Abstract:
Betel quid chewing, being the main cause of oral cancer in Sri Lanka, has
been driving its mortality rate rapidly over the years. The low five-year survival rate has been
witnessed since oral cancer is typically detected in advanced stages. With the advancement
of medicine and technology, the issues must have been addressed to date, but have not.
Thus, there is a paramount need to revise the therapeutic strategies of oral squamous cell
carcinoma in order to identify the cancer in its early stages and also to identify the molecular
sub-classes so as a mean to individualize the treatment. This research paper mainly reports
on several methods and tools that have been used to analyze the gene expression dataset
of oral carcinoma in order to identify the differentially expressed genes. Further, several
other clustering methods that are used to extract molecular sub-types has also been
discussed. An array of literature has been critically reviewed to find the current insights on
methods used for the computational genomic analysis in order to identify the potential gap in
cancer diagnosis, prognosis or drug response as to support oral cancer mitigation through
the identification of molecular sub-types and the stage-specific genes of Oral Cancer with the
support of computational biology and bioinformatics.
