Liquid Biopsy Based on Cell-Free DNA and RNA

Wide-spectrum profiling of plasma cell-free RNA and the potential for health-monitoring

Circulating cell-free RNA (cfRNA) has emerged as a promising analyte for disease detection. However, the comprehensive profiling of diverse cfRNA types remains under-characterized. Here, we applied a new wide-spectrum cfRNA sequencing method and simultaneously captured rRNA, tRNA, mRNA, miRNA, lncRNA and all mitochondrial RNA. The cfRNA compositions, size distributions and highly abundant cfRNA genes were analysed for each type of cfRNA. We depicted the cfRNA cell types of origin profiles of 66 generally healthy individuals and found that BMI showed a significant impact on the kidney-derived cfRNA proportion. Three individuals with some liver problems were identified because of relatively high levels of hepatocyte-specific cfRNA. The abundance levels of different genes and RNA types, including mRNA, miRNA and lncRNA, were significantly correlated with the liver function test results. The genes of individual cfRNA variances were enriched in pathways associated with common diseases such as liver diseases, virus infections, cancers and metabolic diseases. This study provided a profiling of cfRNA and displayed the potential of cfRNA as a biomarker in health monitoring.

RNA methylation: A new promising biomaker in cancer liquid biopsy

RNA methylation is a vital epigenetic modification that regulates gene expression by influencing RNA processes such as transcription, degradation, translation, and transport. Aberrant methylation, including modifications like m6A, m5C, m1A, m7G, and m3C, is closely linked to tumorigenesis and progression. Liquid biopsy, a non-invasive technique analyzing tumor markers in body fluids, offers significant potential for early diagnosis and dynamic monitoring. In this context, RNA methylation, due to its tumor-specific properties, is emerging as a valuable marker. However, significant challenges remain in its clinical application. This review explores the roles of RNA methylation in cancer, recent advances in detection technologies, and its potential as a liquid biopsy marker in tumor management. It highlights its promising applications in cancer diagnosis, prognosis, and personalized treatment in the era of precision oncology.

The impact of liquid biopsy in breast cancer: Redefining the landscape of non-invasive precision oncology

Breast cancer (BC) remains a leading cause of morbidity and mortality among women worldwide, necessitating the development of innovative diagnostic and monitoring strategies. Liquid biopsy (LB), a minimally invasive approach that analyzes circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), and other tumor-derived biomarkers in body fluids, has emerged as a transformative tool in BC management. This review comprehensively explores the role of LB in early detection, disease monitoring, treatment stratification, and resistance surveillance in BC. We discuss the latest advancements in LB technologies, including next-generation sequencing (NGS), digital PCR, and single-cell analysis, highlighting their sensitivity and specificity. Additionally, we examine the clinical utility of LB in guiding personalized therapy, particularly in the context of hormone receptor-positive, HER2positive, and triple-negative BC subtypes. Despite its promise, several challenges, including standardization, validation, and integration into clinical practice, remain to be addressed. By summarizing current evidence and future directions, this review underscores the potential of LB to revolutionize BC diagnosis and treatment, paving the way for a more precise and dynamic approach to disease management.

Integrating multi-cohort machine learning and clinical sample validation to explore peripheral blood mRNA diagnostic biomarkers for prostate cancer

BACKGROUND

The global incidence of prostate cancer (PCa) has been rising annually, and early diagnosis and treatment remain pivotal for improving therapeutic outcomes and patient prognosis. Concurrently, advancements in liquid biopsy technology have facilitated disease diagnosis and monitoring, with its minimally invasive nature and low heterogeneity positioning it as a promising approach for predicting disease progression. However, current liquid biopsy strategies for PCa predominantly rely on prostate-specific antigen (PSA), which lacks specificity and compromises diagnostic accuracy. Thus, there is an urgent need to identify novel liquid biopsy biomarkers to enable early and precise PCa diagnosis.

METHODS

We integrated 12 machine learning algorithms to construct 113 combinatorial models, screening and validating an optimal PCa diagnostic panel across five datasets from TCGA and GEO databases. Subsequently, the biological feasibility of the selected predictive model was verified in one prostate epithelial cell line and five PCa cell lines. Robust RNA diagnostic targets were further validated for their expression in plasma samples to establish an RNA-based liquid biopsy strategy for PCa. Finally, plasma samples from PCa and benign prostatic hyperplasia (BPH) patients at Wuhan Tongji Hospital were collected to evaluate the strategy's clinical significance.

