ABSTRACT
Background and Aim: The hepatitis B virus (HBV) is one of the most common causes of liver cancer in the world. This study aims to provide a better understanding of the mechanisms involved in the development and progression of HBV-associated hepatocellular carcinoma (HCC) by identifying hub genes and the pathways related to their functions. Methods: GSE83148 and GSE94660 were selected from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and a |logFC| ≥1 were identified. Common DEGs of two data sets were identified using the GEO2R tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) databases were used to identify pathways. Protein-protein interactions (PPIs) analysis was performed by using the Cytoscap and Gephi. A Gene Expression Profiling Interactive Analysis (GEPIA) analysis was carried out to confirm the target genes. Results: One hundred and ninety-eight common DEGs and 49 hub genes have been identified through the use of GEO and PPI, respectively. The GO and KEGG pathways analysis showed DEGs were enriched in the G1/S transition of cell cycle mitotic, cell cycle, spindle, and extracellular matrix structural constituent. The expression of four genes (TOP2A, CDK1, CCNA2, and CCNB2) with high scores in module 1 were more in tumor samples and have been identified by GEPIA analysis. Conclusion: In this study, the hub genes and their related pathways involved in the development of HBV-associated HCC were identified. These genes, as potential diagnostic biomarkers, may provide a potent opportunity to detect HBV-associated HCC at the earliest stages, resulting in a more effective treatment.
ABSTRACT
Background: As SARS-CoV-2 becomes a major global health, the authors aimed to predict the severity of the disease, the length of hospitalization, and the death rate of COVID-19 patients based on The Acute Physiology and Chronic Health Evaluation II (APACHE II) criteria, neutrophil-lymphocyte ratio (NLR), and C-reactive protein (CRP) levels to prioritize, and use them for special care facilities. Methods: In a retrospective study, 369 patients with COVID-19 hospitalized in the ICU from March 2021 to April 2022, were evaluated. In addition to the APACHE II score, several of laboratory factors, such as CRP and NLR, were measured. Results: The values of CRP, NLR, and APACHE II scores were significantly higher in hospitalized and intubated patients, as well as those who died 1 month and 3 months after hospital discharge than those in surviving patients. The baseline NLR levels were the strongest factor that adversely affected death in the hospital, death 1 month and 3 months after discharge, and it was able to predict death, significantly. Conclusion: CRP, NLR, and APACHE II were all linked to prognostic factors in COVID-19 patients. NLR was a better predictor of disease severity, the need for intubation, and death than the other two scoring tools.
ABSTRACT
Background: Colorectal cancer (CRC), is the third most prevalent cancer across the globe, and is often detected at advanced stage. Late diagnosis of CRC, leave the chemotherapy and radiotherapy as the main options for the possible treatment of the disease which are associated with severe side effects. In the present study, we seek to explore CRC gene expression data using a systems biology framework to identify potential biomarkers and therapeutic targets for earlier diagnosis and treatment of the disease. Methods: The expression data was retrieved from the gene expression omnibus (GEO). Differential gene expression analysis was conducted using R/Bioconductor package. The PPI network was reconstructed by the STRING. Cystoscope and Gephi software packages were used for visualization and centrality analysis of the PPI network. Clustering analysis of the PPI network was carried out using k-mean algorithm. Gene-set enrichment based on Gene Ontology (GO) and KEGG pathway databases was carried out to identify the biological functions and pathways associated with gene groups. Prognostic value of the selected identified hub genes was examined by survival analysis, using GEPIA. Results: A total of 848 differentially expressed genes were identified. Centrality analysis of the PPI network resulted in identification of 99 hubs genes. Clustering analysis dissected the PPI network into seven interactive modules. While several DEGs and the central genes in each module have already reported to contribute to CRC progression, survival analysis confirmed high expression of central genes, CCNA2, CD44, and ACAN contribute to poor prognosis of CRC patients. In addition, high expression of TUBA8, AMPD3, TRPC1, ARHGAP6, JPH3, DYRK1A and ACTA1 was found to associate with decreased survival rate. Conclusion: Our results identified several genes with high centrality in PPI network that contribute to progression of CRC. The fact that several of the identified genes have already been reported to be relevant to diagnosis and treatment of CRC, other highlighted genes with limited literature information may hold potential to be explored in the context of CRC biomarker and drug target discovery.
