PNAS Nexus. 2026 Feb 25;5(3):pgag047. doi: 10.1093/pnasnexus/pgag047. eCollection 2026 Mar.

ABSTRACT

Liver fibrosis is a significant health concern that affects ∼300 million people globally, characterized by the excessive accumulation of extracellular matrix (ECM) components in the liver. A major contributor to liver fibrosis is fatty liver disease, which can progress to steatohepatitis when the accumulation of fat in the liver causes inflammation, cell death, and scarring. Long-standing steatohepatitis leads to liver fibrosis as scar tissue builds up and replaces healthy liver tissue, potentially progressing to life-threatening conditions, such as cirrhosis, liver failure, or hepatocellular carcinoma. DNA methylation plays a critical role in the progression of fatty liver disease and liver fibrosis by altering gene expression without modifying the DNA sequence. The integration of spatial analysis with protein profiling enhances our ability to explore the spatial organization of cellular interactions and protein expression in liver diseases, fostering a deeper understanding of the disease mechanisms. Multiplex immunofluorescence (mIF) imaging was performed to understand the spatial organization of 10 molecular targets and the cellular interaction between them across four distinct liver tissue types: wild-type (WT) regular, WT high-fat, fibrosis regular, and fibrosis high-fat. Notably, fibrotic high-fat samples displayed increased pan-cytokeratin and vascular cell adhesion molecule-1 (VCAM-1) expression, suggesting diet-aggravated injury and inflammation. Our findings highlight the interplay between epigenetic regulation, ECM remodeling, and cellular crosstalk in liver fibrosis. The spatial profiling approach provides insights into microenvironmental changes, revealing how DNA methylation influences protein localization and fibrotic progression. These results underscore the potential of spatial omics in elucidating disease mechanisms and guiding targeted therapies for metabolic liver disorders.

PMID:41834947 | PMC:PMC12988774 | DOI:10.1093/pnasnexus/pgag047

Oncoscience. 2026 Mar 5;13:44-54. doi: 10.18632/oncoscience.645. eCollection 2026.

ABSTRACT

BACKGROUND: High grade serous ovarian cancer (HGSOC) recurs frequently and commercial tests have emerged for tumor-informed, cell-free DNA (cfDNA)-based detection of minimal residual disease. These tests are based on somatic single nucleotide variants prevalent in many cancers and thus are not well matched to HGSOC, which is dominated by structural genomic rearrangements. The purpose of this study was to evaluate the feasibility of a structural-variant (SV)-informed, cfDNA-based method for detecting clonal and subclonal HGSOC disease burden.

METHODS: A method was developed for detecting patient-specific SV breakpoints using digital droplet PCR (ddPCR) with custom tumor-informed primer/probe pairs. Test parameters were first estimated using synthetic cfDNA generated by ultrasonication of genomic DNA from ovarian cancer cell lines. The optimized workflow was implemented in which whole genome sequencing of multisite pre-treatment HGSOC biopsies performed and high confidence SVs were called by multiple published SV callers. Real-time PCR and ddPCR were used for assay development.

RESULTS: Following the optimized workflow, tumor-specific SV breakpoint-spanning primers/probe sets of four HGSOC patients' multisite biopsies were designed and validated by real-time PCR and ddPCR. Together with four HGSOCs, a total of 29 SVs breakpoints-spanning tumor-informed primers/probe sets were designed and validated in multisite biopsies. 15 validated tumor-specific SVs were selected for quantification in their corresponding liquid biopsies using the validated ddPCR, and 9 had measurements in liquid biopsies.

CONCLUSIONS: Our result shows the detection of SVs from pre-treatment cfDNA using tumor-informed breakpoints-spanning ddPCR is feasible and may enable a novel and sensitive method for monitoring on-treatment disease burden.

PMID:41835357 | PMC:PMC12981705 | DOI:10.18632/oncoscience.645

CPT Pharmacometrics Syst Pharmacol. 2026 Mar;15(3):e70178. doi: 10.1002/psp4.70178.

