ReviewBiomolecules2026
Stage-Specific Mucin Reprogramming Across the Gastric Cancer Cascade: Unveiling Molecular Mechanisms and Novel Therapeutic Vulnerabilities.
Review in Biomolecules, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
The trial behind it
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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Who cites it
0 citing papers in PubMed.
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Authors and funding
10 authors.
Funding
Abstract
Mucins are a class of highly glycosylated macromolecules that constitute a cornerstone of the gastrointestinal mucosal barrier and play multiple essential roles in maintaining tissue homeostasis. During the progression from normal gastric mucosa through precancerous lesions to gastric cancer and metastasis, the expression profiles, glycosylation patterns, and spatial distribution of mucins undergo systematic and programmatic alterations, a process referred to as "mucin reprogramming." Recent studies have revealed that this reprogramming is by no means a passive bystander phenomenon accompanying tumorigenesis; rather, it is a central biological event that actively drives malignant transformation, shapes an immunosuppressive microenvironment, and influences therapeutic response. This article aims to systematically delineate the dynamic landscape of mucin expression changes during gastric cancer progression, to provide an in-depth analysis of the underlying molecular mechanisms, and to focus on its clinical value and translational potential in the early diagnosis, molecular classification, prognostic assessment, and targeted therapy of gastric cancer. In addition, this review also discusses recent applications of artificial intelligence in the precise identification of mucin phenotypic features. By integrating the latest research advances, this review seeks to provide new perspectives and a theoretical basis for a deeper understanding of the mechanisms of mucin reprogramming during gastric cancer progression and for the development of novel diagnostic and therapeutic strategies.
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Registered trials
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