What question did this study set out to answer?

The study aims to identify phenotypes of low-grade gliomas through the TP53 signaling pathway and develop a scoring system to predict chemotherapy response.

April 1, 2026Open Access

Stratified and Quantified Score Construction of Low‐Grade Glioma Samples Based on TP53 Signaling Pathway

Key Points

The study aims to identify phenotypes of low-grade gliomas through the TP53 signaling pathway and develop a scoring system to predict chemotherapy response.
Obtained transcriptomic data from the TCGA database.
Performed consensus clustering of glioma samples based on P53 pathway gene expression.
Conducted univariate COX analysis to identify prognostic factors.
Utilized principal component analysis to create the TP53 score from significant genes.
Identified 14 prognostic risk factors related to TP53.
Classified glioma samples into distinct TP53 clusters indicating heterogeneity.
Highlighted differences in prognosis and immune microenvironment among TP53 clusters.
The TP53 score positively correlates with immune cell subtypes and negatively with tumor stemness.

Abstract

Background This study is aimed at distinguishing the phenotypes of low‐grade gliomas based on the P53 signaling pathway gene set, revealing the transcriptomic changes in different phenotypes, screening phenotype‐related feature genes, constructing a TP53 score, quantitatively describing TP53‐related phenotypes, and predicting the response of glioma patients to chemotherapy. Methods We obtained transcriptomic sequencing data of low‐grade glioma samples from the Cancer Genome Atlas (TCGA) database. Based on the expression levels of genes related to prognosis in the P53 signaling pathway, we performed consensus clustering on glioma samples to describe sample heterogeneity. Through differentially expressed genes (DEGs), we showed the transcriptomic changes between different TP53 phenotypes. We used univariate COX analysis to remove redundant information and retain prognostic factors. We used principal component analysis to retain the first principal component of the prognostic factors as the TP53 score. We elucidated the correlation between the TP53 score and mRNAsi, immune cell subtypes, and EMT markers. Results We obtained the TP53 genes and screened 14 prognostic risk factors through univariate COX analysis. Based on prognostic risk factors, we classified glioma samples into TP53 clusters using consensus clustering, describing sample heterogeneity. The prognosis and immune cell microenvironment characteristics differed between TP53 clusters. Among the DEGs between TP53 clusters, we screened 38 as prognostic factors. We used the first principal component of these 38 genes as the TP53 score. The TP53 score was positively correlated with immune cell subtypes and EMT markers and negatively correlated with mRNAsi. High TP53 glioma samples were more sensitive to vorinostat, elesclomol, gefitinib, AICAR, axitinib, and bosutinib. Conclusion The TP53 score based on the P53 signaling pathway can describe the heterogeneity of glioma samples and distinguish different immune microenvironment characteristics and prognostic features. A high TP53 score indicates more active epithelial–mesenchymal transition and lower tumor stemness.

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