Indian Journal of Dental Sciences (Jun 2024)

Development of Quantifiable Assays for Detection of Hypersialylation in Oral Cancer Diagnosis

  • Shrutam Somkuwar,
  • Shravasti Jangle,
  • Vedanti Morey,
  • Devyani Salotkar,
  • Shreya Reddy,
  • Aparna Nair,
  • Deovrat Begde

DOI
https://doi.org/10.4103/ijds.ijds_152_23
Journal volume & issue
Vol. 16, no. 2
pp. 68 – 75

Abstract

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Aim: The aim of this study is to Quantify sialic acid for oral cancer diagnosis using different method using densitometric analysis. Background: Early detection of OSCC is critical for improving survival rates and reducing morbidity, which has prompted researchers to look for potential biomarkers to aid in early diagnosis. Saliva is an excellent diagnostic tool due to its noninvasive nature. Sialic acid levels in saliva have been found to be higher in cancer patients. Hypersialylation is associated with poor prognosis and accelerated cancer progression, as well as immune escape, tumor growth and spread, angiogenesis, and resistance to apoptosis and treatment. This study addresses the imperative need for adaptive, effective, and non-invasive strategies in the diagnosis and treatment of oral cancer, emphasising the importance of early detection for improved prognosis. This study introduces various methods for quantify levels of sialic acid in saliva, including resorcinol and orcinol assays, using colorimetric and densitometric assays. Material and Method: In this study, sialic acid detection methods have been standardized using standards as well as spent media from cancer cell lines to develop noninvasive diagnostic tools for early detection. Two methods used for the detection of sialic acid Resorcinal and Orcinol method by densitometric analysis using Image J software. Results: The study has successfully quantified sialic acid from spent media and saliva using resorcinol and orcinol method by densitometric analysis. Conclusion: In this study we aim to develop a reliable and cost-effective method for quantification of sialic acid using Image j software. The approach presents a non-invasive and efficient alternative for early cancer diagnosis showcasing its potential impact on advancing diagnostic technologies.

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