Objective: Subarachnoid hemorrhage (SAH) and tumorigenesis share numerous biological complexities; nevertheless, the specific gene expression profiles and underlying mechanisms remain poorly understood. This study aims to identify differentially expressed genes (DEGs) that could serve as biomarkers for diagnosis and prognosis.
Methods: Gene expression datasets (GSE122063, GSE13353, GSE161870) were analyzed using machine learning algorithms and logistic regression to identify DEGs associated with both SAH and tumorigenesis. Lasso regression and receiver operating characteristic (ROC) curve analysis were employed to evaluate the classification accuracy of these genes. Validation of critical DEGs was performed through pan-cancer analysis and experimental studies, focusing on the role of DOK3 in modulating inflammation and oxidative stress in U251MG glioblastoma and BV2 microglia cells.
Results: Fifteen common DEGs were identified, with DOK3 and PAPOLA highlighted as crucial genes implicated in SAH and neurodegenerative processes. Experimental validation demonstrated that DOK3 overexpression significantly reduced pro-inflammatory cytokine levels and oxidative stress markers while enhancing antioxidant enzyme activity. Additionally, DOK3 influenced tumorigenic processes such as apoptosis, cell cycle regulation, and proliferation, effectively mitigating LPS-induced cytotoxicity and inflammation in BV2 microglial cells.
Conclusions: DOK3 and PAPOLA play critical roles in both SAH and related neurodegeneration, presenting themselves as potential prognostic biomarkers and therapeutic targets. Notably, DOK3 exhibits potential as an antitumor agent with anti-inflammatory and antioxidative properties, offering therapeutic benefits for both cancer and neuroinflammatory conditions.
Keywords: DOK3; PAPOLA; biomarkers; differentially expressed genes; inflammation; machine learning; oxidative stress; pan-cancer.
Copyright © 2024 Wang, Bian and Chen.