Gliomas represent one of the most common types of primary brain tumor. Due to their poor prognosis and propensity for recurrence, new therapeutic targets are urgently required. A consensus is emerging that there is a significant relationship between tumor formation and embryonic development. However, the precise mechanisms and regulatory targets remain unclear. A variety of bioinformatics techniques, including GSVA, differential expression analysis, machine learning algorithms and others, were employed to elucidate the significance of germ layer development (GLD) in glioma and identify MEST as the key gene. To validate the results, in vivo and in vitro experiments were conducted, including tumor xenografts, RT-qPCR, immunocytofluorescence, transwell assays and others, which confirmed the central role of the selected oncogenic gene. Here, we performed a comprehensive bioinformatics analysis of GLD genes, providing a novel insight into the landscape of the GLD in gliomas, and confirmed the GLD-related gene MEST as a key oncogenic therapeutic target via machine learning feature selection framework. Furthermore, we have identified the core gene MEST and have conducted extensive research to elucidate its pivotal role in glioma progression through in vivo and in vitro experiments. We leveraged the GLD patterns in glioma and found that the MEST might promote the glioma development through activating RAS signaling and Wnt signaling.
Keywords: Germ layer development; Glioma; MEST; Machine learning; Prognosis.
© 2026. The Author(s).