Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research.

Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas.

Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available.

Identification of the copy number variant biomarkers for breast cancer subtypes.

Breast cancer is a common and threatening malignant disease with multiple biological and clinical subtypes. It can be categorized into subtypes of luminal A, luminal B, Her2 positive, and basal-like. Copy number variants (CNVs) have been reported to be a potential and even better biomarker for cancer diagnosis than mRNA biomarkers, because it is considerably more stable and robust than gene expression.

miRNA-150-5p associate with antihypertensive effect of epigallocatechin-3-gallate revealed by aorta miRNome analysis of spontaneously hypertensive rat.

Aims: The antihypertensive mechanism (s) of the epigallocatechin-3-gallate (EGCG), a major effective component in green tea, might associate with microRNAs (miRNAs). Here, we aimed to investigate which microRNA in aorta of spontaneously hypertensive rats (SHRs) were modulated by administration of EGCG and its mechanism.

Main Methods: The pharmacokinetic behaviors of EGCG and epigallocatechin (EGC) in Sprague-Dawley rats were analyzed by HPLC and DRUG AND STATISTICS software.

Investigation of the roles of trace elements during hepatitis C virus infection using protein-protein interactions and a shortest path algorithm.

Background: Hepatitis is a type of infectious disease that induces inflammation of the liver without pinpointing a particular pathogen or pathogenesis. Type C hepatitis, as a type of hepatitis, has been reported to induce cirrhosis and hepatocellular carcinoma within a very short amount of time. It is a great threat to human health.

Prediction of bioactive compound pathways using chemical interaction and structural information.

The functional screening of compounds is an important topic in chemistry and biomedicine that can uncover the essential properties of compounds and provide information concerning their correct use. In this study, we investigated the bioactive compounds reported in Selleckchem, which were assigned to 22 pathways. A computational method was proposed to identify the pathways of the bioactive compounds.