J Liq Biopsy. 2026 Sep 18;14:100501. doi: 10.1016/j.jlb.2026.100501. eCollection 2026 Dec.

ABSTRACT

INTRODUCTION: Circulating tumor DNA (ctDNA) is a valuable tool for assessing minimal residual disease (MRD) and predicting recurrence in resected non-small cell lung cancer (NSCLC) patients. Combining ctDNA-detection with radiological tumor volume may improve risk stratification.

METHODS: Patients with stage I-III resectable NSCLC were prospectively enrolled in the RESIDUAL study. Plasma samples were collected before surgery (T0), at landmark (10 days after surgery), during surveillance (T2, 20 days, T3, 1 months after surgery and every 3 months for the first year and then at the end of the second year after surgery), and at relapse. Samples were analyzed using Guardant Reveal, a tissue-free methylation-based ctDNA assay. Receiver operating characteristic analysis associated T1 ctDNA status with tumor volume; volume thresholds were calculated via Youden's J, and Cox regression analysis was performed.

RESULTS: Forty-eight patients were enrolled (median age 72 years; 64.6% male). Most had stage I disease (54.2%) and adenocarcinoma histology (79.2%). Median follow-up was 41.8 months, and 19 patients (39.6%) relapsed. Overall ctDNA detection rate was 15.2% across all timepoints. Pre-surgical ctDNA detection was higher in squamous histology and stage II-III disease and was associated with worse disease-free survival (DFS; p = 0.022). Landmark MRD detection was also associated with worse DFS (p = 0.024). Serial surveillance sampling anticipated radiologic recurrence by a median of 2.6 months (range 2.0-7.5). Patients with tumor volume >26,378 mm³ had a significantly higher relapse risk (p < 0.001).

CONCLUSIONS: MRD detection in NSCLC resected patients predicts relapse and poor outcome; integrating ctDNA with tumor volume enhances identification of high-risk patients.

PMID:42828040 | PMC:PMC13631347 | DOI:10.1016/j.jlb.2026.100501

Nat Med. 2026 Oct 2. doi: 10.1038/s41591-026-04673-3. Online ahead of print.

ABSTRACT

Urine biomarkers promise to improve noninvasive detection and molecular characterization of genitourinary malignancies. Here we describe urine random priming and affinity capture of cell-free RNA (cfRNA) fragments for enrichment analysis by sequencing (uRARE-seq), a liquid biopsy method for urine cfRNA profiling, and apply it to 683 urine samples from patients with cancer and controls. Urine cfRNA contained transcripts from genitourinary tissues and, in patients with prostate, kidney or bladder cancer, tumor-derived transcripts. uRARE-seq demonstrated 95% sensitivity at 90% specificity for detecting localized bladder cancer. The method outperformed urine tumor DNA analysis and was unaffected by the presence of field-effect mutations. Urine cfRNA analysis also sensitively detected minimal residual disease and distinguished complete molecular responses after surgery from those after intravesical Bacillus Calmette-Guérin (BCG). Pretreatment urine from complete responders to BCG was enriched for T cell and other immune signatures, suggesting a preexisting antitumor immune response, whereas nonresponders showed higher expression of proliferation-related genes. In pretreatment urine from 114 patients, this biological difference enabled development of a biomarker predicting likelihood of response to BCG versus chemotherapy (area under the curve 0.93) that was strongly associated with risk of recurrence. Urine cfRNA analysis is therefore a promising biomarker approach for bladder cancer and potentially other urologic malignancies, although prospective studies are needed to assess its clinical utility.

PMID:42827132 | DOI:10.1038/s41591-026-04673-3

Clin Gastroenterol Hepatol. 2026 Oct 1:S1542-3565(26)00737-8. doi: 10.1016/j.cgh.2026.09.034. Online ahead of print.

