# NSUN5 as a Prognostic Biomarker Correlates with Malignant Phenotype and Therapeutic Target in Glioma

## TL;DR

NSUN5 is linked to worse outcomes in glioma patients and could serve as a biomarker and treatment target.

## Contribution

This study identifies NSUN5 as a prognostic biomarker and therapeutic target in glioma using machine learning and experimental validation.

## Key findings

- NSUN5 is significantly upregulated in glioma and correlates with tumor malignancy and poor prognosis.
- NSUN5 is associated with increased M2 macrophage infiltration and immune checkpoint expression.
- A machine learning model (STRICOM) based on NSUN5-associated genes shows strong prognostic performance.

## Abstract

NSUN5 is a conserved RNA methyltransferase whose oncogenic role has been demonstrated in various cancers. However, its function and prognostic value in gliomas remain unclear.

In this study, we systematically analyzed the expression and functional associations of NSUN5 in glioma using data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A total of 117 machine learning algorithm combinations were employed to construct and validate a prognostic model for glioma patients. In addition, in vitro experiments were performed to further validate the expression and biological functions of NSUN5.

NSUN5 expression is significantly upregulated in glioma and is positively associated with tumor malignancy and poor prognosis. Immune infiltration analysis revealed a marked increase in M2 macrophages in the NSUN5 high‐expression group, and NSUN5 levels were positively correlated with the expression of multiple inhibitory immune checkpoints. In addition, drug sensitivity analysis and molecular docking suggested that NSUN5 may influence the response to Olaparib. Finally, based on NSUN5‐associated genes, we constructed 117 machine learning models and identified the optimal prognostic model, STRICOM, which demonstrated robust predictive performance for patient survival.

High NSUN5 expression is closely associated with poor prognosis in glioma patients, highlighting its potential as a prognostic biomarker and therapeutic target.

This study systematically analyzed the expression levels, clinical features, prognostic value, immune‐related roles, drug sensitivity, and biological functions of NSUN5 in glioma based on the CGGA and TCGA databases. Finally, a machine learning‐based prognostic model was constructed, and the function of NSUN5 was validated through in vitro experiments.