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TRUVACE RECORD VERSION
record: TRV-2026-1190
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-26T06:54:03.412270Z
status: published
lens: p_space
sector: health
headline: Identification of key molecular targets in nicotine-induced spontaneous abortion through network toxicology and multi-omics integration
dek: 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 elu…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: (none)
quick_read: 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. 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 ).
limitation: 
tag: Evidence-backed problem
key_points: 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.
rundown: 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.
sources:
- peer_reviewed | Tobacco Induced Diseases | https://doi.org/10.18332/tid/230997 | 2026-09-22
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