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Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases.

ABSTRACT: Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China. Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes' protein expression levels. Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC. Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

SUBMITTER: Zhang P

PROVIDER: S-EPMC8262246 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLaryngeal squamous cell carcinoma (LSCC) is the second most aggressive head and neck squamous cell carcinoma. Although much work has been done to optimize its treatment, patients with LSCC still have poor prognosis. Therefore, figuring out differentially expressed genes (DEGs) contained in the progression of LSCC and employing them as potential therapeutic targets or biomarkers for LSCC is extremely meaningful.MethodsOverlapping DEGs were screened from two standalone Gene Expression Omnibus datasets, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. By applying STRING and Cytoscape, a protein-protein network was built, and module analysis was carried out. The hub genes were selected by maximal clique centrality with the CytoHubba plugin of Cytoscape. UALCAN and GEPIA data were examined to validate the gene expression findings. Moreover, the connection of the hub genes with LSCC patient overall survival was studied employing The Cancer Genome Atlas. Then, western blot, qRT-PCR, CCK-8, wound healing and transwell assays were bring to use for further verify the key genes.ResultsA total of 235 DEGs were recorded, including 83 upregulated and 152 downregulated genes. A total of nine hub genes that displayed a high degree of connectivity were selected. UALCAN and GEPIA databases verified that these genes were highly expressed in LSCC tissues. High expression of the SPP1, SERPINE1 and Matrix metalloproteinases 1 (MMP1) genes was connected to worse prognosis in patients with LSCC, according to the GEPIA online tool. Western blot and qRT-PCR testify SPP1, SERPINE1 and MMP1 were upregulated in LSCC cells. Inhibition of SPP1, SERPINE1 and MMP1 suppressed cell proliferation, invasion and migration.ConclusionThe work here identified effective and reliable diagnostic and prognostic molecular biomarkers by unified bioinformatics analysis and experimental verification, indicating novel and necessary therapeutic targets for LSCC.

Project description:Glioblastoma (GBM) is the most common type of malignant tumor of the central nervous system. The prognosis of patients with GBM is very poor, with a survival time of ~15 months. GBM is highly heterogeneous and highly aggressive. Surgical removal of intracranial tumors does provide a good advantage for patients as there is a high rate of recurrence. The understanding of this type of cancer needs to be strengthened, and the aim of the present study was to identify gene signatures present in GBM and uncover their potential mechanisms. The gene expression profiles of GSE15824 and GSE51062 were downloaded from the Gene Expression Omnibus database. Normalization of the data from primary GBM samples and normal samples in the two databases was conducted using R software. Then, joint analysis of the data was performed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the protein?protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed using Cytoscape software. Identification of prognostic biomarkers was conducted using UALCAN. In total, 9,341 DEGs were identified in the GBM samples, including 9,175 upregulated genes and 166 downregulated genes. The top 1,000 upregulated DEGs and all of the downregulated DEGs were selected for GO, KEGG and prognostic biomarker analyses. The GO results showed that the upregulated DEGs were significantly enriched in biological processes (BP), including immune response, cell division and cell proliferation, and the downregulated DEGs were also significantly enriched in BP, including cell growth, intracellular signal transduction and signal transduction by protein phosphorylation. KEGG pathway analysis showed that the upregulated DEGs were enriched in circadian entrainment, cytokine?cytokine receptor interaction and maturity onset diabetes of the young, while the downregulated DEGs were enriched in the TGF?? signaling pathway, MAPK signaling pathway and pathways in cancer. All of the downregulated genes and the top 1,000 upregulated genes were selected to establish the PPI network, and the sub?networks revealed that these genes were involved in significant pathways, including olfactory transduction, neuroactive ligand?receptor interaction and viral carcinogenesis. In total, seven genes were identified as good prognostic biomarkers. In conclusion, the identified DEGs and hub genes contribute to the understanding of the molecular mechanisms underlying the development of GBM and they may be used as diagnostic and prognostic biomarkers and molecular targets for the treatment of patients with GBM in the future.

Project description:Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

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Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases.

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