Research on identification of key genes and immune-metabolic mechanisms in atrial fibrillation through integrated multi-cohort transcriptomic analysis and machine learning.

Notably, DHRS9 and CHGB exhibited higher median values in the experimental group, whereas TNNI1 and DNER showed lower median values, with highly significant differences ( P < .001).

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Concurrently, we applied conservative filtering criteria (FDR < 0.05 and |log 2 FC|>1), along with low-expression gene removal and post-integration normalization – measures that collectively minimize false discovery in public data integration, albeit at the cost of reduced sensitivity for marginally significant transcripts.

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