7 research outputs found
Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24054881/s1.Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often
exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in
insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker
approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions
of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer
detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-
circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal
selected signature is then used to generate the predictive classification model using machine learning
algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models
reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial
analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA),
enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified
five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept
study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers,
providing a potential combinatorial diagnostic signature for lung cancer detection.European Unionâs Horizon 2020 research and innovation program under the Marie SkĆodowska-Curie 765492
Assessing the complementary information from an increased number of biologically relevant features in liquid biopsy-derived RNA-Seq data
Liquid biopsy-derived RNA sequencing (lbRNA-seq) exhibits significant promise for clinicoriented
cancer diagnostics due to its non-invasiveness and ease of repeatability. Despite substantial
advancements, obstacles like technical artefacts and process standardisation impede
seamless clinical integration. Alongside addressing technical aspects such as normalising fluctuating
low-input material and establishing a standardised clinical workflow, the lack of result
validation using independent datasets remains a critical factor contributing to the often low
reproducibility of liquid biopsy-detected biomarkers.
Considering the outlined drawbacks, our objective was to establish a workflow/methodology
characterised by: 1. Harness the rich diversity of biological features accessible through lbRNA-seq
data, encompassing a holistic range of molecular and functional attributes. These components are
seamlessly integrated via a Machine Learning-based Ensemble Classification framework, enabling
a unified and comprehensive analysis of the intricate information encoded within the data. 2.
Implementing and rigorously benchmarking intra-sample normalisation methods to heighten
their relevance within clinical settings. 3. Thoroughly assessing its efficacy across independent
test sets to ascertain its robustness and potential utility.
Using ten datasets from several studies comprising three different sources of biological material,
we first show that while the best-performing normalisation methods depend strongly on the
dataset and coupled Machine Learning method, the rather simple Counts Per Million method is
generally very robust, showing comparable performance to cross-sample methods. Subsequently, we demonstrate that the innovative biofeature types introduced in this study, such as the Fraction
of Canonical Transcript, harbour complementary information. Consequently, their inclusion
consistently enhances prediction power compared to models relying solely on gene expressionbased
biofeatures. Finally, we demonstrate that the workflow is robust on completely independent
datasets, generally from different labs and/or different protocols. Taken together, the
workflow presented here outperforms generally employed methods in prediction accuracy and
may hold potential for clinical diagnostics application due to its specific design.European Unionâs Horizon 2020 research and innovation program under the Marie
SkĆodowska-Curie grant agreement ELBA No 76549
Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection
Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection
The Analysis of Platelet-Derived circRNA Repertoire as Potential Diagnostic Biomarker for Non-Small Cell Lung Cancer
Tumor-educated Platelets (TEPs) have emerged as rich biosources of cancer-related RNA
profiles in liquid biopsies applicable for cancer detection. Although human blood platelets have been
found to be enriched in circular RNA (circRNA), no studies have investigated the potential of circRNA
as platelet-derived biomarkers for cancer. In this proof-of-concept study, we examine whether the
circRNA signature of blood platelets can be used as a liquid biopsy biomarker for the detection
of non-small cell lung cancer (NSCLC). We analyzed the total RNA, extracted from the platelet samples collected from NSCLC patients and asymptomatic individuals, using RNA sequencing
(RNA-Seq). Identification and quantification of known and novel circRNAs were performed using
the accurate CircRNA finder suite (ACFS), followed by the differential transcript expression analysis
using a modified version of our thromboSeq software. Out of 4732 detected circRNAs, we identified
411 circRNAs that are significantly (p-value < 0.05) differentially expressed between asymptomatic
individuals and NSCLC patients. Using the false discovery rate (FDR) of 0.05 as cutoff, we selected
the nuclear receptor-interacting protein 1 (NRIP1) circRNA (circNRIP1) as a potential biomarker
candidate for further validation by reverse transcriptionâquantitative PCR (RT-qPCR). This analysis
was performed on an independent cohort of platelet samples. The RT-qPCR results confirmed the
RNA-Seq data analysis, with significant downregulation of circNRIP1 in platelets derived from
NSCLC patients. Our findings suggest that circRNAs found in blood platelets may hold diagnostic
biomarkers potential for the detection of NSCLC using liquid biopsies.Marie SkĆodowskaCurie Grant Agreement No. 765492.Cancer Center Amsterdam
(CCA) Foundation (Grant #CCA2017-2-16)
Global and single-nucleotide resolution detection of 7-methylguanosine in RNA
RNA modifications, including N-7-methylguanosine (m7G), are pivotal in governing RNA stability and gene expression regulation. The accurate detection of internal m7G modifications is of paramount significance, given recent associations between altered m7G deposition and elevated expression of the methyltransferase METTL1 in various human cancers. The development of robust m7G detection techniques has posed a significant challenge in the field of epitranscriptomics. In this study, we introduce two methodologies for the global and accurate identification of m7G modifications in human RNA. We introduce borohydride reduction sequencing (Bo-Seq), which provides base resolution mapping of m7G modifications. Bo-Seq achieves exceptional performance through the optimization of RNA depurination and scission, involving the strategic use of high concentrations of NaBH4, neutral pH and the addition of 7-methylguanosine monophosphate (m7GMP) during the reducing reaction. Notably, compared to NaBH4-based methods, Bo-Seq enhances the m7G detection performance, and simplifies the detection process, eliminating the necessity for intricate chemical steps and reducing the protocol duration. In addition, we present an antibody-based approach, which enables the assessment of m7G relative levels across RNA molecules and biological samples, however it should be used with caution due to limitations associated with variations in antibody quality between batches. In summary, our novel approaches address the pressing need for reliable and accessible methods to detect RNA m7G methylation in human cells. These advancements hold the potential to catalyse future investigations in the critical field of epitranscriptomics, shedding light on the complex regulatory roles of m7G in gene expression and its implications in cancer biology.</p
Tumor-educated platelet blood tests for Non-Small Cell Lung Cancer detection and management
Abstract Liquid biopsy approaches offer a promising technology for early and minimally invasive cancer detection. Tumor-educated platelets (TEPs) have emerged as a promising liquid biopsy biosource for the detection of various cancer types. In this study, we processed and analyzed the TEPs collected from 466 Non-small Cell Lung Carcinoma (NSCLC) patients and 410 asymptomatic individuals (controls) using the previously established thromboSeq protocol. We developed a novel particle-swarm optimization machine learning algorithm which enabled the selection of an 881 RNA biomarker panel (AUC 0.88). Herein we propose and validate in an independent cohort of samples (nâ=â558) two approaches for blood samples testing: one with high sensitivity (95% NSCLC detected) and another with high specificity (94% controls detected). Our data explain how TEP-derived spliced RNAs may serve as a biomarker for minimally-invasive clinical blood tests, complement existing imaging tests, and assist the detection and management of lung cancer patients