Transcriptomic approaches are increasingly used in reproductive medicine to identify candidate endometrial biomarkers. However, it is known that endometrial progression in the molecular biology of the menstrual cycle is a main factor that could affect the discovery of disorder-related genes. Therefore, the aim of this study was to systematically review current practices for considering the menstrual cycle effect and to demonstrate its bias in the identification of potential biomarkers. From the 35 studies meeting the criteria, 31.43% did not register the menstrual cycle phase. We analysed the menstrual cycle effect in 11 papers (including 12 studies) from Gene Expression Omnibus: three evaluating endometriosis, two evaluating recurrent implantation failure, one evaluating recurrent pregnancy loss, one evaluating uterine fibroids and five control studies, which collected endometrial samples throughout menstrual cycle. An average of 44.2% more genes were identified after removing menstrual cycle bias using linear models. This effect was observed even if studies were balanced in the proportion of samples collected at different endometrial stages or only in the mid-secretory phase. Our bias correction method increased the statistical power by retrieving more candidate genes than per-phase independent analyses. Thanks to this practice, we discovered 544 novel candidate genes for eutopic endometriosis, 158 genes for ectopic ovarian endometriosis and 27 genes for recurrent implantation failure. In conclusion, we demonstrate that menstrual cycle progression masks molecular biomarkers, provides new guidelines to unmask them and proposes a new classification that distinguishes between biomarkers of disorder or/and menstrual cycle progression.

中文翻译：

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子宫内膜生物标志物发现指南：纠正月经周期偏差揭示与子宫疾病相关的新基因

转录组学方法越来越多地用于生殖医学来识别候选子宫内膜生物标志物。然而，众所周知，月经周期分子生物学中的子宫内膜进展是可能影响疾病相关基因发现的主要因素。因此，本研究的目的是系统地回顾当前考虑月经周期影响的做法，并证明其在识别潜在生物标志物方面的偏差。在符合标准的 35 项研究中，31.43% 没有记录月经周期阶段。我们分析了 Gene Expression Omnibus 中的 11 篇论文（包括 12 项研究）中的月经周期效应：三篇评估子宫内膜异位症、两篇评估复发性着床失败、一篇评估复发性流产、一篇评估子宫肌瘤以及五项对照研究，这些研究收集了整个月经期间的子宫内膜样本循环。使用线性模型消除月经周期偏差后，平均发现的基因多了 44.2%。即使研究在不同子宫内膜阶段或仅在分泌中期收集的样本比例方面进行平衡，也可以观察到这种效应。我们的偏差校正方法通过检索比每阶段独立分析更多的候选基因来提高统计功效。通过这一实践，我们发现了 544 个在位子宫内膜异位症的新候选基因、158 个异位卵巢子宫内膜异位症的候选基因和 27 个复发性着床失败的基因。总之，我们证明月经周期进展掩盖了分子生物标志物，提供了揭开它们的新指南，并提出了区分疾病或/和月经周期进展的生物标志物的新分类。