RESUMO
BACKGROUND: Currently, there is still a lack of understanding about the mechanism and therapeutic targets of pancreatic adenocarcinoma (PAAD). The potential of miRNA-mRNA networks for the identification of regulatory mechanisms involved in PAAD development remains unexplored. METHODS: We compared differentially expressed miRNAs (DEMIs) and differentially expressed genes (DEGs) in PAAD and normal tissues from the Gene Expression Omnibus (GEO) database. Transcription factors (TFs) were obtained from FunRich. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs and DEMIs were implemented using Database for Annotation, Visualization and Integrated Discovery (DAVID). Then, key miRNAs and targeted mRNAs were identified by assessment of their expression and prognosis in UALCAN and Kaplan-Meier plotters. In the last step, the candidate miRNA-mRNA selected was confirmed by real-time quantitative polymerase chain reaction (qRT-PCR). RESULTS: We distinguished 62 significant DEMIs, 1314 upregulated DEGs, and 1110 downregulated DEGs. The top 10 TFs were identified. In total, there were 160 hub genes obtained by intersecting the set of 2224 predicted targets with the set of significant DEGs. And we selected 8 key miRNAs. Furthermore, low expression of miR-455-3p in PAAD tissue was closely connected with poor prognosis, and only 5 target mRNAs were predicted to be increased in PAAD tissue with poor prognosis. Therefore, a novel miRNA-hub gene regulatory network in PAAD was constructed. Finally, in vitro experiments indicated that miR-455-3p expression was decreased in PAAD sample. HOXC4, DLG4, DYNLL1 and FBXO45 were validated by qRT-PCR as highly probable targets of miR-455-3p. CONCLUSION: A novel miRNA-mRNA axis has been discovered that may be involved in the regulation of transcriptional disorders and affected the survival of PAAD patients, which would provide a novel strategy for the treatment of PAAD.
RESUMO
BACKGROUND: Pancreatic adenocarcinoma (PC), is a type of digestive tract cancer with the highest mortality all over the word, and its exact pathogenesis is not clear. Therefore, it is of great significance to search for genes related to PC and elucidate its molecular mechanism. METHODS: We integrated and analyzed 8 microarray datasets from the Gene Expression Comprehensive Database (GEO) and PC patient information from the Cancer Genome Atlas (TCGA) database to identified differentially expressed genes (DEGs) based on standardized annotation information. The overlapped DEGs both in the GEO and TCGA datasets were identified as key genes. Kaplan-Meier comprehensive expression scoring method was conducted to determine whether the key genes are related to the survival rate of PC. The expression of those key genes was analyzed by GEPIA and UALCAN. Lastly, Cox regression model was used to construct a gene prognosis signature. RESULTS: The TSPAN1 gene was identified that might be highly related to the pathogenesis of PC. Further analysis showed high expression of TSPAN1 was closely related to the stage 2, moderately differentiated (intermediate grade), and poorly differentiated (high grade) of PC. Finally, we build a four-gene prognosis signature (AIM2, B3GNT3, MATK and BEND4), which can be applied to predict overall survival (OS) effectively. CONCLUSIONS: We revealed promising genes that may participate in the pathophysiology of PC, and found available biomarkers for PC prognosis prediction, which were significant for researchers to further understand the molecular basis of PC and direct the synthesis medicine of PC.
RESUMO
Pancreatic adenocarcinoma (PAAD) is a type of malignant tumor with the highest mortality rate among all neoplasms worldwide, and its exact pathogenesis is still poorly understood. Timely diagnosis and treatment are of great importance in order to decrease the mortality rate of PAAD. Therefore, identifying new biomarkers for diagnosis and prognosis is essential to enable early detection of PAAD and to improve the overall survival (OS) rate. In order to screen and integrate differentially expressed genes (DEGs) between PAAD and normal tissues, a total of seven datasets were downloaded from the Gene Expression Omnibus database and the 'limma' and 'robustrankggreg' packages in R software were used. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery website, and the protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. A gene prognostic signature was constructed using the Cox regression model. A total of 10 genes (CDK1, CCNB1, CDC20, ASPM, UBE2C, TPX2, TOP2A, NUSAP1, KIF20A and DLGAP5) that may be associated with pancreatic adenocarcinoma were identified. According to the differentially expressed genes in The Cancer Genome Atlas, the present study set up four prognostic signatures (matrix metalloproteinase 12, sodium voltage-gated channel α subunit 11, tetraspanin 1 and SH3 domain and tetratricopeptide repeats-containing 2), which effectively predicted OS. The hub genes that were highly associated with the occurrence, development and prognosis of PAAD were identified, which may be helpful to further understand the molecular basis of pancreatic cancer and guide the synthesis of drugs for PPAD.