ABSTRACT

Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent worldwide, yet most affected individuals remain undiagnosed, and pathways for non-invasive risk stratification and linkage to specialty care remain inconsistently implemented. With the emergence of pharmacologic therapies for metabolic dysfunction-associated steatohepatitis (MASH), accurate patient identification, disease staging, treatment selection, and longitudinal monitoring have become increasingly consequential. In this narrative review, we examine the evolving role of artificial intelligence (AI) across three levels of MASLD care, defined by where an AI output informs a decision and who acts on it: the individual patient, the health system, and health policy. At the patient level, machine-learning and deep-learning approaches have demonstrated potential applications in fibrosis assessment, histopathologic and imaging interpretation, multi-omics integration, treatment-response prediction, and hepatocellular carcinoma risk stratification. Across selected datasets, several models have performed comparably to or better than conventional non-invasive tests and expert interpretation, although no validated tool currently predicts treatment response before therapy. At the health-system level, AI may support population-level case finding, extraction of clinically relevant information from unstructured health records, prognostic assessment, and more efficient allocation of confirmatory testing and specialty care. At the policy level, AI-informed disease-burden modeling and risk stratification may help guide decisions regarding workforce capacity, resource allocation, reimbursement, and treatment coverage. However, most available studies are retrospective, frequently originate from single centers, and have limited external validation. No randomized trial has yet demonstrated that AI-guided management improves clinical outcomes in MASLD, while governance, transparency, and equity frameworks remain underdeveloped. Realizing the clinical value of AI will require prospective multicenter studies embedded within clearly defined MASLD care decisions, validation across genetically and socioeconomically diverse populations and health-system settings, standardized reporting and risk-of-bias assessment, and sustained attention to implementation, governance, and equity.

PMID:42822578 | DOI:10.1016/j.cgh.2026.09.034

Cancer Treat Res Commun. 2026 Oct 1;49:101463. doi: 10.1016/j.ctarc.2026.101463. Online ahead of print.

ABSTRACT

Liquid biopsy has transformed the management of non-small cell lung cancer (NSCLC). However, current plasma-based approaches face inherent limitations in early-stage and low-burden disease, where tumour-derived DNA fractions are frequently below reliable detection thresholds. As lung cancer care shifts toward earlier detection and minimal residual disease assessment, these constraints expose a critical gap in tumour-proximal molecular sampling. This has prompted growing interest in organ-specific liquid biopsy strategies designed to interrogate biological compartments closer to the site of tumour origin. Exhaled breath condensate (EBC), a non-invasive and lung-derived matrix containing airway lining fluid, represents a biologically rational candidate for such an approach. Emerging translational studies demonstrate the feasibility of detecting tumour-associated genomic alterations and nucleic acids within EBC, suggesting potential utility in early detection, molecular profiling, and longitudinal monitoring. However, substantial challenges remain, including pre-analytical standardisation, analytical validation, and prospective clinical evaluation. This narrative review examines the conceptual rationale for lung-specific liquid biopsy, summarises the evolving evidence supporting EBC, and outlines critical and translational steps required to determine whether EBC can become an integral component of lung cancer diagnostics.

PMID:42822136 | DOI:10.1016/j.ctarc.2026.101463

Front Digit Health. 2026 Sep 16;8:1851723. doi: 10.3389/fdgth.2026.1851723. eCollection 2026.

ABSTRACT

Artificial intelligence (AI) is being applied across diagnostic and therapeutic workflows in hepatology. This narrative review summarizes recent advances in AI for liver disease. In medical imaging and digital pathology, computer vision enables automated quantitative analysis of ultrasound, CT, MRI, and histologic images, with the aim of improving the consistency of lesion detection, disease staging, and prognostic assessment. In biomarker research, machine learning can analyze high-dimensional liquid-biopsy and multi-omics data to develop diagnostic and prognostic models; some have outperformed conventional markers in their study cohorts. Electronic health records (EHRs) and large language models (LLMs) are also being investigated for clinical decision support and personalized management. However, most reported evidence remains retrospective, and clinical adoption is limited by data heterogeneity, poor interpretability, uncertain generalizability, and regulatory requirements. Progress will require standardized datasets, external and prospective validation, clinically relevant endpoints, and human-centered implementation before gains in model performance can be translated into better patient outcomes.

