A systematic review of transcriptomic studies of the human endometrium reveals inconsistently reported differentially expressed genes - Evangeline R Walker; Mollie McGrane; John D Aplin; Daniel R Brison; Peter T Ruane (Bioscientifica, 2023)
Genome-wide analysis of gene expression has been widely applied to study the endometrium, although to our knowledge no systematic reviews have been performed. Here, we identified 74 studies that described transcriptomes from whole (unprocessed) endometrium samples and found that these fitted into three broad investigative categories: endometrium across the menstrual cycle, endometrium in pathology and endometrium during hormone treatment. Notably, key participant information such as menstrual cycle length and body mass index was often not reported. Fertility status was frequently not defined and fertility-related pathologies, such as recurrent implantation failure (RIF) and recurrent pregnancy loss, were variably defined, while hormone treatments differed between almost every study. A range of 1307-3637 reported differentially expressed genes (DEGs) were compared in four to seven studies in five sub-categories: (i) secretory vs proliferative stage endometrium, (ii) mid-secretory vs early secretory stage endometrium, (iii) mid-secretory endometrium from ovarian stimulation-treated participants vs controls, (iv) mid-secretory endometrium from RIF patients vs controls, and (v) mid-secretory eutopic endometrium from endometriosis patients vs controls. Only the first two sub-categories yielded consistently reported DEG between ≥3 studies, albeit in small numbers (<40), and these were enriched in developmental process and immune response annotations. This systematic review, though not PROSPERO registered, reveals that limited demographic detail, variable fertility definitions and differing hormone treatments in endometrial transcriptomic studies hinder their comparison, and that the large majority of reported DEG do not advance the identification of underlying biological mechanisms. Future studies should apply network biology approaches and experimental validation to establish causal gene expression signatures.