運用系統生物學建構卵巢癌中微小核醣核酸與基因間調控網路

Abstract

卵巢癌死亡率於女性癌症中排名第五，婦科癌症中高居首位，造成高死亡率的主要原因為早期難以診斷，大部分病患確診癌症時，幾乎已經處於疾病後期，且病患經常在治療期間產生藥物抗藥性，現今卵巢癌的標準治療流程都是手術配合鉑類藥物化療，然而對於產生抗藥性的病患，腫瘤容易復發，化療反應較差，死亡率也相對提高。 過去多項研究證實，微小核醣核酸、基因及DNA甲基化在癌症中扮演著重要的角色，許多文獻針對各種癌症化療反應做研究，但確切影響卵巢癌化療反應的機制目前尚未被明確定義，我們希望利用生物資訊分析方法，以探討卵巢癌中，微小核醣核酸、基因及DNA甲基化三者與化療反應彼此之間的關聯性為研究動機，同時考慮化療藥物抗藥性，研究對象鎖定接受標準治療法的病患，針對手術後第一次輔助性化療包含Carboplatin或是Cisplatin配合Paclitaxel的病患，將病患分為化療反應佳(CR)及化療反應差(IR)兩組，利用癌症基因圖譜(TCGA)資料庫的資料進行全面性的分析，找出可能與化療反應相關的微小核醣核酸，並建立卵巢癌中微小核醣核酸與基因間調控網路及表觀基因調控網路。 結果顯示19個微小核醣核酸的表現量，在兩組病患間具有統計上顯著的差異，並有21個基因可能受DNA甲基化影響，微小核醣核酸與基因間的調控網路包含8個微小核醣核酸與12個基因， 1個微小核醣核酸和41個基因與整體存活期(overall survival)相關，2個微小核醣核酸和21個基因與無病存活期(disease-free survival)相關。 我們期望能將此種分析方法應用在其他癌症，且我們的研究結果可作為卵巢癌臨床相關與預後的生物標記，有助於識別化療失敗的患者。

Ovarian cancer ranks fifth in cancer deaths among women. It also has the highest mortality rate among all gynecologic cancers, mainly because most patients are diagnosed at late stage and the development of drug resistance. The standard treatment of ovarian cancer is the combination of debulking surgery and platinum-based chemotherapy. However, drug resistance exists and leads to recurrence of tumors and poor prognosis of patients. Previous studies have confirmed that microRNAs, genes and DNA methylation play important roles in cancer. Several mechanisms have been demonstrated to affect chemotherapy response, but the exact mechanisms are not fully investigated. This research aims to identify the relation between microRNAs, genes, DNA methylation and chemotherapy response. In order to clarify the exact mechanism affected chemotherapy response, we attempted to construct the microRNA and target-gene interaction network and epigenetic regulatory network in ovarian cancer by using The Cancer Genome Atlas (TCGA) data. We identified 19 significantly differentially expressed miRNAs and 21 hypermethylated genes between two groups of patients with different chemotherapy response. We also constructed the miRNA and target gene interaction network which included 8 miRNAs and 12 genes. One miRNA (miR-363-3p) and 41 genes were associated with overall survival. Two miRNAs (miR-181a-5p and miR-30e-5p) and 21 genes were associated with disease-free survival. We expect to apply this computational method to the other types of human cancer. This study provides a novel prognostic biomarker for ovarian cancer patients and our findings may identify patients who will fail to chemotherapy.