ABSTRACT

Azathioprine (AZA), a widely used immunosuppressant, can induce acute pancreatitis (AP), yet the underlying molecular mechanisms remain unclear. This study employed an integrative multiomics strategy-combining network toxicology, machine learning, Mendelian randomization (MR), and molecular docking-to elucidate the biological basis of AZA-induced AP. AZA-associated genes were first identified through bioinformatics databases and analyzed using protein-protein interaction networks and GO/KEGG functional enrichment. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) were applied to prioritize key differentially expressed genes for diagnostic modeling. MR was then used to examine potential causal links between gene expression and AP risk, followed by molecular docking to assess AZA-protein interactions. Sixty-eight candidate genes related to AZA-induced AP were identified. Enrichment analyses indicated involvement in lipid metabolic regulation, inflammatory pathways, and energy homeostasis. Machine learning highlighted seven key genes-CES1, CTSK, JAK1, NR3C2, PLIN5, WEE1, and RORA-as central to AP development. MR analysis further demonstrated that decreased expression of CES1 and CTSK may mediate AZA-related AP susceptibility. Docking simulations revealed strong, specific binding between AZA and both CES1 and CTSK. Overall, this study identifies CES1 and CTSK as genetically protective factors and mechanistic mediators in AZA-triggered AP. These findings offer new molecular insights into the genomic and biochemical pathways underlying this adverse drug reaction.

PMID:41832938 | DOI:10.1002/psp4.70178

Clin Chim Acta. 2026 Mar 12;587:120973. doi: 10.1016/j.cca.2026.120973. Online ahead of print.

ABSTRACT

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.

PMID:41831666 | DOI:10.1016/j.cca.2026.120973

Biomarkers. 2026 Mar 14:1-51. doi: 10.1080/1354750X.2026.2644329. Online ahead of print.

ABSTRACT

Though critical, traditional diagnostic approaches such as X-ray, CT scans, bronchoscopy and tissue biopsy don't reliably detect lung cancer at early stages, paradigm shift has occurred recently with lung cancer diagnostics based on recent advances of molecular biology and computational technologies. Present review analyses incorporation of molecular biomarkers- EGFR, ALK, KRAS, BRAF, MET and PD-L1 expression into routine diagnostics facilitating precise subtyping and selection of appropriate therapy. Advanced technologies like liquid biopsy, circulating tumor DNA provide noninvasive alternatives to characterize tumor and monitor disease in real-time. Next generation sequencing and multiomic approaches like genomics, transcriptomics, proteomics supply detailed molecular profile of tumor microenvironment. Same tools help to transform ability to use medical imaging to detect early lesions on low dose CT scans allowing risk stratification through radiomics and pattern recognition with AI, specifically machine learning and deep learning. Recently, AI powered computer aided detection systems and predictive models are forming clinical decision support while creating ground for personalized diagnostics. Potential of AI and biomarker data integration is transformative, they possess many challenges on data standardization, interpretability, clinical validation, and ethical matters. Digital innovation and biological insights are still converging, though, offering faster, more precise, more patient specific lung cancer diagnosis.

PMID:41830914 | DOI:10.1080/1354750X.2026.2644329

Clin Chim Acta. 2026 Mar 12;587:120973. doi: 10.1016/j.cca.2026.120973. Online ahead of print.

ABSTRACT

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.

PMID:41831666 | DOI:10.1016/j.cca.2026.120973

Diagnostics (Basel). 2026 Feb 24;16(5):645. doi: 10.3390/diagnostics16050645.

