Search Results (117)

Cancer diagnosis requires alternative techniques that allow for early, non-invasive, or minimally invasive identification. Traditional methods, like tissue biopsies, are highly invasive and can be traumatic for patients. Liquid biopsy, a less invasive option, detects cancer biomarkers in body fluids such as blood and urine. However, early-stage cancer often presents low biomarker levels, making sensitivity a challenge for integrating liquid biopsy into early diagnosis. Recent studies revealed that extracellular vesicles (EVs) secreted by cells are apt markers for liquid biopsy. Detecting extracellular vesicles (EVs) for liquid biopsy faces challenges like low sensitivity, EV subtype heterogeneity, and difficulty isolating pure populations. Label-free methods, such as plasmonic biosensors and Raman spectroscopy, offer potential solutions by enabling direct analysis without markers, improving accuracy, and reducing complexity. This review paper discusses current challenges in EV-based liquid biopsy for cancer diagnosis and prognosis. It addresses the effective use of microfluidics and nano-plasmonic approaches to address these challenges. Enhancing label-free EV detection in liquid biopsy could revolutionize early cancer diagnosis by offering non-invasive, cost-effective, and rapid testing. This could improve patient outcomes through personalized treatment and ease the burden on healthcare systems. Full article

Background: Breast cancer remains a leading cause of cancer-related deaths despite advances in its diagnosis and treatment. Accurate evaluation of the response to neoadjuvant chemotherapy (NAC), especially in HER2-positive and triple-negative subtypes, is critical. The current methods, including imaging and liquid biopsies, have limitations. N-NOSE, a novel urine-based cancer screening test using Caenorhabditis elegans (C. elegans) chemotaxis, offers a non-invasive alternative. This study investigates the potential of N-NOSE to predict the NAC response in breast cancer patients for improved treatment evaluations. Materials and Methods: This prospective study enrolled 36 breast cancer patients undergoing NAC and surgery to assess the predictive power of the N-NOSE method using urine samples. A chemotaxis analysis of C. elegans was used to calculate the index reduction scores (IRS1–3), reflecting the changes in tumor-related odorants across the treatment stages. Results: Between August 2020 and May 2023, 36 breast cancer patients were enrolled to evaluate the predictive value of N-NOSE IRSs for NAC response. A pathological complete response (pCR) was achieved in 36.1% of the patients. Among the three IRS types analyzed in the 35 patients, IRS3, which showed the IRS at pre-treatment minus that after surgery, showed the highest predictive performance for a pCR, with an AUC of 0.75, indicating its potential utility as a non-invasive biomarker for treatment response evaluations. Conclusions: Index reduction scores evaluated using the N-NOSE method may reflect the efficacy of NAC in breast cancer patients. Future large-scale and multi-institutional prospective studies are warranted. Full article

Exosomes are 30–150 nm extracellular vesicles that play crucial roles in intercellular communication and hold significant potential as biomarkers for non-invasive liquid biopsy. However, the current isolation methods have limitations including being time-consuming, producing low yields, and having high costs. This study presents a novel automated exosome isolation method using EGCG-modified magnetic beads (EGCG@T) optimized for diverse biofluids including plasma, serum, urine, and saliva. We systematically investigated the optimal EGCG:T-Fe₃O₄ ratio (0.1:1), binding time, elution parameters, and extraction buffer composition for each biofluid type. The developed protocol was successfully integrated into an automated workflow using the Nextractor^® NX-Junior platform, combining exosome isolation and protein extraction into a single step. Western blot and ELISA analyses confirmed that the EGCG@T method yielded a significantly higher recovery of exosomal markers (CD9, CD63, CD81, TSG101, and ALIX) compared to conventional PEG precipitation, with the efficiency varying depending on the biofluid. Notably, CD63-positive exosomes were isolated with approximately two-fold higher efficiency from urine and 1.3-fold higher efficiency from saliva using the EGCG@T method. Our findings demonstrated that biofluid-specific optimization is essential for effective exosome isolation, as exosome subpopulations exhibited distinct physicochemical properties across different sample types. This automated, rapid, and efficient exosome isolation method provides a valuable platform for future clinical applications in non-invasive disease diagnosis and monitoring through liquid biopsy. Full article

