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  • 學位論文

Differentially Expressed Genes and MicroRNAs Reveal the Role of Potential Biomarkers in Steven-Johnsons Syndrome

Differentially Expressed Genes and MicroRNAs Reveal the Role of Potential Biomarkers in Steven-Johnsons Syndrome

指導教授 : 劉建財

摘要


研究背景 早期檢測引起史蒂文生 - 約翰遜綜合徵和毒性表皮壞死(SJS 和TEN)的藥物的免疫應 答可以降低其死亡率和發病率。早期超敏感患者和其他疾病階段的不同基因和 microRNA 表達譜比較可以為SJS 和TEN 的臨床症狀和潛在原因提供重要的了解,有助 於早期檢測策略。在本研究中,我們進行了大規模基因和MicroRNA 表達分析,以確定 嚴重皮膚不良藥物反應(SCAR)患者和健康對照之間已經差異表達的基因(DEG)和 差異表達MicroRNA 的潛在遺傳途徑和網絡。我們的目標是通過使用矽片拓撲和途徑分 析來構建SJS 的聚類模型或基因和/或MicroRNA 特徵。 研究方法 從Gene Expression Omnibus 數據庫中下載GSE12829 的基因表達譜。 數據集來自患有 史蒂文生 - 約翰遜綜合徵(SJS)和中毒性表皮壞死松解症(TEN)患者。一開始,我 們部署了GEO2R 在線工具來分析數據集。 之後,已差異表達的基因(DEG)超過6000。 我們使用Excel 工具(過濾/升序,降序)來仔細檢查基因列表,例如將p 值降低到0.005 以下。在仔細檢查後,共獲得了193 個差異表達基因(DEG)。我們將基因應用於基因 MANIA 數據庫,以消除模糊和重複的基因。該數據庫突出顯示了重複和不明確的基因。 因此,我們手動刪除重複和歧義。 此外,基因列表應用於基因MANIA,DAVID, VII REACTOME,STRING 和GENECODIS 在線軟件工具和數據庫,以幫助其途徑和表徵。 隨後,在我們的隨訪研究中,從患有SJS / TEN 病患者的MicroRNA 表達譜中下載了 microRNA 表達集。總MicroRNAs 是372 個差異表達的MicroRNA,從www.jacionline.org 在線獲得。患者樣本為8 名TEN,10 名SJS 患者和22 名健康個體。檢查倍數變化(FC) 後,超過兩個值的microRNA 被認為過表達。與健康皮膚對照和患者相比,共發現192 個微RNA,具有獨特的表達模式(過表達)。 之後,將基因列表上傳到以下數據庫工具中的每一個; 基因MANIA,DIANA-miR Path 版本-3,DIANA-TarBase 版本7.0 和Ingenuity Pathway Analysis 綜合網絡工具(IPA)。此 外,DAVID,STRING 和GENECODIS 在線工具被用來幫助他們的路徑和表徵。選擇粒 細胞素基因(GNLY)作為SJS 和TEN 中的驅動基因。通過基因MANIA 產生21 個基 因,並使用其他數據庫跟踪通路,通過DIANA-TarBase 數據庫預測GNLY 和21 個基因 microRNAs 靶標。. 研究結果 我們的工作結果產生了193 個基因,僅使用91 個(在手工從GeneMania 網絡工具中除 去模糊和重複的基因)進行拓撲分析。通過geneMANIA 數據庫檢索發現,這些基因中 的大多數共表達產生84.63%的共表達。發現10 個基因在物理相互作用中幾乎佔14.33 VIII %。分別有0.97%和0.06%的途徑和遺傳相互作用的<1%。最終分析顯示,有兩組基因 相互作用,13 個基因顯示出與超敏反應相關的明顯關係。後來,在MicroRNA 調查研究 中,Granulysin(GNLY)基因MANIA 數據庫檢索產生了21 個相互作用的基因,在物理 相互作用中為64.6%,共表達模式為17%。針對21 個基因的MicroRNA 靶向預測和功 能研究數據庫(MicroRNADB)潛在的MicroRNA 是79 個MicroRNA。18 個MicroRNA 在來自SJS / TEN 樣品的過表達MicroRNA 和靶向21 個基因的MicroRNA 之間重疊。 此外,遺傳性途徑分析(IPA)顯示MicroRNA 涉及炎症。 之後,將基因列表上傳到以下數據庫工具中的每一個; 基因MANIA,DIANA-MicroRNA Path 版本-3,DIANA-TarBase 版本7.0 和Ingenuity Pathway Analysis 綜合網絡工具(IPA)。 此外,DAVID,STRING 和GENECODIS 在線工具被用來幫助他們的路徑和表徵。選擇 粒細胞素基因(GNLY)作為SJS 和TEN 中的驅動基因。通過基因MANIA 產生21 個 基因,並使用其他數據庫跟踪通路,通過DIANA-TarBase 數據庫預測GNLY 和21 個基 因MicroRNAs 靶標。 結論 本研究通過拓撲和數據庫方法對差異基因表達的分析揭示了兩個基因網絡簇。這些基因 是CD3G,CD3E,CD3D,TK1,TOP2A,CDK1,CDKN3,CCNB1 和CCNF。超敏反 IX 應中有9 種關鍵的蛋白質相互作用,可作為SJS 和TEN 的生物標誌物。已經鑑定了途 徑相關基因簇,並且已經開發了使用這些生物標誌基因預測SJS 和TEN 早期發病的遺 傳模型。 如果將來這些蛋白質可以在SCAR 疾病的早期階段進行測試,它們可以成為避免SJS / TEN 並發症的有用工具。此外,該簽名可以降低疾病的死亡率。 Belon 等人提供了急性 期疾病期間的差異表達基因具有明顯的表達模式,並強調了基因簽名理論。MicroRNA 的 調查研究分析揭示了靶向顆粒溶素(miR-483-5p / miR-28-5p)的兩個顯著的MicroRNA。 超敏反應中的MicroRNA-GNLY 環相互作用可以用作SCAR 的生物標誌物,包括SJS 和 TEN。仍然需要對這些基因/ miRs 表達和規則的模式進行實驗驗證。

