Ratio-Based Method To Identify True Biomarkers by Normalizing Circulating ncRNA Sequencing and Quantitative PCR Data

sRNAfrag: a pipeline and suite of tools to analyze fragmentation in small RNA sequencing data

Fragments derived from small RNAs such as small nucleolar RNAs are biologically relevant but remain poorly understood. To address this gap, we developed sRNAfrag, a modular and interoperable tool designed to standardize the quantification and analysis of small RNA fragmentation across various biotypes. The tool outputs a set of tables forming a relational database, allowing for an in-depth exploration of biologically complex events such as multi-mapping and RNA fragment stability across different cell types. In a benchmark test, sRNAfrag was able to identify established loci of mature microRNAs solely based on sequencing data. Furthermore, the 5' seed sequence could be rediscovered by utilizing a visualization approach primarily applied in multi-sequence-alignments. Utilizing the relational database outputs, we detected 1411 snoRNA fragment conservation events between two out of four eukaryotic species, providing an opportunity to explore motifs through evolutionary time and conserved fragmentation patterns. Additionally, the tool's interoperability with other bioinformatics tools like ViennaRNA amplifies its utility for customized analyses. We also introduce a novel loci-level variance-score which provides insights into the noise around peaks and demonstrates biological relevance by distinctly separating breast cancer and neuroblastoma cell lines after dimension reduction when applied to small nucleolar RNAs. Overall, sRNAfrag serves as a versatile foundation for advancing our understanding of small RNA fragments and offers a functional foundation to further small RNA research. Availability: https://github.com/kenminsoo/sRNAfrag.

Identifying MicroRNAs Suitable for Detection of Breast Cancer: A Systematic Review of Discovery Phases Studies on MicroRNA Expression Profiles

The analysis of circulating tumor cells and tumor-derived materials, such as circulating tumor DNA, circulating miRNAs (cfmiRNAs), and extracellular vehicles provides crucial information in cancer research. CfmiRNAs, a group of short noncoding regulatory RNAs, have gained attention as diagnostic and prognostic biomarkers. This review focuses on the discovery phases of cfmiRNA studies in breast cancer patients, aiming to identify altered cfmiRNA levels compared to healthy controls. A systematic literature search was conducted, resulting in 16 eligible publications. The studies included a total of 585 breast cancer cases and 496 healthy controls, with diverse sample types and different cfmiRNA assay panels. Several cfmiRNAs, including MIR16, MIR191, MIR484, MIR106a, and MIR193b, showed differential expressions between breast cancer cases and healthy controls. However, the studies had a high risk of bias and lacked standardized protocols. The findings highlight the need for robust study designs, standardized procedures, and larger sample sizes in discovery phase studies. Furthermore, the identified cfmiRNAs can serve as potential candidates for further validation studies in different populations. Improving the design and implementation of cfmiRNA research in liquid biopsies may enhance their clinical diagnostic utility in breast cancer patients.

sRNAfrag: A pipeline and suite of tools to analyze fragmentation in small RNA sequencing data

Fragments derived from small RNAs such as small nucleolar RNAs hold biological relevance. However, they remain poorly understood, calling for more comprehensive methods for analysis. We developed sRNAfrag, a standardized workflow and set of scripts to quantify and analyze sRNA fragmentation of any biotype. In a benchmark, it is able to detect loci of mature microRNAs fragmented from precursors and, utilizing multi-mapping events, the conserved 5' seed sequence of miRNAs which we believe may extraoplate to other small RNA fragments. The tool detected 1411 snoRNA fragment conservation events between 2/4 eukaryotic species, providing the opportunity to explore motifs and fragmentation patterns not only within species, but between. Availability: https://github.com/kenminsoo/sRNAfrag.

