非小细胞肺癌中代谢相关 lncRNA 的单细胞和批量转录组分析揭示肿瘤异质性和预后标志物

Non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), exhibits marked tumor heterogeneity and poor prognosis. Metabolic reprogramming is a hallmark of cancer, and long non-coding RNAs (lncRNAs) have emerged as important regulators of tumor metabolism and immune interactions. This study aimed to systematically characterize metabolism-related lncRNAs in NSCLC using single-cell and bulk transcriptomic data, and to evaluate their associations with tumor heterogeneity, immune microenvironment, and clinical outcomes.

RNA sequencing and clinical data for LUAD and LUSC were obtained from The Cancer Genome Atlas. Metabolism-related lncRNAs were identified through partial correlation analysis combined with KEGG pathway-based gene set enrichment analysis. Tumor microenvironment characteristics were assessed using CIBERSORTx. Consensus clustering based on the top 20 metabolism-related lncRNAs was applied to define molecular subtypes. Cis- and trans-regulatory relationships were explored using Spearman correlation, and competing endogenous RNA networks were constructed by integrating TargetScan-predicted miRNA-mRNA interactions. Findings were validated across six independent datasets, and single-cell transcriptomic data were used to assess cell-type-specific expression patterns.

A subset of metabolism-related lncRNAs was significantly enriched among differentially expressed and survival-associated lncRNAs in NSCLC. AL365181.2 was identified as a key lncRNA associated with multiple metabolic pathways and poor prognosis. Single-cell analysis revealed immune cell-specific expression patterns, particularly in B cells, CD8 + T cells, and natural killer cells. Metabolic lncRNA-based clustering defined distinct molecular subtypes with significant differences in immune profiles and clinical outcomes.

Metabolism-related lncRNAs contribute substantially to tumor heterogeneity and prognosis in NSCLC and may serve as valuable biomarkers for patient stratification and personalized therapeutic strategies.