Humoral immune disorders affect clinical outcomes of oral lichen planus.

OBJECTIVES

The molecular characteristics of oral lichen planus (OLP) are still unclear, and it is not possible to distinguish the clinical outcome of OLP patients in a short period of time for follow-up. Here, we investigate the molecular characteristics of lesions in patients with stable lichen planus (SOLP) and recalcitrant erosive oral lichen planus (REOLP).

METHODS

Our clinical follow-up cohort was split into SOLP and REOLP groups based on the follow-up clinical data. The core modules associated with the clinical information were identified by weighted gene co-expression network analysis (WGCNA). The OLP cohort samples were divided into two groups by molecular typing, and a prediction model for OLP was created by training neural networks with the neuralnet package.

RESULTS

We screened 546 genes in five modules. After doing a molecular type of OLP, it was determined that B cells might have a significant impact on the clinical outcome of OLP. In addition, by means of machine learning, a prediction model was developed to predict the clinical regression of OLP with greater accuracy than the existing clinical diagnostic.

CONCLUSIONS

Our study revealed humoral immune disorders may make an important contribution to the clinical outcome of OLP.