臺灣博碩士論文加值系統

肺癌是全球當前癌症盛行率最高的一種惡性腫瘤，其中約85%以上為非小細胞肺癌，癌症隨者目前研究的進展，逐漸將其視為一種系統性、複雜且動態的基因疾病，且癌症具有產生抗藥性和進一步惡化和轉移的特性。這樣的腫瘤特性稱作癌細胞可塑性和幹細胞特性。從系統生物學的觀點，癌細胞可塑性和幹細胞特性可視為其由底層分子的功能、參與的路徑、形成的調控網絡等一層一層疊加而湧現的生物表型，已知癌細胞的可塑性和幹細胞特性跟其與腫瘤微環境中的互動有關。腫瘤微環境中除了免疫細胞外，占最多數的便是支持細胞為主，又以纖維母細胞在其中佔最多數，而本研究團隊在2014年於自然通訊期刊發表了腫瘤纖維母細胞以旁分泌的方式，藉由分泌IGF-II影響肺癌幹細胞內的IGF1R訊息傳討路徑激活癌症幹細胞的Nanog表現量，藉此維持其癌症幹細胞特性。
此研究主要是使用系統生物學中的逆向工程的分析方法，結和已知的生物分子功能和調控關係，基於前次研究團隊所發表在癌症幹細胞及纖維母細胞培養實驗之基因表現資料，系統性來分析肺癌幹細胞在腫瘤相關纖維母細胞培養下，其分子調控機制的變化，其中從顯著基因分析取得顯著表現基因，進一步採取基因富集分析和路徑分析，最後建立此群顯著表現基因內可能調控網絡，並且進一步辨識其中的主調控基因，並且從中建立可能經由旁分泌方式影響癌症幹細胞調控機制的基因。
最後本研究發現有2609個顯著表現基因跟在纖維母細胞影響下的癌症幹細胞特性相關，從基因功能富集分析中，可以發現顯著表現基因中，活化基因與核酸和蛋白質代謝、細胞外基質重塑、染色體結構調控相關，抑制基因跟細胞凋亡、細胞自嗜、脂質代謝相關，活化和抑制基因接共同與能量代謝路徑調控、細胞週期相關，在路徑分析中可以發現顯著表現基因在細胞週期路徑、去氧核醣核酸修復和複製、能量代謝及免疫相關，在免疫系統相關路徑中，顯著表現的基因位在類鐸受體集聯激活路徑、抗原呈現路徑、FLT3訊息路徑、RAF/MAP激酶路徑和Dectin-1訊息傳遞路徑中，最後註解顯著表現基因中的調控關係和蛋白質-蛋白質互動關係後，重建顯著基因中可能調控網路，進一步進行主調控網絡分析，其中共有64個轉錄因子和35個轉錄輔因子為癌症幹細胞特性之主調控基因，從中發現類鐸受體集聯激活路徑佔有重要角色，從調控網路中辨識出相關的主調控基因如CREB, SIN3A, SMAD, YY1, SP1, NF-κB參與類鐸受體2,3,4,5,7,8,9,10等集聯路徑，其中進一步追溯細胞激素CD14在類鐸受體集聯激活路徑中，上游調控主調控基因FOS, SIN3A, RBFOX2, GTF2F1, BHLHE40及可能經由纖維母細胞旁泌素影響此主調控基因的角色 。
本研究的主要價值除了找尋肺癌幹細胞在腫瘤纖維母細胞下的調控關鍵外，同時建立一個系統性分析顯著基因表現的完整流程以及發展複雜網絡和數據的視覺化方法，希望本研究可以提供未來個體化系統分析病人腫瘤變異狀況和調控關係的一種可行的思路。

