Data_Sheet_1_Weighted Gene Co-expression Network Analysis of Endometriosis and Identification of Functional Modules Associated With Its Main Hallmarks.pdf

Although many genes have been identified using high throughput technologies in endometriosis (ES), only a small number of individual genes have been analyzed functionally. This is due to the complexity of the disease that has different stages and is affected by various genetic and environmental factors. Many genes are upregulated or downregulated at each stage of the disease, thus making it difficult to identify key genes. In addition, little is known about the differences between the different stages of the disease. We assumed that the study of the identified genes in ES at a system-level can help to better understand the molecular mechanism of the disease at different stages of the development. We used publicly available microarray data containing archived endometrial samples from women with minimal/mild endometriosis (MMES), mild/severe endometriosis (MSES) and without endometriosis. Using weighted gene co-expression analysis (WGCNA), functional modules were derived from normal endometrium (NEM) as the reference sample. Subsequently, we tested whether the topology or connectivity pattern of the modules was preserved in MMES and/or MSES. Common and specific hub genes were identified in non-preserved modules. Accordingly, hub genes were detected in the non-preserved modules at each stage. We identified sixteen co-expression modules. Of the 16 modules, nine were non-preserved in both MMES and MSES whereas five were preserved in NEM, MMES, and MSES. Importantly, two non-preserved modules were found in either MMES or MSES, highlighting differences between the two stages of the disease. Analyzing the hub genes in the non-preserved modules showed that they mostly lost or gained their centrality in NEM after developing the disease into MMES and MSES. The same scenario was observed, when the severeness of the disease switched from MMES to MSES. Interestingly, the expression analysis of the new selected gene candidates including CC2D2A, AEBP1, HOXB6, IER3, and STX18 as well as IGF-1, CYP11A1 and MMP-2 could validate such shifts between different stages. The overrepresented gene ontology (GO) terms were enriched in specific modules, such as genetic disposition, estrogen dependence, progesterone resistance and inflammation, which are known as endometriosis hallmarks. Some modules uncovered novel co-expressed gene clusters that were not previously discovered.

尽管采用高通量技术已在子宫内膜异位症（ES）中鉴定出众多基因，但仅有少数基因的功能被进行了分析。这主要归因于该疾病的复杂性，其具有不同的阶段并受多种遗传和环境因素的影响。许多基因在疾病的不同阶段呈现上调或下调，从而使得关键基因的识别变得困难。此外，关于疾病不同阶段之间差异的了解甚少。我们假设，在系统层面上对ES中鉴定出的基因进行研究，有助于更好地理解疾病在不同发展阶段分子机制的复杂性。我们使用了公开的微阵列数据，其中包含来自患有轻微/轻度子宫内膜异位症（MMES）、轻度/重度子宫内膜异位症（MSES）以及无子宫内膜异位症女性的存档子宫内膜样本。利用加权基因共表达分析（WGCNA），从正常子宫内膜（NEM）作为参考样本推导出功能模块。随后，我们测试了这些模块在MMES和/或MSES中的拓扑结构或连接模式是否得到保留。在非保留模块中识别了共有和特定枢纽基因。相应地，在各个阶段非保留模块中检测到了枢纽基因。我们鉴定出十六个共表达模块。在这16个模块中，有九个在MMES和MSES中均未保留，而五个在NEM、MMES和MSES中均得到保留。重要的是，在MMES或MSES中发现了两个非保留模块，这突显了疾病两个阶段之间的差异。分析非保留模块中的枢纽基因表明，它们在疾病发展为MMES和MSES后，大多在NEM中失去了或获得了其中心性。当疾病的严重性从MMES转变为MSES时，同样的情况也得以观察。有趣的是，包括CC2D2A、AEBP1、HOXB6、IER3和STX18以及IGF-1、CYP11A1和MMP-2在内的新选基因候选者的表达分析，可以验证这种在不同阶段之间的转变。在特定模块中富集了过度表达的基因本体（GO）术语，如遗传易感性、雌激素依赖性、孕酮抵抗和炎症，这些都是子宫内膜异位症的标志。一些模块揭示了以前未曾发现的新的共表达基因簇。