Search Results (366)

Background: Emerging evidence suggests that biological sex shapes glioma biology and therapeutic response. Methods: We performed a sex-stratified analysis of CGGA (Chinese Glioma Genome Atlas) RNA sequencing data comparing low-grade glioma (LGG) with high-grade glioma (HGG) and glioblastoma (GBM). Using the 3PodR framework, we integrated differential expression analysis with Gene Set Enrichment Analysis (GSEA), EnrichR, leading-edge analysis, and iLINCS drug repurposing. Results: These comparisons provide a proxy for biological processes underlying malignant transformation. In LGG vs. HGG, 973 significantly differentially expressed genes (DEGs) were identified in females and 1236 in males, with 15.5% and 33.5% unique to each sex, respectively. In LGG vs. GBM, 2011 DEGs were identified in females and 2537 in males, with 12.6% and 30.7% being unique. Gene-level contrasts included GLI1 upregulation in males and downregulation in females, GCGR upregulation in males, MYOD1 upregulation in females, and HIST1H2BH downregulation in males. Additional top DEGs included PRLHR, DGKK, DNMBP-AS1, HOXA9, CTB-1I21.1, RP11-47I22.1, HPSE2, SAA1, DLK1, H19, PLA2G2A, and PI3. In both sexes, LGG–HGG and LGG–GBM grade comparisons converged on neuronal and synaptic programs, with enrichment of glutamatergic receptor genes and postsynaptic modules, including GRIN2B, GRIN2A, GRIN2C, GRIN1, and CHRNA7. In contrast, collateral pathways diverged by sex: females showed downregulation of mitotic and chromosome-segregation programs, whereas males showed reduction of extracellular matrix and immune-interaction pathways. Perturbagen analysis nominated signature-reversing compounds across sexes, including histone deacetylase inhibitors, Aurora kinase inhibitors, microtubule-targeting agents such as vindesine, and multi-kinase inhibitors targeting VEGFR, PDGFR, FLT3, PI3K, and MTOR. Conclusions: Glioma grade comparisons reveal a shared neuronal–synaptic program accompanied by sex-specific transcriptional remodeling. These findings support sex-aware therapeutic strategies that pair modulation of neuron–glioma coupling with chromatin- or receptor tyrosine kinase/angiogenic-targeted agents, and they nominate biomarkers such as GLI1, MYOD1, GCGR, PRLHR, and HIST1H2BH for near-term validation. Full article

The integration of molecular features into histopathological diagnoses has become central to the World Health Organization (WHO) classification of central nervous system (CNS) tumors, improving prognostic accuracy and supporting precision medicine. However, unequal access to molecular testing limits the universal application of integrated diagnosis. To address this, artificial intelligence (AI) models are being developed to predict molecular alterations directly from histological data. In gliomas, deep learning applied to whole-slide images (WSIs) of permanent sections achieves neuropathologist-level accuracy in predicting biomarkers such as IDH mutation and 1p/19q co-deletion, as well as in molecular subtype classification and outcome prediction. Recent advances extend these approaches to intraoperative cryosections, enabling real-time glioma grading, molecular prediction, and label-free tissue analysis using modalities such as stimulated Raman histology and domain-adaptive image translation. Beyond gliomas, AI-powered histology is being explored in other brain tumors, including morphology-based molecular classification of spinal cord ependymomas and intraoperative discrimination of gliomas from primary CNS lymphomas. This review summarizes current progress in AI-assisted molecular profiling prediction of brain tumors from tissue, highlighting opportunities for rapid, accurate, and globally accessible diagnostics. The integration of histology and computational methods holds promise for the development of smart AI-assisted neuro-oncology. Full article

