Search Results (6,123)

Gut microbiota changes have emerged as central players in the pathogenesis of both metabolic dysfunction-associated steatohepatitis (MASH) and inflammatory bowel disease (IBD). Although these diseases affect distinct primary organs, they share converging mechanisms driven by dysbiosis, including loss of beneficial short-chain fatty acid-producing taxa such as Faecalibacterium prausnitzii and Roseburia, enrichment of pro-inflammatory Enterobacteriaceae, and disruption of bile acid and tryptophan metabolism. These shifts compromise epithelial barrier integrity, promote the translocation of microbial products such as lipopolysaccharide, and trigger toll-like receptor 4-mediated activation of inflammatory cascades dominated by tumor necrosis factor-alpha, interleukin-6, and transforming growth factor-beta. In MASH, this dysbiotic environment fuels hepatic inflammation, insulin resistance, and fibrogenesis, while in IBD it sustains chronic mucosal immune activation. Shared features include impaired butyrate availability, altered bile acid pools affecting farnesoid X receptor and Takeda G protein-coupled Receptor 5 signaling, and defective aryl hydrocarbon receptor activation, all of which link microbial dysfunction to host metabolic and immune dysregulation. Understanding these overlapping pathways provides a deeper understanding of the role of the gut-liver and gut-immune axes as unifying frameworks in disease progression. This narrative review synthesizes current evidence on gut microbiota in MASH and IBD, underscoring the need for longitudinal, multi-omics studies and microbiome-targeted strategies to guide personalized therapeutic approaches. Full article

Background: The incidence and prognosis of high-grade gliomas differ according to histopathological and molecular features. The WHO 2021 CNS classification emphasized IDH status, but historical cohorts often lacked systematic molecular profiling. Methods: We conducted a retrospective population-based study including adult patients diagnosed with IDH-wildtype glioblastoma or IDH-mutant astrocytoma in a Spanish tertiary center (2011–2021). Incidence trends and survival outcomes were analyzed according to treatment modality and sex. Results: A total of 1057 patients were included: 530 (50.1%) with IDH-wildtype glioblastoma and 137 (13%) with IDH-mutant astrocytoma. Incidence of both subtypes significantly increased during the study period (p < 0.01). Median overall survival (OS) was 12.3 months for IDH-wildtype glioblastoma and 38.4 months for IDH-mutant astrocytoma. Multimodal therapy (surgery, radiotherapy, chemotherapy) significantly improved OS and progression-free survival (PFS) in both subgroups (p < 0.001). Male sex was associated with longer OS in both tumor types (p < 0.05). Conclusions: IDH-wildtype glioblastoma shows persistently poor outcomes despite increasing incidence, while IDH-mutant astrocytoma demonstrates better survival, particularly in male patients and those receiving multimodal therapy. These findings reflect real-world practice and provide epidemiological and survival data from Southern Europe to guide future clinical and public health strategies. Full article

The tumor microenvironment (TME) is characterized by hypoxia; acidic pH; oxidative stress; and immune suppression; all of which severely impair the efficacy of conventional cancer therapies. Recent advances in inorganic nanotechnology have led to the development of smart nanomaterials capable of modulating these abnormal features; thereby reprogramming the TME toward a more therapy-responsive state. Inorganic nanomaterials such as manganese dioxide; iron oxide; and cerium oxide can selectively alleviate hypoxia; buffer acidity; regulate redox balance; and even stimulate anti-tumor immunity through catalytic or structural mechanisms. These materials can further serve as carriers for stimuli-responsive drug delivery; enabling synergistic therapies that include chemodynamic; photothermal; and immunomodulatory treatments. This review summarizes recent developments in smart inorganic nanomaterials for TME modulation; discusses design considerations including biosafety and biodegradability; and evaluates the current translational status and future directions. Such strategies represent a promising leap toward precise and personalized cancer nanomedicine Full article