Plasma microRNA ratios associated with breast cancer detection in a nested case-control study from a mammography screening cohort

Mammographic breast cancer screening is effective in reducing breast cancer mortality. Nevertheless, several limitations are known. Therefore, developing an alternative or complementary non-invasive tool capable of increasing the accuracy of the screening process is highly desirable. The objective of this study was to identify circulating microRNA (miRs) ratios associated with BC in women attending mammography screening. A nested case-control study was conducted within the ANDROMEDA cohort (women of age 46-67 attending BC screening). Pre-diagnostic plasma samples, information on life-styles and common BC risk factors were collected. Small-RNA sequencing was carried out on plasma samples from 65 cases and 66 controls. miR ratios associated with BC were selected by two-sample Wilcoxon test and lasso logistic regression. Subsequent assessment by RT-qPCR of the miRs contained in the selected miR ratios was carried out as a platform validation. To identify the most promising biomarkers, penalised logistic regression was further applied to candidate miR ratios alone, or in combination with non-molecular factors. Small-RNA sequencing yielded 20 candidate miR ratios associated with BC, which were further assessed by RT-qPCR. In the resulting model, penalised logistic regression selected seven miR ratios (miR-199a-3p_let-7a-5p, miR-26b-5p_miR-142-5p, let-7b-5p_miR-19b-3p, miR-101-3p_miR-19b-3p, miR-93-5p_miR-19b-3p, let-7a-5p_miR-22-3p and miR-21-5p_miR-23a-3p), together with body mass index (BMI), menopausal status (MS), the interaction term BMI * MS, life-style score and breast density. The ROC AUC of the model was 0.79 with a sensitivity and specificity of 71.9% and 76.6%, respectively. We identified biomarkers potentially useful for BC screening measured through a widespread and low-cost technique. This is the first study reporting circulating miRs for BC detection in a screening setting. Validation in a wider sample is warranted.Trial registration: The Andromeda prospective cohort study protocol was retrospectively registered on 27-11-2015 (NCT02618538).

Mitochondria-derived small RNAs as diagnostic biomarkers in lung cancer patients through a novel ratio-based expression analysis methodology

Small non-coding RNAs are potential diagnostic biomarkers for lung cancer. Mitochondria-derived small RNA (mtRNA) is a novel regulatory small non-coding RNA that only recently has been identified and cataloged. Currently, there are no reports of studies of mtRNA in human lung cancer. Currently, normalization methods are unstable, and they often fail to identify differentially expressed small non-coding RNAs (sncRNAs). In order to identify reliable biomarkers for lung cancer screening, we used a ratio-based method using mtRNAs newly discovered in human peripheral blood mononuclear cells. In the discovery cohort (AUC = 0.981) and independent validation cohort (AUC = 0.916) the prediction model of eight mtRNA ratios distinguished lung cancer patients from controls. The prediction model will provide reliable biomarkers that will allow blood-based screening to become more feasible and will help make lung cancer diagnosis more accurate in clinical practice.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Mesoporous Nanozyme-Enhanced DNA Tetrahedron Electrochemiluminescent Biosensor with Three-Dimensional Walking Nanomotor-Mediated CRISPR/Cas12a for Ultrasensitive Detection of Exosomal microRNA

Exosomal microRNAs (exomiRNAs) have emerged as ideal biomarkers for early clinical diagnostics. The accurate detection of exomiRNAs plays a crucial role in facilitating clinical applications. Herein, an ultrasensitive electrochemiluminescent (ECL) biosensor was constructed using three-dimensional (3D) walking nanomotor-mediated CRISPR/Cas12a and tetrahedral DNA nanostructures (TDNs)-modified nanoemitters (TCPP-Fe@HMUiO@Au-ABEI) for exomiR-155 detection. Initially, the 3D walking nanomotor-mediated CRISPR/Cas12a strategy could effectively convert the target exomiR-155 into amplified biological signals for improving the sensitivity and specificity. Then, TCPP-Fe@HMUiO@Au nanozymes with excellent catalytic performance were used to amplify ECL signals because of the enhanced mass transfer and increased catalytic active sites, originating from its high surface areas (601.83 m²/g), average pore size (3.46 nm), and large pore volumes (0.52 cm³/g). Meanwhile, the TDNs as the scaffold to fabricate "bottom-up" anchor bioprobes could improve the trans-cleavage efficiency of Cas12a. Consequently, this biosensor achieved the limit of detection down to 273.20 aM ranging from 1.0 fM to 1.0 nM. Furthermore, the biosensor could discriminate breast cancer patients evidently by analyzing exomiR-155, and these results conformed to that of qRT-PCR. Thus, this work provides a promising tool for early clinical diagnostics.

Meta-analysis of diagnostic cell-free circulating microRNAs for breast cancer detection

Background

Breast cancer (BC) is the most frequently diagnosed cancer among women. Numerous studies explored cell-free circulating microRNAs as diagnostic biomarkers of BC. As inconsistent and rarely intersecting microRNA panels have been reported thus far, we aim to evaluate the overall diagnostic performance as well as the sources of heterogeneity between studies.

