Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

J Cardiovasc Dev Dis. 2026 Apr 30;13(5):195. doi: 10.3390/jcdd13050195.

ABSTRACT

(1) Aim: The incidence of high-altitude pulmonary hypertension (HAPH) has risen in recent years and is expected to continue increasing; however, its diagnosis remains challenging. In this study, we employed proteomics and metabolomics to identify the proteins and metabolic biomarkers that contribute to the development of HAPH. (2) Methods: We applied integrated proteomics and metabolomics to match blood samples from 40 HAPH patients and 40 healthy controls in Yunnan's high-altitude regions to characterize molecular profiles, identify biomarkers, and develop a predictive model. (3) Results: Proteomic analysis identified four proteins (A2IPH7, K1C14, PSME2, SERPINE2) commonly dysregulated in HAPH patients from two high-altitude regions. SERPINE2 was notably downregulated and showed a negative correlation with clinical severity, which was further validated in HAPH rat lung tissues and supported by UK Biobank data for idiopathic PAH. Concurrent metabolomics uncovered 11 shared metabolites, largely acyl fatty acids, enriched in pathways such as unsaturated fatty acid synthesis. Integration of these multi-omics data enabled the development of a robust predictive model. (4) Conclusion: Our study identified key protein and metabolic biomarkers involved in HAPH development, which were validated in animal models. Based on these findings, a predictive model was developed, highlighting SERPINE2 and 11 metabolites as promising targets for the prediction and prevention of HAPH.

PMID:42188081 | DOI:10.3390/jcdd13050195

Metabolites. 2026 May 15;16(5):333. doi: 10.3390/metabo16050333.

ABSTRACT

Background: Children with obesity-related asthma exhibit poorer symptom control and more frequent exacerbations than their normal-weight peers, but the underlying metabolic mechanisms are unclear. This study aimed to identify drivers of obesity-related asthma through untargeted plasma metabolomic and lipidomic profiling. Methods: Plasma was obtained from normal weight (NW) asthmatic (n = 95) and non-asthmatic (n = 67) and overweight/obese (OO) asthmatic (n = 99) and non-asthmatic (n = 100) children (6-17 years). We assessed metabolic and lipidomic differences between asthmatics and controls within each BMI group using orthogonal partial least squares discriminant analysis (OPLS-DA), examined overlap with the adult Qatar Biobank cohort, and mapped metabolic-clinical interactions using Gaussian Graphical Models. Results: In the fitted OPLS-DA models, separation between asthmatic and control groups was stronger in the NW group (R²Y = 0.72/0.52) than in OO (R²Y = 0.65/0.63) children. Asthma was associated with altered tricarboxylic acid (TCA) intermediates, ether-linked phosphatidylethanolamines, and sphingomyelins (SM) in NW, and with phosphatidylcholines, lysophosphatidylcholines, and phosphatidylethanolamines in OO. Integrating metabolomic, lipidomic, and clinical data revealed connections between altered SMs and interleukins, and TCA intermediates and electrolytes, all associated with elevated leptin in NW. An increased residual volume to total lung capacity ratio in OO was associated with phospholipid shifts. The overall dynamics in lipid metabolism with asthma, conditioned on BMI, was also observed in the adult Qatar Biobank cohort. Conclusions: Among NW children with asthma, we found enhanced TCA cycle activity and inflammation linked to altered SM metabolism, whereas in OO, the findings suggest oxidative stress arising from chronic obesity-related inflammation. These data reveal BMI-specific metabolic mechanisms of pediatric asthma that might inform precision approaches to disease management.

PMID:42188042 | DOI:10.3390/metabo16050333

Ecotoxicol Environ Saf. 2026 May 25;319:120306. doi: 10.1016/j.ecoenv.2026.120306. Online ahead of print.

