Abstract
Current treatments for lower-grade glioma (LGG) do not effectively improve life expectancy rates, and this is a major global health concern. Improving our knowledge of this disease will ultimately help to improve prevention, accurate prognosis, and treatment strategies. Pyroptosis is an inflammatory form of regulated cell death, which plays an important role in tumor progression and occurrence. There is still a lack of effective markers to evaluate the prognosis of LGG patients. We collected paraffin-embedded tissue samples and prognostic information from 85 patients with low-grade gliomas and fabricated them into a tissue microarray. Combining data from public databases, we explored the relationship between pyroptosis-related genes (PRGs) and the prognoses of patients with LGG and investigated their correlations with the tumor microenvironment (TME) by means of machine learning, single-cell, immunohistochemical, nomogram, GSEA, and Cox regression analyses. We developed a six-gene PRG-based prognostic model, and the results have identified CASP4 as an effective marker for LGG prognosis predictions. Furthermore, the effects on immune cell infiltration may also provide guidance for future immunotherapy strategies.