ABSTRACT

Background/Objectives: Colorectal cancer (CRC) liver metastases present a significant clinical challenge due to high recurrence risks post-resection. Traditional diagnostics often fail to detect early-stage minimal residual disease (MRD). This preliminary pilot study evaluated ctDNA dynamics in 10 patients with liver metastases using a personalized multistep approach. Methods: Following primary tumor Next-Generation Sequencing (NGS) to identify somatic mutations in KRAS, NRAS, TP53, RET, APC, and WRN, custom TaqMan assays were designed for longitudinal plasma analysis. Four methodologies were compared: HRM-PCR, PNA-enhanced qPCR, and two digital platforms (dPCR and ddPCR). Results: While HRM-PCR sensitivity was limited in plasma, digital platforms demonstrated 100% qualitative concordance. MRD-negative status (VAF 0.00%) was identified in 70% of cases (P01, P03, P06, P07, P08, P09, P10), while detectable ctDNA in patients P02, P04, and P05 strongly correlated with aggressive progression. Digital PCR enabled the ultra-low detection of Variant Allele Frequencies (VAFs), identifying high molecular burdens (e.g., P05, VAF 49%) correlating with rapid decline, and capturing early molecular residue in P04 (VAF 0.62%). Conclusions: Our preliminary findings confirm that personalized longitudinal VAF tracking via digital PCR provides superior prognostic value, serving as a robust tool for recurrence monitoring in personalized CRC therapy.

PMID:41827921 | PMC:PMC12984390 | DOI:10.3390/diagnostics16050645

Cancers (Basel). 2026 Feb 28;18(5):800. doi: 10.3390/cancers18050800.

ABSTRACT

BACKGROUND AND OBJECTIVE: Liquid biopsy has transformed the management of advanced prostate cancer, yet its clinical role in non-metastatic disease remains uncertain. Conventional biomarkers such as PSA, imaging, and pathology have limited ability to capture minimal residual disease and biological aggressiveness. The objective of this review was to critically evaluate the current evidence on circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in non-metastatic prostate cancer, focusing on feasibility, prognostic value, and potential clinical applications.

METHODS: A narrative review of PubMed-indexed original studies evaluating liquid biopsy in clinically localized or non-metastatic prostate cancer was performed. Eligible studies included patients treated with curative-intent local therapy or experiencing biochemical recurrence without radiologic metastases. Study designs were predominantly prospective or retrospective observational cohorts. Liquid biopsy analytes included CTCs and ctDNA assessed from peripheral blood plasma using EpCAM-based enrichment, targeted next-generation sequencing, whole-genome sequencing, or ultra-sensitive tumor-informed assays. Primary outcomes included detection rates, associations with clinicopathologic features, biochemical recurrence, metastasis-free survival, and overall survival. Key Findings and Limitations: Across 11 studies, CTC detection using EpCAM-based platforms was infrequent in localized disease and biochemical recurrence and showed limited prognostic value (10-11% in preoperative settings). In contrast, ctDNA was detectable in a minority of patients but consistently identified biologically aggressive disease and a higher risk of recurrence when present, particularly using tumor-informed ultra-sensitive assays. Limitations include low detection rates, heterogeneous methodologies, small sample sizes, and predominantly exploratory study designs.

CONCLUSIONS AND CLINICAL IMPLICATIONS: Currently, its most promising application is not broad screening, but as a selective, biology-driven tool for detecting minimal residual disease and refining risk assessment. CtDNA acts as a biological risk modifier, potentially guiding the escalation or de-escalation of adjuvant therapy. However, prospective biomarker-driven trials are required to validate these strategies before routine clinical implementation.

PMID:41827734 | PMC:PMC12984391 | DOI:10.3390/cancers18050800

Curr Opin Urol. 2026 Mar 6. doi: 10.1097/MOU.0000000000001387. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: Personalised medicine has rapidly reshaped the management of urothelial carcinoma, driven by advances in tumour genomics, immune profiling and targeted drug development. This review is timely as multiple biomarker-driven therapies have recently entered routine clinical practice across disease stages, necessitating an integrated appraisal of how precision approaches should be applied and sequenced in contemporary care.

