Journal of translational medicine · 2026
Improving pancreatic adenocarcinoma prognosis models through ligand receptor interactions and histopathological integration.
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Liu Kejun, Tan Zhenyao, Tang Yezhen, Lv Yongxue, Bu Yang
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.