ArticleDiscover oncology2025
Genetic insights into the shared molecular mechanisms of Crohn's disease and breast cancer: a Mendelian randomization and deep learning approach.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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Who cites it
1 citing paper in PubMed.
- A multi-model prediction of a stage-specific prognosis for colorectal cancer using attention-driven deep ensemble learning on genomic profiling data.Frontiers in artificial intelligence · 2026Article
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Authors and funding
4 authors.
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Abstract
The objective of this study was to explore the potential genetic link between Crohn's disease and breast cancer, with a focus on identifying druggable genes that may have therapeutic relevance. We assessed the causal relationship between these diseases through Mendelian randomization and investigated gene-drug interactions using computational predictions. This study sought to identify common genetic pathways possibly involved in immune responses and cancer progression, providing a foundation for future targeted treatment research. The dataset comprises single nucleotide polymorphisms used as instrumental variables for Crohn's disease, analyzed to explore their possible impact on breast cancer risk. Gene ontology and pathway enrichment analyses were conducted to identify genes shared between the two conditions, supported by protein-protein interaction networks, colocalization analyses, and deep learning-based predictions of gene-drug interactions. The identified hub genes and predicted gene-drug interactions offer preliminary insights into possible therapeutic targets for breast cancer and immune-related conditions. This dataset may be valuable for researchers studying genetic links between autoimmune diseases and cancer and for those interested in the early identification of potential drug targets.
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