BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.

ABSTRACT

BACKGROUND: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.

OBJECTIVE: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE therapy.

DESIGN: This study enrolled 1448 HCC patients, including a TACE cohort (n=1349), a biomarker subset from a randomised trial (n=41), a single-cell RNA sequencing cohort and The Cancer Genome Atlas (TCGA) HCC cohort (n=50). Pre-treatment contrast-enhanced CT images were used to construct deep learning and conventional radiomic models. The early-fusion and late-fusion models (LFMs) were compared, and a clinical-radiologic model (CRM) was formed by integrating the better-performing LFM with clinical variables. Using TCGA data and single-cell transcriptomic profiles, the differences between high-score and low-score groups in tumour immune microenvironment, cellular functional states and key signalling pathways were investigated.

RESULTS: The CRM effectively stratified patients' survival across multiple independent cohorts and achieved more granular risk stratification than the existing clinical models. Multi-omic analyses revealed that in the LFM high-score group, myelocytomatosis oncogene was activated, epithelial-mesenchymal transition enhanced, glycolysis upregulated and hypoxia pathway activated. Single-cell transcriptomic data confirmed that virtually all cell types in high-risk patients scored high in hypoxia, and cytotoxic T cells had a reduced cytotoxic activity.

CONCLUSION: The CRM model can non-invasively predict the prognosis of HCC patients treated by TACE therapy.

PMID:41856522 | DOI:10.1136/gutjnl-2025-337938