Glycine Decarboxylase Suppresses the Renal Cell Carcinoma Growth and Regulates Its Gene Expressions and Functions.

Background: Glycine decarboxylase (GLDC), a key metabolic enzyme, participates in the regulation of the glycine metabolic pathway. Differential expression of GLDC is linked to the malignant growth of renal cell carcinoma (RCC) and may regulate tumor progression through other genes. However, the regulatory function of GLDC in RCC is currently unknown. The purpose of this work was to evaluate the roles of GLDC in the invasion, proliferation, and migration of RCC cells and elucidate the processes underlying RCC development. Methods: The expression of GLDC in RCC cell lines and tissues was identified by quantitative reverse transcription polymerase chain reaction (PCR) and western blot. A stably transfected cell line overexpressing GLDC was constructed using a lentiviral vector. Cell proliferation was detected using Cell Counting Kit-8 (CCK8) and EdU experiments, and scratch and transwell assays were used to determine migration and invasion capabilities. Furthermore, differential proteins were identified and obtained using high-performance liquid chromatography (HPLC)-tandem mass spectrometry (MS/MS) analysis. Finally, these differential proteins were analyzed by bioinformatics, including cluster analysis, subcellular localization, domain annotation, annotation of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, and study of protein-protein interactions. Results: GLDC expression was found to be lower in six RCC cell lines (786-O, A498, Caki-1, 769-P, OSRC-2, and ACHN) than in 293T cells and decreased in kidney cancer tissues compared to neighboring normal tissues. Overexpression of GLDC inhibited the proliferation of RCC cells as well as their migration and invasion abilities. Tandem mass tag analysis showed that 317 and 236 genes were downregulated and upregulated, respectively, when GLDC was overexpressed in A498 cells. Tandem mass tag analysis showed that 317 and 236 genes were downregulated and upregulated, respectively, when GLDC was overexpressed in A498 cells. Volcano plot showed these upregulated and downregulated proteins. Cluster analysis showed that differentially expressed protein screening can represent the effect of biological treatment on samples. Subcellular localization analysis showed differential proteins are mainly distributed in the nucleus, cytoplasm, mitochondria, plasma membrane, extracellular matrix, and lysosome. GO annotation showed many biological processes in the cells were changed, including "positive regulation of histone H3-K4 methylation", "cofactor binding", and "nuclear body". KEGG pathway analysis showed key pathways have all undergone considerable alterations, such as "cell cycle", "glyoxylate and dicarboxylate metabolism", and "threonine, glycine, and serine metabolism". Finally, highly aggregated proteins with the same or similar functions were acquired by analysis of the protein-protein interaction (PPI) network. Conclusions: These studies indicate that GLDC overexpression suppresses the invasion, proliferation, and migration of RCC cells and leads to the upregulation and downregulation of 236 and 317 genes, respectively.

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data.

Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene-gene interactions involved in these susceptible pathways with their protein-protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

Pathway-based analysis tools for complex diseases: a review.

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehensive understanding of the molecular mechanisms underlying complex diseases. Extensive studies utilizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods-the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available pathway-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are discussed. This review will provide a useful guide to dissect complex diseases.