Comprehensive Transcriptomic and Single-Cell Analyses Enhanced by Artificial Neural Networks Reveal a Distinct Gene Signature for Early Differentiation of MASL and MASH

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The study identifies a distinct gene signature for early differentiation between metabolic dysfunction-associated steatotic liver disease (MASL) and steatohepatitis (MASH).
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A total of 656 differentially expressed genes were identified, with six key genes consistently highlighted: MMP9, FABP5, TREM2, CTSD, UBD, and MAP2K1.
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The artificial neural network model achieved an area under the curve of 0.893 in the validation cohort, indicating high diagnostic accuracy for distinguishing MASL from MASH.
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Immune infiltration analysis revealed increased monocytes and activated dendritic cells in MASH, suggesting a role of immune mechanisms in disease progression.
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The study emphasizes the potential of machine learning and transcriptomic data integration to enhance early detection and risk stratification in liver diseases.

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