Interpreting the molecular and cellular landscape of PCOS through bulk transcriptomics, single-cell transcriptomics...

Area of Science:

• Endocrinology
• Genomics
• Computational Biology

Background:

• Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women, characterized by hyperandrogenism and anovulation.
• Understanding PCOS molecular mechanisms and cellular players is crucial for improved diagnostics and therapeutics.

Purpose of the Study:

• To elucidate PCOS molecular mechanisms and cellular constituents using multi-omics and machine learning.
• To identify novel diagnostic markers and therapeutic targets for PCOS.

Main Methods:

• Integrated bulk and single-cell RNA sequencing to identify granulosa cell subpopulations and gene expression in PCOS.
• Applied machine learning to build a diagnostic model and screen for key gene signatures.
• Validated gene signatures at mRNA and protein levels using qPCR and western blotting in clinical samples.

Main Results:

• Identified an increased proportion of the GC9 granulosa cell subset in PCOS patients, showing active proliferation.
• Discovered up-regulated genes in PCOS associated with immune function and cellular processes.
• Pinpointed a three-gene signature (HLA-DRA, SRM, CTSL) with a highly accurate diagnostic model for PCOS.
• Confirmed significant upregulation of HLA-DRA, SRM, and CTSL in PCOS follicular cells.

Conclusions:

• Delineated a novel cellular landscape and gene signature in PCOS.
• Proposed potential new diagnostic and therapeutic targets for polycystic ovary syndrome.