Search Results (1,901)

Clear cell renal cell carcinoma (ccRCC) is characterized by marked biological heterogeneity, which limits the prognostic accuracy of conventional clinicopathological models. Increasing attention has therefore focused on identification of biomarkers that can enhance risk stratification throughout all stages of the disease. Starting from the current state of the art, this narrative review summarizes and critically appraises the evidence published over the past decade regarding prognostic biomarkers in ccRCC. The analysis is structured into four overarching domains: (i) genomic biomarkers, covering somatic alterations and transcriptomic signatures; (ii) tissue-based biomarkers, including immunohistochemical surrogates and immune microenvironment features; (iii) circulating biomarkers, such as systemic inflammation parameters and indices; and (iv) integrated predictive models, represented by emerging multi-omic approaches. Going through the broad framework of potential prognostic biomarkers, emphasis is placed on their individual and integrative value in relation to classic clinical-pathological factors and survival parameters. At the tissue level, chromosome 3p-related alterations constitute a central molecular feature of ccRCC. Among these, BAP1 loss has emerged as one of the most consistently validated indicators of aggressive tumor behavior. Disruption of the SETD2/H3K36me3 axis and immune-related biomarkers, including PD-L1 expression, have demonstrated prognostic associations in selected settings, although with variable and context-dependent performance. In the circulating compartment, plasma KIM-1 has shown prognostic relevance following nephrectomy, while postoperative detection of circulating tumor DNA (ctDNA) may identify patients at increased risk of recurrence. However, limited analytical sensitivity and methodological heterogeneity currently restrict the broader clinical applicability of ctDNA-based strategies. Systemic inflammatory indices, such as the neutrophil-to-lymphocyte ratio, show reproducible associations with outcomes but largely reflect host inflammatory status rather than tumor-specific biology. However, no single biomarker currently supports routine prognostic implementation in ccRCC. Future progress will likely depend on integrative models combining genomic, tissue-based, immune, and circulating parameters with established clinical variables. Prospective validation and clear demonstration of incremental clinical utility will be essential before such strategies can meaningfully inform therapeutic decision-making. Full article

Disease-specific alpha-synuclein (αsyn) strains have been linked to different synucleinopathies. Current αsyn biomarkers are limited to binary detection of pathogenic αsyn in peripheral tissue biopsies or fluids, limiting differential diagnosis. Hence, there is an urgent need for methods that allow strain-specific detection and characterization of αsyn strain architecture. Notably, luminescent conjugated oligothiophenes (LCOs) have been successfully used to detect distinct protein strain conformers in prion diseases and Alzheimer’s disease, highlighting their utility in differentiating disease-specific amyloid structures. Species-dependent differences in αsyn structure are increasingly recognized as one of the critical aspects that shape how fibrils form, propagate and interact with molecular LCO probes. Here, we evaluate the potential of the LCO h-FTAA to differentiate species-specific αsyn strains and conduct a translational investigation using peripheral cardiac tissue of a gut-first synucleinopathy rodent model. Our in vitro data demonstrate strain-specific probe–fibril interactions, reflecting a differential strain architecture and cellular micro-environment. While h-FTAA binds with comparable efficiency to mouse (mo-) and human (hu-) pre-formed fibrils (PFFs), h-FTAA exhibits markedly lower quantum yield when bound to moPFFs versus huPFFs. Spectral imaging revealed h-FTAA-moPFF binding produces blue-shifted maxima (505–550 nm), contrasting with the red-shifted maxima (545–580 nm) of huPFFs. Fluorescence lifetime imaging microscopy confirmed h-FTAA’s intrinsic sensitivity to species-dependent variations through distinct temporal fluorescence signatures (moPFFs: ~0.60–1.5 ns vs. huPFFs: ~0.65–1.0 ns). Our translational investigation showed h-FTAA binding to peripheral cardiac pathology exhibits comparable red-shifted emission, but distinct fluorescence lifetimes of h-FTAA-bound aggregates in moPFF-injected (~1.0–1.4 ns) versus huPFF-injected (~0.69–0.8 ns) rats. Interestingly, we observed distinct blue-shifted emission profiles in a few selected regions of the heart of moPFF-injected rodents, further characterized by extra-long fluorescence decay shifts (~1.5–1.9 ns), reflecting differences in both aggregate conformation and maturity in moPFF-induced compared with huPFF-induced rats. Taken together, our findings underscore the potential of LCO ligands, like h-FTAA, to enable more precise disease staging and diagnosis through peripheral biopsies, complementing existing αsyn biomarker methods. Full article

