Genomic instabilities in hepatocellular carcinoma: biomarkers and application in immunotherapies.

Hepatocellular carcinoma (HCC) is one of the deadliest cancers. For patients with advanced HCC, liver function decompensation often occurs, which leads to poor tolerance to chemotherapies and other aggressive treatments. Therefore, it remains critical to develop effective therapeutic strategies for HCC. Etiological factors for HCC are complex and multifaceted, including hepatitis virus infection, alcohol, drug abuse, chronic metabolic abnormalities, and others. Thus, HCC has been categorized as a "genomically unstable" cancer due to the typical manifestation of chromosome breakage and aneuploidy, and oxidative DNA damage. In recent years, immunotherapy has provided a new option for cancer treatments, and the degree of genomic instability positively correlates with immunotherapy efficacies. This article reviews the endogenous and exogenous causes that affect the genomic stability of liver cells; it also updates the current biomarkers and their detection methods for genomic instabilities and relevant applications in cancer immunotherapies. Including genomic instability biomarkers in consideration of cancer treatment options shall increase the patients' well-being.

Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication.

Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.