Glioblastoma (GBM) is one of the most common and aggressive primary malignant tumors of the central nervous system, accounting for about half of all gliomas in adults. Despite intensive research and advances in molecular biology, genomics, and modern neuroimaging techniques, the prognosis for patients with GBM remains extremely poor. Despite the implementation of multimodal treatment involving surgery, radiotherapy, and chemotherapy with temozolomide, the average survival time of patients is only about 15 months. This is primarily due to the complex biology of this cancer, which involves numerous genetic and epigenetic abnormalities, as well as a highly heterogeneous tumor structure and the presence of glioblastoma stem cells with self renewal capacity. Mutations and abnormalities in genes such as IDH-wt, EGFR, PTEN, TP53, TERT, and CDKN2A/B are crucial in the pathogenesis of GBM. In particular, IDH-wt status (wild-type isocitrate dehydrogenase) is one of the most important identification markers distinguishing GBM from other, more favorable gliomas with IDH mutations. Frequent EGFR amplifications and TERT gene promoter mutations lead to the deregulation of tumor cell proliferation and increased aggressiveness. In turn, the loss of function of suppressor genes such as PTEN or CDKN2A/B promotes uncontrolled cell growth and tumor progression. The immunosuppressive tumor microenvironment also plays an important role, promoting immune escape and weakening the effectiveness of systemic therapies, including immunotherapy. The aim of this review is to summarize the current state of knowledge on the epidemiology, classification, pathogenesis, diagnosis, and treatment of glioblastoma multiforme, as well as to discuss the impact of recent advances in molecular and imaging diagnostics on clinical decision-making. A comprehensive review of recent literature (2018–2025) was conducted, focusing on WHO CNS5 classification updates, novel biomarkers (IDH, TERT, MGMT, EGFR), and modern diagnostic techniques such as liquid biopsy, radiogenomics, and next-generation sequencing (NGS). The results of the review indicate that the introduction of integrated histo-molecular diagnostics in the WHO 2021 classification has significantly increased diagnostic precision, enabling better prognostic and therapeutic stratification of patients. Modern imaging techniques, such as advanced magnetic resonance imaging (MRI), positron emission tomography (PET), and radiomics and radiogenomics tools, allow for more precise assessment of tumor characteristics, prediction of response to therapy, and monitoring of disease progression. Contemporary molecular techniques, including DNA methylation profiling and NGS, enable in-depth genomic and epigenetic analysis, which translates into a more personalized approach to treatment. Despite the use of multimodal therapy, which is based on maximum safe tumor resection followed by radiotherapy and temozolomide chemotherapy, recurrence is almost inevitable. GBM shows a high degree of resistance to treatment, which results from the presence of stem cell subpopulations, dynamic clonal evolution, and the ability to adapt to unfavorable microenvironmental conditions. Promising preclinical and early clinical results show new therapeutic strategies, including immunotherapy (cancer vaccines, checkpoint inhibitors, CAR-T therapies), oncolytic virotherapy, and Tumor Treating Fields (TTF) technology. Although these methods show potential for prolonging survival, their clinical efficacy still needs to be confirmed in large studies. The role of artificial intelligence in the analysis of imaging and molecular data is also increasingly being emphasized, which may contribute to the development of more accurate predictive models and therapeutic decisions. Despite these advancements, GBM remains a major therapeutic challenge due to its high heterogeneity and treatment resistance. The integration of molecular diagnostics, artificial intelligence, and personalized therapeutic strategies that may enhance survival and quality of life for GBM patients. Full article

Background and Objectives: Grade 4 adult-type diffuse gliomas remain the most aggressive primary central nervous system malignancies, with limited prognostic tools beyond molecular classification. This study introduces the HALLMOUNT score, a multidimensional prognostic index integrating hematologic, biochemical, and clinical parameters to capture the interplay between tumor biology and systemic host response. Materials and Methods: A total of 227 patients with histologically confirmed grade 4 adult-type diffuse glioma were retrospectively analyzed. The HALLMOUNT score incorporated nine pretreatment variables: hemoglobin, albumin, lactate dehydrogenase (LDH), lymphocyte, monocyte, Eastern Cooperative Oncology Group (ECOG) performance status, uric acid, neutrophil, and thrombocyte counts. Receiver operating characteristic (ROC) analyses determined optimal cut-offs, and Cox regression models evaluated prognostic performance for overall (OS) and progression-free survival (PFS). Results: High HALLMOUNT scores (≥2.5) were significantly associated with older age, comorbidities, poor ECOG status, isocitrate dehydrogenase (IDH)-wild phenotype, lower resection rates, and reduced treatment responses. ROC analysis showed predictive accuracy comparable to CAR and PIV (AUC = 0.650). High scores independently predicted inferior OS (HR = 2.78, p < 0.001) and PFS (HR = 2.76, p < 0.001). Conclusions: The HALLMOUNT score provides a simple, cost-effective, and biologically grounded biomarker reflecting both tumor aggressiveness and host vulnerability. It enables refined risk stratification, supports individualized therapeutic planning, and warrants prospective validation in molecularly defined and multicenter cohorts. Full article

