A Comprehensive Bioinformatics Analysis of UBE2C in Cancers

Dastsooz, Hassan; Cereda, Matteo; Donna, Daniela; Oliviero, Salvatore

doi:10.3390/ijms20092228

Cited by 87 publications

(105 citation statements)

References 57 publications

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“…malignant tumors, and this upregulation is significantly associated with lymph node metastasis and tumor stage. In addition, high expression of UBE2C is also closely associated with breast cancer, colon cancer and other malignant tumors (47)(48)(49).…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al ( 46 ), revealed that UBE2C is highly expressed in gastric cancer, which could lead to the occurrence and development of malignant tumors, and this upregulation is significantly associated with lymph node metastasis and tumor stage. In addition, high expression of UBE2C is also closely associated with breast cancer, colon cancer and other malignant tumors ( 47 – 49 ).…”

Section: Discussionmentioning

confidence: 99%

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Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Cao

Zhang

et al. 2020

Oncol Lett

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Hepatocellular carcinoma (HCC) is a heterogeneous malignancy, which is a major cause of cancer morbidity and mortality worldwide. Thus, the aim of the present study was to identify the hub genes and underlying pathways of HCC via bioinformatics analyses. The present study screened three datasets, including GSE112790, GSE84402 and GSE74656 from the Gene Expression Omnibus (GEO) database, and downloaded the RNA-sequencing of HCC from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) in both the GEO and TCGA datasets were filtered, and the screened DEGs were subsequently analyzed for functional enrichment pathways. A protein-protein interaction (PPI) network was constructed, and hub genes were further screened to create the Kaplan-Meier curve using cBioPortal. The expression levels of hub genes were then validated in different datasets using the Oncomine database. In addition, associations between expression and tumor grade, hepatitis virus infection status, satellites and vascular invasion were assessed. A total of 126 DEGs were identified, containing 70 upregulated genes and 56 downregulated genes from the GEO and TCGA databases. By constructing the PPI network, the present study identified hub genes, including cyclin B1 (CCNB1), cell-division cycle protein 20 (CDC20), cyclin-dependent kinase 1, BUB1 mitotic checkpoint serine/ threonine kinase β (BUB1B), cyclin A2, nucleolar and spindle associated protein 1, ubiquitin-conjugating enzyme E2 C (UBE2C) and ZW10 interactor. Furthermore, upregulated CCNB1, CDC20, BUB1B and UBE2C expression levels indicated worse disease-free and overall survival. Moreover, a meta-analysis of tumor and healthy tissues in the Oncomine database demonstrated that BUB1B and UBE2C were highly expressed in HCC. The present study also analyzed the data of HCC in TCGA database using univariate and multivariate Cox analyses, and demonstrated that BUB1B and UBE2C may be used as independent prognostic factors. In conclusion, the present study identified several genes and the signaling pathways that were associated with tumorigenesis using bioinformatics analyses, which could be potential targets for the diagnosis and treatment of HCC.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Cao

Zhang

et al. 2020

Oncol Lett

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“…UBE2C (Ubiquitin-conjugating enzyme E2C) protein is involved in the ubiquitination process that modifies the abnormal or short-lived proteins with ubiquitin and target toward degradation (van Wijk et al, 2010). A good number of studies showed high expression of UBE2C is associated with aggressive progression and poor outcomes of several cancer (Dastsooz et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers in subtypes of epithelial ovarian cancer

