ArticleBMC pharmacology & toxicology2025
Multi-omics-based decoding of circulating biomarkers in amyotrophic lateral sclerosis and risks in environmental toxins.
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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Abstract
backgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the interplay of genetic and environmental factors, and currently, there there is a lack of effective diagnostic or therapeutic strategies available. This study aims to identify circulating biomarkers for ALS and investigate their interactions with environmental toxins.
methodsThis research utilizes plasma proteomic genome-wide association study (GWAS) data and whole blood transcriptomic data from ALS patients to screen for potential circulating biomarkers through Mendelian randomization (MR). Subsequently, functional enrichment analysis and immune infiltration analysis were performed. An integrated machine learning approach will be used to construct a diagnostic model, with hub genes selected based on SHAP values. The model's performance will be validated using receiver operating characteristic (ROC) curves, nomogram, and decision curve analysis (DCA). Finally, reverse network toxicology will be used to explore the interaction mechanisms between hub genes and environmental toxins.
resultsBased on a MR analysis of plasma proteomics, we identified 68 plasma proteins significantly associated with the risk of ALS. By integrating differentially expressed genes (DEGs) from whole blood transcriptomics (1,116 DEGs), we selected four potential circulating biomarkers: FCRL3, HTATIP2, RNASE6, and SF3B4. Functional enrichment analysis indicated that the pathogenesis of ALS is closely related to autophagy, apoptosis, the endoplasmic reticulum unfolded protein response, and the NF-κB signaling pathway. Immune infiltration analysis revealed a disruption of the immune microenvironment mediated by T cells/myeloid cells in ALS patients. Validation through 113 machine learning algorithms showed that the random forest model exhibited the best diagnostic performance (AUC = 0.786), while SHAP analysis confirmed the contribution ranking of hub biomarkers: RNASE6 > FCRL3 > HTATIP2 > SF3B4. Further validation of their diagnostic value was performed using ROC curves, nomograms, and DCA. Environmental toxins analysis revealed that substances such as benzo(a)pyrene exhibit significant neurotoxicity, and molecular docking confirmed that they can interfere with the function of hub biomarkers through strong binding (∆G < -5 kcal·mol⁻¹), suggesting potential environmental pathogenic mechanisms in ALS.
conclusionsThis study not only highlights the value of FCRL3, HTATIP2, RNASE6, and SF3B4 as potential diagnostic biomarkers and therapeutic targets for ALS but also provides new evidence for the involvement of environmental toxins, particularly benzo(a)pyrene, in the pathogenesis of ALS through gene-environment interactions.
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