RESULTS

Differential analysis identified 1,071 candidate mRNAs, which were input into the integrated machine learning framework. Among the 113 combinatorial models, the 9-gene diagnostic panel selected by the Stepglm[both] and Enet[alpha = 0.4] algorithms demonstrated the highest diagnostic efficacy (mean AUC = 0.91), including JPH4, RASL12, AOX1, SLC18A2, PDZRN4, P2RY2, B3GNT8, KCNQ5, and APOBEC3C. Cell line experiments further validated AOX1 and B3GNT8 as robust RNA biomarkers, both exhibiting consistent PCa-specific expression in human plasma samples. In liquid biopsy analyses, AOX1 and B3GNT8 outperformed PSA in diagnostic accuracy, achieving a combined AUC of 0.91. Notably, these biomarkers also demonstrated diagnostic utility in patients with ISUP ≤ 2.

CONCLUSIONS

Through an integrated machine learning approach and clinical validation, we developed an RNA-based diagnostic panel for PCa. Specifically, we identified AOX1 and B3GNT8 as novel liquid biopsy biomarkers with promising clinical diagnostic value. These findings provide new targets and insights for early and precise PCa diagnosis.

The Potential and Limitations of the MinION/Yenos Platform for miRNA-Enabled Early Cancer Detection

The 2024 Nobel Prize in Physiology or Medicine was awarded to the pioneers who reported that microRNAs (miRNAs) regulate and direct the switch between physiological and pathological pathways via their over- or underexpression. The discovery changed the medical landscape and there are many completed and on-going clinical studies based on miRNAs. MiRNAs occur at the femtomolar level in biological fluids and are typically quantified using amplification-based techniques. Experimental nanopores have illustrated potential for trace analysis including amplification-free miRNA quantification. We repurposed the MinION, the only commercially available nanopore array device, and developed unique probes and protocols to detect and measure miRNA copies in blood and urine. Here, we report that miRNA copies are proportional to the total RNA isolated from the biospecimen, and that three known miRNA cancer biomarkers, i.e., miR-21, miR-375, and miR-141, were more than 1.5-fold overexpressed in blood samples from breast, ovarian, prostate, pancreatic, lung, and colorectal cancer patients compared to healthy patients. In these cancer samples, miR-15b was not overexpressed, in agreement with earlier studies. In contrast to literature reports, sample variability was undetectable in this study. The potential and limitations of this ready-to-use MinION/Yenos platform for multiple-cancer early detection (MCED) using blood or urine are discussed.

Chromatin accessibility states affect transrenal clearance of plasma DNA: Implications for urine-based diagnostics

BACKGROUND

Urinary cell-free DNA (ucfDNA) is a valuable resource for truly non-invasive liquid biopsy. UcfDNA comprises transrenal ucfDNA passing from the bloodstream through the glomeruli and locally shed urinary-tract ucfDNA. Understanding their differences in characteristics may enable new diagnostic applications.

METHODS

We analyzed 136 ucfDNA samples from healthy controls, pregnant women, patients with chronic kidney diseases (CKDs), and bladder cancer using massively parallel sequencing. Fragmentomic characteristics including fragment sizes and 5' end motifs were deduced. The relationship between ucfDNA and chromatin accessibility was examined by overlapping ucfDNA with open chromatin regions (OCRs, lacking histones) and heterochromatin regions (HCRs, tightly packed with histones).

FINDINGS

Compared with urinary-tract ucfDNA, the transrenal ucfDNA was shorter and enriched for C-ends. The transrenal ucfDNA was over-represented in OCRs but depleted in HCRs, indicating an interplay between the glomerular filtration barrier and the effective cfDNA size. In patients with proteinuria (preeclampsia and CKDs), the amount of ucfDNA from HCRs increased, suggesting elevated glomerular permeability of histone-bound plasma DNA molecules. In oncology, the use of hypomethylation signals in HCRs enhanced bladder cancer detection, with an area under the receiver operating characteristic curve of 0.93.

CONCLUSIONS

Chromatin accessibility states impact the transrenal clearance of plasma DNA, likely through the size restriction of the glomerular barrier. This realization has enabled the rational development of novel approaches for detecting or monitoring renal dysfunction and urological cancers.

FUNDING

The Innovation and Technology Commission of the Hong Kong SAR Government (InnoHK initiative) and the Li Ka Shing Foundation supported this study.

The Potential of cfDNA as Biomarker: Opportunities and Challenges for Neurodegenerative Diseases

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.

