The potential of circulating cell free RNA as a biomarker in cancer

Multibiomarker panels in liquid biopsy for early detection of pancreatic cancer - a comprehensive review

Pancreatic ductal adenocarcinoma (PDAC) is frequently detected in late stages, which leads to limited therapeutic options and a dismal overall survival rate. To date, no robust method for the detection of early-stage PDAC that can be used for targeted screening approaches is available. Liquid biopsy allows the minimally invasive collection of body fluids (typically peripheral blood) and the subsequent analysis of circulating tumor cells or tumor-associated molecules such as nucleic acids, proteins, or metabolites that may be useful for the early diagnosis of PDAC. Single biomarkers may lack sensitivity and/or specificity to reliably detect PDAC, while combinations of these circulating biomarkers in multimarker panels may improve the sensitivity and specificity of blood test-based diagnosis. In this narrative review, we present an overview of different liquid biopsy biomarkers for the early diagnosis of PDAC and discuss the validity of multimarker panels.

Recent Technologies towards Diagnostic and Therapeutic Applications of Circulating Nucleic Acids in Colorectal Cancers

Liquid biopsy has emerged as a promising noninvasive approach for colorectal cancer (CRC) management. This review focuses on technologies detecting circulating nucleic acids, specifically circulating tumor DNA (ctDNA) and circulating RNA (cfRNA), as CRC biomarkers. Recent advancements in molecular technologies have enabled sensitive and specific detection of tumor-derived genetic material in bodily fluids. These include quantitative real-time PCR, digital PCR, next-generation sequencing (NGS), and emerging nanotechnology-based methods. For ctDNA analysis, techniques such as BEAMing and droplet digital PCR offer high sensitivity in detecting rare mutant alleles, while NGS approaches provide comprehensive genomic profiling. cfRNA detection primarily utilizes qRT-PCR arrays, microarray platforms, and RNA sequencing for profiling circulating microRNAs and discovering novel RNA biomarkers. These technologies show potential in early CRC detection, treatment response monitoring, minimal residual disease assessment, and tumor evolution tracking. However, challenges remain in standardizing procedures, optimizing detection limits, and establishing clinical utility across disease stages. This review summarizes current circulating nucleic acid detection technologies, their CRC applications, and discusses future directions for clinical implementation.

Increased levels of circulating cell-free double-stranded nucleic acids in the plasma of glioblastoma patients

Circulating cell-free nucleic acids are considered a promising source of biomarkers for diseases and cancer. Liquid biopsy biomarkers for brain tumours represent a major, still unmet, clinical need. In plasma, nucleic acids can be free or be associated with extracellular vesicles (EVs). Here we report an easy and reproducible method to analyse cell-free nucleic acids in plasma and EVs by conventional flow cytometry easy to translate into the clinics. Nucleic acids associated with the EVs or present in plasma samples are stained by Pyronin Y, which is a fluorescent dye that is preferably binding double-stranded nucleic acids. Fluorescent staining of EVs isolated from cell-conditioned media is suitable for DNA and RNA detection by flow cytometry. The nucleic acids are partially protected from degradation by the EVs' membrane. Additionally, DNA and RNA can be stained in plasma samples and plasma-derived EVs. Remarkably, analysis of plasma from patients and healthy individuals reveals a difference in their nucleic acid profiles. Taken together, our results indicate that the proposed methodology, which is based on conventional direct flow cytometry, is a promising easy tool for plasma nucleic acid analysis.

