Neurodegenerative disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS), are characterized by the progressive and gradual degeneration of neurons. The prevalence and rates of these disorders rise significantly with age. As life spans continue to increase in many countries, the number of cases is expected to grow in the foreseeable future. Early and precise diagnosis, along with appropriate surveillance, continues to pose a challenge. The high heterogeneity of neurodegenerative diseases calls for more accurate and definitive biomarkers to improve clinical therapy. Cell-free DNA (cfDNA), including fragmented DNA released into bodily fluids via apoptosis, necrosis, or active secretion, has emerged as a promising non-invasive diagnostic tool for various disorders including neurodegenerative diseases. cfDNA can serve as an indicator of ongoing cellular damage and mortality, including neuronal loss, and may provide valuable insights into disease processes, progression, and therapeutic responses. This review will first cover the key aspects of cfDNA and then examine recent advances in its potential use as a biomarker for neurodegenerative disorders.

Current endometrial receptivity analysis is invasive, preventing embryo transfer during the biopsy cycle. This study aims to screen serum sncRNAs as non-invasive biomarkers for ERA tests.

The study included 12 infertile patients undergoing IVF-ET and ERA, whose serum samples were collected for high-energy sequencing technology to detect sncRNA expression profiles. We overexpressed and knocked down tsRNA-35:73-Asp-GTC-1 in the decidualized Immortalized Human Eutopic Endometrial Stromal Cells (HESC) model cultured in vitro to further investigate the its effect on decidualization. The predicted tsRNA-35:73-Asp-GTC-1 target gene was verified by PCR analysis.

We screened 286 differentially expressed tsRNAs, 46 miRNAs, and 106 piRNAs. KEGG analysis indicated that differentially expressed tsRNAs were associated with pathways such as 'Calcium signaling pathway,' 'Sphingolipid signaling pathway,' etc. The results of RT-qPCR validation showed that the trends of four significantly differentially expressed tsRNAs in serum and endometrium were consistent with sequencing results. ROC curves demonstrated that these four tsRNAs have good predictive value for endometrial receptivity. Overexpression of tsRNA-35:73-Asp-GTC-1 affected the morphology of decidualized cells, and the decidualization indicators also showed a decreasing trend. While knocking down tsRNA-35:73-Asp-GTC-1 had the opposite effect. The RT-qPCR results showed that tsRNA-35:73-Asp-GTC-1 was associated with the Wnt3 target gene.

Serum sncRNA analysis shows potential for studying the molecular mechanisms of endometrial receptivity. Four serum tsRNAs can serve as novel biomarkers for non-invasive endometrial receptivity detection. TsRNA-35:73-Asp-GTC-1 may further regulate endometrial receptivity by targeting Wnt3.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, with metastatic disease posing significant therapeutic challenges. While anti-EGFR therapy has improved outcomes for patients with RAS and BRAF wild-type tumors, resistance remains a major hurdle, limiting treatment efficacy. The concept of negative hyperselection has emerged as a refinement of molecular profiling, identifying additional genomic alterations-such as HER2 and MET amplificationsand MAP2K1 mutations-that predict resistance to anti-EGFR agents. Studies incorporating these expanded assessments have demonstrated that nearly half of patients with RAS/BRAF wild-type tumors harbor alternative resistance biomarkers, underscoring the need for expanded selection criteria. Liquid biopsies, particularly circulating tumor DNA (ctDNA) analysis, have revolutionized precision oncology by providing a minimally invasive, real-time assessment of tumor dynamics. ctDNA-based hyperselection enables the detection of resistance-associated alterations, guiding treatment decisions with greater accuracy than conventional tissue biopsies. Recent trials support the predictive value of ctDNA-defined negative hyperselection, revealing superior outcomes for patients stratified through liquid biopsy. This narrative review explores the evolving role of molecular hyperselection in first-line anti-EGFR therapy, emphasizing the integration of ctDNA to refine patient selection, enhance therapeutic efficacy, and pave the way for personalized treatment strategies in metastatic CRC.

This review provides a comprehensive overview of the latest advancements in the clinical utility of liquid biopsy, with a particular focus on epigenetic approaches aimed at overcoming challenges in cancer diagnosis and treatment. It begins by elucidating key epigenetic terms, including methylomics, fragmentomics, and nucleosomics. The review progresses to discuss methods for analyzing circulating cell-free DNA (cfDNA) and highlights recent studies showcasing the clinical relevance of epigenetic modifications in areas such as diagnosis, drug treatment response, minimal residual disease (MRD) detection, and prognosis prediction. While acknowledging hurdles like the complexity of interpreting epigenetic data and the absence of standardization, the review charts a path forward. It advocates for the integration of multi-omic data through machine learning algorithms to refine predictive models and stresses the importance of collaboration among clinicians, researchers, and data scientists. Such cooperative efforts are essential to fully leverage the potential of epigenetic features in clinical practice.

Pancreatic cancer remains one of the most challenging malignancies to treat due to its late-stage diagnosis, aggressive progression, and high resistance to existing therapies. This review examines the latest advancements in early detection, and therapeutic strategies, with a focus on emerging biomarkers, tumor microenvironment (TME) modulation, and the integration of artificial intelligence (AI) in data analysis. We highlight promising biomarkers, including microRNAs (miRNAs) and circulating tumor DNA (ctDNA), that offer enhanced sensitivity and specificity for early-stage diagnosis when combined with multi-omics panels. A detailed analysis of the TME reveals how components such as cancer-associated fibroblasts (CAFs), immune cells, and the extracellular matrix (ECM) contribute to therapy resistance by creating immunosuppressive barriers. We also discuss therapeutic interventions that target these TME components, aiming to improve drug delivery and overcome immune evasion. Furthermore, AI-driven analyses are explored for their potential to interpret complex multi-omics data, enabling personalized treatment strategies and real-time monitoring of treatment response. We conclude by identifying key areas for future research, including the clinical validation of biomarkers, regulatory frameworks for AI applications, and equitable access to innovative therapies. This comprehensive approach underscores the need for integrated, personalized strategies to improve outcomes in pancreatic cancer.

Precision medicine in breast cancer is a revolutionary approach that customizes diagnosis and treatment based on individual and tumor characteristics, departing from the traditional one-size-fits-all approach. Breast cancer is diverse, with various subtypes driven by distinct genetic mutations. Understanding this diversity is crucial for tailored treatment strategies that target specific vulnerabilities in each tumor. Genetic testing, particularly for mutations in breast cancer gene (BRCA) DNA repair-associated genes, helps assess hereditary risks and influences treatment decisions. Molecular subtyping guides personalized treatments, such as hormonal therapies for receptor-positive tumors and human epidermal growth factor receptor 2 (HER2)-targeted treatments. Targeted therapies, including those for HER2-positive and hormone receptor-positive breast cancers, offer more effective and precise interventions. Immunotherapy, especially checkpoint inhibitors, shows promise, particularly in certain subtypes such as triple-negative breast cancer, with ongoing research aiming to broaden its effectiveness. Integration of big data and artificial intelligence enhances personalized treatment strategies, while liquid biopsies provide real-time insights into tumor dynamics, aiding in treatment monitoring and modification. Challenges persist, including accessibility and tumor complexity, but emerging technologies and precision prevention offer hope for improved outcomes. Ultimately, precision medicine aims to optimize treatment efficacy, minimize adverse effects and enhance the quality of life for patients with breast cancer.

Over the past decade, liquid biopsy (LB) has become a routine diagnostic test essential for the treatment of malignant tumors of various localizations. Its capabilities include early diagnosis, molecular genotyping, prognosis, prediction, and monitoring of tumor response. Typically, liquid biopsy involves the extraction of a single type of tumor-derived molecules or cellular elements from blood and subsequent molecular analysis. These elements may include circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), circulating tumor RNA (ctRNA), or contents of extracellular vesicles (exosomes). Despite the technical sophistication of molecular analysis methods for circulating biomarkers, this diagnostic approach has limited relevance. In a significant proportion of cancer patients (ranging from 10 to 50%, depending on the tumor type), none of these analytes can be detected and analyzed, even in the presence of large, progressing neoplastic foci in the body. It seems reasonable to suggest that heterogeneous fractions of the circulating tumor-specific biomarkers complement each other, thus simultaneous analysis of several fractions will not only increase sensitivity of the method but also more accurately characterize and predict the clinical situation. This review examines the possibilities and advantages of applying a combined multiparametric approach to liquid biopsy, which involves testing multiple circulating analytes in a single blood sample.