High-grade gliomas—particularly glioblastoma (GBM)—remain refractory to standard-of-care surgery followed by chemoradiation, with a median overall survival of ~15 months. Oncolytic viruses (OVs), which selectively infect and lyse tumor cells while engaging antitumor immunity, offer a mechanistically distinct therapeutic modality. This review synthesizes clinical progress of OVs in GBM, with emphasis on oncolytic herpes simplex virus (oHSV) and coverage of other vectors (adenovirus, reovirus, Newcastle disease virus, vaccinia virus) across phase I–III trials, focusing on efficacy and safety. Key observations include the encouraging clinical trajectory of oHSV exemplars—T-VEC (approved for melanoma) and G47Δ (approved in Japan for recurrent GBM)—the multi-center exploration of the adenovirus DNX-2401 combined with programmed death-1 (PD-1) blockade, and the early-stage status of reovirus (pelareorep) and Newcastle disease virus programs. Emerging evidence indicates that oHSV therapy augments immune infiltration within the tumor microenvironment and alleviates immunosuppression, with synergy when combined with chemotherapy or immune checkpoint inhibitors. Persistent challenges include GBM’s inherently immunosuppressive milieu, limitations imposed by the blood–brain barrier, intrapatient viral delivery and biodistribution, and concerns about viral shedding. Future directions encompass programmable vector design, optimization of systemic delivery, biomarker-guided patient selection, and rational combination immunotherapy. Collectively, OVs represent a promising immunotherapeutic strategy in GBM; further gains will hinge on vector engineering and precision combinations to translate mechanistic promise into durable clinical benefit. Full article

MicroRNA-221 (miR-221), a conserved small non-coding RNA, acts as a pivotal modulator of biological processes across multiple organ systems, the dysregulation of which is closely linked to the pathogenesis of various human diseases. This review systematically summarizes its multifaceted roles in cancer, cardiovascular diseases (CVDs), neurological disorders, digestive system diseases, respiratory conditions, and adipose-endocrine dysfunction. In cancer, miR-221 exerts context-dependent oncogenic/tumor-suppressive effects by targeting phosphatase and tensin homolog (PTEN), cyclin-dependent kinase inhibitor 1c (CDKN1C/p57), and BCL2 modifying factor (Bmf), thereby regulating cell proliferation, invasion, stemness, and resistance to cancer therapy; it also serves as a non-invasive biomarker for glioma, papillary thyroid carcinoma, and colorectal cancer. In the cardiovascular system, it balances antiviral defense in viral myocarditis, modulates ventricular fibrotic remodeling in heart failure, and regulates endothelial function in atherosclerosis, with cell-type/ventricle-specific effects. In neurological disorders, it protects dopaminergic neurons in Parkinson’s disease and modulates microglial activation in epilepsy. It also regulates hepatic pathogen defense and intestinal mucosal immunity. Mechanistically, miR-221 alters cellular phenotypes by targeting tumor suppressors or signaling components (e.g., PI3K/AKT, TGF-β/suppressor of mothers against decapentaplegic homolog(SMAD), Wnt/β-catenin). Therapeutically, miR-221-targeting strategies show preclinical promise in cancer and CVDs. Despite this progress, further studies are needed to resolve context-dependent functional discrepancies, validate biomarker utility, and develop cell-specific delivery systems. This review provides a framework to understand its pathophysiologcial roles and potential application as a biomarker and therapeutic target. Full article

