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