An Integrative Data Mining for the Identification and Validation of Oncogenic Biomarkers in Pancreatic Carcinoma

Background: Pancreatic carcinoma (PC) is a severe disease associated with high mortality. Although strategies for cancer therapy made great progress, outcomes of pancreatic ductal adenocarcinoma patients remain extremely poor. Therefore, it is urgent to find novel biomarkers and therapeutic targets to improve outcomes of patients. Methods: To identify general applicable targets for early diagnosis and therapy, we selected related microarray data which including three mRNA microarray datasets GSE62165, GSE15471, GSE32676 and two miRNA datasets GSE24279, GSE32678, and combinative analysis was performed by GEO2R. Functional and pathway enrichment analysis were performed using the DAVID database. MiRTarBase, miRWalk, Diana Tools and TBtools were used to get keys. TCGA database, HPA database and western blot experiments were used to verify diagnostic and prognostic value of key genes.Results: By integrating mRNA and miRNA expression profiles, we identified 114 differentially expressed genes (DEGs) and 114 differentially expressed miRNAs (DEMs), respectively. Furthermore, three overlapping key genes, RUNX2, LAMC2 and FBXO32, were found by compared with DEMs target genes and DEGs. In detail, deregulation of 8 key miRNAs were closely related to poor outcomes and participated in crucial genes regulation which may contribute to build a miRNA biomarker panel for prognosis. Moreover, we confirmed that RUNX2 showed a potential property for distinguishing PC and normal people. We also demonstrated that aberrant over-expression of LAMC2 was associated with poor prognosis of PC patients as well as human tumor status and subtypes. The protein levels of RUNX2 and LAMC2 in PC patients were further verified by IHC from Human Protein Atlas and western blot experiments. Conclusions: In summary, our current study identified that RUNX2 and LAMC2 may be promising targets for early diagnosis and therapy of PC patients.