ArticleOncology letters2018
Identification of potential target genes in pancreatic ductal adenocarcinoma by bioinformatics analysis.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 32 citations in OpenAlex.
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- Recent advances in drug delivery and targeting for the treatment of pancreatic cancer.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
- Correlation of NNMT and DKK1 Protein Expression With Clinicopathological Characteristics and Prognosis of Breast Cancer.Clinical Medicine Insights. Oncology · 2023Article
- A novel metabolic-immune related signature predicts prognosis and immunotherapy response in lung adenocarcinoma.Heliyon · 2022Article
- Establishment of prognostic risk model and drug sensitivity based on prognostic related genes of esophageal cancer.Scientific reports · 2022Article
- Identification of Hub Genes in Pancreatic Ductal Adenocarcinoma Using Bioinformatics Analysis.Iranian journal of public health · 2021Article
- GSK2126458 has the potential to inhibit the proliferation of pancreatic cancer uncovered by bioinformatics analysis and pharmacological experiments.Journal of translational medicine · 2021Article
- ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis.Scientific reports · 2021Article
- The Hydroxyquinoline Analogue YUM70 Inhibits GRP78 to Induce ER Stress-Mediated Apoptosis in Pancreatic Cancer.Cancer research · 2021Article
- Machine Learning of Single Cell Transcriptomic Data From anti-PD-1 Responders and Non-responders Reveals Distinct Resistance Mechanisms in Skin Cancers and PDAC.Frontiers in genetics · 2021Article
- Expression and Role of Dickkopf-1 (Dkk1) in Tumors: From the Cells to the Patients.Cancer management and research · 2021Review
- Context Matters-Why We Need to Change From a One Size Fits all Approach to Made-to-Measure Therapies for Individual Patients With Pancreatic Cancer.Frontiers in cell and developmental biology · 2021Review
- Identification of key candidate genes associated with prognosis of lung adenocarcinoma by integrated bioinformatical analysis.Translational cancer research · 2020Article
- The A818-6 system as an in-vitro model for studying the role of the transportome in pancreatic cancer.BMC cancer · 2020Article
- The Implications for Cells of the Lipid Switches Driven by Protein-Membrane Interactions and the Development of Membrane Lipid Therapy.International journal of molecular sciences · 2020Review
- Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode.Frontiers in molecular biosciences · 2020Article
- Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer.Journal of pancreatic cancer · 2020Article
- The Clinical Diagnostic and Prognostic Value of Dickkopf-1 in Cancer.Cancer management and research · 2020Review
- Identification for Exploring Underlying Pathogenesis and Therapy Strategy of Oral Squamous Cell Carcinoma by Bioinformatics Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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