Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Epidemiological studies suggested that obesity increases the risk of colorectal cancer (CRC). The genetic connection between CRC and obesity is multifactorial and inconclusive. In this study, we hypothesize that the study of shared comorbid diseases between CRC and obesity can offer unique insights into common genetic basis of these two diseases. We constructed a comorbidity network based on mining health data for millions of patients. We developed a novel approach and extracted the diseases that play critical roles in connecting obesity and CRC in the comorbidity network. Our approach was able to prioritize metabolic syndrome and diabetes, which are known to be associated with obesity and CRC through insulin resistance pathways. Interestingly, we found that osteoporosis was highly associated with the connection between obesity and CRC. Through gene expression meta-analysis, we identified novel genes shared among CRC, obesity and osteoporosis. Literature evidences support that these genes may contribute in explaining the genetic overlaps between obesity and CRC.
Pubmed ID: 26306270
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentions