Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer.

Background: Ovarian cancer (OC) is a globally prevalent malignancy with significant morbidity and mortality, yet its heterogeneity poses challenges in treatment and prognosis. Recognizing the crucial role of the tumor microenvironment (TME) in OC progression, this study leverages integrative multi-omics and machine learning to uncover TME-associated prognostic biomarkers, paving the way for more personalized therapeutic interventions.

Methods: Employing a rigorous multi-omics approach, this study analyzed single-cell RNA sequencing (scRNA-seq) data from OC and normal tissue samples, including high-grade serous OC (HGSOC) from the Gene Expression Omnibus (GEO: GSE184880) and The Cancer Genome Atlas (TCGA) OC cohort, utilizing the Seurat package to annotate 700 TME-related genes.

The forkhead box protein P3 gene rs3761548 promoter polymorphism confers a genetic contribution to the risk of preeclampsia: A systematic review and meta-analysis.

Various studies have investigated the risk of preeclampsia with the forkhead box protein P3 (FOXP3) gene rs2232365 and rs3761548 polymorphisms. However, the results remained contradictory. A comprehensive literature search was conducted using the Cochrane Library, PubMed, and Web of Science (up to Oct 11, 2021).

Contribution of Rs780094 and Rs1260326 Polymorphisms in GCKR Gene to Non-alcoholic Fatty Liver Disease: A Meta-Analysis Involving 26,552 Participants.

Background: Many published studies attempted to elucidate the implication of glucokinase regulator gene (GCKR) polymorphisms in the susceptibility to non-alcoholic fatty liver disease (NAFLD), but the results among them were still controversial.

Objective: This meta-analysis aims to precisely assess the relationship between the GCKR polymorphisms and the risk of NAFLD.

Methods: Systematic computerized searches in six databases were performed and updated on April 6, 2020.