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Liquid biopsy has gained attention in oncology as a non-invasive diagnostic tool, offering valuable insights into tumor biology through the analysis of circulating nucleic acid (cfDNA and cfRNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and tumor-educated platelets (TEPs). In this review, we summarize the clinical use of liquid biopsies in cancer now and look forward to its future, with a particular emphasis on some the methods used to isolate the liquid biopsy analytes. This technique provides real-time information on tumor dynamics, treatment response, and disease progression, with the potential for early diagnosis and personalized treatment. Despite its advantages, liquid biopsy faces several challenges, particularly in detecting analytes in early-stage cancers and evaluating the tumor molecular fraction. Tumor burden, molecular fraction, and the presence of subclones can impact the sensitivity and specificity of the analysis. Recent advancements in artificial intelligence (AI) have enhanced the diagnostic accuracy of liquid biopsy by integrating data, and multimodal approaches that combine multiple biomarkers such as ctDNA, CTCs, EVs, and TEPs show promise in providing a more comprehensive view of tumor characteristics. Liquid biopsy has the potential to revolutionize cancer care by providing rapid, non-invasive, and cost-effective diagnostics, enabling timely interventions and personalized treatment strategies.

Early detection of cancer holds high promise for reducing cancer-related mortality. Detection of circulating tumor-specific nucleic acids holds promise, but sensitivity and specificity issues remain with current technology. We studied cell-free RNA (cfRNA) in patients with non-small cell lung cancer (NSCLC; n = 56 stage IV, n = 39 stages I-III), pancreatic cancer (PDAC, n = 20 stage III), malignant melanoma (MM, n = 12 stage III-IV), urothelial bladder cancer (UBC, n = 22 stage II and IV), and 65 healthy controls by means of next generation sequencing (NGS) and real-time droplet digital PCR (RT-ddPCR). We identified 192 overlapping upregulated transcripts in NSCLC and PDAC by NGS, more than 90% of which were noncoding. Previously reported transcripts (e.g., HOTAIRM1) were identified. Plasma cfRNA transcript levels of POU6F2-AS2 discriminated NSCLC from healthy donors (AUC = 0.82 and 0.76 for stages IV and I–III, respectively) and significantly associated (p = 0.017) with the established tumor marker Cyfra 21-1. cfRNA yield and POU6F2-AS transcript abundance discriminated PDAC patients from healthy donors (AUC = 1.0). POU6F2-AS2 transcript was significantly higher in MM (p = 0.044). In summary, our findings support further validation of cfRNA detection by RT-ddPCR as a biomarker for early detection of solid cancers.

Characterizing the cellular origin and disease‐driven dynamics of cfRNA is essential for integrating cfRNA profiling into clinical workflows and precision‐medicine strategies. Most cfRNA studies are restricted to bulk‐level analyses, which preclude detailed analysis of alterations in the cellular origins of cfRNA. Single‐cell RNA sequencing reveals cellular heterogeneity and communication, but its application to cfRNA is limited by diverse cellular origins, leaving a critical gap in understanding functional alterations in cfRNA biomarker‐originating cells. In this work, we propose CellFreeGMF, a tool designed to enable diagnosis classification of clinical samples, identify cfRNA biomarkers, and analyze the alterations in their originating cells based on graph matrix factorization. Furthermore, by utilizing cell–cell communication analysis, CellFreeGMF investigates the functional alterations occurring in the cfRNA originating cells under disease conditions. We validate CellFreeGMF on diverse cell‐free RNA transcriptome clinical datasets. In the case of pancreatic ductal adenocarcinoma (PDAC), CellFreeGMF not only identified cfRNA biomarkers but also traced their cellular origins to myeloid and T‐cell populations. Further analysis revealed significant transcriptomic differences in these cell populations between disease and normal groups. Our user‐friendly CellFreeGMF toolkit (https://cellfreegmf.readthedocs.io/) enables identifying cfRNA biomarkers and elucidating pathophysiological changes in their originating cells. CellFreeGMF traces plasma cfRNA to likely originating cell types by integrating single‐cell atlases with graph‐regularized matrix factorization. The method decomposes cfRNA profiles into sample–cell contributions to reconstruct pseudo single‐cell expression. This originating‐cell analysis suggests which cell types may contribute to disease‐associated signals and supports exploratory mapping of altered cell–cell communication patterns from liquid biopsy data.

