Improving pancreatic adenocarcinoma prognosis models through ligand receptor interactions and histopathological integration.

Authors

Kejun Liu, Zhenyao Tan, Yezhen Tang, Yongxue Lv, Yang Bu

Published in

Journal of translational medicine. Jul 03, 2026. Epub Jul 03, 2026.

Abstract

Pancreatic adenocarcinoma (PAAD) has an extremely poor prognosis, and existing prognostic markers fail to fully capture the complex heterogeneity of the tumor microenvironment. This study aimed to integrate ligand-receptor (L-R) interactions, multi-omics data, and deep learning-based pathological images to construct an interpretable multimodal prognostic model and to elucidate the mechanisms underlying the cancer-associated fibroblast (CAF) microenvironment.
Significant L-R interactions were identified using BulkSignalR, followed by sequential Cox, least absolute shrinkage and selection operator (LASSO)-Cox, and random survival forest analyses to construct a prognostic model. Multi-omics profiling characterized molecular distinctions between risk groups. Key L-R pairs were evaluated with single-cell and spatial transcriptomics, validated in 39 paired clinical specimens via immunofluorescence, and linked to histopathological features through deep learning on hematoxylin and eosin-stained whole-slide images.
We identified 236 significant L-R pairs, with 47 associated with prognosis. Integration of LASSO-Cox and random survival forest analyses yielded five key pairs: IL16_KCND1, PLAU_ITGA5, FN1_ITGB3, GNAS_ADCY1, and CALM1_PDE1B. The resulting risk model effectively stratified overall survival. The high-risk group showed higher tumor mutational burden, more frequent KRAS and TP53 mutations, and enrichment of extracellular matrix remodeling, transforming growth factor‑β signaling, and glycolysis pathways. Single-cell and spatial analyses revealed preferential enrichment of PLAU_ITGA5 and FN1_ITGB3 in fibroblast-related compartments. Immunofluorescence confirmed upregulation of these pairs in tumor tissues, and deep learning identified fibroblast-associated histopathological features with strong concordance to the risk axes.
This study established the first multimodal prognostic framework integrating L-R interactions and histopathological features, revealing the central role of CAF-mediated L-R signaling in remodeling the PAAD microenvironment and providing a novel strategy for precise prognostic stratification and targeted microenvironmental therapy.

PMID:
42399741
Bibliographic data and abstract were imported from PubMed on 04 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.