Evaluation of Plasma-Derived Cell-Free RNA Isolation Methods Using PCR- Based Quantification

Evaluation of Plasma-Derived Cell-Free RNA Isolation Methods Using PCR- Based Quantification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of Plasma-Derived Cell-Free RNA Isolation Methods Using PCR- Based Quantification Gizem Inetas-Yengin, Zehra Omeroglu-Ulu, Fikrettin Sahin, Dilek Telci This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7848426/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2026 Read the published version in Molecular Biology Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Background: RNA biomarker research has grown significantly in recent years, with a focus on blood-based biomarker studies using cell-free nucleic acids (cf-NAs). These RNA derivatives are useful in diagnostic procedures for genetic changes and can be found in a variety of physiological fluids, such as plasma, brain fluid, and urine. RNA biomarkers are composed of different coding and non-coding transcripts. Among these, cfRNAs extracted from blood provide a minimally invasive source for disease diagnosis and monitoring. However, cfRNA isolation is challenging due to degradation, instability, lack of standardization, and contamination by microbial, environmental, and intrasample DNA. Therefore, it is crucial to obtain cell-free RNAs in high concentration using appropriate methods and process them using downstream PCR systems. ​​Methods and Results: To compare the purity and quality of cfRNAs isolated from plasma, five different isolation methods using commercial cfRNA kits were evaluated by digital PCR (dPCR) and qRT-PCR. RNA quality was assessed across kits, and expression levels were measured using cfRNA reference genes such as GAPDH and B2M. The results of analysis revealed that the GAPDH housekeeping gene showed greater consistency than B2M, and both genes exhibited statistically significant expression. Conclusions: Our comparative analysis of five cfRNA isolation methods demonstrated that commercial kits outperformed phenol–chloroform–based procedures. These results underscore the need for optimized isolation strategies and careful reference gene selection to enhance the reliability of cfRNA-based biomarker discovery. liquid biopsy plasma cell-free RNA RNA derivatives digital PCR Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION RNA biomarker research has accelerated in recent years, leading to the development of more advanced RNA detection technologies [ 1 ]. There has been considerable expansion in blood-based biomarker studies using cell-free nucleic acids (cf-NAs). cf-NAs can be detected in various physiological fluids such as plasma, brain fluid and urine [ 2 ] and used as biomarkers in a variety of clinical settings, including cancer and tumor monitoring, non-invasive prenatal diagnostics, solid organ transplantation [ 3 ] and neurodegenerative diseases [ 4 ]. Cell-free RNA (cfRNA) shows a potential for monitoring disease progression and therapeutic response, highlighting the translational importance of developing reliable cfRNA workflows. The different circulating cfRNA types in human blood include messenger RNA (mRNA), long-noncoding RNA (lncRNA), circular RNA (circRNA), miRNA, Piwi-interacting RNA (piRNA), transfer RNAs (tRNA), and other classes of non-coding RNA molecules [ 5 ]. These circulating RNAs fall under the broader category of cfNAs and are increasingly recognized as potential biomarkers for genetic alterations and disease diagnostics [ 6 ]. Recent studies emphasize that cfRNA captures dynamic transcriptional changes in real time, thus offering complementary diagnostic and prognostic value beyond cfDNA [ 1 , 5 ]. Moreover, cfNAs including cfRNA have already demonstrated clinical utility across diverse applications such as cancer, metabolic disorders, transplantation, and prenatal testing [ 2 – 4 , 6 ]. Although challenges remain in isolation and detection, continuous advances in sequencing, quantitative PCR (qPCR), digital PCR (dPCR), and especially in workflows for long RNA species are improving the reliability of cfRNA biomarker discovery [ 7 ]. The simplicity of sample collection and the fast-declining prices of sequencing make cfRNA-based biomarkers potentially a cost-effective therapeutic tool [ 7 ]. Nevertheless, several technical and biological challenges limit the reliable use of cfRNA. cfRNA is found in blood in the form of ribonucleoprotein complexes or encapsulated within microvesicles [ 6 ]. Reducing RNA contamination is typically achieved by using serum or plasma instead of whole blood. cfRNA isolation is challenging due to degradation, instability, and lack of standardization, resulting in inconsistent results across different studies and laboratories [ 8 ]. Because of its low biomass, cfRNA can be contaminated by microbial, environmental, and intrasample DNA. Cellular RNA contamination of plasma is another problem for isolation and the cfRNA profile is also impacted by centrifugation speed and duration. These preanalytical variables provide a considerable hurdle for reproducibility [ 9 ]. International initiatives such as the Extracellular RNA Communication Consortium (ERCC) have highlighted the need for standardized protocols and emphasized the need to reduce these problems and increase inter-study comparability [ 10 ]. In addition to isolation challenges, quantification and normalization remain major obstacles, owing to low cfRNA abundance, instability, and the lack of standardized reference controls. qPCR is widely used for cfRNA analysis due to its affordability and availability, while dPCR provides a higher sensitivity for transcripts at low concentrations, such as cfRNA. Another challenge is the selection of appropriate reference genes for normalization. Transcriptomic gene expressions can vary among individuals and biofluids, and a universally stable host gene has not yet been identified. Therefore, systematic assessment of reference gene stability is necessary for a reliable cfRNA quantification. For cfRNA isolation, several commercial kits are available that differ in RNA yield, quality, processing time, and ease of use. However, the small amounts of cfRNA recovered after isolation complicate downstream PCR-based applications, as both input RNA quantity and quality critically influence subsequent analyses [ 7 ]. Therefore, obtaining cfRNAs in sufficient yield and integrity using appropriate isolation strategies is essential before applying methods such as PCR. The aim of this study was to compare the purity and quality of cfRNAs isolated from plasma using different isolation methods and commercial cfRNA kits, and to evaluate the performance of these extracts using dPCR and qRT-PCR. MATERIALS AND METHODS Collection of peripheral blood All optimization experiments with peripheral blood were approved by the Yeditepe University Clinical Research Ethics Committee (Istanbul, Turkey; Decision No. 1678, dated 10.11.2022). Written informed consent was obtained from all healthy donors, and the study was conducted in accordance with the principles of the World Medical Association Declaration of Helsinki. Healthy individuals who do not have high blood pressure, diabetes, or other neurological disorders were included in this study. Peripheral blood samples (30 mL) were taken from all subjects (n = 5 women between 35 and 45 years of age) with BD Vacutainer EDTA blood collection tubes (Cat #367525, Becton Dickinson). Tubes were immediately transported to the laboratory for plasma preparation and tubes were centrifuged at 1900 g for 10 min at 4°C. Supernatant was collected into new falcon tube and centrifuged at 3000 g for 15 min at 4°C. Plasma samples were separated for RNA isolation immediately. Hemolysis measurements for plasma samples Fresh plasma samples were read using the nanodrop (NanoDrop 2000, Thermo Fisher Scientific) at 414 nm in 3 replicates. Hemoglobin obtained from bovine blood (Cat #H2500, Sigma-Aldrich) was used between 0.25 to 1 g/L for the standard curve. The results of plasma samples were calculated both clinically and using the Beer-Lambert equation. cfRNA isolation cfRNAs were extracted from fresh plasma samples with four different