Integrated profiling of microRNAs and mRNAs: microRNAs located on Xq27.3 associate with clear cell renal cell carcinoma
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE24952 | GEO | 2013/09/29
SECONDARY ACCESSION(S): PRJNA131991
REPOSITORIES: GEO
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Project description:BackgroundWith the advent of second-generation sequencing, the expression of gene transcripts can be digitally measured with high accuracy. The purpose of this study was to systematically profile the expression of both mRNA and miRNA genes in clear cell renal cell carcinoma (ccRCC) using massively parallel sequencing technology.MethodologyThe expression of mRNAs and miRNAs were analyzed in tumor tissues and matched normal adjacent tissues obtained from 10 ccRCC patients without distant metastases. In a prevalence screen, some of the most interesting results were validated in a large cohort of ccRCC patients.Principal findingsA total of 404 miRNAs and 9,799 mRNAs were detected to be differentially expressed in the 10 ccRCC patients. We also identified 56 novel miRNA candidates in at least two samples. In addition to confirming that canonical cancer genes and miRNAs (including VEGFA, DUSP9 and ERBB4; miR-210, miR-184 and miR-206) play pivotal roles in ccRCC development, promising novel candidates (such as PNCK and miR-122) without previous annotation in ccRCC carcinogenesis were also discovered in this study. Pathways controlling cell fates (e.g., cell cycle and apoptosis pathways) and cell communication (e.g., focal adhesion and ECM-receptor interaction) were found to be significantly more likely to be disrupted in ccRCC. Additionally, the results of the prevalence screen revealed that the expression of a miRNA gene cluster located on Xq27.3 was consistently downregulated in at least 76.7% of ?50 ccRCC patients.ConclusionsOur study provided a two-dimensional map of the mRNA and miRNA expression profiles of ccRCC using deep sequencing technology. Our results indicate that the phenotypic status of ccRCC is characterized by a loss of normal renal function, downregulation of metabolic genes, and upregulation of many signal transduction genes in key pathways. Furthermore, it can be concluded that downregulation of miRNA genes clustered on Xq27.3 is associated with ccRCC.
| S-EPMC3013074 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-09-29 | E-GEOD-24952 | biostudies-arrayexpress
Project description:Integrated profiling of microRNAs and mRNAs: microRNAs located on Xq27.3 associate with clear cell renal cell carcinoma
| PRJNA131991 | ENA
Project description:To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.
| S-EPMC7331093 | biostudies-literature
Project description:Clear cell papillary renal cell carcinoma (CCPRCC) is a low-grade renal neoplasm with morphological characteristics mimicking both clear cell renal cell carcinoma (CCRCC) and papillary renal cell carcinoma (PRCC). However, despite some overlapping features, their morphological, immunohistochemical, and molecular profiles are distinct. Micro-RNAs (miRNAs) are small noncoding RNAs that play a crucial role in regulating gene expression and are involved in various biological processes, including cancer development. To better understand the biology of this tumor, we aimed to analyze the miRNA expression profile of a set of CCPRCC using microarray and quantitative reverse transcription-polymerase chain reaction. A total of 15 cases diagnosed as CCPRCC were used in this study. Among the most differentially expressed miRNA in CCPRCC, we found miR-210, miR-122, miR-34a, miR-21, miR-34b*, and miR-489 to be up-regulated, whereas miR-4284, miR-1202, miR-135a, miR-1973, and miR-204 were down-regulated compared with normal renal parenchyma. To identify consensus of differentially regulated miRNA between CCPRCC, CCRCC, and PRCC, we additionally determined differential miRNA expression using 2 publically available microarray data sets from the NCBI Gene Expression Omnibus database (GSE41282 and GSE3798). This comparison revealed that the miRNA expression profile of CCPRCC shows some overlapping characteristics between CCRCC and PRCC. Moreover, CCPRCC lacks dysregulation of important miRNAs typically associated with aggressive behavior. In summary, we describe the miRNA expression profile of a relatively infrequent type of renal cancer. Our results may help in understanding the molecular underpinning of this newly recognized entity.
| S-EPMC4332813 | biostudies-literature
Project description:Dysregulated metabolism is a hallmark of cancer, manifested through alterations in metabolites. We performed metabolomic profiling on 138 matched clear cell renal cell carcinoma (ccRCC)/normal tissue pairs and found that ccRCC is characterized by broad shifts in central carbon metabolism, one-carbon metabolism, and antioxidant response. Tumor progression and metastasis were associated with metabolite increases in glutathione and cysteine/methionine metabolism pathways. We develop an analytic pipeline and visualization tool (metabolograms) to bridge the gap between TCGA transcriptomic profiling and our metabolomic data, which enables us to assemble an integrated pathway-level metabolic atlas and to demonstrate discordance between transcriptome and metabolome. Lastly, expression profiling was performed on a high-glutathione cluster, which corresponds to a poor-survival subgroup in the ccRCC TCGA cohort.
