Search Results (1,008)

Thyroid cancer is the most common endocrine malignancy, and preoperative distinction between benign and malignant nodules remains challenging, especially in cytologically indeterminate cases. Circulating microRNAs (miRNAs) have gained interest as non-invasive biomarkers due to their stability and involvement in tumorigenesis. This study aimed to assess the preoperative diagnostic value of circulating miR-146a and miR-221 in patients undergoing thyroidectomy. A total of 56 patients were included, of whom 24 had malignant and 32 had benign thyroid lesions confirmed by histopathology. Preoperative plasma levels of miR-146a and miR-221 were quantified using qRT-PCR, and relative expression was calculated with the 2^−ΔΔCt method. miR-221 expression was significantly higher in malignant cases, with an area under the ROC curve of 1.00, achieving 100% sensitivity and specificity at the optimal threshold. miR-146a showed no significant discriminatory ability. Weak correlations were observed between miRNA expression and clinical parameters such as age, TIRADS score, or thyroid volume. Logistic regression including miR-221 led to perfect separation, indicating strong predictive capacity but precluding multivariate modeling. These findings suggest that circulating miR-221 may serve as a highly accurate biomarker for thyroid malignancy and warrant further validation in larger, prospective cohorts. Full article

To deliver the most effective cancer treatment, clinicians require rapid and accurate diagnoses that delineate tumor type, stage, and prognosis. Consequently, minimizing the need for repetitive and invasive procedures like biopsies and myelograms, along with their associated risks, is a critical challenge. Non-invasive monitoring offers a promising avenue for tumor detection, screening, and prognostication. While the identification of oncogenes and biomarkers from circulating tumor cells or tissue biopsies is currently standard practice for cancer diagnosis and classification, accumulating evidence underscores the significant role of epigenetics in regulating stem cell fate, including proliferation, self-renewal, and malignant transformation. This highlights the importance of analyzing the methylome, exosomes, and circulating RNA for detecting cellular transformation. The development of diagnostic assays that integrate liquid biopsies with epigenetic analysis holds immense potential for revolutionizing tumor management by enabling rapid, non-invasive diagnosis, real-time monitoring, and personalized treatment decisions. This review covers current studies exploring the use of epigenetic regulation, specifically the methylome and circulating RNA, as diagnostic tools derived from liquid biopsies. This approach shows promise in facilitating the differentiation between primary central nervous system lymphoma and other central nervous system tumors and may enable the detection and monitoring of acute myeloid/lymphoid leukemia. We also discuss the current limitations hindering the rapid clinical translation of these technologies. Full article

Bladder cancer pathogenesis is closely linked to epigenetic alterations, particularly DNA methylation and demethylation processes. Environmental carcinogens and persistent inflammatory stimuli—such as recurrent urinary tract infections—can induce aberrant DNA methylation, altering gene expression profiles and contributing to malignant transformation. This review synthesizes current evidence on the role of DNA methyltransferases (DNMT1, DNMT3a, DNMT3b) and the hypermethylation of key tumour suppressor genes, including A2BP1, NPTX2, SOX11, PENK, NKX6-2, DBC1, MYO3A, and CA10, in bladder cancer. It also evaluates the therapeutic application of DNA-demethylating agents such as 5-azacytidine and highlights the impact of chronic inflammation on epigenetic regulation. Promoter hypermethylation of tumour suppressor genes leads to transcriptional silencing and unchecked cell proliferation. Urine-based DNA methylation assays provide a sensitive and specific method for non-invasive early detection, with single-target approaches offering high diagnostic precision. Animal models are increasingly employed to validate these findings, allowing the study of methylation dynamics and gene–environment interactions in vivo. DNA methylation represents a key epigenetic mechanism in bladder cancer, with significant diagnostic, prognostic, and therapeutic implications. Integration of human and experimental data supports the use of methylation-based biomarkers for early detection and targeted treatment, paving the way for personalized approaches in bladder cancer management. Full article

