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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Vydáno v:	Journal of cellular and molecular medicine Ročník 24; číslo 1; s. 711 - 721
Hlavní autoři:	Liu, Tongyan, Fang, Panqi, Han, Chencheng, Ma, Zhifei, Xu, Weizhang, Xia, Wenjia, Hu, Jingwen, Xu, Youtao, Xu, Lin, Yin, Rong, Wang, Siwei, Zhang, Qin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England John Wiley & Sons, Inc 01.01.2020 John Wiley and Sons Inc
Témata:	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
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Shrnutí:	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. In the discovery phase, differentially expressed transcripts were filtered; in the training phase, two adjusted Cox regressions and two machine leaning models were used to construct and estimate signatures; and in the validation phase, prognostic signatures were validated in the testing dataset and the independent external cohort. We constructed two signatures from three types of RNA markers by Akaike information criterion (AIC) and least absolute shrinkage and selection operator (LASSO) Cox regressions, respectively, and all candidate markers were further estimated by Random Forest (RFS) and Support Vector Machine (SVM) algorithms. Both signatures had good predictive performances in the independent external oesophageal squamous cell carcinoma (ESCC) cohort and performed better than common clinicopathological indicators in the TCGA dataset. Machine learning algorithms predicted prognosis with high specificities and measured the importance of markers to verify the risk weightings. Furthermore, the cell function and immunohistochemical (IHC) staining assays identified that the common risky marker FABP3 is a novel oncogene in ESCA.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Liu, Fang and Han contributed equally to the paper.
ISSN:	1582-1838 1582-4934 1582-4934
DOI:	10.1111/jcmm.14779