commercial purification kits by following the manufacturers’ manual. QIAamp ccfDNA/RNA Kit (Cat #55184, Qiagen), miRNeasy Serum/Plasma Advanced Kit (Cat #217204, Qiagen), Plasma/Serum cfc-RNA Advanced Purification Kit (Cat #68200, Norgen), Quick-cfDNA/cfRNA Serum & Plasma Kit (Cat #R1072, Zymo Research) and TRIzol extraction (Cat #15596018 Thermo Fisher Scientific) were used for cfRNA isolations. Briefly, cfRNA was isolated from 600 µL of plasma using the miRNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions, and the final RNA was eluted in 20 µL of RNase-free water. For the QIAamp ccfDNA/RNA Kit, 1 mL of plasma was used, and samples were dissolved in 20 µL RNase-free water as recommended by the manufacturer. For Plasma/Serum cfc-RNA Advanced Purification Kit, 1 ml of plasma was used, and samples were dissolved in 25 µL Elution Buffer A. In Quick-cfDNA/cfRNA Serum & Plasma Kit, 1 mL of plasma was used, and samples were dissolved in 15 µL RNase-free water. In addition to commercial kits, a classical phenol–chloroform extraction was performed using TRIzol reagent (Cat #15596018 Thermo Fisher Scientific). Briefly, 1 mL of plasma was mixed with 3 mL of TRIzol and incubated for 5 min at 4°C. Next, 1200 µL of chloroform was added to each sample and vortexed for 30 s. The mixture was centrifuged at 12,000 × g for 5 min at 4°C, and the aqueous phase was transferred to a fresh tube containing 800 µL of isopropanol. Samples were incubated at − 20°C for 12–16 h and centrifuged again at 12,000 × g for 5 min. The resulting RNA pellets were washed with 1 mL of 80% ethanol, air-dried at room temperature for 5 min, and resuspended in 10 µL of RNase-free water [ 11 ]. All eluates were stored at − 80°C until further processing, and the concentrations of the samples were measured using the DeNovix QFX Fluorometer (DeNovix Inc). cfRNA samples were measured with DeNovix RNA Assay (Cat #EVAL-50, DeNovix) kit. dPCR design After fluorometric measurements of cfRNAs obtained from plasma were taken, they were synthesized into cDNA using the iScript cDNA Synthesis Kit (Cat #1708890, BioRad) according to the manufacturer's instructions, in a final volume of 120 ng/µL. Synthesized cDNA samples were measured with Qubit dsDNA HS Assay (Cat #Q32854, Thermo Fisher Scientific) and Qubit ssDNA Assay (Cat #Q10212, Thermo Fisher Scientific) kits using DeNovix QFX Fluorometer (DeNovix Inc). Subsequently, samples were prepared for dPCR and sample cDNAs with a total volume of 10 ng/µL were used. dPCR The dPCR method was employed to quantify cfRNA expression levels using the nanoplate-based Qiagen QIAcuity One device and QIAcuity EvaGreen (EG) PCR Kit (Cat #250111, Qiagen). The dPCR mixture was prepared by adding 3x EvaGreen PCR Master Mix, primer pairs, RNase-free water and 2 µL cDNA. Below the list of primers, GAPDH: Forward Primer 5’ GTC TCC TCT GAC TTC AAC AGC G 3’ and Reverse Primer 5’ ACC ACC CTG TTG CTG TAG CCA A 3’, B2M: Forward Primer 5’ CCA CTG AAA AAG ATG AGT ATG CCT 3’ and Reverse Primer 5’ CCA ATC CAA ATG CGG CAT CTT C 3’. Each sample was analyzed using QIAcuity One 5plex System (Qiagen) with at least duplicate technical replicates. The reaction was initiated by loading the sample into a QIAcuity Nanoplate 8.5k 24-well (Cat #250011, Qiagen) with 8500 portions. Reaction; initial heat activation: 2 min at 95°C, denaturation 15 s at 95°C, binding 15 s at 55°C and extension 15 s at 72°C for 40 cycles, and cooling 5 min at 40°C was carried out. QIAcuity Software Suite v.2.0. was conducted for the dPCR results. qRT-PCR qRT-PCR studies were carried out using the QuantiNova SYBR Green PCR Kit (Cat# 208152, Qiagen, Germany) as recommended by the manufacturer. cDNAs (10 ng/µL) were amplified using a master mix containing 2× QuantiNova SYBR Green PCR Master Mix, ROX dye, and primer pairs. Each sample was analyzed using QIAquant 96 5plex Real Time PCR (Qiagen) with at least two technical replicates. Reaction; initial heat activation: 2 min at 95°C, denaturation: 5 s at 95°C, binding/extension: 10 s at 60°C, and melting curve in 0.5°C increments between 65°C and 95°C for 40 cycles it was carried out for 2–5 seconds per cycle. Statistical analysis All statistical analyses were performed using GraphPad Prism 10 software (GraphPad Software, USA). To compare cfRNA expression levels ( GAPDH and B2M ) across different isolation methods, the Friedman test was used as a non-parametric alternative to repeated-measures ANOVA. When the Friedman test indicated a statistically significant difference (p < 0.05), post-hoc multiple comparisons were performed using the Dunn’s test to identify which methods differed significantly. Correlation analyses between dPCR and qRT-PCR results were performed using Pearson correlation coefficients. The strength and direction of the correlation were interpreted based on the r-value. Agreement between the two methods was further evaluated using Bland-Altman analysis. RESULTS To provide an overview of the cfRNA extraction strategies, we compared the key features of the commercial kits and the phenol–chloroform method. Table 1 summarizes the cfRNA isolation kits evaluated in this study, including their processing time, cost, plasma input volume, and RNA elution yield. cfRNA isolation was initiated using the minimum plasma volumes recommended in the commercial kits' user manuals, and the resulting cfRNAs were subsequently eluted in the lowest µL volumes specified by the same protocols. Table 1 Comparison of cfRNA isolation methods with respect to plasma input, elution volume, processing time, and cost. Methods Plasma volume Elution volume Time consuming Price Difficulty miRNeasy Serum/Plasma Advanced Kit (MIR) 600 µL 20 µL ≈ 1 hr $$ ++ QIAamp ccfDNA/RNA Kit (QIA) 1 mL 20 µL ≈ 1 hr $$$$ ++ Plasma/Serum cfc-RNA Advanced Purification Kit Norgen (NOR) 1 mL 25 µL ≈ 1,5 hr $$ +++ Quick-cfDNA/cfRNA Serum & Plasma Kit Zymo (ZYM) 1 mL 15 µL ≈ 3 hrs $$$$ +++ TRIzol (TRI) 1 mL 10 µL 15 min + 12–16 hrs incubations $ + For reliable cfRNA analysis, plasma samples must have a hemoglobin concentration below 50 g/L to exclude hemolysis. To assess hemolysis, absorbance at 414 nm was measured in plasma collected from healthy individuals, and hemoglobin concentration was calculated using a standard curve (y = 0.2857x – 0.0201, R² = 0.9805). The results are presented in Table 2 . All plasma samples showed negligible hemolysis, with sample #5 exhibiting the lowest absorbance value. Table 2 Hemolysis analysis of plasma samples based on absorbance measurements at 414 nm. Samples Gender Age mean Std dev Clinical Evaluation of Hemolysis g/L Beer-Lambert equation g/L hemolysis status #1 F 42 0,120 0,00400 49,037 0,420021 mild hemolysis #2 F 35 0,092 0,00173 39,237 0,3220161 mild hemolysis #3 F 38 0,122 0,00153 49,971 0,4293548 mild hemolysis #4 F 45 0,111 0,00265 45,887 0,38851943 mild hemolysis #5 F 36 0,046 0,00462 23,370 0,1633415 No hemolysis/ very mild hemolysis Following isolation, cfRNA and dsDNA/ssDNA concentrations were quantified using a DeNovix QFX Fluorometer (DeNovix Inc.) ( Supplementary Fig. 1A ). Among the tested methods, the TRIzol-based approach (TRI) yielded the highest cfRNA concentrations. A Friedman test was performed to compare cfRNA concentrations across different isolation methods, yielding a statistically significant result (p = 0.0199). Post-hoc multiple comparisons using the Dunn’s test revealed a significant difference between Method NOR and Method TRI (p = 0.0270) ( Supplementary Fig. 1B ). Our dPCR results demonstrate that plasma samples from different individuals exhibit varying expression levels, as evidenced by the number of copies/µL measured for each marker (Figs. 1 and 2 ). Both GAPDH and B2M expression were identified in cfRNA samples by digital PCR analysis (Fig. 1 A& 2 A). GAPDH expression ranged from 0.663 to 11.442 copies/µL across the tested samples (Fig. 1 B). In order to compare GAPDH expression in the same samples across different isolation methods, a Friedman test was performed, yielding a statistically significant result (p-value of 0.0366); however, the differences in GAPDH expression among the groups were not statistically significant by Dunn’s test (Fig. 1 C). In order to further assess the