MicroRNA-211 (miR-211) is a versatile regulatory molecule that plays critical roles in cellular homeostasis and disease progression through the post-transcriptional regulation of gene expression. This review comprehensively examines miR-211’s multifaceted functions across various biological systems, highlighting its context-dependent activity as both a tumor suppressor and oncogene. In physiological contexts, miR-211 regulates cell cycle progression, metabolism, and differentiation through the modulation of key signaling pathways, including TGF-β/SMAD and PI3K/AKT. miR-211 participates in retinal development, bone physiology, and protection against renal ischemia–reperfusion injury. In pathological conditions, miR-211 expression is altered in various diseases, particularly cancer, where it may be a useful diagnostic and prognostic biomarker. Its stability in serum and differential expression in various cancer types make it a promising candidate for non-invasive diagnostics. The review also explores miR-211’s therapeutic potential, discussing both challenges and opportunities in developing miRNA-based treatments. Understanding miR-211’s complex regulatory interactions and context-dependent functions is crucial for advancing its clinical applications for diagnosis, prognosis, and targeted therapy in multiple diseases. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

Non-melanoma skin cancer (NMSC), comprising basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), represents the most common type of cancer worldwide, particularly among Caucasians. While BCC is locally invasive with minimal metastatic potential, cSCC is a highly aggressive tumor with a significant potential for metastasis, particularly in elderly populations. Tumor development and progression and the metastasis of cSCC are influenced by a complex interplay between tumor cells and the tumor microenvironment. Recent research highlights the importance of various immune cell subsets, including T cells, tumor-associated macrophages (TAMs), and dendritic cells, in influencing tumor progression, immune evasion, and treatment resistance. This review outlines key regulatory mechanisms in the immune tumor microenvironment (TME) of cSCC and explores the role of cytokines, immune checkpoints, and stromal interactions. We further discuss the relevance of three-dimensional (3D) in vitro models such as spheroids, organoids, and tumor-on-chip systems as tools to mimic immune–tumor interactions with higher physiological relevance, such as macrophage activation and polarization against cSCC cells. Globally, 3D models offer new opportunities for immunotherapy screening and mechanistic studies. Understanding the immune landscape in cSCC through advanced modeling techniques holds strong clinical potential for improving diagnostic and therapeutic strategies. Full article

Hair, as a non-invasive biospecimen, retains metabolic deposits from sebaceous glands and capillaries, reflecting substances from the peripheral circulation, and provides valuable biochemical information linked to phenotypes, yet its application in animal disease research remains limited. This work applied ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) to compare the hair metabolomic characteristics of healthy forest musk deer (FMD, Moschus berezovskii) and those diagnosed with hemorrhagic pneumonia (HP), phytobezoar disease (PD), and abscess disease (AD). A total of 2119 metabolites were identified in the FMD hair samples, comprising 1084 metabolites in positive ion mode and 1035 metabolites in negative ion mode. Differential compounds analysis was conducted utilizing the orthogonal partial least squares–discriminant analysis (OPLS-DA) model. In comparison to the healthy control group, the HP group displayed 85 upregulated and 92 downregulated metabolites, the PD group presented 124 upregulated and 106 downregulated metabolites, and the AD group exhibited 63 upregulated and 62 downregulated metabolites. Functional annotation using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the differential metabolites exhibited significant enrichment in pathways associated with cancer, parasitism, energy metabolism, and stress. Receiver operating characteristic (ROC) analysis revealed that both the individual and combined panels of differential metabolites exhibited area under the curve (AUC) values exceeding 0.7, demonstrating good sample discrimination capability. This research indicates that hair metabolomics can yield diverse biochemical insights and facilitate the development of non-invasive early diagnostic techniques for diseases in captive FMD. Full article

Background: MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. Results: Sixteen studies (n = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63–0.72), 0.73 (95% CI, 0.68–0.78), and 0.70 (95% CI, 0.65–0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75–0.80) and 6.03 (95% CI, 4.65–7.82). Funnel plot asymmetry and Egger’s test (p = 0.025) indicated potential publication bias. Conclusions: Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice. Full article

