Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

J Intern Med. 2026 Apr 3. doi: 10.1111/joim.70092. Online ahead of print.

ABSTRACT

Tuberculosis (TB) remains a major global health challenge, with multi-drug antibiotic regimens as the current standard of care. While effective at killing Mycobacterium tuberculosis, these treatments do not resolve persistent inflammation, prevent lung damage, or reverse immune dysregulation that contribute to poor outcomes and disease recurrence. Precision medicine offers a promising alternative but requires deeper insight into disease mechanisms to enable tailored interventions. This comprehensive review introduces the concept of immune endotyping to define the underlying disease mechanisms as tools to decode clinical and immunological heterogeneity in TB. TB displays a wide spectrum of clinical phenotypes, from latent or asymptomatic infection to mild or severe disease with characteristic non-cavitary or cavitary lung pathology. Instead, distinct immune endotypes capture the diverse biological pathways that shape disease progression and treatment response. Similar clinical presentations may arise from different immune dysfunctions, underscoring the need to move beyond broad phenotypic classifications. Advances in multi-omics and computational analyses uncover immune signatures that enable stratification for host-directed therapies (HDTs) targeting hyperinflammation, immunosuppression, coagulopathy or metabolic exhaustion. Integrating clinical, radiological, and immunological data through multimodal profiling is essential for developing personalized interventions. We also explore how endotyping has transformed treatment in other diseases, offering valuable insights for TB. Additionally, we present examples of how putative immune endotypes may be targeted with appropriate HDTs. In summary, this review underscores the potential of immune endotypes to advance precision medicine in TB, moving beyond one-size-fits-all treatment to improve outcomes, especially in severe and drug-resistant cases.

PMID:41930636 | DOI:10.1111/joim.70092

Oncol Res. 2026 Mar 23;34(4):11. doi: 10.32604/or.2026.076176. eCollection 2026.

ABSTRACT

Lung cancer remains the leading cause of cancer-related mortality worldwide, primarily driven by metabolic reprogramming and immune evasion mechanisms within tumor cells. To adapt to the nutrient-deprived tumor microenvironment (TME), lung cancer cells undergo profound metabolic reprogramming, characterized by enhanced glycolysis (the Warburg effect), increased glutamine dependency (mediated by GLS1), and accelerated lipid synthesis (involving enzymes such as FASN). These metabolic alterations not only remodel the TME but also dampen antitumor immune responses by promoting immunosuppressive cell populations (e.g., Tregs and M2 macrophages) and inhibiting effector functions of CD8⁺ T cells and natural killer (NK) cells. Critically, a bidirectional crosstalk operates between tumor cell metabolism and the immunosuppressive TME: metabolic reprogramming drives immune suppression through metabolite accumulation, whereas the immunosuppressive TME, in turn, promotes tumor cell adaptability-thus forming a positive feedback loop that reinforces immune evasion and therapy resistance. This review elucidates key molecular pathways governing metabolic reprogramming in lung cancer-spanning glucose, amino acid, and lipid metabolism-and their dynamic crosstalk with immune regulation, including epigenetic modifications and non-coding RNA-mediated mechanisms. Additionally, it evaluates emerging therapeutic strategies targeting the metabolic-immune axis, such as inhibitors of HK2 or GLS1 combined with anti-PD-1/PD-L1 agents, which aim to reverse immunosuppression and improve clinical outcomes. By synthesizing recent advances, this work provides a theoretical framework for precision oncology interventions, highlighting the potential of metabolic immunotherapies and future directions integrating AI and multi-omics data to overcome resistance in lung cancer.

PMID:41930159 | PMC:PMC13040304 | DOI:10.32604/or.2026.076176

Oncogenesis. 2026 Apr 2. doi: 10.1038/s41389-026-00603-7. Online ahead of print.

