Wntβ-Catenin, Carbohydrate Metabolism, and PI3K-Akt Signaling Pathway-Related Genes as Potential Cancer Predictors

الملخص EN

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.