Background: Most patients with hepatocellular carcinoma (HCC) present with advanced disease and have limited systemic treatment options. Oxaliplatin shows clinical activity in HCC but its effectiveness is frequently curtailed by intrinsic and acquired resistance. We sought to systematically identify genetic vulnerabilities that increase oxaliplatin sensitivity in HCC. Methods: Genome-scale negative-selection CRISPR–Cas9 screens were conducted in two genetically distinct HCC cell lines (Hep3B and MHCC-97H) under low-dose oxaliplatin to discover conserved determinants of sensitivity. Selected DNA damage response (DDR) hits were validated. An oxaliplatin-resistant MHCC-97H subline was generated for transcriptomic profiling to characterize resistance-associated programs. Screen results were integrated with TCGA-LIHC expression and survival data to evaluate clinical relevance. Additionally, we analyzed bulk RNA-seq data from biopsy specimens collected from 36 HCC patients prior to initiation of hepatic arterial infusion chemotherapy (HAIC), comparing expression levels of the DDR genes between patients with objective response and non-responders. Results: Screens in both cell lines converged on DDR pathways, particularly nucleotide excision repair (NER) and the Fanconi anemia/interstrand crosslink repair network; shared sensitizers included ERCC4 (XPF), FANCE and SLX4. Validation experiments showed that disruption of representative DDR factors (POLH and XPA) synergistically increased oxaliplatin efficacy at concentrations as low as 0.5 μM. Transcriptomic analysis of the resistant MHCC-97H subline revealed coordinated upregulation of DNA repair programs, G2/M checkpoint and E2F target signatures, and epithelial–mesenchymal transition features. Integration with TCGA-LIHC data demonstrated frequent overexpression of many screen-identified DDR genes in primary HCC and an association between higher expression of selected factors and poorer patient survival. In the HAIC cohort, several DDR genes, including ATR, BRCA2, CDK7, MUS81, MUTYH, PARG, POLH, POLK and XPA, were significantly lower in the objective response group. Conclusions: DDR components represent candidate biomarkers and therapeutic targets whose inhibition may enhance oxaliplatin efficacy in HCC. Full article

Sepsis-associated acute kidney injury (SA-AKI) remains a major diagnostic challenge in critically ill patients, as conventional functional criteria—serum creatinine and urine output—often detect AKI after clinically relevant pathophysiological derangement has already evolved. Increasing evidence suggests that SA-AKI reflects a heterogeneous process characterized by early cellular stress, microcirculatory dysfunction, inflammation-associated injury, and maladaptive repair preceding overt functional decline. In this context, biomarker-based approaches have been investigated to improve early risk stratification, phenotypic characterization, and prognostic assessment in septic patients. This narrative review synthesizes current evidence on established and emerging biomarkers relevant to SA-AKI, encompassing stress markers ([TIMP-2]•[IGFBP7]), tubular injury markers (e.g., NGAL, KIM-1, IL-18), functional markers (e.g., proenkephalin/penKid, cystatin C), and exploratory molecular signatures such as circulating microRNAs (miRNAs). We examine their temporal dynamics, performance estimates, and context-dependent applicability in sepsis, and discuss limitations related to heterogeneity, assay variability, and threshold standardization. Particular attention is given to multimodal and longitudinal strategies integrating biomarkers with KDIGO criteria and clinical phenotyping. Finally, we outline a stratified framework for biomarker interpretation in SA-AKI anchored to pathophysiological windows and clinical decision points. While available evidence supports the potential of selected biomarkers for short-term risk stratification and trajectory assessment, implementation requires prospective validation demonstrating incremental value beyond established models and measurable impact on patient-centered outcomes. Full article

