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首页> 外文期刊>Endocrinology >Molecular Classification of Endometriosis and Disease Stage Using High-Dimensional Genomic Data
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Molecular Classification of Endometriosis and Disease Stage Using High-Dimensional Genomic Data

机译：使用高维基因组数据进行子宫内膜异位症和疾病分期的分子分类

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摘要

Endometriosis (E), an estrogen-dependent, progesterone-resistant, inflammatory disorder, affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Because the uterine lining (endometrium) in women with E has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n = 148 archived endometrial samples from women with E or without E (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with E or no E involved 2 binary decisions, each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no E from E, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified E with 90%-100% accuracy, were cycle phase-specific or independent, and used relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism, and growth factor signaling in endometrium of women with E. Similar findings were observed with other disorders vs controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic E with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of E and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue.
机译：子宫内膜异位症（E）是一种依赖雌激素，抗孕激素的炎症性疾病，影响了10％的育龄妇女。它在手术中被诊断和分阶段，导致从症状发作到诊断的11年潜伏期，从而强调了对侵入性更小，成本更低的方法的需求。因为患有E的女性的子宫内膜（子宫内膜）已经改变了分子特征，所以我们测试了该组织的分子分类是否可以区分疾病并分期。我们使用来自月经周期内有E或没有E（正常对照或其他常见子宫/盆腔病变）妇女的n = 148个已存档子宫内膜样本的基因组数据开发了分类器，并评估了它们在独立样本集上的表现。使用边缘树分类法对特定激素环境中的样本分别进行分类训练，并对独立的验证样本进行准确性评分。来自患有E或没有E的女性的样品分类涉及2个二元决策，每个决策均基于特定基因的表达。这些首先区分是否存在子宫/盆腔病理，然后从E中无E，后者根据严重程度（最低/轻度或中度/重度）进一步分类。表现最好的分类器以90％-100％的准确度鉴定出E，它们是周期特异性的或独立的，并且使用相对较少的基因来确定疾病和严重程度。差异基因表达和途径分析揭示了E病女性子宫内膜的免疫激活，类固醇和甲状腺激素信号/代谢改变以及生长因子信号传导。其他疾病与对照组相比，观察到相似的发现。因此，来自子宫内膜的基因组数据的分类器分析可以以高准确度检测和分期骨盆E，依赖或独立于激素环境。我们建议有限的分类候选基因在发展诊断和确定治疗目标方面具有很高的价值。在存在E和其他子宫/盆腔病变的情况下发现子宫内膜分子差异引起了更广泛的生物学问题，即它们对类固醇激素反应和该组织的正常功能的影响。

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