J Asthma Allergy. 2026 Mar 19;19:575312. doi: 10.2147/JAA.S575312. eCollection 2026.

ABSTRACT

Systems biology approaches have contributed to advancing our understanding of complex respiratory diseases including asthma and chronic obstructive pulmonary disease (COPD). This systematic review evaluates the application of systems biology methodologies in respiratory medicine, focusing on multi-omics data integration and computational techniques for biomarker discovery and mechanistic understanding. Following PRISMA 2020 guidelines, we conducted a comprehensive literature search across Web of Science and Scopus databases, identifying 117 peer-reviewed documents published from 2010 to 2024. The review methodology employed bibliometric analysis combined with qualitative synthesis of included studies. Results demonstrate steady growth in systems biology applications for asthma and COPD research, with publication rates increasing by approximately 0.5 articles per year (R² = 0.73, p < 0.001). Bibliometric analysis identified five major research clusters: systems biology as a foundational methodological framework (Basic Theme), COPD-focused research as the most developed area (Motor Theme), gene expression analysis, disease classification approaches, and specialized lung disease investigations (Niche Theme). Multi-omics integration studies achieved 82-91% accuracy in disease classification tasks, with transcriptomics-based asthma endotyping validated in over 1500 patients across multiple cohorts. Network analysis approaches identified hub genes (IL-6, TNF-α, MMP9) replicated across three independent studies. Machine learning applications demonstrated 80-90% accuracy for diagnostic and prognostic tasks, though external validation remains limited, with only 15% of reviewed studies including independent validation cohorts. Significant challenges persist in data integration, computational reproducibility, and clinical translation. Most studies employed modest sample sizes (median n=89), and population diversity was limited, with 89% conducted in European-ancestry populations. This review provides a comprehensive assessment of systems biology progress in respiratory medicine, identifies methodological gaps, and highlights the need for standardized protocols, larger collaborative studies, and rigorous external validation to advance clinical implementation of systems biology findings in asthma and COPD management.

PMID:41878747 | PMC:PMC13007689 | DOI:10.2147/JAA.S575312

Cell Metab. 2026 Mar 23:S1550-4131(26)00087-2. doi: 10.1016/j.cmet.2026.02.018. Online ahead of print.

ABSTRACT

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent chronic liver disease worldwide, affecting one-third of the global population. Most patients exhibit simple steatosis, whereas up to 20% develop metabolic dysfunction-associated steatohepatitis (MASH), potentially culminating in liver cirrhosis and hepatocellular carcinoma. Diverse parallel mechanisms contribute to the development of MASH, which are fueled by hepatic lipotoxicity, intestinal dysbiosis, and pro-inflammatory diets shaping innate and adaptive immune responses. Moreover, adipose tissue is driving systemic inflammation in obesity, contributing to the inflammatory burden in obesity-related MASH. Polygenetic and multiomic risk scores identify distinct types of MASLD with dominant aggressive liver disease or extrahepatic cardiometabolic disease. Here, we review the complexity of multiple parallel inflammatory hits in MASH and delineate that most current MASH drugs exert pleiotropic metabolic and anti-inflammatory properties. These new therapies will change the clinical management of this disease in the near future.

PMID:41875884 | DOI:10.1016/j.cmet.2026.02.018

Ecotoxicol Environ Saf. 2026 Mar 23;314:120063. doi: 10.1016/j.ecoenv.2026.120063. Online ahead of print.

NO ABSTRACT

PMID:41875554 | DOI:10.1016/j.ecoenv.2026.120063

Immunotherapy. 2026 Mar 24:1-13. doi: 10.1080/1750743X.2026.2648431. Online ahead of print.

