ArticleCancer medicine2023
Comprehensive transcriptomic analysis to identify biological and clinical differences in cholangiocarcinoma.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Deep molecular profiling of biliary tract cancer uncovers novel biological mechanisms and therapeutic opportunities.ESMO open · 2026Article
- Transforming growth factor-β1 triggers the proliferation of human cholangiocyte spheroids via interleukin-6-mediated STAT3 signaling.Molecular biology reports · 2025Article
- Genetic Uncertainties Fueling Cholangiocarcinoma Progression.Current genomics · 2025Review
- Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches.Cancers · 2024Review
- Multi-Omics Classification of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Dual Inhibition of PI3 Kinase and MAP Kinase Signaling Pathways in Intrahepatic Cholangiocellular Carcinoma Cell Lines Leads to Proliferation Arrest but Not Apoptosis.Current issues in molecular biology · 2024Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Tumor Microenvironment Remodeling in Gastrointestinal Cancer: Role of miRNAs as Biomarkers of Tumor Invasion.Biomedicines · 2023Review
- Comprehensive transcriptomic analysis to identify biological and clinical differences in cholangiocarcinoma.Cancer medicine · 2023Article
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression-based classification with clinical relevance.
methodsA total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques.
resultsBy unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis.
conclusionsThe developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice.
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