potential impact of isolation methods, we also analyzed the expression of another widely used cfRNA housekeeping gene, B2M , by digital PCR (Fig. 2 A). B2M expression ranged from 0.178 to 207.385 copies/µL across the tested samples (Fig. 2 B). A Friedman test performed on the same samples across different methods, yielding a statistically significant result (p = 0.0299). Post-hoc multiple comparisons using the Dunn’s test revealed a significant difference between Method ZYM and TRI (p = 0.0137 (Fig. 2 C). To determine whether the differences observed in digital PCR could also be detected by real-time PCR, we repeated the experiments using the same cfRNA samples. All samples were derived from healthy individuals and analyzed with the housekeeping genes GAPDH and B2M ; Ct values were assessed at least in dublicate for each sample, and Friedman analysis was applied to the qRT-PCR data (Fig. 3 ). Similar to dPCR results, for GAPDH , the Friedman test yielded a p-value of 0.0279, but the differences in GAPDH expression among methods were not statistically significant by Dunn’s test (Fig. 3 A). For B2M , the Friedman test produced a p-value of 0.0199, with a significant difference in the expression levels of B2M between Method ZYM and Method TRI according to Dunn’s test (p = 0.0137) (Fig. 3 B). To evaluate the consistency between qRT-PCR and digital PCR, correlation and agreement analyses were performed for the reference genes GAPDH and B2M . Ct values of qRT-PCR for GAPDH showed a strong and significant negative correlation with copy numbers determined by dPCR (y = − 0.54x + 31.25, Pearson r = − 0.76, R² = 0.58, p < 0.0001) (Fig. 4 A). Similarly, analysis of B2M revealed a negative correlation between qRT-PCR Ct values and dPCR copy numbers (y = − 0.039x + 31.00, Pearson r = − 0.72, R² = 0.52, p < 0.0001) (Fig. 4 B). Systematic deviations between the two methods for both reference genes were further assessed using Bland-Altman plots (Figs. 4 C and 4 D). DISCUSSION Liquid biopsy-based diagnostic platforms using plasma or serum as input sources are increasingly gaining acceptance in clinical practice, generating substantial interest in circulating cfDNA and cfRNA as clinically relevant biomarkers [ 6 ]. Due to its fragile structure, cfRNA is highly susceptible to degradation, and both the isolation process and downstream analyses can be affected by pre-analytical factors such as blood collection, tube type, processing time, freeze–thaw cycles, hemolysis, and cellular contamination. These challenges, combined with the inherently low yield of cfRNA, limit the efficiency and reliability of downstream analyses. Nevertheless, cfRNA offers unique advantages as it reflects real-time transcriptional activity and includes both coding and non-coding RNA species, enabling the detection of tissue- and disease-specific molecular signatures [ 5 ]. These characteristics make cfRNA a promising tool for early diagnosis, disease monitoring, and therapeutic stratification [ 5 ]. Despite technical challenges, various methods, including RNA sequencing, microarray, qPCR, and dPCR, are widely used for cfRNA validation [ 12 ]. Among these, dPCR is particularly advantageous because its high sensitivity allows for the reliable detection and quantification of low abundance cfRNA species in plasma. In this study, we systematically compared five cfRNA isolation methods with respect to RNA quality and downstream performance using dPCR and qRT-PCR. In addition, the expression stability of potential reference genes was evaluated in the context of these isolation methods. The novelty of this work lies in the direct comparison of five distinct isolation strategies using fresh plasma samples, combined with reference gene–based expression analyses, thereby providing a comprehensive evaluation of both methodological performance and normalization approaches in cfRNA studies. The protocols evaluated were selected based on practical considerations, including time efficiency, ease of use, and compatibility with small sample volumes. Since clinical specimens are often limited in availability, methods allowing multiple downstream analyses from minimal input material are particularly valuable for biomarker discovery and diagnostic applications. As shown in Figs. 1 – 3 , both dPCR and qRT-PCR analyses demonstrated that Zymo Quick-cfDNA/cfRNA kit (Method ZYM) achieved the highest expression levels of GAPDH and B2M , while miRNeasy Serum/Plasma Advanced kit (Method MIR) and the QIAamp ccfDNA/RNA kit (Method QIA) yielded comparable outcomes. The higher yields observed with the Zymo Quick-cfDNA/cfRNA kit (Method ZYM) may be related to its use of a vacuum manifold during the isolation step, whereas the other kits relied solely on centrifugation. In the literature, further comparisons between the miRNeasy Serum/Plasma Advanced Kit (Method MIR), the Zymo Quick-cfDNA/cfRNA Kit (Method ZYM), and the QIAamp Circulating Nucleic Acids Kit have evaluated the abundance of hsa-miR-16-5p, a specific microRNA found in blood plasma, using qRT-PCR [ 13 ]. Consistent with our findings, that study reported that the Zymo Quick-cfDNA/cfRNA Kit yielded approximately tenfold higher levels of hsa-miR-16-5p compared with the other kits. Similarly, a study evaluating six cfRNA isolation kits using ovine plasma reported that the Zymo Quick-cfDNA/cfRNA kit (ZYM) outperformed the miRNeasy Serum/Plasma Advanced kit (MIR) in fresh samples but exhibited reduced efficiency in frozen samples. However, the miRNeasy Serum/Plasma Advanced Kit (MIR) yielded more consistent results across both fresh and frozen samples and demonstrated higher reproducibility, whereas the Zymo Quick-cfDNA/cfRNA kit (ZYM) showed greater variability between replicates and sample types [ 14 ]. In our analyses, no statistically significant differences were observed between Methods ZYM, MIR, and QIA; however, Method ZYM consistently yielded higher expression across blood plasma samples. Previous studies using animal plasma have reported that while the Zymo Quick-cfDNA/cfRNA Kit can achieve higher cfRNA recovery, its performance may vary depending on sample type and storage conditions, whereas the Qiagen kits generally provide more consistent outcomes across different conditions [ 13 , 14 ]. These reports suggest that performance differences are primarily attributable to the distinct chemistries of the kits, with Zymo’s design favoring cfRNA recovery and Qiagen’s optimization being more suitable for miRNA enrichment. After Method ZYM, the highest levels of GAPDH and B2M were obtained with Methods MIR and QIA, which performed comparably in our analyses. In the literature, another comparative study assessed six commercial kits, including the miRNeasy Serum/Plasma Advanced Kit (MIR), using healthy human plasma samples. Comparisons were made between whole plasma and plasma-derived ultracentrifugation (UC) fractions. To evaluate overall cfRNA yield and concentration, four endogenous miRNAs (miR-451a, miR-21-1, miR-30d, and miR-122), present at varying levels in plasma, were quantified by qRT-PCR [ 15 ]. The results demonstrated that the miRNeasy Serum/Plasma Advanced Kit (MIR) and the Macherey-Nagel NucleoSpin miRNA Plasma Kit provided the highest miRNA yield and purity among the kits tested and further indicated that the incorporation of an ultracentrifugation step in Method MIR enhanced miRNA recovery [ 15 ]. In a related study, eight RNA purification methods, including the miRNeasy Serum/Plasma Advanced Kit (MIR) and the QIAamp ccfDNA/RNA Kit (QIA), were systematically evaluated for extracellular RNA (exRNA) isolation from plasma, generating 456 transcriptomic datasets that encompassed both plasma- and serum-derived microRNAs and mRNAs [ 16 ]. Significant performance differences were observed across the evaluated methods in terms of RNA concentration, yield, and the number of detectable transcripts. The miRNeasy Serum/Plasma Advanced Kit (Method MIR) showed the highest number of detectable miRNAs with low replicate variability [ 16 ]. In contrast, the QIAamp ccfDNA/RNA Kit (Method QIA) performed best with larger plasma input volumes (up to 4 mL), providing higher mRNA concentrations and recovery, but it was less