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PubMed2026

Cost-Effectiveness and the Economics of Genomic Testing and Molecularly Matched Therapies.

Cost-effectiveness analysis of precision oncology can help guide value-driven care. Next-generation sequencing is increasingly cost-efficient over single gene testing because diagnostic algorithms require multiple individual gene tests to determine biomarker status. Matched targeted therapy is often not cost-effective due to the high cost associated with drug treatment. However, genomic profiling can promote cost-effective care by identifying patients who are unlikely to benefit from therapy. Additional applications of genomic profiling such as universal testing for hereditary cancer syndromes and germline testing in patients with cancer may represent cost-effective approaches compared with traditional history-based diagnostic methods.

Humans↗

PubMed2026

Precision Oncology in Hepatopancreatobiliary Cancer Surgery.

Advances in technology have allowed for the characterization of tumors at the genomic, transcriptomic, and proteomic levels. There are well-established targets for biliary tract cancers, with exciting new targets emerging in pancreatic ductal adenocarcinoma and potential targets in hepatocellular carcinoma. Taken together, these data suggest an important role for molecular profiling for personalizing cancer therapy in advanced disease and need for design of novel neoadjuvant studies to leverage these novel therapeutics perioperatively in the surgical patient.

Humans↗

PubMed2026

Intramuscular patient-derived xenografts achieve high engraftment rates in gastric cancer: implications for pharmacodynamic testing and genomic biomarker discovery.

BACKGROUND: Gastric cancer (GC) exhibits marked inter-patient heterogeneity, limiting empirical chemotherapy efficacy. Patient-derived xenograft (PDX) models preserve the molecular features of parental tumors and can serve as pharmacodynamic surrogates, but conventional subcutaneous PDX suffers from low engraftment rates. This study evaluated an optimized intramuscular PDX platform for individualized drug testing in GC and applied whole exome sequencing (WES) for biomarker identification (Clinical trial registry: ChiCTR-OOC-17012731). MATERIALS AND METHODS: Ninety-eight treatment-naive GC patients were enrolled between April 2018 and December 2020. Fresh tumor tissues were engrafted into NCG mice by intramuscular transplantation. Drug efficacy was evaluated using tumor cell necrosis rate and Ki-67 expression. WES was performed on 32 engrafted tumorgrafts to characterize driver mutations in fast- and slow-growing subgroups. RESULTS: An engraftment rate of 71.7% (43/60) was achieved, substantially exceeding rates reported in prior studies. Clinical characteristics were independent of engraftment success and outgrowth time (all p > 0.05). Fast- and slow-growing tumorgrafts diverged in frequently altered genes: KMT2C, APOB, CDK12 and MSH2 predominated in fast-growing grafts, whereas TP53, CHD3 and TET2 were enriched in slow-growing grafts. Slow-growing tumorgrafts correlated with longer progression-free survival (p = 0.02). PDX-guided treatment was associated with improved prognosis. CONCLUSIONS: Intramuscular transplantation into NCG mice yields high engraftment rates for GC PDX. PDX-guided chemotherapy selection is associated with favorable outcomes. Driver mutation divergence between fast- and slow-growing tumorgrafts provides candidate prognostic biomarkers.

Animals↗

PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Genomic science and the nurse educator's role: Promoting integration from curriculum to clinical practice.

BACKGROUND: Registered nurses and nurse educators play a critical role in preparing future clinicians to translate genomic discoveries into practice. However, emerging evidence suggests that both groups may lack sufficient knowledge and confidence in genomics, potentially limiting their ability to teach, mentor, and apply genomics in real-world settings. This gap is especially concerning in Aotearoa New Zealand, where the genomic literacy of nurse educators and clinicians remains underexplored. OBJECTIVE: This study aims to: (1) assess nurse educators' genomic literacy and confidence in teaching genomics; and (2) evaluate registered nurses' knowledge and confidence in applying and teaching genomics in clinical practice. DESIGN: Exploratory descriptive qualitative. SETTING: This study was conducted in the greater Auckland area. PARTICIPANTS: A total of 17 participants were recruited using purposive sampling to ensure a diverse range of perspectives across varying levels of teaching experience, disciplinary backgrounds, and exposure to genomic content. METHODS: Data were collected using semi-structured focus group interviews, a method well-suited for generating in-depth discussion and facilitating interaction among participants with shared professional interests. The collected data were analysed using thematic analysis methods. RESULTS: The findings offer insight into the preparedness of New Zealand's nursing workforce to engage with genomic-informed healthcare and inform strategies for integrating genomics into nursing curricula and continuing professional development. Given the interdisciplinary nature of genomic healthcare, these insights may also be relevant to other health professionals-including midwives, pharmacists, and allied health practitioners-who increasingly encounter genomic information in clinical practice and require foundational competencies to support patient care. CONCLUSION: Addressing this educational gap is critical to ensuring that nurses-key facilitators of patient care and public health-are equipped to deliver safe, equitable, and evidence-based genomic healthcare.