Analysis of Free Circulating Messenger Ribonucleic Acids in Serum Samples from Late-Onset Spinal Muscular Atrophy Patients Using nCounter NanoString Technology

5q-related Spinal muscular atrophy (SMA) is a hereditary multi-systemic disorder leading to progressive muscle atrophy and weakness caused by the degeneration of spinal motor neurons (MNs) in the ventral horn of the spinal cord. Three SMN-enhancing drugs for SMA treatment are available. However, even if these drugs are highly effective when administrated early, several patients do not benefit sufficiently or remain non-responders, e.g., adults suffering from late-onset SMA and starting their therapy at advanced disease stages characterized by long-standing irreversible loss of MNs. Therefore, it is important to identify additional molecular targets to expand therapeutic strategies for SMA treatment and establish prognostic biomarkers related to the treatment response. Using high-throughput nCounter NanoString technology, we analyzed serum samples of late-onset SMA type 2 and type 3 patients before and six months under nusinersen treatment. Four genes (AMIGO1, CA2, CCL5, TLR2) were significantly altered in their transcript counts in the serum of patients, where differential expression patterns were dependent on SMA subtype and treatment response, assessed with outcome scales. No changes in gene expression were observed six months after nusinersen treatment, compared to healthy controls. These alterations in the transcription of four genes in SMA patients qualified those genes as potential SMN-independent therapeutic targets to complement current SMN-enhancing therapies.

OncoUroMiR: Circulating miRNAs for Detection and Discrimination of the Main Urological Cancers Using a ddPCR-Based Approach

The three most common genitourinary malignancies (prostate/kidney/bladder cancers) constitute a substantial proportion of all cancer cases, mainly in the elderly population. Early detection is key to maximizing the patients' survival, but the lack of highly accurate biomarkers that might be used through non-/minimally invasive methods has impaired progress in this domain. Herein, we sought to develop a minimally invasive test to detect and discriminate among those urological cancers based on miRNAs assessment through ddPCR. Plasma samples from 268 patients with renal cell (RCC; n = 119), bladder (BlCa; n = 73), and prostate (PCa; n = 76) carcinomas (UroCancer group), and 74 healthy donors were selected. Hsa-miR-126-3p, hsa-miR-141-3p, hsa-miR-153-5p, hsa-miR-155-5p, hsa-miR-182-5p, hsa-miR-205-5p, and hsa-miR-375-3p levels were assessed. UroCancer cases displayed significantly different circulating hsa-miR-182-5p/hsa-miR-375-3p levels compared to healthy donors. Importantly, the hsa-miR-155-5p/hsa-miR-375-3p panel detected RCC with a high specificity (80.54%) and accuracy (66.04%). Furthermore, the hsa-miR-126-3p/hsa-miR-375-3p panel identified BlCa with a 94.87% specificity and 76.45% NPV whereas higher hsa-miR-126-3p levels were found in PCa patients. We concluded that plasma-derived miRNAs can identify and discriminate among the main genitourinary cancers, with high analytical performance. Although validation in a larger cohort is mandatory, these findings demonstrate that circulating miRNA assessment by ddPCR might provide a new approach for early detection and risk stratification of the most common urological cancers.

Exploring the Role of Circulating Cell-Free RNA in the Development of Colorectal Cancer

Circulating tumor RNA (ctRNA) has recently emerged as a novel and attractive liquid biomarker. CtRNA is capable of providing important information about the expression of a variety of target genes noninvasively, without the need for biopsies, through the use of circulating RNA sequencing. The overexpression of cancer-specific transcripts increases the tumor-derived RNA signal, which overcomes limitations due to low quantities of circulating tumor DNA (ctDNA). The purpose of this work is to present an up-to-date review of current knowledge regarding ctRNAs and their status as biomarkers to address the diagnosis, prognosis, prediction, and drug resistance of colorectal cancer. The final section of the article discusses the practical aspects involved in analyzing plasma ctRNA, including storage and isolation, detection technologies, and their limitations in clinical applications.

Advances in biomarker discovery using circulating cell-free DNA for early detection of hepatocellular carcinoma

