Identification of metastasis associated genes in clear cell renal cell carcinoma
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PROVIDER: S-DIXA-D-1153 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:This study investigates the genes that promote clear cell renal cell carcimoma (ccRCC) metastasis using 4 primary metastatic and 5 non-metastatic tumor samples. U133 plus 2.0 array was used to identify the diffrently expressed genes between the primary metastatic and non metastatic ccRCC samples To identify differences in gene expression assoctiated with ccRCC metastasis, 4 primary metastatic and 5 non-metastatic ccRCC samples were used to perform expression profiling.
2013-05-25 | E-GEOD-47352 | biostudies-arrayexpress
Project description:This study investigates the genes that promote clear cell renal cell carcimoma (ccRCC) metastasis using 4 primary metastatic and 5 non-metastatic tumor samples. U133 plus 2.0 array was used to identify the diffrently expressed genes between the primary metastatic and non metastatic ccRCC samples
2013-05-25 | GSE47352 | GEO
Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a tumor that frequently shows the hematogenous pathway and tends to be resistant to radiotherapy and chemotherapy. However, the exact mechanism of ccRCC metastasis remains unknown.MethodsDifferentially expressed genes (DEGs) of three gene expression profiles (GSE85258, GSE105288 and GSE22541) downloaded from the Gene Expression Omnibus (GEO) database were analyzed by GEO2R analysis, and co-expressed DEGs among the datasets were identified using a Venn drawing tool. The co-expressed DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and hub genes were determined based on the protein-protein interaction network established by STRING. After survival analysis performed on UALCAN website, possible key genes were selected and verified in ccRCC cell lines and ccRCC tissues (n = 44). Statistical analysis was conducted using GraphPad Prism (Version 8.1.1).ResultsA total of 104 co-expressed DEGs were identified in the three datasets. Pathway analysis revealed that these genes were enriched in the extracellular matrix (ECM)-receptor interaction, protein digestion and absorption and focal adhesion. Survival analysis on 17 hub genes revealed that four key genes with a significant impact on survival: procollagen C-endopeptidase enhancer (PCOLCE), prolyl 4-hydroxylase subunit beta (P4HB), collagen type VI alpha 2 (COL6A2) and collagen type VI alpha 3 (COL6A3). Patients with higher expression of these key genes had worse survival than those with lower expression. In vitro experiments revealed that the mRNA expression levels of PCOLCE, P4HB and COL6A2 were three times higher and that of COL6A3 mRNA was 16 times higher in the metastatic ccRCC cell line Caki-1 than the corresponding primary cell line Caki-2. Immunohistochemistry revealed higher expression of the proteins encoded by these four genes in metastatic ccRCC compared with tumors from the corresponding primary sites, with statistical significance.ConclusionPCOLCE, P4HB, COL6A2 and COL6A3 are upregulated in metastatic ccRCC and might be related to poor prognosis and distant metastases.
| S-EPMC8740509 | biostudies-literature
Project description:The present study aimed to screen potential target genes for the early diagnosis and treatment of early metastatic clear cell renal cell carcinoma (ccRCC) using the microarray data of early metastatic and non-metastatic ccRCC samples. The DNA microarray dataset GSE47352 was downloaded from Gene Expression Omnibus and included 4 early metastatic and 5 non-metastatic ccRCC samples. Differentially expressed genes (DEGs) were screened using the limma package. Then, pheatmap package was used to conduct two-way clustering for the DEGs. Subsequently, MAPPFinder and GenMAPP were employed separately to perform functional and pathway enrichment analysis for the DEGs. Additionally, a protein-protein interaction (PPI) network was constructed using Cytoscape, and small drug molecules were searched using Connectivity map (cmap). In total, 196 upregulated and 163 downregulated genes were identified. DEGs, including JUN, tumor necrosis factor (TNF), Ras homolog family member B (RHOB) and transforming growth factor β2 (TGFβ2) were significantly enriched in the signaling pathway of renal cell carcinoma. Furthermore, nuclear receptor subfamily 4 group A member 1 (NR4A1) was significantly enriched in the mitogen-activated protein kinase signaling pathway; in addition, laminin subunit α (LAMA) 1, LAMA2 and LAMA4 were significantly enriched in extracellular matrix-receptor interaction. JUN (degree=6) had the highest degree in the PPI network. Thapsigargin (score=-0.913) possessed the highest performance in terms of the treatment of early metastatic ccRCC. In the present study, it was discovered that certain DEGs, including JUN, TNF, RHOB, NR4A1, TGFβ2, LAMA1, LAMA2 and LAMA4 were potential target genes associated with early metastatic ccRCC. In addition, thapsigargin could be used as an efficient small drug molecule for the treatment of early metastatic ccRCC.
