ArticleDiscover oncology2025
Impact of tertiary lymphoid structure-associated biomarkers on pancreatic cancer via a dual-disease analysis of psoriasis and pancreatic cancer.
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 2 papers.
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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
2 citing papers in PubMed.
- EMMA-STRAT: a multi-omics based machine learning framework for stratification of endometrial carcinoma molecular subtypes and MSI status.BioData mining · 2026Article
- Predictive modeling of axillary web syndrome in Chinese postoperative breast cancer patients using interpretable machine learning.Frontiers in oncology · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Pancreatic cancer (PC), often referred to as the "king of cancers", has demonstrated limited success with immunotherapies, many of which are still under development. Psoriasis, a common hereditary skin disorder involving genetic, immune, and environmental factors, is only partially understood and has been linked to an elevated risk of various cancers, including PC. However, the precise mechanisms through which psoriasis may influence PC progression remain unclear. A comparative analysis of the immune microenvironments in both diseases revealed that tertiary lymphoid structures (TLS) are downregulated in both conditions. Gene expression profiles from public databases were analyzed, and TLS-associated genes were subjected to Cox regression and Kaplan-Meier analyses. Machine learning algorithms identified GBP2 as a risk factor and ZNF814 as a protective factor. The TLS pathway was underexpressed in PC, and GBP2, PARP9, ESRP1, FERMT1, and ZNF814 were identified as critical determinants of survival outcomes. GBP2 was linked to poor prognosis, while ZNF814 was associated with favorable outcomes. Clinical risk plots, ROC curves, and Kaplan-Meier analyses were used to assess model performance. Additionally, qPCR experiments validated the expression of these genes in pancreatic cancer cell lines. These results offer valuable insights into PC progression, providing a foundation for improved clinical management and future research.
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