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pmid:28901457

Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis.

Lin Li · Qingsong Lei · Shujun Zhang · Lingna Kong · Bo Qin

Oncology reports · 2017 doi:10.3892/or.2017.5946

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:28901457
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title
Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis.
doi
10.3892/or.2017.5946
pmid
28901457
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[0]
Lin Li
[1]
Qingsong Lei
[2]
Shujun Zhang
[3]
Lingna Kong
[4]
Bo Qin
journal
Oncology reports
year
2017
abstract
Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the molecular mechanisms of HCC are still not well understood. To identify the candidate genes in the carcinogenesis and progression of HCC, microarray datasets GSE19665, GSE33006 and GSE41804 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. A total of 273 DEGs were identified, consisting of 189 downregulated genes and 84 upregulated genes. The enriched functions and pathways of the DEGs include protein activation cascade, complement activation, carbohydrate binding, complement and coagulation cascades, mitotic cell cycle and oocyte meiosis. Sixteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell division, cell cycle and nuclear division. Survival analysis showed that BUB1, CDC20, KIF20A, RACGAP1 and CEP55 may be involved in the carcinogenesis, invasion or recurrence of HCC. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of HCC, and provide candidate targets for diagnosis and treatment of HCC.
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The adjusted P-values (adj. P) and Benjamini and Hochberg false discovery rate were applied to provide a balance between discovery of statistically significant genes and limitations of false-positives.
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The adjusted P-values (adj. P) and Benjamini and Hochberg false discovery rate were applied to provide a balance between discovery of statistically significant genes and limitations of false-positives.
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The GSE19665 dataset contained 10 HCC tissue samples and 10 non-cancerous samples.
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Using GEO2R analysis of three GEO microarray datasets (GSE19665, GSE33006, and GSE41804), the study identified 273 overlapping differentially expressed genes between HCC and non-cancerous tissues: 189 downregulated and 84 upregulated genes.
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microarray datasets GSE19665, GSE33006 and GSE41804 were downloaded from Gene Expression Omnibus (GEO) database.
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The DEGs between HCC and non-cancerous samples were screened using GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r).
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The adjusted P-values (adj. P) and Benjamini and Hochberg false discovery rate were applied to provide a balance between discovery of statistically significant genes and limitations of false-positives.
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sections[11] "Identification of DEGs in HCC" paragraph 2
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The overlap among the 3 datasets contained 273 genes as shown in the Venn diagram (Fig. 1A), consisting of 189 downregulated genes and 84 upregulated genes between liver cancer tissues and non-cancerous tissues.
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Using GEO2R analysis of three GEO microarray datasets (GSE19665, GSE33006, and GSE41804), the study identified 273 overlapping differentially expressed genes between HCC and non-cancerous tissues: 189 downregulated and 84 upregulated genes.
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Hepatic gene expression of HCV related Hepatocellular carcinoma and non-cancerous tissue with Il28B rs8099917 TT genotype and TG/GG genotype
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To identify the candidate genes in the carcinogenesis and progression of HCC, microarray datasets GSE19665, GSE33006 and GSE41804 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. A total of 273 DEGs were identified, consisting of 189 downregulated genes and 84 upregulated genes. The enriched functions and pathways of the DEGs include protein activation cascade, complement activation, carbohydrate binding, complement and coagulation cascades, mitotic cell cycle and oocyte meiosis. Sixteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell division, cell cycle and nuclear division. 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