ABSTRACT

The gut microbiota (GM) has emerged as a key modulator of cancer development and therapeutic response beyond the gastrointestinal tract. In extra-intestinal cancers, GM composition influences oncogenesis, with specific microbial taxa and their metabolites linked to either increased or decreased cancer risk, as highlighted by Mendelian Randomization studies. Beyond cancer initiation, GM plays a critical role in shaping the efficacy and toxicity of anticancer therapies, particularly immunotherapy. We searched PubMed and ClinicalTrials.gov using the terms"gut microbiota," "immune checkpoint inhibitors," "faecal microbiota transplantation," "solid tumor" in oncology patients. Evidence indicates that SCFA-producing bacteria, Akkermansia muciniphila, and members of Lachnospiraceae and Ruminococcaceae families enhance responses to immune checkpoint inhibitors (ICIs), whereas dysbiosis and immunosuppressive bacteria are associated with poor outcomes and immune-related adverse events. Therapeutic modulation of the GM through probiotics, prebiotics, fecal microbiota transplantation, and dietary interventions shows promise in optimizing immunotherapy efficacy, yet standardized clinical protocols remain lacking. Integrating GM profiling with multi-omics and artificial intelligence approaches offers a path toward personalized microbiota-targeted interventions to improve patient outcomes. This review critically summarizes current evidence linking GM to cancer immunotherapy, discusses mechanistic insights, and outlines future perspectives for translating microbiota modulation into clinical practice.

PMID:41873461 | DOI:10.1080/1750743X.2026.2648431

Nat Commun. 2026 Mar 23. doi: 10.1038/s41467-026-70798-1. Online ahead of print.

ABSTRACT

Glioblastoma (GBM) remains a lethal brain tumor due to therapy resistance. While autophagy contributes to temozolomide (TMZ) resistance, its regulation is incompletely understood. This study investigates the role of replication factor RFC4, which is associated with poor prognosis and TMZ resistance in GBM. Multi-omics analyses and molecular experiments reveal that TMZ-induced chromatin accessibility enables transcription factor YY1 to bind the RFC4 promoter and upregulate its expression. RFC4, in turn, stabilizes the kinase STK38, which is essential for autophagosome formation. The RFC4-STK38 interaction facilitates BECN1 recruitment, thereby activating autophagy. Phosphorylation of STK38 at T444 stabilizes this complex, whereas a phospho-deficient mutant impairs autophagy. In vivo, RFC4 overexpression confers TMZ resistance, reversible by autophagy inhibition. Thus, our findings identify the RFC4-STK38-BECN1 axis as a mechanism underlying TMZ resistance and a potential target for precision therapy in GBM.

PMID:41872171 | DOI:10.1038/s41467-026-70798-1

J Ethnopharmacol. 2026 Mar 21:121548. doi: 10.1016/j.jep.2026.121548. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: The Tingli Dazao Xiefei Decoction (TD) is a traditional phlegm-eliminating prescription composed of Descurainia sophia (L.) Webb. ex Prantl (TLZ) and Ziziphus jujuba Mill. (DZ), which can relieve lung, heart and kidney injury in asthma. TLZ acts as the monarch drug in the TD. Based on the research mode of "material basis of traditional Chinese medicinal properties can be divided and combined", we have confirmed that the flavonoid glycosides components /the oligosaccharide components/the fatty oil component (FG/Oli/FO) are effective components of TLZ. However, the compatibility mechanism of the TD, and the contribution of the effective components of TLZ to the efficacy were still unclear.

AIM OF THE STUDY: To clarify the compatibility mechanism of TD, and the contribution of the effective components of TLZ to the efficacy from a comprehensive perspective of lung, heart, and kidney.

METHODS: First, we chose the asthma model corresponding to the efficacy of TD in purging the lungs and relieving asthma, and the rats were divided into the normal (NC) group, model (M) group, dexamethasone (DEX) group, and treatment groups of TD/TLZ/DZ/FO+DZ/Oli+DZ/FG+DZ. Second, metabolomics and network pharmacology were applied to elucidate the comprehensive protective effect of TD/FG+DZ/Oli+DZ/FO+DZ. Third, the multi-omics results were validated using Western blotting, RT-qPCR, flow cytometry, and immunofluorescence.

RESULTS: FO+DZ/Oli+DZ/FG+DZ had different degrees of protective effects against lung/heart/kidney injury in asthma. In metabolomics research, the principal component analysis (PCA) and cluster analysis results showed that the TLZ group was closer to TD group than DZ group, the FO+DZ and Oli+DZ group clustered with TD/NC groups in the lung and kidney, and the FO+DZ and FG+DZ group clustered with TD/NC groups in the heart. Pathway enrichment analysis suggested that the comprehensive protective effect of TLZ and its effective components combined with DZ on lung/heart/kidney may be achieved by regulating the arginine and proline metabolism, alanine, aspartate and glutamate metabolism, and unsaturated fatty acid biosynthesis. Multi-organ metabolomics and network pharmacology revealed consistent biological functions in KEGG pathways. Validation experiment showed that TLZ and its effective components combined with DZ could reverse the abnormal expression of proteins and RNA related to inflammation, airway remodeling, excitotoxicity, and energy-supply, apoptosis at different levels. Furthermore, FO+DZ may reduce asthma damage by inhibiting the FABP4/PPAR-γ/NF-κB signaling pathway.

