Upregulation of miRNA 146a in Oral Squamous Cell Carcinoma Compared to Oral Lichen Planus: A Potential Diagnostic Biomarker

Abstract

Background: Oral lichen planus (OLP) is a chronic inflammatory disorder with a reported global prevalence of 0.5% to 2.6% and a 0 to 5.6% potential for malignant transformation into oral squamous cell carcinoma (OSCC), which accounts for over 90% of oral cancers and causes approximately 177,000 deaths annually worldwide. Identifying molecular markers that can distinguish high-risk OLP lesions is critical for early intervention. MicroRNA (miRNA) 146a has been implicated in inflammation-mediated carcinogenesis and may play a regulatory role in the progression from OLP to OSCC. Objectives: To evaluate and compare the expression of miRNA 146a in OLP and OSCC tissues and assess its diagnostic potential. Methods: Thirty formalin-fixed, paraffin-embedded (FFPE) tissue samples of well-differentiated OSCC and 18 samples of erosive OLP were retrospectively selected from the archives of Imam Khomeini Cancer Institute. These specific subtypes were included to minimize histopathological variability and ensure diagnostic consistency for accurate expression analysis. RNA was extracted and quantified using NanoDrop, followed by cDNA synthesis and qPCR using SYBR Green chemistry. ACTB was used as the reference gene for normalization. Expression analysis was conducted using REST 2009 software. Group differences were evaluated using the Mann-Whitney U test (P < 0.05). Results: The expression of miRNA 146a was significantly higher in OSCC (4.77 ± 4.54) compared to OLP (1.91 ± 0.96), with a P-value of 0.011, indicating its potential role as a biomarker for malignancy risk in OLP lesions. This corresponds to an approximate 2.5-fold increase in raw expression (non-log-transformed). Conclusions: The study identified a significant difference in miRNA 146a expression between OLP and OSCC samples. This suggests its potential use as a biomarker for distinguishing OLP from OSCC, though longitudinal studies are required to assess predictive value.