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.

Cell-free DNA (cf-DNA) has been reported to represent a suitable material for liquid biopsy in the diagnosis and prognosis of various cancers. We performed a meta-analysis of published data to investigate the diagnostic value of cf-DNA for renal cancer (RCa). Systematic searches were conducted using Pubmed, Embase databases, Web of Science, Medline and Cochrane Library to identify relevant publications until the 31st March 2021. For all patients, we evaluated the true diagnostic value of cf-DNA by calculating the number of true positive, false positive, true negative, and false negative, diagnoses by extracting specificity and sensitivity data from the selected literature. In total, 8 studies, featuring 754 RCa patients, and 355 healthy controls, met our inclusion criteria. The overall diagnostic sensitivity and specificity for cf-DNA was 0.71 (95% confidence interval (CI), 0.55–0.83) and 0.79 (95% CI, 0.66–0.88), respectively. The pooled positive likelihood ratio and pooled negative likelihood ratio were 3.42 (95% CI, 2.04–5.72) and 0.36 (95% CI, 0.23–0.58), respectively. The area under the summary receiver operating characteristic curve was 0.82 (95% CI, 0.79–0.85), and the diagnostic odds ratio was 7.80 (95% CI, 4.40–13.85). Collectively, our data demonstrate that cf-DNA has high specificity and sensitivity for diagnosing RCa. Therefore, cf-DNA is a useful biomarker for the diagnosis of RCa.