ABSTRACT

The respiratory toxicity of heated tobacco products (HTPs) remains incompletely characterized, and traditional models often fail to capture human-specific responses. Here, we established a human pluripotent stem cell (hPSC)-derived airway organoid (AO) platform and systematically compared the toxicological profiles of two HTP aerosols using an integrated framework encompassing conventional cytotoxicity assays, lineage-specific analysis, network perturbation modeling and multi-omics profiling. Both HTPs induced time- and concentration-dependent cytotoxicity, oxidative stress, DNA damage, and apoptosis in AOs. Exposure also triggered epithelial chemokine response characterized by elevated IL-8, MCP-1, MIP-1β, GM-CSF, and RANTES, with concomitant suppression of IP-10, indicating epithelial-derived inflammatory alarm signals. Lineage-specific transcriptional changes revealed mucociliary dysfunction characterized by goblet cell hyperplasia (MUC5AC upregulation) and ciliated cell impairment (FOXJ1 downregulation), key features of airway remodeling in chronic respiratory diseases. To delineate underlying mechanisms, we employed Network Perturbation Amplitude (NPA) analysis, which uncovered qualitatively distinct toxicity architectures: HTP-1 exhibited higher overall toxicity and elicited broad-spectrum network perturbations involving cell stress, proliferation, and immune regulation, correlating with greater apoptotic induction; HTP-2 triggered focused activation of damage-sensing pathways, consistent with its earlier membrane disruption and more pronounced genotoxicity. Multi-omics analysis further linked these mechanistic perturbations to human disease-relevant pathways, with HTP-1 showing stronger enrichment for COPD-associated expression patterns and HTP-2 for lung cancer-related signatures, suggesting the acute molecular response to each product exhibits similarity to specific pulmonary disease-associated molecular signatures. These findings establish human-derived airway organoids as a sensitive, human-relevant platform within the New Approach Methodologies‌ (NAMs) framework for qualitative comparison and mechanistic interrogation of product-specific toxicity.

PMID:42184653 | DOI:10.1016/j.ecoenv.2026.120306

Front Immunol. 2026 May 8;17:1780459. doi: 10.3389/fimmu.2026.1780459. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and its ligand programmed death-ligand 1 (PD-L1) have transformed the management of advanced lung cancer, yet most patients experience primary resistance, hyperprogressive disease (HPD), or clinically significant immune-related adverse events (irAEs). Multi-omics technologies now enable integrated interrogation of tumor, microenvironmental, host, and clinical determinants of these divergent outcomes. In this review, we first discuss the biological and clinical foundations of PD-1/PD-L1 blockade in non-small cell and small cell lung cancer, and summarize the spectrum of resistance, HPD, and irAEs observed in trials and real-world practice. We then describe multi-omics study frameworks that connect genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and microbiome profiling with these outcome phenotypes. Building on this foundation, we synthesize evidence for composite biomarkers of primary and acquired resistance, delineate emerging multi-omics signatures of HPD, and examine host- and tumor-derived multi-omics correlates of organ-specific and systemic irAEs. We further propose an efficacy-risk quadrant framework to guide clinical decision-making when favorable efficacy predictors coexist with elevated risk of severe adverse outcomes, and outline a three-step approach for high-efficacy/high-risk patients: joint probability reporting, multi-omics guided mitigation, and dynamic reassessment. Finally, we evaluate translational strategies that integrate multi-omics scores into baseline risk stratification, dynamic monitoring with attention to technical challenges such as distinguishing true progression from ctDNA pseudoprogression, and biomarker-driven trial design, while assessing the evidence level and translational readiness of candidate assays from retrospective discovery to clinical implementation. A clinical case illustrates how multi-omics can link baseline risk stratification, regimen selection, and longitudinal monitoring into a coherent action plan, while acknowledging that artificial intelligence-driven models remain investigational and real-world application still relies on clinician judgment. Collectively, this review defines how integrated multi-omics biomarkers can be leveraged to predict resistance, HPD, and immune-related toxicity, and to refine patient selection and management during PD-1/PD-L1 therapy in lung cancer.

PMID:42183274 | PMC:PMC13194140 | DOI:10.3389/fimmu.2026.1780459

J Thorac Dis. 2026 Apr 30;18(4):417. doi: 10.21037/jtd-2025-1-2697. Epub 2026 Mar 26.

ABSTRACT

BACKGROUND AND OBJECTIVE: Lung cancer remains the leading cause of cancer related mortality worldwide, and early detection and precise stratified management are crucial for improving patient outcomes. Tumor growth kinetics, as a characterization of its proliferation and malignant differentiation, is a key decision-making factor and research hotspot in clinical practice today. This study aimed to elucidate the growth kinetics of solid nodular non-small cell lung cancer (NSCLC) as a critical determinant of early diagnosis, prognostic evaluation, and treatment strategy selection, and to address the challenge that significant heterogeneity in tumor growth poses to risk stratification and clinical decision-making.

METHODS: We conducted a retrospective search of PubMed, Embase, Web of Science, and Scopus databases, focusing on the current research status of solid nodular NSCLC, particularly in terms of molecular mechanisms, prognosis, modeling prediction, and management strategies related to its growth heterogeneity, with the aim of exploring future research directions.

