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Whole-transcriptome sequencing identified gene expression signatures associated with aggressive clear cell renal cell carcinoma.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer, yet molecular biomarkers have not been used for the prognosis of ccRCC to aide clinical decision making. This study aimed to identify genes associated with ccRCC aggressiveness and overall survival (OS). Samples of ccRCC tumor tissue were obtained from 33 patients who underwent nephrectomy. Gene expression was determined using whole-transcriptome sequencing. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) RNA-seq data was used to test association with OS. 290 genes were differentially expressed between tumors with high and low stage, size, grade, and necrosis (SSIGN) score (?7 vs. ?3) with P ADJ<0.05. Four genes, G6PD, APLP1, GCNT3, and PLPP2, were also over-expressed in advanced stage (III and IV) and high grade (3 and 4) ccRCC and tumor with necrosis (P ADJ<0.05). Investigation stratifying by stage found that APLP1 and PLPP2 overexpression were significantly associated with poorer OS in the early stage (Quartile 1 vs. Quartile 4, HR = 3.87, 95% CI:1.25-11.97, P = 0.02 and HR = 4.77, 95% CI:1.37-16.57, P = 0.04 respectively). These genes are potential biomarkers of ccRCC aggressiveness and prognosis that direct clinical and surgical management.

SUBMITTER: Batai K

PROVIDER: S-EPMC6305109 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Whole-transcriptome sequencing identified gene expression signatures associated with aggressive clear cell renal cell carcinoma.

Batai Ken K Imler Elliot E Pangilinan Jayce J Bell Robert R Lwin Aye A Price Elinora E Milinic Tijana T Arora Amit A Ellis Nathan A NA Bracamonte Erika E Seligmann Bruce B Lee Benjamin R BR

Genes & cancer 20180501 5-6

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer, yet molecular biomarkers have not been used for the prognosis of ccRCC to aide clinical decision making. This study aimed to identify genes associated with ccRCC aggressiveness and overall survival (OS). Samples of ccRCC tumor tissue were obtained from 33 patients who underwent nephrectomy. Gene expression was determined using whole-transcriptome sequencing. The Cancer Genome Atlas Kidney Renal Clear Cell Car ...[more]

PMID: 30603059

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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis.MethodsWe collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis.ResultsUsing this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC.ConclusionsThe integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation.

Project description:BackgroundRenal cell carcinoma (RCC) accounts for 90% of renal cancers, of which clear cell carcinoma (ccRCC) is the most usual histological type. The process of alternative splicing (AS) contributes to protein diversity, and the dysregulation of protein diversity may have a great influence on tumorigenesis. We developed a prognostic signature and comprehensively analyzed the role of tumor immune microenvironment (TIME) and immune checkpoint blocking (ICB) treatment in ccRCC.MethodsTo identify prognosis-related AS events, univariate Cox regression was used and functional annotation was performed using gene set enrichment analysis (GSEA). In this study, prognostic signatures were developed based on multivariate Cox, univariate Cox, and LASSO regression models. Moreover, to assess the prognostic value, the proportional hazards model, Kruskal-Wallis analysis, and ROC curves were used. To obtain a better understanding of TIME in ccRCC, the ESTIMATE R package, single sample gene set enrichment analysis (ssGSEA) algorithm, CIBERSORT method, and the tumor immune estimation resource (TIMER) were applied. The database was searched to verify the expression of C4OF19 in tumor and normal samples. Regulatory networks for AS-splicing factors (SFs) were visualized using Cytoscape 3.9.1.ResultsThere were 9,347 AS cases associated with the survival of ccRCC patients screened. A total of eight AS prognostic signatures were developed with stable prognostic predictive accuracy based on splicing subtypes. In addition, a qualitative prognostic nomogram was developed, and the prognostic prediction showed high effectiveness. In addition, we found that the combined signature was significantly associated with the diversity of TIME and ICB treatment-related genes. C4ORF19 might become an important prognostic factor for ccRCC. Finally, the AS-SF regulatory network was established to clearly reveal the potential function of SFs.ConclusionWe found novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.) for the prognostic prediction of ccRCC. A new and reliable prognostic nomogram was established to quantitatively predict the clinical outcome. The AS-SF networks could provide a new way for the study of potential regulatory mechanisms, and the important roles of AS events in the context of TIME and immunotherapy efficiency were exhibited. C4ORF19 was found to be a vital gene in TIME and ICB treatment.

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Whole-transcriptome sequencing identified gene expression signatures associated with aggressive clear cell renal cell carcinoma.

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Whole-transcriptome sequencing identified gene expression signatures associated with aggressive clear cell renal cell carcinoma.

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