RECENT FINDINGS: Key advances include the expanding role of immune biomarkers beyond PD-L1, such as tumour mutational burden and DNA damage response alterations, to refine the use of immune checkpoint inhibitors. FGFR3 alterations represent the first validated genomic target in urothelial carcinoma, with FGFR inhibitors now established treatment options. Antibody-drug conjugates targeting Nectin-4 and HER2 have demonstrated substantial clinical activity, redefining treatment paradigms in both first-line and refractory settings. In parallel, circulating tumour DNA has emerged as a powerful dynamic biomarker for minimal residual disease detection and adjuvant treatment selection.

SUMMARY: Urothelial carcinoma has transitioned into a biomarker-driven disease, enabling more precise, biologically informed treatment decisions. Integrating genomic, immunologic and liquid biopsy biomarkers will be essential to optimise patient selection, treatment sequencing and toxicity management, and represents a critical direction for future research and clinical practice.

PMID:41827087 | DOI:10.1097/MOU.0000000000001387

Comb Chem High Throughput Screen. 2026 Mar 11. doi: 10.2174/0113862073398293251205055042. Online ahead of print.

ABSTRACT

INTRODUCTION: This study aimed to investigate the protective effects of Siji Kangbingdu Mixture (SKM) against acute lung injury (ALI) in mice and to elucidate its underlying mechanisms.

METHODS: ALI was induced in Kunming mice via intranasal administration of LPS (5 mg/kg), followed by oral SKM treatment for 7 days. Lung wet-to-dry (W/D) ratio, histopathology, multiomics analysis, and network pharmacology were performed. Key targets and pathways were identified through dynamic KEGG analysis and validated by Western blotting.

RESULTS: SKM treatment ameliorated alveolar hemorrhage, alveolar wall disruption, septal thickening, edema, and inflammatory cell infiltration. Integrated multi-omics analysis revealed that SKM primarily modulated the Ras signaling pathway, reducing the protein expression of Phospho- MEK1/2, Raf1, Phospho-ERK1/2, and RASH/RASK/RASN, thereby contributing to the treatment of ALI.

DISCUSSION: SKM alleviated LPS-induced ALI in mice by inhibiting the Ras pathway, highlighting the pathway's role in ALI pathogenesis. However, due to limitations of the animal model and incomplete validation, further studies combining clinical research and in vitro experiments are needed to confirm its efficacy and mechanism.

CONCLUSIONS: SKM shows potential to ameliorate ALI by suppressing inflammatory responses and reducing local tissue fibrosis. The combination of metabolomics, transcriptomics, and network pharmacology elucidated its mechanism, while Western blot analysis suggested that its therapeutic effect is associated with downregulation of the Ras signaling pathway.

PMID:41830142 | DOI:10.2174/0113862073398293251205055042

Nutrients. 2026 Mar 4;18(5):834. doi: 10.3390/nu18050834.

ABSTRACT

Background: Gut microbiota dysbiosis is critically implicated in the pathogenesis of allergic airway inflammation (AAI) via the gut-lung axis. While Latilactobacillus curvatus is a promising probiotic candidate, its specific immunomodulatory mechanisms in respiratory diseases remain poorly understood. Objective: In this study, we investigated the protective effects and underlying mechanisms of L. curvatus IM01 in an ovalbumin (OVA)-induced murine AAI model using an integrated multi-omics approach. Results: Our results demonstrated that oral administration of L. curvatus IM01 significantly attenuated airway inflammation, suppressed Th2-type immune responses, and reduced serum IgE levels. Crucially, our multi-omics integration revealed a coherent gut-lung axis narrative driven by microbial and metabolic crosstalk. Specifically, 16S rRNA sequencing indicated that L. curvatus IM01 was closely linked to structural shifts in the gut microbial community, notably characterized by an enrichment trend for beneficial genera such as Odoribacter and Lactobacillus. This microbial restructuring was closely associated with a modulated cecal metabolic profile, as untargeted metabolomics exhibited a clear trend toward the restoration of key systemically active immunoregulatory metabolites, including indolelactic acid (ILA) and choline, which have been previously linked to the alleviation of AAI symptoms. Further linking this metabolic shift to respiratory immune tolerance, lung transcriptomic analysis showed that the treatment is strongly associated with the promotion of the differentiation of CD4⁺ T cells into Foxp3⁺ regulatory T cells (Tregs). Conclusions: Collectively, these findings suggest a novel potential pathway by which L. curvatus IM01 modulates the gut-lung axis through coordinated microbial and metabolic interventions, highlighting its potential as a therapeutic functional food ingredient for AAI.

