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

Abstract

Background: Renal 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.

Methods: We 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.

Results: We 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.

Conclusions: Our 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.

Keywords: Gene expression; Molecular classification; Pathway; ccRCC.

Fig. 1

a Consensus matrices. Both rows and columns represent samples and consensus values range from 0(never clustered together) to 1 (always clustered together) marked by white to dark blue. The cluster memberships are marked by colored rectangles. b Consensus Cumulative Distribution Function (CDF) Plot. CDF plot shows the cumulative distribution functions of the consensus matrix for each k (indicated by colors) c Delta Area Plot. This graphic shows the relative change in area under the CDF curve. In k = 3, the shape of the curve approaches the ideal step function, and shape hardly changes as we increase K past 3

Fig. 2

a Kaplan-Meier Overall Survival Curves. survival plot by Kaplan-Meier method, EC1 has worse prognosis compared with the other. b The heatmap of ccRCC expression data. Using consensus clustering algorithm, samples are classified into three types. The heatmap shows that EC1 subtype has higher mortality and more patients in stage III, IV than the other groups

Fig. 3

a Enrichment plot of upregulation pathways in EC1. GSEA of expression data from EC1 441 with worse prognosis, as compared to EC2–3. X-axis is the enrichment score of each gene. Y-axis represents the order of the gene in dataset. b Volcano plot of differential genes. Red color: up-regulated in EC1. blue color: down-regulated in EC1. Grey: not differential genes. Size of the bubble: mean expression of each gene C box plot of mean expression level on G1/S and G2/M gene set. EC1 is higher than EC2–3

Fig. 4

a Volcano plot of differential methylation sites. Data are obtained from HM450K methylation data. β-values represent mean methylation level of CpG sites. b Kaplan meier survival plot of four genes. Red line indicates the median survival time