| S-EPMC5453167 | biostudies-literature
Project description:IntroductionRenal cell carcinoma (RCC) is one of the most common malignancies globally, among which clear cell carcinoma (ccRCC) accounts for 85-90% of all pathological types. This study aims to screen out potential genes in metastatic ccRCC so as to provide novel insights for ccRCC treatment.MethodsGSE53757 and GSE84546 datasets in the Gene Expression Omnibus (GEO) were profiled to identify differentially expressed genes (DEGs) from ccRCC samples with or without metastasis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and the gene ontology (GO) analysis were performed to analyze pathway enrichment and functional annotation of DEGs. Protein-protein interaction (PPI) network was constructed, and survival analysis was conducted to evaluate the clinical values of the identified hub genes. In vitro loss-of-function assays were performed to explore the biological roles of these genes.ResultsThe bioinformatic analysis indicated that 312 DEGs were identified, including 148 upregulated genes and 164 downregulated ones. Using PPI and Cytoscape, 10 hub genes were selected (C3, CXCR4, CCl4, ACKR3, KIF20A, CCNB2, CDCA8, CCL28, S1PR5, and CCL20) from DEGs which might be closely related with ccRCC metastasis. In Kaplan-Meier analysis, three potential prognostic biomarkers (KIF20A, CCNB2 and CDCA8) were identified. Finally, cell proliferative and invasive assays further verified that KIF20A, CCNB2 and CDCA8 were associated with the proliferation and invasion of ccRCC cells.ConclusionOur results demonstrated that metastatic ccRCC was partially attributed to the aberrant expression of KIF20A, CCNB2 and CDCA8, and more personalized therapeutic approaches should be explored targeting these hub genes.
| S-EPMC7779301 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal malignant tumor in adults. The aim of the present study was to identify the key genes involved in ccRCC metastasis. Expression profiling data for ccRCC patients with metastasis and without metastasis were obtained from The Cancer Genome Atlas database. The datasets were used to identify differentially expressed genes (DEGs) between the metastasis group and the non-metastasis group using the DESeq2 package. Function enrichment analyses of DEGs were performed. The protein-protein interaction (PPI) network was constructed and analyzed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape for further analysis of the identified hub genes. A total of 472 DEGs were identified, including 247 that were upregulated and 225 that were downregulated in the metastasis group. Gene Ontology enrichment analysis revealed that DEGs were mainly enriched in cell transmembrane movement and mitotic cell cycle process. Kyoto Encyclopedia of Genes Genomes pathway analysis revealed that the DEGs were mainly involved in the 'cell cycle' (hsa04110), 'collecting duct acid secretion' (hsa04966), 'complement and coagulation cascades' (hsa04610) and 'aldosterone-regulated sodium reabsorption' (hsa04960) pathways. Using the PPI network, 35 hub genes were identified, and the majority of them were upregulated in ccRCC tissue compared with normal kidney tissue. The expression levels of certain hub genes (CDKN3, TPX2, BUB1B, CDCA8, UBE2C, NDC80, RRM2, NCAPG, NCAPH, PTTG1, FAM64A, ANLN, KIF4A, CEP55, CENPF, KIF20A, ASPM and HJURP) were significantly associated with overall survival and recurrence-free survival in ccRCC. The present study has identified key genes associated with the metastasis of ccRCC.