Lung cancer, most common diagnosed malignancies in the world, is regarded as a system-level, complex and dynamic genetic disease with treatment adaptability. Cancer cells are able to spread and develop treatment resistance. This characteristics is described as cancer plasticity and stemness, which is modulated by the cancer stem cells(CSCs) and its interaction to the components of the cancer microenvironment such as cancer-associated fibroblasts(CAFs). In our research team's previous publication on Nature Communication in 2014, we showed that CAFs can modulate cancer stem cells through paracrine via IGF-II and induce the IGFIR signaling pathway in CSCs and promote the Nanog expression in CSCs.
In this thesis, we used the reverse engineering approach, which is one of the methods in system biology, to decipher the complexity of the cancer plasticity and stemness related to CAFs. Beginning with our CSCs and CAFs gene expression profiles from previously established data to acquire the differential expression(DE) genes , we step by step from functional enrichment analysis of DE genes, pathway impact analysis to regulatory network reconstruction. In the end, we can identify the possible mechanism from the perturbation in CSCs to the paracrine regulation from CAFs to CSCs.
Our result showed 2609 DE genes related to cancer stemness and plasticity under CAFs nurture. There are 64 transcription factors and 35 transcription cofactors identified as master regulators in modulating cancer stemness and plasticity. One of the significant perturbed pathways, toll-like receptor cascades, stand a central role in immune system. From our regulatory network, the toll-like receptor cascades identified in our data linked to the regulation of functionality in cell cycle, metabolism, IGF activity and multiple signaling pathways such as ERK1/ERK2, FLT3, PERK and MAPK pathways. We identified master regulators including CREB, SIN3A, SMAD, YY1, SP1, NF-kappaB involved in TLR cascades. In the regulatory network, CD14 is identified as important cytokine in the network and we traced upstream to the related master regulator including FOS, SIN3A, EBFOX2, GTF2F1, BHLH40 and possible paracrine protein from CAFs to interact these master regulators.
In this thesis, we not only identify the possible master regulars in the regulation of cancer stemness under CAFs nurture but also establish a systemic method to thoroughly dissecting the complex biological system and related visualization tools.

序言 i
誌謝 ii
中文摘要 iii
Abstract v
I.Introduction 1
II.Background 2
A.Biology as a information science 2
B.From Systems biology to Precision Medicine 3
C.Growing complexity of cancer hallmarks and its niche 3
D.The debating issues on the origin of the cancer 4
E.The ecosystem of tumor microenvironment 6
F.Nurture of the cancers by cancer-associated fibroblasts 8
G.Reverse engineering for regulatory network 9
H.Dive into the regulatory network with master regulator analysis 10
III.Material and Method 11
A.Data Source 11
B.A Reverse engineering approach 13
IV.Result 24
A.DE genes set related to the plasticity of lung CSCs co-culture with lung CAFs 24
B.Relative elevated DE genes in CAFs comparing to CSCs 25
C.Functional enrichment of stemness and plasticity related DE genes 25
D.Perturbation pathways with the involvement of the DE genes 26
E.Regulatory network underling the plasticity and stemness 27
F.Master regulators related to the cancer stemness and plasticity 27
G.Transcriptional categories of the master regulators 28
H. Intersection between the regulons of the master regulators 28
J. Master regulators involved in toll-like receptor cascades 29
K. TLR cascades in the multilayer visualization 30
L. The paracrine effect of CAFs on CSCs on TLR cascades 30
M. Upstream of CD14 in the TLR cascades 31
V.Discussion 31
A.Reverse engineering to decipher the phenotype from bottom-up 31
B.Enrichment genetic functions involved cancer metabolism and extracellular organization 32
C. The perturbation of pathways involved in ECM, metabolism and immune system 34
D. Master regulator related to cancer stemness and plasticity in CAFs nurture 35
G. Toll-like receptor cascades and cancer stemness 36
J. Biological function linked to the TLRs between CSCs and CAFs 38
K. Possible paracrine interaction between CAFs and CSCs via CD14 in TLR cascades 38
M. Toward the precision oncology with system biology 39
VI.Reference 75
VII. Appendix 87
Microarray data preprocess 87
Differential expression analysis 92
GO enrichment analysis and GO term retrieve 98
Impact analysis on perturbed pathways 107
Reconstruct the regulatory network: annotation data aggregation 112
Conducting Panda algorithm for building regulatory network 122
Master regulator analysis 125
Upset visualization of regulon intersection 130
Multilayer visualization with three.js on ObservableHQ website 132

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