Dataset Information

Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) was the most aggressive histological type of renal cell carcinoma (RCC) and accounted for 70-80% of cases of all RCC. The aim of this study was to identify the potential biomarker in ccRCC and explore their underlying mechanisms. Four profile datasets were downloaded from the GEO database to identify DEGs. GO and KEGG analysis of DEGs were performed by DAVID. A protein-protein interaction (PPI) network was constructed to predict hub genes. The hub gene expression within ccRCC across multiple datasets and the overall survival analysis were investigated utilizing the Oncomine Platform and UALCAN dataset, separately. A meta-analysis was performed to explore the relationship between the hub genes: EGFR and ccRCC. 127 DEGs (55 upregulated genes and 72 downregulated genes) were identified from four profile datasets. Integrating the result from PPI network, Oncomine Platform, and survival analysis, EGFR, FLT1, and EDN1 were screened as key factors in the prognosis of ccRCC. GO and KEGG analysis revealed that 127 DEGs were mainly enriched in 21 terms and 4 pathways. The meta-analysis showed that there was a significant difference of EGFR expression between ccRCC tissues and normal tissues, and the expression of EGFR in patients with metastasis was higher. This study identified 3 importance genes (EGFR, FLT1, and EDN1) in ccRCC, and EGFR may be a potential prognostic biomarker and novel therapeutic target for ccRCC, especially patients with metastasis.

SUBMITTER: Wang S

PROVIDER: S-EPMC6393869 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis.

Wang Sheng S Yu Zhi-Hong ZH Chai Ke-Qun KQ

BioMed research international 20190213

Clear cell renal cell carcinoma (ccRCC) was the most aggressive histological type of renal cell carcinoma (RCC) and accounted for 70-80% of cases of all RCC. The aim of this study was to identify the potential biomarker in ccRCC and explore their underlying mechanisms. Four profile datasets were downloaded from the GEO database to identify DEGs. GO and KEGG analysis of DEGs were performed by DAVID. A protein-protein interaction (PPI) network was constructed to predict hub genes. The hub gene exp ...[more]

PMID: 30895194

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Project description:BackgroundClear Cell Renal Cell Carcinoma (CCRCC) is the most aggressive subtype of Renal Cell Carcinoma (RCC) with high metastasis and recurrence rates. This study aims to find new potential key genes of CCRCC.MethodsFour gene expression profiles (GSE12606, GSE53000, GSE68417, and GSE66272) were downloaded from the Gene Expression Omnibus (GEO) database. The TCGA KIRC data was downloaded from The Cancer Genome Atlas (TCGA). Using GEO2R, the differentially expressed genes (DEG) in CCRCC tissues and normal samples were analyzed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed in DAVID database. A protein-protein interaction (PPI) network was constructed and the hub gene was predicted by STRING and Cytoscape. GEPIA and Kaplan-Meier plotter databases were used for further screening of Key genes. Expression verification and survival analysis of key genes were performed using TCGA database, GEPIA database, and Kaplan-Meier plotter. Receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of key genes in CCRCC, which is plotted by R software based on TCGA database. UALCAN database was used to analyze the relationship between key genes and clinical pathology in CCRCC and the methylation level of the promoter of key genes in CCRCC.ResultsA total of 289 up-regulated and 449 down-regulated genes were identified based on GSE12606, GSE53000, GSE68417, and GSE66272 profiles in CCRCC. The upregulated DEGs were mainly enriched with protein binding and PI3K-Akt signaling pathway, whereas down-regulated genes were enriched with the integral component of the membrane and metabolic pathways. Next, the top 35 genes were screened out from the PPI network according to Degree, and three new key genes ITGAX, LAPTM5 and SERPINE1 were further screened out through survival and prognosis analysis. Further results showed that the ITGAX, LAPTM5, and SERPINE1 levels in CCRCC tumor tissues were significantly higher than those in normal tissues and were associated with poor prognosis. ROC curve shows that ITGAX, LAPTM5, and SERPINE1 have good diagnostic value with good specificity and sensitivity. The promoter methylation levels of ITGAX, LAPTM5 and SERPINE1 in CCRCC tumor tissues were significantly lower than those in normal tissues. We also found that key genes were associated with clinical pathology in CCRCC.ConclusionITGAX, LAPTM5, and SERPINE1 were identified as novel key candidate genes that could be used as prognostic biomarkers and potential therapeutic targets for CCRCC.

