Evidence map›Paper›PMID 40247428›Full record

ArticleBMC pharmacology & toxicology2025

Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

Qiang Ma, Longnian Zhou, Zhongde Li

Abstract read
In one paragraph

Article in BMC pharmacology & toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Qiang MaDepartment of Neurosurgery II, Hexi University Affiliated Zhangye People's Hospital, No. 67 Xihuan Road, Ganzhou District, Zhangye, Gansu Province, 734000, China.
Longnian ZhouDepartment of Neurosurgery II, Hexi University Affiliated Zhangye People's Hospital, No. 67 Xihuan Road, Ganzhou District, Zhangye, Gansu Province, 734000, China.
Zhongde LiDepartment of Neurosurgery II, Hexi University Affiliated Zhangye People's Hospital, No. 67 Xihuan Road, Ganzhou District, Zhangye, Gansu Province, 734000, China. lizhongde01231@aliyun.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into the toxicological mechanisms of nicotine in IA, aiming to identify key biomarkers and therapeutic targets.

methodsGene Set Variation Analysis (GSVA), Weighted Gene Co-Expression Network Analysis (WGCNA), and enrichment analyses were conducted on differentially expressed genes (DEGs) from the GSE122897 dataset. Additionally, nicotine-related targets were identified using CTD, SwissTargetPrediction, and Super-PRED databases. Integrative machine learning approaches, such as Random Forest (RF) and Support Vector Machine (SVM), were employed to pinpoint key toxicity targets. Molecular docking and immune cell infiltration analyses were also performed.

resultsDEGs in IA showed significant alterations in metabolic, secretory, signaling, and homeostatic pathways. Several immune and metabolic response pathways were notably disrupted. WGCNA identified 1127 DEGs with 37 overlapping toxic targets between IA and nicotine. ssGSEA revealed substantial upregulation in immune response and inflammation-related processes. Integrative analyses highlighted TGFB1, MCL1, and CDKN1A as core toxicity targets, confirmed via molecular docking studies. Immune cell infiltration analysis indicated significant correlations between these core targets and various immune cell populations.

conclusionThis study uncovers significant disruptions in metabolic and immune pathways in IA under nicotine influence, identifying TGFB1, MCL1, and CDKN1A as critical biomarkers. These findings offer a deeper understanding of IA's molecular mechanisms and potential therapeutic targets for nicotine-related toxicity.

Indexed as

Intracranial AneurysmMachine LearningNicotineAnimalsComputational BiologyGene Regulatory NetworksHumansMolecular Docking SimulationNicotineBioinformaticsCerebral aneurysmCigarette smokingMachine learningMolecular dockingNicotine toxicity

Identifiers

PMID40247428
PMCPMC12007307

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.