Development and validation of an m6A and autophagy related lncRNAs signature for predicting survival and modulating the immune microenvironment in esophageal squamous cell carcinoma

Objective: This research is designed to establish a prognostic framework derived from m6A- and autophagy-related lncRNAs (m6aARLncs) to enhance survival prediction in esophageal squamous cell carcinoma (ESCC). Concurrently, it endeavors to elucidate the role of prognostic framework in modulating the tumor immune microenvironment. Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were employed as the training and independent validation cohorts, respectively. m6A-related genes (m6aRGs) and autophagy-related genes (ARGs) were curated from published literature and the Human Autophagy Database (HADb), respectively. Subsequently, differentially expressed m6aARLncs (DE-m6aARLncs) were identified by integrating co-expression analysis with differential expression profiling. A prognostic risk signature was constructed using univariate Cox and LASSO regression analyses. The model's predictive efficacy was rigorously validated through risk heatmap, survival analysis, ROC curves, differential analysis and independent prognostic analysis. Furthermore, the association between riskScores and clinical features was assessed. To elucidate the model's biological relevance, Gene Set Enrichment Analysis (GSEA) was performed. The impact of the risk signature on the tumor immune microenvironment was comprehensively evaluated via tumor microenvironment analysis, immune cell correlation analysis, single-sample gene set enrichment analysis (ssGSEA), and immune checkpoint profiling. Single-cell sequencing data analysis was carried out. Drug sensitivity profiling was conducted to identify putative therapeutic agents tailored to distinct risk subgroups. The putative m6A-autophagy-lncRNA regulatory axis may implicated in ESCC pathogenesis was proposed. Finally, the mRNA expression levels of m6aARLncs were validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: We established a robust risk prognostic model based on five m6aARLncs (LINC00847, UBL7-AS1, LINC01554, LINC00601, and FAM222A-AS1), which demonstrated significant efficacy in predicting overall survival in ESCC patients. Comprehensive immune profiling delineated the unique immune phenotype of the prognostic model, characterized by the infiltration of mast cells, B cells, neutrophils, plasmacytoid dendritic cells (pDCs), helper T cells, and tumor-infiltrating lymphocytes (TILs), as well as human leukocyte antigens (HLA), etc. The B cells, neutrophils and dendritic cells identified by immune microenvironment analysis results may have been verified to some extent through single-cell sequencing analysis. Furthermore, the expression of two immune checkpoint molecules, TNFRSF18 and LAIR1, was significantly correlated with risk stratification, suggesting their potential therapeutic relevance. Pharmacogenomic analysis identified nine compounds (Shikonin, Bicalutamide, Bryostatin-1, Epothilone-B, JNK-9L, LFM-A13, QS11, VX-680, and Z-LLNle-CHO) exhibiting differential sensitivity between the dichotomous risk strata. We propose novel m6A-autophagy-lncRNA regulatory axis implicating the 5 m6aARLncs, 5 m6aRGs and 21 ARGs, which may play a pivotal role in ESCC pathogenesis. Finally, the results of RT-qPCR verification indicated that the expressions of FAM222A-AS1, LINC00601, LINC00847, LINC01554 and UBL7-AS1 were upregulated. Conclusion: This study yields findings that hold significant clinical implications for refining prognostic stratification and deciphering the immune landscape in ESCC. Moreover, they offer novel perspectives that may pave the way for more precise prognostic assessment and the formulation of targeted therapeutic interventions.