image: On the left, workflow of the Identification of high-confidence risk genes for CRC via iRIGS. On the right (top), Functional characterization of predicted risk genes. On the right (bottom), Graphical representation of the regulation and function of CEBPB in CRC.
Credit: ©Science China Press
This study was led by Prof. Xiao-Ping Miao’ team from the School of Public Health, Wuhan University, in collaboration with Prof. Shaokai Zhang from the Henan Cancer Hospital. The researchers utilized a Bayesian approach, integrative risk gene selector (iRIGS), to prioritize risk genes at CRC GWAS loci by integrating multi-omics data (as shown in the figure). The iRIGS framework is designed to take advantage of the high-dimensional omics data, and the more relevant genomic features are included, the more accurate the prediction is. The researchers identified a total of 105 HRGs with the highest posterior probability for each CRC GWAS locus, through integrating genomic features including differential expression, mutation frequency, DRE-promoter links, and distance to index SNP. They observed that HRGs captured more genomic features that were characteristics of CRC risk genes compared with LBGs and the nearest genes. Therefore, iRIGS was proved to provide a powerful way to probabilistically rank candidate genes at each GWAS locus. Although the results might be not a direct proof that these genes were genuine cancer genes, it was encouraging to see that they corresponded to genes that, when altered, significantly affected CRC cell growth. In fact, both CRISPR-based and RNAi-based dependency scores suggested that HRGs were essential for the growth of CRC cells. These findings strongly suggest that integrating multi-omics data is able to propose candidate risk genes which exhibit high confidence and show promise to be further experimentally validated.
Among the 105 HRGs, CEBPB, located at the 20q13.13 locus, acted as a transcription factor playing critical roles in cancer. The subsequent assays indicated the tumor promoter function of CEBPB that facilitated CRC cell proliferation by regulating multiple oncogenic pathways such as MAPK, PI3K-Akt, and Ras signaling. Next, by integrating a fine-mapping analysis and three independent case-control studies in Chinese populations consisting of 8,039 cases and 12,775 controls, they elucidated that rs1810503, a putative functional variant regulating CEBPB, was associated with CRC risk (OR = 0.90, 95% CI = 0.86-0.93, P = 1.07×10-7). The association between rs1810503 and CRC risk was further validated in three additional multi-ancestry populations consisting of 24,254 cases and 58,741 controls. Mechanistically, the rs1810503 A to T allele change weakened the enhancer activity in an allele-specific manner to decrease CEBPB expression via long-range promoter-enhancer interactions, mediated by the transcription factor, REST, and thus decreased CRC risk.
In summary, the HRGs identified in this study greatly expand potential candidate targets of risk SNPs for CRC, which can be further verified by future studies to help elucidate the genetic etiology of CRC. Moreover, the critical roles of one particular HRG, CEBPB, and its regulatory variant in colorectal carcinogenesis, validated by functional experiments, further shed light on the pathogenesis of CRC. Together, they anticipate that this gene-centric map of genetic etiology of CRC is valuable for the refinement of GWAS association signals and can provide risk gene sets for future applications in precision oncology and beyond.
See the article:
Prioritization of risk genes in colorectal cancer by integrative analysis of multi-omics data and gene networks
Journal
Science China Life Sciences