Diabetes and ovarian cancer: risk factors, molecular mechanisms and impact on prognosis

Impact of sodium-glucose cotransporter-2 inhibitors on ovarian cancer risk in patients with type 2 diabetes mellitus: A multi-institutional TriNetX study

INTRODUCTION

Women with type 2 diabetes mellitus (T2DM) have an increased risk of ovarian cancer. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) impact on the risk of ovarian cancer in women with T2DM remains unclear. Therefore, this study aims to assess the association between SGLT2i use and the risk of ovarian cancer in patients with T2DM.

METHODS

We conducted a retrospective cohort study using the TriNetX network between January 2013 and December 2023. We employed a new-user study design with an active comparator, comparing patients using dipeptidyl peptidase-4 inhibitors (DPP4i). Propensity score matching (PSM) was applied to balance baseline characteristics. The primary outcome was the incidence of ovarian cancer.

RESULTS

Following PSM, 92,090 patients were included in each group. The SGLT2i gorup was associated with a significantly lower risk of ovarian cancer compared to the DPP4i group (HR: 0.758, 95 % CI: 0.596-0.965). Subgroup analyses revealed a consistent trend across various populations, with a notably significant risk reduction observed in white patients (HR: 0.667, 95 % CI: 0.485-0.917).

CONCLUSIONS

Our study suggests that SGLT2i may be associated with lower risk of ovarian cancer in T2DM patients. These findings highlight the need for further investigation into the underlying mechanisms.

Exploring Metabolic Approaches for Epithelial Ovarian Cancer Therapy

Epithelial ovarian cancer (EOC) has the highest mortality rate among malignant tumors of the female reproductive system and the lowest survival rate. This poor prognosis is due to the aggressive nature of EOC, its late-stage diagnosis, and the tumor's ability to adapt to stressors through metabolic reprogramming. EOC cells sustain their rapid proliferation by altering the uptake, utilization, and regulation of carbohydrates, lipids, and amino acids. These metabolic changes support tumor growth and contribute to metastasis, chemotherapy resistance, and immune evasion. Targeting these metabolic vulnerabilities has shown promise in preclinical studies, with some therapies advancing to clinical trials. However, challenges remain due to tumor heterogeneity, adaptive resistance mechanisms, and the influence of the tumor microenvironment. This review provides a comprehensive summary of metabolic targets for EOC treatment and offers an overview of the current landscape of clinical trials focusing on ovarian cancer metabolism. Future efforts should prioritize combination therapies that integrate metabolic inhibitors with immunotherapies or chemotherapy. Advances in precision medicine and multi-omics approaches will be crucial for identifying patient-specific metabolic dependencies and improving outcomes. By addressing these challenges, metabolism-based therapies can significantly transform the treatment of this devastating disease.

Development and validation of a nomogram for predicting outcomes in ovarian cancer patients with liver metastases

PURPOSE

To develop and validate a nomogram for predicting the overall survival (OS) of ovarian cancer patients with liver metastases (OCLM).

METHODS

This study identified 821 patients in the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly divided in a ratio of 7:3 into a training cohort (n = 574) and a validation cohort (n = 247). Clinical factors associated with OS were assessed using univariate and multivariate Cox regression analyses, and backward stepwise regression was applied using the Akaike information criterion (AIC) to select the optimal predictor variables. The nomogram for predicting the OS of the OCLM patients was constructed based on the identified prognostic factors. Their prediction ability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curves analysis (DCA) in both the training and validation cohorts.

RESULTS

We identified factors that predict OS for OCLM patients and constructed a nomogram based on the data. The ROC, C-index, and calibration analyses indicated that the nomogram performed well over the 1, 2, and 3-year OS in both the training and validation cohorts. Additionally, in contrast to the External model from multiple perspectives, our model shows higher stability and accuracy in predictive power. DCA curves, NRI, and IDI index demonstrated that the nomogram was clinically valuable and superior to the External model.

CONCLUSION

We established and validated a nomogram to predict 1,2- and 3-year OS of OCLM patients, and our results may also be helpful in clinical decision-making.

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

BACKGROUND

There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

METHODS

Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.

RESULTS

The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.

CONCLUSION

This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

Drug Repositioning for Ovarian Cancer Treatment: An Update

Ovarian cancer (OC) is one of the most prevalent malignancies in female reproductive organs, and its 5-year survival is below 45%. Despite the advances in surgical and chemotherapeutic options, OC treatment is still a challenge, and new anticancer agents are urgently needed. Drug repositioning has gained significant attention in drug discovery, representing a smart way to identify new clinical applications for drugs whose human safety and pharmacokinetics have already been established, with great time and cost savings in pharmaceutical development endeavors. This review offers an update on the most promising drugs repurposable for OC treatment and/or prevention.