Version 1
: Received: 17 November 2021 / Approved: 22 November 2021 / Online: 22 November 2021 (13:05:31 CET)
How to cite:
DDS, A.V.; Zollanvari, A. Modulating Cancer Progression from Leukoplakia via Bayesian Gene Networks. Preprints2021, 2021110392. https://doi.org/10.20944/preprints202111.0392.v1
DDS, A.V.; Zollanvari, A. Modulating Cancer Progression from Leukoplakia via Bayesian Gene Networks. Preprints 2021, 2021110392. https://doi.org/10.20944/preprints202111.0392.v1
DDS, A.V.; Zollanvari, A. Modulating Cancer Progression from Leukoplakia via Bayesian Gene Networks. Preprints2021, 2021110392. https://doi.org/10.20944/preprints202111.0392.v1
APA Style
DDS, A.V., & Zollanvari, A. (2021). Modulating Cancer Progression from Leukoplakia via Bayesian Gene Networks. Preprints. https://doi.org/10.20944/preprints202111.0392.v1
Chicago/Turabian Style
DDS, A.V. and Amin Zollanvari. 2021 "Modulating Cancer Progression from Leukoplakia via Bayesian Gene Networks" Preprints. https://doi.org/10.20944/preprints202111.0392.v1
Abstract
Oral squamous cell carcinoma often arises from an oral potentially malignant disorder called oral leukoplakia (OL). With this work we aimed to develop a novel data-driven predictive model based on gene expression profiles to distinguish OL patients who underwent malignant transformation from those who did not. We used the Tree Augmented Naïve (TAN) Bayes classifier to predict the posterior probability of having oral cancer given the data. 86 patients were included with a median follow-up of 7.11 years. Fifty-one patients (51/86; 59%) underwent malignant transformation. We found that 16 genes were predictors of oral cancer in patients with OL and these included SLC7A11, SPINK6, SERPINA12, VIT, ATP1B3, CST6, FLRT2, ELMOD1, AZGP1, RNASE13, DIO2, ECM1, CYP4F11, SYTL4, AKR1C1, and AKR1C3. In conclusion, we showed that Bayesian gene networks are a data-driven approach which could be used also in other predictor models in oncology.
Medicine and Pharmacology, Dentistry and Oral Surgery
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