Like most tumors, glioblastoma multiforme (GBM), the deadliest brain tumor in human adulthood, releases extracellular vesicles (EVs). Their content, reflecting that of the tumor of origin, can be donated to nearby and distant cells which, by acquiring it, become more aggressive. Therefore, the study of EV-transported molecules has become very important. Particular attention has been paid to EV proteins to uncover new GBM biomarkers and potential druggable targets. Proteomic studies have mainly been performed by "bottom-up" mass spectrometry (MS) analysis of EVs isolated by different procedures from conditioned media of cultured GBM cells and biological fluids from GBM patients. Although a great number of dysregulated proteins have been identified, the translation of these findings into clinics remains elusive, probably due to multiple factors, including the lack of standardized procedures for isolation/characterization of EVs and analysis of their proteome. Thus, it is time to change research strategies by adopting, in addition to harmonized EV selection techniques, different MS methods aimed at identifying selected tumoral protein mutations and/or isoforms due to post-translational modifications, which more deeply influence the tumor behavior. Hopefully, these data integrated with those from other "omics" disciplines will lead to the discovery of druggable pathways for novel GBM therapies.

Gastric cancer is the fifth most common disease in the world and the fourth most common cause of death. It is diagnosed through esophagogastroduodenoscopy with biopsy; however, there are limitations in finding lesions in the early stages. Recently, research has been actively conducted to use liquid biopsy to diagnose various cancers, including gastric cancer. Various substances derived from cancer are reflected in the blood. By analyzing these substances, it was expected that not only the presence or absence of cancer but also the type of cancer can be diagnosed. However, the amount of these substances is extremely small, and even these have various variables depending on the characteristics of the individual or the characteristics of the cancer. To overcome these, we collected methylated DNA fragments using MeDIP and compared them with normal plasma to characterize gastric cancer tissue or patients' plasma. We attempted to diagnose gastric cancer using the characteristics of cancer reflected in the blood through the cancer tissue and patients' plasma. As a result, we confirmed that the consistency of common methylated fragments between tissue and plasma was approximately 41.2% and we found the possibility of diagnosing and characterizing cancer using the characteristics of the fragments through SFR and 5'end-motif analysis.

Liquid biopsy is emerging as a pivotal tool in precision oncology, offering a noninvasive and comprehensive approach to cancer diagnostics and management. By harnessing biofluids such as blood, urine, saliva, cerebrospinal fluid, and pleural effusions, this technique profiles key biomarkers including circulating tumor DNA, circulating tumor cells, microRNAs, and extracellular vesicles. This review discusses the extended scope of liquid biopsy, highlighting its indispensable role in enhancing patient outcomes through early detection, continuous monitoring, and tailored therapy. While the advantages are notable, we also address the challenges, emphasizing the necessity for precision, cost-effectiveness, and standardized methodologies in its broader application. The future trajectory of liquid biopsy is set to expand its reach in personalized medicine, fueled by technological advancements and collaborative research.

Vascular malformations are congenital lesions that occur due to mutations in major cellular signalling pathways which govern angiogenesis, cell proliferation, motility, and cell death. These pathways have been widely studied in oncology and are substrates for various small molecule inhibitors. Given their common molecular biology, there is now a potential to repurpose these cancer drugs for vascular malformation care; however, a molecular diagnosis is required in order to tailour specific drugs to the individual patient's mutational profile. Liquid biopsies (LBs), emerging as a transformative tool in the field of oncology, hold significant promise in this feat. This paper explores the principles and technologies underlying LBs and evaluates their potential to revolutionize the management of vascular malformations. The review begins by delineating the fundamental principles of LBs, focusing on the detection and analysis of circulating biomarkers such as cell-free DNA, circulating tumor cells, and extracellular vesicles. Subsequently, an in-depth analysis of the technological advancements driving LB platforms is presented. Lastly, the paper highlights the current state of research in applying LBs to various vascular malformations, and uses the aforementioned principles and techniques to conceptualize a liquid biopsy framework that is unique to vascular malformation research and clinical care.

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

The technological advances of recent years have significantly enhanced medical discoveries [...].