KEY CONTENT AND FINDINGS: Volume doubling time (VDT) serves as a key metric for evaluating nodule dynamics. While earlier studies suggested a generally rapid growth pattern (VDT <400 days) in solid nodular NSCLC, recent evidence reveals considerable heterogeneity, with some tumors demonstrating indolent growth pattern (VDT >40-600 days). The prognosis of rapidly growing nodules is usually poor, so nodule management recommendations should be personalized based on growth dynamics and patient characteristics. Traditional radiological features, and deep learning models show promise for growth risk stratification but require large-scale external validation and refinement. Molecular and pathological studies suggest that the tumor microenvironment and immune cell infiltration may contribute to growth heterogeneity, though direct mechanistic evidence remains limited. Artificial intelligence (AI) based approaches exhibit significant potential in predicting individual tumor growth behavior.

CONCLUSIONS: Growth heterogeneity in solid nodular NSCLC carries substantial clinical significance but remains insufficiently studied. Future research should prioritize imaging based modeling to predict individualized growth dynamics. Integrating multi-omics analyses may help elucidate the molecular factors underlying growth heterogeneity. AI driven risk stratification based on large-scale multi center sequence data can achieve truly personalized and growth oriented management strategies.

PMID:42182806 | PMC:PMC13190150 | DOI:10.21037/jtd-2025-1-2697

J Thorac Dis. 2026 Apr 30;18(4):353. doi: 10.21037/jtd-2025-1-2610. Epub 2026 Mar 20.

ABSTRACT

BACKGROUND: Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor characterized by rapid progression, early metastasis, and high mortality, with limited effective long-term treatment options. B4GALT1, a β-1,4-galactosyltransferase, has been implicated in the malignant progression of various cancers, but its specific role and underlying mechanisms in SCLC remain largely unexplored. We conducted a multi-omics analysis and clinical sample study to explore the function of B4GALT1 in SCLC.

METHODS: This study comprehensively investigated the expression pattern, functional significance, and clinical relevance of B4GALT1 in SCLC. We conducted multi-omics analyses, including single-cell data processing, InferCNV analysis, and immune infiltration analysis, to explore the association between B4GALT1 and the immune microenvironment of SCLC and patient survival. To determine B4GALT1 as a potential circulating biomarker, quantitative data-independent acquisition (DIA) proteomics analysis was performed on serum samples from SCLC patients and healthy controls. Enzyme-linked immunosorbent assay (ELISA) was used to further verify the differential expression of serum B4GALT1 in a larger cohort of SCLC patients, to evaluate its diagnostic, prognostic, and treatment response predictive value.

RESULTS: Multi-omics analysis revealed that B4GALT1 expression was significantly associated with patient survival. The expression of B4GALT1 positively correlated with macrophage infiltration in the tumor and negatively correlated with CD4⁺ T cells in the tumor. There was a negative correlation in inactivated naïve B cells, eosinophils, and CD4 naïve T cells, while it showed a positive correlation in dendritic cells, M0/M1/M2 macrophages, natural killer (NK) cells, CD8 T cells, follicular helper T cells, and regulatory T cells. ELISA results showed that serum protein B4GALT1 expression was higher in patients with SCLC than in healthy controls. Elevated serum B4GALT1 protein levels correlated with poor treatment outcomes in patients with SCLC undergoing chemoradiotherapy.

CONCLUSIONS: Our findings establish B4GALT1 as a critical prognostic, diagnostic, and predictive biomarker in SCLC, with its expression closely linked to the tumor immune microenvironment and treatment response. Targeting B4GALT1 or its related pathways may represent a novel therapeutic strategy, and serum B4GALT1 holds promise as a liquid biopsy marker for SCLC patient stratification, monitoring, and guiding treatment decisions.

PMID:42182735 | PMC:PMC13190155 | DOI:10.21037/jtd-2025-1-2610

J Thorac Dis. 2026 Apr 30;18(4):406. doi: 10.21037/jtd-2026-0547. Epub 2026 Apr 27.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, posing a substantial global health burden. Despite advances in early detection, molecular profiling, and targeted therapies, patient outcomes remain unsatisfactory due to tumor heterogeneity, therapeutic resistance, and the lack of reliable preclinical models. In recent years, lung cancer organoids (LCOs), patient-derived three-dimensional (3D) culture systems, have demonstrated the ability to preserve the histological architecture and genomic features of primary tumors more faithfully than conventional models, making them a promising platform for translational research and precision medicine. This study aims to quantitatively evaluate the global research output, identify major contributors and collaboration patterns, and systematically uncover research hotspots and emerging trends in the field of LCOs through bibliometric analysis.

