Kaitlin A Goettsch1, Ling Zhang1, Amar B Singh2,3, Punita Dhawan2, Dhundy K Bastola1. 1. School of Interdisciplinary Informatics, College of Information Science & Technology, University of Nebraska at Omaha, 1110 S. 67th Street, Omaha, NE 68182, USA. 2. Department of Biochemistry & Molecular Biology, University of Nebraska Medical Center, 42nd & Emile Streets, Omaha, NE 68198, USA. 3. Veterans Affairs Nebraska - Western Iowa Health Care System, Research Service, Omaha, NE 68105, USA.
Abstract
Aims: To combat increases in colorectal cancer (CRC) incidence and mortality, biomarkers among differentially expressed genes (DEGs) have been identified to objectively detect cancer. However, DEGs are numerous, and additional parameters may identify more reliable biomarkers. Here, CRC DEGs were filtered into a prioritized list of biomarkers. Materials & methods: Two independent datasets (COAD-READ [n = 698] and GSE50760 [n = 36]) were input alternatively to the recently published data-driven reference method. Results were filtered based on epithelial-mesenchymal transition enrichment (χ-square statistic: 919.05; p = 2.2e-16) to produce 37 potential CRC biomarkers. Results: All 37 genes reliably classified CRC samples and ETV4, CLDN1 and CA2 together were top-ranked by DDR (accuracy: 89%; F1 score: 0.89). Conclusion: Biological and statistical information were combined to produce a better set of CRC detection biomarkers.
Aims: To combat increases in colorectal cancer (CRC) incidence and mortality, biomarkers among differentially expressed genes (DEGs) have been identified to objectively detect cancer. However, DEGs are numerous, and additional parameters may identify more reliable biomarkers. Here, CRC DEGs were filtered into a prioritized list of biomarkers. Materials & methods: Two independent datasets (COAD-READ [n = 698] and GSE50760 [n = 36]) were input alternatively to the recently published data-driven reference method. Results were filtered based on epithelial-mesenchymal transition enrichment (χ-square statistic: 919.05; p = 2.2e-16) to produce 37 potential CRC biomarkers. Results: All 37 genes reliably classified CRC samples and ETV4, CLDN1 and CA2 together were top-ranked by DDR (accuracy: 89%; F1 score: 0.89). Conclusion: Biological and statistical information were combined to produce a better set of CRC detection biomarkers.
Authors: Nikhil Tyagi; Sachin K Deshmukh; Sanjeev K Srivastava; Shafquat Azim; Aamir Ahmad; Ahmed Al-Ghadhban; Ajay P Singh; James E Carter; Bin Wang; Seema Singh Journal: Mol Cancer Res Date: 2017-11-08 Impact factor: 5.852
Authors: Robert L Grossman; Allison P Heath; Vincent Ferretti; Harold E Varmus; Douglas R Lowy; Warren A Kibbe; Louis M Staudt Journal: N Engl J Med Date: 2016-09-22 Impact factor: 91.245
Authors: S Cherradi; A Ayrolles-Torro; N Vezzo-Vié; N Gueguinou; V Denis; E Combes; F Boissière; M Busson; L Canterel-Thouennon; C Mollevi; M Pugnière; F Bibeau; M Ychou; P Martineau; C Gongora; M Del Rio Journal: J Exp Clin Cancer Res Date: 2017-06-28