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

Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation

Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatment, suppressing ferroptosis or circumventing it holds significant promise. Beyond cancer, ferroptosis affects aging, organs, metabolism, and nervous system. Understanding ferroptosis mechanisms holds promise for uncovering novel therapeutic strategies across a spectrum of diseases. However, detection and regulation of this regulated cell death are still mired with challenges. The dearth of cell, tissue, or organ-specific biomarkers muted the pharmacological use of ferroptosis. This review covers recent studies on ferroptosis, detailing its properties, key genes, metabolic pathways, and regulatory networks, emphasizing the interaction between cellular signaling and ferroptotic cell death. It also summarizes recent findings on ferroptosis inducers, inhibitors, and regulators, highlighting their potential therapeutic applications across diseases. The review addresses challenges in utilizing ferroptosis therapeutically and explores the use of machine learning to uncover complex patterns in ferroptosis-related data, aiding in the discovery of biomarkers, predictive models, and therapeutic targets. Finally, it discusses emerging research areas and the importance of continued investigation to harness the full therapeutic potential of targeting ferroptosis.

Molecular Landscape and Diagnostic Model of MASH: Transcriptomic, Proteomic, Metabolomic, and Lipidomic Perspectives

Metabolic dysfunction-associated steatohepatitis (MASH), a progressive form of fatty liver disease, presents a significant global health challenge. Despite extensive research, fully elucidating its complex pathogenesis and developing accurate non-invasive diagnostic tools remain key goals. Multi-omics approaches, integrating data from transcriptomics, proteomics, metabolomics, and lipidomics, offer a powerful strategy to achieve these aims. This review summarizes key findings from multi-omics studies in MASH, highlighting their contributions to our understanding of disease mechanisms and the development of improved diagnostic models. Transcriptomic studies have revealed widespread gene dysregulation affecting lipid metabolism, inflammation, and fibrosis, while proteomics has identified altered protein expression patterns and potential biomarkers. Metabolomic and lipidomic analyses have further uncovered significant changes in various metabolites and lipid species, including ceramides, sphingomyelins, phospholipids, and bile acids, underscoring the central role of lipid dysregulation in MASH. These multi-omics findings have been leveraged to develop novel diagnostic models, some incorporating machine learning algorithms, with improved accuracy compared to traditional methods. Further research is needed to validate these findings, explore the complex interplay between different omics layers, and translate these discoveries into clinically useful tools for improved MASH diagnosis and prognosis.

Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor's genetic complexity and heterogeneity.

METHODS

This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. Five publicly available gene expression datasets were analyzed to identify differentially expressed genes (DEGs) associated with GBM. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify GBM-related gene modules. Further, gene set enrichment and variation analyses were conducted to explore the biological pathways involved. The machine learning models were evaluated using Receiver Operating Characteristic (ROC) curves and confusion matrices to assess their predictive accuracy, with the best-performing model validated across external datasets. MR analysis was performed to establish causal relationships between genetically predicted gene expression levels and GBM outcomes.

RESULTS

The study identified 286 DEGs between GBM and adjacent normal tissues across five datasets. WGCNA highlighted the yellow module as the most relevant to GBM, containing key genes such as KLHL3, FOXO4, and MAP1A. Of the 113 machine learning models tested, Ridge regression achieved the highest area under the curve (AUC) of 0.92, demonstrating robust predictive accuracy. Validation using external datasets confirmed the model's reliability, with a classification accuracy of 89.5% in the training set and 85.3% in the validation sets. MR analysis provided strong evidence of a causal relationship between the expression levels of the identified genes and GBM risk.

CONCLUSIONS

This study demonstrates the power of combining machine learning and Mendelian Randomization to uncover novel genetic markers for GBM. The identified genes offer promising potential as biomarkers for GBM diagnosis and therapy, providing new avenues for personalized treatment strategies.

Ferroptosis as a potential therapeutic target for age-related macular degeneration

Cell death plays a crucial part in the process of age-related macular degeneration (AMD), but its mechanisms remain elusive. Accumulating evidence suggests that ferroptosis, a novel form of regulatory cell death characterized by iron-dependent accumulation of lipid hydroperoxides, has a crucial role in the pathogenesis of AMD. Numerous studies have suggested that ferroptosis participates in the degradation of retinal cells and accelerates the progression of AMD. Furthermore, inhibitors of ferroptosis exhibit notable protective effects in AMD, underscoring the significance of ferroptosis as a pivotal mechanism in the death of retinal cells during the process of AMD. This review aims to summarize the molecular mechanisms of ferroptosis in AMD, enumerate potential inhibitors and discuss the challenges and future opportunities associated with targeting ferroptosis as a therapeutic strategy, providing important information references and insights for the prevention and treatment of AMD.