Project description:To discuss the dual energy spectral computer tomography (CT) imaging features of the pathological grading of clear cell renal cell carcinoma (ccRCC) and the correlation between spectral CT imaging features and pathology.We performed analyses of 62 patients with confirmed diagnosis of ccRCC. All patients underwent non-enhanced CT and dual-phase (cortex phase, CP and parenchyma phase, PP) contrast-enhanced CT with dual energy spectral mode. The subjects were pathologically divided into two groups: low-grade group (Fuhrman 1/2) and high-grade group (Fuhrman 3/4). The CT value of each lesion was measured on the monochromatic image at 70 keV. The normalized iodine concentrations (NIC) and the slope of the spectrum curve were calculated. The qualitative morphological parameters, including tumor shape, calcification, pseudocapsule, necrosis, and enhancement mode, were compared between the two groups. The quantitative data were compared using Student's t-test, and the enumeration data were analyzed using the chi-square test between low-grade and high-grade ccRCC.Receiver operating characteristic curve was used to determine the sensitivity and specificity of the quantitative parameters in two groups.The CT value, NIC, and mean slope of the low-grade group were increased compared with the high-grade group during CP (P = 0.001, P = 0.043, and P<0.001, respectively). The CT did not differ significantly during PP (P = 0.134); however, the NIC and mean slope varied considerably in the low grade compared with the high-grade group (P = 0.048, P = 0.017, respectively). The CT threshold value, NIC, and slope had high sensitivity and specificity in differentiating low-grade ccRCC from high-grade ccRCC. The tumor shape, pseudocapsule, and necrosis differed significantly between the two groups (P<0.05).Dual energy spectral CT with the quantitative analysis of iodine concentration and qualitative analysis of morphological characteristics increases the accuracy of diagnosing the pathological grade of ccRCC.

Project description:Tumorigenic processes are understood to be driven by epi-/genetic and genomic alterations from single point mutations to chromosomal alterations such as insertions and deletions of nucleotides up to gains and losses of large chromosomal fragments including products of chromosomal rearrangements e.g. fusion genes and proteins. Overall comparisons of copy number alterations (CNAs) presented in 48 clear cell renal cell carcinoma (ccRCC) genomes resulted in ratios of gene losses versus gene gains between 26 ccRCC Fuhrman malignancy grades G1 (ratio 1.25) and 20 G3 (ratio 0.58). Gene losses and gains of 15762 CNA genes were mapped to 795 chromosomal cytoband loci including 280 KEGG pathways. CNAs were classified according to their contribution to Fuhrman tumour gradings G1 and G3. Gene gains and losses turned out to be highly structured processes in ccRCC genomes enabling the subclassification and stratification of ccRCC tumours in a genome-wide manner. CNAs of ccRCC seem to start with common tumour related gene losses flanked by CNAs specifying Fuhrman grade G1 losses and CNA gains favouring grade G3 tumours. The appearance of recurrent CNA signatures implies the presence of causal mechanisms most likely implicated in the pathogenesis and disease-outcome of ccRCC tumours distinguishing lower from higher malignant tumours. The diagnostic quality of initial 201 genes (108 genes supporting G1 and 93 genes G3 phenotypes) has been successfully validated on published Swiss data (GSE19949) leading to a restricted CNA gene set of 171 CNA genes of which 85 genes favour Fuhrman grade G1 and 86 genes Fuhrman grade G3. Regarding these gene sets overall survival decreased with the number of G3 related gene losses plus G3 related gene gains. CNA gene sets presented define an entry to a gene-directed and pathway-related functional understanding of ongoing copy number alterations within and between individual ccRCC tumours leading to CNA genes of prognostic and predictive value.

Dataset Information

Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis.

Publications

Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis.

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