Comprehensive machine learning-generated classifier identifies pro-metastatic characteristics and predicts individual treatment in pancreatic cancer: A multicenter cohort study based on super-enhancer profiling

Rationale: Accumulating evidence illustrated that the reprogramming of the super-enhancers (SEs) landscape could promote the acquisition of metastatic features in pancreatic cancer (PC). Given the anatomy-based TNM staging is limited by the heterogeneous clinical outcomes in treatment, it is of grea...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Dongjie, Cao, Yizhi, Tang, Haoyu, Zang, Longjun, Yao, Na, Zhu, Youwei, Jiang, Yongsheng, Zhai, Shuyu, Liu, Yihao, Shi, Minmin, Zhao, Shulin, Wang, Weishen, Wen, Chenlei, Peng, Chenghong, Chen, Hao, Deng, Xiaxing, Jiang, Lingxi, Shen, Baiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2023
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283048/
https://www.ncbi.nlm.nih.gov/pubmed/37351165
http://dx.doi.org/10.7150/thno.84978

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283048/
https://www.ncbi.nlm.nih.gov/pubmed/37351165
http://dx.doi.org/10.7150/thno.84978

Ejemplares similares