Abstract
Uterine fibroid (UF) is the most prevalent benign tumour that affects millions of women globally, with a high incidence of 70% amongst women of reproductive age. UF has been associated with various complications, such as recurrent surgeries, infertility, anemia and pregnancy loss. Notably, women of African descent often experience more severe symptoms and complications. Although hormones, growth factors, and genetic alterations are widely associated with UF, the precise mechanism underlying its pathogenesis is not fully understood. Recent evidence suggests altered microbiota may serve as a potential risk factor for UF development. Altered microbiota can contribute to tumorigenesis via epigenetic changes to host cells or toxic effects from invasion. The lack of curative-drug treatment poses significant challenges to patients with UF. Patients often undergo surgeries that require the removal of the uterus or tumour, which can negatively impact fertility. Furthermore, uterine fibroids’ diagnosis relies on expensive imaging technologies such as ultrasound, which may not be readily available in developing countries. Moreso, diagnosis is often conducted only after patients’ symptoms become severe. Although late presentation may contribute to severe symptoms and complications among women with UF in Africa, other factors that influence severity and increase incidence in this population remain unknown. A comprehensive assessment of UF predisposing factors in high-risk populations such as Ghana could give better insights into disease pathogenesis. Hence, this study aims to assess: UF-associated demographic factors, the role of uterine microbiota dysbiosis on UF tumorigenesis; and molecular markers associated with UF in the Ghanaian population. Epidemiological data and clinical samples (tissues, blood and cervico-vaginal swabs) will be obtained. The characterization of samples will involve metagenomics, whole genome sequencing, functional validation of SNPs and SNP genotyping. The association of risk alleles with disease phenotypes will be assessed via regression analysis using PLINK v.1.9. The findings will provide information on potential disease markers that can be explored for better management strategies for UF in high-risk populations.