PMID:41830004 | PMC:PMC12987261 | DOI:10.3390/nu18050834

Animals (Basel). 2026 Mar 6;16(5):825. doi: 10.3390/ani16050825.

ABSTRACT

BACKGROUND: Respiratory bacterial infections represent a major health challenge in swine production, highlighting the need for novel immunomodulatory strategies that enhance host resistance. In this study, we investigated whether porcine intestinal lactobacilli could modulate the gut-lung axis and improve respiratory innate immunity in a mouse model of Streptococcus pneumoniae infection, as a surrogate of Streptococcus suis pneumonia.

METHODS: Three strains of Ligilactobacillus salivarius (LAFF998, LAFF1071, and LAFF1095) were orally administered to Swiss mice prior to pneumococcal challenge. The resistance to the infection, the lung damage and the respiratory innate immune response were evaluated.

RESULTS: Only strain LAFF998 significantly reduced pulmonary bacterial loads, prevented bacteremia, and attenuated lung injury. This protective effect was associated with selective modulation of respiratory immunity, characterized by reduced neutrophilic inflammation, increased lymphocyte recruitment, and enhanced activation of alveolar macrophages expressing MHC-II. LAFF998 markedly increased the production of IFN-β, IFN-γ, IL-6, IL-10, and IL-27 in the respiratory tract, without inducing excessive inflammatory damage. Ex vivo and in vitro analyses confirmed that alveolar macrophages from LAFF998-treated mice exhibited a primed phenotype with heightened cytokine responses to pneumococcal stimulation. In contrast, strains LAFF1071 and LAFF1095 failed to confer protection or significantly modulate respiratory immune responses.

CONCLUSIONS: These findings demonstrate a strict strain-dependent effect among porcine L. salivarius isolates and identify LAFF998 as a potent immunobiotic capable of enhancing respiratory innate immunity through the gut-lung axis. This work supports further studies of LAFF998 as an immunobiotic strategy for the prevention of respiratory infections in pigs.

PMID:41829035 | PMC:PMC12985233 | DOI:10.3390/ani16050825

Int J Mol Sci. 2026 Feb 28;27(5):2271. doi: 10.3390/ijms27052271.

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide, yet only a fraction of smokers develops the disease, suggesting protective mechanisms in resilient individuals. Identifying airway-localized molecular signatures may improve our understanding of disease pathomechanisms and support hypothesis generation for biomarker research. In this pilot study, induced sputum from smokers with COPD (n = 28) and smokers without COPD (n = 16; Global Initiative for Chronic Obstructive Lung Disease (GOLD)-defined pre-COPD) was analyzed by untargeted proteomics, metabolomics, and lipidomics. After quality control, 1180 proteins, 187 metabolites, and 1234 lipids were retained. Analyses included univariate models with false discovery rate adjustment and multivariate analyses (PCA, PLS-DA), followed by pathway enrichment and protein interaction network analysis. While few features remained significant after FDR correction, consistent cross-omics patterns were observed. COPD was characterized by ↑ glutathione, creatine, and L-arginine; ↓ CCDC88A and ↑ STAT3 and SYDE2; and broad lipid remodeling involving phosphatidylcholines, sphingolipids, and eicosanoids. Network analysis highlighted STAT3 as a highly connected node linking COPD-related genes. These findings suggest that the multi-omic profiling of induced sputum can capture coherent airway-localized molecular signatures such as oxidative stress, cytoskeletal remodeling, and Rho-family GTPase signaling. However, the results should be interpreted as exploratory and require validation in functional studies.

