A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Mingming Wang; Ke An; Jing Huang; Richard Mprah; Huanhuan Ding

doi:10.3389/fendo.2023.1193992

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Front Endocrinol (Lausanne). 2023 Sep 7:14:1193992. doi: 10.3389/fendo.2023.1193992. eCollection 2023.

Authors

Mingming Wang¹, Ke An¹, Jing Huang², Richard Mprah¹, Huanhuan Ding¹

Affiliations

¹ Department of Physiology, Xuzhou Medical University, Xuzhou, Jiangsu, China.
² Department of Medical Informatics Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China.

Abstract

Background: Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.

Methods: Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.

Results: Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.

Conclusion: In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.

Keywords: diagnostic model; gene signature; necroptosis; polycystic ovary syndrome; therapeutic drugs.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Area Under Curve
B-Lymphocytes
Female
Humans
Mice
Necroptosis / genetics
Polycystic Ovary Syndrome* / drug therapy
Polycystic Ovary Syndrome* / genetics
Tumor Microenvironment

Grants and funding

This research was supported financially by the Natural Science Research of the Jiangsu Higher Education Institutions of China (Grant No. KY13022201), the Outstanding Talent Research Funding of Xuzhou Medical University (Grant No. RC20552029), the Natural Science Foundation of Jiangsu (Grant No. BK2021090) and the Shuangchuang Ph.D award of Jiangsu (Grant No. JSSCBS20211249).