Zhang Fan, Cui Yukun
Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong 515041, P.R. China.
Oncol Lett. 2019 Aug;18(2):1287-1303. doi: 10.3892/ol.2019.10405. Epub 2019 May 27.
Endocrine therapy (ET) is one of a number of targeted therapies for estrogen receptor-positive breast cancer (BRCA); however, resistance to ET has become the primary issue affecting treatment outcome. In the present study, a predictive classifier was created using a DNA methylation dataset to identify patients susceptible to endocrine resistance. DNA methylation and RNA sequencing data, and the clinicopathological features of BRCA, were obtained from The Cancer Genome Atlas. Stringent criteria were set to select and classify patients into two groups, namely those resistant to ET (n=11) and sensitive to ET (n=21) groups. Bump hunting analysis revealed that 502 out of 135,418 genomic regions were differentially methylated between these two groups; these regions were differentially methylated regions (DMRs). The majority of the CpG sites contained in the DMRs mapped to the promoter region. Functional enrichment analyses indicated that a total of 562 specific genes encompassing these DMRs were primarily associated with 'biological progress of organ morphogenesis and development' and 'cell-cell adhesion' gene ontologies. Logistic regression and Pearson's correlation analysis were conducted to construct a predictive classifier for distinguishing patients resistant or sensitive to ET. The highest areas under the curve and relatively low Akaike information criterion values were associated with a total of 60 DMRs; a risk score retained from this classifier was revealed to be an unfavorable predictor of survival in two additional independent datasets. Furthermore, the majority of genes (55/63) exhibited a statistically significant association between DNA methylation and mRNA expression (P<0.05). The association between the mRNA expression of a number of genes (namely calcium release activated channel regulator 2A, Schlafen family member 12, chromosome 3 open reading frame 18, zinc finger protein 880, dual oxidase 1, major histocompatibility complex, class II, DP β1, C-terminal binding protein 1, ALG13 UDP-N-acetylglucosaminyltransferase subunit and RAS protein activator like 2) and the prognosis of patients with estrogen receptor-positive BRCA and ET resistance was determined using Kaplan-Meier Plotter. In summary, the predictive classifier proposed in the present study may aid the identification of patients sensitive or resistant to ET, and numerous genes maybe potential therapeutic targets to delay the development of resistance to ET.
内分泌治疗(ET)是雌激素受体阳性乳腺癌(BRCA)的多种靶向治疗方法之一;然而,对ET的耐药性已成为影响治疗效果的主要问题。在本研究中,利用DNA甲基化数据集创建了一个预测分类器,以识别易发生内分泌耐药的患者。DNA甲基化和RNA测序数据以及BRCA的临床病理特征取自癌症基因组图谱。设定了严格的标准来选择患者并将其分为两组,即对ET耐药的患者(n = 11)和对ET敏感的患者(n = 21)组。碰撞搜寻分析显示,在这两组之间,135,418个基因组区域中有502个区域存在差异甲基化;这些区域为差异甲基化区域(DMR)。DMR中包含 的大多数CpG位点映射到启动子区域。功能富集分析表明,共有562个包含这些DMR的特定基因主要与“器官形态发生和发育的生物学进程”以及“细胞 - 细胞粘附”基因本体相关。进行逻辑回归和Pearson相关性分析以构建用于区分对ET耐药或敏感患者的预测分类器。曲线下面积最高且赤池信息准则值相对较低与总共60个DMR相关;从该分类器得出的风险评分在另外两个独立数据集中被证明是生存的不利预测指标。此外,大多数基因(55/63)在DNA甲基化和mRNA表达之间表现出统计学上的显著关联(P<0.05)。使用Kaplan-Meier Plotter确定了一些基因(即钙释放激活通道调节因子2A、 Schlafen家族成员12、3号染色体开放阅读框18、锌指蛋白880、双氧化酶1、主要组织相容性复合体II类DPβ1、C末端结合蛋白1、ALG13 UDP-N-乙酰葡糖胺基转移酶亚基和RAS蛋白激活剂样2)的mRNA表达与雌激素受体阳性BRCA和ET耐药患者预后之间的关联。总之,本研究中提出的预测分类器可能有助于识别对ET敏感或耐药的患者,并且许多基因可能是延迟ET耐药发展的潜在治疗靶点。