ArticleJournal of gastroenterology2026
Emerging omics diagnosis of perihilar cholangiocarcinoma.
Article in Journal of gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
Funding
Abstract
Perihilar cholangiocarcinoma (pCCA) frequently presents as an indeterminate perihilar biliary stricture. The diagnosis is often delayed or uncertain because of the limited sensitivity of conventional endobiliary sampling and blood-based markers, especially in patients with additional confounding conditions such as primary sclerosing cholangitis (PSC). This diagnostic ambiguity can significantly impact clinical outcomes as timely detection and accurate classification are critical for referral and curative-intent treatment. As such, research focused on advancing diagnostic methodologies is a priority. pCCA has a distinct proteomic signature, pattern of activated signaling cascades, epigenetic modifications, and metabolic interactions within its anatomic niche. Recent advances in omics-based diagnostics have leveraged these biological features to identify biomarkers for accurate pCCA diagnosis. In this review, we summarize emerging evidence for omics-based diagnostics in pCCA using clinically accessible samples, including biliary brushings, bile and serum/plasma samples. We discuss the key aspects of pCCA biology that form the foundation for development of novel techniques including glyco-/phospho-/proteomic, genomic, methylomic, metabolomic, and lipidomic analyses as well as multiomic approaches. We conclude by outlining how artificial intelligence may help integrate the data into diagnostic algorithms and identifying areas requiring further investigation to advance these diagnostics into routine clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.