Humans↗

PubMed2026

A therapeutic atlas of monogenic inflammatory bowel disease.

BACKGROUND AND AIMS: Evidence-based, mechanism-guided therapies are urgently needed for treating monogenic inflammatory bowel disease (mIBD). For such rare diseases, mechanistic insight is essential to guide treatment when conventional clinical trials are often not feasible. We aimed to summarize literature-based evidence and to identify knowledge gaps. METHODS: We conducted a systematic review of published manuscripts evaluating the therapeutic efficacy in mIBD. We quantified and compared the global therapeutic response score across treatments and conditions. In a subset of conditions, biomarkers of longitudinal therapeutic response were evaluated in comparison to non-monogenic pediatric IBD cohorts. RESULTS: Responses to 35 therapeutics across the 102 known genetic causes of mIBD were evaluated in 241 articles and 669 patients, summarizing 302 gene-drug responses. The efficacy of at least one pharmacological intervention was identified in 61% (n = 62/102) of the mIBD conditions, highlighting a major unmet need for effective medications in many others. Gene- and pathway-specific responses were demonstrated for several therapies, including allogeneic hematopoietic stem cell transplantation, gene therapy, and advanced therapies such as anti-TNF agents, IL-1 inhibitors, mTOR inhibitors, as well as eculizumab in CD55 deficiency, abatacept in CTLA4 deficiency, and the immunometabolic agent empagliflozin in glycogen storage disease type 1b. CONCLUSIONS: This study highlights the potential of precision medicine approaches tailored to genetic and pathway-specific mechanisms, while underscoring the urgent need for effective therapies in many monogenic conditions that remain without established treatment options.

Humans↗

PubMed2026

Pharmacogenomic and drug interactions risk in cardio-oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a considerable polypharmacy burden. This significantly increases the prospect of drug-drug interactions (DDIs) and gene-drug interactions. The majority of these interactions arise from comparable pharmacokinetic and pharmacological pathways associated with drug transporters and cytochrome P450 enzymes. The significance of pharmacogenomics in tailored treatment strategies are emphasised by the fact that genetic variability enhances individual differences in drug response, safety, and efficacy. This narrative review focus on the effects of key genetic polymorphisms (e.g., DPYD, CYP2C19, and CYP2C9) on the metabolism and efficacy of commonly prescribed anticancer and cardiovascular medications such as fluoropyrimidines, clopidogrel, and warfarin. In addition it explore the role of pharmacogenomic variants on drug-drug interactions within the field of cardio-oncology. The study ultimately emphasizes the necessity of precision medicine in India to address the genetic diversity and underrepresentation in global genomic databases. The absence of pharmacogenomic testing, infrastructural deficiencies, financial constraints, and insufficient clinical integration hinder the widespread use of this technology in India. The Genome India Project and other national initiatives establish the foundation for pharmacogenomic-guided therapy. Utilizing genetic data, together with artificial intelligence-based predictive tools, for clinical decision-making may enhance medication safety and yield optimal outcomes in Indian cardio-oncology patients.

Humans↗

PubMed2026

CRISPR-Cas and Infectious Diseases: A Decade of Translational Advances in Molecular Biotechnology.