The past several decades have witnessed unprecedented progress in basic and clinical cancer research, and our understanding of the molecular mechanisms and pathogenesis of cancers have been greatly improved. More recently, with the availability of high-throughput sequencing and profiling platforms as well as sophisticated analytical tools and high-performance computing capacity, there have been tremendous advances in the development of diagnostic approaches in clinical oncology, especially the discovery of novel biomarkers for cancer early detection. Although tissue biopsy-based pathology has been the "gold standard" for cancer diagnosis, notable limitations such as the risk due to invasiveness and the bias due to intra-tumoral heterogeneity have limited its broader applications in oncology (e.g., screening, regular disease monitoring). Liquid biopsy analysis that exploits the genetic and epigenetic information contained in DNA/RNA materials from body fluids, particularly circulating cell-free DNA (cfDNA) in the blood, has been an intriguing alternative approach because of advantageous features such as sampling convenience and minimal invasiveness. Taking advantage of innovative enabling technologies, cfDNA has been demonstrated for its clinical potential in cancer early detection, including hepatocellular carcinoma (HCC), the most common liver cancer that causes serious healthcare burden globally. Hereby, we reviewed the current advances in cfDNA-based approaches for cancer biomarker discovery, with a focus on recent findings of cfDNA-based early detection of HCC. Future clinical investigations and trials are warranted to further validate these approaches for early detection of HCC, which will contribute to more effective prevention, control, and intervention strategies with the ultimate goal of reducing HCC-associated mortality. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics.

Cell-free chromatin immunoprecipitation can determine tumor gene expression in lung cancer patients

Cell-free DNA (cfDNA) in blood plasma can be bound to nucleosomes that contain post-translational modifications representing the epigenetic profile of the cell of origin. This includes histone H3 lysine 36 trimethylation (H3K36me3), a marker of active transcription. We hypothesised that cell-free chromatin immunoprecipitation (cfChIP) of H3K36me3-modified nucleosomes present in blood plasma can delineate tumour gene expression levels. H3K36me3 cfChIP followed by targeted NGS (cfChIP-seq) was performed on blood plasma samples from non-small-cell lung cancer (NSCLC) patients (NSCLC, n = 8), small-cell lung cancer (SCLC) patients (SCLC, n = 4) and healthy controls (n = 4). H3K36me3 cfChIP-seq demonstrated increased enrichment of mutated alleles compared with normal alleles in plasma from patients with known somatic cancer mutations. Additionally, genes identified to be differentially expressed in SCLC and NSCLC tumours had concordant H3K36me3 cfChIP enrichment profiles in NSCLC (sensitivity = 0.80) and SCLC blood plasma (sensitivity = 0.86). Findings here expand the utility of cfDNA in liquid biopsies to characterise treatment resistance, cancer subtyping and disease progression.

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.

Integrated analysis of circulating cell free nucleic acids for cancer genotyping and immune phenotyping of tumor microenvironment

The circulating cell-free nucleic acids (ccfNAs) consist of a heterogenous cocktail of both single (ssNA) and double-stranded (dsNA) nucleic acids. These ccfNAs are secreted into the blood circulation by both healthy and malignant cells via various mechanisms including apoptosis, necrosis, and active secretion. The major source of ccfNAs are the cells of hematopoietic system under healthy conditions. These ccfNAs include fragmented circulating cell free DNA (ccfDNA), coding or messenger RNA (mRNA), long non-coding RNA (lncRNA), microRNA (miRNA), and mitochondrial DNA/RNA (mtDNA and mtRNA), that serve as prospective biomarkers in assessment of various clinical conditions. For, e.g., free fetal DNA and RNA migrate into the maternal plasma, whereas circulating tumor DNA (ctDNA) has clinical relevance in diagnostic, prognostic, therapeutic targeting, and disease progression monitoring to improve precision medicine in cancer. The epigenetic modifications of ccfDNA as well as circulating cell-free RNA (ccfRNA) such as miRNA and lncRNA show disease-related variations and hold potential as epigenetic biomarkers. The messenger RNA present in the circulation or the circulating cell free mRNA (ccf-mRNA) and long non-coding RNA (ccf-lncRNA) have gradually become substantial in liquid biopsy by acting as effective biomarkers to assess various aspects of disease diagnosis and prognosis. Conversely, the simultaneous characterization of coding and non-coding RNAs in human biofluids still poses a significant hurdle. Moreover, a comprehensive assessment of ccfRNA that may reflect the tumor microenvironment is being explored. In this review, we focus on the novel approaches for exploring ccfDNA and ccfRNAs, specifically ccf-mRNA as biomarkers in clinical diagnosis and prognosis of cancer. Integrating the detection of circulating tumor DNA (ctDNA) for cancer genotyping in conjunction with ccfRNA both quantitatively and qualitatively, may potentially hold immense promise towards precision medicine. The current challenges and future directions in deciphering the complexity of cancer networks based on the dynamic state of ccfNAs will be discussed.

Identifying stage-associated hub genes in bladder cancer via weighted gene co-expression network and robust rank aggregation analyses

BACKGROUND

Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated.

METHODS

The robust rank aggregation approach was adopted to integrate 4 eligible bladder urothelial carcinoma microarray datasets from the Gene Expression Omnibus. Differentially expressed gene sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using weighted gene co-expression network to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the weighted gene co-expression network and robust rank aggregation to screen differentially expressed genes.

RESULTS

CDH11, COL6A3, EDNRA, and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, receiver operating characteristics curves and Kaplan-Meier plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on gene set enrichment analysis for single hub gene, all the genes were closely linked to BC cell proliferation.

CONCLUSIONS

These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA, and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.

CRISPR nuclease off-target activity and mitigation strategies

The discovery of CRISPR has allowed site-specific genomic modification to become a reality and this technology is now being applied in a number of human clinical trials. While this technology has demonstrated impressive efficacy in the clinic to date, there remains the potential for unintended on- and off-target effects of CRISPR nuclease activity. A variety of in silico-based prediction tools and empirically derived experimental methods have been developed to identify the most common unintended effect-small insertions and deletions at genomic sites with homology to the guide RNA. However, large-scale aberrations have recently been reported such as translocations, inversions, deletions, and even chromothripsis. These are more difficult to detect using current workflows indicating a major unmet need in the field. In this review we summarize potential sequencing-based solutions that may be able to detect these large-scale effects even at low frequencies of occurrence. In addition, many of the current clinical trials using CRISPR involve ex vivo isolation of a patient's own stem cells, modification, and re-transplantation. However, there is growing interest in direct, in vivo delivery of genome editing tools. While this strategy has the potential to address disease in cell types that are not amenable to ex vivo manipulation, in vivo editing has only one desired outcome-on-target editing in the cell type of interest. CRISPR activity in unintended cell types (both on- and off-target) is therefore a major safety as well as ethical concern in tissues that could enable germline transmission. In this review, we have summarized the strengths and weaknesses of current editing and delivery tools and potential improvements to off-target and off-tissue CRISPR activity detection. We have also outlined potential mitigation strategies that will ensure that the safety of CRISPR keeps pace with efficacy, a necessary requirement if this technology is to realize its full translational potential.

Ultra-sensitive molecular detection of gene fusions from RNA using ASPYRE

Background

RNA is a critical analyte for unambiguous detection of actionable mutations used to guide treatment decisions in oncology. Currently available methods for gene fusion detection include molecular or antibody-based assays, which suffer from either being limited to single-gene targeting, lack of sensitivity, or long turnaround time. The sensitivity and predictive value of next generation sequencing DNA-based assays to detect fusions by sequencing intronic regions is variable, due to the extensive size of introns. The required depth of sequencing and input nucleic acid required can be prohibitive; in addition it is not certain that predicted gene fusions are actually expressed.

Results

Herein we describe a method based on pyrophosphorolysis to include detection of gene fusions from RNA, with identical assay steps and conditions to detect somatic mutations in DNA [1], permitting concurrent assessment of DNA and RNA in a single instrument run.

Conclusion

The limit of detection was under 6 molecules/ 6 µL target volume. The workflow and instrumentation required are akin to PCR assays, and the entire assay from extracted nucleic acid to sample analysis can be completed within a single day.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01363-0.

Rapid diagnosis of malignant pleural mesothelioma and its discrimination from lung cancer and benign exudative effusions using blood serum

Malignant pleural mesothelioma (MPM), an aggressive cancer associated with exposure to fibrous minerals, can only be diagnosed in the advanced stage because its early symptoms are also connected with other respiratory diseases. Hence, understanding the molecular mechanism and the discrimination of MPM from other lung diseases at an early stage is important to apply effective treatment strategies and for the increase in survival rate. This study aims to develop a new approach for characterization and diagnosis of MPM among lung diseases from serum by Fourier transform infrared spectroscopy (FTIR) coupled with multivariate analysis. The detailed spectral characterization studies indicated the changes in lipid biosynthesis and nucleic acids levels in the malignant serum samples. Furthermore, the results showed that healthy, benign exudative effusion, lung cancer, and MPM groups were successfully separated from each other by applying principal component analysis (PCA), support vector machine (SVM), and especially linear discriminant analysis (LDA) to infrared spectra.

Evaluation of circulating cell-free nucleic acids in health workers occupationally exposed to ionizing radiation

Radiology workers might constantly be exposed to low-dose ionizing radiation due to their profession. Low doses of radiation in a short exposure time have the potential to alter the genome, which might potentially lead to diseases. The main objective of this study was to determine whether the amount of cell-free nucleic acids in plasma samples of radiation-exposed workers was different from the general public, in other words, non-exposed individuals. In this context, we investigated the association between radiation exposure and cell-free nucleic acids concentration by using radiation exposure parameters. The study consisted of 40 radiology workers and 40 individuals who were not exposed to ionizing radiation. The plasma concentrations of cell-free DNA, RNA, and miRNA were measured fluorometrically. We found that the ccfRNA concentration of the radiation-exposed group was significantly different from that of the non-exposed group (p = 0.0001). However, there are no differences between both groups in terms of ccfDNA and ccfmiRNA concentration. The concentration of ccfDNA is significantly correlated with working time in the fluoroscopy field (p < 0.05). We found that the concentration of ccfmiRNA was significantly correlated with working time in plain radiography (p < 0.01) and computed tomography (p < 0.05) and with total working time (p < 0.01). Similarly, the concentrations of ccfRNA were significantly correlated with working time in computed tomography (p < 0.01) and with the total working time (p < 0.05) of the workers. We found that imaging number in computed tomography significantly altered the level of ccfRNA (p = 0.006) and that working time in the computed tomography field significantly affected the ccfRNA concentration (p = 0.03, R² = 0.36 for model). Finally, we determined that total working time was significantly associated with total ccfRNA concentration (p < 0.05, R² = 0.25 for model). In conclusion, total RNA measured in radiation-exposed workers has the potential to predict the radiation exposure risk. Furthermore, total working time and working time in the tomography field significantly alter the level of free nucleic acids.

Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.

Circulating RNAs in prostate cancer patients

Growing bodies of evidence have demonstrated that the identification of prostate cancer (PCa) biomarkers in the patients' blood and urine may remarkably improve PCa diagnosis and progression monitoring. Among diverse cancer-derived circulating materials, extracellular RNA molecules (exRNAs) represent a compelling component to investigate cancer-related alterations. Once outside the intracellular environment, exRNAs circulate in biofluids either in association with protein complexes or encapsulated inside extracellular vesicles (EVs). Notably, EV-associated RNAs (EV-RNAs) were used for the development of several assays (such as the FDA-approved Progensa Prostate Cancer Antigen 3 (PCA3 test) aiming at improving early PCa detection. EV-RNAs encompass a mixture of species, including small non-coding RNAs (e.g. miRNA and circRNA), lncRNAs and mRNAs. Several methods have been proposed to isolate EVs and relevant RNAs, and to perform RNA-Seq studies to identify potential cancer biomarkers. However, EVs in the circulation of a cancer patient include a multitude of diverse populations that are released by both cancer and normal cells from different tissues, thereby leading to a heterogeneous EV-RNA-associated transcriptional signal. Decrypting the complexity of such a composite signal is nowadays the major challenge faced in the identification of specific tumor-associated RNAs. Multiple deconvolution algorithms have been proposed so far to infer the enrichment of cancer-specific signals from gene expression data. However, novel strategies for EVs sorting and sequencing of RNA associated to single EVs populations will remarkably facilitate the identification of cancer-related molecules. Altogether, the studies summarized here demonstrate the high potential of using EV-RNA biomarkers in PCa and highlight the urgent need of improving technologies and computational approaches to characterize specific EVs populations and their relevant RNA cargo.

Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis.

METHODS

To identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models.

RESULTS

From the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2-202 and SPON2-203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development.

CONCLUSION

Using RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2-202, SPON2-203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers.

Liquid Biopsy in Cervical Cancer: Hopes and Pitfalls

Simple Summary

Cervical cancer is the fourth most common cancer in women worldwide, and its incidence is variably distributed between developed and less-resourced countries, in which socio-economic issues and religious beliefs often limit the widespread diffusion and the access to screening campaigns. In the “liquid biopsy” era, the application of non-invasive and repeatable techniques to the identification of diagnostic, prognostic, and predictive biomarkers might facilitate the management of this disease and, hopefully, improve its outcome. The purpose of this review is to explore the progress status of liquid biopsy in cervical cancer patients. Several methods are described, which include the analysis of circulating tumor cells, the search for pathogenic mutations on circulating tumor DNA, as well as the identification of circulating RNAs, focusing on their potential clinical applications and current limitations.

Abstract

Cervical cancer (CC) is the fourth most common cancer in women worldwide, with about 90% of cancer-related deaths occurring in developing countries. The geographical influence on disease evolution reflects differences in the prevalence of human papilloma virus (HPV) infection, which is the main cause of CC, as well as in the access and quality of services for CC prevention and diagnosis. At present, the most diffused screening and diagnostic tools for CC are Papanicolaou test and the more sensitive HPV-DNA test, even if both methods require gynecological practices whose acceptance relies on the woman’s cultural and religious background. An alternative (or complimentary) tool for CC screening, diagnosis, and follow-up might be represented by liquid biopsy. Here, we summarize the main methodologies developed in this context, including circulating tumor cell detection and isolation, cell tumor DNA sequencing, coding and non-coding RNA detection, and exosomal miRNA identification. Moreover, the pros and cons of each method are discussed, and their potential applications in diagnosis and prognosis of CC, as well as their role in treatment monitoring, are explored. In conclusion, it is evident that despite many advances obtained in this field, further effort is needed to validate and standardize the proposed methodologies before any clinical use.

Lateral flow assay-based detection of long non-coding RNAs: A point-of-care platform for cancer diagnosis

Lateral flow assay (LFA) is a flexible, simple, low-costpoint-of-care platform for rapid detection of disease-specific biomarkers. Importantly, the ability of the assay to capture the circulating bio-molecules has gained significant attention, as it offers a potential minimal invasive system for early disease diagnosis and prognosis. In the present article, we review an innovative concept of LFA-based detection of circulating long non-coding RNAs (lncRNAs), one of the key regulators of fundamental biological processes. In addition, their disease-specific expression pattern and presence in biological fluids at differential levels make them excellent biomarker candidates for cancer detection. Our article also provides an update on the requirements for developing and improving such systems and discusses the key aspects of material selection, operational concepts, principles and conceptual design. We assume that the reviewed points will be helpful to improve the diagnostic applicability of LFA based lncRNA detection in cancer diagnosis.

Circulating non-coding RNAs as new biomarkers and novel therapeutic targets in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignant tumors, and a large number of patients are diagnosed and die every year. Due to the lack of appropriate diagnosis, prediction and treatment, early diagnosis rate of CRC is low and the prognosis is poor. Studies have found that abnormally expressed non-coding RNAs (ncRNAs) (including microRNAs (miRNAs), circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs),etc.) play an important regulatory role in the occurrence and development of CRC. Some studies have shown that they are stable in the blood and can be detected repeatedly. They are expected to be non-invasive biomarkers for early diagnosis, prognosis evaluation, and prediction of drug sensitivity of CRC, as well as potential applications in the treatment of CRC.

