Search Results (772)

Background: Statins exhibit pleiotropic anti-inflammatory, antioxidant, and immunomodulatory effects, suggesting their potential in non-cardiovascular conditions. However, evidence supporting their repurposing remains limited, and off-label prescribing policies vary globally. Objective: To systematically review evidence on statin repurposing in oncology and infectious diseases, and to assess Hungarian regulatory practices regarding off-label statin use. Methods: A systematic literature search (PubMed, Web of Science, Scopus, ScienceDirect; 2010–May 2025) was conducted using the terms “drug repositioning” OR “off-label prescription” AND “statin” NOT “cardiovascular,” following PRISMA guidelines. Hungarian off-label usage data from the NNGYK (2008–2025) were also analyzed. Results: Out of 205 publications, 12 met the inclusion criteria—75% were oncology-focused, and 25% focused on infectious diseases. Most were preclinical (58%); only 25% offered strong clinical evidence. Applications included hematologic malignancies, solid tumors, Cryptococcus neoformans, SARS-CoV-2, and dengue virus. Mechanisms involved mevalonate pathway inhibition and modulation of host immune responses. Hungarian data revealed five approved off-label statin uses—three dermatologic and two pediatric metabolic—supported by the literature and requiring post-treatment reporting. Conclusions: While preclinical findings are promising, clinical validation of off-label statin use remains limited. Statins should be continued in cancer patients with cardiovascular indications, but initiation for other purposes should be trial-based. Future directions include biomarker-based personalization, regulatory harmonization, and cost-effectiveness studies. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Bone metastasis remains a significant cause of morbidity and diminished quality of life in patients with advanced breast, prostate, and lung cancers. Emerging research highlights the pivotal role of reversible epigenetic alterations, including DNA methylation, histone modifications, chromatin remodeling complex dysregulation, and non-coding RNA networks, in orchestrating each phase of skeletal colonization. Site-specific promoter hypermethylation of tumor suppressor genes such as HIN-1 and RASSF1A, alongside global DNA hypomethylation that activates metastasis-associated genes, contributes to cancer cell plasticity and facilitates epithelial-to-mesenchymal transition (EMT). Key histone modifiers, including KLF5, EZH2, and the demethylases KDM4/6, regulate osteoclastogenic signaling pathways and the transition between metastatic dormancy and reactivation. Simultaneously, SWI/SNF chromatin remodelers such as BRG1 and BRM reconfigure enhancer–promoter interactions that promote bone tropism. Non-coding RNAs, including miRNAs, lncRNAs, and circRNAs (e.g., miR-34a, NORAD, circIKBKB), circulate via exosomes to modulate the RANKL/OPG axis, thereby conditioning the bone microenvironment and fostering the formation of a pre-metastatic niche. These mechanistic insights have accelerated the development of epigenetic therapies. DNA methyltransferase inhibitors (e.g., decitabine, guadecitabine) have shown promise in attenuating osteoclast differentiation, while histone deacetylase inhibitors display context-dependent effects on tumor progression and bone remodeling. Inhibitors targeting EZH2, BET proteins, and KDM1A are now advancing through early-phase clinical trials, often in combination with bisphosphonates or immune checkpoint inhibitors. Moreover, novel approaches such as CRISPR/dCas9-based epigenome editing and RNA-targeted therapies offer locus-specific reprogramming potential. Together, these advances position epigenetic modulation as a promising axis in precision oncology aimed at interrupting the pathological crosstalk between tumor cells and the bone microenvironment. This review synthesizes current mechanistic understanding, evaluates the therapeutic landscape, and outlines the translational challenges ahead in leveraging epigenetic science to prevent and treat bone metastases. Full article

