Dysregulated immunological and metabolic functions discovered by a polygenic integrative analysis for PCOS

Abstract

Research question: Polycystic ovary syndrome (PCOS) is a complex disease and its pathophysiology is still unclear. This polygenic study may provide some clues.

Design: A polygenic, functionome-based study with the ovarian gene expression profiles downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, including 48 PCOS and 181 normal control samples. These profiles were converted to the gene set regularity (GSR) indices, which were computed by the modified differential rank conversion algorithm and were defined by the gene ontology terms.

Results: Machine learning could accurately recognize the patterns of functional regularities between PCOS and normal controls. The significantly aberrant functions in PCOS included transporter activity, catalytic activity, the receptor signalling pathway via signal transducer and activator of transcription (STAT), the cellular metabolic process, and immune response.

Conclusion: This study provided a comprehensive view of the dysregulated functions and information for further studies on the management of PCOS.

Keywords: Gene set regularity; Machine learning; Polycystic ovary syndrome; Polygenic analysis.