Integrating Single-Cell and RNA Sequencing to Predict Glioma Prognosis Through Lactylation

TL;DR

This study uses single-cell and RNA sequencing to identify a lactylation-related gene signature that predicts glioma prognosis and informs personalized treatment.

Contribution

The study introduces a novel lactylation-related gene signature validated at single-cell resolution for glioma prognosis and treatment guidance.

Findings

A lactylation-related risk model was developed and validated for predicting glioma patient survival.

Risk groups showed distinct immune profiles and drug sensitivity patterns, aiding personalized treatment.

The model integrates clinical variables and demonstrates potential for clinical application.

Abstract

Gliomas are the most prevalent primary malignant neoplasms of the central nervous system, distinguished by their high recurrence rates and poor prognosis. Aerobic glycolysis in tumors generates excess lactate, which promotes lactylation, a post-translational modification (PTM). Although accumulating evidence implicates lactylation in glioma initiation and progression, previous lactylation-focused prognostic studies lacked single-cell resolution and broad validation, limiting their generalizability and clinical relevance. Single-cell and bulk RNA sequencing (RNA-seq) data were integrated to identify lactylation-enriched tumor cell populations and derive candidate genes. A risk model was developed using univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO), and its predictive performance was validated in independent cohorts from the China Glioma Genome Atlas (CGGA). To improve clinical applicability, a nomogram integrating the risk score incorporating key clinical variables was constructed and externally validated. The risk groups showed distinct immune microenvironment profiles and differential drug sensitivity patterns. In this study, we established and validated a lactylation-related gene signature, with the derived risk score serving as a reliable prognostic biomarker for glioma. Furthermore, the model not only predicts overall survival (OS) but also exhibits the potential to inform drug selection and stratify patients for more precise and personalized therapeutic interventions.

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