Methods

Based on the search of three online search engines performed up to March 21^st 2022, 56 eligible publications that investigated diagnostic circulating microRNAs by utilizing Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) were obtained. Primary studies’ potential for bias was evaluated with the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2). A bivariate generalized linear mixed-effects model was applied to obtain pooled sensitivity and specificity. A novel methodology was utilized in which the sample and study models’ characteristics were analysed to determine the potential preference of studies for sensitivity or specificity.

Results

Pooled sensitivity and specificity of 0.85 [0.81—0.88] and 0.83 [0.79—0.87] were obtained, respectively. Subgroup analysis showed a significantly better performance of multiple (sensitivity: 0.90 [0.86—0.93]; specificity: 0.86 [0.80—0.90]) vs single (sensitivity: 0.82 [0.77—0.86], specificity: 0.83 [0.78—0.87]) microRNA panels and a comparable pooled diagnostic performance between studies using serum (sensitivity: 0.87 [0.81—0.91]; specificity: 0.83 [0.78—0.87]) and plasma (sensitivity: 0.83 [0.77—0.87]; specificity: 0.85 [0.78—0.91]) as specimen type. In addition, based on bivariate and univariate analyses, miRNA(s) based on endogenous normalizers tend to have a higher diagnostic performance than miRNA(s) based on exogenous ones. Moreover, a slight tendency of studies to prefer specificity over sensitivity was observed.

Conclusions

In this study the diagnostic ability of circulating microRNAs to diagnose BC was reaffirmed. Nonetheless, some subgroup analyses showed between-study heterogeneity. Finally, lack of standardization and of result reproducibility remain the biggest issues regarding the diagnostic application of circulating cell-free microRNAs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09698-8.

mtTB: A Web-Based R/Shiny App for Pulmonary Tuberculosis Screening

Pulmonary tuberculosis caused by Mycobacterium tuberculosis remains a global issue. However, the diagnosis of active pulmonary tuberculosis (TB) remains a challenge in the clinic. Small non-coding RNAs are potential diagnostic biomarkers for pulmonary tuberculosis. However, the current normalization methods are not stable and usually fail to reliably detect differentially expressed sncRNAs. To identify reliable biomarkers for pulmonary tuberculosis screening, we utilized the ratio-based method on the newly discovered mitochondria-derived small RNAs in human peripheral blood mononuclear cells. The prediction model of seven mtRNA biomarkers noteworthily enables the discrimination between pulmonary tuberculosis patients and controls in discovery (AUC = 0.906, 23 patients) and independent validation cohort (AUC = 0.968, 20 patients). Moreover, we present mtTB (https://tuberculosis.shinyapps.io/mtTB/), a novel R Graphical User Interface (GUI) that provides reliable biomarkers for the feasibility of blood-based screening, and produce a more accurate tool for pulmonary tuberculosis diagnosis in real clinical practice.

Comprehensive Analysis of a tRNA-Derived Small RNA in Colorectal Cancer

Colorectal cancer often presents as a highly variable disease with myriad forms that are at times difficult to detect in early screenings with sufficient accuracy, for which novel diagnostic methods are an attractive and valuable area of improvement. To improve colorectal cancer diagnosis and prognosis, new biomarkers that can be assembled into a diagnostic panel must be identified, and tRNA-derived small RNAs (tsRNAs) are a particularly interesting and increasingly visible new class of molecules to examine. In this study, small RNA-seq data were profiled for the expression of 104 human tsRNAs in tumor tissue and adjacent normal tissue samples, and a diagnostic model was built based on four differentially expressed tsRNAs: tRF-22-WB86Q3P92, tRF-22-WE8SPOX52, tRF-22-WE8S68L52, tRF-18-8R1546D2. Furthermore, the diagnostic model was validated by two independent validation datasets (AUC was 0.97 and 0.99), and a LASSO model was applied to develop a seven-tsRNA-based risk score model for colorectal cancer prognosis. Finally, a tsRNA-mRNA interaction network was established according to potential mRNA targets predicted by bioinformatic methods. In conclusion, the results suggest that abnormal expression of tsRNA in colorectal cancer may have a functional effect on tumor action and moreover, that some of the tsRNAs identified in this study with diagnostic and prognostic potential could be of clinical significance.