ABSTRACT

Gallbladder cancer (GBC) is an aggressive malignancy characterized by metabolic plasticity and profound immune evasion. However, the functional role of glutathione peroxidase 3 (GPX3), a secreted antioxidant enzyme, in these processes remains unclear. Multi-omics analyses of paired GBC and adjacent non-tumor tissues revealed consistent downregulation of GPX3, which correlated with reactive oxygen species (ROS) accumulation and enhanced glycolytic activity. Functional restoration of GPX3 in GBC cells reduced intracellular ROS levels, suppressed the expression of glycolysis-related enzymes, and consequently impaired tumor proliferation, migration, and invasion. In xenograft models, GPX3 overexpression markedly attenuated tumor growth and lung metastasis. Notably, GPX3 restoration also enhanced CD8⁺ T cell infiltration and elevated pro-inflammatory cytokine production, suggesting reversal of tumor-associated immunosuppression. These findings identify GPX3 as a critical tumor suppressor that integrates redox regulation, metabolic reprogramming, and immune activation to restrict malignant progression. Targeting GPX3 or its downstream pathways may represent a promising therapeutic strategy to simultaneously suppress gallbladder cancer aggressiveness and reinforce anti-tumor immunity.

PMID:41927557 | DOI:10.1038/s41389-026-00603-7

Curr Comput Aided Drug Des. 2026 Mar 31. doi: 10.2174/0115734099433388260204214424. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung disease with a poor prognosis. BI-1015550 is an oral phosphodiesterase 4B (PDE4B) inhibitor that has shown anti-inflammatory and anti-fibrotic effects; the exact molecular target(s) and mechanism of action in fibrosis are unknown. BI-1015550, an orally available PDE4B inhibitor with a possible anti-fibrotic effect, whose molecular mechanism of action is unknown Methods: We adopt an integrative approach that combines network pharmacology for identifying putative targets, molecular docking, and Molecular Dynamics (MD) simulations to assess the binding, ML-based target prioritization. Predicted targets/pathways were verified by Western blotting and Immunohistochemistry (IHC).

RESULTS: Network pharmacology analysis identified eight key targets: PTGS2, VCAM1, MMP1, IGF1, MMP7, CCL5, MMP13, and SELE. Docking results and MD simulation demonstrated that the predicted major targets of BI-1015550 include MMP1, PTGS2, and VCAM1. Therapeutic targets were also prioritized using machine learning methods. BI-1015550 treatment significantly decreased collagen deposition and HYP content of lung tissues in vivo. It down-regulated PTGS2, MMP1, and VCAM1 proteins via modulation of the NF-κB signaling pathway.

DISCUSSION: We presented an integrative multi-omics approach based on in silico prediction and wet-lab experiments to dissect the antifibrotic activity of BI-1015550. We showed here that BI- 1015550 mainly acts by inhibiting the NF-κB axis, resulting in downstream suppression of profibrotic and proinflammatory mediators. Our work on integrating network pharmacology with molecular simulation and ML is promising in both identifying reliable targets and providing a solid basis for further drug repurposing and mechanistic investigations. The better performance of BI-1015550 than current drugs (i.e., nintedanib and pirfenidone) demonstrates that it could be considered as an effective multi-target therapy against IPF.

CONCLUSION: BI-1015550 attenuates idiopathic pulmonary fibrosis through the suppression of the NF-kB signalling pathway and up-regulation of PTGS2, MMP1, and VCAM1. This provides some theoretical basis to treat IPF with this compound and indicates that a combination study is beneficial for revealing drug mechanisms.

PMID:41926303 | DOI:10.2174/0115734099433388260204214424

Cancer Immunol Immunother. 2026 Apr 2;75(4):131. doi: 10.1007/s00262-026-04374-3.

ABSTRACT

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcriptomics to dissect the spatial localization of these regulators. We identified the SNHG6-BIRC5 axis as a critical driver of the "immune-cold" phenotype in lung adenocarcinoma. We provide visual evidence that this axis localizes to tumor nests and negatively correlates with T- cell infiltration, elucidating a mechanism of spatial immune exclusion. Validating the clinical relevance of these findings, genome-scale CRISPR-Cas9 screening data confirmed the functional essentiality of these targets for cancer cell survival. Furthermore, pharmacogenomic analysis revealed that high expression of this axis correlates with sensitivity to chemotherapy agents like Vinblastine, suggesting a potential stratification strategy for patients with immune-excluded tumors. To expand the clinical utility to immunotherapy prediction, we developed a pan-cancer XGBoost machine learning model incorporating 14 high-performance regulatory features. This model achieved robust performance in distinguishing immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1. Collectively, this study highlights spatial determinants of immune exclusion and chemotherapy sensitivity- and presents a generalized machine- learning tool for precision immunotherapy stratification. The developed online resource is freely available to facilitate community-wide biomarker discovery.