CONCLUSION: TLZ played the key role in TD, and FO had the best therapeutic effect on each organ; the efficacy of Oli was mainly reflected in reducing lung and kidney damage, and FG was mainly involved in enhancing energy metabolism in the heart. These findings proved that traditional Chinese medicine could exert comprehensive efficacy in a 'multi-components trigger multi-channel' way.

PMID:41871629 | DOI:10.1016/j.jep.2026.121548

Cell Oncol (Dordr). 2026 Mar 23;49(2):60. doi: 10.1007/s13402-026-01194-6.

NO ABSTRACT

PMID:41870836 | DOI:10.1007/s13402-026-01194-6

Sci China Life Sci. 2026 Mar 16. doi: 10.1007/s11427-025-3273-6. Online ahead of print.

ABSTRACT

Phenotypic diversity of malignant cells within a tumor underlies intratumor heterogeneity (ITH), a key determinant of cancer metastasis and treatment failure. However, the molecular mechanisms driving this heterogeneity are poorly understood. Here, we curated and analyzed a cohort of 3' tag-based single-cell RNA-seq covering 12 common cancer types. We identified thousands of poly(A) site (PAS) peaks representing the 3' ends of previously unannotated transcripts, whose expression is widely associated with diverse malignant cellular states. By integrating multi-omics data, we characterized the expression patterns and epigenetic landscape of these unannotated PAS peak-associated transcripts (UPTs). The expression heterogeneity of UPTs was supported by multi-region sampling bulk RNA-seq data and recapitulated within cancer cell lines. As proof of principle validation, functional experiments confirmed that two noncoding UPTs promoted the proliferation and migration of lung cancer cells. Our results suggest that epigenetic activation of unannotated noncoding transcripts might represent a previously unrecognized mechanism contributing to transcriptomic ITH.

PMID:41870780 | DOI:10.1007/s11427-025-3273-6

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Technol Cancer Res Treat. 2026 Jan-Dec;25:15330338261435022. doi: 10.1177/15330338261435022. Epub 2026 Mar 23.

ABSTRACT

IntroductionHepatocellular carcinoma (HCC) screening in patients with hepatic cirrhosis (HC) relies on ultrasound and alpha-fetoprotein (US + AFP), which has limitations in sensitivity, particularly for early-stage HCC detection. This study aims to evaluate the performance of a novel multi-omics blood test, HCCscreen, with its individual components (methylation, AFP, Des-γ-Carboxy Prothrombin (DCP), mutations) and the standard US + AFP for HCC screening in a hepatic cirrhotic population.MethodsA total of 5078 patients with known high-risk for HCC were recruited. A prospective screening study was conducted on 650 patients with hepatic cirrhosis identified by ultrasound. Blood samples were collected from all patients before the confirmation of diagnosis by imaging and/or pathological examinations. The performance of HCCscreen, individual markers and US + AFP were calculated and compared. Statistics was performed with Graphpad Prism 5.0.ResultsHCCscreen exhibited a sensitivity of 86.3% at a specificity of 81.3%, with a positive predictive value (PPV) of 28.2% and a negative predictive value (NPV) of 98.6%. The positive likelihood ratio (LR+) was 4.61 and the negative LR (LR-) was 0.17. The positive detection rate (PDR) for all markers increased with more advanced HCC stages, whether Barcelona Clinic Liver Cancer (BCLC) or clinical staging. Among the single-omics, methylation showed the highest PDR, followed by AFP, DCP and mutations. HCCscreen demonstrated superior overall performance with an AUC of 0.87, outperforming individual markers like methylation (AUC = 0.76), AFP (AUC = 0.83), and DCP (AUC = 0.77). Crucially, HCCscreen's PDR was significantly higher than US + AFP in early-stage HCC (BCLC-0 and clinical stage I). Furthermore, while AFP's PDR varied significantly by sex, HCCscreen's performance remained consistent across all demographics. Correlation analysis revealed a significant association only between the HCCscreen score and the methylation score.ConclusionsThe multi-omics approach of HCCscreen significantly enhances early HCC detection in patients with hepatic cirrhosis compared to both its individual components and the current standard of US + AFP. Its robust and consistent performance across patient demographics underscores its potential as a superior tool for population-wide early HCC screening.