METHODS: A systematic bibliometric analysis was conducted using publications on LCOs retrieved from the Web of Science Core Collection (WoSCC). Articles published between 2015 and 2024 were included. A total of 356 publications were analyzed. Publication outputs, country and institutional contributions, collaboration networks, and keyword co-occurrence were evaluated using Bibliometrix (R package), VOSviewer, and CiteSpace.

RESULTS: The number of publications on LCOs has increased steadily over the past decade, reflecting growing research interest and technological advancement. China and the United States were identified as the leading contributors, accounting for the majority of publications, while Germany, South Korea, and Japan also demonstrated strong research capacity and active collaboration. Keyword and thematic analyses revealed several major research hotspots, including personalized medicine, drug response and resistance mechanisms, tumor microenvironment modeling, and immune-related interactions. Burst keyword analysis further identified emerging trends, such as co-culture systems, immunotherapy evaluation, and the integration of LCOs with high-throughput screening and multi-omics approaches.

CONCLUSIONS: LCOs have evolved into a versatile platform bridging basic research and clinical applications in lung cancer. This study provides a comprehensive overview of the current research landscape and highlights emerging directions in the field. Future research should focus on methodological standardization, optimization of organoid construction and evaluation, integration with multi-omics and immune models, and strengthened international collaboration to facilitate clinical translation and improve patient outcomes.

PMID:42182656 | PMC:PMC13190222 | DOI:10.21037/jtd-2026-0547

Drug Des Devel Ther. 2026 May 18;20:603491. doi: 10.2147/DDDT.S603491. eCollection 2026.

ABSTRACT

This review systematically summarizes the chemical composition, mechanisms of action, and recent progress in research and clinical applications of Yupingfeng San (YPFS) in various diseases. Research indicates that YPFS contains abundant active constituents-such as flavonoids, coumarins, and terpenoids, and demonstrates diverse biological activities,including immune modulation, anti-inflammatory, antiviral, antibacterial, and antitumor activities. Recent pharmacological and network pharmacology studies have elucidated that YPFS regulates key signaling pathways - such as PI3K-Akt, NF-κB, TLR4/MyD88, and JAK-STAT - through multi-target and multi-pathway mechanisms. These effects contribute to its therapeutic role in allergic rhinitis (AR), asthma, atopic dermatitis (AD), liver cancer, lung cancer, and other conditions. Although limited clinical trials and meta-analyses suggest that YPFS, when combined with conventional therapies, can improve treatment efficacy, relieve symptoms, and demonstrate good safety and tolerability, current research is constrained by low evidence quality and the absence of standardized quality control measures. Future research should prioritize large-scale, multi-center clinical trials and integrate multi-omics approaches to identify the main active components and mechanisms of action.

PMID:42182585 | PMC:PMC13196821 | DOI:10.2147/DDDT.S603491

Stem Cells Int. 2026 May 21;2026:3060138. doi: 10.1155/sci/3060138. eCollection 2026.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD), a predominant subtype of non-small cell lung cancer (NSCLC), is associated with a high mortality rate. Currently, there are no reliable or sensitive biomarkers or prognostic methodologies available for its early detection or diagnosis. Gamma-aminobutyric acid (GABA), a pivotal inhibitory neurotransmitter within the central nervous system (CNS), primarily exerts its effects through interactions with GABA receptors (GABARs). Recent studies have increasingly highlighted GABA's significant role in mediating the initiation and progression of various tumors beyond the CNS. Nonetheless, research investigating the role of GABA in LUAD is limited, and the specific molecular and cellular mechanisms underlying its interactions remain to be fully elucidated.

METHODS: We developed an innovative machine learning framework designed to screen GABA-related genes (GABARgenes) at both single-cell and large transcriptomic levels. This framework encompasses 10 algorithms and 101 combinatorial pairing patterns, which facilitate the construction of consistent GABA-related features (GABARFs). The framework's performance was assessed using both a training set and an external validation set. To provide a quantitative prognostic tool for clinical application, we established a nomogram that incorporates GABARF. Additionally, we conducted multiomics analyses, including genomics, single-cell transcriptomics, and comprehensive transcriptomics, to derive and consolidate more extensive prognostic features. We also evaluated the response of GABARF-defined risk subgroups to immunotherapy and identified potential personalized therapeutic agents for specific risk categories.