efficient than the miRNeasy kit for miRNA detection [ 16 ]. In a complementary study, the expression of CAVIN2 , NRGN , AIF1 , and B2M genes from human plasma was analyzed using six co-purification kits for cfDNA and cfRNA, including Methods MIR and QIA [ 17 ]. Using an optimized framework, the authors compared the co-purification efficiency of two manual kits: miRNeasy Serum/Plasma Advanced Kit (MIR) and QIAamp ccfDNA/RNA Kit (QIA) along with two semi-automated platforms (MagNA Pure 24 Total NA Isolation Kit and iCatcher Circulating cfDNA/cfRNA 4000 Kit) across different plasma input volumes. All kits were able to successfully co-purify cfDNA and cfRNA, as confirmed by duplex assays, demonstrating their applicability for parallel nucleic acid recovery. Notably, the study highlighted that in some cases equivalent or even higher eluate concentrations could be achieved with smaller plasma input volumes—for example, Method MIR with 0.6 mL yielded cfDNA and cfRNA concentrations comparable to or greater than those obtained with Method QIA using 1 mL input. These findings emphasize that plasma input requirements, in addition to kit chemistry, critically influence recovery efficiency and may account for performance variability observed between different platforms. In summary, consistent with numerous reports, Qiagen kits—particularly Method MIR—have been shown to achieve high efficiency and yield in cfRNA isolation from plasma samples. In our study, Method MIR was applied at its maximum input volume (600 µL), whereas the other methods were tested with 1 mL plasma, corresponding to their lowest recommended input volumes. Together with published evidence, this underscores that sample input volume is a critical factor influencing method performance. Since Method ZYM requires at least 1 mL of plasma, Method MIR emerges as a practical alternative when sample availability is limited, making it the second-best option in settings where input material is scarce. In this study, TRIzol-based extraction (Method TRI) produced the highest cfRNA concentration measurements ( Supplementary Fig. 1B ); however, the apparent recovery of cfRNA was the lowest (Figs. 1 – 3 ). This discrepancy is most likely attributable to residual phenol from the isolation procedure, which can artificially inflate spectrophotometric RNA measurements while simultaneously inhibiting downstream PCR applications by interfering with polymerase and reverse transcriptase activity in a concentration-dependent manner [ 18 ]. In agreement with previous reports, commercial column-based kits provided higher cfRNA yield and purity from small plasma volumes compared with guanidinium thiocyanate/phenol–chloroform methods [ 14 , 19 ]. Phenol–chloroform approaches such as TRIzol generally result in lower dPCR/qRT-PCR signals from plasma-derived cfRNA, likely due to their limited ability to enrich fragmented RNAs and to efficiently remove inhibitory contaminants [ 7 , 14 , 18 – 20 ]. Nonetheless, these methods remain widely used for total RNA isolation from cells and tissues, and hybrid protocols that integrate column- and phenol-based chemistries may improve the consistency and reliability of cfRNA analyses. Another critical aspect of the comparison involved the selection of candidate reference genes, as this choice is essential for reliable relative quantification in cfRNA studies. In our study, both GAPDH and B2M were evaluated as potential reference genes [ 12 , 21 ]. Following cfRNA extraction, expression levels of GAPDH and B2M were assessed by dPCR and qRT-PCR. As shown in Fig. 4 , a stronger linear correlation was observed between dPCR and qRT-PCR results for GAPDH compared with B2M . GAPDH also displayed lower variation across plasma cfRNA samples, suggesting greater stability as a housekeeping gene, although its abundance still varied among individuals. Importantly, the stability of reference genes is context-dependent, influenced by factors such as tissue type, disease state, and sample handling, and no single universal reference gene has yet been established. For instance, Wagner et al. reported significant inter-individual variation in GAPDH transcript levels in plasma-derived cfRNA, likely reflecting donor-specific basal expression [ 22 ]. Likewise, other studies emphasize that pre-analytical and biological variables—including storage, handling, and processing—strongly affect reference gene stability, underscoring the need for careful validation in cfRNA biomarker discovery [ 7 ]. Supporting this, a study conducted with umbilical cord blood and healthy adult samples evaluated the suitability of eight reference genes, including GAPDH and B2M , using algorithms such as BestKeeper, GeNorm, and NormFinder [ 23 ]. The results indicated that GAPDH and PPIB were among the most stable reference genes across both pediatric and adult cohorts, suggesting broad applicability and reliable performance in RNA expression datasets. Taken together, these observations underscore the importance of systematically assessing cfRNA normalization transcripts across time and individuals as a prerequisite for robust cfRNA diagnostic applications. Ultimately, successful translation of cfRNA analyses into clinical practice will require standardized Standard Operating Procedures (SOPs) spanning both pre-analytical and analytical stages. Moreover, integration with complementary biomarkers (cfDNA, proteins, imaging) and the adoption of advanced computational normalization strategies, including machine learning, are expected to further enhance robustness and diagnostic accuracy. CONCLUSIONS In summary, our comparative evaluation of five cfRNA isolation methods demonstrated that commercial kits generally outperformed phenol–chloroform–based approaches. When assessed for qRT-PCR and dPCR performance, Method ZYM produced the most consistent results, followed by Methods MIR and QIA, underscoring that protocol selection can markedly influence cfRNA recovery and detectability. Beyond cfRNA quality, additional factors such as processing time, cost, and ease of application should also be considered when evaluating commercial kits. In our reference gene analysis, GAPDH exhibited greater stability than B2M in plasma-derived cfRNA; however, the observed interindividual variability highlights the need for careful validation of normalization strategies in future studies. Taken together, these findings emphasize the importance of method optimization and reference gene selection to improve the reliability of cfRNA-based biomarker discovery. Large-scale validation, rigorous control of pre-analytical variables, and integration with complementary analytes will be essential to facilitate the translation of cfRNA analyses into routine clinical diagnostics. Abbreviations cfRNA cell-free RNA dPCR Digital PCR ERCC Extracellular RNA Communication Consortium Declarations AUTHOR CONTRIBUTIONS Conceptualization: G.I.Y (Equal) and Z.O.U (Equal), Methodology: G.I.Y (Equal), Z.O.U (Equal) and D.T (Lead), Validation: G.I.Y (Lead), Investigation: G.I.Y (Lead), Formal analysis: G.I.Y (Equal) and Z.O.U (Equal), Writing - Original Draft Preparation: G.I.Y (Lead), Z.O.U (Equal), F.S (Equal) and D.T (Equal), Writing - Review & Editing: G.I.Y (Equal), Z.O.U (Equal), F.S (Equal) and D.T (Equal), Visualization: G.I.Y (Equal) and Z.O.U (Equal), Supervision: F.S (Equal) and D.T (Lead), Project administration: D.T (Lead), Funding acquisition: F.S (Lead). FUNDING STATEMENT This study is provided by the Scientific and Technological Research Council of Türkiye (TUBITAK) TUBİTAK-1004, Project No. 23AG009. CONFLICT OF INTEREST Authors declare no competing financial and/or non-financial interests. DATA AVAILABILITY STATEMENT The data used to support the findings of this study are included within the article. No datasets were generated or analyzed during the current study. ETHICS STATEMENT This study was conducted in accordance with the decision of Yeditepe University Clinical Research Ethics Committee (Date 10.11.2022/No:1678) and all requirements of the World Medical Association Helsinki Declaration. References Islam MN, Masud MK, Haque MH, Hossain MSA, Yamauchi Y, Nguyen NT, Shiddiky MJ (2017) RNA biomarkers: diagnostic and