Cell-Free Nucleic Acids for Early Diagnosis of Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Rapid identification of acute ischemic stroke (AIS) is challenging in both pre-hospital and hospital settings. We aimed to identify the most promising cell-free nucleic acids (cfNAs) as diagnostic biomarkers for IS within 72 h from symptom onset. We searched PubMed, Web of Science, EMBASE, and Cochrane Library for published articles that evaluated blood cfNAs in the early diagnosis of AIS until 10 May 2023. The diagnostic performances of individual cfNAs were pooled by random-effects meta-analysis based on the fold change of biomarkers' level between AIS and non-AIS patients. Of 2955 records, 66 articles reporting 143 different cfNAs met the inclusion criteria. The median sample size was 110, and 21.4% of the studies performed validation. Among selected high-quality studies, miR-106b-5p, miR-124, miR-155, lncRNA H19, and cfDNA showed good diagnostic performance. Data from four studies on cfDNA involving 355 AIS patients and 97 controls were pooled in the meta-analysis, which showed a significant fold change between AIS and controls (pooled ratio 1.48, 95% confidence interval 1.23-1.79, p < 0.001). This review highlights that cfDNA, miR-106b-5p, miR-124, miR-155, and lncRNA H19 are the most promising biomarkers for AIS diagnosis, and further research is needed for verification.

Advancements in noninvasive techniques for transplant rejection: from biomarker detection to molecular imaging

Transplant rejection remains a significant barrier to the long-term success of organ transplantation. Biopsy, although considered the gold standard, is invasive, costly, and unsuitable for routine monitoring. Traditional biomarkers, such as creatinine and troponin, offer limited predictive value owing to their low specificity, and conventional imaging techniques often fail to detect early organ damage, increasing the risk of undiagnosed rejection episodes. Considering these limitations, emerging noninvasive biomarkers and molecular imaging techniques hold promise for the early and accurate detection of transplant rejection, enabling personalized management strategies. This review highlights noninvasive biomarkers that predict, diagnose, and assess transplant prognosis by reflecting graft injury, inflammation, and immune responses. For example, donor-derived cell-free DNA (dd-cfDNA) is highly sensitive in detecting early graft injury, whereas gene expression profiling effectively excludes moderate-to-severe acute rejection (AR). Additionally, microRNA (miRNA) profiling enhances the diagnostic specificity for precise AR detection. Advanced molecular imaging techniques further augment the monitoring of rejection. Fluorescence imaging provides a high spatiotemporal resolution for AR grading, ultrasound offers real-time and portable monitoring, and magnetic resonance delivers high tissue contrast for anatomical assessments. Nuclear imaging modalities such as single photon emission computed tomography and positron emission tomography, enable dynamic visualization of immune responses within transplanted organs. Notably, dd-cfDNA and nuclear medicine imaging have already been integrated into clinical practice, thereby demonstrating the translational potential of these techniques. Unlike previous reviews, this work uniquely synthesizes advancements in both noninvasive biomarkers and molecular imaging, emphasizing their complementary strengths. Biomarkers deliver molecular-level insights, whereas imaging provides spatial and temporal resolution. Together, they create a synergistic framework for comprehensive and precise transplant monitoring. By bridging these domains, this review underscores their individual contributions and collective potential to enhance diagnostic accuracy, improve patient outcomes, and guide future research and clinical applications in transplant medicine.

Clinical use of circulating tumor DNA analysis in patients with lymphoma

The analysis of circulating tumor DNA (ctDNA) in liquid biopsy specimens has an established role for the detection of predictive molecular alterations and acquired resistance mutations in several tumors. The low-invasiveness of this approach allows for repeated sampling and dynamic monitoring of disease evolution. Originating from the entire body tumor bulk, plasma-derived ctDNA reflects intra- and interlesional genetic heterogeneity. In the management of lymphoma patients, ctDNA quantification at various timepoints of the patient's clinical history is emerging as a complementary tool that may improve risk stratification, assessment of treatment response and early relapse detection during follow-up, most prominently in patients with diffuse large B-cell lymphoma or classic Hodgkin lymphoma. While liquid biopsies have not yet entered standard-of-care treatment protocols in these settings, several trials have provided evidence that at least a subset of lymphoma patients may benefit from the introduction of liquid biopsies into daily clinical care. In parallel, continuous technological developments have enabled highly sensitive ctDNA assessment methods, which span from locus-specific techniques identifying single hotspot mutations, to sequencing panels and genome-wide approaches that explore broader genetic and epigenetic alterations. Here, we provide an overview of current methods and ongoing technical developments for ctDNA evaluation. We also summarize the most important data from a selection of clinical studies that have explored the clinical use of ctDNA in several lymphoma entities.