PMID:41828494 | PMC:PMC12984585 | DOI:10.3390/ijms27052271

Int J Mol Sci. 2026 Feb 25;27(5):2171. doi: 10.3390/ijms27052171.

ABSTRACT

Gastric cancer (GC), also known as stomach adenocarcinoma (STAD), remains a highly lethal malignancy due to late diagnosis, limited therapeutic efficacy, and frequent metastasis. Although extensive molecular profiling has been performed, post-transcriptional regulatory mechanisms underlying GC progression are still incompletely characterized. In this study, we applied an integrative multi-omics framework to elucidate the regulatory roles and clinical relevance of circular RNAs (circRNAs) in GC. Transcriptomic data of mRNAs, microRNAs, and circRNAs from eight independent GEO datasets were jointly analyzed, resulting in the identification of 249 differentially expressed genes (DEGs), 8 differentially expressed microRNAs (DEmiRNAs), and 4 differentially expressed circRNAs (DEcircRNAs). These molecules were integrated into a competing endogenous RNA (ceRNA) network, enabling systems-level characterization of GC-associated regulatory interactions. Network topology and survival analyses prioritized 13 hub molecules, including IGF2BP3, COL4A1, MMP14, and TGM2, which showed both central network positions and significant associations with patient survival. To explore therapeutic implications, transcriptomics-guided drug repositioning combined with molecular docking analysis identified five candidate compounds-celastrol, fedratinib, pevonedistat, tozasertib, and withaferin A-predicted to target key network hubs. Overall, this in silico study provides a ceRNA-centered regulatory framework for GC and prioritizes biologically informed biomarkers and repositioned drug candidates with potential applicability across other malignancies to converge precision oncology.

PMID:41828401 | PMC:PMC12985316 | DOI:10.3390/ijms27052171

Int J Mol Sci. 2026 Feb 25;27(5):2170. doi: 10.3390/ijms27052170.

ABSTRACT

Carbosulfan is a widely used carbamate insecticide, yet its mechanisms of respiratory toxicity remain poorly understood. This study integrated network toxicology, untargeted metabolomics, and molecular docking to systematically investigate the potential mechanisms of carbosulfan-induced respiratory toxicity in male Sprague Dawley rats. Rats were administered a single oral dose of carbosulfan (125 or 250 mg/kg) and assessed after 12 h. Exposure resulted in significant pathological lung damage, characterized by disrupted alveolar architecture, inflammatory cell infiltration, and increased serum levels of the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α. Network toxicology analysis identified 51 potential targets associated with respiratory toxicity, with core targets including SRC, EGFR, PTGS2, CXCL8, CYP3A4, and NR3C1. Enriched pathways were primarily related to neuroactive ligand-receptor interaction, VEGF signaling, and arachidonic acid metabolism. Untargeted metabolomics revealed significant metabolic perturbations in pathways central to antioxidant defense and energy homeostasis, including glutathione metabolism, the tricarboxylic acid cycle, and arginine biosynthesis. Molecular docking confirmed stable in silico binding affinities between carbosulfan and the predicted core targets. Integrative analysis suggests that carbosulfan exposure is associated with respiratory damage, potentially through interconnected mechanisms involving oxidative stress, inflammation, and disruption of cell signaling and metabolic enzyme systems. However, given the acute high-dose nature of the model and the interpretative integration of multi-omics data, these findings should be considered hypothesis-generating. This study provides a novel system-level perspective on carbosulfan-induced respiratory toxicity and highlights key pathways and targets for future validation in chronic exposure models.

PMID:41828400 | PMC:PMC12984169 | DOI:10.3390/ijms27052170

Int J Mol Sci. 2026 Feb 25;27(5):2137. doi: 10.3390/ijms27052137.

