Four transcription profile–based models identify novel prognostic signatures in oesophageal cancer

Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health‐related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk‐related signatures. A total of 159 ESCA patients of The Cancer Genome Atlas (TCGA) were sorted by three phases....

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Veröffentlicht in:	Journal of cellular and molecular medicine Jg. 24; H. 1; S. 711 - 721
Hauptverfasser:	Liu, Tongyan, Fang, Panqi, Han, Chencheng, Ma, Zhifei, Xu, Weizhang, Xia, Wenjia, Hu, Jingwen, Xu, Youtao, Xu, Lin, Yin, Rong, Wang, Siwei, Zhang, Qin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England John Wiley & Sons, Inc 01.01.2020 John Wiley and Sons Inc
Schlagworte:	Algorithms Apoptosis Artificial intelligence Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Biomedical research Cancer therapies Cell adhesion & migration Cell growth Cell Proliferation Datasets Esophageal cancer Esophageal Neoplasms - genetics Esophageal Neoplasms - metabolism Esophageal Neoplasms - pathology Esophageal Squamous Cell Carcinoma - genetics Esophageal Squamous Cell Carcinoma - metabolism Esophageal Squamous Cell Carcinoma - pathology Esophagus Ethics Feature selection Female Gene expression Gene Expression Regulation, Neoplastic Genomes Human subjects Humans Learning algorithms Liver cancer Machine Learning Male Medical prognosis Metastasis MicroRNAs Middle Aged oesophageal cancer Original Pore size Prognosis prognostic signature Quality of life Ribonucleic acid RNA Software Squamous cell carcinoma Studies Support vector machines Survival analysis Survival Rate Transcription transcription profile Transcriptome Transcriptomes Tumor Cells, Cultured machine learning transcription profile oesophageal cancer prognostic signature
ISSN:	1582-1838, 1582-4934, 1582-4934
Online-Zugang:	Volltext
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