Background/Objectives: Short inverted repeats (SIRs) are abundant DNA motifs capable of forming secondary structures, such as hairpins and cruciforms, that can induce genome instability. However, their mutational consequences in cancer, particularly in osteosarcoma (OS), remain largely unexplored. Methods: In this study, we systematically identified over 5.2 million SIRs in the human genome and analyzed their mutational patterns across six common cancer types. Results: We found that increased small insertion and deletion (INDEL) density within SIR spacer regions represents a consistent feature across cancers, whereas elevated single nucleotide variant (SNV) and structural breakpoint density is cancer-type specific. Integrating whole-genome sequencing data from 13 OS patients, we found that both SNVs and INDELs are significantly enriched within SIR spacer regions in OS. Notably, genomic regions with higher SIR density tend to accumulate more somatic mutations, suggesting a link between SIR abundance and local genome instability. SIR-associated mutations frequently occur in oncogenes and tumor suppressor genes, including TP53, NFATC2, MECOM, LRP1B, RB1, CNTNAP2, and PTPRD, as well as in long non-coding RNAs. Mutational signature analysis further suggests that defective DNA mismatch repair and homologous recombination may act in concert with SIR-induced DNA structural instability to drive OS development. Conclusions: Our findings highlight SIRs as mutational hotspots and potential drivers of osteosarcoma pathogenesis. Full article

Objectives: Employing 18F-FDG PET/CT radiomic properties both within and surrounding tumors, in conjunction with clinical attributes, to precisely differentiate among several pathological subtypes of non-small-cell lung cancer (NSCLC). Approaches: The study comprised 222 patients who received 18F-FDG PET/CT scans from January 2015 to December 2020 and were later diagnosed with NSCLC, encompassing 169 cases of lung adenocarcinoma (LUAD) and 53 cases of lung squamous cell carcinoma (LUSC). They were arbitrarily allocated into a training group and a validation group in a 7:3 ratio. Radiomics feature extraction was conducted on 18F-FDG PET/CT images of primary tumors and adjacent tumor regions with LIFE-x (5.2.0). A multivariate logistic regression analysis was employed to develop a nomogram for differentiating lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC). The clinical efficacy of each model was assessed and contrasted utilizing accuracy (Acc), sensitivity (Sen), specificity (Spe), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Outcomes: The nomogram model that integrates 18F-FDG PET/CT radiomics features with clinical characteristics showed superior efficacy in differentiating adenocarcinoma from squamous cell carcinoma in NSCLC patients, surpassing models based only on PET or CT radiomics. The validation set exhibited an Area under curve (AUC) of 0.880, an Acc of 0.929, a Sen of 0.808, and a Spe of 0.962. This model exhibits the most superior overall performance in DCA. Conclusions: A nomogram model integrating radiomic features derived from 18F-FDG PET/CT images of tumors and adjacent tissues with clinical characteristics can effectively differentiate between LUAD and LUSC. Full article

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults. This review explains the connections between the genesis and progression of GBM and particular cellular tumorigenic mechanisms, such as angiogenesis, invasion, migration, growth factor overexpression, genetic instability, and apoptotic disorders, as well as possible therapeutic targets that help predict the course of the disease. Glioblastoma multiforme (GBM) diagnosis relies heavily on histopathological features, molecular markers, extracellular vesicles, neuroimaging, and biofluid-based glial tumor identification. In order to improve miRNA stability and stop the proliferation of cancer cells, nanoparticles, magnetic nanoparticles, contrast agents, gold nanoparticles, and nanoprobes are being created for use in cancer treatments, neuroimaging, and biopsy. Targeted nanoparticles can boost the strength of an MRI signal by about 28–50% when compared to healthy tissue or controls in a preclinical model like mouse lymph node metastasis. Combining the investigation of CNAs and noncoding RNAs with deep learning-driven global profiling of genes, proteins, RNAs, miRNAs, and metabolites presents exciting opportunities for creating new diagnostic markers for malignancies of the central nervous system. Artificial intelligence (AI) advances precision medicine and cancer treatment by enabling the real-time analysis of complex biological and clinical data through wearable sensors and nanosensors; optimizing drug dosages, nanomaterial design, and treatment plans; and accelerating the development of nanomedicine through high-throughput testing and predictive modeling. Full article

Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor, characterized by rapid proliferation, extensive vascularization, and resistance to conventional therapies. A feature of the GBM microenvironment is hypoxia, which activates a wide range of adaptive responses orchestrated mainly by the hypoxia-inducible factor (HIF). The Von Hippel–Lindau protein (pVHL) is a central regulator of HIF stability, inducing proteasomal degradation under physiological conditions. However, in GBM, the pVHL is frequently mutated or functionally inactivated by several mechanisms, including microRNA regulation, post-translational modifications, or degradation by specific E3 ubiquitin ligases. This loss of function results in persistent HIF activation, thereby enhancing the oncogenic and pro-angiogenic environment that contributes to the progression and aggressiveness of GBM. This review focuses on the multifaceted roles of the pVHL-HIF axis and proposes it as a key driver of GBM malignancy. Full article

The tumor microenvironment (TME) plays a crucial role in regulating cancer cell proliferation, invasion, and drug resistance. Traditional two-dimensional (2D) in vitro models and animal models often fail to replicate the biochemical and biophysical complexity of human tumors, leading to low predictive power in preclinical drug screening. In recent years, scaffold-based three-dimensional (3D) in vitro models have emerged as promising alternatives, offering a more physiologically relevant context for studying tumor behavior. Among these, biomimetic scaffolds capable of replicating the composition, stiffness, porosity, and signaling features of the tumor extracellular matrix (ECM) are of particular interest. This review provides a comprehensive overview of scaffold-based approaches for mimicking the TME in vitro. After outlining the key characteristics of the tumor ECM, we discuss various scaffold typologies, including those based on natural, synthetic, and hybrid biomaterials, as well as decellularized ECM. Recent advancements in fabrication technologies, such as electrospinning and 3D bioprinting, are also highlighted for their role in replicating the geometric and mechanical features of tumor tissues. Special attention is given to the integration of vascular components and stromal cells to recapitulate the complexity of the TME. Finally, we explore current limitations and future directions, emphasizing the need for standardized and reproducible models, particularly in the context of personalized cancer therapy. Full article

The integration of multi-omics technologies is transforming the landscape of cancer management, offering unprecedented insights into tumor biology, early diagnosis, and personalized therapy. This review provides a comprehensive overview of the current state of omics approaches, with a particular focus on the application of genomics, NMR-based metabolomics, and radiomics in non-small cell lung cancer (NSCLC). Genomics currently represents one of the most established omics technologies in oncology, as it enables the identification of genetic alterations that drive tumor initiation, progression, and therapeutic response. Interestingly, genomic analyses have revealed that many tumors harbor mutations in genes encoding metabolic enzymes, thus establishing a tight connection between genomics and tumor metabolism. In parallel, metabolomics profiling—by capturing the metabolic phenotype of tumors—has, in recent years, identified specific biomarkers associated with tumor burden, progression, and prognosis. Such findings have catalyzed growing interest in metabolomics as a complementary approach to better characterize cancer biology and discover novel diagnostic and therapeutic targets. Moreover, radiomics, through the extraction of quantitative features from standard imaging modalities, captures tumor heterogeneity and contributes predictive information on tumor biology, treatment response, and clinical outcomes. As a non-invasive and widely available technique, radiomics has the potential to support longitudinal monitoring and individualized treatment planning. Both metabolomics and radiomics, when integrated with genomic data, could support a more comprehensive understanding of NSCLC and pave the way for the development of non-invasive, predictive models and personalized therapeutic strategies. In addition, we explore the specific contributions of these technologies in enhancing clinical decision-making for lung cancer patients, with particular attention to their potential in early diagnosis, treatment selection, and real-time monitoring. Full article

