  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.
