基于机器学习的糖脂代谢基因特征预测食管鳞状细胞癌的预后和免疫景观

Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycolipid metabolism-related genes. Through integrated analysis of TCGA and GEO datasets, we established a robust 15-gene signature that effectively stratified patients into distinct risk groups. This signature demonstrated superior prognostic value and revealed significant associations with immune infiltration patterns. High-risk patients exhibited reduced immune cell infiltration, particularly in B cells and NK cells, alongside increased tumour purity.

Single-cell RNA sequencing analysis uncovered unique cellular composition patterns and enhanced interaction intensities in the high-risk group, especially within epithelial and smooth muscle cells. Functional validation confirmed MECP2 as a promising therapeutic target, with its knockdown significantly inhibiting tumour progression both in vitro and in vivo. Drug sensitivity analysis identified specific therapeutic agents showing potential efficacy for high-risk patients.

Our study provides both a practical prognostic tool and novel insights into the relationship between glycolipid metabolism and tumour immunity in ESCC, offering potential strategies for personalised treatment.