Exopolysaccharides (EPSs) are emerging as sustainable polymers for biomedical hydrogels. Here, we report hydrogels from sulfated EPSs produced by Porphyridium cruentum and ionically crosslinked with Ca²⁺, Ce³⁺, or Cu²⁺ to generate tunable networks with bioactive potential. Rheological analysis showed viscoelastic behavior was primarily governed by cation nature and accessible binding site density, with diminishing gains above 2.5 wt% EPS and limited benefit beyond 10 wt% crosslinker. Ce³⁺ produced the most solid-like gel, Ca²⁺ yielded more thixotropic networks, and Cu²⁺ promoted rapid, heterogeneous crosslinking consistent with fast surface complexation. These network signatures showed distinct in vitro performances. Cation selection tuned antibacterial activity against Staphylococcus aureus and Escherichia coli, with Cu²⁺ achieving rapid bactericidal effects and Ce³⁺ enabling an 8-log reduction after 24 h. The ABTS assay showed that Ca²⁺- and Ce³⁺-crosslinked gels had antioxidant potential (≥40 µM Trolox eq.mg⁻¹); however, antioxidant capacity was assay dependent. Conditioned-medium assays showed ≥75% viability at day 3 for Ca²⁺- and Ce³⁺-crosslinked gels against human dermal fibroblasts (HDFs), while only Ce³⁺-crosslinked gels were cytocompatible against human keratinocytes (HaCaTs). Cu²⁺-crosslinked gels were highly cytotoxic across all tested conditions. Macrophage cytokine readouts (TNF-α and IL-6) indicated formulation-dependent immunobiological response. This work establishes microalgal EPSs as versatile polymers and links crosslinking chemistry to rheological modulation and multifunctional biomedical performance, while direct wound-healing efficacy remains to be demonstrated in future in vivo or wound repair functional models. Full article

Lipidomics has emerged as a transformative discipline in biomedical research, providing high-resolution insights into metabolic signaling and disease pathophysiology. The R programming language provides a widely adopted framework for extensible analysis of complex lipidomic datasets due to its robust biostatistical infrastructure. Herein, we present a comprehensive roadmap for lipidomics in R, structured around a standardized analytical lifecycle: from raw data acquisition and preprocessing to structural annotation, statistical modeling and functional interpretation. We critically contextualize and integrate a curated suite of widely adopted R packages (version 4.3.0), including xcms and MSnbase for feature extraction, LipidMS 3.0 for fragmentation-based identification, and lipidr for quality control and normalization. Furthermore, we demonstrate how advanced tools such as mixOmics and clusterProfiler can be integrated to bridge the gap between differential lipid abundance and systems-level biological insights. Particular emphasis is placed on reproducibility, nomenclature standardization and the emerging role of machine learning in biomarker discovery. By synthesizing these resources into a coherent pipeline, this guide provides a structured reference for researchers. Further discussion addresses methodological pitfalls, statistical assumptions and reproducibility constraints that frequently compromise lipidomics studies. Ultimately, this structured approach facilitates systematic tool selection, accelerating the translation of complex lipidomic signatures into reproducible and clinically meaningful discoveries. Full article

