ArticleMolecular medicine (Cambridge, Mass.)2019
Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer.
Article in Molecular medicine (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bioinformatic Analysis and Integration of Transcriptome and Proteome Results Identify Key Coding and Noncoding Genes Predicting Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas.BioMed research international · 2021Pooled it
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- Insight into the shared pathogenic link between COVID-19 and pterygium: a systematic bioinformatics analysis with experimental validation.Hereditas · 2025Article
- Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer.Scientific reports · 2025Article
- Metabolic reprogramming and prognostic modeling in pancreatic cancer: insights from WGCNA.Frontiers in genetics · 2025Article
- Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.PloS one · 2025Article
- Targeting the Exonic CircularResearch (Washington, D.C.) · 2025Article
- Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast.PloS one · 2025Article
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- CAP2 promotes gastric cancer metastasis by mediating the interaction between tumor cells and tumor-associated macrophages.The Journal of clinical investigation · 2023Article
- Exosome and lipid metabolism-related genes in pancreatic adenocarcinoma: a prognosis analysis.Aging · 2023Article
- Transcriptomic analysis of pancreatic adenocarcinoma specimens obtained from Black and White patients.PloS one · 2023Article
- Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis.MethodsX · 2023Article
- LAMC2 marks a tumor-initiating cell population with an aggressive signature in pancreatic cancer.Journal of experimental & clinical cancer research : CR · 2022Article
- Identification and prognostic analysis of biomarkers to predict the progression of pancreatic cancer patients.Molecular medicine (Cambridge, Mass.) · 2022Article
- Identification of immune-related features involved in Duchenne muscular dystrophy: A bidirectional transcriptome and proteome-driven analysis.Frontiers in immunology · 2022Article
- The connection between innervation and metabolic rearrangements in pancreatic cancer through serine.Frontiers in oncology · 2022Review
- Article
- Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments.BioMed research international · 2022Article
- Fibroblast activation protein-based theranostics in pancreatic cancer.Frontiers in oncology · 2022Review
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9 authors.
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Abstract
backgroundThe hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.
methodsRobust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.
resultsBased on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log
conclusionIntegrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.
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