Renal cell carcinoma (RCC) presents a significant diagnostic challenge, particularly in small renal masses. The search for non-invasive screening methods and biomarkers has directed research toward liquid biopsy, which focuses on microRNAs (miRNAs), exosomes, and circulating tumor cells (CTCs). miRNAs are small non-coding RNA molecules that show considerable dysregulation in RCC, and they have potential for both diagnostic and prognostic applications. Research has highlighted their utility on biofluids, such as plasma, serum, and urine, in detecting RCC and characterizing its subtypes. Promising miRNA signatures have been associated with overall survival, suggesting their potential importance in the management of RCC. Exosomes, which carry a variety of molecular components, including miRNAs, are emerging as valuable biomarkers, whereas CTCs, released from primary tumors into the bloodstream, provide critical information on cancer progression. However, translation of these findings into clinical practice requires additional validation and standardization through large-scale studies and robust evidence. Although there are currently no approved diagnostic tests for RCC, the future potential of liquid biopsy in monitoring, treatment decision-making, and outcome prediction in patients with this disease is significant. This review examined and discussed recent developments in liquid biopsy for RCC, assessing both the strengths and limitations of these approaches for managing this disease. Full article

Background: Surface-enhanced Raman spectroscopy (SERS) analysis of urine is a promising liquid biopsy technique for cancer detection. However, its clinical translation is hindered by two major challenges that impact classification efficacy. First, the SERS signal of urine is confounded by fluctuations induced by physiological differences in urine composition such as pH and dilution. Second, the molecular origin of the SERS signal of urine is incompletely understood, limiting the interpretability of machine learning classifiers in terms of specific biochemical markers. Methods: In this pilot study, we analyzed urine samples from breast cancer patients (n = 18) and control subjects (n = 10) at three pH levels (5, 7, and 9). Additionally, we analyzed simulated urine mixtures consisting of uric acid, hypoxanthine, xanthine, and creatinine in physiological concentrations to explain the variation in the SERS spectra at different pH values. Results: Urine at pH 9 yielded the most detailed spectral features. The SERS spectral pattern under alkaline pH reflected greater contributions from hypoxanthine, uric acid, and creatinine, while xanthine contributions diminished due to competitive interactions at the SERS substrate surface. Normalizing SERS signals to the creatinine band at 1420 cm⁻¹ effectively mitigated the confounding effects of urine dilution. Conclusions: Optimizing the pH to 9 and normalizing to creatinine significantly enhances the interpretability and accuracy of SERS-based urine analysis for cancer detection. These findings offer important theoretical and practical advancements for the development of SERS-based liquid biopsy tools for cancer detection. Full article

Background/Objectives: The timely diagnostics of bladder cancer is still a challenge in clinical settings. The reliability of conventional testing methods does not reach desirable accuracy and sensitivity, and it has an invasive nature. The present study examines the application of machine learning to improve bladder cancer diagnostics by integrating miRNA expression levels, demographic routine laboratory test results, and clinical data. We proposed that merging these datasets would enhance diagnostic accuracy. Methods: This study combined molecular biology methods for liquid biopsy, routine clinical data, and application of machine learning approach for the acquired data analysis. We evaluated urinary exosome miRNA expression data in combination with patient test results, as well as clinical and demographic data using three machine learning models: Random Forest, SVM, and XGBoost classifiers. Results: Based solely on miRNA data, the SVM model achieved an ROC curve area of 0.75. Patient analysis’ clinical and demographic data obtained ROC curve area of 0.80. Combining both data types enhanced performance, resulting in an F1 score of 0.79 and an ROC of 0.85. The feature importance analysis identified key predictors, including erythrocytes in urine, age, and several miRNAs. Conclusions: Our findings indicate the potential of a multi-modal approach to improve the accuracy of bladder cancer diagnosis in a non-invasive manner. Full article

Towards new and improved techniques in liquid biopsy for the diagnosis of diseases, this study reports experimental evidence of a rapid and reliable method based on terahertz (THz) time-domain spectroscopic ellipsometry (TDSE) for the early diagnosis of kidney-related diseases, using the detection of uric acid (UA) content in urine. Employing a custom-built THz-TDSE system, we analyzed the absorption and dispersion response of synthetic urine samples with varying concentrations of UA. The technique provides a prompt indication of UA presence and concentration, thanks to the sensitivity of THz waves to intermolecular interactions such as hydrogen bonding. The results clearly show a linear correlation between the UA concentration and changes in the absorption spectra of urine in the frequency window 0.2–1.2 THz, with the minimum detectable UA concentration being approximately close to the upper limit of normal UA levels in urine. The increase in the absorption coefficient as a function of the UA concentration provides a means for a quantifiable measure of the UA biomarker in urine for assessing disease stage. This study proves that THz-TDSE is capable of detecting UA at concentrations relevant for early-stage diagnosis of renal diseases, with an estimated sensitivity of 0.2 g/L in the region where the material response is linear. Full article

