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

Li, Lin; Lei, Qingsong; Zhang, Shujun; Kong, Lingna; Qin, Bo

doi:10.3892/or.2017.5946

Cited by 190 publications

(169 citation statements)

References 64 publications

(64 reference statements)

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“…Sixteen genes with degrees ≥10 were defined as hub genes (40). Two of these hub genes, TOP2A and AURKA, had the highest node degrees at 31 and 22, respectively.…”

Section: Discussionmentioning

confidence: 99%

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

et al. 2020

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Lung adenocarcinoma is one of the most common malignant tumors worldwide. Although efforts have been made to clarify its pathology, the underlying molecular mechanisms of lung adenocarcinoma are still not clear. The microarray datasets GSE75037, GSE63459 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify biomarkers for effective lung adenocarcinoma diagnosis and therapy. The differentially expressed genes (DEGs) were identified by GEO2R, and function enrichment analyses were conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The STRING database and Cytoscape software were used to construct and analyze the protein-protein interaction network (PPI). We identified 376 DEGs, consisting of 83 upregulated genes and 293 downregulated genes. Functional and pathway enrichment showed that the DEGs were mainly focused on regulation of cell proliferation, the transforming growth factor β receptor signaling pathway, cell adhesion, biological adhesion, and responses to hormone stimulus. Sixteen hub genes were identified and biological process analysis showed that these 16 hub genes were mainly involved in the M phase, cell cycle phases, the mitotic cell cycle, and nuclear division. We further confirmed the two genes with the highest node degree, DNA topoisomerase IIα (TOP2A) and aurora kinase A (AURKA), in lung adenocarcinoma cell lines and human samples. Both these genes were upregulated and associated with larger tumor size. Upregulation of AURKA in particular, was associated with lymphatic metastasis. In summary, identification of the DEGs and hub genes in our research enables us to elaborate the molecular mechanisms underlying the genesis and progression of lung adenocarcinoma and identify potential targets for the diagnosis and treatment of lung adenocarcinoma.

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“…Sixteen genes with degrees ≥10 were defined as hub genes (40). Two of these hub genes, TOP2A and AURKA, had the highest node degrees at 31 and 22, respectively.…”

Section: Discussionmentioning

confidence: 99%

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

et al. 2020

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“…GEO2R is an interactive web tool that allows users to compare two or more datasets in the GEO series in to identify DEGs across experimental conditions [7] . The DEGs with log FC<0 was taken to be down-regulated genes, while the DEGs with log FC>0 was considered as an up-regulated gene.…”

Section: Data Identification Of Degsmentioning

confidence: 99%

Screening and identification of significant genes in esophageal squamous cell carcinoma by bioinformatical analysis

Ren

Zhang

Pan

et al. 2020

Preprint

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Background: Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers with notably high incidence and mortality rates. However, the molecular mechanism underlying ESCC pathogenesis and prognosis is very complicated. The main objective of our investigation has been to obtain some knowledge of significant genes with poor outcome and their underlying mechanisms.Methods: Gene expression profiles of GSE26886, GSE23400, GSE20347 and GSE17351 were available from 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 software.Results: A total of 105 DEGs were identified between normal esophagus and ESCC bioinformatical analysis samples. Functional annotations of the common DEGs indicate that extracellular matrix (ECM) remodeling plays a key role in tumor formation and progression.18 hub genes were identified and disease free survival analysis showed that CDKN3, RAD51AP1, KIF4A may be involved in poor prognosis in ESCC patients.Conclusions: DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of ESCC, and provide candidate targets for diagnosis and treatment of ESCC.

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“…The screening of DEGs between AR and no rejection samples was performed by using GEO2R((http://www.ncbi.nlm.nih.gov/geo/geo2r), which is a online analysis tool for the GEO database according to R language. Compliance with the previous criteria [12][13] , we regulated DEGs as differentially expressed with logFC > 1 (upregulated genes) or logFC < -1 (downregulated genes). In the meantime, the adjusted P value < 0.05 was regarded statistically signi cant to reduce the false positive rate.…”

Section: Screen Genes Of Differential Expressionmentioning

confidence: 99%

Noninvasive Identification of Core Gene Biomarkers in Patients with Acute Kidney Transplant Rejection

Zhao

2020

Preprint

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Background: Acute rejection (AR) is one of common and critical complications after kidney transplantation, which gives rise to an increasing of allograft loss and even death. However, the potential mechanisms of AR remain incompletely understood at present. This study aimed to reveal the underlying mechanisms and identify core biomarkers of AR.Methods: The AR gene expression profile GSE1563 involving 6 renal transplant patients undergoing AR and 9 patients with well-functioning transplant with no clinical evidence of rejection was selected to analyze the differentially expressed genes (DEGs) from purified RNA of peripheral blood lymphocytes. DAVID was used to carry out Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A protein-protein interaction (PPI) network was built to display the interactions among these DEGs. To get further reliable significant genes and mechanisms, gene set enrichment analysis (GSEA) was applied to evaluate the hub gene analyzing via PPI network.Results: A total of 347 DEGs were captured, including 301 upregulated genes and 46 downregulated genes. Go and KEGG pathway analysis showed the DEGs were particularly enriched in immune response, inflammatory response to antigenic stimulus, RNA transport and protein stabilization. The PPI network indicated 3 modules were also mainly involved in immune response and RNA transport. 18 hub genes were selected in PPI network, among which there were 6 core genes evaluated by DAVID. By the way of GSEA, Oxidative stress was another potential mechanism besides immunity and ncRNA transport. Conclusion: Our analysis uncovered the immune response including humoral and cellular immunity, ncRNA transport as well as oxidative stress was the major mechanisms of AR associated respectively with MAPK8, CCL5, HMGB1, NCBP2, XPO1 and APP, which could be novel noninvasive biomarkers in peripheral blood for early diagnosis and could be helpful to the treatment of AR.

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

Cited by 190 publications

References 64 publications

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

Identification of lung adenocarcinoma biomarkers based on bioinformatic analysis and human samples

Screening and identification of significant genes in esophageal squamous cell carcinoma by bioinformatical analysis

Noninvasive Identification of Core Gene Biomarkers in Patients with Acute Kidney Transplant Rejection

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