Machine Learning‐Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma DOI Creative Commons
Lin Zhu, Liang Feng,

Xue Han

и другие.

Journal of Cellular and Molecular Medicine, Год журнала: 2025, Номер 29(6)

Опубликована: Март 1, 2025

ABSTRACT 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 GEO datasets, established robust 15‐gene signature that effectively stratified patients into distinct risk groups. This demonstrated superior value revealed significant associations with immune infiltration patterns. High‐risk exhibited reduced infiltration, particularly in B cells NK cells, alongside increased tumour purity. Single‐cell RNA sequencing uncovered unique cellular composition patterns enhanced interaction intensities the high‐risk group, especially within epithelial smooth muscle cells. Functional validation confirmed MECP2 as promising therapeutic target, its knockdown significantly inhibiting progression both vitro vivo. Drug sensitivity identified specific agents showing potential efficacy patients. Our study provides practical tool insights relationship between metabolism immunity ESCC, offering strategies personalised treatment.

Язык: Английский

PADI4 facilitates stem‐like properties and cisplatin resistance through upregulating PRMT2/IDs family in oesophageal squamous cell carcinoma DOI Creative Commons
Zeyu Wang, Hao Wu, Zhaoxing Li

и другие.

Clinical and Translational Medicine, Год журнала: 2025, Номер 15(3)

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Machine Learning‐Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma DOI Creative Commons
Lin Zhu, Liang Feng,

Xue Han

и другие.

Journal of Cellular and Molecular Medicine, Год журнала: 2025, Номер 29(6)

Опубликована: Март 1, 2025

ABSTRACT 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 GEO datasets, established robust 15‐gene signature that effectively stratified patients into distinct risk groups. This demonstrated superior value revealed significant associations with immune infiltration patterns. High‐risk exhibited reduced infiltration, particularly in B cells NK cells, alongside increased tumour purity. Single‐cell RNA sequencing uncovered unique cellular composition patterns enhanced interaction intensities the high‐risk group, especially within epithelial smooth muscle cells. Functional validation confirmed MECP2 as promising therapeutic target, its knockdown significantly inhibiting progression both vitro vivo. Drug sensitivity identified specific agents showing potential efficacy patients. Our study provides practical tool insights relationship between metabolism immunity ESCC, offering strategies personalised treatment.

Язык: Английский

Процитировано

0