Machine learning models decipher environmental pollutant-driven core genes in glioblastoma: Biomarkers for diagnosis, recurrence, and prognosis.

Environmental pollution, particularly from industrial production and automobile exhaust emissions, is increasingly recognized as a significant factor in the development of various diseases, including glioblastoma (GBM). However, the relationship between environmental exposure and GBM has not been systematically analyzed. In this study, we utilized machine learning techniques to develop three predictive models for GBM diagnosis, recurrence, and prognosis, considering core genes identified through environmental and clinical data. We applied Random Forest, LASSO regression, logistic regression, and Cox regression models, and validated them with independent external datasets. GO and KEGG functional enrichment analyses were also performed to explore the potential underlying mechanisms. Molecular docking was used to examine interactions between key genes and air pollutants. Our results indicated that polycyclic aromatic hydrocarbons, nitrogen oxides, and particulate matter contribute to the onset and progression of GBM by affecting cell proliferation pathways. Using machine learning and molecular docking, we identified several key genes: NFKBIA and BCL2L12 for onset prediction; DKK3, FGFR1, GLIPR1, and TRIM8 for recurrence prediction; and STAT3, NF1, and KDM5A for prognosis prediction. These genes may serve as potential biomarkers for early diagnosis, recurrence monitoring, and prognosis of GBM. This study underscores the importance of environmental gene interactions in GBM, offering valuable insights into clinical diagnosis and treatment strategies.