PMID:42819088 | PMC:PMC13624904 | DOI:10.3389/fdgth.2026.1851723

Cell. 2026 Sep 29:S0092-8674(26)01081-0. doi: 10.1016/j.cell.2026.09.012. Online ahead of print.

ABSTRACT

Cachexia is a major cause of morbidity in pancreatic cancer, but the cellular circuitry linking tumor progression to systemic wasting remains incompletely understood. Integrating single-cell RNA sequencing, Xenium spatial transcriptomics, multiplex immunohistochemistry, bulk transcriptomics, and functional studies across human non-cachexia, pre-cachexia, and cachexia samples, together with mouse models, we define a cachexia-associated microenvironmental niche composed of SEMA4A⁺ tumor cells, AQP9⁺ macrophages, and LOXL2⁺ cancer-associated fibroblasts. Mechanistically, SEMA4A-associated signaling promotes bone morphogenetic protein-2 (BMP2)-dependent acquisition of an AQP9-associated macrophage phenotype, and macrophage-derived CXCL8 activates LOXL2⁺ fibroblasts. LOXL2⁺ fibroblasts reciprocally enhance tumor cell FOSL1/SEMA4A signaling through exosomal N-glycosylated LOXL2. Spatial analyses demonstrate progressive enrichment of this niche with cachexia severity and association with postoperative development of cachexia in previously non-cachectic patients. These findings provide a framework linking local tumor ecosystem dynamics to cachexia progression.

PMID:42810340 | DOI:10.1016/j.cell.2026.09.012

Adv Sci (Weinh). 2026 Sep 27:e78005. doi: 10.1002/advs.78005. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) patients show heterogeneous responses to immune checkpoint inhibitors (ICIs). This study developed ECOS-Net, a transformer-based multitask network integrating CT-derived habitat and 2.5-dimensional (2.5D) deep learning features for simultaneously predicting early disease control (DC) and overall survival (OS). Of 1,234 patients with HCC enrolled from eight institutions and public databases, 832 ICI-treated patients were used for model development. ECOS-Net fused features using multi-head attention and generated early DC probabilities and OS risk scores. ECOS-DC achieved AUCs of 0.836, 0.822, and 0.817 in training, internal validation, and external test sets, outperforming clinical models (all p values < 0.05). ECOS-OS yielded C-indices of 0.730, 0.722, and 0.720, respectively. Integrated models also showed favorable external performance (early DC AUC: 0.825; OS C-index: 0.741). Patients with higher ECOS-DC probabilities had a higher likelihood of early DC, whereas those with higher ECOS-OS risk had shorter OS, with directionally consistent associations across most subgroups. Exploratory biological analyses suggested that the higher ECOS-DC probability and lower ECOS-OS risk groups were associated with immune-active tumor microenvironment features. Therefore, ECOS-Net shows potential as a non-invasive imaging-based risk stratification framework for simultaneously predicting early DC and OS in ICI-treated HCC patients.

PMID:42801546 | PMC:PMC13616327 | DOI:10.1002/advs.78005

Biomedicines. 2026 Aug 25;14(9):1898. doi: 10.3390/biomedicines14091898.

