Machine learning integration identifying an eight-gene diagnostic signature for acute mountain sickness.

Background: Acute mountain sickness (AMS) is highly prevalent at high altitudes, with estimated incidence rates ranging from 25 to 90%. However, current AMS diagnosis primarily relies on self-reported questionnaires, highlighting the need for reliable biomarkers. Thus, we aimed to establish a diagnostic model for AMS. Methods: We applied scRNA-seq ( Results: We analyzed cellular heterogeneity through scRNA-seq data, revealing significant enrichment of myeloid (MD) and platelet (PLT) cells during AMS progression. Subsequently, we identified 526 differentially expressed genes (DEGs) associated with the progression of AMS using pseudobulk differential expression analysis on the MD and PLT subsets between the AMS and control groups. We further screened for AMS-associated genes using bulk RNA-seq based differential analysis and WGNCA. Finally, we screened 12 AMS-related genes using scRNA-seq and bulk-RNA-seq data. These genes were utilized as features across 113 distinct combinations of machine learning models to develop an AMS diagnostic model. The model of Stepglm[both] + NaiveBayes (ATP6V0C, BCL2A1, CD52, CSTA, GZMA, HINT1, PFDN5, and RNF11) demonstrated optimal diagnostic accuracy. It obtained an AUC of 0.948 on the training cohort ( Conclusion: Using machine learning, we identified and validated a minimal blood biomarker signature for AMS diagnosis. This approach offered a practical approach for the early detection of AMS, especially in resource-limited populations residing in high-altitude regions.