Pancreatic cancer is one of the most aggressive cancers with a very poor 5-year survival rate and reduced therapeutic options when diagnosed in an advanced stage. The dismal prognosis of pancreatic cancer has guided significant efforts to discover novel biomarkers in order to anticipate diagnosis, increasing the population of patients who can benefit from curative surgical treatment. CA 19-9 is the reference biomarker that supports the diagnosis and guides the response to treatments. However, it has significant limitations, a low specificity, and is inefficient as a screening tool. Several potential biomarkers have been discovered in the serum, urine, feces, and pancreatic juice of patients. However, most of this evidence needs further validation in larger cohorts. The advent of advanced omics sciences and liquid biopsy techniques has further enhanced this field of research. The aim of this review is to analyze the historical evolution of the research on novel biomarkers for the early diagnosis of pancreatic cancer, focusing on the current evidence for the most promising biomarkers from different body fluids and the novel trends in research, such as omics sciences and liquid biopsy, in order to favor the application of modern personalized medicine. Full article

Circulating tumor cells and cell-free nucleic acids are novel diagnostic, prognostic and predictive tools for non-invasive and cost-effective cancer detection in liquid biopsy. Carbonic anhydrase IX (CAIX) has been proposed as a biomarker in urogenital tumors and urine sediment. Our aim was to evaluate CAIX full-length percentage (CAIX FL%) in urine-cell-free RNA (cfRNA) and its relationship with tumor-cell-associated RNA (TC-RNA). CAIX FL% was quantified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in patients with prostate, kidney or bladder carcinoma. When cfRNA and TC-RNA were analyzed, CAIX FL% was significantly higher in urine samples from cancer patients than from controls. Using a 10% cutoff for CAIX FL%, specificity, sensitivity, positive and negative predictive values, as well as accuracy for TC-RNA were higher than for cfRNA in all urogenital cancers, but varied according to tumor type. CAIX FL% distribution in TC-RNA differed significantly (p < 0.001) between control and tumor samples (37.5% and 96.2%, respectively); similar results were obtained for each tumor type. Additionally, the 10% cutoff showed a 77.9% concordance between TC-RNA and cfRNA. In conclusion, urine is proposed as an alternative biofluid for investigating CAIX FL% in urogenital cancers, and this parameter can be reliably measured as cfRNA and TC-RNA with different predictive capabilities depending on tumor type. Full article

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques—including statistical, regularization, deep learning, decision trees, and Bayesian methods—were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of ≥0.7, sensitivity ≥ 0.6, and selectivity ≥ 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation. Full article

Lung cancer is responsible for the death of over a million people worldwide every year. With its high mortality rate and exponentially growing number of new cases, lung cancer is a major threat to public health. The high mortality and poor survival rates of lung cancer patients can be attributed to its stealth progression and late diagnosis. For a long time, intrusive tissue biopsy has been considered the gold standard for lung cancer diagnosis and subtyping; however, the intrinsic limitations of tissue biopsy cannot be overlooked. In addition to being invasive and costly, it also suffers from limitations in sensitivity and specificity, is not suitable for repeated sampling, provides restricted information about the tumor and its molecular landscape, and is inaccessible in several cases. To cope with this, advancements in diagnostic technologies, such as liquid biopsy, have shown great prospects. Liquid biopsy is an innovative non-invasive approach in which cancer-related components called biomarkers are detected in body fluids, such as blood, urine, saliva and others. It offers a less invasive alternative with the potential for applications such as routine screening, predicting treatment outcomes, evaluating treatment effectiveness, detecting residual disease, or disease recurrence. A large number of research articles have indicated extracellular vesicles (EVs) as ideal biomarkers for liquid biopsy. EVs are a heterogeneous collection of membranous nanoparticles with diverse sizes, contents, and surface markers. EVs play a critical role in pathophysiological states and have gained prominence as diagnostic and prognostic biomarkers for multiple diseases, including lung cancer. In this review, we provide a detailed overview of the potential of EV-based liquid biopsy for lung cancer. Moreover, it highlights the strengths and weaknesses of various contemporary techniques for EV isolation and analysis in addition to the challenges that need to be addressed to ensure the widespread clinical application of EV-based liquid biopsies for lung cancer. In summary, EV-based liquid biopsies present interesting opportunities for the development of novel diagnostic and prognostic platforms for lung cancer, one of the most abundant cancers responsible for millions of cancer-related deaths worldwide. Full article

