基于原始数据的多个微阵列综合分析鉴定了复发性种植失败中的新型基因特征。
Integrated Analysis of Multiple Microarrays Based on Raw Data Identified Novel Gene Signatures in Recurrent Implantation Failure.
机构信息
Department of Reproductive Medicine Center, Foshan Maternal and Child Health Care Hospital, Southern Medical University, Foshan, China.
Department of Gynecology and Obstetrics, NanFang Hospital, Southern Medical University, Guangzhou, China.
出版信息
Front Endocrinol (Lausanne). 2022 Feb 7;13:785462. doi: 10.3389/fendo.2022.785462. eCollection 2022.
BACKGROUND
Recurrent implantation failure (RIF) is an intricate complication following IVF-ET, which refers to the situation that good-quality embryos repeatedly fail to implant following two or more IVF cycles. Intrinsic molecular mechanisms underlying RIF have not yet been fully elucidated. With enormous improvement in high-throughput technologies, researchers screened biomarkers for RIF using microarray. However, the findings of published studies are inconsistent. An integrated study on the endometrial molecular determinants of implantation will help to improve pregnancy outcomes.
OBJECTIVE
To identify robust differentially expressed genes (DEGs) and hub genes in endometrium associated with RIF, and to investigate the diagnostic role of hub genes in RIF.
METHODS
Raw data from five GEO microarrays regarding RIF were analyzed. Integrated genetic expression analyses were performed using the Robust Rank Aggregation method to identify robust DEGs. Enrichment analysis and protein-protein interaction (PPI) analysis were further performed with the robust DEGs. Cytohubba was used to screen hub genes based on the PPI network. GSE111974 was used to validate the expression and diagnostic role of hub genes in RIF.
RESULTS
1532 Robust DEGs were identified by integrating four GEO datasets. Enrichment analysis showed that the robust DEGs were mainly enriched in processes associated with extracellular matrix remodeling, adhesion, coagulation, and immunity. A total of 18 hub genes (HMGCS1, SQLE, ESR1, LAMC1, HOXB4, PIP5K1B, GNG11, GPX3, PAX2, TF, ALDH6A1, IDH1, SALL1, EYA1, TAGLN, TPD52L1, ST6GALNAC1, NNMT) were identified. 10 of the 18 hub genes were significantly differentially expressed in RIF patients as validated by GSE111974. The 10 hub genes (SQLE, LAMC1, HOXB4, PIP5K1B, PAX2, ALDH6A1, SALL1, EYA1, TAGLN, ST6GALNAC1) were effective in predicting RIF with an accuracy rate of 85%, specificity rate of 100%, and sensitivity rate of 88.9%.
CONCLUSIONS
Our integrated analysis identified novel robust DEGs and hub genes in RIF. The hub genes were effective in predicting RIF and will contribute to the understanding of comprehensive molecular mechanisms in RIF pathogenesis.
背景
反复着床失败（RIF）是体外受精-胚胎移植（IVF-ET）后的一种复杂并发症，指的是在经历了两次或更多次 IVF 周期后，优质胚胎反复着床失败的情况。RIF 的内在分子机制尚未完全阐明。随着高通量技术的巨大进步，研究人员使用微阵列筛选 RIF 的生物标志物。然而，已发表研究的结果并不一致。对子宫内膜着床分子决定因素进行综合研究将有助于改善妊娠结局。
目的
鉴定与 RIF 相关的子宫内膜中稳健的差异表达基因（DEGs）和枢纽基因，并研究枢纽基因在 RIF 中的诊断作用。
方法
对五个关于 RIF 的 GEO 微阵列的原始数据进行分析。使用稳健秩聚合方法进行综合遗传表达分析，以鉴定稳健的 DEGs。使用稳健的 DEGs 进一步进行富集分析和蛋白质-蛋白质相互作用（PPI）分析。基于 PPI 网络使用 Cytohubba 筛选枢纽基因。使用 GSE111974 验证枢纽基因在 RIF 中的表达和诊断作用。
结果
通过整合四个 GEO 数据集，鉴定出 1532 个稳健的 DEGs。富集分析表明，稳健的 DEGs 主要富集在与细胞外基质重塑、粘附、凝血和免疫相关的过程中。共鉴定出 18 个枢纽基因（HMGCS1、SQLE、ESR1、LAMC1、HOXB4、PIP5K1B、GNG11、GPX3、PAX2、TF、ALDH6A1、IDH1、SALL1、EYA1、TAGLN、TPD52L1、ST6GALNAC1、NNMT）。通过 GSE111974 验证，在 RIF 患者中发现其中 10 个枢纽基因（SQLE、LAMC1、HOXB4、PIP5K1B、PAX2、ALDH6A1、SALL1、EYA1、TAGLN、ST6GALNAC1）的表达差异显著。这 10 个枢纽基因（SQLE、LAMC1、HOXB4、PIP5K1B、PAX2、ALDH6A1、SALL1、EYA1、TAGLN、ST6GALNAC1）在预测 RIF 方面具有 85%的准确率、100%的特异性和 88.9%的敏感性。
结论
本研究通过综合分析鉴定了 RIF 中新型稳健的 DEGs 和枢纽基因。枢纽基因可有效预测 RIF，并有助于理解 RIF 发病机制中的综合分子机制。
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