Prognostic Biomarker Identification for Pancreatic Cancer by Analyzing Multiple mRNA Microarray and microRNA Expression Datasets

Abstract: Possessing the five-year durability rate of nearly 5%, currently, the fourth leading cause for cancer-related deaths is pancreatic cancer. Previously, several works have resolved that early diagnosis performs a meaningful function in enhancing the durability rate and diverse online tools have been utilized to distinguish prognostic biomarker which is a lengthy process. We believe that the statistical feature selection method can produce a better and faster result here. To authenticate our statement, we picked three different mRNA microarray (GSE15471, GSE28735, and GSE16515) and a microRNA (GSE41372) dataset for identification of differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs). By adopting some feature selecting methods, 178 DEGs and 16 DEMs were elected. After identifying target genes of DEMs, we selected two DEGs (ECT2 and NRP2) which were also identified among DEMs target genes. Moreover, overall durability report established that ECT2 and NRP2 were associated with poor overall survival. Hence, we concluded that for pancreatic cancer, statistical feature selection approaches certainly perform better for biomarker identification than pre-defined online programs, and here, ECT2 and NRP2 can act as possible prognostic biomarkers. All the resources, programs and snippets of our literature can be discovered at https://github.com/Srizon143005/PancreaticCancerBiomarkers.