prognostic potentials and recent developments of electrochemical biosensors. Small Methods 1(7):1700131. https://doi.org/10.1002/smtd.201700131 Szilágyi M, Pös O, Márton É, Buglyó G, Soltész B, Keserű J, Nagy B (2020) Circulating cell-free nucleic acids: main characteristics and clinical application. Int J Mol Sci 21(18):6827. https://doi.org/10.3390/ijms21186827 Drag MH, Kilpeläinen TO (2021) Cell-free DNA and RNA—measurement and applications in clinical diagnostics with focus on metabolic disorders. Physiol Genom 53(1):33–46. https://doi.org/10.1152/physiolgenomics.00086.2020 Cai X, Janku F, Zhan Q, Fan JB (2015) Accessing genetic information with liquid biopsies. Trends Genet 31(10):564–575. https://doi.org/10.1016/j.tig.2015.06.001 Mareboina M, Deng E, Mouratidis I, Yee NS, Pitteloud N, Georgakopoulos-Soares I, Chartoumpekis DV (2024) A review on cell-free RNA profiling: Insights into metabolic diseases and predictive value for bariatric surgery outcomes. Mol metabolism 87:101987. https://doi.org/10.1016/j.molmet.2024.101987 Pös O, Biró O, Szemes T, Nagy B (2018) Circulating cell-free nucleic acids: characteristics and applications. Eur J Hum Genet 26(7):937–945. https://doi.org/10.1038/s41431-018-0132-4 Cabús L, Lagarde J, Curado J, Lizano E, Pérez-Boza J (2022) Current challenges and best practices for cell-free long RNA biomarker discovery. Biomark Res 10(1):62. https://doi.org/10.1186/s40364-022-00409-w Kan CM, Pei XM, Yeung MHY, Jin N, Ng SSM, Tsang HF, Wong SCC (2023) Exploring the role of circulating cell-free RNA in the development of colorectal cancer. Int J Mol Sci 24(13):11026. https://doi.org/10.3390/ijms241311026 Loy C, Ahmann L, De Vlaminck I, Gu W (2024) Liquid biopsy based on cell-free DNA and RNA. Annu Rev Biomed Eng 26:169–195. https://doi.org/10.1146/annurev-bioeng-110222-111259 Amolegbe SM, Johnston NC, Ambrosi A, Ganguly A, Howcroft TK, Kuo LS, Happel C (2025) Extracellular RNA communication: A decade of NIH common fund support illuminates exRNA biology. J Extracell Vesicles 14(1):e70016. https://doi.org/10.1002/jev2.70016 Zhou D, Tang W, Liu X, An HX, Zhang Y (2017) Clinical verification of plasma messenger RNA as novel noninvasive biomarker identified through bioinformatics analysis for lung cancer. Oncotarget 8(27):43978–43989. https://doi.org/10.18632/oncotarget.16701 Cheung KWE, Choi SYR, Lee LTC, Lee NLE, Tsang HF, Cheng YT, Wong SCC (2019) The potential of circulating cell free RNA as a biomarker in cancer. Expert Rev Mol Diagn 19(7):579–590. https://doi.org/10.1080/14737159.2019.1633307 Kim H, Wu G, Kemp R, Okobi Q, Barberan-Solder S, Jia XY Assessment of Cell-Free Nucleic Acid Isolation Efficiencies for Liquid Biopsy Research. Zymo Res, 1–8 Wright K, de Silva K, Purdie AC, Plain KM (2020) Comparison of methods for miRNA isolation and quantification from ovine plasma. Sci Rep 10(1):825. https://doi.org/10.1038/s41598-020-57659-7 Meerson A, Ploug T (2016) Assessment of six commercial plasma small RNA isolation kits using qRT-PCR and electrophoretic separation: higher recovery of microRNA following ultracentrifugation. Biology Methods Protocols 1(1):bpw003. https://doi.org/10.1093/biomethods/bpw003 The exRNAQC Consortium (2025) Blood collection tube and RNA purification method recommendations for extracellular RNA transcriptome profiling. Nature communications , 2025, 16.1: 4513. https://doi.org/10.1038/s41467-025-58607-7 Deleu, J., Schoofs, K., Decock, A., Verniers, K., Roelandt, S., Denolf, A., … Vandesompele,J. (2022). Digital PCR-based evaluation of nucleic acid extraction kit performance for the co-purification of cell-free DNA and RNA. Human Genomics, 16(1), 73. https://doi.org/10.1186/s40246-022-00446-4 Unger C, Lokmer N, Lehmann D, Axmann IM (2019) Detection of phenol contamination in RNA samples and its impact on qRT-PCR results. Anal Biochem 571:49–52. https://doi.org/10.1016/j.ab.2019.02.002 Le APH, Tran TT, Cao THM, Le TM, Le PT, Huong HTT (2020), July Evaluate and Optimize Cell-Free RNA Extraction Methods to Apply for Alzheimer’s Disease Biomarkers Detection. In International Conference on the Development of Biomedical Engineering in Vietnam (pp. 591–609). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-75506-5_50 Eldh M, Lötvall J, Malmhäll C, Ekström K (2012) Importance of RNA isolation methods for analysis of exosomal RNA: evaluation of different methods. Mol Immunol 50(4):278–286. https://doi.org/10.1016/j.molimm.2012.02.001 Lak NSM, Seijger A, van Zogchel LMJ, Gelineau NU, Javadi A, Zappeij-Kannegieter L, Bongiovanni L, Andriessen A, Stutterheim J, van der Schoot CE, de Bruin A, Tytgat GAM (2023) Cell-Free RNA from Plasma in Patients with Neuroblastoma: Exploring the Technical and Clinical Potential. Cancers 15(7). https://doi.org/10.3390/cancers15072108 Wagner JT, Kim HJ, Johnson-Camacho KC, Kelley T, Newell LF, Spellman PT, Ngo TT (2020) Diurnal stability of cell-free DNA and cell-free RNA in human plasma samples. Sci Rep 10(1):16456. https://doi.org/10.1038/s41598-020-73350-3 Hieronymus, K., Dorschner, B., Schulze, F., Vora, N. L., Parker, J. S., Winkler, J.L., … Winkler, S. (2021). Validation of reference genes for whole blood gene expression analysis in cord blood of preterm and full-term neonates and peripheral blood of healthy adults. BMC genomics, 22(1), 489. https://doi.org/10.1186/s12864-021-07801-0 Additional Declarations No competing interests reported. Supplementary Files floatimage5.png Supplementary Figure 1: Quantification of plasma-derived cfRNA using different isolation methods. (A) Table of fluorometric concentrations of cfRNAs and cDNAs obtained from plasma. (B) Comparison of cfRNA extractions across different isolation methods Friedman test, p = 0.0199, comparison between Method NOR and TRI *p=0.0270). Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2026 Read the published version in Molecular Biology Reports → Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 12 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Editor assigned by journal 14 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 13 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7848426","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535148653,"identity":"62818646-698f-4882-85de-55f19e39f79a","order_by":0,"name":"Gizem Inetas-Yengin","email":"","orcid":"","institution":"Yeditepe University","correspondingAuthor":false,"prefix":"","firstName":"Gizem","middleName":"","lastName":"Inetas-Yengin","suffix":""},{"id":535148654,"identity":"ec65eb03-b64e-4212-a104-a603a961319d","order_by":1,"name":"Zehra Omeroglu-Ulu","email":"","orcid":"","institution":"Yeditepe University","correspondingAuthor":false,"prefix":"","firstName":"Zehra","middleName":"","lastName":"Omeroglu-Ulu","suffix":""},{"id":535148657,"identity":"19684f6f-0a59-491b-bd5d-13a6002dbcc2","order_by":2,"name":"Fikrettin Sahin","email":"","orcid":"","institution":"Yeditepe University","correspondingAuthor":false,"prefix":"","firstName":"Fikrettin","middleName":"","lastName":"Sahin","suffix":""},{"id":535148658,"identity":"5d8d4ec5-2a3d-40c0-a436-21196f924146","order_by":3,"name":"Dilek Telci","email":"data:image/png;base64,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","orcid":"","institution":"Yeditepe University","correspondingAuthor":true,"prefix":"","firstName":"Dilek","middleName":"","lastName":"Telci","suffix":""}],"badges":[],"createdAt":"2025-10-13 11:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7848426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7848426/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11033-025-11410-5","type":"published","date":"2026-01-03T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94698037,"identity":"816d3aed-f057-4507-b8f6-312a9f867e34","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1179504,"visible":true,"origin":"","legend":"","description":"","filename":"InetascfRNAManuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/373cd19d25405073830c7b5f.docx"},{"id":94728991,"identity":"937bc24b-d8b2-44ba-b1b8-e8e4aeb97e4c","added_by":"auto","created_at":"2025-10-30 