Evaluation of triphenyl phosphate's pathogenic potential and molecular mechanisms in glioblastoma: an integrated network toxicology investigation employing multiple machine learning approaches.

Background: Glioblastoma (GBM) is a highly aggressive brain tumor with a poor prognosis, and its etiology involves both genetic and environmental factors. Triphenyl phosphate (TPHP), an organophosphorus flame retardant widely used in various consumer products, has raised public health concerns due to its environmental prevalence and detection in human samples. However, its potential role in GBM pathogenesis remains unclear. This study combined machine learning and molecular simulation docking technology to investigate the effects of TPHP on the pathogenesis and related molecular mechanisms of glioma. Methods: Our study made use of a variety of computational learning methods along with online databases to carry out differential transcriptional expression analysis on different datasets. The aim was to identify target genes related to glioma. Based on the expression levels of key genes, we constructed a risk prediction model. Network toxicology and molecular docking technologies were adopted to investigate how TPHP binds to target proteins. Results: Fourteen genes in total were determined to be potential target genes in relation to TPHP-induced glioma. Further machine learning analysis identified three core target genes ( Conclusions: It is possible that TPHP influences the pathogenic mechanism of glioma through targeting particular genes and pathways. Results from molecular docking simulations demonstrated a remarkable binding specificity effect between TPHP and target proteins. This effect is highly likely to be the crucial factor contributing to the development of glioma.