RESULTS: Among the 124 GABARgenes analyzed, 38 demonstrated a significant correlation with overall survival (OS) in patients. Our machine learning-derived GABARF exhibited exceptional performance in predicting prognosis and clinical outcomes, showing promise in forecasting the onset and progression of LUAD. Multivariate analysis confirmed that GABARF serves as an independent prognostic factor for OS in LUAD. Furthermore, distinct GABARF risk subgroups exhibited significant differences in biological function, mutation status, and tumor immune infiltration. Notably, there were significant variations in the immunophenoscore (IPS) across the risk subgroups. GABARF risk stratification aligns with stemness properties of tumor cells, indicating that high-risk patients may harbor tumors with enhanced stemness features that contribute to their poor prognosis and reduced immunotherapy response. Sensitivity analyses of conventional LUAD therapies indicated that patients in the low-risk group may derive greater benefit from immune checkpoint inhibitors (ICIs), while those in the high-risk group may exhibit heightened sensitivity to first-line chemotherapy agents. Furthermore, LDHA overexpression was found to promote proliferation and migration, while inhibiting apoptosis. In addition, overexpression of LDHA can upregulate the expression of stemness markers CD133, SOX2, and OCT4 in LUAD cells, enhancing the malignant phenotype of tumor cells.

CONCLUSION: This study presents a novel machine learning-based model for GABARF, which shows promise as a potential tool to aid in prognostic prediction, targeted prevention, and individualized treatment planning in LUAD. Initial investigations into the interaction mechanisms of GABARF at the molecular, cellular, and tumor immune microenvironment (TIME) levels in LUAD have commenced. The GABARF model not only serves as a prognostic indicator but may also reflect the stemness status of LUAD tumors, offering insights into personalized treatment strategies that account for both neural-immune-stemness interactions.

PMID:42181964 | PMC:PMC13191820 | DOI:10.1155/sci/3060138

Hum Mutat. 2026 May 21;2026:6434376. doi: 10.1155/humu/6434376. eCollection 2026.

ABSTRACT

Immune checkpoint inhibitors have become integral to the management of non-small cell lung cancer (NSCLC), yet both primary and acquired resistance remain frequent and are only partially captured by routine biomarkers such as programmed death-ligand 1 (PD-L1) immunohistochemistry and tumor mutational burden (TMB). Resistance is increasingly viewed as a multiaxis functional phenotype shaped by antigenicity and neoantigen quality, antigen processing and presentation competence, interferon signaling and adaptive resistance programs, tumor-immune spatial organization, suppressive myeloid/stromal ecosystems, and metabolic constraints that limit effector function. Multiomics profiling provides a practical route to translate genomic event anchors into reproducible, mechanistically interpretable biomarker outputs by assembling coherent evidence chains across genomics, transcriptomics, epigenomics, proteomics, and metabolomics, complemented by spatial assays, digital pathology, and imaging-derived surrogates.

PMID:42181742 | PMC:PMC13191777 | DOI:10.1155/humu/6434376

Oncol Rev. 2026 May 8;20:1809836. doi: 10.3389/or.2026.1809836. eCollection 2026.

ABSTRACT

Cancer biomarkers are now recognized as major tools for early detection, diagnosis, and prognosis. In the last 10 years or so, molecular biology, proteomics, and genomics have been rapidly advancing to find tissue-specific biomarkers used in clinical practice. In this review, the development of the cancer biomarkers published in 2011-2025, for lung, breast, liver, and kidney cancers, is reviewed. A systematic review of peer-reviewed literature searched PubMed, Scopus, and Web of Science, including human and published English studies. A variety of detection approaches, including immunohistochemistry, next-generation sequencing, and liquid biopsy technology, were assessed with an explicit focus on clinically relevant biomarkers. The main trends indicate that while classic protein markers, particularly carcinoembryonic antigen and neuron-specific enolase in lung cancer, hormone receptor status and HER2 in breast cancer, and alpha-fetoprotein in liver cancer, have evolved, many modern genomic markers including epidermal growth factor receptor mutations, anaplastic lymphoma kinase rearrangements, TP53 mutations, vascular endothelial growth factor pathways, and von Hippel-Lindau gene alterations have evolved, resulting in a large gap in their knowledge. These innovations underscore the importance of molecular biomarkers in supporting early detection, targeted therapy, and enhanced surveillance of disease progression. Cancer biomarker studies have evolved from protein-based biomarkers to encompass both genomic and transcriptomic targets, which may allow for more targeted and individualized cancer interventions. Multi-omics integration and novel types of biomarkers like circulating tumor DNA, circulating tumor cells, and microRNAs will focus on developing early diagnosis, prognosis, and personalized treatment strategies as a prerequisite of such work.