07:04:27","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6297,"visible":true,"origin":"","legend":"","description":"","filename":"1ea8d9ee44b547d7b8c7f406ba10fa9c.json","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/f7fe8a26d24f5603308d6bbf.json"},{"id":94729138,"identity":"a3dca1a1-adb5-4b8b-8937-ace26d14b25e","added_by":"auto","created_at":"2025-10-30 07:04:38","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99126,"visible":true,"origin":"","legend":"","description":"","filename":"1ea8d9ee44b547d7b8c7f406ba10fa9c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/8cbaf5e7ebe169696f9b8e2e.xml"},{"id":94698042,"identity":"ce481b71-9ff7-4881-b373-c86ed965f483","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":324296,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/69d130c8d798ea2c4bd90b78.png"},{"id":94728761,"identity":"2b0e9c95-4d22-4e34-98d4-57b3a5159cb6","added_by":"auto","created_at":"2025-10-30 07:04:15","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":399491,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/967f1b6bcad9ce8fd49b5bae.png"},{"id":94728798,"identity":"aa02338e-742b-435b-85d7-eb4393736cb1","added_by":"auto","created_at":"2025-10-30 07:04:17","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63220,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/ab517781506a14f343051b5c.png"},{"id":94698040,"identity":"aaacd522-9f1e-4d7d-9661-70b9378761b9","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161374,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/228d8380dbb12137a0e78f40.png"},{"id":94822730,"identity":"a669a397-0a3c-40a9-990e-358651a15ac7","added_by":"auto","created_at":"2025-10-31 06:43:20","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65211,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/adeaf24a08cc2da26727e725.png"},{"id":94698048,"identity":"d607f711-d048-472c-a673-a24a44c1b0b2","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80888,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/194d96c5963a0736f0095783.png"},{"id":94728539,"identity":"c81472db-122f-4bc1-9894-4db443b5effd","added_by":"auto","created_at":"2025-10-30 07:04:00","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11374,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/1f9160d1563c2d90bb45fa4d.png"},{"id":94728197,"identity":"3bcac8f4-6752-448b-b233-e99d7ed592bb","added_by":"auto","created_at":"2025-10-30 07:03:19","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20669,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/e6c0a8b12e829ea32096e320.png"},{"id":94728649,"identity":"0846697a-224d-47ec-b1aa-0d2bc6fcd6f7","added_by":"auto","created_at":"2025-10-30 07:04:08","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31839,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/789ea25d216b3027b561236f.png"},{"id":94698045,"identity":"f5057076-2be2-4f07-a450-c2b579ae1d94","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96254,"visible":true,"origin":"","legend":"","description":"","filename":"1ea8d9ee44b547d7b8c7f406ba10fa9c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/cddc0be44d496368566b5b96.xml"},{"id":94698047,"identity":"25e6f193-86b7-4ecb-9a2d-94bf04ef6b46","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106689,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/11686b188375384bf6ad3074.html"},{"id":94728908,"identity":"9852ee10-4af0-4f23-ac6b-67688568b6df","added_by":"auto","created_at":"2025-10-30 07:04:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantification of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGAPDH\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression in plasma-derived cfRNA samples by dPCR\u003c/strong\u003e. (A) Representative scatter plots of \u003cem\u003eGAPDH\u003c/em\u003eexpression by dPCR in plasma-derived cfRNA samples. Copy number results for all samples, each analyzed in at least duplicate technical replicates. (C) Comparison of copy number across different isolation methods; Friedman test, p = 0.0366.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/2432f9306967504c9b4a2fa2.png"},{"id":94728989,"identity":"871ba800-7f01-4c2a-80cc-0633ad6ab06e","added_by":"auto","created_at":"2025-10-30 07:04:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantification of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression in plasma-derived cfRNA samples by dPCR\u003c/strong\u003e. (A) Representative scatter plots of \u003cem\u003eB2M\u003c/em\u003eexpression. (B) Copy number results for all samples, each analyzed in at least two technical replicates. (C) Comparison of copy number across different isolation methods (Friedman test, p = 0.0299, comparison between Method ZYM and TRI *p=0.0137).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/c254734e367eb38b9f3aa9df.png"},{"id":94698030,"identity":"2fa64bfc-0c56-405b-83fa-000fe8f354fc","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eqRT-PCR expression results of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGAPDH\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ereference genes\u003c/strong\u003e (A) Ct values for \u003cem\u003eGAPDH\u003c/em\u003eand (B) \u003cem\u003eB2M\u003c/em\u003e, with all samples analyzed in at least duplicate technical replicates. Friedman test, p = 0.0199, comparison between Method ZYM and TRI *p=0.0137).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/3421f4ad61333b6e65f5cad1.png"},{"id":94698035,"identity":"3fb4fbd5-f18e-4094-ba8f-9d916531dc1f","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":132865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between dPCR and qRT-PCR results of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGAPDH\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eB2M\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e reference genes\u003c/strong\u003e. (A) dPCR-qRT-PCR correlation graph of \u003cem\u003eGAPDH\u003c/em\u003eand (B) \u003cem\u003eB2M\u003c/em\u003e with Pearson correlation analysis. (C) Bland-Altman plot graph for \u003cem\u003eGAPDH\u003c/em\u003e (D) Bland-Altman plot graph for \u003cem\u003eB2M\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/1bb3412cab4826a4abf32f4d.png"},{"id":99545442,"identity":"84c84035-59aa-4470-bac1-2ff995eb69a8","added_by":"auto","created_at":"2026-01-05 16:07:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1181722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/f2478762-6a5f-446c-afe1-9ec6a91ff841.pdf"},{"id":94698034,"identity":"b126908f-70e2-4840-ae3b-26be55365d88","added_by":"auto","created_at":"2025-10-29 18:41:28","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":185033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1: Quantification of plasma-derived cfRNA using different isolation methods. \u003c/strong\u003e(A) Table of fluorometric concentrations of cfRNAs and cDNAs obtained from plasma. (B) Comparison of cfRNA extractions across different isolation methods Friedman test, p = 0.0199, comparison between Method NOR and TRI *p=0.0270).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7848426/v1/5a6f893378e68b386f4b5c23.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Plasma-Derived Cell-Free RNA Isolation Methods Using PCR- Based Quantification","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRNA biomarker research has accelerated in recent years, leading to the development of more advanced RNA detection technologies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There has been considerable expansion in blood-based biomarker studies using cell-free nucleic acids (cf-NAs). cf-NAs can be detected in various physiological fluids such as plasma, brain fluid and urine [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and used as biomarkers in a variety of clinical settings, including cancer and tumor monitoring, non-invasive prenatal diagnostics, solid organ transplantation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and neurodegenerative diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCell-free RNA (cfRNA) shows a potential for monitoring disease progression and therapeutic response, highlighting the translational importance of developing reliable cfRNA workflows. The different circulating cfRNA types in human blood include messenger RNA (mRNA), long-noncoding RNA (lncRNA), circular RNA (circRNA), miRNA, Piwi-interacting RNA (piRNA), transfer RNAs (tRNA), and other classes of non-coding RNA molecules [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These circulating RNAs fall under the broader category of cfNAs and are increasingly recognized as potential biomarkers for genetic alterations and disease diagnostics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies emphasize that cfRNA captures dynamic transcriptional changes in real time, thus offering complementary diagnostic and prognostic value beyond cfDNA [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, cfNAs including cfRNA have already demonstrated clinical utility across diverse applications such as cancer, metabolic disorders, transplantation, and prenatal testing [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although challenges remain in isolation and detection, continuous advances in sequencing, quantitative PCR (qPCR), digital PCR (dPCR), and especially in workflows for long RNA species are improving the reliability of cfRNA biomarker discovery [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The simplicity of sample collection and the fast-declining prices of sequencing make cfRNA-based biomarkers potentially a cost-effective therapeutic tool [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNevertheless, several technical and biological challenges limit the reliable use of cfRNA. cfRNA is found in blood in the form of ribonucleoprotein complexes or encapsulated within microvesicles [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Reducing RNA contamination is typically achieved by using serum or plasma instead of whole blood. cfRNA isolation is challenging due to degradation, instability, and lack of standardization, resulting in inconsistent results across different studies and laboratories [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Because of its low biomass, cfRNA can be contaminated by microbial, environmental, and intrasample DNA. Cellular RNA contamination of plasma is another problem for isolation and the cfRNA profile is also impacted by centrifugation speed and duration. These preanalytical variables provide a considerable hurdle for reproducibility [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. International initiatives such as the Extracellular RNA Communication Consortium (ERCC) have highlighted the need for standardized protocols and emphasized the need to reduce these problems and increase inter-study comparability [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to isolation challenges, quantification and normalization remain major obstacles, owing to low cfRNA abundance, instability, and the lack of standardized reference controls. qPCR is widely used for cfRNA analysis due to its affordability and availability, while dPCR provides a higher sensitivity for transcripts at low concentrations, such as cfRNA. Another challenge is the selection of appropriate reference genes for normalization. Transcriptomic gene expressions can vary among individuals and biofluids, and a universally stable host gene has not yet been identified. Therefore, systematic assessment of reference gene stability is necessary for a reliable cfRNA quantification.\u003c/p\u003e\u003cp\u003eFor cfRNA isolation, several commercial kits are available that differ in RNA yield, quality, processing time, and ease of use. However, the small amounts of cfRNA recovered after isolation complicate downstream PCR-based applications, as both input RNA quantity and quality critically influence subsequent analyses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, obtaining cfRNAs in sufficient yield and integrity using appropriate isolation strategies is essential before applying methods such as PCR. The aim of this study was to compare the purity and quality of cfRNAs isolated from plasma using different isolation methods and commercial cfRNA kits, and to evaluate the performance of these extracts using dPCR and qRT-PCR.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCollection of peripheral blood\u003c/h2\u003e\u003cp\u003eAll optimization experiments with peripheral blood were approved by the Yeditepe University Clinical Research Ethics Committee (Istanbul, Turkey; Decision No. 1678, dated 10.11.2022). Written informed consent was obtained from all healthy donors, and the study was conducted in accordance with the principles of the World Medical Association Declaration of Helsinki. Healthy individuals who do not have high blood pressure, diabetes, or other neurological disorders were included in this study. Peripheral blood samples (30 mL) were taken from all subjects (n\u0026thinsp;=\u0026thinsp;5 women between 35 and 45 years of age) with BD Vacutainer EDTA blood collection tubes (Cat #367525, Becton Dickinson). Tubes were immediately transported to the laboratory for plasma preparation and tubes were centrifuged at 1900 g for 10 min at 4\u0026deg;C. Supernatant was collected into new falcon tube and centrifuged at 3000 g for 15 min at 4\u0026deg;C. Plasma samples were separated for RNA isolation immediately.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHemolysis measurements for plasma samples\u003c/h3\u003e\n\u003cp\u003eFresh plasma samples were read using the nanodrop (NanoDrop 2000, Thermo Fisher Scientific) at 414 nm in 3 replicates. Hemoglobin obtained from bovine blood (Cat #H2500, Sigma-Aldrich) was used between 0.25 to 1 g/L for the standard curve. The results of plasma samples were calculated both clinically and using the Beer-Lambert equation.\u003c/p\u003e\n\u003ch3\u003ecfRNA isolation\u003c/h3\u003e\n\u003cp\u003ecfRNAs were extracted from fresh plasma samples with four different commercial purification kits by following the manufacturers\u0026rsquo; manual. QIAamp ccfDNA/RNA Kit (Cat #55184, Qiagen), miRNeasy Serum/Plasma Advanced Kit (Cat #217204, Qiagen), Plasma/Serum cfc-RNA Advanced Purification Kit (Cat #68200, Norgen), Quick-cfDNA/cfRNA Serum \u0026amp; Plasma Kit (Cat #R1072, Zymo Research) and TRIzol extraction (Cat #15596018 Thermo Fisher Scientific) were used for cfRNA isolations. Briefly, cfRNA was isolated from 600 \u0026micro;L of plasma using the miRNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions, and the final RNA was eluted in 20 \u0026micro;L of RNase-free water. For the QIAamp ccfDNA/RNA Kit, 1 mL of plasma was used, and samples were dissolved in 20 \u0026micro;L RNase-free water as recommended by the manufacturer. For Plasma/Serum cfc-RNA Advanced Purification Kit, 1 ml of plasma was used, and samples were dissolved in 25 \u0026micro;L Elution Buffer A. In Quick-cfDNA/cfRNA Serum \u0026amp; Plasma Kit, 1 mL of plasma was used, and samples were dissolved in 15 \u0026micro;L RNase-free water. In addition to commercial kits, a classical phenol\u0026ndash;chloroform extraction was performed using TRIzol reagent (Cat #15596018 Thermo Fisher Scientific). Briefly, 1 mL of plasma was mixed with 3 mL of TRIzol and incubated for 5 min at 4\u0026deg;C. Next, 1200 \u0026micro;L of chloroform was added to each sample and vortexed for 30 s. The mixture was centrifuged at 12,000 \u0026times; g for 5 min at 4\u0026deg;C, and the aqueous phase was transferred to a fresh tube containing 800 \u0026micro;L of isopropanol. Samples were incubated at \u0026minus;\u0026thinsp;20\u0026deg;C for 12\u0026ndash;16 h and centrifuged again at 12,000 \u0026times; g for 5 min. The resulting RNA pellets were washed with 1 mL of 80% ethanol, air-dried at room temperature for 5 min, and resuspended in 10 \u0026micro;L of RNase-free water [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. All eluates were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further processing, and the concentrations of the samples were measured using the DeNovix QFX Fluorometer (DeNovix Inc). cfRNA samples were measured with DeNovix RNA Assay (Cat #EVAL-50, DeNovix) kit.\u003c/p\u003e\n\u003ch3\u003edPCR design\u003c/h3\u003e\n\u003cp\u003eAfter fluorometric measurements of cfRNAs obtained from plasma were taken, they were synthesized into cDNA using the iScript cDNA Synthesis Kit (Cat #1708890, BioRad) according to the manufacturer's instructions, in a final volume of 120 ng/\u0026micro;L. Synthesized cDNA samples were measured with Qubit dsDNA HS Assay (Cat #Q32854, Thermo Fisher Scientific) and Qubit ssDNA Assay (Cat #Q10212, Thermo Fisher Scientific) kits using DeNovix QFX Fluorometer (DeNovix Inc). Subsequently, samples were prepared for dPCR and sample cDNAs with a total volume of 10 ng/\u0026micro;L were used.