PROGNOSTIC ROLE OF ΓH2AX AND OTHER BIOMARKERS IN MALIGNANT TRANSFORMATION OF ORAL POTENTIALLY MALIGNANT DISORDERS: A SYSTEMATIC REVIEW AND META-ANALYSIS
DOI:
https://doi.org/10.4238/sqk4kb04Keywords:
γH2AX; Oral potentially malignant disorders; Biomarkers; Malignant transformation; Meta-analysisAbstract
Oral potentially malignant disorders (OPMD) represent a heterogeneous group of lesions with an increased risk of progression to oral squamous cell carcinoma (OSCC), and conventional histopathological assessment has limited predictive accuracy, highlighting the need for reliable molecular biomarkers. Among these, γH2AX, a marker of DNA damage, along with other genomic, epigenetic, and protein biomarkers, has gained increasing attention. This study aimed to systematically evaluate and quantitatively synthesize the prognostic role of γH2AX and other biomarkers in predicting malignant transformation in OPMD. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar, and observational cohort studies assessing biomarkers with reported malignant transformation outcomes were included. Data extraction and quality assessment using the Newcastle–Ottawa Scale were performed, and a random-effects meta-analysis was used to calculate pooled effect sizes, along with subgroup, sensitivity, and publication bias analyses. A total of 13 studies were included, and the pooled analysis demonstrated a significant association between biomarker expression and malignant transformation (HR/OR = 4.05, 95% CI: 2.71–6.06). Subgroup analysis indicated that genomic and epigenetic biomarkers showed stronger predictive value compared to protein biomarkers. Moderate heterogeneity was observed (I² = 48.8%), while sensitivity analysis confirmed the robustness of the findings, and no significant publication bias was detected. In conclusion, molecular biomarkers, particularly γH2AX and other genomic and epigenetic markers, are strong predictors of malignant transformation in OPMD, and their integration into clinical practice may enhance risk stratification, early detection, and personalized management strategies.
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