Mobile Unmanned Aerial Vehicles (UAVs) forming Flying Ad Hoc Networks (FANETs) offer promising applications, but dynamic network structures, limited resources, and potential single points of failure create security challenges. While cluster-based data aggregation, where data is collected and combined at Cluster Heads (CHs) before transmission, improves efficiency, traditional techniques can compromise data privacy. This paper introduces SSDBFAN, a scalable and secure cluster-based data aggregation framework for Flying Ad Hoc Networks (FANETs). The proposed approach integrates the Frilled Lizard Optimization Algorithm (FLOA) for efficient cluster head selection with blockchain technology and post-quantum cryptographic techniques, including lattice-based homomorphic encryption and the Chinese Remainder Theorem, to ensure privacy-preserving data aggregation. Additionally, a hybrid online/offline signature mechanism is employed to achieve secure and efficient authentication with reduced computational overhead. The performance of the proposed framework is evaluated using NS-3 simulations under varying network sizes. Experimental results demonstrate that SSDBFAN significantly improves communication efficiency, reduces computational cost, and enhances network stability compared to existing schemes. Furthermore, scalability analysis with up to 500 UAV nodes confirms that the proposed framework effectively controls blockchain overhead, including bandwidth consumption, consensus latency, and storage requirements. Comparative evaluation with existing optimization algorithms shows that FLOA achieves superior performance in terms of cluster stability, delay, and throughput. These results validate the effectiveness of SSDBFAN as a scalable and security-aware solution for large-scale FANET environments. Full article

The Rhiniinae (Diptera: Calliphoridae), a recently reclassified subfamily of blowflies, comprise approximately 400 species across 30 to 39 genera, which occupy diverse ecological associations, including flower visitation and specialized associations with social insects, yet their phylogenetic relationships remain poorly established due to lack of molecular data. We sequenced and characterized the complete mitochondrial genome of six representative Rhiniinae species, with which the phylogenetic analyses were conducted. The monophyly of Rhiniinae was robustly supported and the internal relationships were clarified. Rhiniini and Cosminini were both recovered as well-supported monophyletic tribes using comprehensive mitogenomic evidence for the first time. In contrast to the purifying selection prevailing in most protein-coding genes, the COII gene showed consistent signatures of positive selection, potentially linked to the functional optimization of cytochrome c oxidase. Overall, this study provides foundational mitogenomic data and a robust phylogenetic framework, offering valuable resources for future research on mitochondrial evolution and systematics within this ecologically intriguing lineage. Full article

Background/Objectives: Essential oils (EOs) have multi-target antifungal activity, but their translation is limited by volatility and poor aqueous dispersibility. Randomly methylated β-cyclodextrin (RAMEB) inclusion may enhance effective exposure and thereby alter susceptibility, stress responses, and biofilm outcomes in a species-dependent manner. This study quantified species-specific planktonic and biofilm susceptibility to four EOs and their RAMEB complexes across clinically relevant Candida species. Methods: Lavender (L), lemon balm (B), peppermint (P), and thyme (T) oils and their RAMEB complexes (RL, RB, RP, and RT) were tested against C. albicans and non-albicans Candida. Susceptibility thresholds were used to derive phase plasticity metrics. Functional inhibition was assessed via planktonic metabolism/viability and established biofilm metabolism/viability/biomass. Mechanistic signatures were captured by ROS/RNS measurements and a qPCR analysis of antioxidant genes (CAT1, GPX1, and SOD1) was performed. Mixed-effects models and multivariate/unsupervised and interpretable classification approaches (k-means, PCA, and CRT) were used to integrate endpoints and stratify response phenotypes. Results: Susceptibility thresholds were strongly species-structured (lowest MIC₉₀/EC₁₀ for C. albicans; higher thresholds and broader sublethal windows in non-albicans species). RAMEB complexation produced formulation-dependent shifts in efficacy, with RT emerging as the most consistent broad-spectrum inhibitory condition across compartments. Biofilm biomass was comparatively insensitive even when viability was suppressed, indicating a decoupling of structural biomass from biocidal activity. Mechanistic signatures were broadly conserved across species and linked to antioxidant-program engagement, with CAT1-related rules contributing to responder/tolerant classification. Conclusions: Integrating MIC/EC plasticity with functional and mechanistic markers supports the rational selection of EO formulations; RAMEB complexation, particularly RT, prioritizes candidates for further pharmaceutical optimization while highlighting species-specific vulnerabilities. Full article

