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 transcription–quantitative 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.

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.

Background: Platelet-derived Extracellular Vesicles, or “Platelet Dust” (PD), are reported as the most-abundant extracellular vesicles in plasma. However, the PD molecular content, especially the small RNA profile, is still poorly characterized. This study aims to characterize PD and other extracellular vesicles (EVs) in patients with non-small-cell lung cancer (NSCLC), focusing on their small RNA signatures and diagnostic potential. Methods: The EVs were isolated directly from the plasma of healthy donors and patients with NSCLC using the surface markers CD9, CD63, CD81 (overall EVs), and CD61 (PD). Small RNA sequencing was then performed to comprehensively profile the miRNAs. Results: Our analysis revealed distinct small RNA profiles in the EVs and the PD from the patients with NSCLC. The EVs (CD9-, CD63-, and CD81-positive) showed the enrichment of four miRNAs and the depletion of ten miRNAs, while the PD (CD61-positive) exhibited a more complex profile, with nineteen miRNAs enriched and nine miRNAs depleted in the patients with NSCLC compared to those of the healthy controls. Conclusions: This exploratory study enhances our understanding of miRNA composition within different plasma vesicle populations, shedding light on the biology of plasma vesicles and their contents. Furthermore, utilizing an extracellular vesicle isolation method with potential clinical applicability offers the prospect of improved cancer characterization and detection by selecting the most informative subpopulation of plasma vesicles.

Background: Liquid biopsy has gained significant attention as a non-invasive method for cancer detection and monitoring. IsomiRs and tRNA-derived fragments (tRFs) are small non-coding RNAs that arise from non-canonical microRNA (miRNAs) processing and the cleavage of tRNAs, respectively. These small non-coding RNAs have emerged as pro-mising cancer biomarkers, and their distinct expression patterns highlight the need for further exploration of their roles in cancer research. Methods: In this study, we investigated the differential expression profiles of miRNAs, isomiRs, and tRFs in plasma extracellular vesicles (EVs) from colorectal and prostate cancer patients compared to healthy controls. Subsequently, a combinatorial analysis using the CombiROC package was performed to identify a panel of biomarkers with optimal diagnostic accuracy. Results: Our results demonstrate that a combination of miRNAs, isomiRs, and tRFs can effectively di- stinguish cancer patients from healthy controls, achieving accuracy and an area under the curve (AUC) of approximately 80%. Conclusions: These findings highlight the potential of a combinatorial approach to small RNA analysis in liquid biopsies for improved cancer diagnosis and management.