The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Gene Expression Omnibus (GEO) contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server GEO. A total of 91 samples were collected, including 81 HCC samples and 10 healthy samples as control. GSE62232 was analyzed through GEO2R, and functional enrichment analysis was performed to extract rational information from a set of DEGs. The protein-protein relationship networking search method was used for extracting interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analyzed using GEPIA to estimate the effect of their differential expression on cancer progression. We identified the top 10 hub genes through Cytohubba plugin. These genes include cell cycle regulatory cyclins and cyclin-dependent proteins CCNA2, CCNB1 and CDK1. The pathogenesis and prognosis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarker studies that could promote selective molecular therapy for HCC.
The continuous expansion of nucleic acid detection applications has resulted in constant developments in rapid, low‐consumption, and highly automated nucleic acid extraction methods. Nucleic acid extraction using magnetic beads across an immiscible phase interface offers significant simplification and parallelization potential. The gas–liquid immiscible phase valve eliminates the requirement for complicated cassettes and is suitable for automation applications. By analyzing the process of magnetic beads crossing the gas‐liquid interface, we utilized a low magnetic field strength to drive large magnetic bead packages to cross the gas‐liquid interface, providing a solution of high magnetic bead recovery rate for solid‐phase extraction with a low‐surfactant system based on gas‐liquid immiscible phase valve. The recovery rate of magnetic beads was further improved to 90%–95% and the carryover of the reagents was below 1%. Consequently, a chip and an automatic system were developed to verify the applicability of this method for nucleic acid extraction. The Hepatitis B virus serum standard was used for the extraction test. The extraction of four samples was performed within 7 minutes, with nucleic acid recovery maintained above 80% and good purity. Thus, through analysis and experiments, a fast, highly automated, and low‐consumption nucleic acid recovery method was proposed in this study.
The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Gene Expression Omnibus (GEO) contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server GEO. A total of 91 samples were collected, including 81 HCC samples and 10 healthy samples as control. GSE62232 was analyzed through GEO2R, and functional enrichment analysis was performed to extract rational information from a set of DEGs. The protein-protein relationship networking search method was used for extracting interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analyzed using GEPIA to estimate the effect of their differential expression on cancer progression. We identified the top 10 hub genes through Cytohubba plugin. These genes include cell cycle regulatory cyclins and cyclin-dependent proteins CCNA2, CCNB1 and CDK1. The pathogenesis and prognosis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarker studies that could promote selective molecular therapy for HCC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.