Dual disease co-expression analysis reveals potential roles of estrogen-related genes in postmenopausal osteoporosis and Parkinson's disease.

INTRODUCTION

The deficiency of estrogen correlates with a range of diseases, notably Postmenopausal osteoporosis (PMO) and Parkinson's disease (PD). There is a possibility that PMO and PD may share underlying molecular mechanisms that are pivotal in their development and progression. The objective of this study was to identify critical genes and potential mechanisms associated with PMO by examining co-expressed genes linked to PD.

METHODS

Initially, pertinent data concerning PMO and PD were obtained from the GWAS database, followed by conducting a Bayesian colocalization analysis. Subsequently, co-expressed genes from the PMO dataset (GSE35956) and the PD dataset (GSE20164) were identified and cross-referenced with estrogen-related genes (ERGs). Differentially expressed genes (DEGs) among PMO, PD, and ERGs were subjected to an array of bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses, in addition to protein-protein interaction (PPI) network analysis. The study also involved constructing TF-gene interactions, TF-microRNA coregulatory networks, interactions of hub genes with diseases, and validation through quantitative reverse transcription polymerase chain reaction (qRT-PCR).

RESULTS

The colocalization analysis uncovered shared genetic variants between PD and osteoporosis, with a posterior probability of colocalization (PPH4) measured at 0.967. Notably, rs3796661 was recognized as a shared genetic variant. A total of 11 genes were classified as DEGs across PMO, PD, and ERGs. Five principal KEGG pathways were identified, which include the p53 signaling pathway, TGF-beta signaling pathway, cell cycle, FoxO signaling pathway, and cellular senescence. Additionally, three hub genes-WT1, CCNB1, and SMAD7-were selected from the PPI network utilizing Cytoscape software. These three hub genes, which possess significant diagnostic value for PMO and PD, were further validated using GEO datasets. Interactions between transcription factors and genes, as well as between microRNAs and hub genes, were established. Ultimately, the expression trends of the identified hub genes were confirmed through qRT-PCR validation.

CONCLUSIONS

This study is anticipated to offer innovative approaches for identifying potential biomarkers and important therapeutic targets for both PMO and PD.