Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Liu, Li; Lin, Jiajing; He, Haiyang

doi:10.3389/fgene.2019.00373

Cited by 71 publications

(60 citation statements)

References 94 publications

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“…After co-expression and correlation analysis using the Oncomine, bc-GenExMiner, and UCSC Xena web-based tools in the present study, we confirmed that CDC20 gene was positively correlated with BIRC5 expression. As a matter of fact, both CDC20 and BIRC5 were found to be co-expressed in lung adenocarcinoma, endometrial cancer, renal cell carcinoma, and thyroid carcinoma [31][32][33][34]. Given that cell cycle process is often coordinated with apoptotic proteins to maintain tissue homeostasis, silencing the expressions of cell cycle protein as well as anti-apoptotic proteins simultaneously could potentially lead to cell cycle arrest and reduce the proliferation of breast cancer cells.…”

Section: Discussionmentioning

confidence: 99%

Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis

et al. 2020

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Aims: Baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) plays vital roles in carcinogenesis by influencing cell division and proliferation and by inhibiting apoptosis. However, the prognostic significance of BIRC5 remains unclear in breast cancer. Methods: BIRC5 expression and methylation status were evaluated using the Oncomine and The Cancer Genome Atlas (TCGA) databases. The relevance between BIRC5 and different clinicopathological features as well as survival information was analyzed using the bc-GenExMiner database and Kaplan-Meier Plotter. BIRC5-drug interaction network was obtained using the Comparative Toxicogenomics Database. Results: Based on the results from databases and own hospital data, BIRC5 was higher expressed in different breast cancer subtypes compared with the matched normal individuals. Hormone receptors were negatively correlated with BIRC5 expression, whereas the Scarff-Bloom-Richardson (SBR) grade, Nottingham Prognostic Index (NPI), human epidermal growth factor receptor-2 (HER-2) status, basal-like status, and triple-negative status were positively related to BIRC5 level in breast cancer samples with respect to normal tissues. High BIRC5 expression was responsible for shorter relapse-free survival, worse overall survival, reduced distant metastasis free survival, and increased risk of metastatic relapse event. BIRC5-drug interaction network indicated that several common drugs could modulate BIRC5 expression. Furthermore, a positive correlation between BIRC5 andcell-division cycle protein 20 (CDC20) gene was confirmed. Conclusion: BIRC5 may be adopted as a promising predictive marker and potential therapeutic target in breast cancer. Further large-scale studies are needed to more precisely confirm the value of BIRC5 in treatment of breast cancer.The Breast Cancer Gene-Expression Miner v4.1 (bc-GenExMiner v4.1, http://bcgenex.centregauducheau.fr/BC-GEM), an open access database of published annotated genomics data, was utilized to analyze the relevance between BIRC5 and specific clinicopathological features of breast cancer [16,17]. Association between BIRC5 and metastatic relapse event was assessed using the prognostic module, and correlation of BIRC5 and co-expressed cell-division cycle protein 20 (CDC20) was evaluated using the correlation module. UCSC XenaThe UCSC Xena browser (http://xena.ucsc.edu/) was used to analyze the TCGA Breast Cancer data using the level 3 data. The heatmap and correlation between BIRC5 and CDC20 were then constructed. Kaplan-Meier PlotterThe Kaplan-Meier Plotter (http://kmplot.com/analysis/), a web tool capable to check the effect of 54675 genes on survival using 5143 clinical breast cancer samples, was applied to show the prognostic value of BIRC5 in relapse-free survival, overall survival, and distant metastasis-free survival [18]. The hazard ratio (HR) with 95% confidence interval (CI), and log-rank P-value were calculated automatically.

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Section: Discussionmentioning

confidence: 99%

Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis

et al. 2020

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“…Chen et al found that ESPL1 may be associated with bladder cancer development and recurrence [33]. In addition, Liu et al identi ed 7 pivotal genes involved in endometrial cancer prognosis and constructed a prognostic gene signature, among which ESPL1 was one of the genes that were viewed as risky prognostic genes [34]. ESPL1 expression was found to be increased in endometrial cancer tissues, but the clinical signi cance and functional mechanism of ESPL1 in EC remains to be veri ed [34].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, Liu et al identi ed 7 pivotal genes involved in endometrial cancer prognosis and constructed a prognostic gene signature, among which ESPL1 was one of the genes that were viewed as risky prognostic genes [34]. ESPL1 expression was found to be increased in endometrial cancer tissues, but the clinical signi cance and functional mechanism of ESPL1 in EC remains to be veri ed [34]. Nevertheless, it has also been reported that ESPL1 plays an opposite role in gastric adenocarcinoma.…”

Section: Discussionmentioning

confidence: 99%

Identification and Analysis of Genes Associated with Epithelial Ovarian Cancer by Integrated Bioinformatics Methods

Gui

Yao²,

Jia³

et al. 2020

Preprint

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Abstract Background: Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets for EOC is highly valuable. Methods: Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed in the STRING database. The top 20 hub genes were selected using cytoHubba. The expression of hub genes was detected in GEPIA, Oncomine, and human protein atlas (HPA) databases. The relationship of hub genes with the pathological stage and the overall survival and progression-free survival in EOC patients was investigated using the cancer genome atlas data. Results: A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through the PPI network analysis, the top 20 genes were screened out, among which 4 hub genes were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues and the expression of these four genes decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival. Conclusions: Two hub genes, CDCA5 and ESPL1, identified as playing tumor-promotive roles, could be utilized as potential novel therapeutic targets for EOC treatment.

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“…The DNA topoisomerase 2-alpha (TOP2A) is involved in the cell cycle, and it is crucial to cancer development as indicated by its expression, described as upregulated in several types of cancer, including pancreatic cancer [60,61]. Network analyses identified TOP2A among the hub proteins in the protein-protein interaction network in glioblastoma, rectal adenocarcinoma, and endometrial cancer [60][61][62]. In pancreatic cancer, TOP2A is associated with tumor size, metastasis, tumor stage, poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression

Almeida

Cardoso

Freitas

2019

Preprint

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Background: Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. Methods:We performed a gene expression microarray meta-analysis of the tumor against healthy tissues in order to identify differentially expressed genes shared among all datasets, named core-genes (CG). We confirmed the pancreatic expressed proteins of the CG through The Human Protein Atlas. The five most expressed proteins in the tumor group were selected to train an artificial neural network to classify samples. 2 Results: This microarray included 110 tumor and 77 healthy samples. We identified a CG composed of 60 genes, 58 upregulated and two downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that thirteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our artificial neural network, we used the five most expressed genes (KRT19, LAMC2, MELK, MET, TOP2A). The artificial neural network model (PDAC-ANN) classified the train samples with sensitivity of 0.95, specificity of 0.9, and f1-score of 0.93. The PDAC-ANN could classify the test samples with a sensitivity of 0.97, specificity of 0.88, and f1-score 0.94. Conclusion:The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on mRNA expression. This software can be useful in the PDAC diagnosis.1 5 migration, and metastasis. The PDAC-ANN trained using gene expression information could classify the samples in normal and PDAC with an f1-score of 0.94 and sensitivity = 0.97. The PDAC-ANN tool can only be used when the gene expression information from KRT19, LAMC2, MELK, MET, and TOP2A are available, in addition to min-max gene expression values rescaling. The PDAC-ANN is a free tool (Additional file 4) that can support in the pancreatic ductal adenocarcinoma diagnosis.

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Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer

Cited by 71 publications

References 94 publications

Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis

Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis

Identification and Analysis of Genes Associated with Epithelial Ovarian Cancer by Integrated Bioinformatics Methods

PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression

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