Efforts by the World Health Organization (WHO) have clarified the descriptive nomenclature and histologic grading of neuroendocrine neoplasms (NENs) in human medicine. Employing a standardized stratification scheme in conjunction with specific immunohistochemical markers, such as gastrin, enhances prognostic accuracy and guides treatment recommendations. Yet, this classification system has yet to be applied consistently in veterinary pathology. Histopathologic features and gastrin expression were analyzed in a group of canine gallbladder (GB) NENs. Based on the human WHO histologic system, which stratifies grade based on proliferative indices (mitotic count and Ki67%), all gradable GB NENs were classified as neuroendocrine tumors (NETs) rather than neuroendocrine carcinomas (NECs). Only one GB NET was positive for gastrin using immunohistochemical staining. Collectively, our data suggest that canine GB NENs have a lower grade than most human GB NENs and rarely express gastrin. The use of proliferative indices in the histologic characterization of canine GB NENs is likely to improve prognostic information. Given the limited expression of gastrin in these neoplasms in dogs, this marker is unlikely to be widely applicable as a druggable target. Full article

Osteosarcoma (OS) is the most common primary malignant bone tumor, predominantly affecting adolescents and young adults. Despite advances in surgery and multi-agent chemotherapy, survival rates for metastatic or recurrent OS remain poor, highlighting the need for novel prognostic and therapeutic strategies. This review integrates histopathologic, molecular, and immune perspectives to provide a comprehensive understanding of OS biology in the context of precision medicine. We discuss classic morphologic and radiographic features alongside recent insights into the tumor microenvironment, including the role of tumor-infiltrating lymphocytes, tumor-associated macrophages, and immune checkpoint expression. Emerging molecular markers, such as gene expression–based immune risk signatures, circulating tumor DNA, and gasdermin D overexpression, are evaluated for their prognostic and therapeutic relevance. Key dysregulated pathways, including WNT/β-catenin and JAK/STAT, are examined in relation to metastasis, chemoresistance, and immune evasion, with emphasis on current targeted approaches under development. By bridging histopathology, immunogenomics, and translational research, this work outlines how integrated biomarker assessment can refine patient stratification and guide the implementation of individualized treatment strategies in OS. Full article

Background: Large-cell carcinoma (LCC) and large-cell neuroendocrine carcinoma (LCNEC) are kinds of rare lung tumors classified as distinct forms of non-small-cell lung cancer (NSCLC). They both differ in cellular morphology, neuroendocrine marker expression, and clinical outcomes. Thus, LCC and LCNEC exhibit different clinicopathological characteristics and survival outcomes. This study seeks to assess how clinicopathological and immunohistochemical features influence the need for adjuvant chemotherapy in individuals with early-stage, surgically resected LCC or LCNEC. Methods: This multicenter retrospective analysis included 79 patients who underwent surgical resection for large-cell carcinoma (LCC) or large-cell neuroendocrine carcinoma (LCNEC) between January 2008 and March 2025. We evaluated prognostic factors that influence survival in patients with LCC and LCNEC and assessed the effect of adjuvant chemotherapy on survival outcomes. Results: This study included 79 patients—39 diagnosed with LCC and 40 diagnosed with LCNEC. All patients were in stages I–III and received curative surgery. The median age was 61 years for LCC patients and 58.5 years for LCNEC patients. The median overall survival (mOS) was 80.1 months for patients with LCC and 34.2 months for those with LCNEC. Multivariate Cox regression analysis revealed that age (HR: 0.279), stage (HR: 0.198), and chromogranin A expression (HR: 0.088) were independent prognostic factors for overall survival in LCC patients. In LCNEC patients, stage (HR: 0.20), synaptophysin expression (HR: 0.30), type of surgery (HR: 0.31), and adjuvant chemotherapy (HR: 0.264) were identified as factors influencing overall survival. Adjuvant chemotherapy improved overall survival in LCNEC patients (67.0 vs. 17.8 months). Conclusions: Patients with LCNEC generally have poorer prognoses than those with LCC, exhibiting reduced overall survival periods. Disease stage is the most significant factor influencing overall survival for both groups. Notably, in LCNEC patients, adjuvant chemotherapy was found to independently improve survival outcomes regardless of stage. Full article

