Identification of potential diagnostic and therapeutic apoptosis-related casual targets for osteoporosis: an integrated multi-omics analysis.

INTRODUCTION

Apoptosis plays a significant role in osteoporosis (OP), yet a causal relationship between apoptosis gene expressions and OP remains unexplored. This study applies an integrated multi-omics analysis to establish causality between them, offering clinical treatment and prediction insights.

MATERIALS AND

METHODS

Apoptosis-related genes are sourced from GeneCards, and 6 transcriptomic datasets from the cells in the circulation are obtained from GEO. Meta-analysis integrated differentially expressed apoptosis-related genes (DEGs) from the above 6 datasets. Causality between gene expressions, epigenetic changes, and OP is examined using OP genome-wide association study (GWAS), plasma expression quantitative trait loci (eQTL), and methylation quantitative trait loci (mQTL) data, while analysis of skeletal muscle eQTL and OP GWAS data is conducted. External validation is performed with the UK Biobank datasets.

RESULTS

Meta-analysis of 6 GEO datasets identified 384 DEGs, including 78 apoptosis-related genes. The three-step analysis indicates 8 candidate causal genes in blood, including MAP3K3, DPP8, RPL3, PPP2CA, CD86, LRRFIP1, TRAP1, and DUSP6, with LRRFIP1 influenced by four methylation sites. Analysis of skeletal muscle data reveals 4 causal genes, including SIPA1L3, PDLIM7, CTNNB1, and DPP8. Among apoptosis-related genes causally linked to OP in both circulation and skeletal muscle, LRRFIP1 was validated based on methylation-associated regulation and demonstrated consistent, reproducible expression patterns.

CONCLUSIONS

This study uses a multi-omics strategy to clarify the roles of apoptosis-related gene expressions and their corresponding methylation in OP, providing targets and a basis for early diagnosis, personalized treatment, and monitoring of OP.