Open AccessLipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer
Author(s) -
Hiroki Ishii,
Masao Saitoh,
Kiyomi Sakamoto,
Kei Sakamoto,
Daisuke Saigusa,
Hirotake Kasai,
Kei Ashizawa,
Keiji Miyazawa,
Sén Takeda,
Keisuke Masuyama,
Kenichi Yoshimura
Publication year - 2020
Publication title -
british journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.833
H-Index - 236
eISSN - 1532-1827
pISSN - 0007-0920
DOI - 10.1038/s41416-020-0732-y
Subject(s) - head and neck squamous cell carcinoma , lipidome , transforming growth factor , head and neck , cancer research , cancer , cytokine , head and neck cancer , biology , pathology , medicine , lipidomics , bioinformatics , immunology , surgery
Several pro-oncogenic signals, including transforming growth factor beta (TGF-β) signalling from tumour microenvironment, generate intratumoural phenotypic heterogeneity and result in tumour progression and treatment failure. However, the precise diagnosis for tumour areas containing subclones with cytokine-induced malignant properties remains clinically challenging.