CtDNA is emerging as a non-invasive clinical detection method for several cancers, including genitourinary (GU) cancers such as prostate cancer, bladder cancer, and renal cell carcinoma (RCC). CtDNA assays have shown promise in early detection of GU cancers, providing prognostic information, assessing real-time treatment response, and detecting residual disease and relapse. The ease of obtaining a “liquid biopsy” from blood or urine in GU cancers enhances its potential to be used as a biomarker. Interrogating these “liquid biopsies” for ctDNA can then be used to detect common cancer mutations, novel genomic alterations, or epigenetic modifications. CtDNA has undergone investigation in numerous clinical trials, which could address clinical needs in GU cancers, for instance, earlier detection in RCC, therapeutic response prediction in castration-resistant prostate cancer, and monitoring for recurrence in bladder cancers. The utilization of liquid biopsy for ctDNA analysis provides a promising method of advancing precision medicine within the field of GU cancers. Full article

Most of the annual 10 million cancer-related deaths are caused by metastatic disease. Survival rates for cancer are strongly dependent on the type of cancer and its stage at detection. Early detection remains a challenge due to the lack of reliable biomarkers and cost-efficient screening methods. Phage biosensors can offer a solution for early detection using non-invasive liquid biopsies. Here, we report the first results of the bifunctional phage biosensor to detect metastatic urological cancers from urine. A dye-sensitized phage library was used to select metastasis-related phage binders. After selection rounds, the most promising phage candidate was used to classify metastatic cancer from controls. Additionally, we applied one chemical sensor (phenoxazine non-fluorescent dye) to classify cancer from urine. A statistical significance (p = 0.0002) was observed between metastatic and non-metastatic cancer, with sensitivity of 70% and specificity of 79%. Furthermore, the chemical sensor demonstrated significance in detecting cancer (p < 0.0001) with a sensitivity of 71% and a specificity of 75%. Our data suggest a new promising field for urine biomarker research, and further evaluation with prospectively collected samples is ongoing. In conclusion, we report, for the first time, the potential of a chemical- and phage-based biosensor method to detect metastatic cancer using urine. Full article

Liquid biopsy involves the utilization of minimally invasive or noninvasive techniques to detect biomarkers in biofluids for disease diagnosis, monitoring, or guiding treatments. This approach is promising for the early diagnosis of childhood cancer, especially for brain tumors, where tissue biopsies are more challenging and cause late detection. Extracellular vesicles offer several characteristics that make them ideal resources for childhood cancer liquid biopsy. Extracellular vesicles are nanosized particles, primarily secreted by all cell types into body fluids such as blood and urine, and contain molecular cargos, i.e., lipids, proteins, and nucleic acids of original cells. Notably, the lipid bilayer-enclosed structure of extracellular vesicles protects their cargos from enzymatic degradation in the extracellular milieu. Proteins and nucleic acids of extracellular vesicles represent genetic alterations and molecular profiles of childhood cancer, thus serving as promising resources for precision medicine in cancer diagnosis, treatment monitoring, and prognosis prediction. This review evaluates the recent progress of extracellular vesicles as a liquid biopsy platform for various types of childhood cancer, discusses the mechanistic roles of molecular cargos in carcinogenesis and metastasis, and provides perspectives on extracellular vesicle-guided therapeutic intervention. Extracellular vesicle-based liquid biopsy for childhood cancer may ultimately contribute to improving patient outcomes. Full article

Background: Prostate cancer (PCa) is one of the most frequently occurring malignancies. Although most cases are not life-threatening, approximately 20% endure an unfavorable outcome. PSA-based screening reduced mortality but at the cost of an increased overdiagnosis/overtreatment of low-risk (lrPCa) and favorable intermediate-risk (firPCa) PCa. PCa risk-groups are usually identified based on serum Prostate-Specific Antigen (PSA), the Gleason score, and clinical T stage, which have consistent although variable specificity or subjectivity. Thus, more effective and specific tools for risk assessment are needed, ideally making use of minimally invasive methods such as liquid biopsies. In this systematic review we assessed the clinical potential and analytical performance of liquid biopsy-based biomarkers for pre-treatment risk stratification of PCa patients. Methods: Studies that assessed PCa pre-treatment risk were retrieved from PubMed, Scopus, and MedLine. PCa risk biomarkers were analyzed, and the studies’ quality was assessed using the QUADAS-2 tool. Results: The final analysis comprised 24 full-text articles, in which case-control studies predominated, mostly reporting urine-based biomarkers (54.2%) and biomarker quantification by qPCR (41.7%). Categorization into risk groups was heterogeneous, predominantly making use of the Gleason score. Conclusion: This systematic review unveils the substantial clinical promise of using circulating biomarkers in assessing the risk for prostate cancer patients. However, the standardization of groups, categories, and biomarker validation are mandatory before this technique can be implemented. Circulating biomarkers might represent a viable alternative to currently available tools, obviating the need for tissue biopsies, and allowing for faster and more cost-effective testing, with superior analytical performance, specificity, and reproducibility. Full article