Survivin (BIRC5) plays a key role in inhibiting apoptosis and is highly expressed in many cancers, including gliomas and breast cancer, where it contributes to tumor progression, therapeutic resistance and poor patient outcomes. With a dual function in promoting cell proliferation and survival, coupled with its potential immunogenicity, survivin is a compelling therapeutic target for cancer; yet, it has no FDA-approved agents to date. Here, we review key findings from preclinical models that emphasize how survivin contributes to chemoresistance and radioresistance; summarize the clinical landscape of survivin-targeted strategies, highlighting both the successes and limitations of these approaches; and outline next steps to optimize survivin-targeted therapies, including the need to integrate biomarker-focused patient selection and the potential for combination therapies. These insights establish survivin as a key driver of cancer progression and a promising target for future therapeutic development. Full article

Background/Objectives: Lower-grade gliomas (LGGs) are a biologically and clinically heterogeneous group of brain tumors, for which molecular stratification plays essential role in diagnosis, prognosis, and therapeutic decision-making. Conventional unimodal classifiers do not necessarily describe cross-layer regulatory dynamics which entail the heterogeneity of glioma. Methods: This paper presents a protein–protein interaction (PPI)-informed hybrid model that combines multi-omics profiles, including RNA expression, DNA methylation, and microRNA expression, with a Graph Attention Network (GAT), Random Forest (RF), and logistic stacking ensemble learning. The proposed model utilizes ElasticNet-based feature selection to obtain the most informative biomarkers across omics layers, and the GAT module learns the biologically significant topological representations in the PPI network. The Synthetic Minority Over-Sampling Technique (SMOTE) was used to mitigate the class imbalance, and the model performance was assessed using a repeated five-fold stratified cross-validation approach using the following performance metrics: accuracy, precision, recall, F1-score, ROC-AUC, and AUPRC. Results: The findings illustrate that a combination of multi-omics data increases subtype classification rates (up to 0.984 ± 0.012) more than single-omics methods, and DNA methylation proves to be the most discriminative modality. In addition, analysis of interpretability using attention revealed the major subtype-specific biomarkers, including UBA2, LRRC41, ANKRD53, and WDR77, that show great biological relevance and could be used as diagnostic and therapeutic tools. Conclusions: The proposed multi-omics based on a biological and explainable framework provides a solid computational approach to molecular stratification and biomarker identification in lower-grade glioma, bridging between predictive power, biological clarification, and clinical benefits. Full article

Diffuse intrinsic pontine glioma (DIPG), or as it is newly redefined, diffuse midline glioma (DMG), remains one of the most horrific diagnoses in pediatric oncology. Aggressive and inaccessible to standard treatments, it is generally considered incurable. Focused ultrasound technology has developed over the last several decades as a noninvasive means to target various types of tumors in both adults and children. Recent advances, particularly in low-intensity focused ultrasound (LIFU), have opened new avenues for enhancing drug delivery and modulating the tumor microenvironment in these challenging tumors. This review provides a comprehensive overview of preclinical and clinical research developments in the use of LIFU for pediatric DMGs. We highlight key findings from animal models demonstrating improved blood–brain barrier (BBB) permeability, increased chemotherapeutic and nanoparticle delivery, and potential immunomodulatory effects of LIFU. Emerging clinical studies, including early-phase safety and feasibility trials, are also discussed, with attention to technical parameters, imaging guidance strategies, and biomarkers of response. The review concludes by addressing the challenges of translating LIFU into routine clinical practice, including device optimization for pediatric anatomy, regulatory hurdles, and the need for standardized treatment protocols. Collectively, these recent advances underscore the promise of LIFU as a minimally invasive, image-guided adjunct to current and future therapies for pediatric DMGs, warranting continued research and collaborative clinical efforts. Full article