Identification of syncytiotrophoblast-derived cf-RNA OPA1 to predict the occurrence of preeclampsia

BACKGROUND

Pre-eclampsia (PE) poses a significant threat to mothers and infants worldwide. Studies indicate that taking low-dose aspirin before the 16th week of pregnancy may prevent approximately 70 % of PE cases, highlighting the importance of predicting PE. Cell-free RNA (cf-RNA) exhibits significant changes in the maternal peripheral blood during early pregnancy, making cf-RNA analysis a promising and less invasive method for predicting PE.

METHODS

The two datasets, GSE192902 and GSE149440, were analyzed to identify differentially expressed cf-RNAs, followed by the calculation of their AUC values Subsequently, these cf-RNAs were validated using placental tissues, as well as late- and early-stage plasma samples collected from both healthy individuals and patients with PE. Furthermore, we performed tissue localization and functional analyses on the ultimate candidate gene.

RESULTS

Mitochondrial Dynamin-Like GTPase (OPA1) emerged as the molecule with the most consistent and statistically significant alterations in placental tissues and serum samples from patients with PE across various gestational weeks. Notably, the combination of OPA1 levels and mean arterial pressure (MAP) yielded an AUC of 0.825 (95 % CI: 0.759-0.879) for predicting PE. Additionally, we verified that OPA1 is predominantly expressed in placental syncytiotrophoblast (STB) cells, and its downregulation negatively impacts STB mitochondrial function, angiogenic potential, and cell proliferation.

CONCLUSIONS

OPA1 holds the potential to emerge as a novel cf-RNA for predicting PE.

Inherently imperfect, inherently evolving - The pursuit of precision through biomarkers

Featuring a special section on cancer biomarkers, this issue of the Biomedical Journal highlights research on cfDNA, fecal miRNA, mitochondrial pathways, Epstein-Barr virus DNA, multi-biomarker panels combined with LC-MS-based methods, and circulating tumor cell (CTC) growth status as potential tools for cancer detection and management, including colorectal cancer, ovarian cancer, nasopharyngeal carcinoma, and hepatocellular carcinoma. Additionally, it explores the social stigma surrounding mpox, and discusses the achievements in miRNA research that earned the 2024 Nobel Prize in Physiology or Medicine. Advances in deep learning for trauma imaging are examined, alongside a review of decades of enterovirus research and current vaccination strategies. Other studies investigate the combined use of anthelmintic and antibiotic therapy for roundworm infection, and the application of a well-established Traditional Chinese Medicine herbal formula, commonly prescribed for gynecological conditions, against autoimmune hepatitis. Further articles elucidate the role of the nuclear receptor HNF4 in Schistosoma, mitochondrial dysfunction in acute kidney injury, the effects of astragalus polysaccharides on prostate cancer, and the use of deep learning to predict mortality from electrocardiograms.

Evaluation of cfDNA fragmentation characteristics in plasma for the diagnosis of lung cancer: A prospective cohort study

Lung cancer is one of the most prevalent cancers worldwide, yet only approximately 16% of patients are diagnosed in early stage, highlighting the urgent need for novel, highly accurate detection models. In our study, patients with suspected lung cancer or lung disease, as identified through radiographic imaging, along with healthy individuals, were consecutively recruited from Beijing Chest Hospital. Circulating free DNA (cfDNA) was extracted from plasma samples, and low-depth whole-genome sequencing was performed to identify fragmentomic features for model construction. A total of 265 participants were prospectively enrolled, comprising 124 lung cancer patients and 141 noncancer individuals. The model we developed was based on four cfDNA fragmentation characteristics, including 20-bp breakpoint nucleotides motif, fragmentation size coverage, fragmentation size distribution, and copy number variation. In an independent test cohort, the model achieved an area under the curve (AUC) of 0.861 (95% CI: 0.781-0.942) and demonstrated a sensitivity of 70% (95% CI: 53.5%-83.4%) at a specificity of 89.4% (95% CI: 76.9%-96.5%). Notably, the model was also effective in detecting early-stage cancer, with an AUC of 0.808 (95% CI: 0.69-0.925). In summary, our lung cancer detection model shows strong screening capabilities by leveraging four cfDNA fragmentation characteristics, exhibiting robust performance in early cancer diagnosis.

Rectal Cancer: Exploring Predictive Biomarkers Through Molecular Pathways Involved in Carcinogenesis

In 2022, colorectal cancer (CCR) had the second-highest incidence in Europe, preceded only by breast cancer [...].

