Subtype cluster analysis unveiled the correlation between m6A- and cuproptosis-related lncRNAs and the prognosis, immune microenvironment, and treatment sensitivity of esophageal cancer

Objective: Esophageal cancer (EC) is highly malignant with a poor prognosis. N6-methyladenosine (m6A), a key post-transcriptional modification of mRNA in mammalian cells, plays an essential role in regulating various cellular and biological processes. Similarly, cuproptosis has emerged as an area of interest for its potential relevance in cancer biology. This study aims to explore the impact of m6A- and cuproptosis-related long non-coding RNAs (m6aCRLncs) on the prognosis of EC patients.

Methods: Transcriptional data and corresponding clinical information for EC were obtained from The Cancer Genome Atlas (TCGA) database, which included 11 normal samples and 159 EC samples. Data on 23 m6A regulators and 25 cuproptosis-related genes were collected from recent literature. Co-expression analysis was used to identify m6aCRLncs associated with EC. Differentially expressed m6aCRLncs linked to EC prognosis were screened using the limma package in R and univariate Cox regression analysis. Subtype clustering was conducted to classify EC patients and examine variations in clinical outcomes and immune microenvironment across patient clusters. A risk prognostic model was built using least absolute shrinkage and selection operator (LASSO) regression, and its robustness was evaluated through survival analysis, risk stratification curves, and receiver operating characteristic (ROC) curves. The model’s applicability across different clinical features and molecular subtypes of EC patients was assessed. To further investigate its potential for predicting the immune microenvironment, single-sample gene set enrichment analysis (ssGSEA), immune cell infiltration analysis, and immune checkpoint differential expression analysis were performed. Drug sensitivity analysis was conducted to identify potential therapeutic agents for EC. Finally, the mRNA expression of m6aCRLncs in EC cell lines was validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results: A prognostic risk model based on five m6aCRLncs—ELF3-AS1, HNF1A-AS1, LINC00942, LINC01389, and MIR181A2HG—was developed to predict survival outcomes and characterize the immune microenvironment in EC patients. Molecular subtype and clinical feature analysis revealed significant differences in cluster distribution, disease stage, and N stage between high- and low-risk groups. Immune profiling identified distinct immune cell populations and functional pathways associated with risk scores, with positive correlations to naive B cells, resting CD4+ T cells, and plasma cells, and negative correlations to macrophages M0 and M1. Key immune checkpoint-related genes, including TNFRSF14, TNFSF15, TNFRSF18, LGALS9, CD44, HHLA2, and CD40, were significantly differentially expressed between the risk groups. Additionally, nine potential therapeutic agents for EC were identified: Bleomycin, Cisplatin, Cyclopamine, PLX4720, Erlotinib, Gefitinib, RO.3306, XMD8.85, and WH.4.023. RT-qPCR validation revealed that ELF3-AS1 expression was significantly upregulated in the EC cell lines KYSE-30 and KYSE-180 compared to normal esophageal epithelial cells.

Conclusion: This study highlights the role of m6aCRLncs in influencing the prognosis and immune microenvironment of EC. It also identifies potential therapeutic agents for EC treatment. These findings offer promising avenues to improve survival outcomes for EC patients and provide valuable insights to guide clinical WH-4-023 decision-making in managing the disease.