Establishment of glioma prognosis nomogram based on the function of meox1 in promoting the progression of cancer

Abstract

BackgroundGliomas stand out as highly predominant malignant nervous tumors and are linked to adverse treatment outcomes and short survival periods. Current treatment options are limited, emphasizing the need to identify effective therapeutic targets. The heterogeneity of tumors necessitates a personalized treatment approach with an effective grouping system. Meox1 has been implicated in promoting tumor progression in diverse cancers; nonetheless, its role in gliomas remains unelucidated. Material/methodsUtilized immunohistochemistry to assess the expression of Meox1 protein in glioma tissues. Proliferation and invasion assays were conducted on wild-type and meox1-overexpressed glioma cells using the CCK8 and Transwell assays, respectively. The expression levels of meox1 and its related genes in gliomas were obtained from Chinese Glioma Genome Atlas (CGGA), along with the corresponding patient survival periods. LASSO regression modeling was employed to construct a scoring system for patients with gliomas, categorizing them into high-/low-risk groups. Additionally, a nomogram for predicting the survival period of patients with glioma was developed using multivariate logistic analysis. ResultsWe attempted, for the first time, to demonstrate heightened expression of Meox1 in glioma tumor tissues, correlating with significantly increased invasion and proliferation abilities of glioma cells following meox1 overexpression. The scoring system effectively stratified patients with glioma into high-/low-risk groups, revealing differences in the survival period and immunotherapy efficacy between the two groups. The integration of this scoring system with other clinical indicators yielded a nomogram capable of effectively predicting the survival period of individuals with gliomas. ConclusionsOur study established a stratified investigation system based on the levels of meox1 and its related genes, providing a novel, cost-effective model for facilitating the prognosis prediction of individuals with glioma.