A bioinformatics filtering strategy for identifying radiation response biomarker candidates

Abstract

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response.

Figure 1. Direct protein-protein interaction network.

A network representation that illustrates the complexity of direct connections among genes identified via literature review.

Figure 2. A normal quantile plot of t-scores for GSE1977.

Significant genes have red circles.

Figure 3. Significant gene detection.

A volcano plot that depicts the –log10 of q-values against log2 of fold changes for all genes in GSE1977.

Figure 4. Comparison of significant genes among three sources.

A Venn diagram depicting the number of shared and unique genes among a set of genes identified by literature review and two sets of genes identified in the analysis of two gene microarray datasets.

Figure 5. The most probable interaction network when 20 genes were entered into MetaCore software.

The resulting interacting network uses only 7 genes. Red, green, and gray lines indicate inhibitory, stimulatory, and unspecified interactions, respectively.