Background/Objectives: Hepatic cancers, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are major global health concerns due to rising incidence and limited therapeutic success. While traditional risk factors include chronic liver disease and environmental exposures, recent evidence underscores the significance of genetic alterations and gut microbiota in liver cancer development and progression. This review aims to integrate emerging knowledge on the interplay between host genomic changes and gut microbial dynamics in the pathogenesis and treatment of hepatic cancers. Methods: We conducted a comprehensive review of current literature on genetic and epigenetic drivers of HCC and CCA, focusing on commonly mutated genes such as TP53, CTNNB1, TERT, IDH1/2, and FGFR2. In parallel, we evaluated studies addressing the gut–liver axis, including the roles of dysbiosis, microbial metabolites, and immune modulation. Key clinical and preclinical findings were synthesized to explore how host–microbe interactions influence tumorigenesis and therapeutic response. Results: HCC and CCA exhibit distinct but overlapping genomic landscapes marked by recurrent mutations and epigenetic reprogramming. Alterations in the gut microbiota contribute to hepatic inflammation, genomic instability, and immune evasion, potentially enhancing oncogenic signaling pathways. Furthermore, microbiota composition appears to affect responses to immune checkpoint inhibitors. Emerging therapeutic strategies such as probiotics, fecal microbiota transplantation, and precision oncology based on mutational profiling demonstrate potential for personalized interventions. Conclusions: The integration of host genomics with microbial ecology provides a promising paradigm for advancing diagnostics and therapies in liver cancer. Targeting the gut–liver axis may complement genome-informed strategies to improve outcomes for patients with HCC and CCA. Full article

Natural compounds, particularly flavonoids, have emerged as promising anticancer agents due to their various biological activities and no or negligible toxicity towards healthy tissues. Among these, isorhamnetin, a methylated flavonoid, has gained significant attention for its potential to target multiple cancer hallmarks. This review comprehensively explores the mechanisms by which isorhamnetin exerts its anticancer effects, including cell cycle regulation, apoptosis, suppression of metastasis and angiogenesis, and modulation of oxidative stress and inflammation. Notably, isorhamnetin arrests cancer cell proliferation by regulating cyclins, and CDKs induce apoptosis via caspase activation and mitochondrial dysfunction. It inhibits metastatic progression by downregulating MMPs, VEGF, and epithelial–mesenchymal transition (EMT) markers. Furthermore, its antioxidant and anti-inflammatory properties mitigate reactive oxygen species (ROS) and pro-inflammatory cytokines, restricting cancer progression and modulating tumor microenvironments. Combining isorhamnetin with other treatments was also discussed to overcome multidrug resistance. Importantly, this review integrates the recent literature (2022–2024) and highlights isorhamnetin’s roles in modulating cancer-specific signaling pathways, immune evasion, tumor microenvironment dynamics, and combination therapies. We also discuss nanoformulation-based strategies that significantly enhance isorhamnetin’s delivery and bioavailability. This positions isorhamnetin as a promising adjunct in modern oncology, capable of improving therapeutic outcomes when used alone or in synergy with conventional treatments. The future perspectives and potential research directions were also summarized. By consolidating current knowledge and identifying critical research gaps, this review positions Isorhamnetin as a potent and versatile candidate in modern oncology, offering a pathway toward safer and more effective cancer treatment strategies. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background/Objectives: Increasing evidence suggests that lncRNAs are core regulators in the field of tumor progression, with context-specific functions in oncogenic tumorigenesis. LINC01133, a lncRNA that has been identified as both an oncogene and a tumor suppressor, remains largely unexplored in terms of its molecular mechanisms. The purpose of this study was to conduct an in silico analysis, incorporating literature research on various cancer types, to investigate the structural and functional duality of LINC01133. This analysis aimed to identify pathways influenced by LINC01133 and evaluate its mechanism of action as a potential therapeutic target and diagnostic biomarker. Methods: In silico analyses and a narrative review of the literature were performed to predict conserved structural elements, functional internal loops, and overall conservation of the LINC01133 sequence among different vertebrate organisms, summarizing the empirical evidence regarding its roles as a tumor suppressor and tumor-promoting roles in various types of tumors. Results: LINC01133 harbors the evolutionarily conserved structural regions that might allow for binding to relevant driver signaling pathways, substantiating its specific functionality. Its action extends beyond classical tumor mechanisms, affecting proliferation, migration, invasion, and epigenetic pathways in various types of tumors, as indicated by the in silico results and narrative review of the literature we present here. Clinical outcome associations pointed to its potential as a biomarker. Conclusions: The dual character of LINC01133 in tumor biology further demonstrates its prospective therapeutic value, but complete elucidation of its mechanisms of action requires further investigation. This study establishes LINC01133 as a multifaceted lncRNA, supporting context-specific strategies in targeting its pathways, and calls for expanded research to harness its full potential in oncology. Full article

