In the post-genomic era, the main aim of cancer research is organizing the large amount of data on gene expression and protein abundance into a meaningful biological context. Performing integrated analysis of genomic and proteomic data sets is a challenging task. To comprehensively assess the correlation between mRNA and protein expression, we focused on the gene set enrichment analysis, a recently described powerful analytical method. When the differentially expressed proteins in 12 colorectal cancer tissue samples were considered a collective set, they exhibited significant concordance with primary tumor gene expression data in 180 colorectal cancer tissue samples. We found that 53 upregulated proteins were significantly enriched in genes exhibiting elevated gene expression levels (P<0.001, ES=0.53), indicating a positive correlation between the proteomic and transcriptomic data. Similarly, 44 downregulated proteins were significantly enriched in genes exhibiting elevated gene expression levels (P<0.001, ES -0.65). Moreover, we applied gene set enrichment analysis to identify functional genetic pathways in CRC. A relatively large number of upregulated proteins were related to the two principal pathways; ECM receptor interaction was related to heparan sulfate proteoglycan 2 and vitronectin, and ribosome to RPL13, RPL27A, RPL4, RPS18, and RPS29. In conclusion, the integrated understanding of both genomic and proteomic data sets can lead to a better understanding of functional inference at the physiological level and potential molecular targets in clinical settings.