| S-EPMC6447949 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most common tumor types of the urinary system. Bioinformatics tools have been used to identify new biomarkers of ccRCC and to explore the mechanisms underlying development and progression of ccRCC. The present study analyzed the differentially expressed genes (DEGs) associated with RCC using data obtained from Gene Expression Omnibus datasets and GEO2R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of these DEGs was performed and analyzed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes to identify the hub genes, defined as the genes with the highest degree of interrelation. Subsequently, differential expression and survival analyses of hub genes was performed using The Cancer Genome Atlas database and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Using GEO2R, 1,650 DEGs were identified, including 743 upregulated and 907 downregulated genes. GO and KEGG pathway analyses indicated that the upregulated DEGs were primarily involved in blood vessel and vasculature development, whereas downregulated DEGs were primarily involved in organic acid metabolic processes and carboxylic acid metabolic processes. Subsequently, important modules were identified in the PPI network using Cytoscape's Molecular Complex Detection. The 15 most connected hub genes were identified among DEGs, including glycine decarboxylase (GLDC), enolase 2 (ENO2) and topoisomerase II alpha. GEPIA revealed the association between expression levels of hub genes and survival. Specifically, GLDC and ENO2 were associated with the prognosis of patients with RCC and thus, the effects of GLDC and ENO2 involvement in renal cancer were investigated in vitro. GLDC and ENO2 affected the proliferation and apoptosis of renal cancer cells. These hub genes may reveal a new mechanism underlying development or progression of RCC and identify new markers for its diagnosis and prognosis.
| S-EPMC7068649 | biostudies-literature
Project description:BackgroundRenal epithelium lesions can cause renal cell carcinoma. This kind of tumor is common among all renal cancers with poor prognosis, of which more than 70% belong to kidney renal clear cell carcinoma. As the pathogenesis of KIRC has not been elucidated, it is necessary to be further explored.MethodsThe Genomic Spatial Event database was used to obtain the analysis dataset (GSE126964) based on the GEO database, and The Cancer Genome Atlas was applied for KIRC data collection. edgeR and limma analyses were subsequently conducted to identify differentially expressed genes. Based on the systems biology approach of WGCNA, potential biomarkers and therapeutic targets of this disease were screened after the establishment of a gene coexpression network. GO and KEGG enrichment used cluster Profiler, enrichplot, and ggplot2 in the R software package. Protein-protein interaction network diagrams were plotted for hub gene collection via the STRING platform and Cytoscape software. Hub genes associated with overall survival time of KIRC patients were ultimately identified using the Kaplan-Meier plotter.ResultsThere were 1863 DEGs identified in total and ten coexpressed gene modules discovered using a WGCNA method. GO and KEGG analysis findings revealed that the most enrichment pathways included Notch binding, cell migration, cell cycle, cell senescence, apoptosis, focal adhesions, and autophagosomes. Twenty-seven hub genes were identified, among which FLT1, HNRNPU, ATP6V0D2, ATP6V1A, and ATP6V1H were positively correlated with OS rates of KIRC patients (p < 0.05).ConclusionsIn conclusion, bioinformatic techniques can be useful tools for predicting the progression of KIRC. DEGs are present in both KIRC and normal kidney tissues, which can be considered the KIRC biomarkers.
| S-EPMC9357261 | biostudies-literature
Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.MethodsTranscriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.ResultsIn the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.ConclusionsThe present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.
| S-EPMC8516333 | biostudies-literature
Project description:The aim of this study was to investigate the effects of chromatin-remodeling genes on the prognosis of patients with clear cell renal cell carcinoma (ccRCC). In TCGA-KIRC patients, two subgroups based on 86 chromatin-remodeling genes were established. The random forest algorithm was used for feature selection to identify BPTF, SIN3A and CNOT1 as characterized chromatin remodelers in ccRCC with good prognostic value. YY1 was indicated to be a transcription factor of genes highly related to BPTF, SIN3A and CNOT1. Functional annotations indicated that BPTF, SIN3A, CNOT1 and YY1 are all involved in the ubiquitin-mediated proteolysis process and that high expression of any of the five associated E3 ubiquitin ligases found in the pathway suggests a good prognosis. Protein network analysis indicated that BPTF has a targeted regulatory effect on YY1. Another independent dataset from International Cancer Genome Consortium (ICGC) showed a strong consistency with results in TCGA. In conclusion, we demonstrate that BPTF, SIN3A and CNOT1 are novel prognostic factors that predict good survival in ccRCC. We predicted that the good prognostic value of chromatin-remodeling genes BPTF and SIN3A is related to the regulation of YY1 and that YY1 regulates E3 ubiquitin ligases for further degradation of oncoproteins in ccRCC.
| S-EPMC7803503 | biostudies-literature