Integrated genomic analysis identifies clinically relevant subtypes of renal clear cell carcinoma

Abstract

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.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4176-1) contains supplementary material, which is available to authorized users.

m6A modification patterns and tumor immune landscape in clear cell renal carcinoma

“…26 In this study, we focused on the role of m 6 A modification in the immune landscape in ccRCC to enhance our understanding of the TIME antitumor immune response and provide more effective immunotherapeutic strategies for patients with ccRCC. Many previous studies have identified ccRCC subtypes based on genomic profiling, [42][43][44] improving the future application of precision-focused, personalized treatments for ccRCC. A 4-mRNA pattern with significant differences in patient survival was identified by unsupervised analyses based on mRNA expression data.…”

Long Non-coding RNA LINC01094 Promotes the Development of Clear Cell Renal Cell Carcinoma by Upregulating SLC2A3 via MicroRNA-184

“…Clear cell renal cell carcinoma causes more than 80% of renal malignancies ( Wu et al, 2018 ) and reported to be most prevalently diagnosed subtype with more aggressive symptoms than papillary and chromophobe RCC ( Lee et al, 2018 ). Moreover, ccRCC is a complex disease and its molecular mechanism underlying metastasis remained unclear ( Yang et al, 2019 ).…”

Emerging Biomarkers in Bladder Cancer Identified by Network Analysis of Transcriptomic Data

“…To overcome this problem, large sample size expression datasets should be used and, in particular, datasets that include samples isolated from different points of a single cancer tissue should be preferred. Moreover, we suggest performing sample clustering based on expression data before WGCNA analysis, in order to process less heterogeneous samples, as recently carried out for ccRCC ( 88 ).…”