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Wnt/?-Catenin, Carbohydrate Metabolism, and PI3K-Akt Signaling Pathway-Related Genes as Potential Cancer Predictors.

ABSTRACT: 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/?-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.

SUBMITTER: Chen P

PROVIDER: S-EPMC6855054 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Wnt/<i>β</i>-Catenin, Carbohydrate Metabolism, and PI3K-Akt Signaling Pathway-Related Genes as Potential Cancer Predictors.

Chen Pengliang P Shi Pengwei P Du Gang G Zhang Zhen Z Liu Liang L

Journal of healthcare engineering 20191020

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/<i>β</i>-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 gene ...[more]

PMID: 31781361

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Project description:Lung cancer is the most prevalent in cancer-related deaths, while breast carcinoma is the second most dominant cancer in women, accounting for the most number of deaths worldwide. Cancers are heterogeneous diseases that consist of several subtypes based on the presence or absence of hormone receptors and human epidermal growth factor receptor 2. Several drugs have been developed targeting cancer biomarkers; nonetheless, their efficiency are not adequate due to the high reemergence rate of cancers and fundamental or acquired resistance toward such drugs, which leads to partial therapeutic possibilities. Recent studies on cardiac glycosides (CGs) positioned them as potent cytotoxic agents that target multiple pathways to initiate apoptosis and autophagic cell death in many cancers. In the present study, our aim is to identify the anticancer activity of a naturally available CG (strophanthidin) in human breast (MCF-7), lung (A549), and liver cancer (HepG2) cells. Our results demonstrate a dose-dependent cytotoxic effect of strophanthidin in MCF-7, A549, and HepG2 cells, which was further supported by DNA damage on drug treatment. Strophanthidin arrested the cell cycle at the G2/M phase; this effect was further validated by checking the inhibited expressions of checkpoint and cyclin-dependent kinases in strophanthidin-induced cells. Moreover, strophanthidin inhibited the expression of several key proteins such as MEK1, PI3K, AKT, mTOR, Gsk3α, and β-catenin from MAPK, PI3K/AKT/mTOR, and Wnt/β-catenin signaling. The current study adequately exhibits the role of strophanthidin in modulating the expression of various key proteins involved in cell cycle arrest, apoptosis, and autophagic cell death. Our in silico studies revealed that strophanthidin can interact with several key proteins from various pathways. Taken together, this study demonstrates the viability of strophanthidin as a promising anticancer agent, which may serve as a new anticancer drug.

Project description:ObjectivesThe study aimed to examine the anti-diabetic effects of Gynura divaricata (GD) and the underlying mechanism.MethodsInformation about the chemical compositions of GD was obtained from extensive literature reports. Potential target genes were predicted using PharmMapper and analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). To validate the results from bioinformatics analyses, an aqueous extract of GD was administered to type 2 diabetic rats established by feeding a high-fat and high-sugar diet followed by STZ injection. Key proteins of the PI3K/AKT signaling pathway and fatty acid metabolism signaling pathway were investigated by immunoblotting.ResultsThe blood glucose of the rats in the GD treatment group was significantly reduced compared with the model group without treatment. GD also showed activities in reducing the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine (CREA). The levels of urine sugar (U-GLU) and urine creatinine (U-CREA) were also lowered after treatment with GD. Bioinformatics analysis showed that some pathways including metabolic pathways, insulin resistance, insulin signaling pathway, PPAR signaling pathway, bile secretion, purine metabolism, etc. may be regulated by GD. Furthermore, GD significantly increased the protein expression levels of PKM1/2, p-AKT, PI3K p85, and GLUT4 in the rat liver. In addition, the expression levels of key proteins in the fatty acid metabolism signaling pathway including AMPK, p-AMPK, PPARα, and CPT1α were significantly upregulated. The anti-apoptotic protein BCL-2/BAX expression ratio in rats was significantly upregulated after GD intervention. These results were consistent with the bioinformatics analysis results.ConclusionsOur study suggests that GD can exert hypoglycemic effects in vivo by regulating the genes at the key nodes of the PI3K/AKT signaling pathway and fatty acid metabolism signaling pathway.

Dataset Information

Wnt/?-Catenin, Carbohydrate Metabolism, and PI3K-Akt Signaling Pathway-Related Genes as Potential Cancer Predictors.

Publications

Wnt/<i>β</i>-Catenin, Carbohydrate Metabolism, and PI3K-Akt Signaling Pathway-Related Genes as Potential Cancer Predictors.

Similar Datasets

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