並列摘要


Background Early detection of immune response to drugs that cause Stevens-Johnson syndrome and toxic epidermal necrosis (SJS and TEN) can reduce their mortality and morbidity. Distinct gene and microRNA expression profiles comparison between early hypersensitive patients and other stage disease could provide important understanding of clinical symptoms and underlying causes of SJS and TEN and help in their early detection strategies. In this study, we performed a large-scale gene and microRNA expression analysis to identify potential genetic pathways and networks to already differentially expressed genes (DEGs) and differentially expressed microRNAs between severe cutaneous adverse drug reactions (SCAR) patients and healthy controls. We aim to construct a cluster model or a gene and/or microRNA signature for SJS by using in-silico topological and pathways analyses. Methods Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. The datasets were obtained from patients suffering from Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). In the beginning, we deployed the GEO2R online tool to analyze the dataset. Later, the already differentially expressed genes (DEG) were over 6000. We used Excel tools (filtering/ascending, descending orders) to scrutinize the gene list, such as decreasing p-Value to below 0.005. A total of 193 differentially expressed genes (DEG’s) were obtained after scrutinization. We applied the genes to geneMANIA database to remove ambiguous and duplicated genes. This database highlights the duplicated and ambiguous genes. Therefore, we manually deleted the duplication and ambiguity. Moreover, the gene list were applied to geneMANIA, DAVID, REACTOME, STRING and GENECODIS online software tools and databases to help in their pathway and characterization. Later, in our follow-up study, microRNA expression sets were downloaded from the miRNA expression profile of patients’ suffering from SJS/TEN disease. The total miRNAs were of 372 differentially expressed miRNAs, obtain from online at www.jacionline.org. The patients’ samples were eight TEN, ten SJS patients and twenty-two healthy individuals. After checking the fold change (FC), microRNAs over value of two were considered overexpressed. A total of 192 microRNAs were found with unique expressions patterns (overexpressed) in contrast with healthy skin controls and patients. XI Afterwards gene list was uploaded to each one of the following database tools; geneMANIA, DIANA-miR Path version-3, DIANA-TarBase version 7.0, and Ingenuity Pathway Analysis comprehensive web tool (IPA). In addition, DAVID, STRING and GENECODIS online tools were used to help in their pathway and characterization. Granulysin gene (GNLY) was selected as a driving gene in SJS and TEN. Twenty-one genes were generated by geneMANIA and used other database to follow the pathways, predicted GNLY, and the 21 genes microRNAs targets by DIANA-TarBase database. Results The results of our work yielded 193 genes, only 91 were used (after manually removing the ambiguous and duplicated genes from GeneMania web tool) for topological analysis. It was found by geneMANIA database search that majority of these genes were co- expressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction concerning hypersensitivity. Later, in the microRNA investigation study, Granulysin (GNLY) geneMANIA database search yielded 21 interacting genes that were 64.6 % in physical interaction, 17 % in co-expression pattern. MicroRNA Target Prediction And Functional Study Database (miRDB) potential microRNAs that target the 21 genes were 79 miRs.18 miRs overlap between the overexpressed miRNAs from SJS/TEN samples and the miRs targeting the 21 genes. Moreover, Ingenuity pathway analysis (IPA) revealed that the microRNAs were involved in inflammation. Conclusions The analysis of differential gene expressions by topological and database approaches in the current study reveals two gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are nine key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed. If in the future these proteins can be tested in early stage of SCAR disease, they can be a useful tool to avoid complications of SJS/TEN. In addition, this signature can reduce the mortality of the disease. Differentially expressed genes XII during the acute phase of disease were provided by Belon et al. to have a distinct pattern of expression and emphasized the gene signature theory. The microRNA investigation study analysis of differential microRNA expressions reveals two significant miRs that target Granulysin (miR-483-5p/miR-28-5p). MiR-GNLY loop interactions in hypersensitivity reactions may function as biomarkers for SCAR including SJS and TEN. There is still need for experimental validation for these genes/miRs patterns of expressions and regulations.

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