Obstructive sleep apnea (OSA) is a highly prevalent chronic disease affecting nearly a billion people globally and increasing the risk of multi-organ morbidity and overall mortality. However, the mechanisms underlying such adverse outcomes remain incompletely delineated. Extracellular vesicles (exosomes) are secreted by most cells, are involved in both proximal and long-distance intercellular communication, and contribute toward homeostasis under physiological conditions. A multi-omics integrative assessment of plasma-derived exosomes from adult OSA patients prior to and after 1-year adherent CPAP treatment is lacking. We conducted multi-omic integrative assessments of plasma-derived exosomes from adult OSA patients prior to and following 1-year adherent CPAP treatment to identify potential specific disease candidates. Fasting morning plasma exosomes isolated from 12 adult patients with polysomnographically-diagnosed OSA were analyzed before and after 12 months of adherent CPAP therapy (mean ≥ 6 h/night) (OSAT). Exosomes were characterized by flow cytometry, transmission electron microscopy, and nanoparticle tracking analysis. Endothelial cell barrier integrity, wound healing, and tube formation were also performed. Multi-omics analysis for exosome cargos was integrated. Exosomes derived from OSAT improved endothelial permeability and dysfunction as well as significant improvement in tube formation compared with OSA. Multi-omic approaches for OSA circulating exosomes included lipidomic, proteomic, and small RNA (miRNAs) assessments. We found 30 differentially expressed proteins (DEPs), 72 lipids (DELs), and 13 miRNAs (DEMs). We found that the cholesterol metabolism (has04979) pathway is associated with lipid classes in OSA patients. Among the 12 subjects of OSA and OSAT, seven subjects had complete comprehensive exosome cargo information including lipids, proteins, and miRNAs. Multi-omic approaches identify potential signature biomarkers in plasma exosomes that are responsive to adherent OSA treatment. These differentially expressed molecules may also play a mechanistic role in OSA-induced morbidities and their reversibility. Our data suggest that a multi-omic integrative approach might be useful in understanding how exosomes function, their origin, and their potential clinical relevance, all of which merit future exploration in the context of relevant phenotypic variance. Developing an integrated molecular classification should lead to improved diagnostic classification, risk stratification, and patient management of OSA by assigning molecular disease-specific therapies.

The most promising method for monitoring patients with minimal morbidity is the detection of circulating melanoma cells (CMCs). We have shown that CD45^-CD146⁺ABCB5⁺ CMCs identify a rare primitive stem/mesenchymal CMCs population associated with disease progression. The epithelial-to-mesenchymal transition (EMT) confers cancer cells a hybrid epithelial/mesenchymal phenotype promoting metastatization. Thus, we investigated the potential clinical value of the EMT gene signature of these primitive CMCs. A reliable quantitative real-time polymerase chain reaction (qRT-PCR) protocol was settled up using tumor cell lines RNA dilutions. Afterwards, immune-magnetically isolated CMCs from advanced melanoma patients, at onset and at the first checkpoint (following immune or targeted therapy), were tested for the level of EMT hallmarks and EMT transcription factor genes. Despite the small cohort of patients, we obtained promising results. Indeed, we observed a deep gene rewiring of the EMT investigated genes: in particular we found that the EMT gene signature of isolated CMCs correlated with patients' clinical outcomes. In conclusion, We established a reliable qRT-PCR protocol with high sensitivity and specificity to characterize the gene expression of isolated CMCs. To our knowledge, this is the first evidence demonstrating the impact of immune or targeted therapies on EMT hallmark gene expressions in CMCs from advanced melanoma patients.

Liquid biopsy has become a significant tool in personalized medicine, enabling real-time monitoring of cancer evolution and patient follow-up. This minimally invasive procedure analyzes circulating tumor cells (CTCs) and circulating tumor-derived materials, such as ctDNA, miRNAs, and EVs. CTC analysis significantly impacts prognosis, detection of minimal residual disease (MRD), treatment selection, and monitoring of cancer patients. Liquid biopsy is an attractive option for mouth cancer detection and treatment progress monitoring in many countries. It is not invasive and requires no surgical expertise, making it an attractive option for mouth cancer detection. Liquid biopsy is a diagnostic repeatable test that can profile cancer genomes in real-time with minimal invasiveness and tailor oncological decision-making. It analyzes different blood-circulating biomarkers, with ctDNA being the preferred one. While tissue biopsy remains the gold standard for molecular evaluation of solid tumors, liquid biopsy is a complementary tool in various clinical settings, including treatment selection, monitoring response, cancer clonal evolution, prognostic evaluation, early disease detection, and minimal residual disease (MRD).