ABSTRACT

Protein Kinase N1 (PKN1) is a PKC-related serine/threonine kinase of the AGC group within the eukaryotic protein kinase superfamily (ePK) that orchestrates oncogenic, metabolic, and cytoskeletal signaling. Despite these critical roles, the phosphorylation-dependent regulatory network of PKN1 remains largely undefined. We performed a large-scale phosphoproteomic data integration of publicly available human datasets (892 profiling datasets and 191 differential datasets) to identify recurrent PKN1 phosphorylation sites. This analysis identified two predominant PKN1 phosphosites, S562 and S916, that were frequently observed and differentially regulated across studies. The S916 maps to a turn motif (TM) in the AGC group of kinases, which is evolutionarily conserved among PKN paralogs, while S562 is non-conserved and appears to be PKN1-specific. Co-regulation and enrichment analyses suggest that S916 is associated with insulin/AMPK signaling and metabolic pathways, whereas S562 co-occurs with phosphosites involved in cell division, cytoskeletal regulation, and microtubule cytoskeleton organization. Integrating predicted and experimentally validated kinases, substrates, and interactors, we reconstructed a phospho-regulatory network that positions PKN1 at the crossroads of cytoskeleton organization and metabolic signaling. To assess the disease relevance of these phosphorylation events, we integrated transcriptomic and phosphoproteomic data from the hepatocellular carcinoma database (HCCDB). PKN1 was markedly up-regulated in HCC, and its phosphorylation at S916 was positively co-regulated with multiple oncogenic and proliferation-associated protein phosphosites. These results predict S562 and S916 as potential sites for targeted biochemical validation and functional experiments. The identification of S562 and S916 as key regulatory sites provides new mechanistic insight into PKN1 activation and highlights potential avenues for therapeutic targeting.

PMID:41828364 | PMC:PMC12984926 | DOI:10.3390/ijms27052137

Cancers (Basel). 2026 Mar 9;18(5):878. doi: 10.3390/cancers18050878.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), the major subtypes of non-small cell lung cancer (NSCLC), exhibit distinct molecular landscapes that demand precision in prognosis and therapy. While deep learning models can achieve high predictive accuracy, their black-box nature limits clinical translation.

METHODS: We introduce AttentioFuse, an interpretable deep learning framework employing a Reactome-guided mid-fusion strategy for multi-omics integration. AttentioFuse builds on three pillars: (i) dual-phase learning with omics-specific encoders to preserve modality-unique patterns, (ii) hierarchical attention mechanisms (cross-omics, feature-level, and fusion-layer) to quantify layer contributions dynamically, and (iii) integrated explainability combining DeepSHAP and global attention weights for gene-to-pathway interpretation. Two depth variants are instantiated under identical priors: a three-layer configuration (3F) for main discrimination and a five-layer configuration (AttentioFuse-5X) for deeper hierarchical interpretation; the 5X variant is trained end-to-end and yields comparable accuracy while enhancing pathway-level resolution.

RESULTS: Evaluated on The Cancer Genome Atlas (TCGA) LUAD/LUSC cohorts, AttentioFuse matches state-of-the-art performance in TNM staging while uncovering actionable biological insights, including pan-NSCLC AKT/mTOR metabolic control, histology-divergent Notch signaling roles, and additional pathways related to developmental reactivation, microbiota-associated metastasis, and extracellular matrix remodeling.

CONCLUSIONS: By design, AttentioFuse-5X bridges predictive performance with hierarchical, pathway-resolved explanations, advancing oncology by transforming black-box predictions into biologically grounded decision support.

PMID:41827812 | PMC:PMC12985206 | DOI:10.3390/cancers18050878

Cancers (Basel). 2026 Feb 25;18(5):735. doi: 10.3390/cancers18050735.

ABSTRACT

BACKGROUND: Lactate accumulation is increasingly recognized as a feature of tumor metabolic reprogramming that can coincide with immune dysregulation and aggressive phenotypes. The prognostic and immunologic relevance of lactate-associated heterogeneity in lung cancer remains to be clarified.