Background Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understanding their biological functions and clinical value. However, many challenges still need to be addressed for their application, including developing specific analysis methods and translating cfRNA fragments with biological support into clinical applications. Results We present cfPeak, a novel method combining statistics and machine learning models to detect the fragmented cfRNA signals effectively. When test in real and artificial cfRNA sequencing (cfRNA-seq) data, cfPeak shows an improved performance compared with other applicable methods. We reveal that narrow cfRNA peaks preferentially overlap with protein binding sites, vesicle-sorting sites, structural sites, and novel small non-coding RNAs (sncRNAs). When applied in clinical cohorts, cfPeak identified cfRNA peaks in patients’ plasma that enable cancer detection and are informative of cancer types and metastasis. Conclusions Our study fills the gap in the current small cfRNA-seq analysis at fragment-scale and builds a bridge to the scientific discovery in cfRNA fragmentomics. We demonstrate the significance of finding low abundant tissue-derived signals in small cfRNA and prove the feasibility for application in liquid biopsy. Graphical Abstract

Glioblastoma (GBM) is a highly aggressive and lethal primary brain cancer that necessitates early detection and accurate diagnosis for effective treatment and improved patient outcomes. Traditional diagnostic methods, such as imaging techniques and tissue biopsies, have limitations in providing real-time information and distinguishing treatment-related changes from tumor progression. Liquid biopsies, used to analyze biomarkers in body fluids, offer a non-invasive and dynamic approach to detecting and monitoring GBM. This article provides an overview of GBM biomarkers in body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), cell-free RNA (cfRNA), microRNA (miRNA), and extracellular vesicles. It explores the clinical utility of these biomarkers for GBM detection, monitoring, and prognosis. Challenges and limitations in implementing liquid biopsy strategies in clinical practice are also discussed. The article highlights the potential of liquid biopsies as valuable tools for personalized GBM management but underscores the need for standardized protocols and further research to optimize their clinical utility.

Gastric cancer (GC) is a common malignancy with early detection being crucial for survival. Liquid biopsy analysis using cell-free nucleic acid is a preferred method for detection. Hence, we conducted a systematic review to assess the diagnostic efficacy of cell-free nucleic acid markers for GC. We searched PubMed and ISI Web of Science databases for articles that conformed to our inclusion and exclusion criteria from 2012 to 2022. The following information was abstracted: first author, year of publication, country/region, age, male proportion, tumor stage for cases, specimen type, measurement method, targeted markers and diagnostic related indicators (including sensitivity, specificity, AUC, P-value). Fifty-eight studies examined cell-free RNAs (cfRNAs) with a total of 62 individual circulating markers and 7 panels in serum or plasma, while 21 studies evaluated cell-free DNAs (cfDNAs) with 29 individual circulating markers and 7 panels. For individual cfRNAs, the median (range) sensitivity and specificity were 80% (21% - 98%) and 80% (54% - 99%), respectively. The median (range) sensitivity and specificity for cfRNA panels were 86% (83% - 90%) and 75% (60% - 98%), respectively. In comparison, the median (range) sensitivity and specificity reported for individual cfDNAs were 50% (18% - 96%) and 93% (57% - 100%), respectively, while cfDNA panels had a median (range) sensitivity and specificity of 85% (41% - 92%) and 73.5% (38% - 90%), respectively. The meta results indicate that cfRNA markers exhibit high sensitivity (80%) and low specificity (80%) for detecting GC, while cfDNA markers have lower sensitivity (59%) but higher specificity (92%). This review has demonstrated that cell-free nucleic acids have the potential to serve as useful diagnostic markers for GC. Given that both cfRNA and cfDNA markers have shown promising diagnostic performance for GC, the combination of the two may potentially enhance diagnostic efficiency.

Despite extensive technological advances and an ever-growing body of literature, liquid biopsy has yet to achieve reliable early detection of pancreatic ductal adenocarcinoma (PDA). Numerous studies have investigated circulating tumor-derived components, including cell-free DNA (cfDNA), cell-free RNA (cfRNA), extracellular vesicles (EVs), and circulating tumor cells (CTCs), primarily using peripheral blood samples; however, their clinical utility for early-stage disease remains limited. The fundamental obstacles are biological rather than purely technical: early PDA and its precursor lesions, such as pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMN), are characterized by minimal tumor burden, low levels of nucleic acid shedding, and substantial background signals from non-neoplastic tissues. Increasing analytical complexity through multilayered liquid biopsy approaches, including analyses from pancreas-associated fluid, has not consistently translated into improved diagnostic performance and, in some cases, has amplified issues related to specificity, reproducibility, and interpretability. Moreover, molecular alterations detected in body fluids may reflect clonal expansion without inevitable malignant progression, raising concerns regarding overdiagnosis and clinical decision-making. Pre-analytical variability, lack of standardization, and limited access to tumor-adjacent fluids further hinder clinical implementation. Liquid biopsy should therefore be regarded as a complementary modality rather than a substitute for histopathological diagnosis, with its precise clinical role in early detection still ill-defined. In this review, we critically examine why liquid biopsy has not yet succeeded in early PDA detection, highlighting the key biological, technical, and clinical barriers that must be addressed to move the field beyond exploratory research toward meaningful clinical application.

