Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinoma
Background Developments in high‐throughput genomic technologies have led to improved understanding of the molecular underpinnings of esophageal squamous cell carcinoma (ESCC). However, there is currently no model that combines the clinical features and gene expression signatures to predict outcomes....
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Thoracic Cancer |
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| Online Access: | https://doi.org/10.1111/1759-7714.12902 |
| _version_ | 1819158347616616448 |
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| author | Lin Wang Gaochao Dong Wenjie Xia Qixing Mao Anpeng Wang Bing Chen Weidong Ma Yaqin Wu Lin Xu Feng Jiang |
| author_facet | Lin Wang Gaochao Dong Wenjie Xia Qixing Mao Anpeng Wang Bing Chen Weidong Ma Yaqin Wu Lin Xu Feng Jiang |
| author_sort | Lin Wang |
| collection | DOAJ |
| description | Background Developments in high‐throughput genomic technologies have led to improved understanding of the molecular underpinnings of esophageal squamous cell carcinoma (ESCC). However, there is currently no model that combines the clinical features and gene expression signatures to predict outcomes. Methods We obtained data from the GSE53625 database of Chinese ESCC patients who had undergone surgical treatment. The R packages, Limma and WGCNA, were used to identify and construct a co‐expression network of differentially expressed genes, respectively. The Cox regression model was used, and a nomogram prediction model was constructed. Results A total of 3654 differentially expressed genes were identified. Bioinformatics enrichment analysis was conducted. Multivariate analysis of the clinical cohort revealed that age and adjuvant therapy were independent factors for survival, and these were entered into the clinical nomogram. After integrating the gene expression profiles, we identified a “2‐gene score” associated with overall survival. The combinational model is composed of clinical data and gene expression profiles. The C‐index of the combined nomogram for predicting survival was statistically higher than the clinical nomogram. The calibration curve revealed that the combined nomogram and actual observation showed better prediction accuracy than the clinical nomogram alone. Conclusions The integration of gene expression signatures and clinical variables produced a predictive model for ESCC that performed better than those based exclusively on clinical variables. This approach may provide a novel prediction model for ESCC patients after surgery. |
| first_indexed | 2024-12-22T16:23:13Z |
| format | Article |
| id | doaj.art-ed7a7a297b4e4bf5b80fe779e2419f79 |
| institution | Directory Open Access Journal |
| issn | 1759-7706 1759-7714 |
| language | English |
| last_indexed | 2024-12-22T16:23:13Z |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Thoracic Cancer |
| spelling | doaj.art-ed7a7a297b4e4bf5b80fe779e2419f792022-12-21T18:20:13ZengWileyThoracic Cancer1759-77061759-77142019-01-01101607010.1111/1759-7714.12902Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinomaLin Wang0Gaochao Dong1Wenjie Xia2Qixing Mao3Anpeng Wang4Bing Chen5Weidong Ma6Yaqin Wu7Lin Xu8Feng Jiang9Department of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaDepartment of Thoracic Surgery, Jiangsu Key Laboratory of Molecular and Translational Cancer Research Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & Nanjing Medical University Affiliated Cancer Hospital Nanjing ChinaBackground Developments in high‐throughput genomic technologies have led to improved understanding of the molecular underpinnings of esophageal squamous cell carcinoma (ESCC). However, there is currently no model that combines the clinical features and gene expression signatures to predict outcomes. Methods We obtained data from the GSE53625 database of Chinese ESCC patients who had undergone surgical treatment. The R packages, Limma and WGCNA, were used to identify and construct a co‐expression network of differentially expressed genes, respectively. The Cox regression model was used, and a nomogram prediction model was constructed. Results A total of 3654 differentially expressed genes were identified. Bioinformatics enrichment analysis was conducted. Multivariate analysis of the clinical cohort revealed that age and adjuvant therapy were independent factors for survival, and these were entered into the clinical nomogram. After integrating the gene expression profiles, we identified a “2‐gene score” associated with overall survival. The combinational model is composed of clinical data and gene expression profiles. The C‐index of the combined nomogram for predicting survival was statistically higher than the clinical nomogram. The calibration curve revealed that the combined nomogram and actual observation showed better prediction accuracy than the clinical nomogram alone. Conclusions The integration of gene expression signatures and clinical variables produced a predictive model for ESCC that performed better than those based exclusively on clinical variables. This approach may provide a novel prediction model for ESCC patients after surgery.https://doi.org/10.1111/1759-7714.12902ESCCnomogramprediction modelprognosis |
| spellingShingle | Lin Wang Gaochao Dong Wenjie Xia Qixing Mao Anpeng Wang Bing Chen Weidong Ma Yaqin Wu Lin Xu Feng Jiang Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinoma Thoracic Cancer ESCC nomogram prediction model prognosis |
| title | Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinoma |
| title_full | Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinoma |
| title_fullStr | Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinoma |
| title_full_unstemmed | Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinoma |
| title_short | Integrative analysis of differential genes and identification of a “2‐gene score” associated with survival in esophageal squamous cell carcinoma |
| title_sort | integrative analysis of differential genes and identification of a 2 gene score associated with survival in esophageal squamous cell carcinoma |
| topic | ESCC nomogram prediction model prognosis |
| url | https://doi.org/10.1111/1759-7714.12902 |
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