Background/Objectives: Vestibular schwannoma (VS) is a benign cerebellopontine angle tumor causing audiological and vestibular symptoms. This pilot study aimed to describe the application of video Head Impulse Test (vHIT) in the diagnosis and follow-up of patients with unilateral VS treated surgically. The objective was to describe a detailed interpretation of vHIT—not only numerical parameters such as gain and corrective saccades, but also a visual analysis of vHIT curves. Methods: The results were presented in four cases for better understanding and more straightforward explanation. The patients underwent surgery through the middle cranial fossa and translabyrinthine approach. In each patient, vHIT examinations were performed preoperatively and at one month, three months, and one year after the surgery. Results: Before treatment, vestibular loss features varied within the presented cases. Findings of vestibulo-ocular reflex deficiency were most pronounced in the lateral semicircular canals. After the surgery, severe signs of acute labyrinth denervation were found during the first follow-up visit. Over time, features indicating central compensation became more pronounced, despite a decrease in gain in subsequent vHIT examinations. Conclusions: Detailed analysis of vHIT curves is crucial to analyze vestibulo-ocular reflex in patients with VS. Our preliminary data confirms that vHIT examination can be helpful in the postoperative follow-up assessment and compensation evaluation. Full article

Cancer-associated fibroblasts (CAFs) are key components of the tumor microenvironment (TME) that influence cancer progression via extracellular matrix (ECM) remodeling and secretion of growth factors and cytokines. Endothelial-to-mesenchymal transition (EndMT) is emerging as an important axis among the heterogeneous origins of CAFs. This review introduces the diverse methods used to induce EndMT in cancer—mouse tumor models, conditioned-medium treatment, co-culture, targeted gene perturbation, ligand stimulation, exosome exposure, irradiation, viral infection, and three-dimensional (3D) culture systems—and summarizes EndMT cell-type evidence uncovered using transcriptomic and proteomic technologies. Hallmark EndMT features include spindle-like morphology, increased motility, impaired angiogenesis and barrier function, decreased endothelial markers (CD31, VE-cadherin), and increased mesenchymal markers (α-SMA, FN1). Reported mechanisms include signaling via TGF-β, cytoskeletal/mechanical stress, reactive oxygen species, osteopontin, PAI-1, IL-1β, GSK-3β, HSP90α, Tie1, TNF-α, HSBP1, and NOTCH. Cancer-induced EndMT affects tumors and surrounding TME—promoting tumor growth and metastasis, expanding cancer stem cell-like cells, driving macrophage differentiation, and redistributing pericytes—and is closely associated with poor survival and therapy resistance. Finally, we indicate each study’s stance: some frame cancer-induced EndMT as a source of CAFs, whereas others, from an endothelial perspective, emphasize barrier weakening and promotion of metastasis. Full article

Breast cancer is the most commonly diagnosed cancer in women globally and represents the leading cause of mortality related to malignant tumors. Currently, healthcare professionals are focused on developing and implementing innovative techniques to improve the early detection of this disease. Thermography, studied as a complementary method to traditional approaches, captures infrared radiation emitted by tissues and converts it into data about skin surface temperature. During tumor development, angiogenesis occurs, increasing blood flow to support tumor growth, which raises the surface temperature in the affected area. Automatic classification techniques have been explored to analyze thermographic images and develop an optimal classification tool to identify thermal anomalies. This study aims to design a concise description using statistical and texture features to accurately classify thermograms as control or highly probable to be cancer (with thermal anomalies). The importance of employing a short description lies in facilitating interpretation by medical professionals. In contrast, a characterization based on a large number of variables could make it more challenging to identify which values differentiate the thermograms between groups, thereby complicating the explanation of results to patients. A maximum accuracy of 91.97% was achieved by applying only seven features and using a Coarse Decision Tree (DT) classifier and robust Machine Learning (ML) model, which demonstrated competitive performance compared with previously reported studies. Full article