A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection

Cell-free RNA (cfRNA) is a promising analyte for cancer detection. However, a comprehensive assessment of cfRNA in individuals with and without cancer has not been conducted. We perform the first transcriptome-wide characterization of cfRNA in cancer (stage III breast [n = 46], lung [n = 30]) and non-cancer (n = 89) participants from the Circulating Cell-free Genome Atlas (NCT02889978). Of 57,820 annotated genes, 39,564 (68%) are not detected in cfRNA from non-cancer individuals. Within these low-noise regions, we identify tissue- and cancer-specific genes, defined as “dark channel biomarker” (DCB) genes, that are recurrently detected in individuals with cancer. DCB levels in plasma correlate with tumor shedding rate and RNA expression in matched tissue, suggesting that DCBs with high expression in tumor tissue could enhance cancer detection in patients with low levels of circulating tumor DNA. Overall, cfRNA provides a unique opportunity to detect cancer, predict the tumor tissue of origin, and determine the cancer subtype.

Cell-free RNA (cfRNA) is a promising analyte for cancer diagnosis. Here, the authors determine the baseline cell-free transcriptome in the absence of cancer and identify tissue- and subtype-specific cfRNA biomarkers in breast and lung cancer patients.

Circulating miR-320a Acts as a Tumor Suppressor and Prognostic Factor in Non-small Cell Lung Cancer

Dysregulated expression profiles of microRNAs (miRNAs) have been observed in several types of cancer, including non-small cell lung cancer (NSCLC); however, the diagnostic and prognostic potential of circulating miRNAs in NSCLC remains largely undefined. Here we found that circulating miR-320a was significantly down-regulated (~5.87-fold; p < 0.0001) in NSCLC patients (n = 80) compared to matched control plasma samples from healthy subjects (n = 80). Kaplan-Meier survival analysis revealed that NSCLC patients with lower levels of circulating miR-320a had overall poorer prognosis and survival rates compared to patients with higher levels (p < 0.0001). Moreover, the diagnostic and prognostic potential of miR-320a correlated with clinicopathological characteristics such as tumor size, tumor node metastasis (TNM) stage, and lymph node metastasis. Functionally, depletion of miR-320a in human A549 lung adenocarcinoma cells induced their metastatic potential and reduced apoptosis, which was reversed by exogenous re-expression of miR-320a mimics, indicating that miR-320a has a tumor-suppressive role in NSCLC. These results were further supported by high levels of epithelial-mesenchymal transition (EMT) marker proteins (e.g., Beta-catenin, MMP9, and E-cadherin) in lung cancer cells and tissues via immunoblot and immunohistochemistry experiments. Moreover, through bioinformatics and dual-luciferase reporter assays, we demonstrated that AKT3 was a direct target of miR-320a. In addition, AKT3-associated PI3K/AKT/mTOR protein-signaling pathways were elevated with down-regulated miR-320a levels in NSCLC. These composite data indicate that circulating miR-320a may function as a tumor-suppressor miRNA with potential as a prognostic marker for NSCLC patients.

Systematic evaluation of multiple qPCR platforms, NanoString and miRNA-Seq for microRNA biomarker discovery in human biofluids

Aberrant miRNA expression has been associated with many diseases, and extracellular miRNAs that circulate in the bloodstream are remarkably stable. Recently, there has been growing interest in identifying cell-free circulating miRNAs that can serve as non-invasive biomarkers for early detection of disease or selection of treatment options. However, quantifying miRNA levels in biofluids is technically challenging due to their low abundance. Using reference samples, we performed a cross-platform evaluation in which miRNA profiling was performed on four different qPCR platforms (MiRXES, Qiagen, Applied Biosystems, Exiqon), nCounter technology (NanoString), and miRNA-Seq. Overall, our results suggest that using miRNA-Seq for discovery and targeted qPCR for validation is a rational strategy for miRNA biomarker development in clinical samples that involve limited amounts of biofluids.