Parasitic Helminth-Derived microRNAs and Extracellular Vesicle Cargos as Biomarkers for Helminthic Infections

As an adaption to their complex lifecycles, helminth parasites garner a unique repertoire of genes at different developmental stages with subtle regulatory mechanisms. These parasitic worms release differential components such as microRNAs (miRNAs) and extracellular vesicles (EVs) as mediators which participate in the host-parasite interaction, immune regulation/evasion, and in governing processes associated with host infection. MiRNAs are small (~ 22-nucleotides) non-coding RNAs that regulate gene expression at the post-transcriptional level, and can exist in stable form in bodily fluids such as serum/plasma, urine, saliva and bile. In addition to reports focusing on the identification of miRNAs or in the probing of differentially expressed miRNA profiles in different development stages/sexes or in specific tissues, a number of studies have focused on the detection of helminth-derived miRNAs in the mammalian host circulatory system as diagnostic biomarkers. Extracellular vesicles (EVs), small membrane-surrounded structures secreted by a wide variety of cell types, contain rich cargos that are important in cell-cell communication. EVs have attracted wide attention due to their unique functional relevance in host-parasite interactions and for their potential value in translational applications such as biomarker discovery. In the current review, we discuss the status and potential of helminth parasite-derived circulating miRNAs and EV cargos as novel diagnostic tools.

Predict Colon Cancer by Pairing Plasma miRNAs: Establishment of a Normalizer-Free, Cross-Platform Model

Background

Plasma miRNAs are emerging biomarkers for colon cancer (CC) diagnosis. However, the lack of robust internal references largely limits their clinical application. Here we propose a ratio-based, normalizer-free algorithm to quantitate plasma miRNA for CC diagnosis.

Methods

A miRNA-pair matrix was established by pairing differentially expressed miRNAs in the training group from GSE106817. LASSO regression was performed to select variables. To maximize the performance, four algorithms (LASSO regression, random forest, logistic regression, and SVM) were tested for each biomarker combination. Data from GSE106817 and GSE112264 were used for internal and external verification. RT-qPCR data acquired from another cohort were also used for external validation.

Results

After validation through four algorithms, we obtained a 4-miRNA pair model (miR-1246 miR-451a; miR-1246 miR-4514; miR-654-5p miR-575; miR-4299 miR-575) that showed good performance in differentiating CC from normal controls with a maximum AUC of 1.00 in internal verification and 0.93 in external verification. Tissue validation showed a maximum AUC of 0.81. Further external validation using RT-qPCR data exhibited good classifier ability with an AUC of 0.88.

Conclusion

We established a cross-platform prediction model robust against sample-specific disturbance, which is not only well-performed in predicting CC but also promising in the diagnosis of other diseases.

Unraveling Molecular Pathways Altered in MeCP2-Related Syndromes, in the Search for New Potential Avenues for Therapy

Methyl-CpG-binding protein 2 (MeCP2) is an X-linked epigenetic modulator whose dosage is critical for neural development and function. Loss-of-function mutations in MECP2 cause Rett Syndrome (RTT, OMIM #312750) while duplications in the Xq28 locus containing MECP2 and Interleukin-1 receptor-associated kinase 1 (IRAK1) cause MECP2 duplication syndrome (MDS, OMIM #300260). Both are rare neurodevelopmental disorders that share clinical symptoms, including intellectual disability, loss of speech, hand stereotypies, vasomotor deficits and seizures. The main objective of this exploratory study is to identify novel signaling pathways and potential quantitative biomarkers that could aid early diagnosis and/or the monitoring of disease progression in clinical trials. We analyzed by RT-PCR gene expression in whole blood and microRNA (miRNA) expression in plasma, in a cohort of 20 females with Rett syndrome, 2 males with MECP2 duplication syndrome and 28 healthy controls, and correlated RNA expression with disease and clinical parameters. We have identified a set of potential biomarker panels for RTT diagnostic and disease stratification of patients with microcephaly and vasomotor deficits. Our study sets the basis for larger studies leading to the identification of specific miRNA signatures for early RTT detection, stratification, disease progression and segregation from other neurodevelopmental disorders. Nevertheless, these data will require verification and validation in further studies with larger sample size including a whole range of ages.

