Abstract
Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.
Highlights
Pancreatic cancer (PC) is one of the most common malignant tumors, with a 5-year survival rate of only 9% [1]
We aimed to identify potential diagnostic and prognostic biomarkers for PC using the data available in the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and validate the expression of these biomarkers using the Cancer Cell Line Encyclopedia (CCLE), International Cancer Genome Consortium (ICGC), cBioPortal, Gene Expression Profiling Interactive Analysis (GEPIA), and Human Protein Atlas databases
A total of 5,134 differentially expressed genes (DEGs) were obtained from the mRNA expression profiles derived from TCGA database
Summary
Pancreatic cancer (PC) is one of the most common malignant tumors, with a 5-year survival rate of only 9% [1]. Surgery is the most effective way to improve the survival rate of patients with PC. The prognosis of patients with PC is still very poor because the onset of PC is cryptic, symptoms are atypical, lymph node metastasis occurs early, the degree of malignancy is high, and the progress is rapid [2]. Early diagnosis and intervention are essential for reducing mortality and improving the clinical prognosis of patients with PC. CA19-9, which is considered a better biomarker for the diagnosis and prognosis of PC [4], is highly sensitive, its application in early diagnostic screening for PC is limited owing to a low specificity [5, 6].
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