PMID:41925746 | PMC:PMC13046951 | DOI:10.1007/s00262-026-04374-3

Mucosal Immunol. 2026 Mar 30:S1933-0219(26)00036-X. doi: 10.1016/j.mucimm.2026.03.012. Online ahead of print.

ABSTRACT

Aberrant inflammation and structural lung damage occurs early in life for people with cystic fibrosis (CF). Even in the era of CFTR modulators, anti-inflammatory therapy may still be needed to prevent establishment and lifelong consequences of bronchiectasis. In this study, we integrated transcriptome-wide single-cell RNA sequencing data and highly multiplexed surface protein expression to create the largest comprehensive paediatric lower airway atlas of >190,000 cells from 45 bronchoalveolar lavage (BAL) samples resulting in 43 immune and epithelial cell populations, all available for exploration on CELLxGENE. We then investigated inflammatory cell responses in children with CF to show widespread gene expression dysregulation of macrophage populations in the preschool CF lung. This included alterations in pathways associated with TNF and IFN signalling, cholesterol homeostasis, as well as pulmonary fibrosis, that were further altered by the early development of bronchiectasis. We showed that the CFTR modulator ivacaftor restores some of these macrophage-related functional deficits and reduces expression of pathways associated with neutrophil infiltration, however the modulator lumacaftor/ivacaftor did not result in any detectable changes in transcriptional response. This work represents a comprehensive, multi-omic single-cell analysis of BAL from preschool children and the results may inform the future development of anti-inflammatory therapy for children with CF.

PMID:41921915 | DOI:10.1016/j.mucimm.2026.03.012

Environ Int. 2026 Mar 28;210:110212. doi: 10.1016/j.envint.2026.110212. Online ahead of print.

ABSTRACT

Microplastics (MPs) and nanoplastics (NPs) are pervasive environmental contaminants, raising concerns about their potential to cause inflammation, oxidative stress, and lung injury through respiratory toxicity. Due to their smaller size, larger surface area, and greater reactivity, NPs may pose a greater risk than MPs, yet size-dependent toxicity mechanisms remain unclear. This study investigates the distinct early molecular initiating events and toxicological effects of 1 μm polystyrene MPs (PS-MPs) and 20 nm polystyrene NPs (PS-NPs). Based on the internal exposure dose estimated from Py-GC/MS analysis, in vitro exposure concentrations were set at 0, 62.5, 125, 250, 500, and 1000 μg/mL. Multi-omics sequencing and integrative analysis identify specific proteomic and metabolomic alterations. Molecular dynamics simulations and co-immunoprecipitation assays elucidate binding interactions between PS-NPs-induced proteins and metabolic enzymes. In vitro and in vivo experiments reveal a greater accumulation of PS-NPs through endocytosis compared to PS-MPs; while pronounced histopathological damage with inflammatory response in mice lungs were only induced by PS-NPs, rather than PS-MPs. Compared to control group, PS-MPs partly caused proteomic or metabolomic perturbations, while PS-NPs induced significant differential expression of more extensive proteins and metabolites. PS-NPs exposure specifically upregulates insulin-like growth factor 2 receptor (IGF2R) expression and reduces Hypoxanthine levels when compared with PS-MPs. IGF2R directly interacts with Hypoxanthine-guanine phosphoribosyl transferase (HPRT), a key enzyme in Hypoxanthine metabolism, causing its disruption. This study provides important insights into the comparative toxic effects between PS-NPs with PS-MPs, especially the unique toxicological mechanisms of PS-NPs, thereby advancing the understanding of airborne plastic pollutant risks and supporting future regulatory assessments.

PMID:41921402 | DOI:10.1016/j.envint.2026.110212