Biochemical and biophysical research communications · 2026
Identification of PANoptosis-related gene SIK1 in MASLD by bioinformatics, machine learning and experimental validation.
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Zhang Hong, Liu Xin, Liu Yan, Gong Dacai, Lai Min, Ge Bin, Wang Dan, Tan Mingsheng, Chen Peng
Аннотация
OBJECTIVE: Metabolic dysfunction-associated steatotic liver disease (MASLD) constitutes a chronic inflammatory condition within the spectrum of metabolic liver diseases, characterized by the accumulation of hepatic lipids in the absence of significant alcohol consumption, however, the contribution of PANoptosis to its pathobiology remains largely undefined.
METHODS: We retrieved MASLD transcriptomic datasets from the Gene Expression Omnibus (GEO). Shared genes were first defined by intersecting differentially expressed genes (DEGs) with modules identified by weighted gene co-expression network analysis (WGCNA). To identify the PANoptosis-related hub genes, a comprehensive approach was adopted, integrating three distinct machine-learning algorithms: least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support-vector machine recursive feature elimination (SVM-RFE). Receiver operating characteristic (ROC) curves were constructed to quantify the diagnostic performance of each candidate gene. Immune-infiltration landscapes were delineated with CIBERSORT. Finally, the expression of the hub gene was corroborated in vitro, in vivo and in human biopsies.
RESULTS: The PANoptosis-related gene SIK1 exhibited robust association with MASLD and significant diagnostic value. Immune-infiltration profiling revealed a marked increase in gamma delta T cells and resting mast cells in MASLD tissues relative to controls. Experimental validation further confirmed that SIK1 expression was significantly down-regulated in MASLD samples.
CONCLUSION: Our findings illuminate novel aspects of PANoptosis in MASLD, thereby paving the way for future research into its underlying pathogenesis and the development of targeted therapeutic strategies.
METHODS: We retrieved MASLD transcriptomic datasets from the Gene Expression Omnibus (GEO). Shared genes were first defined by intersecting differentially expressed genes (DEGs) with modules identified by weighted gene co-expression network analysis (WGCNA). To identify the PANoptosis-related hub genes, a comprehensive approach was adopted, integrating three distinct machine-learning algorithms: least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support-vector machine recursive feature elimination (SVM-RFE). Receiver operating characteristic (ROC) curves were constructed to quantify the diagnostic performance of each candidate gene. Immune-infiltration landscapes were delineated with CIBERSORT. Finally, the expression of the hub gene was corroborated in vitro, in vivo and in human biopsies.
RESULTS: The PANoptosis-related gene SIK1 exhibited robust association with MASLD and significant diagnostic value. Immune-infiltration profiling revealed a marked increase in gamma delta T cells and resting mast cells in MASLD tissues relative to controls. Experimental validation further confirmed that SIK1 expression was significantly down-regulated in MASLD samples.
CONCLUSION: Our findings illuminate novel aspects of PANoptosis in MASLD, thereby paving the way for future research into its underlying pathogenesis and the development of targeted therapeutic strategies.