CRISPR-Cas systems have emerged as a versatile tool for diagnosing, treating, and preventing infectious diseases. This review highlights translational advancements in CRISPR-Cas-based applications, concentrating on the past decades in diagnostics, therapeutic genome editing, and vaccine development. The article highlights key platforms like DETECTR and SHERLOCK, which enable rapid, sensitive pathogen detection, and explores CRISPR-Cas9 systems in therapeutic strategies for directly targeting viral genomes and combating antimicrobial resistance. It also examines the role of CRISPR-Cas9 in engineering live-attenuated and personalized neoantigen vaccines. Principal findings demonstrate a clear progression from experimental proof-of-concept to preclinical applications primarily in CRISPR-based diagnostics and the engineering of live-attenuated vaccine candidates, whereas translation in CRISPR-based therapeutics and personalized neoantigen vaccines for infectious diseases remains at earlier, more exploratory stages. CRISPR-based diagnostics have progressed further toward clinical evaluation than therapeutics due to delivery and safety constraints, while personalized neoantigen vaccines are included mainly as an emerging, comparative concept for infectious diseases rather than a mature application. This review uniquely integrates CRISPR-based diagnostics, therapeutics, and vaccine development within a single infectious disease framework, critically assesses their current maturity, and systematically highlights technical, regulatory, and ethical barriers alongside realistic future priorities. The review concludes that while CRISPR-Cas holds transformative potential for infectious disease management, significant challenges in delivery efficiency, off-target effects, and ethical regulation must be addressed to ensure safe and equitable clinical translation.

Humans↗

PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗

PubMed2026

A system-level metastable model of cancer evolution: integrating replication stress, cell cycle deregulation and chromosomal instability.

INTRODUCTION: Cancer cell proliferation occurs within the context of persistent genomic instability. In this review, we propose the RS-CCD-CIN axis as a systems-level framework in which replication stress (RS), cell cycle deregulation (CCD) and chromosomal instability (CIN) form an interdependent triad that shapes tumour evolution. This axis represents a constrained metastable state in which genomic instability is tolerated and buffered. The objective of this review is to synthesize the current understanding of how the RS-CCD-CIN axis contributes to tumour heterogeneity, adaptability and therapy response. DISCUSSION: Evidence indicates that RS, CCD and CIN operate as a dynamic, interconnected network rather than as independent processes. Replication stress induces DNA damage and mutagenesis, while partial checkpoint disruption permits cells with unresolved lesions to proliferate. Chromosomal instability generates both structural and numerical alterations, contributing to intratumoural heterogeneity. Together, these processes facilitate adaptation to environmental and therapeutic pressures. Extrachromosomal DNA, micronuclei formation and cytosolic DNA signalling, including the cGAS-STING pathway, connect genomic instability to adaptive responses and immune modulation. Single-cell and spatial profiling reveal temporal and spatial variability in RS, CCD and CIN states, highlighting the limitations of static biomarkers. Therapeutically, targeting individual components often yields limited durability, whereas approaches that simultaneously perturb multiple aspects of the RS-CCD-CIN axis may improve clinical outcomes. CONCLUSIONS: This review highlights the RS-CCD-CIN axis as a fragile and metastable architecture that supports cancer evolution, while also being susceptible to collapse. A deeper understanding of this interconnected framework may inform the development of therapeutic strategies and enhance the management of resistance.

Humans↗

PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2025

Diagnostic and clinical utility of exome sequencing and chromosomal microarray in children with GDD/iD: a meta-analysis.

BACKGROUND: Global developmental delay/intellectual disability (GDD/ID) is among the most common neurodevelopmental disorders, with up to half of cases are attributed to genetic factors. Chromosome microarray (CMA) has traditionally been the primary genetic test for idiopathic GDD/ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) have recently emerged, substantially increasing diagnostic yields in these populations. METHODS: We conducted a comprehensive literature search of PubMed, Scopus, EMBASE, and the Cochrane Library from inception to April 29, 2025. Studies reporting the diagnostic utility of these tests in children with GDD/ID were included and analyzed. RESULTS: A total of 102 studies, comprising 55,752 children, were reviewed. The pooled diagnostic yield of WES was 0.37 (95% CI: 0.33-0.41; I2 = 93%), significantly higher than that of CMA at 0.19 (95% CI: 0.16-0.21; I2 = 95%). Subgroup analyses showed that WES yielded significantly higher diagnostic rates than CMA in both same-sample comparisons (OR = 2.27, 95% CI: 1.08-4.78) and different-sample comparisons (OR = 1.65, 95% CI: 1.15-2.37). Only one study evaluated WGS, reporting a diagnostic yield of 0.27. Meta-regression revealed a significant association between CMA diagnostic yield and the proportion of male participants (p < 0.01), but not with WES. No significant difference in diagnostic utility was observed between isolated GDD/ID and GDD/ID with comorbidities. CONCLUSION: In children with unexplained GDD/ID, WES demonstrates superior diagnostic and clinical utility compared to CMA. Incorporating WES as a first-line investigation in the diagnostic evaluation of GDD/ID may be warranted.

