Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer

Abstract

In this study, an attempt has been made to identify expression-based gene biomarkers that can discriminate early and late stage of clear cell renal cell carcinoma (ccRCC) patients. We have analyzed the gene expression of 523 samples to identify genes that are differentially expressed in the early and late stage of ccRCC. First, a threshold-based method has been developed, which attained a maximum accuracy of 71.12% with ROC 0.67 using single gene NR3C2. To improve the performance of threshold-based method, we combined two or more genes and achieved maximum accuracy of 70.19% with ROC of 0.74 using eight genes on the validation dataset. These eight genes include four underexpressed (NR3C2, ENAM, DNASE1L3, FRMPD2) and four overexpressed (PLEKHA9, MAP6D1, SMPD4, C11orf73) genes in the late stage of ccRCC. Second, models were developed using state-of-art techniques and achieved maximum accuracy of 72.64% and 0.81 ROC using 64 genes on validation dataset. Similar accuracy was obtained on 38 genes selected from subset of genes, involved in cancer hallmark biological processes. Our analysis further implied a need to develop gender-specific models for stage classification. A web server, CancerCSP, has been developed to predict stage of ccRCC using gene expression data derived from RNAseq experiments.

Figure 1

The protein–protein interaction network among the potential ccRCC biomarkers generated using STRING database (with direct and indirect interactions) ((a) for RCSP-set-Threshold, (b) for RCSP-set-Weka, and (c) for RCSP-set-Weka-Hall).

Figure 2. A box plot diagram representing median log expression distribution of 15 genes differentially expressed in early and late stage of ccRCC with a p-value < 0.01 calculated using Wilcoxon rank-sum test.

These genes are the union of Combo-1 and Combo-2 sets.

Figure 3. The gene ontology analysis depicting percentage distribution of different biomarkers in major biological processes, molecular functions and cellular components from the five gene sets.

In the process of gene enrichment, 56 out of 64 genes, 32 out of 38 genes, 26 out of 28 genes, 8 out of 10 genes and 7 out of 8 genes were annotated respectively.