METHODS: We curated lactate-related genes and identified prognostic candidates in lung cancer cohorts. Consensus clustering was applied to define lactate-associated molecular subtypes, followed by characterization of survival and tumor microenvironment features. A LASSO-based gene signature was developed to generate an individual-level risk score and an integrated nomogram. Multi-omics analyses were used to evaluate concordance between transcriptomic and proteomic alterations. Single-cell transcriptomic data were analyzed to explore cellular heterogeneity in lactate-related programs. In vitro assays evaluated the response of candidate genes to lactate exposure and assessed cell migration and invasion under proliferation-inhibited conditions after genetic perturbation.

RESULTS: Two lactate-associated molecular subtypes were identified with distinct overall survival and divergent immune microenvironment features. Subtype 1 was associated with better outcomes and a more immune-inflamed profile, whereas Subtype 2 was associated with poorer outcomes and a myeloid-enriched, immunosuppressive contexture. Pathway analyses indicated subtype-associated differences in extracellular matrix-related processes and apoptosis-associated signaling. We developed an 11-gene prognostic signature and nomogram that stratified patients by risk across TCGA and GEO cohorts. Multi-omics integration highlighted ANLN, FGA, and DKK1 as consistently dysregulated at both transcript and protein levels. Among these candidates, DKK1 showed lactate-responsive induction in vitro. DKK1 perturbation altered lactate-enhanced migratory and invasive phenotypes and was accompanied by changes in intracellular lactate levels and global protein lactylation, supporting a potential feedforward relationship between lactate exposure, DKK1 expression, and lactylation.

CONCLUSIONS: This study characterizes lactate-associated molecular heterogeneity in lung cancer and provides a lactate-related subtype framework and prognostic risk model for patient stratification. The findings nominate DKK1 as a lactate-responsive candidate linked to migration/invasion phenotypes and lactate/lactylation changes in vitro.

PMID:41827671 | PMC:PMC12985219 | DOI:10.3390/cancers18050735

Cell Commun Signal. 2026 Mar 14. doi: 10.1186/s12964-026-02793-4. Online ahead of print.

NO ABSTRACT

PMID:41826981 | DOI:10.1186/s12964-026-02793-4

Int J Mol Sci. 2026 Feb 25;27(5):2171. doi: 10.3390/ijms27052171.

ABSTRACT

Gastric cancer (GC), also known as stomach adenocarcinoma (STAD), remains a highly lethal malignancy due to late diagnosis, limited therapeutic efficacy, and frequent metastasis. Although extensive molecular profiling has been performed, post-transcriptional regulatory mechanisms underlying GC progression are still incompletely characterized. In this study, we applied an integrative multi-omics framework to elucidate the regulatory roles and clinical relevance of circular RNAs (circRNAs) in GC. Transcriptomic data of mRNAs, microRNAs, and circRNAs from eight independent GEO datasets were jointly analyzed, resulting in the identification of 249 differentially expressed genes (DEGs), 8 differentially expressed microRNAs (DEmiRNAs), and 4 differentially expressed circRNAs (DEcircRNAs). These molecules were integrated into a competing endogenous RNA (ceRNA) network, enabling systems-level characterization of GC-associated regulatory interactions. Network topology and survival analyses prioritized 13 hub molecules, including IGF2BP3, COL4A1, MMP14, and TGM2, which showed both central network positions and significant associations with patient survival. To explore therapeutic implications, transcriptomics-guided drug repositioning combined with molecular docking analysis identified five candidate compounds-celastrol, fedratinib, pevonedistat, tozasertib, and withaferin A-predicted to target key network hubs. Overall, this in silico study provides a ceRNA-centered regulatory framework for GC and prioritizes biologically informed biomarkers and repositioned drug candidates with potential applicability across other malignancies to converge precision oncology.

PMID:41828401 | PMC:PMC12985316 | DOI:10.3390/ijms27052171