Background/Objectives: X-linked Alport syndrome (XLAS) arises from pathogenic variants in COL4A5. Truncating variants are generally classified as severe, but whether clinically meaningful heterogeneity exists within this group remains unclear. This study aimed to establish a novel Col4a5 knock-in mouse model based on a clinical variant and to determine whether truncating mutation position influences disease severity. Methods: A de novo COL4A5 frameshift variant, c.2440delG, was identified in a patient with severe early-onset XLAS. A Col4a5-G814fs knock-in mouse was generated by CRISPR/Cas9 on the C57BL/6J inbred mouse strain background and compared with the established Col4a5-G5X nonsense model using survival analysis, serial functional measurements, kidney histopathology, transmission electron microscopy, and RNA sequencing. Results: The Col4a5-G814fs knock-in mouse was successfully generated and showed loss of glomerular α5(IV) collagen chain expression. Compared with G5X mice, G814fs mice exhibited shorter survival (median 141 vs. 161.5 days, p = 0.0004), earlier onset of proteinuria, and more severe kidney functional decline. By 16 weeks, G814fs mice also showed more severe glomerular basement membrane abnormalities and more extensive glomerulosclerosis. RNA sequencing revealed a shared inflammatory gene signature in both models, together with selective upregulation of genes related to the PPAR signaling pathway and fatty acid metabolism in G814fs kidneys. Conclusions: This study reports a novel de novo COL4A5 frameshift variant and establishes the first Col4a5-G814fs knock-in mouse model. Direct comparison with the G5X model shows that distinct truncating COL4A5 mutations can be associated with substantially different disease severity, providing a useful platform for future mechanistic and therapeutic studies in XLAS. Full article

Background: Acute rejection remains a major complication following liver transplantation, yet reliable noninvasive biomarkers for its early prediction and diagnosis remain unidentified. This exploratory study characterized bile and serum metabolites associated with acute rejection in living donor liver transplantation using comprehensive metabolomic profiling combined with machine learning. Methods: Non-targeted metabolomics were performed on bile samples collected on post-operative day (POD) 1 (n = 38) and serum on POD 14 (n = 45) from liver transplant recipients. Partial least squares discriminant analysis-based variable selection was followed by logistic regression and least absolute shrinkage and selection operator models, which were evaluated via cross-validation in the discovery cohort to explore potential biomarkers for acute rejection. Results: A three-variable, bile-based model for predicting acute rejection achieved a mean cross-validated AUC of 0.872 (95% confidence interval: 0.814–0.930). Glycohyocholic acid and sulfolithocholylglycine were the main contributors. A nine-variable serum model for the Rejection Activity Index, including the change in γ-glutamyl transferase, showed a mean cross-validated R² of 0.728 (95% confidence interval: 0.609–0.846), with methionine, creatine, and oxidized fatty acids contributing prominently. Conclusions: These findings suggest that metabolomic profiling combined with machine learning may provide candidate biomarkers for acute rejection after liver transplantation. However, given the exploratory nature of the study and the lack of external validation, the clinical utility of these metabolite signatures remains to be determined. Therefore, external validation in larger, independent cohorts will be required. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

Background/Objectives: Bud sports (somatic mutations) offer a quick way to develop new bougainvillea varieties by altering specific traits while keeping the desirable genetic background of the original cultivar. However, we still lack a comprehensive understanding of their genomic architecture and the molecular mechanisms behind their formation. This study aimed to characterize the population genomic characteristics of bud sports derived from the commercial variety Bougainvillea × buttiana ‘Miss Manila’. Methods: We employed genotyping by sequencing (GBS) on 39 accessions, including 27 bud sports and 12 conventional varieties. Population genomic analyses, such as principal component analysis (PCA), phylogenetic reconstruction, ADMIXTURE, and diversity statistics (π, He, Tajima’s D), were performed on 64,810 high-quality SNPs. Genome-wide scans for differentiation (FST) and selective sweeps (XP-CLR) were also conducted. Results: Bud sports showed significantly lower genetic diversity (π and He) than conventional varieties, which matches their clonal origin. PCA, phylogenetic, and ADMIXTURE analyses (optimal K = 4) revealed clear genetic differentiation and distinct population structures between the two groups. The bud sport population possessed fewer private alleles and a less negative Tajima’s D value. Genomic scans identified regions under selection in bud sports, with functional annotation pointed to genes involved in ubiquitin-mediated proteolysis and RNA transport. Notably, Bou_119143 (UDP-rhamnose rhamnosyltransferase 1) showed a high mutation frequency specifically in bud sports. Conclusions: We provide the first population-genomic evidence that bud sports of ‘Miss Manila’ are genetically distinct clonal lineages, shaped by somatic mutation and selection. These findings support bud sports as efficient sources for germplasm innovation. The identified genomic regions and candidate genes lay a foundation for future marker-assisted selection and molecular breeding in bougainvillea. Full article

