ArticleJournal of cellular and molecular medicine2020
Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.
Article in Journal of cellular and molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Identification of esophageal cancer tumor antigens and immune subtypes for guiding vaccine development.Journal of thoracic disease · 2025Article
- Crotonylation-Related Prognostic Model of Esophageal Squamous Cell Carcinoma Based on Transcriptome Analysis and Single-Cell Sequencing Analysis.International journal of general medicine · 2025Article
- Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era.World journal of gastroenterology · 2024Review
- A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis.Frontiers in immunology · 2024Article
- Glutamine Deprivation Synergizes the Anticancer Effects of Cold Atmospheric Plasma on Esophageal Cancer Cells.Molecules (Basel, Switzerland) · 2023Article
- Article
- Identification of FABP7 as a Potential Biomarker for Predicting Prognosis and Antiangiogenic Drug Efficacy of Glioma.Disease markers · 2022Article
- Comprehensive analysis of a new immune-related prognostic signature for esophageal cancer and its correlation with infiltrating immune cells and target genes.Annals of translational medicine · 2021Article
- Risk Signature of Cancer-Associated Fibroblast-Secreted Cytokines Associates With Clinical Outcomes of Breast Cancer.Frontiers in oncology · 2021Article
- Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.Journal of cellular and molecular medicine · 2020Article
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Authors and funding
12 authors.
Funding
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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.
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