Abstract
Significantly expressed genes extracted from microarray gene expression data have proved very useful for identifying genetic biomarkers of diseases, including cancer. However, deriving a disease related inference from a list of differentially expressed genes has proven less than straightforward. In a systems disease such as cancer, how genes interact with each other should matter just as much as the level of gene expression. Here, in a novel approach, we used the network and disease progression properties of individual genes in state-specific gene-gene interaction networks (GGINs) to select cancer genes for human colorectal cancer (CRC) and obtain a much higher hit rate of known cancer genes when compared with methods not based on network theory. We constructed GGINs by integrating gene expression microarray data from multiple states – healthy control (Nor), adenoma (Ade), inflammatory bowel disease (IBD) and CRC – with protein-protein interaction database and Gene Ontology. We tracked changes in the network degrees and clustering coefficients of individual genes in the GGINs as the disease state changed from one to another. From these we inferred the state sequences Nor-Ade-CRC and Nor-IBD-CRC both exhibited a trend of (disease) progression (ToP) toward CRC, and devised a ToP procedure for selecting cancer genes for CRC. Of the 141 candidates selected using ToP, ∼50% had literature support as cancer genes, compared to hit rates of 20% to 30% for standard methods using only gene expression data. Among the 16 candidate cancer genes that encoded transcription factors, 13 were known to be tumorigenic and three were novel: CDK1, SNRPF, and ILF2. We identified 13 of the 141 predicted cancer genes as candidate markers for early detection of CRC, 11 and 2 at the Ade and IBD states, respectively.
Highlights
Colorectal cancer (CRC) is the fourth leading cause of cancer death worldwide but rank higher in economically more developed societies
Samples and Microarrays Data provided by the Gyorffy group [30] on genome-wide gene expression profile from tissue samples of 53 human patients evaluated by HG-U133 Plus 2.0 platform microarrays (Affymetrix, Santa Clara), which list 18,267 genes, were downloaded from Gene Expression Omnibus (GEO) database
Significant Differentially Expressed Genes The total set of selected 2,666 differentially expressed genes (DEGs) (FDR,0.001, Student’s ttest p-value,0.05, fold change .1.5; Figure S1) was the union DEGs separately selected from three state pairs; ADE vs. NOR, 1652 genes; CRC vs. NOR, 1100 genes; inflammatory bowel disease (IBD) vs. NOR: 1629 genes
Summary
Colorectal cancer (CRC) is the fourth leading cause of cancer death worldwide but rank higher in economically more developed societies. The overlap between published gene signatures from different studies for CRC tended to be small On it was recognized the identification of differentially expressed genes (DEGs) in two cohort samples was a potentially useful approach [7,8,9]. A method based on priori biological information such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) on modules that are functionally annotated [10], partially meets the challenge The rationale for this approach, which groups DEGs into functional subsets using GO or KEGG (or something equivalent), derives from the observation that most genes function as part of a group rather than singly [11]. A majority of human genes have not yet been assigned a definite pathway or protein complex [12]
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