Analyzing large-scale samples confirms the association between rs16892766 polymorphism and colorectal cancer susceptibility.

Colorectal cancer (CRC) is a common complex disease caused by the combination of genetic variants and environmental factors. Genome-wide association studies (GWAS) have been performed and reported some novel CRC susceptibility variants. The rs16892766 (8q23.3) polymorphism was first identified to be significantly associated with CRC in European ancestry. The following studies investigated this association in Chinese, Japanese, Romanian, Swedish, African American, European American, and Croatian populations. These studies reported consistent and inconsistent results. Here, we reevaluated this association using the relatively large-scale samples from 13 studies (N = 59737, 26237 cases and 33500 controls) using a meta-analysis by searching the PubMed, Google Scholar and CRCgene databases. We observed no significant heterogeneity among the included studies. Our results showed significant association between rs16892766 polymorphism and CRC (P = 1.33E-35, OR = 1.23, 95% CI 1.20-1.27). Collectively, our analysis further supports previous findings that the rs16892766 polymorphism is significantly associated with CRC susceptibility. We believe that our findings will be very useful for future genetic studies on CRC.

Pathway analysis of genome-wide association study and transcriptome data highlights new biological pathways in colorectal cancer.

Colorectal cancer (CRC) is a common malignancy that meets the definition of a complex disease. Genome-wide association study (GWAS) has identified several loci of weak predictive value in CRC, however, these do not fully explain the occurrence risk. Recently, gene set analysis has allowed enhanced interpretation of GWAS data in CRC, identifying a number of metabolic pathways as important for disease pathogenesis. Whether there are other important pathways involved in CRC, however, remains unclear. We present a systems analysis of KEGG pathways in CRC using (1) a human CRC GWAS dataset and (2) a human whole transcriptome CRC case-control expression dataset. Analysis of the GWAS dataset revealed significantly enriched KEGG pathways related to metabolism, immune system and diseases, cellular processes, environmental information processing, genetic information processing, and neurodegenerative diseases. Altered gene expression was confirmed in these pathways using the transcriptome dataset. Taken together, these findings not only confirm previous work in this area, but also highlight new biological pathways whose deregulation is critical for CRC. These results contribute to our understanding of disease-causing mechanisms and will prove useful for future genetic and functional studies in CRC.