PMID:42181045 | PMC:PMC13194566 | DOI:10.3389/or.2026.1809836

J Nanobiotechnology. 2026 May 22. doi: 10.1186/s12951-026-04565-1. Online ahead of print.

ABSTRACT

BACKGROUND: Human umbilical cord mesenchymal stem cell-derived exosomes (hUCMSC-Exos) are a promising treatment for acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), but traditional delivery methods have limitations. Therefore, this study presents a noninvasive therapeutic approach for ALI/ARDS, offering new mechanistic insights and identifying potential therapeutic targets.

RESULTS: We established a nebulized LPS-induced ALI model that was characterized by diffuse lung injury and high homogeneity. Following inhalation, hUCMSC-Exos were observed to be internalized by pulmonary microvascular endothelial cells. Analysis revealed that hUCMSC-Exos alleviated ALI by reducing the severity of histological damage, pulmonary oedema, lung inflammation and ferroptosis. Additionally, hUCMSC-Exos improved the mitochondrial function of human pulmonary microvascular endothelial cells (HPMECs) via the transfer of mitochondrial components. Subsequent proteomic sequencing of mitochondria isolated from HPMECs receiving different treatments revealed the significant differential expression of ribosomal proteins among the groups. The most significantly upregulated protein, RPS11, was identified as a key mediator; its knockdown blocked the ability of hUCMSC-Exos to suppress ferroptosis and restore mitochondrial function in HPMECs. Mechanistically, hUCMSC-Exos exert their effects by enhancing mitochondria-encoded protein translation.

CONCLUSIONS: We report a mechanism whereby hUCMSC-Exos upregulate RPS11 to promote mitochondria-encoded protein translation, rescuing mitochondrial function, inhibiting ferroptosis in HPMECs, and ultimately alleviating ALI. Validated across multiple models and supported by multi-omics analyses, our findings collectively establish nebulized hUCMSC-Exos as a promising cell-free therapy targeting mitochondrial homeostasis in HPMECs for the treatment of ALI.

PMID:42174606 | DOI:10.1186/s12951-026-04565-1

Prog Mol Biol Transl Sci. 2026;222:165-206. doi: 10.1016/bs.pmbts.2026.01.025. Epub 2026 Mar 2.

ABSTRACT

The reports of obstructive lung diseases (OLDs) like asthma, chronic obstructive pulmonary disease (COPD), and bronchiectasis show increasing global prevalence. The available treatment options for these diseases are limited to antibiotics and steroids. Different multi-omics integration approaches have been applied in studying host, microbiome, and host-microbiome interactions in these diseases to get better insights. Artificial intelligence (AI)-based, as well as statistical and other approaches, are used for multi-omics analyses, specifically the integration of multi-omics data in OLDs. This chapter discusses various aspects of multi-omics integration studies in asthma, COPD, and bronchiectasis. Overall, these studies focused on disease subtype classification, risk assessment, association with genetic factors, and several other aspects.

PMID:42173629 | DOI:10.1016/bs.pmbts.2026.01.025

Prog Mol Biol Transl Sci. 2026;222:143-163. doi: 10.1016/bs.pmbts.2026.03.010. Epub 2026 Apr 10.

ABSTRACT

The reports of lung, breast, colorectal, and prostate cancers show increasing global prevalence. Cancer recurrence and drug resistance are open challenges in these fields. Different multi-omics integration approaches have been applied in cancer type sub-classification and prediction of patient survival and recurrence. Artificial intelligence (AI)-based, as well as statistical and other approaches, are used for multi-omics analyses, specifically for integrating multi-omics data in cancer. This chapter discusses multi-omics resources available for reanalyzing cancer data and for developing AI-based prediction models. Different aspects of multi-omics integration studies of major cancers are also discussed in this chapter. Overall, these studies focused on disease subtype classification, risk assessment, cancer recurrence, survivability, and several other aspects.

PMID:42173628 | DOI:10.1016/bs.pmbts.2026.03.010

Cancer Med. 2026 Apr;15(4):e71833. doi: 10.1002/cam4.71833.

