肝纖維化細胞與動物模型以轉錄體資料分析比較;Transcriptomic Data Analysis of Liver Fibrosis Cells and Animal Models

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Title:	肝纖維化細胞與動物模型以轉錄體資料分析比較;Transcriptomic Data Analysis of Liver Fibrosis Cells and Animal Models
Authors:	許偉恩;Hsu, Wei-En
Contributors:	系統生物與生物資訊研究所
Keywords:	纖維化;轉錄體;肝臟;Fibrosis;Transcriptom;Liver
Date:	2023-08-10
Issue Date:	2024-09-19 15:51:21 (UTC+8)
Publisher:	國立中央大學
Abstract:	纖維化是一段組織修復的過程，會由於修復過程中的失調而導致難以逆轉，在肝組織中會受到慢性炎症影響而發展纖維化。而纖維化的過程相當複雜，牽涉許多化學因子、機械應力調控，並會與許多疾病同時發展。此篇研究使用藉由物理刺激和化學刺激的細胞、小鼠及大鼠模型轉錄組數據進行分析，經由免疫組成分析確認模型內的免疫特徵，將差異表現基因以\| log2FC\| > 1的條件進行篩選並用於路徑分析，發現了許多細胞週期調控、免疫調控、組織修復、ECM結構變化等與纖維化相關的路徑，同時也觀察到使用不同方法刺激的模型有明顯參與不同路徑結果。在各個模型中，所顯現的特徵有所不同，在各階段的分析中，也有提出可能參與調控的因子，這些數據可於不同纖維化模型的建立時，提出可參考的資訊來選擇較適合的方法。;Fibrosis is a process of tissue repair that can be difficult to reverse due to dysregulation during the repair process. It develops in liver tissue as a result of chronic inflammation. The process of fibrosis is highly complex, involving various chemical factors and mechanical stress regulation, and it often occurs concomitantly with many diseases. In this study, transcriptomic data from cell cultures, as well as mouse and rat models, stimulated by physical and chemical stimuli, were analyzed. In this study, transcriptomic data from cell cultures, as well as mouse and rat models, stimulated by physical and chemical stimuli, were analyzed. Immuno composition analysis was performed to confirm the immune characteristics within the models. Differential expression genes were filtered based on the condition of \|log2FC\| > 1 and used for pathway analysis. Many pathways related to fibrosis, such as cell cycle regulation, immune regulation, tissue repair, ECM structural changes, and others, were discovered. Additionally, it was observed that models stimulated using different methods significantly participated in different pathways. The features displayed in various models differ, and potential regulatory factors have been proposed in the analysis of different stages. These data can provide valuable information for selecting a suitable approach when constructing different fibrosis models.
Appears in Collections:	[Institute of Systems Biology and Bioinformatics] Electronic Thesis & Dissertation

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