Integrating clinical risk-predictors and liquid biopsies as potential biomarkers of endometrial cancer: Insights into metabolic and molecular processes

Abstract

Endometrial cancer (EC) is the most common gynaecological malignancy in developed countries, with its incidence rising significantly over the last two decades. This increase is largely attributed to an aging population and escalating obesity rates. Systematic reviews and meta-analyses have identified key risk factors for EC development, including elevated body mass index (BMI), diabetes mellitus, polycystic ovarian syndrome (PCOS), and nulliparity. Metabolic syndrome (MetS) is a cluster of conditions closely related to these risk factors. MetS is also independently associated with EC and characterised by low-grade chronic inflammation, driven by abnormal adipokine signalling from white adipose tissue. Large biomedical databases such as the UK Biobank and the Clinical Practice Research Datalink (CPRD) enable robust longitudinal analyses to elucidate EC pathogenesis and identify key risk predictors using advanced statistical methodologies, such as hazard regression analysis and machine learning. Chapter one focuses on analysing components of MetS to assess their association with EC development, highlighting differences in risk between pre- and post-menopausal women. Using six readily available clinical parameters, a neural networking algorithm was externally validated to predict the ten-year risk of EC. While the algorithm did not achieve optimal performance, the findings emphasise the need for further refinement and the incorporation of biomarkers to enhance discriminatory ability of risk prediction models. Even the best performing existing models fail to achieve sufficient predictive accuracy for clinical adoption, underscoring the urgent need for robust risk stratification methods in EC. Biochemical variables, when combined with clinical risk predictors, often enhance the predictive accuracy of diagnostic models and hence chapter two examines the current state of liquid biopsy in EC diagnostics through a systematic review and meta-analysis of blood-based biomarkers. Promising adipokines such as leptin, visfatin, and adiponectin demonstrate potential diagnostic value but lack validation in large studies that would enable widespread clinical adoption. Additionally, asprosin, a fasting-induced adipokine linked to obesity, insulin resistance, and PCOS, is investigated for its potential role in EC pathogenesis and as a predictive biomarker. While no single biomarker achieves sufficient sensitivity or specificity for EC screening, the use of biomarker panels show promise in improving diagnostic accuracy. Chapter three delves into the role of asprosin in EC. This work includes analyses of receptor expression for the posited receptors of asprosin, OR4M1, TLR4, and PTPRD in EC cell lines and tissues. Immunofluorescence confirmed receptor expression of these receptors across four EC cell lines, while immunohistochemistry revealed strong PTPRD expression and minimal TLR4 expression in both normal and cancerous endometrial tissues, with no significant differences established. Short-term treatment with asprosin did not induce changes in receptor expression patterns seen in EC cell lines. Transcriptomic analyses using RNA sequencing revealed upregulation of genes such as DDIT4, which is implicated in glycolytic pathways and hallmarks of cancer. These findings suggest that asprosin’s role in EC may involve complex cancer-associated signalling pathways requiring further investigation. The final chapter presents a clinical study analysing plasma adipokines and their receptor expression in EC cases and controls. Plasma leptin levels were significantly higher in EC patients, independent of age or BMI. The free leptin index (FLI), a measure combining leptin and soluble leptin receptor levels, emerged as the most sensitive adipokine based diagnostic measure with an AUC of 0.79. Furthermore, bioinformatic analyses demonstrated significantly increased expression of the candidate asprosin receptors TLR4 and PTPRD in EC cases compared to controls, supporting their potential as diagnostic targets. This thesis contributes to advancing EC research by highlighting the interplay between metabolic dysfunction and EC pathogenesis. It underscores the importance of integrating clinical and molecular data to develop more accurate risk prediction and diagnostic tools. Future studies focusing on biomarker panels and functional analyses of adipokine signalling pathways are essential for achieving clinically impactful advancements in EC prevention and early detection.