Comprehensive Multi-Omics Analysis Identifies Lactylation-Related Gene RAN as a Novel Prognostic Biomarker and...

Gliomas are the most aggressive primary malignancies of the central nervous system (CNS) and exhibit marked heterogeneity that is closely associated with metabolic reprogramming. Emerging evidence underscores the pivotal role of lactylation modifications in shaping the tumor microenvironment (TME) and facilitating glioma progression. This study aimed to systematically identify key lactylation-related genes (LRGs), elucidate their functional roles and associated pathways, and explore their potential as novel therapeutic targets using multi-omics data.

We combined various datasets from the TCGA, GEO, and CGGA databases, including RNA-seq, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics. Key LRGs were identified through a multi-step analytical pipeline that involved processing scRNA-seq data using (Seurat, scoring), cell-type-specific lactylation scoring (AUCell), high-dimensional weighted gene co-expression network analysis (hdWGCNA) and applying rigorous machine learning-based feature selection utilizing 10 algorithms and 101 combinatorial strategies. We comprehensively assessed the prognostic value associated with the immune microenvironment, and spatiotemporal heterogeneity of the prioritized RAN. Functional validation was executed using shRNA-mediated knockdown in glioma cell lines, including LN229, U87, and U251, while evaluating proliferation (CCK-8, colony formation, EdU), migration (wound healing), invasion (Transwell), and pathway activity (using western blot).

scRNA-seq analysis revealed distinct lactylation enrichment patterns across glioma cell types, with malignant cells exhibiting the highest scores. hdWGCNA identified a gene module (royal blue) strongly correlated with lactylation activity (correlation = 0.75). The intersection of this module with a curated set of LRGs yielded 22 candidate genes. Subsequent machine learning analysis using (ENet, α = 0.4) prioritized six core LRGs (PDAP1, ALYREF, CBX3, MAGOH, RAN, TMSB4X). RAN, an understudied gene in glioma, was selected for further investigation. High RAN expression correlated significantly with poor patient prognosis, reduced immune cell infiltration (assessed by ESTIMATE, CIBERSORT, xCell, ssGSEA), and distinct spatiotemporal heterogeneity within tumors (analyzed using spatial transcriptomics, Monocle2). Glioma cell invasion, migration, colony formation, and proliferation were all markedly inhibited by RAN knockdown. Mechanistically, reduced p-AKT levels following knockdown and functional rescue with a PI3K/AKT activator (SC79) indicate that RAN increased these malignant traits by activating the PI3K/AKT signaling pathway.

Our study established lactylation modifications as a crucial regulator of the TME and glioma progression. Through integrative multi-omics analysis and robust machine learning techniques, we determined that RAN was a novel lactylation-associated gene. RAN is a potent, independent prognostic biomarker that promotes glioma malignancy via the PI3K/AKT pathway. Our results demonstrate RAN as a prospective therapeutic target and establish a novel framework for individualized therapy for glioma.