作用（DTI）模型，我們可以在表6-9中為這些藥物靶標選擇候選藥物。這些候選藥物仍然通過LD50過濾毒性，並通過連接Map（CMap）作為潛在藥物進行調節，然後結合作為潛在的多分子藥物（白藜蘆醇，西羅莫司，潑尼松龍）用於TNBC和（白藜蘆醇，西羅莫司，卡馬西平，維拉帕米）分別用於非TNBC。

Triple-negative breast cancer (TNBC) is a more heterogeneous subtype of them. The etiology of triple-negative breast cancer (TNBC) is involved in various biological signal cascades and multifactorial aberrations of genetic, epigenetic and microenvironment. New therapeutic for TNBC is urgently in need because surgery and chemotherapy are two only available modalities now. However, it is a challenge to design multiple molecular drugs for the TNBC and non-TNBC, respectively. It requires a well understanding of molecular mechanisms triggered by cascade signaling pathways, genetic and epigenetic regulation, characteristic of targets and drug, and drug-target interactions. In this study, in terms of systems biology approaches and deep learning method, we proposed a series of strategies for systems medicine design toward TNBC and non-TNBC. For systems biology approach, we constructed candidate genome-wide genetic and epigenetic network (GWGEN) by big data mining technique and identified real GWGEN of TNBC and non-TNBC by corresponding microarray data via system identification and model order selection methods. Core GWGEN of TNBC and non-TNBC were constructed from their corresponding GWGENs and their denoted by KEGG pathways to obtain core signaling pathways of TNBC and non-TNBC, which were compared to find essential carcinogenic biomarkers to bring about multiple cellular dysfunctions including cell proliferation, autophagy, immune response, apoptosis, metastasis, angiogenesis, epithelial-mesenchymal transition (EMT), and cell differentiation. With the help of the drug-target interaction (DTI) model based on deep neural network trained through feature vectors of drug-target databases, we could select candidate drugs in Tables 6-9 for these drug targets. These candidate drugs were still filtered for the toxicity by LD50 and for regulation ability by connectively Map (CMap) as potential drugs and then combined as potential multiple-molecule drugs (resveratrol, sirolimus, prednisolone) for TNBC and (resveratrol, sirolimus, carbamazepine, verapamil) for non-TNBC, respectively.

Content
Chapter1 Introduction 1
Chapter 2. Results 8
2.1 Investigation of core signaling pathways from identified GWGEN in TNBC and non-TNBC. 8
2.2 Common core pathways from the common core GWGEN between TNBC and non-TNBC 10
2.3 Specific core pathways from core GWGEN in TNBC 12
2.4 Specific core pathways from core GWGEN in non-TNBC 13
2.5 Select drug targets to design multi-molecule drugs for TNBC and non-TNBC 15
Chapter 3. Discussion 17
3.1 The Common Carcinogenic Molecular Mechanism between TNBC and non-TNBC 17
3.2 The Specific Carcinogenic Molecular Mechanism in TNBC 20
3.3 The Specific Carcinogenic Molecular Mechanism in non-TNBC 22
3.4 Overview of Systems Drug Discovery and Design for TNBC and non-TNBC by Deep Learning Approaches and Data Mining Methods. 23
Chapter 4. Conclusion 26
Chapter 5. Method and Materials 28
5.1 Overview of the construction for core genome-wide genetic and epigenetic networks (GWGENs) of TNBC and non-TNBC. 28
5.2 Big data mining and preprocessing of microarray data for constructing candidate GWGENs. 29
5.3 Constructing the Stochastic Models of the GWGEN to identify real GWGEN of TNBC and non-TNBC. 30
5.4 Parameter estimation of the models of candidate GWGENs through system identification scheme, system order detection method and microarray data. 34
5.5 Applying the principal network projection (PNP) method to extract core GWGENs in the real GWGENs. 40
5.6 Systems Discovery and Design Multiple-Molecule drug via DNN of Drug-Target Interaction Model and Drug Toxicity Filter 44
Tables 50
Table 1. The total number of nodes and edges in candidate GWGENs and identified GWGENs of TNBC and non-TNBC. 50
Table 2. The drug targets identified for each of TNBC and non-TNBC. 51
Table 3. The pathway enrichment analysis of proteins by applying the DAVID in core GEGEN of triple-negative breast cancer 52
Table 4. The pathway enrichment analysis of proteins by applying the DAVID in core GEGEN of non-triple-negative breast cancer 53
Table 5. The number of overlap proteins and genes in TNBC and non-TNBC 53
Table 6. The potential drugs identified for target BRCA1 and AKT1 54
Table 8. The potential drugs identified for target STAT3 and MMP2 55
Table 9. The potential drugs identified for target NFE2L1 55
Table 10 Potential multiple-molecule drug and the corresponding target genes for TNBC 56
Table 11 Potential multiple-molecule drug and the corresponding target genes for non-TNBC 57
FIGURES 58
Figure 1. Flowchart of using systems biology method to construct candidate GWGENs, real GWGENs, core GWGENs and core signaling pathways of TNBC and non-TNBC and find potential genetic and epigenetic drug targets for multiple-molecule drug design. 59
Figure 2. Flowchart of the proposed systems drug discovery and design method 61
Figure 3. The real genome-wide genetic and epigenetic network (GWGEN) of TNBC. 62
Figure 4. The real genome-wide genetic and epigenetic network (GWGEN) of non-TNBC. 63
Figure 5. The core genome-wide genetic and epigenetic network (GWGEN) of TNBC 64
Figure 6. The core genome-wide genetic and epigenetic network (GWGEN) of non-TNBC 65
Figure 7. The core signaling pathways are obtained by projecting core GWGENs to KEGG pathways to investigate the carcinogenic progression mechanism of triple-negative breast cancer. 66
Figure 8. The core signaling pathways are obtained by projecting core GWGENs to KEGG pathways to investigate the carcinogenic progression mechanism of non-triple-negative breast cancer. 67
Figure 9. The common and specific core signaling pathways of core signaling pathways of TNBC in Figure 7 and non-TNBC in Figure 8. 69
Figure 10. The ROC curve of different DTI models for predicting drug-target interaction. 70
Reference 71

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