Booth Id:
CBIO057
Category:
Computational Biology and Bioinformatics
Year:
2021
Finalist Names:
Kapasi, Sara (School: The Westminster Schools)
Abstract:
Ulcerative colitis (UC) and type 2 diabetes (T2D) are associated with high risks of colorectal cancer (CRC), yet bacterial biomarkers for CRC present in both remain unclear. In this study, a cross-study analysis was conducted of T2D and UC gut microbiomes to find shared CRC biomarkers for early CRC detection. Metagenomic and 16S rRNA gene sequencing re-analyzation were performed on colon and rectum samples in 6 datasets with UC, T2D, and CRC patients (n=209). CRC alpha diversity was shown to be the lowest compared to UC and T2D datasets. Through random forest machine learning models with 84.91%, 91.67%, and 83.25% accuracy, decreased levels of Ruminococcaceae UCG-002 spp. and Ruminiclostridium 6 spp. were identified as a bacterial biomarker linking UC and T2D. Increased levels of D. longicatena was identified as a common biomarker between T2D and CRC. Inconclusive levels of D. longicatena levels and Prevotella 9 spp. were identified as common CRC biomarkers between UC and T2D, as well as increased Bacteroides spp. and decreased Faecalibacterium sp. CM04-06. Through identifying bacterial CRC biomarkers present in UC and T2D, this study emphasizes a comprehensive bioinformatics approach for microbe analyzation in CRC diagnosis.
Awards Won:
Patent and Trademark Office Society: Second Award of $500