While existing research has established that air pollution-induced “avoidance behavior” significantly drives the growth of online food delivery volumes, the Average Order Value (AOV) remains unexplored. This study utilizes micro-transactional data provided by the store owner and employs machine learning algorithms to detect the impact of air quality (measured by the AQI) on online food delivery AOV and analyze the underlying consumer behavior. The findings indicate that: (1) Air quality deterioration significantly drives up the AOV. The global average response coefficient is 0.0053, showing a 2.4-fold acceleration effect once the AQI crosses the median (66). (2) Crucially, this growth stems from a directional divergence in consumer decision-making. Air pollution leads to the simultaneous occurrence of a reduction in average item quantity (impact coefficient: −0.0014) and a surge in Average Item Price (AIP) (impact coefficient: 0.0066). (3) Causal analysis further identifies a “substitution mechanism.” Specifically, every one-unit decrease in average item quantity induces a CNY 1.098 jump in average item price. These findings suggest a plausible behavioral logic where environmental stress may induce psychological fatigue but does not necessarily trigger “defensive frugality.” Instead, the observed pattern is consistent with a “decision avoidance” mode where consumers streamline item quantities; simultaneously, to hedge against potential experience risks resulting from simplified choices, they appear to utilize saved cognitive resources to target high-value “signature” items. Theoretically, this study fills the gap in environmental stress research regarding the price dimension of online consumption and reveals a behavioral evolution from “pure avoidance” to “value-oriented selection.” Practically, it provides empirical support for online food delivery merchants to optimize product selection, differentiate pricing, and implement precision marketing in dynamic environments. Full article

Background: DNA methylation alterations represent a key epigenetic mechanism linking environmental exposures to disease pathogenesis. The present study aimed to identify differentially methylated genes and shared biological processes associated with lung cancer (LuCa), chronic obstructive pulmonary disease (COPD) and tobacco exposure. Methods: A comprehensive literature search was performed in PubMed to identify studies evaluating DNA methylation in LuCa, COPD and smoking-related models. A total of 117 articles were selected, including 83 studies on lung cancer, 18 on COPD and 16 on smoking exposure. Genes exhibiting statistically significant methylation changes relative to controls were extracted from each study. To provide additional support for these findings, differential methylation signatures were further evaluated using The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) datasets. Functional and transcription factor motif enrichment analyses were subsequently conducted to identify shared biological pathways and regulatory mechanisms. Results: In total, 324 genes displaying altered methylation patterns across these conditions were identified. Seven tumor suppressor genes (CDKN2A, CDH13, MGMT, MIR137, DAPK1, RARB, and RASSF1A) consistently exhibited hypermethylation in both lung cancer and in association with smoking exposure. In addition, AHRR hypomethylation emerged as a shared epigenetic hallmark across all three conditions. TCGA-based analyses confirmed several of these methylation patterns and revealed subtype-specific methylation profiles associated with smoking history. Functional enrichment highlighted common biological processes and signaling pathways, particularly those related to transcriptional regulation, apoptosis and cancer-associated pathways. Conclusions: These results provide an integrative overview of shared DNA methylation alterations associated with smoking exposure, COPD, and lung cancer, and suggest potential DNA methylation candidates that may be relevant for future biomarker development and mechanistic studies. Full article