Objectives: Cancer, characterized by its profound complexity and heterogeneity, arises from a multitude of molecular disruptions. The pursuit of identifying distinct cancer subtypes is driven by the need to stratify patients into clinically coherent subgroups, each exhibiting unique prognostic outcomes. The integration of multi-omics datasets enhances the precision of subtyping and advances precision medicine. Methods: Considering the high-dimensional nature inherent to various multi-omics data types, we introduce an innovative deep learning framework, DAE-MKL, which integrates denoising autoencoders with multi-kernel learning for identifying cancer subtypes. Leveraging the capabilities of DAE, we extract non-linearly transformed features that retain pertinent information while mitigating noise and redundancy. These refined data representations are then funneled into the MKL framework, thereby enhancing the accuracy of subtype identification. We applied the DAE-MKL framework to both simulated studies and empirical datasets derived from two distinct cancer types, low-grade glioma (LGG, $n$ = 86) and kidney renal clear cell carcinoma (KIRC, $n$ = 285), thereby validating its utility and feasibility. Results: In simulations, DAE-MKL achieved superior performance with NMI gains up to 0.78 compared to other state-of-the-art methods. For real datasets, DAE-MKL identified three LGG subtypes and three KIRC subtypes, showing significant survival differences (KIRC log-rank $p$ = 3.33 × 10⁻⁸, LGG log-rank $p$ = 3.99 × 10⁻⁸). Additionally, we explored potential cancer-related biomarkers. Conclusions: The DAE-MKL effectively identifies molecular subtypes, reduces data dimensionality, and improves prognostic stratification in multi-omics cancer datasets, providing an effective tool for precision oncology. Full article

Background/Objectives: Lower-grade gliomas, particularly IDH-mutant astrocytomas, represent a distinct molecular subtype with unique therapeutic challenges. Whole-genome sequencing (WGS) plays a crucial role in uncovering genetic alterations that drive glioma pathogenesis and therapeutic resistance. This study identifies and evaluates a novel GATA2 p.Arg396Trp mutation in a clinical sample of lower-grade glioma, assessing its structural impact and implications for drug binding. Methods: A WHO Grade II astrocytoma specimen from a 33-year-old female patient was analyzed using WGS with Oxford Nanopore sequencing, followed by comprehensive bioinformatics processing to identify genomic variants. The GATA2 p.Arg396Trp mutation was evaluated using protein modeling, structural analysis, and pathogenicity prediction tools. Drug affinity analysis was conducted using molecular docking simulations to assess the computational impact of the mutation on drug binding. Results: The GATA2 p.Arg396Trp mutation was identified as a computationally predicted pathogenic variant, potentially disrupting protein interactions within critical functional domains. Structural analysis revealed altered binding dynamics with key anti-neoplastic agents, suggesting potential implications for therapeutic response. These findings represent computational predictions requiring experimental validation. Conclusions: Our preliminary findings suggest a potential role of the GATA2 p.Arg396Trp mutation in lower-grade glioma pathogenesis. The mutation predicted impact on transcriptional regulation and drug affinity suggests GATA2 as a possible biomarker candidate. Extensive experimental validation in larger patient cohorts is needed to establish clinical relevance and explore targeted therapeutic strategies. Full article

Background: Enhancer RNAs (eRNAs), a subclass of long non-coding RNAs transcribed from enhancer regions, have emerged as dynamic regulators of gene expression, tumor progression, and therapeutic response. In gliomas, their biological and clinical significance is only recently being elucidated. This systematic review aimed to synthesize current evidence regarding the role of eRNAs in gliomagenesis, chemoresistance, and prognosis. Methods: We conducted a systematic review following PRISMA 2020 guidelines. PubMed/MEDLINE and Scopus databases were searched on September 2025 using a predefined strategy. Eligible studies included clinical or pre-clinical analyses of eRNAs in gliomas, reporting associations with tumorigenicity, survival, or resistance to temozolomide (TMZ). Risk of bias was assessed using ROBINS-I (Version 2), and findings were qualitatively synthesized. Results: From 26 retrieved records, 10 studies were included, encompassing 22 unique eRNAs. Two studies demonstrated that TMZR1-eRNA and LINC02454* modulate TMZ sensitivity by regulating STAT3, SORBS2, and DDR1 pathways. Seven studies evaluated prognostic implications: 12 eRNAs (e.g., AC003092.1, CYP1B1-AS1, CRNDE) were consistently associated with poor survival, while seven (e.g., LINC00844, ENSR00000260547) correlated with favorable outcomes, particularly in low-grade gliomas. One mechanistic study showed that HOXDeRNA directly promotes gliomagenesis by displacing PRC2 repression at key transcription factor promoters and activating oncogenic super-enhancers. Conclusions: eRNAs are not passive transcriptional by-products but active modulators of glioma biology. They influence tumor initiation, therapeutic resistance, and survival outcomes, underscoring their potential as prognostic biomarkers and therapeutic targets. Future research should validate these findings in larger clinical cohorts and explore strategies for eRNA-directed therapies in precision neuro-oncology. Full article

