Identification of Prognostic Dosage-Sensitive Genes in Colorectal Cancer Based on Multi-Omics

Abstract

Several studies have already identified the prognostic markers in colorectal cancer (CRC) based on somatic copy number alteration (SCNA). However, very little information is available regarding their value as a prognostic marker. Gene dosage effect is one important mechanism of copy number and dosage-sensitive genes are more likely to behave like driver genes. In this work, we propose a new pipeline to identify the dosage-sensitive prognostic genes in CRC. The RNAseq data, the somatic copy number of CRC from TCGA were assayed to screen out the SCNAs. Wilcoxon rank-sum test was used to identify the differentially expressed genes in alteration samples with |SCNA| > 0.3. Cox-regression was used to find the candidate prognostic genes. An iterative algorithm was built to identify the stable prognostic genes. Finally, the Pearson correlation coefficient was calculated between gene expression and SCNA as the dosage effect score. The cell line data from CCLE was used to test the consistency of the dosage effect. The differential co-expression network was built to discover their function in CRC. A total of six amplified genes (NDUFB4, WDR5B, IQCB1, KPNA1, GTF2E1, and SEC22A) were found to be associated with poor prognosis. They demonstrate a stable prognostic classification in more than 50% threshold of SCNA. The average dosage effect score was 0.5918 ± 0.066, 0.5978 ± 0.082 in TCGA and CCLE, respectively. They also show great stability in different data sets. In the differential co-expression network, these six genes have the top degree and are connected to the driver and tumor suppressor genes. Function enrichment analysis revealed that gene NDUFB4 and GTF2E1 affect cancer-related functions such as transmembrane transport and transformation factors. In conclusion, the pipeline for identifying the prognostic dosage-sensitive genes in CRC was proved to be stable and reliable.

Keywords: colorectal cancer; differential co-expression; gene dosage effect; somatic copy number alteration; survival analysis.

Figure 1

Classification stability of gene prognosis. For each threshold of somatic copy number alteration (SCNA) (from 0.1 to 0.5, at 0.02 step), the p-value was calculated by the log-rank test in corresponding alteration and CNNS samples. The Number of Threshold will increase if the p-value < 0.05.

Figure 2

The dosage sensitivity of six prognostic dosage-sensitive genes (PDSGs). The X-axis represents the somatic copy number alteration (SCNA) value and Y-axis represents the FPKM of genes.

Figure 3

The Kaplan-Meier curves of six PSDGs for samples in CNAS and CNNS. (A–C) with the somatic copy number alteration (SCNA) threshold 0.1, gene GTF2E1 and NDUFB4 had similar prognostic classification efficacy. (D) with the SCNA threshold 0.3, all six PSDGs have similar efficacy. (E, F) with the SCNA threshold 0.5, although the p-value was > 0.05, the two survival curves still separated from each other.

Figure 4

The dosage-sensitive and the correlation scores of somatic copy number alteration (SCNA) of prognostic dosage-sensitive genes (PDSGs). (A) The correlation coefficient of SCNA and gene expression. Both results suggest strong concordance. (B) The heatmap of the SCNA of PDSG. All these six PDSGs show high co-alteration in colorectal cancer (CRC).

Figure 5

Differential co-expression network and function of enrichment of prognostic dosage-sensitive genes (PDSGs). (A) Differential co-expression networks, Triangle represent tumor suppressor genes, lower triangular represent driver gene. Six PDSGs (NDUFB4, WDR5B, IQCB1, KPNA1, GTF2E1, and SEC22A) have the top degree. The edge represents co-expression of the adjacent genes above 0.5 in one group and below 0.1 in another group. (B). The co-expression curve of gene (C) Normal and abstained function of gene NDUFB4 using Cluster Profiler R package (D) Normal and abstained function of gene GTF2E1 using Cluster Profiler R package.