Identification of Biomarkers for Head and Neck Cancer by Analyzing Multiple mRNA Expression Datasets

Abstract

Identifying biomarkers play a vital role in some clinical applications like diagnosis, prognosis, and drug selecting. Being the fifth common and frequent cancer in the world and having a poor survival rate, it makes Head and Neck squamous cell carcinoma (HNSCC) one of the most studied concern. Knowing the lack of effective and reliable biomarkers along with its high mortality rate, finding biomarkers from deferentially expressed genes can reduce the mortality rate. In our research paper, we studied different mRNA micro-array datasets (GSE6631 and GSE12452) for the identification of deferentially expressed genes. With the implementation of a parametric test (Student’s t-test) to identify the deferentially expressed genes with log fold change values. With careful inspection, we found a list of deferentially expressed genes consist of 365 up-regulated and 283 down-regulated genes in total by using Student t-test while keeping the p-value less than 0.05. Finally, after extensive analysis, two genes (PSPH and DST) were selected among them. With an overall endurance inspection, it indicates that PSPH and DST were associated with overall durability. Consequently, PSPH and DST can be the possible biomarkers and can act as a therapeutic objective to enhance the survival rate of Head and Neck Cancers. All the files and code which we used in this research paper are available at https://github.com/AmitHasanShuvo/BiomarkersHNSC