Four transcription profile–based models identify novel prognostic signatures in oesophageal cancer. Issue 1 (19th November 2019)

• Record Type: Journal Article
• Authors: Liu, Tongyan; Fang, Panqi; Han, Chencheng; Ma, Zhifei; Xu, Weizhang; Xia, Wenjia; Hu, Jingwen; Xu, Youtao; Xu, Lin; Yin, Rong; Wang, Siwei; Zhang, Qin
• Is Part Of: Journal of cellular and molecular medicine. Volume 24:Issue 1(2020)
• Page Start: 711
• Page End: 721
• Publication Date: 2019-11-19
• Subjects: machine learning -- oesophageal cancer -- prognostic signature -- transcription profile; Cytology; Medicine; Molecular Biology; Cytologie -- Périodiques; Médecine -- Périodiques; Biologie moléculaire -- Périodiques; Cytology -- Periodicals; Medicine -- Periodicals; Molecular biology -- Periodicals; 611.01805
• DOI: 10.1111/jcmm.14779
• Languages: English
• ISSNs: 1582-1838

Abstract

Abstract: 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.