臺灣博碩士論文加值系統

子宮內膜癌 (Endometrial cancer, EC) 在2020年是全球排名第六常見的婦科癌症。許多研究表明，異常的 DNA 甲基化可以影響基因表現並引發疾病，尤其是癌症。儘管在過去，啟動子過度甲基化且在癌症中表現沉默的基因 (例如：腫瘤抑制基因) 比較受到人們的關注，但近年來，許多全基因組 DNA 甲基化的研究強調低甲基化是癌細胞的另一個特徵。此外，在基因體區域發生 DNA 甲基化的頻率比在啟動子區域高，並已觀察到這與某些癌症相關基因的表現水平呈正相關。

本研究從美國癌症基因體圖譜計畫 (The Cancer Genome Atlas Program, TCGA) 的數據庫下載 DNA 甲基化和基因表現量的數據。與大多數子宮內膜癌甲基化研究相比，本研究選擇了兩個較新穎的探針偏差校正方法：Regression on Correlated Probes (RCP) 和 REgression on Logarithm of Internal Control probes (RELIC) 進行 DNA 甲基化數據的前處理。接著，使用 R package：Limma 和 DESeq2 尋找在子宮內膜癌中腫瘤與正常組織之間的差異甲基化位點 (differentially methylated CpG sites, DMCs) 和差異表現基因 (differentially expressed genes, DEG) 等生物標記。根據上述的甲基化特性，本研究將高甲基化和低甲基化基因與上調或下調基因，根據位點落在 DNA 的區域位置來進行配對，並計算相關性，找出相關性顯著的基因。

之後，將 Cox 比例風險模型 (Cox proportional hazards model, Cox model) 應用於已鑑定的基因，以分析它們與存活時間的關係。同時，為了檢查所選擇基因的共同影響，進行了通路分析 (Pathway analysis) ，並使用檢索相互作用基因數據庫 (STRING) 構建蛋白質與蛋白質相互作用 (protein-protein interaction, PPI) 網絡。此外，也分析已鑑定基因的甲基化值與臨床變量的關係，以及與子宮內膜癌甲基化研究中較少提及的分子亞型變量的關係。透過以上步驟，本研究得到一些與子宮內膜癌甲基化相關的生物標記，可以讓未來的臨床研究做進一步分析。

Endometrial cancer (EC) is ranked 6th among the most common gynecologic cancers worldwide in 2020. Many studies indicate that aberrant DNA methylation can regulate gene expression and cause various diseases, especially cancer. Although in the past, most attention was on the promoter hypermethylation of genes that silenced gene expression in cancer (e.g., tumor-suppressor genes), more resent studies of genome-wide DNA methylation have highlighted hypomethylation as another signature of cancer cells. Besides, DNA methylation within the gene body occurs more frequently than in the promoter, and it has been observed to have a positive correlation with the expression levels of certain cancer-associated genes.

In this study, the methylation and expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Compared with most of the methylation studies in EC, our study innovatively selected two novel probe bias correction methods: REgression on Logarithm of Internal Control probes (RELIC) and Regression on Correlated Probes (RCP) for methylation data preprocessing. After that, R packages, Limma and DESeq2, were used for exploring molecular targets such as differentially methylated CpG sites (DMCs) and differentially expressed genes (DEGs) between tumor and normal tissues in EC. According to the characteristics of methylation mentioned above, both hypermethylated and hypomethylated genes are paired in this thesis with up-regulated or down-regulated genes based on the location of CpG sites to calculate the correlations, and genes with a significant correlation are identified.

Cox proportional hazards model (Cox model) was then applied to identify genes to analyze their relationships with survival. Meanwhile, to examine the joint contribution of selected genes, pathway analysis was performed, and protein-protein interaction (PPI) network was constructed by Retrieval of Interacting Genes Database (STRING) as well. Moreover, the associations of methylation for the identified genes with clinical variables, and with the information regarding molecular subtypes seldom revealed in EC methylation studies were investigated. In summary, this study provides some biomarkers related to methylation in EC, which can be further analyzed in clinical research.

論文審定書...... i
誌謝...... ii
摘要...... iii
Abstract...... iv

1 Introduction...... 1
1.1 Endometrial Cancer Background...... 1
1.2 Literature Review...... 1
2 Materials...... 4
2.1 DNA Methylation Data...... 4
2.2 Gene Expression Data...... 6
2.3 Clinical Data...... 7
3 Methods...... 9
3.1 DNA Methylation Data Processing: RELIC and RCP...... 9
3.2 Identification of DMCs and DMGs: LIMMA...... 11
3.3 Identification of DEGs: DESeq2...... 13
3.4 Pearson Product-moment Correlation Coefficient (PPMCC)...... 17
3.5 Cox Proportional Hazards Model...... 18
3.6 Pathway Analysis: ORA and GSEA...... 19
3.7 PPI Network: STRING...... 21
4 Results...... 23
4.1 DMCs and DMGs and DEGs in EC...... 23
4.2 Correlation of DNA Methylation and Gene Expression...... 26
4.3 Survival Analysis...... 27
4.4 ORA and GSEA Pathway Analysis...... 29
4.5 PPI Network...... 35
4.6 Integrated Analysis of DNA Methylation and Clinical Data...... 36
5 Discussion...... 44
5.1 Comparation...... 44
5.2 Conclusion...... 45
Reference...... 47
A 圖附錄...... 53

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