Machine learning-based integration identifies ferroptosis hub genes in nonalcoholic steatohepatitis

Abstract

Abstract Background Ferroptosis, an iron-dependent mode of cellular demise, precipitates the accumulation of lipid peroxides and perturbation of vital metabolic routes, culminating in hepatic impairment. However, the pivotal genes governing the contribution of ferroptosis to the pathogenesis of nonalcoholic steatohepatitis (NASH) remain elusive, necessitating a thorough and profound investigation. Methods Employing sophisticated machine learning techniques, pivotal ferroptosis hub genes were meticulously identified, culminating in the formulation of a comprehensive ferroptosis-related score (FeRS) model. Sequentially, correlation analyses were harnessed to unravel intricate associations linking the ferroptosis hub genes with immune function scores, as well as distinct immune cell subpopulations. Results An FeRS model, encompassing a set of eight central ferroptosis hub genes, was meticulously fashioned, exhibiting profound diagnostic efficacy within the training dataset and across seven independent testing datasets. Among these genes, ZFP36 emerged as a key hub within the FeRS. Moreover, ZFP36 and IL6 revealed substantial positive correlations with immune function scores and various subsets of immune cells. In contrast, GRIA3 and FADS2 exhibit the opposite pattern. Conclusions The pivotal role of the ferroptosis-related gene ZFP36 in the context of NASH comes to the fore, as its diminished expression serves to propel the trajectory of restrain the infiltration of immune components within the NASH milieu.