CNDP1 Suppresses the Malignant Behavior of Hepatoma Cell Restricting PI3K-AKT-mTOR Activation.

Introduction: Hepatocellular carcinoma (HCC) is a global health problem with increasing morbidity and mortality, and exploring the diagnosis and treatment of HCC at the gene level has become a research hotspot in recent years. As the rate-limiting enzyme of carnosine hydrolysis, CNDP1 participates in the progress of many diseases, but its function in HCC has not been fully elucidated.

Methods: This study firstly screened differentially expressed genes from the biochip related to HCC by bioinformatic analysis, and CNDP1 was finally selected for in-depth study.

CTSG Suppresses Colorectal Cancer Progression through Negative Regulation of Akt/mTOR/Bcl2 Signaling Pathway.

Colorectal cancer (CRC) is the most common gastrointestinal tumor worldwide, which is a severe malignant disease that threatens mankind. Cathepsin G (CTSG) has been reported to be associated with tumorigenesis, whereas its role in CRC is still unclear. This investigation aims to determine the function of CTSG in CRC.

Identification of Prognostic Biomarkers for Bladder Cancer Based on DNA Methylation Profile.

DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation.

Identification of modules related to programmed cell death in CHD based on EHEN.

The formation and death of macrophages and foam cells are one of the major factors that cause coronary heart disease (CHD). In our study, based on the Edinburgh Human Metabolic Network (EHMN) metabolic network, we built an enzyme network which was constructed by enzymes (nodes) and reactions (edges) called the Edinburgh Human Enzyme Network (EHEN). By integrating the subcellular location information for the reactions and refining the protein-reaction relationships based on the location information, we proposed a computational approach to select modules related to programmed cell death.

Cancer-risk module identification and module-based disease risk evaluation: a case study on lung cancer.

Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples.

Identifying colon cancer risk modules with better classification performance based on human signaling network.

Identifying differences between normal and tumor samples from a modular perspective may help to improve our understanding of the mechanisms responsible for colon cancer. Many cancer studies have shown that signaling transduction and biological pathways are disturbed in disease states, and expression profiles can distinguish variations in diseases. In this study, we integrated a weighted human signaling network and gene expression profiles to select risk modules associated with tumor conditions.