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. Risk modules as classification features by our method had a better classification performance than other methods, and one risk module for colon cancer had a good classification performance for distinguishing between normal/tumor samples and between tumor stages. All genes in the module were annotated to the biological process of positive regulation of cell proliferation, and were highly associated with colon cancer. These results suggested that these genes might be the potential risk genes for colon cancer.

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. Our results indicated that CTSG was inhibited in CRC tissues, and patients with CTSG low expression have poor overall survival. Functional experiments revealed that CTSG overexpression suppressed CRC cell progression in vitro and in vivo, whereas CTSG suppression supports CRC development cells in vitro and in vivo. Mechanistically, CTSG overexpression suppressed Akt/mTOR signaling mechanism and elevated apoptotic-associated markers, and CTSG silencing activated Akt/mTOR signaling mechanisms and inhibited apoptotic-associated markers. Furthermore, the Akt suppression signaling pathway by MK2206 abolishes CTSG-silenced expression-induced cell viability and Bcl2 up-regulation in vitro and in vivo. Altogether, these outcomes demonstrate that CTSG may act as a tumor suppressor gene via Akt/mTOR/Bcl2-mediated anti-apoptotic signaling inactivation, and CTSG represents a potential therapeutic target in CRC.

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

Background: 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. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment. Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan. Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients. Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.

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. The identified module was in the EHEN-mitochondria (EHEN-M) and was confirmed to be related to programmed cell death, CHD pathogenesis, and lipid metabolism in the literature. We expected this method could analyze CHD better and more comprehensively from the point of programmed cell death in subnetworks.

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. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms.