Cancer type classification using plasma cell-free RNAs derived from human and microbes

Serum Circulating mRNA Panel for the Early Detection of Gastric Cancer: A Potential Biomarker Test

Circulating free messenger RNAs (cfmRNAs) in serum have emerged as potential noninvasive biomarkers for cancer diagnosis, including gastric cancer (GC). This study utilized RNA-sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify a training set of 100 differentially expressed genes (DEGs) specific to GC patients. Employing a support vector machine (SVM) classification, we narrowed down the candidate gene set to 23, which was further refined to 4 genes-DMBX1, EVX1, MAL, and PIWIL1-after validation through reverse transcription quantitative polymerase chain reaction (RT-qPCR). The diagnostic performance of mRNA panels, particularly the combinations of DMBX1 with EVX1 and EVX1 with PIWIL1, was exceptional, achieving area under the curve (AUC) values of 0.800, sensitivities of 90.0 %, and specificities of 80.0 %. The accuracy of these biomarkers was corroborated through various machine learning algorithms, underscoring their robust diagnostic potential. The findings of this study are poised to significantly influence clinical practice by providing robust tools for early GC detection. As these biomarkers undergo further investigation and validation, they hold promise to become integral to the diagnostic for GC.

The potential of circulating cell-free RNA in CNS tumor diagnosis and monitoring: A liquid biopsy approach

Early detection of malignancies, through regular cancer screening, has already proven to have potential to increase survival rates. Yet current screening methods rely on invasive, expensive tissue sampling that has hampered widespread use. Liquid biopsy is noninvasive and represents a potential approach to precision oncology, based on molecular profiling of body fluids. Among these, circulating cell-free RNA (cfRNA) has gained attention due to its diverse composition and potential as a sensitive biomarker. This review provides an overview of the processes of cfRNA delivery into the bloodstream and the role of cfRNA detection in the diagnosis of central nervous system (CNS) tumors. Different types of cfRNAs such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) have been recognized as potential biomarkers in CNS tumors. These molecules exhibit differential expression patterns in the plasma, cerebrospinalfluid (CSF) and urine of patients with CNS tumors, providing information for diagnosing the disease, predicting outcomes, and assessing treatment effectiveness. Few clinical trials are currently exploring the use of liquid biopsy for detecting and monitoring CNS tumors. Despite obstacles like sample standardization and data analysis, cfRNA shows promise as a tool in the diagnosis and management of CNS tumors, offering opportunities for early detection, personalized therapy, and improved patient outcomes.

DeepPhylo: Phylogeny-Aware Microbial Embeddings Enhanced Predictive Accuracy in Human Microbiome Data Analysis

Microbial data analysis poses significant challenges due to its high dimensionality, sparsity, and compositionality. Recent advances have shown that integrating abundance and phylogenetic information is an effective strategy for uncovering robust patterns and enhancing the predictive performance in microbiome studies. However, existing methods primarily focus on the hierarchical structure of phylogenetic trees, overlooking the evolutionary distances embedded within them. This study introduces DeepPhylo, a novel method that employs phylogeny-aware amplicon embeddings to effectively integrate abundance and phylogenetic information. DeepPhylo improves both the unsupervised discriminatory power and supervised predictive accuracy of microbiome data analysis. Compared to the existing methods, DeepPhylo demonstrates superiority in informing biologically relevant insights across five real-world microbiome use cases, including clustering of skin microbiomes, prediction of host chronological age and gender, diagnosis of inflammatory bowel disease (IBD) across 15 studies, and multilabel disease classification.

An investigation of plasma cell-free RNA for the detection of colorectal cancer: From transcriptome marker selection to targeted validation

Regular screening for colorectal cancer (CRC) is critical for early detection and long-term survival. Despite the current screening options available and advancements in therapies there will be around 53,000 CRC related deaths this year. There is great interest in non-invasive alternatives such as plasma cell-free RNA (cfRNA) for diagnostic, prognostic, and predictive applications. In the current study, our aim was to identify and validate potential cfRNA candidates to improve early CRC diagnosis. In phase 1 (n = 49; 25 controls, 24 cancers), discovery total RNA sequencing was performed. Select exons underwent validation in phase 2 (n = 73; 35 controls, 29 cancers, 9 adenomas) using targeted capture sequencing (n = 10,371 probes). In phase 3 (n = 57; 30 controls, 27 cancers), RT-qPCR was performed on previously identified candidates (n = 99). There were 895 exons that were differentially expressed (325 upregulated, 570 downregulated) among cancers versus controls. In phases 2 and 3, fewer markers were validated than expected in independent sets of patients, most of which were from previously published literature (FGA, FGB, GPR107, CDH3, and RP23AP7). In summary, we optimized laboratory processes and data analysis strategies which can serve as methodological framework for future plasma RNA studies beyond just the scope of CRC detection. Additionally, further exploration is needed in order to determine if the few cfRNA candidates identified in this study have clinical utility for early CRC detection. Over time, advancements in technologies, data analysis, and RNA preservation methods at time of collection may improve the biological and technical reproducibility of cfRNA biomarkers and enhance the feasibility of RNA-based liquid biopsies.

The systematic role of pancreatic cancer exosomes: distant communication, liquid biopsy and future therapy

Pancreatic cancer remains one of the most lethal diseases worldwide. Cancer-derived exosomes, benefiting from the protective role of the lipid membrane, exhibit remarkable stability in the circulatory system. These exosomes, released by tumor microenvironment, contain various biomolecules such as proteins, RNAs, and lipids that plays a pivotal role in mediating distant communication between the local pancreatic tumor and other organs or tissues. They facilitate the transfer of oncogenic factors to distant sites, contributing to the compromised body immune system, distant metastasis, diabetes, cachexia, and promoting a microenvironment conducive to tumor growth and metastasis in pancreatic cancer patients. Beyond their intrinsic roles, circulating exosomes in peripheral blood can be detected to facilitate accurate liquid biopsy. This approach offers a novel and promising method for the diagnosis and management of pancreatic cancer. Consequently, circulating exosomes are not only crucial mediators of systemic cell-cell communication during pancreatic cancer progression but also hold great potential as precise tools for pancreatic cancer management and treatment. Exosome-based liquid biopsy and therapy represent promising advancements in the diagnosis and treatment of pancreatic cancer. Exosomes can serve as drug delivery vehicles, enhancing the targeting and efficacy of anticancer treatments, modulating the immune system, and facilitating gene editing to suppress tumor growth. Ongoing research focuses on biomarker identification, drug delivery systems, and clinical trials to validate the safety and efficacy of exosome-based therapies, offering new possibilities for early diagnosis and precision treatment in pancreatic cancer. Leveraging the therapeutic potential of exosomes, including their ability to deliver targeted drugs and modulate immune responses, opens new avenues for innovative treatment strategies.

Depletion-assisted multiplexed cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicles

BACKGROUND

Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature.

METHODS

To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilised a meticulously tailored set of customised guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimise plasma requirements.

RESULTS

Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs and viral RNAs, while EVs showed enrichment in messenger RNAs (mRNAs) and signal recognition particle RNAs (srpRNAs). Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types.

CONCLUSIONS

Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of cfRNAs and liquid biopsy.

KEYPOINTS

DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing) enabled efficient and specific depletion of sequences derived from fragmented ribosomal and mitochondrial RNAs in plasma. Distinct human and microbial cell-free RNA (cfRNA) signatures in whole Plasma versus extracellular vesicles (EVs) were revealed. Both Plasma and EV cfRNAs were capable of distinguishing cancer patients from normal individuals, while microbial RNAs in Plasma cfRNAs enabled better classification of cancer types than EV cfRNAs.

Utilizing nullomers in cell-free RNA for early cancer detection

Early detection of cancer can significantly improve patient outcomes; however, sensitive and highly specific biomarkers for cancer detection are currently missing. Nullomers are the shortest sequences that are absent from the human genome but can emerge due to somatic mutations in cancer. We examine over 10,000 whole exome sequencing matched tumor-normal samples to characterize nullomer emergence across exonic regions of the genome. We also identify nullomer emerging mutational hotspots within tumor genes. Finally, we provide evidence for the identification of nullomers in cell-free RNA from peripheral blood samples, enabling detection of multiple tumor types. We show multiple tumor classification models with an AUC greater than 0.9, including a hepatocellular carcinoma classifier with an AUC greater than 0.99.

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.

Microbes for lung cancer detection: feasibility and limitations

As the second most common cancer in the world, the development of lung cancer is closely related to factors such as heredity, environmental exposure, and lung microenvironment, etc. Early screening and diagnosis of lung cancer can be helpful for the treatment of patients. Currently, CT screening and histopathologic biopsy are widely used in the clinical detection of lung cancer, but they have many disadvantages such as false positives and invasive operations. Microbes are another genome of the human body, which has recently been shown to be closely related to chronic inflammatory, metabolic processes in the host. At the same time, they are important players in cancer development, progression, treatment, and prognosis. The use of microbes for cancer therapy has been extensively studied, however, the diagnostic role of microbes is still unclear. This review aims to summarize recent research on using microbes for lung cancer detection and present the current shortcomings of microbes in collection and detection. Finally, it also looks ahead to the clinical benefits that may accrue to patients in the future about screening and early detection.

Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data

MOTIVATION

Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. However, conventional integration methods rarely utilize prior biological knowledge and lack interpretability.

RESULTS

To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction, 5.1%-12% for cancer stage prediction, and 8.1%-13.6% for cancer drug response prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformer's potential of clinical applications in cancer screening. Moreover, we revealed deregulation of interesting pathways (e.g. scavenger receptor pathway) and their crosstalk in cancer patients' blood, providing potential candidate targets for cancer microenvironment study.

AVAILABILITY AND IMPLEMENTATION

Pathformer is implemented and freely available at https://github.com/lulab/Pathformer.

Decoding bioactive signals of the RNA secretome: the cell-free messenger RNA catalogue

Despite gene-expression profiling being one of the most common methods to evaluate molecular dysregulation in tissues, the utilization of cell-free messenger RNA (cf-mRNA) as a blood-based non-invasive biomarker analyte has been limited compared to other RNA classes. Recent advancements in low-input RNA-sequencing and normalization techniques, however, have enabled characterization as well as accurate quantification of cf-mRNAs allowing direct pathological insights. The molecular profile of the cell-free transcriptome in multiple diseases has subsequently been characterized including, prenatal diseases, neurological disorders, liver diseases and cancers suggesting this biological compartment may serve as a disease agnostic platform. With mRNAs packaged in a myriad of extracellular vesicles and particles, these signals may be used to develop clinically actionable, non-invasive disease biomarkers. Here, we summarize the recent scientific developments of extracellular mRNA, biology of extracellular mRNA carriers, clinical utility of cf-mRNA as disease biomarkers, as well as proposed functions in cell and tissue pathophysiology.

Robustness of cancer microbiome signals over a broad range of methodological variation

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Circulating small extracellular vesicle-based miRNA classifier for follicular thyroid carcinoma: a diagnostic study

BACKGROUND

The diagnosis of follicular thyroid carcinoma (FTC) prior to surgery remains a major challenge in the clinic.

METHODS

This multicentre diagnostic study involved 41 and 150 age- and sex-matched patients in the training cohort and validation cohort, respectively. The diagnostic properties of circulating small extracellular vesicle (sEV)-associated and cell-free RNAs were compared by RNA sequencing in the training cohort. Subsequently, using a quantitative real-time polymerase chain reaction (qRT‒PCR) assay, high-quality candidates were identified to construct an RNA classifier for FTC and verified in the validation cohort. The parallel expression, stability and influence of the RNA classifier on surgical strategy were also investigated.

RESULTS

The diagnostic properties of sEV long RNAs, cell-free long RNAs and sEV microRNAs (miRNAs) were comparable and superior to those of cell-free miRNAs in RNA sequencing. Given the clinical application, the circulating sEV miRNA (CirsEV-miR) classifier was developed from five miRNAs based on qRT‒PCR data, which could well identify FTC patients (area under curve [AUC] of 0.924 in the training cohort and 0.844 in the multicentre validation cohort). Further tests revealed that the CirsEV-miR score was significantly correlated with the tumour burden, and the levels of sEV miRNAs were also higher in sEVs from the FTC cell line, organoid and tissue. Additionally, circulating sEV miRNAs remained constant after different treatments, and the addition of the CirsEV-miR classifier as a biomarker improves the current surgical strategy.

CONCLUSIONS

The CirsEV-miR classifier could serve as a noninvasive, convenient, specific and stable auxiliary test to help diagnose FTC following ultrasonography.

Circulating cell-free DNA-based multi-cancer early detection

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

The mysterious anelloviruses: investigating its role in human diseases

Anelloviruses (AVs) that infect the human population are members of the Anelloviridae family. They are widely distributed in human populations worldwide. Torque teno virus (TTV) was the first virus of this family to be identified and is estimated to be found in the serum of 80-90% of the human population. Sometime after the identification of TTV, Torque teno mini virus (TTMV) and Torque teno midi virus (TTMDV) were also identified and classified in this family. Since identifying these viruses, have been detected in various types of biological fluids of the human body, including blood and urine, as well as vital organs such as the liver and kidney. They can be transmitted from person to person through blood transfusions, fecal-oral contact, and possibly sexual intercourse. Recent studies on these newly introduced viruses show that although they are not directly related to human disease, they may be indirectly involved in initiating or exacerbating some human population-related diseases and viral infections. Among these diseases, we can mention various types of cancers, immune system diseases, viral infections, hepatitis, and AIDS. Also, they likely use the microRNAs (miRNAs) they encode to fulfill this cooperative role. Also, in recent years, the role of proliferation and their viral load, especially TTV, has been highlighted to indicate the immune system status of immunocompromised people or people who undergo organ transplants. Here, we review the possible role of these viruses in diseases that target humans and highlight them as important viruses that require further study. This review can provide new insights to researchers.

cfOmics: a cell-free multi-Omics database for diseases

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.

Method for the extraction of circulating nucleic acids based on MOF reveals cell-free RNA signatures in liver cancer

Cell-free RNA (cfRNA) allows assessment of health, status, and phenotype of a variety of human organs and is a potential biomarker to non-invasively diagnose numerous diseases. Nevertheless, there is a lack of highly efficient and bias-free cfRNA isolation technologies due to the low abundance and instability of cfRNA. Here, we developed a reproducible and high-efficiency isolation technology for different types of cell-free nucleic acids (containing cfRNA and viral RNA) in serum/plasma based on the inclusion of nucleic acids by metal-organic framework (MOF) materials, which greatly improved the isolation efficiency and was able to preserve RNA integrity compared with the most widely used research kit method. Importantly, the quality of cfRNA extracted by the MOF method is about 10-fold that of the kit method, and the MOF method isolates more than three times as many different RNA types as the kit method. The whole transcriptome mapping characteristics of cfRNA in serum from patients with liver cancer was described and a cfRNA signature with six cfRNAs was identified to diagnose liver cancer with high diagnostic efficiency (area under curve = 0.905 in the independent validation cohort) using this MOF method. Thus, this new MOF isolation technique will advance the field of liquid biopsy, with the potential to diagnose liver cancer.

Microbial Biomarkers in Liquid Biopsy for Cancer: An Overview and Future Directions

In recent years, the relationship between microbes and tumors has led to a new wave of scholarly pursuits. Due to the growing awareness of the importance of microbiota, including those within tumors, for cancer onset, progression, metastasis, and treatment, researchers have come to understand that microbiota and the tumor microenvironment together form a dynamic and complex ecosystem. Liquid biopsy technology, a non-invasive and easily repeatable method for sample collection, combined with emerging multi-omics techniques, allows for a more comprehensive and in-depth exploration of microbial signals and characteristics in bodily fluids. Microbial biomarkers hold immense potential in the early diagnosis, treatment stratification, and prognosis prediction of cancer. In this review, we describe the significant potential of microbial biomarkers in liquid biopsy for clinical applications in cancer, including early diagnosis, predicting treatment responses, and prognosis. Moreover, we discuss current limitations and potential solutions related to microbial biomarkers. This review aims to provide an overview and future directions of microbial biomarkers in liquid biopsy for cancer clinical practice.

Profiling of repetitive RNA sequences in the blood plasma of patients with cancer

Liquid biopsies provide a means for the profiling of cell-free RNAs secreted by cells throughout the body. Although well-annotated coding and non-coding transcripts in blood are readily detectable and can serve as biomarkers of disease, the overall diagnostic utility of the cell-free transcriptome remains unclear. Here we show that RNAs derived from transposable elements and other repeat elements are enriched in the cell-free transcriptome of patients with cancer, and that they serve as signatures for the accurate classification of the disease. We used repeat-element-aware liquid-biopsy technology and single-molecule nanopore sequencing to profile the cell-free transcriptome in plasma from patients with cancer and to examine millions of genomic features comprising all annotated genes and repeat elements throughout the genome. By aggregating individual repeat elements to the subfamily level, we found that samples with pancreatic cancer are enriched with specific Alu subfamilies, whereas other cancers have their own characteristic cell-free RNA profile. Our findings show that repetitive RNA sequences are abundant in blood and can be used as disease-specific diagnostic biomarkers.

Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients' blood

During cancer progression, tumorigenic and immune signals are spread through circulating molecules, such as cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in the blood. So far, they have not been comprehensively investigated in gastrointestinal cancers. Here, we profile 4 categories of cell-free omics data from patients with colorectal cancer and patients with stomach adenocarcinoma and then assay 15 types of genomic, epigenomic, and transcriptomic variations. We find that multi-omics data are more appropriate for detection of cancer genes compared with single-omics data. In particular, cfRNAs are more sensitive and informative than cfDNAs in terms of detection rate, enriched functional pathways, etc. Moreover, we identify several peripheral immune signatures that are suppressed in patients with cancer. Specifically, we establish a γδ-T cell score and a cancer-associated-fibroblast (CAF) score, providing insights into clinical statuses like cancer stage and survival. Overall, we reveal a cell-free multi-molecular landscape that is useful for blood monitoring in personalized cancer treatment.

A Simple, Rapid, and Effective Heparinase Protocol to Enable Nucleic Acid Study from Frozen Heparinized Plasma

Cell-free RNAs (cfRNAs) are promising analytes as non-invasive biomarkers and have even greater potential if tied in with metabolomics. Plasma is an optimal source for cfRNAs but is often derived from a variety of anticoagulants. Plasma obtained in heparin is suitable for metabolomics but is difficult to utilize for qPCR-based downstream analysis. In the present study, we aimed to develop a simple, time-efficient, and cost-effective heparinase protocol, followed by library preparation and sequencing of human plasma cfRNAs drawn and stored in heparin at -80 °C for several years. Blood was collected in CPT™ sodium heparin tubes from patients with chronic HCV infection (NCT02400216) at the National Institutes of Health (NIH) Clinical Center. Plasma cfRNAs were treated with heparinase I and used for library preparation and next-generation sequencing (NGS). Heparinase treatment maintained RNA integrity and allowed for successful library preparation for all the study subjects even with 7 ng of cfRNAs as starting material. The classification report derived from Pavian R package v1.2.0 showed no artificial reads. The abundance of chordate over microbial reads suggests no addition of experimental error through heparinase I treatment. We report a novel and practical approach to heparinase treatment for human plasma collected and frozen in sodium heparin for several years. This is an effective demonstration of utilizing heparin plasma for NGS and downstream transcriptomic research, which could then be integrated with metabolomics from the same samples, maximizing efficiency and minimizing blood draws.

Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer

BACKGROUND

Liquid biopsy, particularly cell-free RNA (cfRNA), has emerged as a promising non-invasive diagnostic tool for various diseases, including cancer, due to its accessibility and the wealth of information it provides. A key area of interest is the composition and cellular origin of cfRNA in the blood and the alterations in the cfRNA transcriptomic landscape during carcinogenesis. Investigating these changes can offer insights into the manifestations of tissue alterations in the blood, potentially leading to more effective diagnostic strategies. However, the consistency of these findings across different studies and their clinical utility remains to be fully elucidated, highlighting the need for further research in this area.

RESULTS

In this study, we analyzed over 350 blood samples from four distinct studies, investigating the cell type contributions to the cfRNA transcriptomic landscape in liver cancer. We found that an increase in hepatocyte proportions in the blood is a consistent feature across most studies and can be effectively utilized for classifying cancer and healthy samples. Moreover, our analysis revealed that in addition to hepatocytes, liver endothelial cell signatures are also prominent in the observed changes. By comparing the classification performance of cellular proportions to established markers, we demonstrated that cellular proportions could distinguish cancer from healthy samples as effectively as existing markers and can even enhance classification when used in combination with these markers.

CONCLUSIONS

Our comprehensive analysis of liver cell-type composition changes in blood revealed robust effects that help classify cancer from healthy samples. This is especially noteworthy, considering the heterogeneous nature of datasets and the etiological distinctions of samples. Furthermore, the observed differences in results across studies underscore the importance of integrative and comparative approaches in the future research to determine the consistency and robustness of findings. This study contributes to the understanding of cfRNA composition in liver cancer and highlights the potential of cellular deconvolution in liquid biopsy.

Cell-free RNA for the liquid biopsy of gastrointestinal cancer

Gastrointestinal (GI) cancer includes many cancer types, such as esophageal, liver, gastric, pancreatic, and colorectal cancer. As the cornerstone of personalized medicine for GI cancer, liquid biopsy based on noninvasive biomarkers provides promising opportunities for early diagnosis and dynamic treatment management. Recently, a growing number of studies have demonstrated the potential of cell-free RNA (cfRNA) as a new type of noninvasive biomarker in body fluids, such as blood, saliva, and urine. Meanwhile, transcriptomes based on high-throughput RNA detection technologies keep discovering new cfRNA biomarkers. In this review, we introduce the origins and applications of cfRNA, describe its detection and qualification methods in liquid biopsy, and summarize a comprehensive list of cfRNA biomarkers in different GI cancer types. Moreover, we also discuss perspective studies of cfRNA to overcome its current limitations in clinical applications. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Comprehensive analysis of circulating cell-free RNAs in blood for diagnosing non-small cell lung cancer

Early screening and detection of non-small cell lung cancer (NSCLC) is crucial due to the significantly low survival rate in advanced stages. Blood-based liquid biopsy is non-invasive test to assistant disease diagnosis, while cell-free RNA is one of the promising biomarkers in blood. However, the disease related signatures have not been explored completely for most cell-free RNA transcriptome sequencing (cfRNA-Seq) datasets. To address this gap, we developed a comprehensive cfRNA-Seq pipeline for data analysis and constructed a machine learning model to facilitate noninvasive early diagnosis of NSCLC. The results of our study have demonstrated the identification of differential mRNA, lncRNAs and miRNAs from cfRNA-Seq, which have exhibited significant association with development and progression of lung cancer. The classifier based on gene expression signatures achieved an impressive area under the curve (AUC) of up to 0.9, indicating high specificity and sensitivity in both cross-validation and independent test. Furthermore, the analysis of T cell and B cell immune repertoire extracted from cfRNA-Seq have provided insights into the immune status of cancer patients, while the microbiome analysis has revealed distinct bacterial and viral profiles between NSCLC and normal samples. In our future work, we aim to validate the existence of cancer associated T cell receptors (TCR)/B cell receptors (BCR) and microorganisms, and subsequently integrate all identified signatures into diagnostic model to improve the prediction accuracy. This study not only provided a comprehensive analysis pipeline for cfRNA-Seq dataset but also highlights the potential of cfRNAs as promising biomarkers and models for early NSCLC diagnosis, emphasizing their importance in clinical settings.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.

Cell-free circulating tumor RNAs in plasma as the potential prognostic biomarkers in colorectal cancer

Background

Cell free RNA (cfRNA) contains transcript fragments from multiple cell types, making it useful for cancer detection in clinical settings. However, the pathophysiological origins of cfRNAs in plasma from colorectal cancer (CRC) patients remain unclear.

Methods

To identify the tissue-specific contributions of cfRNAs transcriptomic profile, we used a published single-cell transcriptomics profile to deconvolute cell type abundance among paired plasma samples from CRC patients who underwent tumor-ablative surgery. We further validated the differentially expressed cfRNAs in 5 pairs of CRC tumor samples and adjacent tissue samples as well as 3 additional CRC tumor samples using RNA-sequencing.

Results

The transcriptomic component from intestinal secretory cells was significantly decreased in the in-house post-surgical cfRNA. The HPGD, PACS1, and TDP2 expression was consistent across cfRNA and tissue samples. Using the Cancer Genome Atlas (TCGA) CRC datasets, we were able to classify the patients into two groups with significantly different survival outcomes.

Conclusions

The three-gene signature holds promise in applying minimal residual disease (MRD) testing, which involves profiling remnants of cancer cells after or during treatment. Biomarkers identified in the present study need to be validated in a larger cohort of samples in order to ascertain their possible use in early diagnosis of CRC.