Multi-omics approaches including single-cell analysis and machine learning were used to identify the roles of telomere-related genes in esophageal squamous cell carcinoma

作者：Shuang Li, Nan Wang, Yazhou Liu, Haitao Ma, Kai Xie, Tao Zheng · 发表于：Discover Oncology · 年份：2025 · DOI：10.1007/s12672-025-04047-0 · 被引用次数：2 · 研究领域：Single-cell and spatial transcriptomics、Esophageal Cancer Research and Treatment、Telomeres, Telomerase, and Senescence

BACKGROUND: Telomeres, consisting of TTAGGG repeats and six shelterin proteins, have been extensively studied in oncogenesis due to their crucial role in maintaining genomic stability. Despite these advances, the prognostic significance of telomere-related genes (TRGs) in esophageal squamous cell carcinoma (ESCC) remains poorly characterized. METHODS: Transcriptomic data, single-cell RNA sequencing (scRNA-seq) data, and clinical information of ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. TRGs were retrieved from the TelNet database. Core TRGs were identified using three machine learning algorithms: least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), and random forest (RF). Based on prognosis-related TRGs, unsupervised clustering was performed to classify ESCC patients into two distinct molecular subtypes, and a prognostic risk model was subsequently constructed. Following risk stratification, survival analysis, immune infiltration analysis, drug sensitivity analysis, and molecular docking were conducted to further evaluate the potential clinical value of the risk model in ESCC. In addition, the expression patterns and intercellular communication of the model genes were examined using single-cell data. Finally, the differential expression of the core genes was validated by quantitative real-time PCR (qRT-PCR). RESULTS: We developed a prognostic risk prediction model us...