Construction of a machine learning-based artificial neural network for discriminating PANoptosis related subgroups to predict prognosis in low-grade gliomas

The poor prognosis of gliomas necessitates the search for biomarkers for predicting clinical outcomes. Recent studies have shown that PANoptosis play an important role in tumor progression. However, the role of PANoptosis in in gliomas has not been fully clarified.Low-grade gliomas (LGGs) from TCGA and CGGA database were classified into two PANoptosis patterns based on the expression of PANoptosis related genes (PRGs) using consensus clustering method, followed which the differentially expressed genes (DEGs) between two PANoptosis patterns were defined as PANoptosis related gene signature. Subsequently, LGGs were separated into two PANoptosis related gene clusters with distinct prognosis based on PANoptosis related gene signature. Univariate and multivariate cox regression analysis confirmed the prognostic values of PANoptosis related gene cluster, based on which a nomogram model was constructed to predict the prognosis in LGGs. ESTIMATE algorithm, MCP counter and CIBERSORT algorithm were utilized to explore the distinct characteristics of tumor microenvironment (TME) between two PANoptosis related gene clusters. Furthermore, an artificial neural network (ANN) model based on machine learning methods was developed to discriminate distinct PANoptosis related gene clusters. Two external datasets were used to verify the performance of the ANN model. The Human Protein Atlas website and western blotting were utilized to confirm the expression of the featured genes involved the ANN model. We developed a machine learning based ANN model for discriminating PANoptosis related subgroups with drawing implications in predicting prognosis in gliomas.