Development of a novel diagnostic model to monitor the progression of metabolic dysfunction-associated steatotic liver disease to hepatocellular carcinoma in females.

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The onset of metabolic dysfunction-associated steatotic liver disease-associated hepatocellular carcinoma (MASLD-HCC) is insidious and exhibits sex-specific variations. Effective methods for monitoring MASLD-HCC progression in females have not yet been developed. Transcriptomic data of female liver tissue samples were obtained from multiple public databases. Differentially expressed genes (DEGs) in MASLD-HCC were identified using differential expression and robust rank aggregation analyses. Diagnostic prediction models for MASLD (DP.MASLD) and HCC (DP.HCC) were developed and validated using elastic net analysis, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Bioinformatics was used to assess the pathogenesis of MASLD-HCC. Seven overlapping DEGs were identified in female patients with MASLD and HCC: AKR1B10, CLEC1B, CYP2C19, FREM2, MT1H, NRG1, and THBS1). The area under the ROC curve (AUC) values for the training and validation groups of the DP.MASLD model were 0.864 and 0.782, 0.932 and 1.000, and 0.920 and 0.969 when differentiating between the steatosis and normal liver, steatohepatitis and steatosis, and steatohepatitis and normal liver groups, respectively. The AUCs for DP.HCC were 0.980 and 0.997 in the training and validation groups, respectively. The oncogenesis of female MASLD-HCC is associated with molecular pathways, including cytochrome P450-associated drug metabolism, tyrosine metabolism, fatty acid degradation, focal adhesion, extracellular matrix receptor interactions, and protein digestion and absorption. A novel and effective method to quantitatively assess the risk of MASLD-HCC progression in female patients was developed, and this method will aid in the generation of precise diagnostic, preventive, and therapeutic strategies.