Humans↗

PubMed2026

Responding to a protracted tuberculosis outbreak: lessons from multiple rounds of investigation in a Chinese boarding school.

PURPOSE: This study analysed a multi-semester pulmonary tuberculosis (PTB) cluster outbreak in a Chinese boarding school to provide evidence for future epidemic control. METHODS: Contacts were screened via symptoms, infection tests and chest radiography. Screening expanded progressively from close contacts to same-floor contacts, then all students and staff. Whole-genome sequencing (WGS) with single nucleotide polymorphism (SNP) and bioinformatics analysis was used for lineage classification, transmission clustering (≤12 SNPs defining a cluster) and drug resistance prediction. RESULTS: From 2020 to 2022, 20 students were diagnosed with PTB, half laboratory-confirmed. Most cases clustered in class 16 and were epidemiologically linked to the primary case (case 0), who had household PTB exposure. Case 0 and case 1 had diagnostic delays exceeding 3 and 6 months, respectively. WGS of five isolates (case 1, 3, 4, 9 and 10) collected over three semesters showed all belonged to lineage 2 and differed by ≤12 SNPs, confirming the same transmission chain. The infection rate in class 16 (46.34%) was significantly higher than other case classes (19.05%) and classes without cases (8.27%) (χ2 = 61.169, p < 0.001). No new cases were detected during a one-year follow-up of students involved in the outbreak after the final round of screening, nor among household contacts of all cases followed up to the present. CONCLUSIONS: Lack of entry health examinations facilitated the outbreak. Delayed diagnosis, incomplete contact screening and absence of preventive treatment led to cross-semester persistence. The infection rate disparity confirms class 16 as the outbreak epicentre. Improving community case management, extending contact follow-up and enhancing cluster outbreak measures are recommended to prevent future outbreaks.

Humans↗

PubMed2026

Published Database Resources for Traditional, Complementary, and Integrative Medicine: Update of a Systematic Review.

BACKGROUND: Traditional, Complementary, and Integrative Medicine (TCIM) has been established in the academic context of universities. In recent years, strategies have been developed worldwide to strengthen the role of TCIM in supporting the health of the population. Online databases are a common way for obtaining evidence-based information. This article is an update of a former systematic review from 2010 on published databases resources for TCIM. METHODS: The databases CINAHL, CAMbase, Web of Science, MEDLINE/PubMed, and Google Scholar search engine were searched for databases related to TCIM published in peer-reviewed journals between 2010 and November 2024. All included databases were visited online, and information on the origin, content, and scope of the database was extracted. RESULTS: A total of 6579 articles were identified through the literature search. After exclusion of irrelevant articles, full-text screening of 127 articles yielded 37 new databases. Together with 16 still available old databases, these mainly contained information on herbal therapies (n = 15) and Traditional Chinese Medicine (n = 11) from 18 different countries. Newly identified medicinal plant databases offer various scientific resources such as crude drugs, indigenous plants, and structures for natural and phytochemical components with molecular biological content. CONCLUSIONS: This literature review illustrates the dynamic development in the database landscape over the last 15 years. While the number of bibliographic databases is shrinking, databases in the field of medical plants/herbal therapy content are on the rise, which might be due to advances in plant genomics and molecular biology.

Humans↗

PubMed2026

Targeting SIRT6: the design and therapeutic implications of activators and inhibitors.