Background The COVID-19 pandemic highlighted the need for novel biomarkers to identify patients at risk of developing severe disease. Circulating cell-free nucleic acids (cfNAs) are released from injured host cells or can be derived from pathogens. As cfNA is suggested to be a useful biomarker in oncologic, autoimmune, and other diseases, the aim of our study was to investigate the role of serum cfNA in predicting the course and outcome of COVID-19 disease. Methods We conducted a retrospective cohort study at Vilnius University Hospital Santaros Klinikos, utilizing serum samples collected upon admission and accompanying health information obtained from the Vilnius Santaros Klinikos Biobank. A total of 108 adult COVID-19 patients hospitalized between November 24, 2020, and November 10, 2021, and 24 healthy controls were enrolled. cfDNA concentration was measured using capillary electrophoresis. cfRNA was detected using quantitative real-time PCR. Results cfDNA concentration was higher in COVID-19 patients compared with controls (4.28 vs. 0.51 ng/µL, p  < 0.001) and increased with disease severity: from 1.06 ng/µL in mild to 2.65 ng/µL in severe, and 6.68 ng/µL in critical disease. cfDNA levels were higher in intensive care unit (ICU) patients (6.68 vs. 2.30 ng/µL, p  < 0.001), patients requiring advanced respiratory support (ARS) (7.11 vs. 2.74 ng/µL, p  < 0.001), and non-survivors (16.68 vs. 3.44 ng/µL, p  < 0.001). cfDNA showed high predictive values for ICU admission (AUC 0.79), ARS (AUC 0.77), and lethal outcome (AUC 0.81), outperforming other routine biomarkers. In logistic regression analysis, cfDNA remained an independent predictor for ICU admission (OR 1.21, 95%CI 1.08–1.36), ARS requirement (OR 1.15, 95%CI 1.03–1.28), and in-hospital mortality (OR 1.08, 95%CI 1.02–1.14), while serum SARS-CoV-2 RNAemia remained an independent predictor of ARS requirement (OR 4.18, 95%CI 1.15–15.20). Conclusions Higher serum cfDNA concentrations were independently associated with greater COVID-19 disease severity, increased likelihood of requiring intensive care, ARS, and in-hospital mortality. cfDNA demonstrated superior prognostic performance, surpassing established biomarkers. Serum SARS-CoV-2 RNAemia was associated with the need for ARS. This indicates that cfNAs may serve as clinically valuable biomarkers for early risk stratification and outcome prediction in COVID-19 patients. Further validation in independent cohorts is required to confirm generalizability. Clinical trial number Not applicable.

Pancreatic ductal adenocarcinoma (PDAC) comprises two clinically relevant molecular subtypes that are currently determined using tissue biopsies, which are spatially biased and highly invasive. We used whole transcriptome sequencing of 10 plasma samples with tumor‐informed subtypes, complemented by proteomic analysis for minimally invasive identification of PDAC subtype markers. Data were validated in independent large cohorts and correlated with treatment response and patient outcome. Differential transcript abundance analyses revealed 32 subtype‐specific, protein‐coding cell‐free RNA (cfRNA) transcripts. The subtype specificity of these transcripts was validated in two independent tissue cohorts comprising 195 and 250 cases, respectively. Three disease‐relevant cfRNA‐defined subtype markers (DEGS1, KDELC1, and RPL23AP7) that consistently associated with basal‐like tumors across all cohorts were identified. In both tumor and liquid biopsies, the overexpression of these markers correlated with poor survival. Moreover, elevated levels of the identified markers were linked to a poor response to systemic therapy and early relapse in resected patients. Our data indicate clinical applicability of cfRNA markers in determining tumor subtypes and monitoring disease recurrence. Subtype determination of the two clinically relevant molecular subtypes of pancreatic cancer has exclusively relied on tumor tissue samples, which are unfortunately not always available, require highly invasive procedures to obtain and, owing to tumor heterogeneity, provide limited results. In this proof‐of‐concept study, we deploy next‐generation sequencing and digital PCR to provide evidence for the clinical utility of cell‐free RNA for determination of PDAC subtypes.

Malignant pleural effusion (MPE) from patients with advanced non-small-cell lung cancer (NSCLC) has been proven valuable for molecular analysis; however, simultaneous detection of driver fusions in MPE is still challenging. In this study, we investigated the Idylla™ GeneFusion Panel, a stand-alone test in tissue samples, in the evaluation of ALK, ROS1, RET and MET ex14 skipping mutations in MPE and compared its performance with routine reference methods (Real-time-based and Next-generation Sequencing—NGS). The inclusion criteria for sample selection were as follows: advanced NSCLC harboring ALK, ROS1, RET fusions or MET exon-skipping alterations and the availability of MPE collected at diagnosis or disease progression. Molecular alterations have been investigated on tissue by fluorescence in situ hybridization (FISH) or Real-time PCR or NGS. For molecular profiling with the Idylla™ GeneFusion, 200 µL of MPE supernatants combined with 50 µL of RNA Later solution were loaded into the Idylla™ cartridge without cfRNA extraction. The Idylla™ GeneFusion Assay performed on MPEs was able to confirm molecular profile, previously diagnosed with conventional methods, in all cases. Our data confirm that MPE are suitable material for investigating fusion alterations. The Idylla™ GeneFusion, although indicated for investigation of tissue samples, offers the possibility of performing a molecular characterization of supernatants without undertaking the entire cfRNA extraction procedure providing a rapid and reliable strategy for the detection of actionable genetic alterations.