Are we closer to robust predictors of recurrent pregnancy loss by means of integrating different types of omics data?

KEYWORDS:

• Recurrent pregnancy loss
• biomarkers
• metabolomics
• proteomics
• transcriptomic

1. Introduction

According to the recent revision of the European Society of Human Reproduction and Embryology (ESHRE), recurrent pregnancy loss (RPL) is defined as the loss of two or more consecutive pregnancies before 24 weeks of gestation [1]. RPL can stem from a variety of factors, including genetic, anatomical, hormonal, immunological, and environmental influences. Regrettably, approximately 50% of RPL cases remain idiopathic or unexplained. Despite extensive research in this domain, predicting RPL remains a formidable challenge.

Recent advancements in omics technologies are reshaping our understanding of the potential causes of RPL by identifying new potential biomarkers, providing new hope for developing robust predictors of RPL. Through the integration of diverse omics data, including genomics, transcriptomics, epigenetics, proteomics, and metabolomics, it is anticipated that the specific risks of RPL in affected couples can be more precisely foreseen [2]. By scrutinizing these diverse strata of molecular data, researchers can pinpoint pivotal genetic and molecular signatures linked to RPL. This integrative approach affords a more comprehensive understanding of the underlying mechanisms and may culminate in the identification of novel biomarkers for predicting RPL.

2. Current key findings and weaknesses

Several key findings have emerged from the research conducted in this field so far. Sanger sequencing (direct sequencing) of candidate genes has identified potential pathogenic genes (and variants) associated with RPL, including those that regulate embryo implantation and pregnancy maintenance. Furthermore, next-generation sequencing (NGS) technology has emerged as a valuable alternative for identifying genetic variants and transcriptome dysregulation contributing to the pathogenesis of monogenic and polygenic disorders [3]. For instance, whole exome sequencing has unveiled 14 candidate gene variations linked to RPL, thereby enhancing our understanding of the genetic underpinnings of this condition [4]. Transcriptomic investigations have unveiled aberrant gene expression patterns in the endometrium and placenta of women with RPL, indicating potential links between RPL and abnormal expression of specific immune regulatory genes involved in T cell activation and differentiation, offering potential targets for therapeutic interventions [5,6]. Additionally, epigenetics, proteomics, and metabolomics studies have underscored alterations in epigenetic, protein, and metabolite profiles in RPL, integrating omics techniques with bioinformatics analysis. Drawing from these omics research findings, it is inferred that the majority of differentially expressed molecules associated with RPL are implicated in processes such as decidualization, embryo implantation, trophoblast cell differentiation, invasion and apoptosis, placental development, fetal development, immune response, and coagulation [7–10]. These insights serve to deepen our understanding of the pathophysiology of this condition.

However, despite these advancements, several limitations persist in the research conducted in this field. One major constraint is that there is a dearth of large-scale, adequately powered studies. The characteristic of many existing studies is their small sample size, which limits their generalizability. Moreover, the absence of standardized protocols and methodologies for omics data analysis poses challenges in comparing and validating findings across different studies. Furthermore, although a few studies have conducted comprehensive analysis of multiple omics [7,11], most have focused on single omics layers, overlooking the potential synergistic effects of integrating multiple omics data. In summary, research on the etiology, treatment, and prevention of RPL based on high-throughput omics still has a long road ahead.

3. Expert opinion

The potential of integrating different types of omics data in RPL research is substantial. It holds the potential to identify robust predictors of RPL, enabling early identification and intervention in high-risk pregnancies. The ultimate goal in this field is to develop personalized approaches for the management of RPL, tailoring interventions based on individual characteristics and underlying mechanisms. By understanding the specific molecular mechanisms that lead to RPL, targeted therapies could potentially be developed to prevent or mitigate the risk of pregnancy loss.

To achieve this goal, further research is required. Firstly, large-scale, well-designed studies with diverse populations are necessary to validate the findings from previous studies and identify robust biomarkers. Standardization of protocols and methodologies for omics data analysis is crucial to ensure reproducibility and comparability of results. Additionally, longitudinal studies are needed to capture the dynamic changes in omics profiles throughout pregnancy and identify predictive patterns. Integration of omics data with clinical and lifestyle factors will also enhance the predictive accuracy and clinical utility of the models. The biggest challenge in achieving this goal lies in the complexity and heterogeneity of RPL. The condition has multifactorial etiology, involving interactions between genetic, environmental, and immunological factors. Integrating these diverse sources of information into a coherent predictive model is a significant challenge. Additionally, the identification of causative factors and the translation of omics findings into clinical practice require interdisciplinary collaboration and the integration of expertise from various fields.

Looking ahead, significant progress is expected in the field of RPL research in the coming years. The advancements in omics technologies, coupled with the increasing availability of large-scale datasets, will enable more comprehensive and robust analyses [12,13]. Integration of omics data with advanced computational approaches, such as machine learning and network analysis, will further enhance our understanding of the complex interactions underlying RPL. Moreover, the increasing use of noninvasive sampling techniques (such as saliva or urine), will facilitate the collection of omics data throughout pregnancy, enabling real-time monitoring and prediction of RPL.

Currently, one particularly intriguing area of research is the study of maternal fetal immunity. Previous studies have confirmed a definite link between immune imbalance and RPL, and understanding the immune response during pregnancy may provide valuable insights into the mechanisms of recurrent loss [5]. Our research group has also conducted in-depth research in the field of maternal fetal immunity in the past, discovering the potential role of immune cell metabolism reprogramming at the maternal fetal interface, and confirming several possible related molecules and pathways [14–16]. Integrating immunological omics data with other omics layers may uncover novel immune-related biomarkers and therapeutic targets.

In conclusion, the integration of different types of omics data holds great potential for developing robust predictors of RPL. Despite the current limitations in research, advancements in omics technologies, larger-scale studies, and interdisciplinary collaborations provide optimism for substantial advancements in this field. By unraveling the complex molecular mechanisms underlying RPL, we can pave the way for personalized interventions and improved pregnancy outcomes for couples affected by this disease.

Declaration of interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [2023YFC2705700] and the Key Research and Development Program of Hubei Province [2022BCA042].