The Mechanism Exploration of Follicular Fluids on Granulose Cell Apoptosis in Endometriosis-Associated Infertility

Objective. To explore the mechanisms of follicular fluids (FFs) on granulose cell (GC) apoptosis in endometriosis-associated infertility. Materials and Methods. 60 infertile women were enrolled. The FFs from 30 endometriosis-associated infertility (EI) patients were collected and processed by ELISA hormone assay and proteomic profiling. The ovary GCs collected from 30 tubal-associated infertility (TI) patients were cultured in follicular fluids of endometriosis-associated infertility patients (EI-FFs), and the apoptosis mechanisms were explored by flow cytometry assay, real-time PCR, Western blotting, and protein-protein interaction (PPI) network analysis. Results. Our results showed that the expression of 22 specific proteins was significantly different in the FFs from EI and TI patients, and the level of testosterone and anti-Mullerian hormone was not obviously different between the two groups. EI-FFs could accelerate the apoptosis process of granulose cells of tubal-associated infertility patients (TI-GCs) by regulating the expression of 5 apoptosis-related proteins including BCL2, BAX, CASP3, CASP9, and TP53. The correlation of these 22 specific proteins and 5 apoptosis-related proteins was analyzed by PPI, and 5 protein biomarkers (INS, CXCL10, ICAM1, WIF1, and TNFRSF13C) and 5 signaling pathways (cytokine-cytokine receptor interaction, apoptosis, regulation of actin cytoskeleton, MAPK, and p53 signaling pathway) were predicted. Conclusion. This research clarified the effect and explored the mechanisms of EI-FFs on the apoptosis of TI-GCs and indicated the protein biomarkers and signaling pathways for further study.

Automated summary

The study found that follicular fluids from patients with endometriosis-associated infertility can accelerate the apoptosis of granulose cells from tubal-associated infertility patients by regulating specific protein expression. Protein-protein interaction network analysis predicted potential protein biomarkers and signaling pathways for further research.