Polycystic ovarian syndrome (PCOS) and recurrent spontaneous abortion (RSA) are associated with the PI3K-AKT pathway activation

Abstract

Aims: We aimed to elucidate the mechanism leading to polycystic ovarian syndrome (PCOS) and recurrent spontaneous abortion (RSA).

Background: PCOS is an endocrine disorder. Patients with RSA also have a high incidence rate of PCOS, implying that PCOS and RSA may share the same pathological mechanism.

Objective: The single-cell RNA-seq datasets of PCOS (GSE168404 and GSE193123) and RSA GSE113790 and GSE178535) were downloaded from the Gene Expression Omnibus (GEO) database.

Methods: Datasets of PSCO and RSA patients were retrieved from the Gene Expression Omnibus (GEO) database. The "WGCNA" package was used to determine the module eigengenes associated with the PCOS and RSA phenotypes and the gene functions were analyzed using the "DAVID" database. The GSEA analysis was performed in "clusterProfiler" package, and key genes in the activated pathways were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Real-time quantitative PCR (RT-qPCR) was conducted to determine the mRNA level. Cell viability and apoptosis were measured by cell counting kit-8 (CCK-8) and flow cytometry, respectively.

Results: The modules related to PCOS and RSA were sectioned by weighted gene co-expression network analysis (WGCNA) and positive correlation modules of PCOS and RSA were all enriched in angiogenesis and Wnt pathways. The GSEA further revealed that these biological processes of angiogenesis, Wnt and regulation of cell cycle were significantly positively correlated with the PCOS and RSA phenotypes. The intersection of the positive correlation modules of PCOS and RSA contained 80 key genes, which were mainly enriched in kinase-related signal pathways and were significant high-expressed in the disease samples. Subsequently, visualization of these genes including PDGFC, GHR, PRLR and ITGA3 showed that these genes were associated with the PI3K-AKT signal pathway. Moreover, the experimental results showed that PRLR had a higher expression in KGN cells, and that knocking PRLR down suppressed cell viability and promoted apoptosis of KGN cells.

Conclusion: This study revealed the common pathological mechanisms between PCOS and RSA and explored the role of the PI3K-AKT signaling pathway in the two diseases, providing a new direction for the clinical treatment of PCOS and RSA.

Keywords: GSEA; GSEA analysis; PI3K-AKT signaling pathway; Polycystic Ovary Syndrome (PCOS); Recurrent spontaneous abortion (RSA); “WGCNA”.

Figure 1. WGCNA for identifying PCOS and RSA phenotype-related gene module.

(A) The correlation between the co-expression modules and the PCOS phenotype. (B) The correlation between the co-expression modules and the RSA phenotype.

Figure 2. Function enrichment analysis of gene module.

(A) The function enrichment analysis of PCOS positive correlated gene module. (B) The function enrichment analysis of PCOS negative correlated gene module. (C) The function enrichment analysis of RSA positive correlated gene module. (D) The function enrichment analysis of RSA negative correlated gene module.

Figure 3. The gene set enrichment analysis (GSEA) for PCOS and RSA related biological process identifying.

(A) The biological process of angiogenesis in PCOS samples. (B) The biological process of the Wnt signaling pathway in PCOS samples. (C) The biological process of chromosome segregation in PCOS samples. (D) The biological process of angiogenesis in RSA samples. (E) The biological process of positive regulation of Wnt signaling pathway in RSA samples. (F) The biological process of positive regulation of MAPK cascade in RSA samples.

Figure 4. Identifying the shared genes between PCOS and RSA samples.

(A) Venn plot of the intersection between the PCOS positive correlated genes and RSA positive correlated genes. (B) Venn plot of the intersection between the PCOS negative correlated genes and RSA negative correlated genes. (C) The biological process of 80 positive correlated genes. (D) The expression of kinase-related genes in PCOS samples. (E) The expression of kinase-related genes in RSA samples. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 5. Kinase-related genes in the PI3K-Akt pathway.

Figure 6. Venn plot of kinase-inhibited drugs.

Figure 7. Validation of experimental.

(A–D) Transcription levels of PDGFC, GHR, PRLR, and ITGA3 in IOSE80 and KGN cell lines were detected by RT-qPCR, and relative quantitative analysis was performed. (E) RT-qPCR was used to verify the inhibition efficiency of small interfering RNA. (F) Changes of cell viability of KGN cells in normal group and KGN cells inhibited by PRLR. (G, H) Changes in cell migration ability of KGN cells in normal group and KGN cells with inhibition of PRLR. (I, J) Changes of apoptotic ability of KGN cells in normal group and KGN cells that inhibit PRLR. ns: no significance, **p < 0.01, ***p < 0.001, ****p < 0.0001.