Journal of Ovarian Research最新文献

Ovarian cancer (OC) remains the most lethal gynecologic malignancy, with the regulatory role of innate immunity in its progression and treatment poorly understood. This study provides a thorough characterization of OC immune microenvironment by combining multi-omics data. Our findings show innate immune signals in myeloid cells are central to communication within tumor microenvironment. Analysis of innate immune scores revealed IFIT3⁺macrophages exhibit the highest innate immune scores. By triggering inflammatory reactions, IFN-α interferon pathways, IFIT3⁺macrophages are essential for the onset of OC. Using high-dimensional weighted gene co-expression network analysis algorithm, we identified 25 genes associated with IFIT3⁺macrophages and developed a prognosis model related to innate immunity, which includes ISG20, DYNLT1, and IL1RN. Validation across three cohorts confirmed the model's strong predictive performance. Low-risk group demonstrated an active immune status, higher immune and microenvironment scores, and higher tumor mutation burden, suggesting they are more likely to benefit from immunotherapy. Furthermore, low-risk group showed increased expression of classical immune checkpoints and trained immunity markers, reinforcing the potential for immunotherapy. Among the genes in the prognostic model, high ISG20 expression was associated with improved overall survival and positively correlated with earlier clinical stages. Enrichment analysis revealed high ISG20 expression activates multiple antitumor pathways. Pan-cancer analysis also suggested ISG20 may be a tumor suppressor, correlating negatively with angiogenesis and epithelial-mesenchymal transition scores. In vitro assays confirmed that knockdown of ISG20 promoted OC cells proliferation, migration and invasion. Molecular docking and molecular dynamics simulations suggest that the Chinese herbal monomer Mulberrofuran K may be a potential therapeutic agent targeting ISG20 for treating OC. In conclusion, we emphasize the significance of innate immunity in OC development and immunotherapy, and find ISG20 to be a prospective biomarker and therapeutic target.

Background: Polycystic ovary syndrome (PCOS) is a prevalent endocrine-metabolic disorder in women, hallmarked by hyperandrogenism, anovulation, and polycystic ovarian morphology. This study integrates multi-omics and machine-learning analyses to elucidate the molecular mechanisms and cellular constituents underlying PCOS, aiming to uncover potential therapeutic targets and enhance diagnostic precision.

Methods: Bulk and single-cell RNA sequencing identified key granulosa cell subpopulations and gene expression patterns in PCOS; subsequently, machine-learning algorithms were applied to construct a diagnostic model and to screen for key gene signatures. Consequently, the identified signatures were validated at both mRNA and protein levels in independent clinical samples using qPCR and western blotting.

Results: Compared with controls, PCOS patients exhibited a markedly increased proportion of the GC9 granulosa cell subset, which displayed an active proliferative phenotype. Up-regulated genes in PCOS were closely associated with immune function, responsiveness to stimuli, and diverse cellular biological processes. Machine-learning analysis further pinpointed a three-gene signature-comprising HLA-DRA, SRM, and CTSL-and yielded a diagnostic model with superior accuracy and specificity. Moreover, validation in clinical samples confirmed significant up-regulation of HLA-DRA, SRM, and CTSL at both mRNA and protein levels in follicular cells of PCOS patients.

Conclusions: Our findings delineate a previously unrecognized cellular landscape and gene signature associated with PCOS, thereby proposing novel diagnostic and therapeutic targets.