RT Journal Article
T1 Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach
A1 Vaquerizo Villar, Fernando
A1 Álvarez Escudero, Julián
A1 Veiras Del Rio, Sonia
A1 Carracedo Álvarez, Ángel
A1 Tamayo Gómez, Eduardo
K1 Explainable artificial intelligence (XAI)
K1 Genome-wide association study (GWAS)
K1 Sepsis
K1 Personalized medicine
K1 Surgical patients
AB Background: Early detection of sepsis is essential for its successful management. Although genome-wide association studies (GWAS) have shown potential in identifying sepsis-related genetic variants, they often involve heterogeneous patient groups and use single-locus analysis methods. Here, we aim to identify new sepsis susceptibility loci in post-surgical patients using an explainable artificial intelligence (XAI) approach applied to GWAS data.Methods: GWAS was performed in 750 post-operative patients with sepsis and 3,500 population controls. We applied a novel XAI-based methodology to GWAS-derived single nucleotide polymorphisms (SNPs) to predict sepsis and prioritize new genetic variants associated with post-operative sepsis susceptibility. We also assessed functional and enrichment effects using empirical data from integrated software tools and datasets, with the top-ranked variants and associated genes.Results: Our XAI-GWAS approach showed a notable performance in predicting post-surgical sepsis and prioritized SNPs (such as rs17653532, rs1575081785, and rs74707084) with higher contribution to post-operative sepsis prediction. It also facilitated the discovery of post-operative sepsis risk loci with important functional implications related to gene expression regulation, DNA replication, cyclic nucleotide signaling, cell proliferation, and cardiac dysfunction.Conclusion: The combination of GWAS and XAI prioritized loci associated with post-operative sepsis susceptibility. The determination of key genes, such as PRIM2, SYNPR, and RBSN, through pre-operative blood tests could enhance risk stratification, enable early detection of post-operative sepsis, and guide targeted interventions to improve patient outcomes. Further research with additional and ethnically diverse cohorts comprising sepsis and non-sepsis patients undergoing major surgery is needed to validate these exploratory findings
PB Frontiers Media
SN 2296-858X
YR 2025
FD 2025-12-15
LK https://hdl.handle.net/10347/45355
UL https://hdl.handle.net/10347/45355
LA eng
NO Vaquerizo-Villar, F., Hernandez-Beeftink, T., Heredia-Rodríguez, M., Gómez-Sánchez, E., Lorenzo-López, M., López-Herrero, R., Bardaji-Carrillo, M., Tamayo-Velasco, Á., Martín-Fernández, M., Sánchez-de-Prada, L., Álvarez-Escudero, J., Veiras, S., Baluja, A., Gonzalo-Benito, H., Martínez-Paz, P., García-Concejo, A., Fernández-Rodríguez, A., Jiménez-Sousa, M. A., Resino, S., … Tamayo, E. (2025). Identifying sepsis susceptibility genes in post-surgical patients using an artificial intelligence approach. Frontiers in Medicine, 12, 1644800. https://doi.org/10.3389/fmed.2025.1644800
NO The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by ‘Instituto de Salud Carlos III (ISCIII)’ PI18/01238, PI19/00141, PI20/00876, PI23/00980, and PI23CIII/00010, by ‘Consorcio Centro de Investigación Biomédica en Red (CIBER) en Enfermedades Respiratorias (CIBERES)’ (CB06/06/1088 and AC_212/00039), by ‘CIBER en Enfermedades Infecciosas (CIBERINFEC)’ (CB21/13/00051, CB21/13/00044, and IM23/INFEC/1), by ‘CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)’ (CB19/01/00012), by ‘CIBER en Enfermedades Raras (CIBERER)’ (CB06/07/0088), by ‘Junta de Castilla y León’ (VA321P18, GRS 1922/A/19, GRS 2057/A/19, GRS 2425/A/21), by ‘Fundación Ramón Areces’ (CIVP19A5953), by ERA PerMed (JTC_2021) by the contract AC21_2/00039 with Instituto de Salud Carlos III and funds from Next Generation EU as part of the actions of the Recovery Mechanism and Resilience (MRR), by ITER agreements (OA17/008 and OA23/043), and by ‘Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación/10.13039/501100011033/’, ERDF A way of making Europe, and NextGenerationEU/PRTR (PID2023-148895OB-I00). FV-V is supported by a ‘Sara Borrell’ grant (CD23/00031) from ISCIII cofounded by the ‘Fondo Social Europeo Plus (FSE+)’. ES-P was supported by “Agencia Canaria de Investigación, Innovación y Sociedad de la Información de la Consejería de Economía, Conocimiento y Empleo y por el Fondo Social Europeo (FSE) Programa Operativo Integrado de Canarias 2014–2020, Eje 3 Tema Prioritario 74 (85%) Gobierno de Canarias, Social European Fund “Canarias Avanza con Europa” (TESIS202201004).” JV is supported by the European Regional Development Funds, Fundación Canaria Instituto de Investigación Sanitaria de Canarias, Spain (PIFIISC24/22) and Asociación Científica Pulmón y Ventilación Mecánica, Las Palmas de Gran Canaria, Spain.
DS Minerva
RD 25 abr 2026