Gliomas are aggressive brain tumours with diverse histological and molecular features, complicating accurate diagnosis and treatment. Dysregulated lipid metabolism contributes to glioma progression, and analysing lipid profiles in plasma and tissue may enhance diagnostic and prognostic accuracy. This study investigated lipid dysregulation to identify key lipid signatures that distinguish glioma from other brain diseases and examined the associations between lipid biomarkers in glioma tissue and plasma. Biospecimens from 11 controls and 72 glioma patients of varying grades underwent lipidomic profiling using liquid chromatography-mass spectrometry. Univariate and multivariate analyses identified differentially abundant lipids, and correlation analysis evaluated the associations between tissue and plasma biomarkers. Lipidomic analysis revealed distinct lipid profiles in the tissues and plasma of glioma patients compared to those of controls. Prominent lipid metabolites in glioma tissues included LPC 21:3 (AUC = 0.925), DG 43:11 (AUC = 0.906), and PC 33:1 (AUC = 0.892), which served as effective biomarkers. Conversely, in plasma, lipid metabolites such as phosphatidylethanolamine (PE 21:3, AUC = 0.862), ceramide-1-phosphate (CerP 26:1, AUC = 0.861), and sphingomyelin (SM 24:3, AUC = 0.858) were identified as the most promising lipid biomarkers. Significant positive and negative correlations were observed between the tissue and plasma lipid biomarkers of glioma patients. Lipidomic profiling revealed aberrant lipid classes and pathways in glioma tissues and plasma, enhancing understanding of glioma heterogeneity and potential clinical applications. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Background/Objectives: Galectin-3 (Gal-3), encoded by LGALS3, is a β-galactoside-binding lectin involved in diverse tumor-associated processes, including immune modulation, cell cycle regulation, and stress adaptation. Despite its known roles in cancer biology, the full extent of its molecular functions and prognostic relevance across tumor types remains incompletely understood. This study aimed to systematically investigate the transcriptomic impact of LGALS3 deletion and assess its clinical significance in cancer. Methods: We analyzed CRISPR-Cas9 knockout transcriptomic data from the SigCom LINCS database to characterize the consensus gene signature associated with LGALS3 loss using functional enrichment analyses. Pan-cancer survival analyses were conducted using TIMER2.0. Differential Gal-3 protein levels in ductal adenocarcinoma and normal pancreatic tissues were evaluated using the Human Protein Atlas. Finally, functional analyses were performed in pancreatic ductal adenocarcinoma (PDAC). Results: LGALS3 deletion across multiple cancer cell lines led to transcriptomic changes involving mitotic progression, stress responses, and axonal guidance pathways. High LGALS3 expression was significantly associated with worse overall survival in lower-grade glioma, PDAC, uveal melanoma, and kidney renal papillary cell carcinoma. LGALS3 knockout in YAPC cells recapitulated the pan-cancer findings, linking LGALS3 to cell morphogenesis and proliferation. Conclusions: These findings identify Galectin-3 as a key regulator of oncogenic programs and a potential prognostic biomarker in PDAC and other malignancies, with implications for therapeutic targeting. Full article