Glioma biomarker analysis and drug targeting prediction based on ferroptosis-related genes

Clinical Snapshot

Key Findings

• P Value: Not specifically reported

• Effect Size: Not quantitatively reported

• Primary Outcome: Identification of 10 hub ferroptosis-related genes in glioma

• Nnt Or Sensitivity: Hub genes: TP53, RRM2, EZH2, CDKN1A, MYCN, KIF20A, BLM, GLS2, HMOX1, GOT1

• Confidence Interval: Not provided

Clinical Application

Requires further clinical validation before implementation Relevant to Australian neuro-oncology practice; would need TGA approval for any resulting therapeutics and consideration for PBS listing of targeted therapies Glioma patients, particularly those being considered for targeted therapy

Abstract

Glioma represents a significant challenge in neuro-oncology due to its highly invasive nature and poor prognosis. Recent studies indicate that ferroptosis, a form of regulated cell death, plays a crucial role in the pathogenesis of glioma. Modulating ferroptosis may offer a novel therapeutic approach for glioma treatment. This study aims to identify ferroptosis-related biomarkers and patterns of immune infiltration in glioma using bioinformatics techniques, and to predict potential therapeutic drugs targeting these biomarkers. Gene expression profiles (GSE16011, GSE50161) were retrieved from the GEO database, while ferroptosis-related genes were sourced from the FerrDb database. Intersection genes were identified using the CytoHubba algorithm and a consensus clustering method. Immune infiltration was assessed using the CIBERSORT algorithm. Functional enrichment analysis and gene set enrichment analysis were conducted with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Drug targeting predictions were made based on identified hub genes. We identified 10 hub genes: TP53, RRM2, EZH2, CDKN1A, MYCN, KIF20A, BLM, GLS2, HMOX1, and GOT1. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that ferroptosis is primarily associated with apoptosis, reactive oxygen metabolism, the p53 signaling pathway, and immune regulation. Immune infiltration analysis showed significant differences in the expression of plasma cells, CD8 T cells, follicular helper T cells, activated NK cells, and macrophages between groups. qRT-PCR validation in glioma cell lines confirmed the differential expression of key ferroptosis-related genes, supporting the robustness of our bioinformatic findings. Furthermore, molecular docking demonstrates excellent binding properties between the drug components and key targets, and these key targets were further validated by PCR analysis in vitro. In summary, this study highlights the potential of ferroptosis-related biomarkers in glioma and suggests novel drug targets, providing a foundation for future therapeutic strategies.

References

1. 1.Zhang, Y., Wu, D., Gu, Y., & Xiao, H. (2024). Glioma biomarker analysis and drug targeting prediction based on ferroptosis-related genes. Medicine, Article 0000000000048175. https://doi.org/10.1097/MD.0000000000048175