Enriched Methylomes of Low-input and Fragmented DNA Using Fragment Ligation EXclusive Methylation Sequencing (FLEXseq)

Methylome profiling is an emerging clinical tool for tumor classification and liquid biopsies. Here, we developed FLEXseq, a genome-wide methylation profiler that enriches and sequences the fragments of DNA flanking the CCGG motif. FLEXseq strongly correlates (Pearson's r = 0.97) with whole genome bisulfite sequencing (WGBS) while enriching 18-fold. To demonstrate the broad applicability of FLEXseq, we verified its usage across cells, body fluids, and formalin-fixed paraffin-embedded (FFPE) tissues. DNA dilutions down to 250 pg decreased CpG coverage, but bias in methylation remained low (Pearson's r ≥ 0.90) compared to a 10 ng input. FLEXseq offers a cost-efficient, base-pair resolution methylome with potential as a diagnostic tool for tissue and liquid biopsies.

Expanding the clinical utility of liquid biopsy by using liquid transcriptome and artificial intelligence

Most of the current utilization of liquid biopsy (LBx) is based on analyzing cell-free DNA(cfDNA). There is limited data on using cell-free RNA (cfRNA) levels (liquid transcriptome) in LBx. The major hurdles for using liquid transcriptome is its low level in circulation and the dilutional effects of various tissues that may pour their RNA into circulation. We explored the potential of using artificial intelligence (AI) to normalize the cancer-specific cfRNA and to enable liquid transcriptome to predict diagnosis. cfRNA transcriptomic data from 1009 peripheral blood samples was generated by hybrid capture next generation sequencing (NGS). Using two-thirds of samples for training and one third for testing, we demonstrate that AI is able to distinguish between normal control (N = 368) and patients with solid tumors (N = 404) with AUC = 0.820 (95 % CI: 0.760-0.879), patients with myeloid neoplasms (N = 99) with AUC = 0.858 (95 % CI: 0.793-0.924) and patients with lymphoid neoplasms (N = 128) with AUC = 0.788 (95 % CI: 0.687-0.888). Specific diagnosis was also possible when patients with lung, breast, colorectal, and myelodysplastic subgroups were tested. This data suggests that liquid transcriptomics when used with AI has the potential of transforming "liquid biopsy" to "true" biopsy, replacing the need for tissue biopsy.

Plasma cell-free RNA signatures of inflammatory syndromes in children

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell-free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), viral infections, and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C-two conditions presenting with overlapping symptoms-with high performance [test area under the curve = 0.98]. We further extended this methodology into a multiclass machine learning framework that achieved 80% accuracy in distinguishing among KD, MIS-C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes.

A Comprehensive Review on Circulating cfRNA in Plasma: Implications for Disease Diagnosis and Beyond

Circulating cfRNA in plasma has emerged as a fascinating area of research with potential applications in disease diagnosis, monitoring, and personalized medicine. Circulating RNA sequencing technology allows for the non-invasive collection of important information about the expression of target genes, eliminating the need for biopsies. This comprehensive review aims to provide a detailed overview of the current knowledge and advancements in the study of plasma cfRNA, focusing on its diverse landscape and biological functions, detection methods, its diagnostic and prognostic potential in various diseases, challenges, and future perspectives.

Cell-free RNA Liquid Biopsy to Monitor Hematopoietic Stem Cell Transplantation

Hematopoietic Stem Cell Transplantation (HSCT) is one of the oldest and most successful immunotherapies. Yet, despite long-standing success in the use of HSCT for the treatment of blood cancers and severe immune disorders, monitoring post-transplant complications remains a challenge due to a lack of informative diagnostic tests. Here, we investigate the utility of cell-free RNA (cfRNA) in plasma as a liquid biopsy to monitor allogeneic HSCT recipients during and after treatment. We assayed longitudinal samples from 92 HSCT recipients by cfRNA sequencing and show that cfRNA provides insight into treatment and recovery trajectories, immune dynamics in response to transplantation, infection, and solid-tissue injury associated with Graft-Versus-Host Disease. Collectively, our results provide support for the use of plasma cfRNA profiling to monitor complications of HSCT.

Plasma Cell-free RNA Signatures of Inflammatory Syndromes in Children

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell-free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), viral infections and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C - two conditions presenting with overlapping symptoms - with high performance (Test Area Under the Curve (AUC) = 0.97). We further extended this methodology into a multiclass machine learning framework that achieved 81% accuracy in distinguishing among KD, MIS-C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes.