Mutational landscape and DNA methylation-based classification of squamous cell carcinoma and urothelial carcinoma

作者：Min Ren, Midie Xu, Chen Chen, Ran Wei, Qianlan Yao, Liqing Jia, Qi Peng, Qifeng Wang, Qianming Bai, Xiaoli Zhu, Sheng Wu, Qinghua Xu, Xiaoyan Zhou · 发表于：Clinical Epigenetics · 年份：2025 · DOI：10.1186/s13148-025-01902-3 · 被引用次数：4 · 研究领域：Cancer Diagnosis and Treatment、Bladder and Urothelial Cancer Treatments、Esophageal Cancer Research and Treatment

BACKGROUND: Identification of the tissue of origin is fundamental for cancer treatment. However, squamous cell carcinomas from different sites lack representative histological and immunohistochemical features. This study aimed to identify mutational profiles and further establish a DNA methylation-based classification for squamous cell carcinoma and urothelial carcinoma. Samples of unambiguous squamous cell carcinomas and urothelial carcinomas were collected for targeted next-generation sequencing and mutational landscape analysis. Moreover, using Illumina methylation BeadChip data from public datasets and a local cohort, we developed a DNA methylation-based classifier utilizing the CatBoost algorithm to identify four common types of squamous cell carcinoma (lung, head and neck, esophagus, and cervix) as well as urothelial carcinoma. RESULTS: The DNA mutational profiles of squamous cell carcinomas from different sites overlapped greatly, and there was no significant difference in tumor mutation burden or microsatellite status. On the basis of public datasets and analyses via various machine learning algorithms, a DNA methylation-based classification containing 106 features by the CatBoost algorithm was constructed and reached an accuracy of 98.79% (490/496) in the training set from PanCanAtlas datasets. The predictive accuracies of the methylation classification in the public validation set and local FUSCC validation set 1 with known primary were 86.96% (340/391) and 84.87% (...