ABSTRACT

Background/Objectives: Hepatocellular carcinoma (HCC) is the most prevalent primary liver tumor and is often diagnosed at advanced stages with very poor therapeutic response, leading to high mortality. Thus, new therapeutic strategies and biomarkers are urgently needed. We previously showed that the combination of loratadine, raloxifene, and sorafenib exerts synergistic cytotoxicity on HCC cells. Here, we explored potential molecular mechanisms underlying the anticancer effects of this combination using multiomics analyses. Methods: We performed proteomic analyses based on mass spectrometry, transcriptomic analyses using the Clariom D Plus human microarray (Affymetrix), and metabolomic analyses based on nuclear magnetic resonance to investigate the profile changes induced by the drug combination in HuH7 cells. Bioinformatic analyses were applied to associate the omics changes with biological functions, molecular interactions, and clinical relevance in terms of patient survival. Results: We identified several molecules whose expression changed in response to treatment across the three omics profiles analyzed. Some of them were found to be involved in hallmarks of cancer, including sustained proliferation, evasion of growth suppressors, and resistance to cell death. Integrated multi-omics analyses revealed that the drug combination suppresses critical oncogenic drivers (C7orf50, NUP188, and HS2ST1) and that the mitotic cell cycle process, DNA synthesis and cholesterol biosynthesis are the primary pathways affected. Protein-protein interaction analysis revealed five key hubs (KIF2C, PCNA, TRIP13, NDC80, and RPA3), whose expression in HCC is associated with poor clinical prognosis. Conclusions: The combined treatment rewired molecular networks involved in HCC progression. These findings identify clinically relevant molecular targets associated with poor prognosis and provide mechanistic insights into the synergistic anticancer activity of this drug combination.

PMID:42792641 | PMC:PMC13604568 | DOI:10.3390/biomedicines14091898

Int J Mol Sci. 2026 Sep 14;27(18):8183. doi: 10.3390/ijms27188183.

ABSTRACT

The detection of circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs) is a minimally invasive approach for diagnosing and monitoring cancer. These liquid biopsy-based strategies enable the identification of primary or metastatic tumors and provide valuable information on tumor biology and treatment response. A variety of techniques are employed to analyze components obtained through liquid biopsy. While CTCs are typically detected using cell-based methods, ctDNA is commonly analyzed using highly sensitive molecular approaches, including quantitative PCR (qPCR), digital PCR (dPCR), and next-generation sequencing (NGS). Extracellular vesicles (EVs), which carry tumor-derived nucleic acids, proteins, and other biomolecules, are increasingly investigated as potential biomarkers, while tumor-educated platelets (TEPs) can reflect tumor-associated molecular changes and may provide additional information for cancer detection and monitoring. Furthermore, emerging multi-cancer early detection (MCED) approaches aim to identify molecular signatures associated with multiple cancer types from a single blood sample, highlighting the broader diagnostic potential of liquid biopsy. Due to the high specificity of tumors and somatic mutations, ctDNA serves as a real-time biomarker for tracking cancer progression. The advantages of ctDNA diagnostics include its low invasiveness and its capacity to detect cancer at early stages. In addition to confirming the presence of a tumor, liquid biopsy approaches facilitate the assessment of malignancy and the evaluation of treatment response. In the field of human medicine, ctDNA plays a pivotal role in diagnostic procedures. It is utilized not only for screening tests that detect the presence of cancer but also for the development of targeted treatment protocols for specific patients. These protocols are informed by the detection of genomic alterations and are designed to monitor the response to therapy over the course of treatment. Similarly, CTC, EV, TEP, and MCED approaches are being investigated as complementary tools for cancer detection, molecular characterization, prognosis, and longitudinal disease monitoring. In the domain of veterinary medicine, ctDNA has been instrumental in the diagnosis of various neoplasms, including osteosarcomas, hemangiosarcomas, lymphomas, canine mammary tumors, and melanomas. Other liquid biopsy components, including CTCs and EVs, also show promise for the detection and characterization of tumors in companion animals, although their clinical application remains less developed than in human medicine. Another significant application is in the detection of minimal residual disease (MRD), where a small number of cancer cells remain undetectable by conventional tests, such as blood counts. This underscores the significance of advanced ctDNA analysis. However, challenges persist, including the low concentration of ctDNA in blood, the low ratio of mutated to normal DNA fragments, potential contamination, biological variability, and the need for standardized protocols. This review synthesizes the current knowledge on liquid biopsy, including ctDNA, CTCs, EVs, TEPs, and emerging MCED approaches, and the potential for transferring these technologies from human medicine to animal medicine. Continued research is necessary to enhance the sensitivity and specificity of detection, which could facilitate early cancer diagnosis in animals and improve survival through timely treatment.

PMID:42794612 | PMC:PMC13607009 | DOI:10.3390/ijms27188183

Anticancer Agents Med Chem. 2026 Sep 23. doi: 10.2174/0118715206470187260914042941. Online ahead of print.