Nuclear receptors (NRs) are ligand-activated transcription factors that regulate gene expression and are involved in diverse physiological and pathological processes, including carcinogenesis. In bladder cancer (BCa), dysregulation of NR signaling pathways has been linked to tumor initiation, progression, therapy resistance, and immune evasion. Recent evidence highlights the intricate crosstalk between NRs and microRNAs (miRNAs), which are small non-coding RNAs that posttranscriptionally modulate gene expression. This review provides an integrated overview of the molecular interactions between key NRs and miRNAs in BCa. We investigated how miRNAs regulate NR expression and function and, conversely, how NRs influence miRNA biogenesis, thereby forming regulatory feedback loops that shape tumor behavior. Specific miRNA–NR interactions affecting epithelial-to-mesenchymal transition, metabolic reprogramming, angiogenesis, and chemoresistance are discussed in detail. Additionally, we highlight therapeutic strategies targeting NR–miRNA networks, including selective NR modulators, miRNA mimics and inhibitors, as well as RNA-based combinatorial approaches focusing on their utility as diagnostic biomarkers and personalized treatment targets. Understanding the molecular complexity of NR–miRNA regulation in BCa may open new avenues for improving therapeutic outcomes and advancing precision oncology in urological cancers. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

The protein p53, often referred to as the “guardian of the genome,” is essential for preserving cellular balance and preventing cancerous transformations. As one of the most commonly altered genes in human cancers, its impaired function is associated with tumor initiation, development, and resistance to treatment. Exploring the diverse roles of p53, which include regulating the cell cycle, repairing DNA, inducing apoptosis, reprogramming metabolism, and modulating immunity, provides valuable insights into cancer mechanisms and potential treatments. This review integrates recent findings on p53′s dual nature, functioning as both a tumor suppressor and an oncogenic promoter, depending on the context. Wild-type p53 suppresses tumors by inducing cell cycle arrest or apoptosis in response to genotoxic stress, while mutated variants often lose these functions or gain novel pro-oncogenic activities. Emerging evidence highlights p53′s involvement in non-canonical pathways, such as regulating tumor microenvironment interactions, metabolic flexibility, and immune evasion mechanisms. For instance, p53 modulates immune checkpoint expression and influences the efficacy of immunotherapies, including PD-1/PD-L1 blockade. Furthermore, advancements in precision diagnostics, such as liquid biopsy-based detection of p53 mutations and AI-driven bioinformatics tools, enable early cancer identification and stratification of patients likely to benefit from targeted therapies. Therapeutic strategies targeting p53 pathways are rapidly evolving. Small molecules restoring wild-type p53 activity or disrupting mutant p53 interactions, such as APR-246 and MDM2 inhibitors, show promise in clinical trials. Combination approaches integrating gene editing with synthetic lethal strategies aim to exploit p53-dependent vulnerabilities. Additionally, leveraging p53′s immunomodulatory effects through vaccine development or adjuvants may enhance immunotherapy responses. In conclusion, deciphering p53′s complex biology underscores its unparalleled potential as a biomarker and therapeutic target. Integrating multi-omics analyses, functional genomic screens, and real-world clinical data will accelerate the translation of p53-focused research into precision oncology breakthroughs, ultimately improving patient outcomes. Full article

Metformin, a long-established antidiabetic agent, is undergoing a renaissance as a prototype gerotherapeutic and immunometabolic oncology adjuvant. Mechanistic advances reveal that metformin modulates an integrated network of metabolic, immunological, microbiome-mediated, and epigenetic pathways that impact the hallmarks of aging and cancer biology. Clinical data now demonstrate its ability to reduce cancer incidence, enhance immunotherapy outcomes, delay multimorbidity, and reverse biological age markers. Landmark trials such as UKPDS, CAMERA, and the ongoing TAME study illustrate its broad clinical impact on metabolic health, cardiovascular risk, and age-related disease trajectories. In oncology, trials such as MA.32 and METTEN evaluate its influence on progression-free survival and tumor response, highlighting its evolving role in cancer therapy. This review critically synthesizes the molecular underpinnings of metformin’s polypharmacology, examines results from pivotal clinical trials, and compares its effectiveness with emerging gerotherapeutics and senolytics. We explore future directions, including optimized dosing, biomarker-driven personalization, rational combination therapies, and regulatory pathways, to expand indications for aging and oncology. Metformin stands poised to play a pivotal role in precision strategies that target the shared roots of aging and cancer, offering scalable global benefits across health systems. Full article

