Protein-protein interaction network analysis of cirrhosis liver disease

Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research

Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task

Methods: Essential analysis, such as gene ontology [GO] enrichment and protein-protein interactions [PPI] was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query

Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins [fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 [beta], lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I] as hub and bottleneck proteins

Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease

Metabolomic analysis of human cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease and non-alcoholic steatohepatitis diseases

Metabolome analysis is used to evaluate the characteristics and interactions of low molecular weight metabolites under a specific set of conditions. In cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease [NAFLD] and non-alcoholic steatotic hepatitis [NASH] the liver does not function thoroughly due to long-term damage. Unfortunately the early detection of cirrhosis, HCC, NAFLD and NASH is a clinical problem and determining a sensitive, specific and predictive novel method based on biomarker discovery is an important task. On the other hand, metabolomics has been reported as a new and powerful technology in biomarker discovery and dynamic field that cause global comprehension of system biology. In this review, it has been collected a heterogeneous set of metabolomics published studies to discovery of biomarkers in researches to introduce diagnostic biomarkers for early detection and the choice of patient-specific therapies

Protein-protein interaction network of celiac disease

Aim: The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease

Background: Celiac disease [CD] is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye andbarley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug targetdiscovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiacdisease

Material and Methods: In the present study, we collected the articles that focused on the proteomic data in celiac disease.According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. Thenetworks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods suchas MCODE and ClueGO

Results: According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha [cytosolic and Grp94]; class A, Band 1 member, Heat shock 70kDa protein, and protein 5 [glucose-regulated protein, 78kDa], T-complex, Chaperon incontaining TCP1; subunit 7 [beta] and subunit 4 [delta] and subunit 2 [beta], have been introduced as hub-bottlnecksproteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins

Conclusion: Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hubbottlneckproteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease

association between polymorphisms in insulin and obesity related genes and risk of colorectal cancer

Colon cancer is the cancer of the large intestine [colon], which is located in the lower part of digestive system. Colon cancer is the third most common cancer in men and the second in women worldwide. Genetic background is thought to play a role in modulating individual risks of this cancer. Many studies support an association between insulin pathway gene polymorphisms and regulation of tumor cell biology in colorectal cancer. This review examines the role of polymorphisms of insulin and obesity pathway genes [IGFs, INS, INSR, ADIPOQ, ADIPOQR, LEP and LEPR] in development of colorectal cancer

[Association of the insulin gene polymorphism and colorectal cancer]

Some studies have determined that polymorphism in insulin gene are associated with increased insulin level and resistant to insulin and also cause to increase risk of colorectal cancer [CRC]. The goal of this study was to evaluate incidence of the insulin gene polymorphism [rs689] in an Iranian population and to investigate the role of this polymorphism in increased risk of CRC. Genotyping of the insulin gene were determined in a series of 110 colorectal cancer patients and 110 controls by using polymerase chain reaction and restriction fragment length polymorphism genotyping assays [PCR-RFLP]. P value for genotype AT compared with AA, was 0.052 [OR=1.88, CI=0.99-3.5] and TT versus AA was 0.57 [OR=1.33 CI=0.48- 3.6]. The results showed that the insulin gene polymorphism [rs689] is not a predisposing factor to increased risk to CRC [P=0.14]. Incidence of mutant allele between patients and controls had no significant differences [OR=1.53 95% CI=0.98- 2.39, Pe=0.057]. These findings suggest that the insulin gene polymorphism [rs689] is not associated with increased risk of CRC