Non-selective beta-blockers and the incidence of hepatocellular carcinoma in patients with cirrhosis: a meta-analysis

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Trial watch: beta-blockers in cancer therapy

Compelling evidence supports the hypothesis that stress negatively impacts cancer development and prognosis. Irrespective of its physical, biological or psychological source, stress triggers a physiological response that is mediated by the hypothalamic-pituitary-adrenal axis and the sympathetic adrenal medullary axis. The resulting release of glucocorticoids and catecholamines into the systemic circulation leads to neuroendocrine and metabolic adaptations that can affect immune homeostasis and immunosurveillance, thus impairing the detection and eradication of malignant cells. Moreover, catecholamines directly act on β-adrenoreceptors present on tumor cells, thereby stimulating survival, proliferation, and migration of nascent neoplasms. Numerous preclinical studies have shown that blocking adrenergic receptors slows tumor growth, suggesting potential clinical benefits of using β-blockers in cancer therapy. Much of these positive effects of β-blockade are mediated by improved immunosurveillance. The present trial watch summarizes current knowledge from preclinical and clinical studies investigating the anticancer effects of β-blockers either as standalone agents or in combination with conventional antineoplastic treatments or immunotherapy.