@article{ec5d185ac7a54a84a34d2dd06aced240,
title = "Reliable epithelial-mesenchymal transition biomarkers for colorectal cancer detection",
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.",
keywords = "CRC, DDR, EMT, biomarkers, classification, colorectal cancer, data-driven reference, epithelial-mesenchymal transition",
author = "Goettsch, {Kaitlin A.} and Ling Zhang and Singh, {Amar B.} and Punita Dhawan and Bastola, {Dhundy K.}",
note = "Funding Information: This work was supported by Collaborative Funding/Nebraska Research Initiative (NRI) Funding 2019 and the School of Interdisciplinary Informatics at the University of Nebraska at Omaha for K Goettsch, L Zhang and D Bastola; the Nebraska collaborative seed grant, VA-merit (BX002761), and DK124095 to A Singh; and VA-merit (BX002086) and CA216746 to P Dhawan. A Singh and P Dhawan are NIH-funded. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Funding Information: This work was supported by Collaborative Funding/Nebraska Research Initiative (NRI) Funding 2019 and the School of Interdisciplinary Informatics at the University of Nebraska at Omaha for K Goettsch, L Zhang and D Bastola; the Nebraska collaborative seed grant, VA-merit (BX002761), and DK124095 to A Singh; and VA-merit (BX002086) and CA216746 to P Dhawan. A Singh and P Dhawan are NIH-funded. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. Publisher Copyright: {\textcopyright} 2022 Future Medicine Ltd.",
year = "2022",
month = aug,
doi = "10.2217/bmm-2022-0071",
language = "English (US)",
volume = "16",
pages = "889--901",
journal = "Biomarkers in Medicine",
issn = "1752-0363",
publisher = "Future Medicine Ltd.",
number = "12",
}