Sirtuin 6 (SIRT6) is an NAD+-dependent deacylase that maintains genomic stability, regulates metabolism, and influences aging, making it an attractive but challenging therapeutic target. Pharmacological modulation of SIRT6 holds promise for cancer and metabolic disorders, yet its context-dependent functions demand precise intervention strategies. Potent, selective, and drug-like chemical probes are therefore essential to dissect SIRT6 biology and to validate its therapeutic potential. This review critically evaluates recent medicinal chemistry advances in SIRT6 modulation. We focus on structure-guided design strategies and structure-activity relationships (SAR) that have transformed initial hits into optimized leads for both activators and inhibitors, highlighting the remaining challenges in achieving isoform selectivity and drug-like properties.

Sirtuins↗

PubMed2026

The mighty microproteins: from versatile cellular regulators to precision medicine therapeutics.

Microproteins, are tiny proteins encoded by small open reading frame (sORF), translation of these non-canonical open reading frames (ncORFs) has been implicated in diverse biological processes and diseases. This review summarizes recent developments in the discovery, biogenesis, and functional characterization of microproteins, and their involvement in various disease, with special focus on their roles in cancer, cardiovascular, metabolic, neurodegenerative and immune-related disorders. We emphasize the regulation of key cellular pathways by microproteins, including mitochondrial homeostasis, apoptosis, metabolic reprogramming, and immune signaling, all of which affect disease initiation and progression. Emerging evidence also supports their potential as disease biomarkers and therapeutic candidates for precision medicine. Finally, the review critically discusses the current challenges including discrepancies in microprotein annotation, the limitations of ribosome profiling and proteogenomic approaches, the gap between computationally predicted and experimentally validated microproteins, and the need for rigorous orthogonal validation by means of CRISPR-based genome editing, ribosome release assays, mutational analysis, high-resolution mass spectrometry, and functional studies. Finally, we review recent development of AI-assisted ORF prediction, single-cell translatomics, spatial proteomics, and integrated multi-omics as emerging technologies reshaping. Microprotein discovery and functional annotation. Finally, we discuss the translational potential of microproteins and highlight the remaining challenges to clinical application, including peptide stability, pharmacokinetics, tissue-specific delivery, immunogenicity, and the need for rigorous preclinical and clinical validation. Together, this review provides an updated and critical overview of the rapidly evolving microprotein field and highlights future research priorities for translating these molecules into clinically useful biomarkers and precision therapeutics.

Microproteins↗

PubMed2026

Integrated phytochemical and bioactivity profiling of Xanthium strumarium fruits from Korea and China: Implications for origin-specific quality specification.

BACKGROUND: Geographic origin influences the phytochemical composition and biological activities of medicinal plant resources. Xanthium strumarium L. (XS) fruit is widely used in East Asian traditional medicine. However, current pharmacopeial standards primarily recognize Chinese-derived material, despite the availability and traditional use of XS in Korea. To address this gap and support origin-informed quality specification, we compared fruits from Korea (XS-K) and China (XS-C) using chloroplast genome sequencing, targeted phytochemical profiling (high-performance liquid chromatography (HPLC) for selected phenolics and gas chromatography-flame ionization detection (GC-FID) for fatty acids and phytosterols, and multivariate chemometric analysis. RESULTS: Chloroplast genome analysis revealed high overall similarity but localized divergence around the rpoC2 locus and a greater mutation burden in XS-C, supporting origin-associated genomic differentiation. Phytochemical profiling revealed distinct origin-dependent metabolic signatures. XS-K showed higher levels of phytosterols, chlorogenic acid, 4,5-dicaffeoylquinic acid (4,5-DCQ), and xanthatin was detected only in XS-K, whereas XS-C exhibited greater abundance of total fatty acids, particularly oleic acid. Unsupervised clustering and log2 fold-change ranking confirmed clear compositional separation, and variable importance in projection (VIP) analysis identified chlorogenic acid, β-sitosterol, oleic acid, 4,5-DCQ, and xanthatin as major discriminators between origins. Bioactivity assays demonstrated that XS-K exerted stronger antioxidant effects in ABTS, DPPH and FRAP assays, stronger skin-related enzyme inhibition, and greater antibacterial activity against Staphylococcus aureus, consistent with its enriched phenolic and sterol profile. CONCLUSION: Together, chloroplast sequence variation, targeted metabolite quantification, and screening bioassays consistently distinguished XS-K from XS-C. These findings support the use of candidate markers for the origin-based authentication and quality control of XS fruit-derived ingredients. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Fruit↗