The last decades of research have shown that the endocannabinoid system may be a promising therapeutic target for the pharmacological treatment of cancer in human medicine and possibly in veterinary medicine as well. Compared with the original cells, the expression of gene encoding for receptors and enzymes belonging to the endocannabinoid system has been found to be altered in several tumor types; it has been hypothesized that this aberrant expression may be related to the course of the neoplasm as well as to the patient’s prognosis. Several studies, conducted both in vitro and in vivo, suggest that both endo- and phytocannabinoids can modulate signaling pathways, controlling cell proliferation and survival. In the complex process of carcinogenesis, cannabinoids seem to intervene at different levels by stimulating cell death, inhibiting the processes of angiogenesis and metastasis, and regulating antitumor immunity. Although the molecular mechanisms by which cannabinoids act are not always clear and defined, their synergistic activity with the most used antineoplastic drugs in clinical oncology is showing promising results, thus providing veterinary medicine with alternative therapeutic targets in disease control. This review aims to summarize current knowledge on the potential role of the endocannabinoid system and exogenous cannabinoids in oncology, with specific reference to the molecular mechanisms by which cannabinoids may exert antitumor activity. Additionally, it explores the potential synergy between cannabinoids and conventional anticancer drugs and considers their application in veterinary oncology. Full article

Pancreatic cancer, specifically pancreatic ductal adenocarcinoma (PDAC), ranks among the most lethal malignancies, with a 5-year survival rate of under 10%. The most prevalent KRAS mutations occur in three hotspot residues: glycine-12 (G12), glycine-13 (G13), and glutamine-61 (Q61), leading to the constant activation of the Ras pathway, making them the primary focus in oncologic drug development. Selective KRAS G12C inhibitors (e.g., sotorasib, adagrasib) have demonstrated moderate efficacy in clinical trials; however, this mutation is infrequent in PDAC. Emerging therapies targeting KRAS G12D and G12V mutations, such as MRTX1133, PROTACs, and active-state inhibitors, show promise in preclinical studies. Pan-RAS inhibitors like ADT-007, RMC-9805, and RMC-6236 compounds provide broader coverage of mutations. Their efficacy and safety are currently being investigated in several clinical trials. A major challenge is the development of resistance mechanisms, including secondary mutations and pathway reactivation. Combination therapies targeting the RAS/MAPK axis, SHP2, mTOR, or SOS1 are under clinical investigation. Immunotherapy alone has demonstrated limited effectiveness, attributed to an immunosuppressive tumor microenvironment, although synergistic effects are noted when paired with KRAS-targeted agents. Furthermore, KRAS mutations reprogram cancer metabolism, enhancing glycolysis, macropinocytosis, and autophagy, which are being explored therapeutically. RNA interference technologies have also shown potential in silencing mutant KRAS and reducing tumorigenicity. Future strategies should emphasize the combination of targeted therapies with metabolic or immunomodulatory agents to overcome resistance and enhance survival in KRAS-mutated PDAC. Full article

Heat shock protein 90 (HSP90) is a molecular chaperone that plays a pivotal role in the stabilization and functional activation of numerous oncoproteins and signaling molecules essential for cancer cell survival and proliferation. Despite the extensive development and clinical evaluation of HSP90 inhibitors, their therapeutic potential as monotherapies has been limited by suboptimal efficacy, dose-limiting toxicity, and the emergence of drug resistance. Recent studies have demonstrated that combination therapies involving HSP90 inhibitors and other anticancer agents such as chemotherapeutics, targeted therapies, and immune checkpoint inhibitors can enhance anticancer activity, overcome resistance mechanisms, and modulate the tumor microenvironment. These synergistic effects are mediated by the concurrent degradation of client proteins, the disruption of signaling pathways, and the enhancement of antitumor immunity. However, the successful clinical implementation of such combination strategies requires the careful optimization of dosage, administration schedules, toxicity management, and patient selection based on predictive biomarkers. In this review, we provide a comprehensive overview of the mechanistic rationale, preclinical and clinical evidence, and therapeutic challenges associated with HSP90 inhibitor-based combination therapies. We also discuss future directions leveraging emerging technologies including multi-omics profiling, artificial intelligence, and nanoparticle-mediated delivery for the development of personalized and effective combination regimens in oncology. Full article

Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods—spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology—enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease. Full article