ArticleBMC cancer2020
PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Peptides as integrative modulators for clinical prognosis and targeted therapy in pancreatic cancer.Discover oncology · 2026Review
- NAT10 regulates tumor progression and immune microenvironment in pancreatic ductal adenocarcinoma via the N4-acetylated LAMB3-mediated FAK/ERK pathway.Cancer communications (London, England) · 2025Article
- Evaluation of tumor targets selected from public genomic databases for imaging of pancreatic ductal adenocarcinoma.Scientific reports · 2025Article
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- Data privacy-aware machine learning approach in pancreatic cancer diagnosis.BMC medical informatics and decision making · 2024Article
- Topographic analysis of pancreatic cancer by TMA and digital spatial profiling reveals biological complexity with potential therapeutic implications.Scientific reports · 2024Article
- AHNAK2 Promotes the Progression of Pancreatic Ductal Adenocarcinoma by Maintaining the Stability of c-MET.Cancer management and research · 2024Article
- Genomic analysis and filtration of novel prognostic biomarkers based on metabolic and immune subtypes in pancreatic cancer.Cellular oncology (Dordrecht, Netherlands) · 2023Article
- The Utility of Artificial Intelligence in the Diagnosis and Management of Pancreatic Cancer.Cureus · 2023Review
- Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2023Article
- Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review.Journal of cancer research and clinical oncology · 2023Review
- Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review.Journal of medical Internet research · 2023Article
- AHNAK2 is a biomarker and a potential therapeutic target of adenocarcinomas.Acta biochimica et biophysica Sinica · 2022Article
- Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma.Scientific reports · 2022Article
- Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review.International journal of molecular sciences · 2022Review
- Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.Chinese clinical oncology · 2022Review
- The Obscure Potential of AHNAK2.Cancers · 2022Review
- Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.Cancer biomarkers : section A of Disease markers · 2022Article
- Artificial intelligence in pancreatic cancer.Theranostics · 2022Review
- Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images.Frontiers in oncology · 2022Article
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3 authors.
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Abstract
backgroundAlthough the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.
methodsWe performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples.
resultsThis microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1.
conclusionThe gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.
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