Background/Objectives: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy; however, reliable biomarkers of therapeutic efficacy remain limited. We investigated the clinical utility of plasma WFDC2 levels in patients receiving anti-PD-1 antibody treatment. Methods: Twenty-one patients with non-small cell lung, gastric, or bladder cancer received nivolumab or pembrolizumab. Plasma WFDC2 concentrations were measured by ELISA before ICI treatment (pre-ICI) and after two and four treatment cycles. Associations between WFDC2 expression changes and overall survival (OS), progression-free survival (PFS), and tumor progression were assessed. ROC curve analyses compared the predictive performance of WFDC2, soluble PD-L1 (sPD-L1), soluble PD-1 (sPD-1), and their combinations, with the area under the curve (AUC) evaluating predictive accuracy. Results: Levels of WFDC2 pre-ICI and those after two cycles were significantly higher than levels in healthy donors. However, no significant differences in WFDC2 levels were found between the time points during treatment. Greater increases in WFDC2 levels were significantly correlated with shorter OS (p = 0.002), shorter PFS (p = 0.037), and tumor progression (p = 0.003). ROC analysis revealed that WFDC2 achieved a higher AUC (0.700) than sPD-L1 (0.538) or sPD-1 (0.650). Combining biomarkers improved the predictive accuracy, with sPD-L1 plus WFDC2 showing the highest AUC (0.825). Conclusions: Serial increases in plasma WFDC2 are associated with poor clinical outcomes, highlighting its potential as a biomarker. Baseline plasma WFDC2 outperformed sPD-L1 and sPD-1 diagnostically. These findings should be interpreted as exploratory and hypothesis-generating, requiring confirmation in larger, tumor-specific cohorts with multivariate adjustment. WFDC2 represents a promising minimally invasive biomarker for the early identification of patients unlikely to benefit from ICI therapy. Full article

Positron emission tomography (PET) and liquid biopsy have independently transformed prostate cancer management. This review explores the complementary roles of PET imaging and liquid biopsy in prostate cancer, focusing on their combined diagnostic, monitoring, and prognostic potential. A systematic search of PubMed, Scopus, and Cochrane Library databases was conducted to identify human studies published in English up to January 2025. Seventeen studies met the inclusion criteria and were analyzed according to PRISMA guidelines. Across the included studies, PET-derived imaging metrics, such as metabolic activity and radiotracer uptake, correlated consistently with liquid biopsy biomarkers, including circulating tumor cells and cell-free DNA. Their joint application demonstrated added value in early detection, treatment monitoring, and outcome prediction, particularly in castration-resistant prostate cancer. Independent and synergistic prognostic value was noted for both modalities, including survival outcomes such as overall survival and progression-free survival. Combining PET imaging and liquid biopsy emerges as a promising, non-invasive strategy for improving prostate cancer diagnosis, monitoring, and therapeutic stratification. While preliminary findings are encouraging, large-scale prospective studies are essential to validate their integrated clinical utility. Full article

Oral squamous cell carcinoma (OSCC) remains one of the most common malignancies in the head and neck region, often preceded by a spectrum of oral potentially malignant disorders (OPMDs). Despite advances in diagnostic methods, reliable and non-invasive biomarkers for early detection and prognostic stratification are still lacking. In recent years, circulating cell-free DNA (cfDNA) has emerged as a promising liquid biopsy tool in several solid tumors, offering insights into tumor burden, heterogeneity, and molecular dynamics. However, its application in oral oncology remains underexplored. This study aims to review and discuss the current evidence on cfDNA quantification and mutation analysis (including TP53, NOTCH1, and EGFR) in patients with OPMDs and OSCC. Particular attention is given to cfDNA fragmentation patterns, methylation signatures, and tumor-specific mutations as prognostic and predictive biomarkers. Moreover, we highlight the challenges in standardizing pre-analytical and analytical workflows in oral cancer patients and explore the potential role of cfDNA in monitoring oral carcinogenesis. Understanding cfDNA dynamics in the oral cavity might offer a novel, minimally invasive strategy to improve early diagnosis, risk assessment, and treatment decision-making in oral oncology. Full article

The prognosis of sarcopenia is poor in cancer patients. Recently, urinary titin, a biomarker of muscle damage, has been suggested as a potential marker for sarcopenia. However, its utility in patients with unresectable digestive malignancies remains unclear. In addition, sex differences have been reported in the association between sarcopenia and urinary titin levels. This study aimed to evaluate urinary titin as a diagnostic marker for unresectable digestive malignancies, focusing on sex differences. This retrospective study enrolled 96 patients (58 males, 38 females; median age 70), and urinary titin was evaluated as a diagnostic biomarker in relation to clinical factors (e.g., age, Eastern Cooperative Oncology Group performance status [ECOG PS], albumin [Alb]) and muscle indicators (e.g., psoas muscle index [PMI], handgrip strength). In male patients, urinary titin levels were significantly higher in the sarcopenia subgroup (5.78 vs. 2.79 pmol/mgCr, p = 0.008), and multivariate analyses identified urinary titin as an independent predictor of sarcopenia (odds ratio 13.4, p = 0.028). The receiver operating characteristic (ROC) analysis demonstrated fair diagnostic performance (area under the curve [AUC] 0.729), with an optimal cutoff value of 3.676 pmol/mgCr. Urinary titin may serve as a useful non-invasive diagnostic biomarker for sarcopenia in patients with unresectable digestive malignancies, particularly in males. These findings suggest that sex-specific approaches are required for sarcopenia assessment with urinary titin. Full article