ABSTRACT

OBJECTIVES: This study aims to elucidate the role of FGFR1 in activating the Wnt/β-catenin signaling pathway and the underlying mechanisms by which it promotes malignant progression in lung squamous cell carcinoma (LUSC). By integrating multi-omics analysis with functional experiments, the clinical heterogeneity of FGFR1 amplification, signaling crosstalk, and their regulatory networks governing tumor phenotypes were revealed.

METHODS: Using TCGA data (n = 490), we analyzed the relationship between FGFR1 copy number variation (CNV) and mRNA expression in LUSC, and validated the correlation with protein expression in a clinical cohort (n = 38). GSEA and single-gene GSEA were performed to identify signaling pathways associated with high FGFR1 expression. The interaction between FGFR1 and the Wnt/β-catenin pathway was investigated by immunohistochemistry, immunofluorescence, stable cell lines, Western blot, qPCR, and functional assays.

RESULTS: FGFR1 amplification correlated with increased mRNA and protein expression. The top 25% FGFR1 high-expression group enriched Wnt/β-catenin, PI3K-Akt, and cAMP pathways. Mechanistically, FGFR1 promoted β-catenin nuclear accumulation and enhanced β-catenin signaling through PKA-associated phosphorylation and Akt/GSK3β-related regulation of β-catenin stability, and these effects were attenuated by AKT inhibition. CTNNB1 knockdown significantly inhibited proliferation, migration, invasion, and tumor growth of LUSC cells.

CONCLUSIONS: Our findings indicate that FGFR1 activates Wnt/β-catenin signaling through coordinated regulation of β-catenin phosphorylation, stability, and subcellular localization, thereby promoting malignant progression in LUSC. These results provide a rationale for targeting the FGFR1-Wnt/β-catenin axis as a potential therapeutic strategy.

PMID:41998829 | DOI:10.1002/cam4.71833

Biotechnol J. 2026 Apr;21(4):e70228. doi: 10.1002/biot.70228.

ABSTRACT

Small cell lung cancer (SCLC) is a highly aggressive malignancy, and chemotherapy frequently causes nausea and vomiting, which can impair treatment tolerance. Because thalidomide (THD) has shown potential clinical benefit in alleviating nausea and anorexia, we investigated whether its effects might be associated with changes in gut microbial composition and metabolite profiles. Fecal samples were collected from patients with SCLC and categorized into THD-treated and control groups. Metagenomic sequencing and nontargeted metabolomic profiling were performed to characterize microbial composition and metabolic signatures. THD treatment was also associated with higher microbial alpha diversity and increased abundance of genera such as Eubacterium and Prevotella. Metabolomic analysis identified several differential metabolites, including hydrogenated MDI, becocalcidiol, β-octylglucoside, and azelaic acid. Collectively, these findings suggest that the gut microbiota-metabolite axis may be associated with the potential effects of THD on CINV and anorexia in patients with SCLC. The identified microbial taxa and metabolites may serve as candidate biomarkers or potential therapeutic targets, although further validation in larger studies is necessary.

PMID:41994961 | PMC:PMC13088213 | DOI:10.1002/biot.70228

Ann Med. 2026 Dec;58(1):2654291. doi: 10.1080/07853890.2026.2654291. Epub 2026 Apr 17.

ABSTRACT

BACKGROUND: To develop and validate an integrated radiopathomics nomogram combining multiphase CT images, H&E-stained slides, and clinicopathological variables for predicting microvascular patterns (MVPs) in non-small cell lung cancer (NSCLC).

METHODS: We retrospectively included consecutive surgically resected NSCLC patients from two centers (n = 258). Patients from center 1 were randomly divided into training and internal validation cohorts, while patients from center 2 formed external validation cohort. CD34-immunohistochemistry was used as the reference standard for MVPs to classify patients into non-angiogenic alveolar (NAA) and non-NAA groups. Radiomics and pathomics features were extracted to construct single-phase radiomics, combined radiomics, and pathomics models. Rad-score and Path-score were derived from combined radiomics and pathomics models, respectively. Rad-score, Path-score, and clinicopathological independent predictors were integrated to develop a nomogram. Model performance was assessed by area under the curve (AUC), calibration curve, decision curve analysis (DCA), and DeLong test.

