Cargando…
A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies
BACKGROUND: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD. METHODS: We analyzed the gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omn...
Autores principales: | Li, Yin, Ge, Di, Gu, Jie, Xu, Fengkai, Zhu, Qiaoliang, Lu, Chunlai |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6729062/ https://www.ncbi.nlm.nih.gov/pubmed/31488089 http://dx.doi.org/10.1186/s12885-019-6101-7 |
Ejemplares similares
-
Transcriptomic and functional network features of lung squamous cell carcinoma through integrative analysis of GEO and TCGA data
por: Li, Yin, et al.
Publicado: (2018) -
High CXCR4 Expression Predicts a Poor Prognosis in Resected Lung Adenosquamous Carcinoma
por: Zhu, Qiaoliang, et al.
Publicado: (2020) -
Neoadjuvant pembrolizumab and chemotherapy in resectable clinical stage III non-small-cell lung cancer: a retrospective cohort study
por: Zhao, Guangyin, et al.
Publicado: (2023) -
Development of a ferroptosis-based model to predict prognosis, tumor microenvironment, and drug response for lung adenocarcinoma with weighted genes co-expression network analysis
por: Cheng, Tao, et al.
Publicado: (2022) -
Calpain-2 Enhances Non-Small Cell Lung Cancer Progression and Chemoresistance to Paclitaxel via EGFR-pAKT Pathway
por: Xu, Fengkai, et al.
Publicado: (2019)