Detection of Pancreatic Ductal Adenocarcinoma by A qPCR-based Normalizer-free Circulating Extracellular Vesicles RNA Signature

Background: Pancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose and many efforts have been made to evaluate EVs-derived RNAs as biomarkers to predict PDAC. However, lack of robust internal references largely limited their clinical application. Here we proposed an RNA ratio-based, normalizer-free algorithm to quantitate EVs-derived RNAs in PDAC. Methods: Differentially expressed RNAs in the training group were identified using "limma" package. The ratio of any two candidate RNAs in the same sample was calculated and used as a new biomarker. LASSO regression was performed to build prediction models based on those RNA ratios. RNA-seq data of 116 plasma samples and RT-qPCR data of 111 plasma samples were used for internal and external validation, separately. Three algorithms (lasso regression, logistic regression, and SVM) were compared to improve the performance of this RNA signature. Results: We developed an RNA-ratio based prediction model which comprised eight EVs-derived RNAs, including FBXO7, MORF4L1, DDX17, TALDO1, AHNAK, TUBA1B, CD44, and SETD3. This model could well differentiate PDAC patients with a minimal AUC of 0.86 in internal verification using testing group. External validation using RT-qPCR data also exhibited a good classifier ability with an AUC of 0.89 when distinguishing PDAC from healthy controls. Conclusion: We've developed a qPCR-based, normalizer-free circulating EVs RNA classifier, which could well distinguish PDAC patients from noncancerous controls.

Identification of plasma lipid species as promising diagnostic markers for prostate cancer

BACKGROUND

Prostate cancer is a very common and highly fatal in men. Current non-invasive detection methods like serum biomarker are unsatisfactory. Biomarkers with high accuracy for diagnostic of prostate cancer are urgently needed. Many lipid species have been found related to various cancers. The purpose of our study is to explore the diagnostic value of lipids for prostate cancer.

RESULTS

Using triple quadruple liquid chromatography electrospray ionization tandem mass spectrometry, we performed lipidomics profiling of 367 lipids on a total 114 plasma samples from 30 patients with prostate cancer, 38 patients with benign prostatic hyperplasia (BPH), and 46 male healthy controls to evaluate the lipids as potential biomarkers in the diagnosis of prostate cancer. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to construct the potential mechanism pathway. After statistical analysis, five lipids were identified as a panel of potential biomarkers for the detection of prostate cancer between prostate cancer group and the BPH group; the sensitivity, specificity, and area under curve (AUC) of the combination of these five lipids were 73.3, 81.6%, and 0.800, respectively. We also identified another panel of five lipids in distinguishing between prostate cancer group and the control group with predictive values of sensitivity at 76.7%, specificity at 80.4%, and AUC at 0.836, respectively. The glycerophospholipid metabolism pathway of the selected lipids was considered as the target pathway.

CONCLUSIONS

Our study indicated that the identified plasma lipid biomarkers have potential in the diagnosis of prostate cancer.

Association of circulating miR-20a, miR-27a, and miR-126 with non-alcoholic fatty liver disease in general population

Non-alcoholic fatty liver disease (NAFLD) is closely associated with obesity, metabolic syndrome, and type II diabetes mellitus. Recently, circulating microRNAs (miRNAs) have been proposed as useful disease biomarkers. We examined whether circulating miRNAs, such as miR-20a, miR-27a, and miR-126, were useful biomarkers for NAFLD. We conducted a cross-sectional analysis of 527 subjects aged 39 years or older who had undergone a health examination in the Yakumo Study. Of the residents, 92 were diagnosed with NAFLD using a registered medical sonographer. Serum miR-20a, miR-27a and miR-126 levels were measured by quantitative real-time PCR. We then calculated the odds ratios for serum miRNA level changes according to the severity of NAFLD using normal liver status as the reference group. Serum levels of miR-20a and 27a, but not miR-126, were significantly lower in NAFLD subjects than normal subjects. Serum miR-20a and miR-27a levels were significantly lower in both male and female severe NAFLD subjects. Logistic regression analysis showed a significant relationship between low circulating miR-20a and 27a levels and severe NAFLD. Down-regulated circulating miR-20a and 27a levels were significantly associated with severe NAFLD in the general population. Circulating miR-20a and miR-27a may be useful biomarkers for severe NAFLD.