| S-EPMC4809063 | biostudies-literature
Project description:Intratumoral heterogeneity (IH) has been recently described as an important contributor to tumor growth through a branched rather than a linear pattern of tumor evolution for renal cell carcinoma. As to whether the miRNA profiling of the different and heterogeneous areas is the same or not, it is not known. This study analyzed the differences and similarities of the miRNA profiles in histologically distinct regions within several RCC tumors. The observed differences may have great implications for the development of predictive biomarkers and the identification of druggable targets with improvement of combinatorial therapeutic approaches for the effective treatment of kidney cancer, as well as for the identification of circulating malignant cells that can be useful to detect tumor recurrences.
| S-EPMC8364632 | biostudies-literature
Project description:Changes in cellular metabolism contribute to the development and progression of tumors, and can render tumors vulnerable to interventions. However, studies of human cancer metabolism remain limited due to technical challenges of detecting and quantifying small molecules, the highly interconnected nature of metabolic pathways, and the lack of designated tools to analyze and integrate metabolomics with other âomics data. Our study generates the largest comprehensive metabolomics dataset on a single cancer type, and provides a significant advance in integration of metabolomics with sequencing data. Our results highlight the massive re-organization of cellular metabolism as tumors progress and acquire more aggressive features. The results of our work are made available through an interactive public data portal for cancer research community. 10 RNA samples from human ccRCC tumors analyzed from the high glutathione cluster
2016-03-08 | E-GEOD-74734 | biostudies-arrayexpress
Project description:BackgroundRenal cell carcinoma (RCC) account for over 80% of renal malignancies. The most common type of RCC can be classified into three subtypes including clear cell, papillary and chromophobe. ccRCC (the Clear Cell Renal Cell Carcinoma) is the most frequent form and shows variations in genetics and behavior. To improve accuracy and personalized care and increase the cure rate of cancer, molecular typing for individuals is necessary.MethodsWe adopted the genome, transcriptome and methylation HMK450 data of ccRCC in The Cancer Genome Atlas Network in this research. Consensus Clustering algorithm was used to cluster the expression data and three subtypes were found. To further validate our results, we analyzed an independent data set and arrived at a consistent conclusion. Next, we characterized the subtype by unifying genomic and clinical dimensions of ccRCC molecular stratification. We also implemented GSEA between the malignant subtype and the other subtypes to explore latent pathway varieties and WGCNA to discover intratumoral gene interaction network. Moreover, the epigenetic state changes between subgroups on methylation data are discovered and Kaplan-Meier survival analysis was performed to delve the relation between specific genes and prognosis.ResultsWe found a subtype of poor prognosis in clear cell renal cell carcinoma, which is abnormally upregulated in focal adhesions and cytoskeleton related pathways, and the expression of core genes in the pathways are negatively correlated with patient outcomes.ConclusionsOur work of classification schema could provide an applicable framework of molecular typing to ccRCC patients which has implications to influence treatment decisions, judge biological mechanisms involved in ccRCC tumor progression, and potential future drug discovery.
| S-EPMC5851245 | biostudies-literature
Project description:Gene expression profiling has been shown to provide prognostic information on patients with solitary sporadic renal cell carcinoma. To our knowledge there is no reliable way to differentiate synchronous renal metastasis from bilateral primary tumors in patients with bilateral renal cell carcinoma. We present data using a custom kidney cancer cDNA array that can predict the outcome in patients with unilateral and bilateral renal cell carcinoma.Fresh frozen tissue from 38 clear cell renal cell carcinomas was analyzed using a cancer cDNA array containing 3,966 genes relevant to cancer or kidney development. Median followup was 5.3 years. Cancer recurred in 12 patients (43%) and 11 (39%) had died by the last followup.Using a training data set of 8 tumors a 44 gene expression profile distinguishing aggressive and indolent clear cell renal cell carcinoma was identified. Of 29 single clear cell renal cell carcinomas 16 and 13 were predicted to be indolent and aggressive, respectively, by the gene expression profile. Recurrence-free survival at 5 years was 68% and 42% in these 2 groups, respectively (p = 0.032). Clear cell renal cell carcinoma classified as indolent or aggressive according to SSIGN (stage, size, grade and necrosis) score showed a 5-year recurrence-free survival rate of 78% and 42%, respectively (p = 0.021). On Cox proportional hazards analysis the gene expression profile was not an independent predictor of recurrence-free survival after accounting for SSIGN score. Gene expression profile classification correlated with cancer specific survival at 5 years in 4 of 4 patients with metachronous clear cell renal cell carcinoma but in only 2 of 4 with bilateral synchronous clear cell renal cell carcinoma.Gene expression profiling using a kidney cancer relevant cDNA array can differentiate between aggressive and indolent clear cell renal cell carcinomas. Gene expression profile results may be most useful for unilateral clear cell renal cell carcinoma when results are discordant with predictions of tumor behavior based on standard clinicopathological features. In addition, gene expression profiling can provide prognostic information that may help characterize tumors of unknown clinical stage, such as bilateral metachronous clear cell renal cell carcinoma.
| S-EPMC2706314 | biostudies-literature