scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets. Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was carried out to explore the interaction between potential crucial genes and AF. Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 or ≤ 2/3 (|log2 FC| ≥ 0.58) and 5 with FC ≥ 2 or ≤ 0.5 (|log2 FC| ≥ 1) of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF. Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2 , may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets. Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was carried out to explore the interaction between potential crucial genes and AF. Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 and 5 with FC ≥ 2 of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF. Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2 , may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets.Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was used to explore the interaction between potential crucial genes and AF.Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 or ≤ 2/3 (|log2 FC| ≥ 0.58) and 5 with FC ≥ 2 or ≤ 0.5 (|log2 FC| ≥ 1) of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF.Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2, may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is clearly heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets.Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were enrolled. After merging all microarray data and adjusted batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out for DEGs. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The potential crucial genes coupled with corresponding predicted microRNAs involved in AF were then assessed.Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 and 5 with FC ≥ 2 of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. Sixteen corresponding predicted microRNAs, of which 5 targeting IGFBP3 and 8 FHL2, might be associated with AF. The comparative toxicogenomics database (CTD) database showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF.Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2, may be associated with risk of AF. MicroRNAs targeting IGFBP3 and FHL2 may be potential biomarkers or therapeutic targets for AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

Identification of MEG8-miR378d-SOBP Axis as a Novel Regulatory Network and Associated With Immune Infiltrates in Ovarian Carcinoma by Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Jian Lei ◽  
Zhen-Yu He ◽  
Jun Wang ◽  
Min Hu ◽  
Ping Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Human Protein Atlas

Abstract BackgroundTo investigate the potential molecular mechanism of ovarian cancer (OC) evolution and immunological correlation using the integrated bioinformatics analysis.MethodsData from the Gene Expression Omnibus (GEO) was used to gain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were completed by utilizing the Database for Annotation, Visualization, and Integrated Discovery (DAVID). After multiple validation via The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis 2 (GEPIA 2), the Human Protein Atlas (HPA) and Kaplan-Meier (KM) plotter, immune logical relationships of the key gene SOBP were evaluated based on Tumor Immune Estimation Resource (TIMER), and Gene Set Enrichment Analysis (GSEA) software. Finally, the lncRNAs-miRNAs-mRNAs sub-network was predicted by starBase, Targetscan, miRBD, and LncBase, individually. Correlation of expression and prognosis for mRNAs, miRNAs and lncRNAs were confirmed by TCGA, GEPIA 2, starBase, and KM.ResultsA total of 192 shared DEGs were discovered from the four data sets, including 125 upregulated and 67 downregulated genes. Functional enrichment analysis presented that they were mainly enriched in cartilage development, pathway in PI3K-Akt signaling pathway. Lower expression of SOBP was the independent prognostic factor for inferior prognosis in OC patients. Intriguingly, downregulated SOBP enhanced the infiltration levels of B cells, CD8+ T cells, Macrophage, Neutrophil and Dendritic cells. GSEA also disclosed low SOBP showed significantly association with the activation of various immune-related pathways. Finally, we firstly reported that MEG8-miR378d-SOBP axis was linked to development and prognosis of ovarian cancer through regulating cytokines pathway.Conclusions Our study establishes a novel MEG8-miR378d-SOBP axis in the development and prognosis of OC, and the triple sub-network probably affects the progression of ovarian tumor by regulating cytokines pathway.

scholarly journals Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis

2020 ◽  
Vol 48 (5) ◽  
pp. 030006052092167
Author(s):  
Yingyuan Li ◽  
Wulin Tan ◽  
Fang Ye ◽  
Shihong Wen ◽  
Rong Hu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Microarray Data Analysis ◽  
Protein Protein Interaction ◽  
Inflammatory Processes ◽  
Key Genes

Objective Stroke is a severe complication of atrial fibrillation (AF). We aimed to discover key genes and microRNAs related to stroke risk in patients with AF using bioinformatics analysis. Methods GSE66724 microarray data, including peripheral blood samples from eight patients with AF and stroke and eight patients with AF without stroke, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AF patients with and without stroke were identified using the GEO2R online tool. Functional enrichment analysis was performed using the DAVID database. A protein–protein interaction (PPI) network was obtained using the STRING database. MicroRNAs (miRs) targeting these DEGs were obtained from the miRNet database. A miR–DEG network was constructed using Cytoscape software. Results We identified 165 DEGs (141 upregulated and 24 downregulated). Enrichment analysis showed enrichment of certain inflammatory processes. The miR–DEG network revealed key genes, including MEF2A, CAND1, PELI1, and PDCD4, and microRNAs, including miR-1, miR-1-3p, miR-21, miR-21-5p, miR-192, miR-192-5p, miR-155, and miR-155-5p. Conclusion Dysregulation of certain genes and microRNAs involved in inflammation may be associated with a higher risk of stroke in patients with AF. Evaluating these biomarkers could improve prediction, prevention, and treatment of stroke in patients with AF.

scholarly journals Integrative analyses of biomarkers and pathways for heart failure

2021 ◽  
Author(s):  
Shaowei Fan ◽  
Yuanhui Hu
Keyword(s):  
Heart Failure ◽  
Extracellular Matrix ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Signal Pathway ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Ppi Networks ◽  
Key Genes