ABSTRACT

INTRODUCTION: Hepatocellular carcinoma (HCC) is one of the largest health burdens in the world because of high mortality rates, low early diagnostic sensitivity, and poor response to conventional treatment. The existing diagnostic and treatment methods tend to be non-specific and linked with systemic toxicity and late cancer detection, which provokes the necessity of more accurate, precision-driven methods.

METHODS: This review analyzes recent developments in HCC diagnosis and treatment, including new biomarkers, liquid biopsy, and nanotechnology-based therapeutic systems. We critically analysed relevant studies on molecular biomarkers, tumour microenvironment targets, and various nanocarrier platforms.

RESULTS: Newer biomarkers such as Glypican-3, Exosomal microRNAs, circulating tumour DNA methylation panels, AFP-L3, IFI44L, miR-375, miR-203, and CD19 on tumour-associated macrophages showed better diagnostic sensitivity and specificity. Nanotherapeutics approaches that use lipid-based nanoparticles, polymeric systems, inorganic nanoparticles, metal-based nanoparticles, and carbon-based nanocarriers displayed enhanced therapy delivery, targeting, and therapeutic effects. Liposomes, dendrimers, polymeric nanoparticles, and mesoporous silica nanoparticles had especially promising outcomes.

DISCUSSION: A combination of biomarker-based diagnostics and nanotechnology-based diagnostic delivery systems will enable earlier detection, treatment, and delivery of therapeutic agents, reducing systemic toxicity. The liquid biopsy, multi-omics profiling, and AI-assisted imaging developments further improve the accuracy of diagnosis and personalised treatment.

CONCLUSION: Biomarker-based diagnostics and nanocarrier-based therapeutics are potential solutions to enhance HCC management by improving early diagnosis, treatment outcomes, and prognosis.

PMID:42786871 | DOI:10.2174/0118715206470187260914042941

Arch Bronconeumol. 2026 Sep 25:S0300-2896(26)00318-2. doi: 10.1016/j.arbres.2026.08.007. Online ahead of print.

ABSTRACT

BACKGROUND: Highly effective CFTR modulator therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has revolutionized clinical outcomes in cystic fibrosis (CF), yet its effects on gut and lung microbiota, especially at the functional level, are poorly understood.

METHODS: In a 12-month prospective study, we enrolled 35 clinically stable CF patients initiating ETI. Paired fecal and sputum samples, collected at baseline and after 12 months, were analyzed using shotgun metagenomics, metaproteomics, and short-chain fatty acid (SCFA) quantification. Multi-omics data were integrated with clinical parameters assessing lung, hepatic, pancreatic, and intestinal function.

RESULTS: ETI drove significant clinical improvements, including increased ppFEV₁, higher fecal elastase, and better nutritional status, despite persistent major lung pathogens and minimal changes in liver or intestinal inflammation markers. Microbiota composition showed limited shifts: alpha diversity was stable, and beta diversity changes accounted for only small variance in both compartments. However, butyrate-producing genera enriched in feces, while oropharyngeal taxa increased in sputum. Metaproteomics revealed broad downregulation of host neutrophil-driven inflammatory proteins; sputum additionally showed increased abundance of extracellular matrix-related proteins. Microbial proteins linked to carbohydrate/lipid metabolism, particularly butanoate pathways, increased in feces alongside a trend for higher butyrate. In sputum, formaldehyde dehydrogenase enzymes rose, indicating enhanced oxidative microbial metabolism.

CONCLUSIONS: ETI is associated with minimal compositional but substantial functional reprogramming in CF microbiota. These changes are accompanied by an increase in butyrate-producing taxa, attenuation of host pro-inflammatory pathways, and a shift in lung metabolism toward oxidation. Despite ongoing pathogenic colonization, these changes suggest CFTR modulation is associated with a less inflammatory, more stable host-microbiota ecosystem.

PMID:42791132 | DOI:10.1016/j.arbres.2026.08.007