Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis

• Published in: Discover. Oncology 2024-09, Vol.15 (1), p.505-26, Article 505
• Main Authors: He, Rongzhang, Tang, Jing, Lai, Haotian, Zhang, Tianchi, Du, Linjuan, Wei, Siqi, Zhao, Ping, Tang, Guobin, Liu, Jie, Luo, Xiufang
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Biomarkers; Cancer Research; Cells; Chemotherapy; Communication; Data analysis; Datasets; Disease; Esophageal cancer; Gene expression; Immunotherapy; Integrated approach; Internal Medicine; Medical prognosis; Medicine; Medicine & Public Health; Metabolism; Molecular Medicine; Oncology; Pathogenesis; Radiotherapy; Signaling pathways; Sphingolipid metabolism; Squamous cell carcinoma; Surgical Oncology; Tumor microenvironment
• ISSN: 2730-6011; 2730-6011
• DOI: 10.1007/s12672-024-01379-1

Background Esophageal squamous cell carcinoma (ESCC) stands as a significant global health challenge, distinguished by its aggressive progression from the esophageal epithelium. Central to this malignancy is sphingolipid metabolism, a critical pathway that governs key cellular processes, including apoptosis and immune regulation, thereby influencing tumor behavior. The advent of single-cell and transcriptome sequencing technologies has catalyzed significant advancements in oncology research, offering unprecedented insights into the molecular underpinnings of cancer. Methods We explored sphingolipid metabolism-related genes in ESCC using scRNA-seq data from GEO and transcriptome data from TCGA. We assessed 97 genes in epithelial cells with AUCell, UCell, and singscore algorithms, followed by bulk RNA-seq and differential analysis to identify prognosis-related genes. Immune infiltration and potential immunotherapeutic strategies were also investigated, and tumor gene mutations and drug treatment strategies were analyzed. Result Our study identified distinct gene expression patterns, highlighting ARSD, CTSA, DEGS1, and PPTQ's roles in later cellular stages. We identified seven independent prognostic genes and created a precise nomogram for prognosis. Conclusion This study integrates single-cell and transcriptomic data to provide a reliable prognostic model associated with sphingolipid metabolism and to inform immunotherapy and pharmacotherapy for ESCC at the genetic level. The findings have significant implications for precision therapy in esophageal cancer.