Preprint Article Version 1 This version is not peer-reviewed

Machine Learning Discoveries of NFκB-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells

Version 1 : Received: 5 September 2024 / Approved: 9 September 2024 / Online: 10 September 2024 (03:34:23 CEST)

How to cite: Sinha, S. Machine Learning Discoveries of NFκB-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024090696. https://doi.org/10.20944/preprints202409.0696.v1 Sinha, S. Machine Learning Discoveries of NFκB-X Synergy in ETC-1922159 Treated Colorectal Cancer Cells. Preprints 2024, 2024090696. https://doi.org/10.20944/preprints202409.0696.v1

Abstract

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regulated, individually. A recently developed search engine ranked combinations of Nuclear factor kappa-light-chain-enhancer of activated B cells (NFκB)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which NFκB-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning between the NFκB and X. In this research work, we cover combinations of caspase (CASP) with receptor interacting serine/threonine kinase (RIPK) family, mucin (MUC) family with RIPK, tumor necrosis factor (TNF) with NF-κB family and NF-κB-Inhibitor (NF-κB-I), NFκB-2/I with STAT family, IκB kinase ε (IKBKE) with STAT family, IKBKE with conjugal transfer protein (TRAF), ATP-binding cassette (ABC) domain transporters with NFκB, IKBKE with ubiquitination modifier enzyme and ubiquitination conjugating enzymes (UBA/UBE) and REL-A/B with NFκB .

Keywords

NFκB; Porcupine inhibitor ETC-1922159; Sensitivity analysis; Colorectal cancer

Subject

Computer Science and Mathematics, Mathematical and Computational Biology

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