Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system
| Autor: | Jing Li, Zhiqiao Zhang, Tingshan He, Liwen Huang, Peng Wang |
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| Rok vydání: | 2021 |
| Předmět: |
Concordance
Biophysics Translational research Logistic regression Biochemistry AJCC the American Joint Committee on Cancer 03 medical and health sciences 0302 clinical medicine Structural Biology GEO the Gene Expression Omnibus Genetics Medicine Disease free survival Immune gene 030304 developmental biology ComputingMethodologies_COMPUTERGRAPHICS 0303 health sciences Framingham Risk Score Prognostic signature business.industry Proportional hazards model Cancer medicine.disease HR hazard ratio DCA decision curve analysis Computer Science Applications ROC receiver operating characteristic CI confidence interval DFS disease free survival 030220 oncology & carcinogenesis GC gastric cancer TCGA The Cancer Genome Atlas Artificial intelligence Transcription factor business SD standard deviation Gastric cancer TP248.13-248.65 Biotechnology Research Article |
| Zdroj: | Computational and Structural Biotechnology Journal Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 2329-2346 (2021) |
| ISSN: | 2001-0370 |
| Popis: | Graphical abstract The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer. Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system. Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score. The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision. |
| Databáze: | OpenAIRE |
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