Cell-free DNA and RNA-measurement and applications in clinical diagnostics with focus on metabolic disorders

Circulating cell-free DNA (cfDNA) and RNA (cfRNA) hold enormous potential as a new class of biomarkers for the development of noninvasive liquid biopsies in many diseases and conditions. In recent years, cfDNA and cfRNA have been studied intensely as tools for noninvasive prenatal testing, solid organ transplantation, cancer screening, and monitoring of tumors. In obesity, higher cfDNA concentration indicates accelerated cellular turnover of adipocytes during expansion of adipose mass and may be directly involved in the development of adipose tissue insulin resistance by inducing inflammation. Furthermore, cfDNA and cfRNA have promising diagnostic value in a range of obesity-related metabolic disorders, such as nonalcoholic fatty liver disease, type 2 diabetes, and diabetic complications. Here, we review the current and future applications of cfDNA and cfRNA within clinical diagnostics, discuss technical and analytical challenges in the field, and summarize the opportunities of using cfDNA and cfRNA in the diagnostics and prognostics of obesity-related metabolic disorders.

Identification of protein-protected mRNA fragments and structured excised intron RNAs in human plasma by TGIRT-seq peak calling

Human plasma contains > 40,000 different coding and non-coding RNAs that are potential biomarkers for human diseases. Here, we used thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) combined with peak calling to simultaneously profile all RNA biotypes in apheresis-prepared human plasma pooled from healthy individuals. Extending previous TGIRT-seq analysis, we found that human plasma contains largely fragmented mRNAs from > 19,000 protein-coding genes, abundant full-length, mature tRNAs and other structured small non-coding RNAs, and less abundant tRNA fragments and mature and pre-miRNAs. Many of the mRNA fragments identified by peak calling correspond to annotated protein-binding sites and/or have stable predicted secondary structures that could afford protection from plasma nucleases. Peak calling also identified novel repeat RNAs, miRNA-sized RNAs, and putatively structured intron RNAs of potential biological, evolutionary, and biomarker significance, including a family of full-length excised intron RNAs, subsets of which correspond to mirtron pre-miRNAs or agotrons.

Cell-free Chromatin Immunoprecipitation (cfChIP) from blood plasma can determine gene-expression in tumors from non-small-cell lung cancer patients

OBJECTIVES

Lung cancer is the leading cause of cancer related death worldwide. Accurate molecular diagnostics from a tumor biopsy is paramount for correct diagnosis, treatment strategy, and prediction of outcome. However, a tumor biopsy can be misleading due to tumor heterogeneity and consecutive biopsies are rarely achievable. Importantly, tumor-specific genetic information concerning mutations and translocations, can also be obtained from liquid biopsies, e.g. blood plasma, containing cell-free DNA (cfDNA) with both systemic and tumor origin. Tumor-specific gene-expression information can also be determined from liquid biopsies using cfDNA methylation and cell-free RNA analyses. However, supplementary methodologies that can determine gene-expression patterns in lung tumors from liquid biopsies could also have diagnostic impact.

MATERIALS AND METHODS

We here present the method cell-free chromatin Immunoprecipitation (cfChIP), which for genes having high expression specifically in the tumor, can determine such gene-expression from blood plasma. In cfChIP cell-free nucleosomes modified with histone H3 lysine 36 tri-methylation (H3K36me3), a mark quantitatively correlated with the transcription of the underlying gene, are isolated, and associated cfDNA quantified.

RESULTS

We demonstrate that cfChIP from lung cancer patient blood plasma can successfully quantify the level of H3K36me3 associated with circulating cell-free nucleosomes and thereby quantify the transcriptional level of genes associated with these nucleosomes. Moreover, as a proof-of-principle we show that in blood plasma from 14 lung cancer patients, H3K36me3 cfChIP can replicate the expected higher expression of KRT6 in lung squamous cell carcinoma relative to adenocarcinoma.

CONCLUSION

This work shows that for genes with a high expression specifically in tumor, cfChIP can determine this gene-expression pattern from blood plasma. cfChIP is a method that determine gene-expression at the transcriptional level and accordingly can supplement cfDNA methylation and cell-free RNA analyses.