Singh

Som

2020

Preprint

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Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer. Due to the lack of specific symptoms, ~80% of epithelial ovarian cancer is diagnosed at an advanced stage and often metastasize to the distant organ. Epithelial ovarian cancer is a heterogeneous disease that is classified into four major histological subtypes namely, serous carcinoma (SC), endometrioid carcinoma (EC), mucinous carcinoma (MC), and clear cell carcinoma (CCC).Ovarian cancer treatment is complicated due to the heterogeneity of the tumors. Patients with different subtypes respond differently to the same treatment and also have different prognoses. This diversity extends to various clinical outcomes of the disease. Thus, identifying new reliable potential biomarkers irrespective of their subtypes is an urgent need for the diagnosis and prognosis of epithelial ovarian cancer. In this study, we performed comparative gene expression analysis for identifying potential biomarkers in four histological subtypes of epithelial ovarian cancer (EOC) that include serous, endometrioid, mucinous, and clear cell carcinomas. Differentially expressed genes (DEGs) between cancerous and normal tissue samples were identified by considering the criteria of absolute logarithmic fold change |log2fc|>1 and adjusted p (padj) value<0.05. Pathway enrichment analysis of the DEGs showed that pathways in cancer, PI3K-AKT signaling pathway, RAP1 signaling pathway, cell cycle, cell adhesion molecules, and proteoglycans in cancer were common among the selected cancer subtypes. Further, we constructed the co-expression network of DEGs and identified 15 candidate genes. Finally, based on the survival analysis of the candidate genes, a total of nine genes namely ASPM, CDCA8, CENPM, CEP55, HMMR, RACGAP1, TPX2, UBE2C, and ZWINT with significant prognostic value was proposed as the potential biomarker.

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“…Data from the Oncomine (https://www.oncomine.org) (16) and TCGA (https://tcga-data. nci.nih.gov/tcga/) databases (17,18), and GEPIA (http://gepia. cancer-pku.cn) (18,19), were used to estimate the expression of MELK in LUAD and adjacent normal tissues (16)(17)(18)(19).…”

Section: Methodsmentioning

confidence: 99%

“…nci.nih.gov/tcga/) databases (17,18), and GEPIA (http://gepia. cancer-pku.cn) (18,19), were used to estimate the expression of MELK in LUAD and adjacent normal tissues (16)(17)(18)(19). The search parameters in Oncomine were as follows: Analysis type, cancer vs. normal; data source, public; cancer type, selected specific cancer type; sample type, clinical specimen and; data type, mRNA.…”

Section: Methodsmentioning

confidence: 99%

Maternal embryonic leucine zipper kinase: A novel biomarker and a potential therapeutic target in lung adenocarcinoma

Chen

et al. 2020

Oncol Lett

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Maternal embryonic leucine zipper kinase (MELK), is an adenosine monophosphate-activated protein kinase-related kinase that serves important roles in tumourigenesis in multiple malignant tumours. However, to the best of our knowledge, the effect of MELK in lung adenocarcinoma (LUAD) has not been elucidated. The present study aimed to explore the clinical significance of MELK in the prognosis of LUAD. Data from Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) were selected to predict the differential mRNA expression levels of MELK mRNA in LUAD and normal tissues. Subsequently, LUAD and adjacent normal tissue samples were collected from 75 patients with the disease, and immunohistochemistry was used to detect the protein expression of MELK. In addition, the Kaplan-Meier Plotter database, GEPIA and TCGA were used to verify the effect of MELK expression on clinical prognosis in patients with LUAD. MELK was significantly upregulated in LUAD tissues compared with that in normal tissues based on Oncomine, GEPIA and TCGA data (P<0.05). In addition, the results from immunohistochemistry demonstrated that the MELK protein level in LUAD tissues was significantly higher compared with that in matched normal tissues (P<0.05). Prognostic analysis performed using the Kaplan-Meier plotter, GEPIA and TCGA suggested that the expression of MELK was negatively associated with the overall survival time of patients with LUAD (P<0.05). In conclusion, MELK was highly expressed in LUAD based on bioinformatics and immunohistochemistry analysis, and increased expression of MELK was associated with a poor patient prognosis. MELK may serve as a potential diagnostic marker and therapeutic target for LUAD.

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A Comprehensive Bioinformatics Analysis of UBE2C in Cancers

Cited by 87 publications

References 57 publications

Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Identification of potential biomarkers in subtypes of epithelial ovarian cancer

Maternal embryonic leucine zipper kinase: A novel biomarker and a potential therapeutic target in lung adenocarcinoma

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