Abstract Background: Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. Methods: The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. Results: We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine / threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. Conclusions: the enrichment pathway of DEGs and go ontology may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

scholarly journals Identification of novel potential biomarkers in hepatocarcinoma cancer; a transcriptome analysis

2021 ◽  
Author(s):  
Mohib kakar ◽  
Muhammad Mehboob ◽  
Muhammad Akram ◽  
Imran Iqbal ◽  
Hafza Ijaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Predictive Biomarkers ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Molecular Therapy ◽  
Hub Genes ◽  
Healthy Control

Abstract Objective The goal of this study was to understand possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis. Methods GEO contains datasets of gene expression, miRNA and methylation patterns of diseased and healthy/control patients. GSE62232 Dataset was selected by employing the server Gene Expression Omnibus. 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 has been used for extracting genes interacting. 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. Results 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. Conclusion In this analysis, we found critical genes for HCC that showed recommendations for more diagnostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

scholarly journals Bioinformatic Identification of Hub Genes and Biological Pathways for Sepsis-Associated Acute Lung Injury

2021 ◽  
Author(s):  
Chao Zhang ◽  
Feng Xu ◽  
Fang Fang
Keyword(s):  
Gene Expression ◽  
Acute Lung Injury ◽  
Lung Injury ◽  
Function Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Biological Pathways ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Sepsis-associated acute lung injury (ALI) is a potentially lethal complication associated with a poor prognosis and high mortality worldwide, especially in the outbreak of COVID-19. However, the fundamental mechanisms of this complication were still not fully elucidated. Thus, we conducted this study to identify hub genes and biological pathways of sepsis-associated ALI, mainly focus on two pathways of LPS and HMGB1. Methods: Gene expression profile GSE3037 were downloaded from Gene Expression Omnibus (GEO) database, including 8 patients with sepsis-induced acute lung injury, with 8 unstimulated blood neutrophils, 8 LPS- induced neutrophils and 8 HMGB1-induced neutrophils. Differentially expressed genes (DEGs) identifications, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network constructions were performed to obtain hub genes and relevant biological pathways.Results: We identified 534 and 317 DEGs for LPS- and HMGB1-induced ALI, respectively. The biological pathways involved in LPS- and HMGB1-induced ALI were also identified accordingly. By PPI network analysis, we found that ten hub genes for LPS-induced ALI (CXCL8, TNF, IL6, IL1B, ICAM1, CXCL1, CXCL2, IL1A, IL1RN and CXCL3) and another ten hub genes for HMGB1-induced ALI (CCL20, CXCL2, CXCL1, CCL4, CXCL3, CXCL9, CCL21, CXCR6, KNG1 and SST). Furthermore, by combining analysis, the results revealed that genes of TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3 were potential biomarkers for sepsis-associated ALI. Conclusions: Our study revealed that ten hub genes associated with sepsis-induced ALI were TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3, which may serve as genetic biomarkers and be further verified in prospective experimental trials.

scholarly journals Identification of ALDH3A2 as a novel prognostic biomarker in gastric adenocarcinoma using integrated bioinformatics analysis

2020 ◽  
Author(s):  
Zhenhua Yin ◽  
Dejun Wu ◽  
Jianping Shi ◽  
Xiyi Wei ◽  
Nuyun Jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reference Value ◽  
Enrichment Analysis ◽  
Predictive Marker ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Fatty Aldehyde ◽  
Low Grade

Abstract Background: Extensive research has revealed that genes play a pivotal role in tumor development and growth. However, the underlying involvement of gene expression in gastric carcinoma (GC) remains to be investigated further.Methods: In this study, we identified overlapping differentially expressed genes (DEGs) by comparing tumor tissue with adjacent normal tissue using the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database.Results: Our analysis identified 79 up-regulated and ten down-regulated genes. Functional enrichment analysis and prognosis analysis were conducted on the identified genes, and the fatty aldehyde dehydrogenase (FALDH) gene, ALDH3A2, was chosen for more detailed analysis. We performed Gene Set Enrichment Analysis (GSEA) and immunocorrelation analysis (infiltration, copy number alterations, and checkpoints) to elucidate the mechanisms of action of ALDH3A2 in depth. The immunohistochemical (IHC) result based on 140 paraffin-embedded human GC samples indicated that ALDH3A2 was over-expressed in low-grade GC cases and the OS of patients with low expression of ALDH3A2 was significantly shorter than those with high ALDH3A2 expression. In vitro results indicated that the expression of ALDH3A2 was negatively correlated with PDCD1, PDCD1LG2, and CTLA-4.Conclusion: We conclude that ALDH3A2 might be useful as a potential reference value for the relief and immunotherapy of GC, and also as an independent predictive marker for the prognosis of GC.

scholarly journals Identification of Novel Potential Biomarkers in Hepatocarcinoma Cancer; A Transcriptome Analysis

2021 ◽  
Author(s):  
Mohib kakar ◽  
Muhammad Mehboob ◽  
Muhammad Akram ◽  
Imran Iqbal ◽  
Hafza Ijaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Predictive Biomarker ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Molecular Therapy ◽  
Hub Genes ◽  
Healthy Control

Abstract 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.

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