PMID:41869803 | PMC:PMC13009828 | DOI:10.1177/15330338261435022

iScience. 2026 Mar 3;29(4):115203. doi: 10.1016/j.isci.2026.115203. eCollection 2026 Apr 17.

ABSTRACT

Mass spectrometry imaging (MSI)-based spatial metabolomics exhibits extensive missing values; yet, practical guidance on how imputation choices affect both imputation accuracy and downstream spatial analyses remains limited. In this study, we evaluated eight imputation methods, including both existing approaches and a graph convolutional network (GCN)-based method specifically designed for spatial metabolomics data, to identify suitable approaches for spatial metabolomics. To enable comprehensive assessment, we developed an evaluation framework focusing on two objective criteria: (a) imputation accuracy and (b) preservation of spatial cluster structure. We assembled six benchmark datasets spanning mouse brain and liver, human kidney and stomach, and plant seed sections, and conducted controlled dropout simulations of missing values. Across both evaluation dimensions, including imputation accuracy and preservation of spatial cluster structure, RF ranked first overall, and GCN ranked second in both dimensions. Overall, this systematic, dual-perspective benchmark study provides guidance for selecting imputation strategies in spatial metabolomics research.

PMID:41869568 | PMC:PMC12999350 | DOI:10.1016/j.isci.2026.115203

J Cancer. 2026 Jan 30;17(3):515-523. doi: 10.7150/jca.127381. eCollection 2026.

ABSTRACT

Gastrointestinal (GI) cancers remain a leading cause of cancer-related morbidity and mortality worldwide, largely due to their molecular heterogeneity, complex tumor microenvironment (TME), and variable treatment responses. In recent years, the emergence of spatially resolved omics technologies-encompassing spatial transcriptomics, proteomics, metabolomics, and epigenomics-has revolutionized the ability to interrogate tumor architecture with unprecedented resolution. These methods enable precise mapping of cellular and molecular interactions within intact tissue contexts, thereby uncovering spatially defined niches that influence tumor progression, immune evasion, and therapeutic resistance. In GI malignancies such as colorectal, gastric, and esophageal cancers, spatial omics have provided critical insights into cancer-stromal-immune crosstalk, identified predictive biomarkers for immunotherapy and targeted agents, and guided the development of novel therapeutic strategies. This review synthesizes the latest advances in spatial omics applied to GI oncology over the past five years, with an emphasis on their integration into early diagnosis, treatment stratification, and real-time monitoring of therapeutic efficacy. We also discuss current challenges, including standardization, data integration, and clinical validation, as well as future directions for incorporating spatial profiling into routine oncology practice. By bridging the gap between bench discoveries and bedside applications, spatial omics hold transformative potential for achieving truly personalized treatment in gastrointestinal cancers.

PMID:41869445 | PMC:PMC13003551 | DOI:10.7150/jca.127381

J Cancer. 2026 Mar 4;17(3):614-625. doi: 10.7150/jca.127511. eCollection 2026.

ABSTRACT

Malignant pleural effusion (MPE) and malignant ascites (MA) are common complications in advanced-stage cancers, often signifying disease progression and resistance to treatment. Compared to tissue biopsies or surgical specimens, materials derived from effusions offer advantages such as minimal invasiveness, ease of accessibility, and the feasibility of repeated collection during therapeutic interventions. Organoids generated from tumor cells in effusions, termed fluid-derived organoids (FDOs), have demonstrated the ability to maintain genetic heterogeneity and accurately replicate patient-specific tumor phenotypes. These characteristics position FDOs as promising models for investigating drug resistance mechanisms and informing personalized oncology strategies. In the context of lung cancer, organoids derived from pleural effusions have been employed to study acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and immunotherapy. Similarly, in ovarian and gastrointestinal cancers, organoids derived from ascites have proven to be valuable platforms for examining chemotherapy resistance and conducting drug sensitivity testing. FDOs have shown significant potential for translational applications by effectively correlating ex vivo drug responses with clinical outcomes, thus facilitating real-time monitoring of resistance evolution. However, several challenges remain, such as achieving culture standardization, maintaining the integrity of tumor microenvironment components, and integrating with multi-omics approaches. This review provides a comprehensive overview of recent advancements in the use of pleural effusion- and ascites-derived organoids for drug resistance research, underscores their applications in personalized oncology, and explores future research directions.

