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 |
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John Wiley & Sons, Inc
01.01.2020
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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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| AbstractList | 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. 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.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. |
| Author | Liu, Tongyan Fang, Panqi Hu, Jingwen Wang, Siwei Xu, Weizhang Xu, Youtao Xu, Lin Ma, Zhifei Xia, Wenjia Yin, Rong Han, Chencheng Zhang, Qin |
| AuthorAffiliation | 3 Department of Clinical Pharmacy School of Basic Medical Sciences and Clinical Pharmacy China Pharmaceutical University Nanjing China 5 Jiangsu Biobank of Clinical Resources Nanjing China 1 Department of Thoracic Surgery The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Nanjing China 2 Department of Scientific Research The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Nanjing China 4 The Fourth Clinical College of Nanjing Medical University Nanjing China |
| AuthorAffiliation_xml | – name: 2 Department of Scientific Research The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Nanjing China – name: 1 Department of Thoracic Surgery The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Nanjing China – name: 3 Department of Clinical Pharmacy School of Basic Medical Sciences and Clinical Pharmacy China Pharmaceutical University Nanjing China – name: 5 Jiangsu Biobank of Clinical Resources Nanjing China – name: 4 The Fourth Clinical College of Nanjing Medical University Nanjing China |
| Author_xml | – sequence: 1 givenname: Tongyan surname: Liu fullname: Liu, Tongyan organization: The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research – sequence: 2 givenname: Panqi surname: Fang fullname: Fang, Panqi organization: China Pharmaceutical University – sequence: 3 givenname: Chencheng surname: Han fullname: Han, Chencheng organization: The Fourth Clinical College of Nanjing Medical University – sequence: 4 givenname: Zhifei surname: Ma fullname: Ma, Zhifei organization: The Fourth Clinical College of Nanjing Medical University – sequence: 5 givenname: Weizhang surname: Xu fullname: Xu, Weizhang organization: The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research – sequence: 6 givenname: Wenjia surname: Xia fullname: Xia, Wenjia organization: The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research – sequence: 7 givenname: Jingwen surname: Hu fullname: Hu, Jingwen organization: The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research – sequence: 8 givenname: Youtao surname: Xu fullname: Xu, Youtao organization: The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research – sequence: 9 givenname: Lin surname: Xu fullname: Xu, Lin organization: The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research – sequence: 10 givenname: Rong surname: Yin fullname: Yin, Rong organization: Jiangsu Biobank of Clinical Resources – sequence: 11 givenname: Siwei orcidid: 0000-0001-5077-060X surname: Wang fullname: Wang, Siwei email: szl_wangsiwei@163.com organization: The Fourth Clinical College of Nanjing Medical University – sequence: 12 givenname: Qin surname: Zhang fullname: Zhang, Qin email: zhangqin804@sohu.com organization: The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31746108$$D View this record in MEDLINE/PubMed |
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| Keywords | machine learning transcription profile oesophageal cancer prognostic signature |
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| Snippet | Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health‐related quality of life. Here, we investigated the transcriptome... Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome... |
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| SubjectTerms | 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 |
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| Title | Four transcription profile–based models identify novel prognostic signatures in oesophageal cancer |
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