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

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

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):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

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

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):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

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

Am J Surg Pathol. 2026 Jun 1;50(6):695-704. doi: 10.1097/PAS.0000000000002533. Epub 2026 Mar 13.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2 :: NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases ( NFATC2::NUTM2A , n=2; NFATC2::NUTM2E , n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2 -associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

Dev Cell. 2026 Mar 12:S1534-5807(26)00079-1. doi: 10.1016/j.devcel.2026.02.014. Online ahead of print.

ABSTRACT

Unexplored biological matter-including uncharacterized genetic elements, molecular entities, and microbial components-remains poorly understood. Here, we use integrated multi-omics approaches to identify and characterize previously unrecognized protein products encoded by circular RNAs (circRNAs) in human tissue specimens and to delineate their roles in the progression of lung adenocarcinoma (LUAD). The transcription of precursor mRNA by RNA polymerase Ⅱ subunit A (RPB1) is crucial for the biogenesis of these potential circRNA-encoded proteins. Functional and translational analyses link their expression to distinct pathological stages of LUAD in patients. The protein RIPK1-98, encoded by circRIPK1, was identified as functionally distinct from its parental gene product, receptor-interacting serine/threonine kinase 1 (RIPK1). RIPK1-98 modulates cyclin-dependent kinase 2 (CDK2)-dependent cell-cycle regulation, thereby facilitating tumor proliferation in cellular and animal models. Together, these findings suggest that RIPK1-98 serves as a biomarker for cell-cycle progression in LUAD and highlight its potential as a therapeutic target to counteract resistance to first-line treatments, such as osimertinib.

PMID:41825439 | DOI:10.1016/j.devcel.2026.02.014

Phys Med Biol. 2026 Mar 13. doi: 10.1088/1361-6560/ae5209. Online ahead of print.

ABSTRACT

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and dosiomic features from functionally defined lung regions, using generated perfusion (Q) and ventilation (V) from pre-radiotherapy planning computed tomography (CT).
ApproachWe retrospectively analyzed data from 121 patients with locally advanced non-small cell lung cancer (NSCLC) treated with curative-intent IMRT between 2015 and 2019, including pre-treatment CT and dose maps. Q and V maps were generated from CT with deep learning-based and supervoxel-based approaches, respectively. Regions of interest (ROIs) combined the planning target volume (PTV) with each of three functional lung regions-high functional lung (HFL), low functional lung (LFL), and whole lung (WL)-defined by thresholds on Q and V maps. Radiomic and dosiomic features were extracted from CT and dose distributions within each ROI. For each ROI, For each ROI, three methods-radiomics (R), dosiomics (D), and dual-omics (RD)-were constructed. 13 machine learning algorithms were trained and evaluated using 10-fold cross-validation, and model performance was assessed by the average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RP was defined as CTCAE grade ≥ 2.
Main resultsOf the 35 selected features, 20 were from HFL. In dual-omics models, using HFL features improved predictive performance for RP (AUC 0.879±0.105) compared to WL (AUC 0.778 ± 0.100). In HFL, the RD method outperformed both R (AUC 0.786± 0.076) and D (AUC 0.791 ± 0.107) methods. Decision curve analysis showed the dual-omics model based on HFL provided the highest net benefit across threshold probabilities.
SignificanceThis study is the first to systematically demonstrate that features extracted from CT-derived HFL capture important functional differences and provide strong predictive value for RP. Compared to conventional methods, integrating radiomics, dosiomics, and CT-based functional information further improves predictive performance.&#xD.

PMID:41825133 | DOI:10.1088/1361-6560/ae5209

Crit Care Explor. 2026 Mar 12;8(3):e1390. doi: 10.1097/CCE.0000000000001390. eCollection 2026 Mar 1.

ABSTRACT

IMPORTANCE: Inflammation, endothelial dysfunction, and complement activation are associated with COVID-19 acute lung injury (ALI) and acute respiratory distress syndrome (ARDS).

OBJECTIVES: We hypothesized that higher levels of inflammation, endothelial dysfunction, and complement activation implicated in more severe COVID-19 ALI/ARDS are associated with post-acute sequelae of COVID-19 (PASC) phenotypes in the 3 years after hospitalization.

DESIGN, SETTING, AND PARTICIPANTS: A single-center prospective cohort of 150 adult survivors of severe and critical COVID-19 from the first wave of the pandemic with sampling weighted to include 50% survivors of mechanical ventilation.

MAIN OUTCOMES AND MEASURES: Eleven serum biomarkers at hospital discharge, 4 months, 15 months, and 3 years, and symptoms and physical function at 15 months and 3 years. PASC presence was defined using the 12 symptoms and scoring from the Researching COVID to Enhance Recovery (RECOVER) definition. We tested associations of biomarkers with PASC and symptom phenotypes of post-exertional malaise, fatigue, and brain fog while adjusting for age, sex, body mass index, comorbidities, and days since COVID-19 diagnosis.

RESULTS: The mean (sd) age of the cohort was 56 years (13); 67% were Hispanic and 25% were Black. PASC was present in 26% of participants at both 15 months and 3 years. PASC and symptom phenotypes at 15 months and 3 years were consistently associated with higher frailty phenotype category, worse short physical performance battery scores, and shorter 6-minute walk distance. Biomarkers of inflammation, including interleukin-6 and soluble tumor necrosis factor receptor-1, endothelial function, including angiopoietin, and complement, including C2, C4b, and C5, were not associated with PASC or symptom phenotypes in cross-sectional or longitudinal analyses.

CONCLUSIONS AND RELEVANCE: PASC persists for 3 years after acute COVID-19 ALI/ARDS, is associated with frailty, but not associated with persistently higher levels of inflammatory, endothelial, and complement biomarkers implicated in worse short-term outcomes in acute COVID-19, non-COVID-19 ARDS, and sepsis. Future studies should employ multiomics to elucidate potential mechanisms of PASC.

PMID:41824803 | DOI:10.1097/CCE.0000000000001390