Related Experiment Video
Updated: Jun 27, 2026

Gene Expression Analyses in Human Follicles
Published on: February 17, 2023
Identification of genes differentially expressed in granulosa cells from women with high vs. low ovarian
Alexa Medica1, Matthew E Kim1, Wei-Ting Hung1
1Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, La Jolla, CA, United States.
Background:
A better understanding of the response of granulosa cells to exogenous hormone stimulation and how this impacts the complex interplay between the granulosa cells and the oocyte is crucial for the optimal management of infertile couples seeking IVF treatment.
Methods:
Granulosa and cumulus cells (G + CCs) from women with different degrees of ovarian responsiveness to controlled ovarian hyperstimulation (COH) were purified and used for RNA sequencing (RNA-seq) analysis and downstream bioinformatic analyses.
Results:
RNA-seq analysis on G + CCs purified from 16 patients undergoing hormone treatment for in vitro fertilization (IVF) identified 291 genes that are statistically downregulated in G + CCs from poor ovarian responders (PORs) as compared with G + CCs from normal ovarian responders (NORs). Downregulated genes are enriched for several gene ontology (GO) categories, including pro-proliferation functions (e.g., AURKB, BUB1, CCNA2, CCNB1, CCNB2, CDK1, CDC20, and NEK2) and DNA damage-response (e.g., BCL2, BLM, and BRCA1). Upregulated genes in POR G + CCs include those encoding secreted proteins with known granulosa cell roles (ANGPTL4, IL1RL1, and STC1) and genes linked to apoptosis and ovarian malignancy. Weighted gene co-expression network analysis identified 6 gene modules significantly correlated with the POR phenotype; integration with differential expression analysis revealed core genes associated with weak ovarian responsiveness, including those involved in canonical folliculogenesis (ESR1, FSHR, HES1, and HEY2), steroidogenesis (ANGPTL4, GSTA1, and HSD17B1), and cell-cycle regulation (CCNA2, CCNB1, CDK1 and FOXM1).
Conclusions:
Our study (i) offers potential insight into the underlying pathophysiologic processes underlying weak ovarian responses, (ii) identifies genes that are candidates to define weak ovarian responders, and (iii) identifies signaling pathways that when modulated, could potentially be used to improve IVF outcomes.
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