BACKGROUND: Salivary gland neoplasms (SGNs) pose a challenge to both pathologists and clinicians. Despite research, the etiology of these neoplasms remains unclear. This study aimed to identify any potential association between the presence of hepatitis C virus (HCV) at the protein or gene level and epithelial salivary gland neoplasms. METHODS: Formalin-fixed paraffin-embedded (FFPE) blocks of epithelial salivary gland neoplasms were retrieved from the archives of the Oral and Maxillofacial Pathology Department, Faculty of Dentistry, Cairo University within the 5-year period from 2016 to 2020. Immunohistochemistry was used to assess HCV core antigen, while reverse transcription polymerase chain reaction was employed for the evaluation of HCV RNA. RESULTS: A total of 44 specimens were collected, 28 of which were benign neoplasms and 16 were malignant neoplasms. There was a statistically significant difference in HCV positivity between the two groups (P-value = 0.036). Benign tumors showed a statistically significant lower percentage of positive cases than malignant tumors. The localization of staining was also evaluated, revealing various patterns of HCV core antigen expression, including diffuse cytoplasmic, patchy cytoplasmic, nuclear, and a combination of nuclear and cytoplasmic expression. There was no statistically significant difference between the expression patterns in benign and malignant tumors (P-value = 0.616). Given that Pleomorphic Adenoma and Mucoepidermoid Carcinoma were the predominant tumor types in this study, four cases were selected for RNA detection. HCV RNA was detected in all cases using RT-PCR. CONCLUSIONS: HCV core antigen is frequently detected in SGNs and is suggested to be a potential risk factor for the development of these neoplasms. Further studies are required to discover other biomarkers, their roles, and the pathways associated with HCV in SGNs.
No clinical trial protocols linked to this paper
Clinical trials are automatically linked when NCT numbers are found in the paper's title or abstract.PICO Elements
No PICO elements extracted yet. Click "Extract PICO" to analyze this paper.
Paper Details
MeSH Terms
Associated Data
No associated datasets or code repositories found for this paper.
Related Papers
Related paper suggestions will be available in future updates.