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. 2023 Apr 28;43(3):602–608. doi: 10.1007/s11596-023-2716-7

Predicting the Activity of Oral Lichen Planus with Glycolysis-related Molecules: A Scikit-learn-based Function

Yan Yang 1,2, Pei Hu 1,2, Su-rong Chen 1,2, Wei-wei Wu 1,2, Pan Chen 1,2, Shi-wen Wang 1,2, Jing-zhi Ma 1,2, Jing-yu Hu 1,2,✉
PMCID: PMC10141813  PMID: 37115394

Abstract

Objective

Oral lichen planus (OLP) is one of the most common oral mucosa diseases, and is mainly mediated by T lymphocytes. The metabolic reprogramming of activated T cells has been shown to transform from oxidative phosphorylation to aerobic glycolysis. The present study investigated the serum levels of glycolysis-related molecules (lactate dehydrogenase, LDH; pyruvic acid, PA; lactic acid, LAC) in OLP, and the correlation with OLP activity was assessed using the reticular, atrophic and erosive lesion (RAE) scoring system.

Methods

Univariate and multivariate linear regression functions based on scikit-learn were designed to predict the RAE scores in OLP patients, and the performance of these two machine learning functions was compared.

Results

The results revealed that the serum levels of PA and LAC were upregulated in erosive OLP (EOLP) patients, when compared to healthy volunteers. Furthermore, the LDH and LAC levels were significantly higher in the EOLP group than in the nonerosive OLP (NEOLP) group. All glycolysis-related molecules were positively correlated to the RAE scores. Among these, LAC had a strong correlation. The univariate function that involved the LAC level and the multivariate function that involved all glycolysis-related molecules presented comparable prediction accuracy and stability, but the latter was more time-consuming.

Conclusion

It can be concluded that the serum LAC level can be a user-friendly biomarker to monitor the OLP activity, based on the univariate function developed in the present study. The intervention of the glycolytic pathway may provide a potential therapeutic strategy.

Electronic supplementary material

The online version of this article (10.1007/s11596-023-2716-7) contains supplementary material, which is available to authorized users.

Key words: oral lichen planus, glycolysis, lactic acid, linear regression, machine learning

Electronic Supplementary Material

11596_2023_2716_MOESM1_ESM.pdf (173.1KB, pdf)

Predicting the Activity of Oral Lichen Planus with Glycolysis-related Molecules: A Scikit-learn-based Function

Footnotes

This work was supported by grants from the National Natural Science Foundation of China (No. 62171193), the Natural Science Foundation of Hubei Province (No. 2021CFB399), the Foundation of Health Commission of Hubei Province (No. WJ2021M125), and the Key Research and Development Project of Hubei Province of China (No. 2022BCA033).

Conflict of Interest Statement

None of the authors have any potential financial conflicts of interest related to the study.

Contributor Information

Yan Yang, Email: yangyantj@hust.edu.cn.

Jing-yu Hu, Email: 2014tj0116@hust.edu.cn.

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Supplementary Materials

11596_2023_2716_MOESM1_ESM.pdf (173.1KB, pdf)

Predicting the Activity of Oral Lichen Planus with Glycolysis-related Molecules: A Scikit-learn-based Function

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