Machine learning identifies a NETs-related four-gene diagnostic signature for abdominal aortic aneurysm

Clinical Snapshot

Key Findings

• P Value: Not reported for diagnostic performance

• Effect Size: AUC = 0.920 in training cohort

• Primary Outcome: Diagnostic accuracy of 4-gene signature for AAA detection

• Nnt Or Sensitivity: Sensitivity and specificity not reported

• Confidence Interval: Not reported

Clinical Application

Limited by requirement for transcriptomic analysis - not readily available in routine clinical practice Would require TGA approval for diagnostic use; cost-effectiveness unclear for PBS listing; may complement current RACGP AAA screening guidelines if validated clinically Patients at risk for AAA requiring early detection, though specific population characteristics unclear

Abstract

Abdominal aortic aneurysm (AAA) is a chronic degenerative disease characterized by localized aortic dilation and persistent inflammation. While neutrophil extracellular traps (NETs) are increasingly recognized as key drivers of vascular inflammation and aneurysm progression, the transcriptomic landscape of NETs-related genes (NRGs) in AAA remains inadequately characterized. This study aimed to identify reliable diagnostic biomarkers and explore the immune heterogeneity of AAA to facilitate early risk stratification. We integrated transcriptomic datasets from the Gene Expression Omnibus to elucidate the role of dysregulated NRGs. A comprehensive bioinformatics pipeline was employed, combining weighted gene co-expression network analysis with differential expression profiling to screen for AAA-specific NRGs. Rigorous feature selection was conducted through the intersection of 3 machine learning algorithms - least absolute shrinkage and selection operator, support vector machine-recursive feature elimination, and random forest - to derive a diagnostic signature. The model was constructed using the GSE232911 cohort and validated in an independent external cohort. A robust 4-gene diagnostic signature comprising CXCR4, GZMB, ITGA6, and CD47 was identified. This signature demonstrated a favorable diagnostic performance, achieving an area under the curve of 0.920 in the training cohort. The model maintained consistent discriminatory ability in the external validation cohort, primarily driven by the high discriminative ability of CXCR4 and GZMB. Consensus clustering based on these hub genes revealed 2 distinct molecular subtypes, with Cluster 2 characterized by significant enrichment of neutrophils and innate immune pathways, suggesting intense NETosis activity. Furthermore, drug prediction analyses identified candidate therapeutic compounds, including Eugenol and Tretinoin, offering potential avenues for targeting the NETs-associated molecular landscape. Our findings underscore the pivotal role of NETs-mediated inflammation in AAA pathogenesis and validate a robust 4-gene signature for early diagnosis. By delineating immune-related molecular subtypes and identifying potential drug candidates, this study provides a foundational framework for precision risk stratification and the development of targeted nonsurgical therapies for aneurysm management.

References

1. 1.Jiang, F., Lv, J., Zheng, Z., & Dong, M. (2026). Machine learning identifies a NETs-related four-gene diagnostic signature for abdominal aortic aneurysm. Medicine, Article 0000000000048987. https://doi.org/10.1097/MD.0000000000048987