Identification of potential target genes in pancreatic ductal adenocarcinoma by bioinformatics analysis

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most complicated and fatally pathogenic human malignancies. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of PDAC. The aim of the present study was to identify the key genes and signaling pathways associated with PDAC using bioinformatics analysis. Four transcriptome microarray datasets (GSE15471, GSE55643, GSE62165 and GSE91035) were acquired from Gene Expression Omnibus datasets, which included 226 PDAC samples and 65 normal pancreatic tissue samples. We screened differentially expressed genes (DEGs) with GEO2R and investigated their biological function by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. The overall survival data was obtained from UALCAN, which calculated the data shared with The Cancer Genome Atlas. In addition, a protein-protein interaction (PPI) network of the DEGs was constructed by STRING and Cytoscape software. The four sets of DEGs exhibited an intersection consisting of 205 genes (142 up-regulated and 63 down-regulated), which may be associated with PDAC. GO analysis showed that the 205 DEGs were significantly enriched in the plasma membrane, cell adhesion molecule activity and the Energy pathways, and glycine, serine, threonine metabolism were the most enriched pathways according to KEGG pathway analysis. Kaplan-Meier survival analysis revealed that 22 of 205 common genes were significantly associated with the overall survival of pancreatic cancer patients. In the PPI network and sub-network, DKK1 and HMGA2 were considered as hub genes with high connectivity degrees. DKK1 and HMGA2 are strongly associated with WNT3A and TP53 separately, which indicates that they may play an important role in the Wnt and P53 signaling pathways. Using integrated bioinformatics analysis, we identified DKK1 and HMGA2 as candidate genes in PDAC, which may improve our understanding of the mechanisms of the pathogenesis and integration; the two genes may be therapeutic targets and prognostic markers for PDAC.

Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode

“…Tumor occurrence is a complicated process along with genetic gene changes at the same time, and these altered genes usually show abnormal expression patterns, thus having clinical significance for the diagnosis and prognosis of cancer (Tang et al, 2018). Presently, some molecules have been considered as markers for the diagnosis and prognosis of LUAD.…”

Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer

“…6 The application of WGCNA to identify gene expression networks potentially involved in crucial molecular pathways for progression of cancer is becoming an increasingly prevalent area in oncogenomic studies among different types of high-morbidity cancers, including breast cancer, colon cancer, gastric cancer, and pancreatic cancer. [7][8][9][10] Implementation of WGCNA on genomic data on FPC could open new perspectives for the characterization of hereditary pancreatic cancer-identifying important pathways and hub genes associated with molecular mechanisms in the progression of FPC.…”

Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets

“…a meta-analysis, as a large-sample study, has an advantage in addressing this limitation due to its enhanced statistical power (19). Prior meta-analyses have been applied to study tumors to confirm deGs between tumor tissue and normal tissue in glioma (20,21), lung cancer (22), bladder cancer (23), breast cancer (24), osteosarcoma (25), liver cancer (26) and pancreatic cancer (27). In the present study, integrative meta-analysis of expression data (inMeX) was used to conduct a meta-analysis based on 11 qualified microarray datasets, with the aim to identify crucial deGs between Pdac samples and normal pancreatic samples that may serve as biomarkers for Pdac treatment and prognosis.…”