ArticleOncology letters2019
Identification of key pathways and candidate genes in pancreatic ductal adenocarcinoma using bioinformatics analysis.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Review
- Integrative multi-cohort analysis of DNA methylation profiles for pancreatic ductal adenocarcinoma biomarker discovery and prognosis.Frontiers in bioinformatics · 2026Article
- The geroprotectors trametinib and rapamycin combine additively to extend mouse healthspan and lifespan.Nature aging · 2025Article
- Article
- Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.Briefings in bioinformatics · 2024Article
- Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.Nature communications · 2023Article
- Identification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data.Journal of gastrointestinal cancer · 2022Article
- Combined analyses of RNA-sequence and Hi-C along with GWAS loci-A novel approach to dissect keloid disorder genetic mechanism.PLoS genetics · 2022Article
- Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy.Nature communications · 2021Article
- Positive Crosstalk Between Hedgehog and NF-κB Pathways Is Dependent on KRAS Mutation in Pancreatic Ductal Adenocarcinoma.Frontiers in oncology · 2021Article
- Gene Expression Network Analysis of Precursor Lesions in Familial Pancreatic Cancer.Journal of pancreatic cancer · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high degree of malignancy that is difficult to diagnose and treat. The present study integrated PDAC cohort profile datasets to identify key candidate genes and pathways involved in the pathogenesis of the disease. The expression profiles of GSE28735 included 45 PDCA and matching pairs of adjacent non-tumor tissue. Differentially expressed genes (DEGs) were sorted and candidate genes and pathway enrichment were analyzed. A DEG-associated protein-protein interaction (PPI) network was constructed. A total of 424 DEGs were identified in PDAC, including 159 upregulated genes and 265 downregulated genes. Gene Ontology analysis results indicated that upregulated DEGs were significantly enriched in biological process, molecular function and cellular component categories. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that the upregulated DEGs were enriched in 'pancreatic secretion', 'protein digestion' and 'absorption'. Downregulated DEGs were enriched in 'ECM-receptor interaction', 'focal adhesion' and 'PI3K/AKT' signaling pathways. The PPI network revealed that these genes were involved in significant pathways, including 'ECM organization' signaling pathways (Hippo signaling pathway, TGF-β signaling pathway, Hedgehog signaling pathway and Wnt signaling pathway), 'serine-type peptidase activity' signaling pathway (PI3K-Akt signaling pathway, TNF-α signaling pathway and Wnt signaling pathway) and 'extracellular region' signaling pathways (RTP signaling pathway, G protein-coupled receptor signaling pathway and RAS-RAF-MAPK signaling pathway). The identification of these candidate genes and pathways sheds light on the etiology and molecular mechanisms of PDAC and may guide the development of novel therapies for pancreatic cancer.
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