Biomarker correlation network in colorectal carcinoma by tumor anatomic location
Qian, Zhi Rong
MetadataShow full item record
CitationNishihara, R., K. Glass, K. Mima, T. Hamada, J. A. Nowak, Z. R. Qian, P. Kraft, et al. 2017. “Biomarker correlation network in colorectal carcinoma by tumor anatomic location.” BMC Bioinformatics 18 (1): 304. doi:10.1186/s12859-017-1718-5. http://dx.doi.org/10.1186/s12859-017-1718-5.
AbstractBackground: Colorectal carcinoma evolves through a multitude of molecular events including somatic mutations, epigenetic alterations, and aberrant protein expression, influenced by host immune reactions. One way to interrogate the complex carcinogenic process and interactions between aberrant events is to model a biomarker correlation network. Such a network analysis integrates multidimensional tumor biomarker data to identify key molecular events and pathways that are central to an underlying biological process. Due to embryological, physiological, and microbial differences, proximal and distal colorectal cancers have distinct sets of molecular pathological signatures. Given these differences, we hypothesized that a biomarker correlation network might vary by tumor location. Results: We performed network analyses of 54 biomarkers, including major mutational events, microsatellite instability (MSI), epigenetic features, protein expression status, and immune reactions using data from 1380 colorectal cancer cases: 690 cases with proximal colon cancer and 690 cases with distal colorectal cancer matched by age and sex. Edges were defined by statistically significant correlations between biomarkers using Spearman correlation analyses. We found that the proximal colon cancer network formed a denser network (total number of edges, n = 173) than the distal colorectal cancer network (n = 95) (P < 0.0001 in permutation tests). The value of the average clustering coefficient was 0.50 in the proximal colon cancer network and 0.30 in the distal colorectal cancer network, indicating the greater clustering tendency of the proximal colon cancer network. In particular, MSI was a key hub, highly connected with other biomarkers in proximal colon cancer, but not in distal colorectal cancer. Among patients with non-MSI-high cancer, BRAF mutation status emerged as a distinct marker with higher connectivity in the network of proximal colon cancer, but not in distal colorectal cancer. Conclusion: In proximal colon cancer, tumor biomarkers tended to be correlated with each other, and MSI and BRAF mutation functioned as key molecular characteristics during the carcinogenesis. Our findings highlight the importance of considering multiple correlated pathways for therapeutic targets especially in proximal colon cancer. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1718-5) contains supplementary material, which is available to authorized users.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:33490951