Identification of key molecular targets in nicotine-induced spontaneous abortion through network toxicology and multi-omics integration.

INTRODUCTION: Spontaneous abortion (SA) remains a prevalent reproductive health challenge, with tobacco-derived nicotine emerging as a significant risk factor. This study sought to decipher the molecular underpinnings of nicotine-induced pregnancy loss through comprehensive multi-omics profiling to identify novel biomarkers and potential intervention targets. METHODS: An integrated bioinformatics approach was implemented combining network toxicology, transcriptomic profiling, and machine learning algorithms to elucidate nicotine's pathological mechanisms in SA. The analytical pipeline encompassed functional enrichment analysis, expression pattern characterization, diagnostic biomarker evaluation, immune microenvironment assessment, causal inference through Mendelian randomization (MR), single-cell RNA sequencing, and molecular docking simulations of nicotine-protein interactions. RESULTS: Differential expression analysis identified 2451 DEGs between SA and control groups. Integration with 645 nicotine targets yielded 69 candidate genes. Five hub genes - CTNNB1, TINAGL1, RAD50, FGF2, and VIM - were identified as critical mediators in nicotine-triggered SA pathogenesis. These genes exhibited good diagnostic capability and significant correlations with immune cell infiltration patterns. MR analysis confirmed causal associations for RAD50 (OR=1.08; 95% CI: 1.01-1.15, p=0.019) and FGF2 (OR=1.21; 95% CI: 1.11-1.33, p=4.69×10-5). Single-cell analyses revealed cell-type-specific expression signatures across trophoblasts and immune populations, while molecular docking showed binding energies ranging from -5.0 to -6.9 kcal/mol. CONCLUSIONS: This study illuminates a complex, multi-target mechanism underlying tobacco-induced pregnancy loss. This work advances understanding of nicotine's reproductive toxicity pathways and identifies promising molecular targets for early diagnosis and further mechanistic studies.