Colorectal carcinoma remains one of the primary contributors to cancer deaths; however, it is also considered a preventable type of cancer, because the prognosis of the disease is directly dependent on its timely detection. Developing accurate risk prediction models for colorectal cancer is crucial for identifying individuals at both low and high risk, as risk stratification determines the need for additional interventions, which carry their own risks. The development of new non-invasive diagnostic methods based on biomaterial analysis, alongside standard diagnostic techniques such as colonoscopy with biopsy, computed tomography scanning, and magnetic resonance imaging, can address multiple objectives: improving screening accuracy, providing a comprehensive assessment of minimal residual disease, identifying patients at a high risk of colorectal cancer, and evaluating the effectiveness of ongoing treatments. The lack of sensitive diagnostic methods drives contemporary research toward the discovery of new tools for detecting tumor cells, particularly through the examination of biological materials, including blood, exhaled air, and tumor tissue itself. In this article, we analyze current studies regarding biomarkers in colorectal cancer and prognostic significance. Full article

(1) Background: Oral cancer (OC) is one of the most frequently diagnosed human cancers and remains a challenge for biologists and clinicians. More than 90% of OC cases are squamous cell carcinomas (OSCCs). Despite the use of modern diagnostic and prognostic methods, the 5-year survival rate remains unsatisfactory due to the late diagnosis of the neoplastic process and its resistance to treatment. This comprehensive review aims to present the latest literature data on the use and effectiveness of saliva as a non-invasive biomarker in patients with oral cancer. (2) Methods: The article reviews the current literature on the use of salivary omics biomarkers as an effective method in diagnosing and modifying treatment in patients with OSCC; the research corpus was acquired from the PubMed/Google/Scopus/Cochrane Library/Web of Science databases in accordance with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA 2020) guidelines. (3) Results: The identification of salivary omics biomarkers involved in carcinogenesis and neoplastic transformation may be a potential alternative to traditional invasive diagnostic methods. Saliva, being both an abundant reservoir of organic and inorganic components derived from epithelial cells as well as a cell-free environment, is becoming an interesting diagnostic material for studies in the field of proteomics, genomics, metagenomics, and metabolomics. (4) Conclusions: Saliva-based analysis is a modern and promising method for the early diagnosis and improvement of treatment outcomes in patients with OSCC and oral potentially malignant disorders (OPMDs), with high diagnostic, therapeutic, and prognostic potential. Full article

Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for early and accurate detection methods. Traditional mammography, although widely used, has limitations, including radiation exposure and challenges in detecting early-stage lesions. Electrical Impedance Mammography (EIM) has emerged as a non-invasive and radiation-free alternative that assesses the density and electrical conductivity of breast tissue. EIM images consist of seven layers, each representing different tissue depths, offering a detailed representation of the breast structure. However, analyzing these layers individually can be redundant and complex, making it difficult to identify relevant features for lesion classification. To address this issue, advanced computational techniques are employed for image integration, such as the Root Mean Square ($C_{\mathrm{RMS}}$) Contrast and Contrast-Limited Adaptive Histogram Equalization (CLAHE), combined with the Coefficient of Variation (CV), CLAHE-based fusion, weighted average fusion, Gaussian pyramid fusion, and Wavelet–PCA fusion. Each method enhances the representation of tissue features, optimizing the image quality and diagnostic utility. This study evaluated the impact of these integration techniques on EIM image analysis, aiming to improve the accuracy and reliability of computational diagnostic models for breast cancer detection. According to the obtained results, the best performance was achieved using Wavelet–PCA fusion in combination with XGBoost as a classifier, yielding an accuracy rate of 89.5% and an F1-score of 81.5%. These results are highly encouraging for the further investigation of this topic. Full article