High PYGL Expression Predicts Poor Prognosis in Human Gliomas

Background: PYGL has been reported as a glycogen degradation-related gene, which is up-regulated in many tumors. This study was designed to investigate the predictive value of high PYGL expression in patients with gliomas through bioinformatics analysis of the gene transcriptome and the single-cell sequencing data. Methods: The gene transcriptome data of 595 glioma patients from the TCGA database and the single-cell RNA sequencing data of 7,930 GBM cells from the GEO database were included in the study. Differential analysis was used to find the distribution of expression of PYGL in different groups of glioma patients. OS analysis was used to assess the influence of the high expression of PYGL on the prognosis of patients. The reliability of its prediction was evaluated by the AUC of ROC and the C-index. The GSEA be used to reveal potential mechanisms. The single-cell analysis was used to observe the high expression of PYGL in different cell groups to further analyze the mechanism of its prediction. Results: Differential analysis identified the expression level of PYGL is positively associated with glioma malignancy. OS analysis and Cox regression analyses showed high expression of PYGL was an independent factor for poor prognosis of gliomas (p < 0.05). The AUC values were 0.838 (1-year ROC), 0.864 (3-year ROC) and 0.833 (5-year ROC). The C index was 0.81. The GSEA showed that gene sets related to MTORC1 signaling, glycolysis, hypoxia, PI3K/AKT/mTOR signaling, KRAS signaling up and angiogenesis were differentially enriched in the high PYGL expression phenotype. The single-cell sequencing data analysis showed TAMs and malignant cells in GBM tissues expressed a high level of PYGL. Conclusion: The high expression of PYGL is an independent predictor of poor prognosis in patients with glioma.

Automated summary

This study found that high expression of PYGL is positively associated with glioma malignancy and serves as an independent predictor of poor prognosis in patients with glioma. The study used bioinformatics analysis to identify potential mechanisms underlying the predictive value of PYGL in gliomas, with different gene sets related to signaling pathways and angiogenesis enriched in high PYGL expression phenotype. Single-cell sequencing data analysis further confirmed the high expression of PYGL in specific cell groups, including tumor-associated macrophages and malignant cells.