PMID:41869438 | PMC:PMC13003542 | DOI:10.7150/jca.127511

Immunotargets Ther. 2026 Mar 17;15:541852. doi: 10.2147/ITT.S541852. eCollection 2026.

ABSTRACT

PURPOSE: NUP85 encodes protein components of the Nup107-160 subunit of the nuclear pore complex, belonging to the Nucleoporins (NUPs) family, potentially implicating its role in human cancer. This study aims to elucidate the potential involvement of NUP85 in cancer pathogenesis.

METHODS: Leveraging data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis (GEPIA), CellMiner, and GeneMANIA databases, we investigated the role of NUP85 across various tumors. Correlations between NUP85 expression and pathological stage, histological grade, survival, immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), drug resistance, DNA methylation, copy number variation (CNV), and single-cell expression were analyzed. Gene functional enrichment analysis was conducted to explore NUP85-associated pathways. Molecular biology experiments including Western blotting, flow cytometry, trans-well migration, and invasion assays were performed to validate NUP85's oncogenic role in lung adenocarcinoma (LUAD) and oral squamous cell carcinoma (OSCC) cell lines.

RESULTS: Our findings reveal up-regulated expression of NUP85 in most tumor tissues, with significant correlations observed with pathological stage, survival, immune infiltration, TMB, MSI, drug resistance, DNA methylation, and CNV. Molecular biology experiments confirm NUP85's tumor-promoting role in LUAD and OSCC cell lines. Single-cell sequencing data suggest elevated NUP85 expression primarily in proliferative T cells (Tprolif).

CONCLUSION: NUP85 emerges as a potential tumor marker associated with tumor immunity and poor prognosis. These insights offer avenues for the development of novel therapeutic targets and anti-neoplastic drugs.

PMID:41869435 | PMC:PMC13005628 | DOI:10.2147/ITT.S541852

Int J Chron Obstruct Pulmon Dis. 2026 Jan 17;21:553092. doi: 10.2147/COPD.S553092. eCollection 2026.

ABSTRACT

BACKGROUND: As a relatively common respiratory disease, chronic obstructive pulmonary disease (COPD) has a high incidence and mortality rate. Mitochondrial dysfunction has been implicated in COPD pathogenesis, but the causal genes and underlying molecular mechanisms remain unclear.

METHODS: We performed a summary data-based Mendelian Randomization (SMR) study integrating summary data from genome-wide association studies (GWAS) with blood-based methylation (mQTL), expression (eQTL), and protein quantitative trait loci (pQTL) to identify mitochondrial-related genes causally associated with COPD. Significant findings were validated using two-sample MR, independent lung tissue transcriptomic data (GSE76925), and weighted gene co-expression network analysis (WGCNA) to assess transcriptional consistency and functional convergence.

RESULTS: Our integrative SMR and colocalization analyses identified 77 mitochondrial genes linked to COPD risk, including GPX1, TUFM, COQ5, BPHL, and NAGS. A methylation-to-expression regulatory cascade was observed, with hypermethylation at GPX1 cg24011261 associated with increased gene expression and higher COPD risk-despite its role as an antioxidant enzyme. Two-sample MR confirmed robust causal effects of GPX1, BPHL, TUFM and COQ5 expression on COPD. These findings were replicated in lung tissue: GPX1, COQ5, and TUFM were significantly upregulated in COPD patients (GSE76925). WGCNA revealed that these genes reside within a highly interconnected turquoise module strongly correlated with COPD status (r = 0.43, p < 0.001) and enriched in oxidative phosphorylation and mitochondrial energy metabolism pathways.

CONCLUSION: This study provides systematic genetic and transcriptomic evidence that mitochondrial-related genes, particularly GPX1 and TUFM, exert causal effects on COPD risk through regulatory cascades and coordinated network dysregulation. The convergence of genetic, epigenetic, and co-expression evidence underscores mitochondrial dysfunction as a central mechanism in COPD pathogenesis and highlights potential targets for future therapeutic development.

PMID:41868721 | PMC:PMC13003660 | DOI:10.2147/COPD.S553092

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

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

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