Wnt/β-Catenin, Carbohydrate Metabolism, and PI3K-Akt Signaling Pathway-Related Genes as Potential Cancer Predictors
Predicting the outcome after a cancer diagnosis is critical. Advances in high-throughput sequencing technologies provide physicians with vast amounts of data, yet prognostication remains challenging because the data are greatly dimensional and complex. We evaluated Wnt/β-catenin, carbohydrate metabo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2019-01-01
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| Series: | Journal of Healthcare Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/9724589 |
| Summary: | Predicting the outcome after a cancer diagnosis is critical. Advances in high-throughput sequencing technologies provide physicians with vast amounts of data, yet prognostication remains challenging because the data are greatly dimensional and complex. We evaluated Wnt/β-catenin, carbohydrate metabolism, and PI3K-Akt signaling pathway-related genes as predictive features for classifying tumors and normal samples. Using differentially expressed genes as controls, these pathway-related genes were assessed for accuracy using support-vector machines and three other recommended machine learning models, namely, the random forest, decision tree, and k-nearest neighbor algorithms. The first two outperformed the others. All candidate pathway-related genes yielded areas under the curve exceeding 95.00% for cancer outcomes, and they were most accurate in predicting colorectal cancer. These results suggest that these pathway-related genes are useful and accurate biomarkers for understanding the mechanisms behind cancer development. |
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| ISSN: | 2040-2295 2040-2309 |