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dc.contributor.authorShahid, Muhammad-
dc.description.abstractHepatitis C virus (HCV) is a major cause of chronic liver disease, affecting approximately 170 million people worldwide. In the population of Pakistan, the prevalence of chronic hepatitis C (CHC) is 6-10%. In 60% to 80% CHC patients, liver fibrosis, cirrhosis and eventually hepatocellular carcinoma (HCC) do occur due to various biological activities. Gene expression is an excellent tool for identifying biological activity and gene pathway mechanisms, and this area has opened up a new era in medical science. Gene expression is used in viral infections and cancers, providing valuable information on the progression of the disease, resistance to treatment, early diagnosis and therapeutic approaches. Several studies have been reported the dysregulated genes in liver and in the blood of HCV infected patients with different HCV genotypes. However, studies on hepatic and blood-based gene expression in CHC patients infected with genotype-3 have not been done till date. As gene expression profiling provides useful predictive information for the outcome of therapy, therefore, such type of studies need to be done. In this study, real time PCR was utilized to study gene expression profiles of HCV-infected patients and also in healthy controls. Based on the inclusion criteria, a total of 144 untreated CHC-positive patients and 20 normal samples were selected for this study. HCV genotype and viral titre were measured for all selected patients. The classification of the biopsy was done using the METAVIR scoring system. Total RNA was extracted from liver tissues, converted it to cDNA and then performed real-time PCR. The relative levels of gene expression were measured using the ΔΔCt method. Fold changes greater than 2.0 together with a P value <0.05 were considered statistically significant. iv A t-test and/or ANOVA was conducted to compare differences between patient groups for clinical and real-time PCR data. The correlation analysis was used to determine the relationship between gene expression data with clinical, virological and histopathological data of CHC patients. The logistic regression model was used to determine predictor of treatment outcome of CHC patients. It is important to mention here that all the HCV infected patients (enrolled for this study) received pegylated interferon plus ribavirin combination therapy as it was the standard treatment for HCV at the time of this study protocol. The present study was divided into three parts. In the first part of the study, we evaluated the clinical, virological and histopathological data. Analysis of variance (ANOVA) was carried out to assess differences among responders, relapsers, and non responders. AST/ALT, γ-GT, HDL, VLDL, PT, globulin, and monocytes% confirmed the statistically significant difference (P <0.05) between responders and non-responders. Logistic regression analysis was performed to determine the predictive variables of response to treatment in patients with CHC infection. The prediction analysis revealed that seven significant γ-GT, ALT, globulin, total bilirubin, albumin and fibrosis stage, and grades are predictors of treatment. In the second part of our study, we determined the gene expression in HCV positive liver tissue and normal samples. We identified 242 differentially expressed genes (DEG) between patients with sustained virological response (SVR) and non-responders (NR) patients (162 up-regulated, 80 down-regulated; P <0.005) compare to healthy controls. In our study, we identified 127 genes the expression levels of which differed between SVR and NR groups. NRs patients had higher gene expression as compare to SVR before treatment and up-regulation of genes in NR predicts non-response to therapy (P<0.05). Univariate and multivariate analysis revealed that the expression of hepatic v genes such as ADAR, IFI30, IFIT1, ISG15, MX1, TNF, SOCS2, DDX58, IL1β, OAS2, and CTGF are strong predictors of the treatment outcome for SVR and NR patients. In the third part of the clinical study, we calculated the gene expression in blood samples. We found 271 differentially expressed genes between SVR and NR patients (235 up-regulated, 36 down-regulated; P <0.005) compare to healthy controls. In our study, we classified 111 genes with different expression levels between SVR and NR groups. Univariate and multivariate analysis showed that the expression of blood genes such as CASP1, IL1β, IFI30, IFI27, ISG15, MX1, TNF, CD80, CTGF, DDX58, and CTGF were strong predictors for the treatment outcome such as SVR or NR. In order to know how reflective the gene expression changes in blood relative to hepatic gene expression, we compared the gene expression in liver and blood. We found that 27 genes were expressed in similar patterns, both in tissue and blood. We performed ROC analysis to determine the diagnostic potential of blood-based gene expression data. In our study, we identified that MX1, IFI27, ALB, CCS, IL1B, and CTGF have a first-class diagnostic value to discriminate NR patient from SVR patients with HCV infection. Analysis of Gene Ontology (GO) enrichment shows that hepatic and blood-based differentially expressed genes are enriched for cytoplasm (cellular function), signal transduction (biological process) and receptor activity (molecular function) respectively. Pathway analysis showed that immune response signaling and integrin cell surface interaction signaling are associated with expressed genes in NR and SVR patients. In addition, differentially expressed genes were found to correlate with biochemical parameters in patients with CHC. To the best of my understanding, this is the first study which determined gene expression in both liver tissue and blood from same CHC patients and correlated with biochemical, virological and histopathological data. The present study highlights the vi differentially expressed genes between SVR and NR in CHC patients. The NR patients showed high expression as compared to the SVR at baseline level before treatment. Our findings showed that blood-based gene expression changes are reflective to hepatic gene expression, so these genes can reliably be used as novel diagnostic markers in clinical practice.en_US
dc.description.sponsorshipHigher Education Commission Pakistanen_US
dc.publisherUniversity of the Punjab , Lahoreen_US
dc.subjectBiological & Medical Sciencesen_US
dc.subjectMolecular Biologyen_US
dc.titleIdentification and Correlation Analysis of Host Genes Expression in Response to Hepatitis C Virus Infection and Therapyen_US
Appears in Collections:PhD Thesis of All Public / Private Sector Universities / DAIs.

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