COX17 and MATK emerged candidate ankylosing spondylitis biomarkers tied to mitochondrial and senescence pathways.
Evidence
This transcriptomic machine-learning analysis compared AS patients with controls, validated candidates in an independent dataset and a CAIA mouse model, and assessed diagnostic performance with ROC analysis.
Caveat
The study identifies biomarker associations and mouse-model validation, not prospective clinical diagnostic performance or causal disease mechanisms.
Simplified
OBJECTIVE: (AS) is a chronic immune-mediated inflammatory disorder characterized by inflammation and pathological bone formation. Growing evidence suggests that and are key drivers of disease progression. This study aimed to identify novel biomarkers linking these processes to AS.
METHODS: Transcriptomic datasets of AS patients and controls were analyzed to identify differentially expressed genes related to mitochondrial function and cellular senescence. Bioinformatics pipelines and multiple machine learning algorithms were used to screen candidate biomarkers, which were further validated in an independent dataset and in a collagen antibody-induced arthritis (CAIA) mouse model. Clinical diagnostic value was assessed using receiver operating characteristic analysis.
RESULTS: We identified 25 mitochondrial- and 8 senescence-related genes differentially expressed in AS. Consensus machine learning analysis highlighted COX17 and MATK as robust candidates with significant diagnostic performance. Immune infiltration analysis suggested strong correlations between these genes and altered immune cell subsets. In vivo validation confirmed upregulation of COX17 and downregulation of MATK in the AS mouse model, accompanied by enhanced osteogenic activity.
CONCLUSION: COX17 and MATK are promising biomarkers linking mitochondrial dysfunction and cellular senescence to AS. Their diagnostic potential highlights new avenues for improving early disease detection and personalized therapeutic strategies.
Key numbers
2249
Differentially Expressed Genes
Total differentially expressed genes identified between patients and healthy controls.
943
Upregulated Genes
Number of genes significantly upregulated in patients compared to controls.
1306
Downregulated Genes
Number of genes significantly downregulated in patients compared to controls.
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