RESULTS: On multivariable analysis, histological grade was an independent predictor of NAA MVP. Combined radiomics model for predicting MVPs achieved AUCs of 0.863, 0.856, and 0.849 in training, internal validation, and external validation cohorts, showing better performance than single-phase models. Pathomics model yielded AUCs of 0.878, 0.860, and 0.833, however, its specificity markedly decreased in validation cohorts. Nomogram model achieved the superior performance across all cohorts, with AUCs of 0.911, 0.903, and 0.901, outperforming single-modality models (DeLong test: all p < 0.05).

CONCLUSION: The nomogram demonstrated high accuracy and robustness in predicting MVPs in NSCLC, offering a promising tool for characterizing the tumor microenvironment and supporting individualized treatment.

PMID:41992828 | DOI:10.1080/07853890.2026.2654291

J Proteome Res. 2026 Apr 16. doi: 10.1021/acs.jproteome.6c00216. Online ahead of print.

ABSTRACT

Nonsmall-cell lung cancer (NSCLC) accounts for more than 80% of lung cancer cases. Epidermal growth factor receptor mutations (EGFRm) occur in 15 and 40% of NSCLC in Western and Asian populations, respectively. Current treatment for advanced NSCLC targets EGFRm with tyrosine kinase inhibitors (TKIs). Osimertinib is a third-generation EGFR-TKI now used as a first-line treatment in advanced/metastatic NSCLC; however, drug resistance frequently develops. Dysregulation of metabolism has been suggested to play a role in the development of drug resistance. Here, we investigated the role of lipid metabolism in the development of osimertinib resistance (OR) using pharmacologically-induced resistant cellular models. We used a multiomics approach, combining lipidomics with proteomics analyses. We found alterations in processes relating to metabolism, such as dysregulated sphingolipid metabolism. In particular, we identified that OR lines reduce free ceramides in favor of complex glycosphingolipids. Mechanistically, this metabolic shift avoids ceramide-mediated apoptosis via caspase-3 activation. Importantly, when we combined osimertinib with D-PDMP, an inhibitor of the key enzyme responsible for the conversion of ceramide to glucosylceramide, we increased the sensitivity to osimertinib. Overall, we have identified the glycosphingolipid metabolic pathway as a potential therapeutic target to reinstate sensitivity to osimertinib in NSCLC.

PMID:41988688 | DOI:10.1021/acs.jproteome.6c00216

Exp Ther Med. 2026 Mar 30;31(6):148. doi: 10.3892/etm.2026.13143. eCollection 2026 Jun.

ABSTRACT

Streptococcus pneumoniae (SP) infection often leads to persistent lung injury even after antibiotic treatment. Despite this phenomenon, the mechanisms underlying host cell recovery remain poorly understood. Upon breaching the epithelial barrier, SP primarily targets the pulmonary interstitial cells, which constitute the major mesenchymal component of the lung. These cells serve as essential effectors of tissue repair, extracellular matrix remodeling and epithelial restoration. Therefore, a feline pulmonary interstitial cell (FCA-L2) model of SP infection was established to investigate the protective effects of glutamine (GLU) and fish collagen peptides (FCP) through integrated transcriptomic and metabolomic analyses. Cells were infected with SP (0.05 McFarland units for 4 h) and then treated with doxycycline (7.5 µg/ml for 18 h) followed by GLU (40 mM) or FCP (500 µg/ml). Notably, SP infection increased lactate dehydrogenase (LDH) release by 3.5-fold, induced secretion of IL-1β, TNF-α and IL-8, disrupted tight-junction proteins (claudin, ZO-1 and occludin) and caused oxidative imbalance and apoptosis despite antibiotic (doxycycline) treatment. However, treatment with GLU or FCP significantly reduced LDH release by ~40%, restored junctional proteins, suppressed inflammatory cytokines and enhanced antioxidant enzyme activities. Multi-omics analysis revealed that GLU promoted amino acid biosynthesis and energy metabolism and suppressed aminoacyl-tRNA synthetases and cell-cycle regulators, thereby enhancing metabolic adaptability. By contrast, FCP activated amino and nucleotide sugar metabolism, increased polyunsaturated fatty-acid synthesis and supported glycocalyx repair and membrane reconstruction. GLU and FCP provided complementary metabolic and structural protection, which mitigated post-infectious stress and promoted cellular recovery. The findings of the present study underscore the potential of bioactive food-derived compounds as adjunctive therapies that may accelerate lung tissue repair and enhance the efficacy of conventional antibiotics.

PMID:41988354 | PMC:PMC13077270 | DOI:10.3892/etm.2026.13143