\u003c/p\u003e\n\u003ch3\u003edPCR\u003c/h3\u003e\n\u003cp\u003eThe dPCR method was employed to quantify cfRNA expression levels using the nanoplate-based Qiagen QIAcuity One device and QIAcuity EvaGreen (EG) PCR Kit (Cat #250111, Qiagen). The dPCR mixture was prepared by adding 3x EvaGreen PCR Master Mix, primer pairs, RNase-free water and 2 \u0026micro;L cDNA. Below the list of primers, GAPDH: Forward Primer 5\u0026rsquo; GTC TCC TCT GAC TTC AAC AGC G 3\u0026rsquo; and Reverse Primer 5\u0026rsquo; ACC ACC CTG TTG CTG TAG CCA A 3\u0026rsquo;, B2M: Forward Primer 5\u0026rsquo; CCA CTG AAA AAG ATG AGT ATG CCT 3\u0026rsquo; and Reverse Primer 5\u0026rsquo; CCA ATC CAA ATG CGG CAT CTT C 3\u0026rsquo;. Each sample was analyzed using QIAcuity One 5plex System (Qiagen) with at least duplicate technical replicates. The reaction was initiated by loading the sample into a QIAcuity Nanoplate 8.5k 24-well (Cat #250011, Qiagen) with 8500 portions. Reaction; initial heat activation: 2 min at 95\u0026deg;C, denaturation 15 s at 95\u0026deg;C, binding 15 s at 55\u0026deg;C and extension 15 s at 72\u0026deg;C for 40 cycles, and cooling 5 min at 40\u0026deg;C was carried out. QIAcuity Software Suite v.2.0. was conducted for the dPCR results.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eqRT-PCR\u003c/h2\u003e\u003cp\u003eqRT-PCR studies were carried out using the QuantiNova SYBR Green PCR Kit (Cat# 208152, Qiagen, Germany) as recommended by the manufacturer. cDNAs (10 ng/\u0026micro;L) were amplified using a master mix containing 2\u0026times; QuantiNova SYBR Green PCR Master Mix, ROX dye, and primer pairs. Each sample was analyzed using QIAquant 96 5plex Real Time PCR (Qiagen) with at least two technical replicates. Reaction; initial heat activation: 2 min at 95\u0026deg;C, denaturation: 5 s at 95\u0026deg;C, binding/extension: 10 s at 60\u0026deg;C, and melting curve in 0.5\u0026deg;C increments between 65\u0026deg;C and 95\u0026deg;C for 40 cycles it was carried out for 2\u0026ndash;5 seconds per cycle.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using GraphPad Prism 10 software (GraphPad Software, USA). To compare cfRNA expression levels (\u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e) across different isolation methods, the Friedman test was used as a non-parametric alternative to repeated-measures ANOVA. When the Friedman test indicated a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), post-hoc multiple comparisons were performed using the Dunn\u0026rsquo;s test to identify which methods differed significantly. Correlation analyses between dPCR and qRT-PCR results were performed using Pearson correlation coefficients. The strength and direction of the correlation were interpreted based on the r-value. Agreement between the two methods was further evaluated using Bland-Altman analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTo provide an overview of the cfRNA extraction strategies, we compared the key features of the commercial kits and the phenol\u0026ndash;chloroform method. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the cfRNA isolation kits evaluated in this study, including their processing time, cost, plasma input volume, and RNA elution yield. cfRNA isolation was initiated using the minimum plasma volumes recommended in the commercial kits' user manuals, and the resulting cfRNAs were subsequently eluted in the lowest \u0026micro;L volumes specified by the same protocols.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of cfRNA isolation methods with respect to plasma input, elution volume, processing time, and cost.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlasma volume\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElution volume\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTime consuming\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDifficulty\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emiRNeasy Serum/Plasma Advanced Kit (MIR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e600 \u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 \u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026asymp;\u0026thinsp;1 hr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e$$\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e++\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQIAamp ccfDNA/RNA Kit\u003c/p\u003e\u003cp\u003e(QIA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 \u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026asymp;\u0026thinsp;1 hr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e$$$$\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e++\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma/Serum cfc-RNA Advanced Purification Kit Norgen\u003c/p\u003e\u003cp\u003e(NOR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 \u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026asymp;\u0026thinsp;1,5 hr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e$$\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuick-cfDNA/cfRNA Serum \u0026amp; Plasma Kit Zymo\u003c/p\u003e\u003cp\u003e(ZYM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 \u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026asymp;\u0026thinsp;3 hrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e$$$$\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+++\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTRIzol\u003c/p\u003e\u003cp\u003e(TRI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 \u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 min\u0026thinsp;+\u0026thinsp;12\u0026ndash;16 hrs incubations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor reliable cfRNA analysis, plasma samples must have a hemoglobin concentration below 50 g/L to exclude hemolysis. To assess hemolysis, absorbance at 414 nm was measured in plasma collected from healthy individuals, and hemoglobin concentration was calculated using a standard curve (y\u0026thinsp;=\u0026thinsp;0.2857x \u0026ndash; 0.0201, R\u0026sup2; = 0.9805). The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All plasma samples showed negligible hemolysis, with sample #5 exhibiting the lowest absorbance value.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHemolysis analysis of plasma samples based on absorbance measurements at 414 nm.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStd dev\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClinical Evaluation of Hemolysis g/L\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBeer-Lambert equation\u003c/p\u003e\u003cp\u003eg/L\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ehemolysis status\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49,037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,420021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emild hemolysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39,237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,3220161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emild hemolysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49,971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,4293548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emild hemolysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45,887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,38851943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emild hemolysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e#5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0,046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,00462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23,370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0,1633415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo hemolysis/ very mild hemolysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFollowing isolation, cfRNA and dsDNA/ssDNA concentrations were quantified using a DeNovix QFX Fluorometer (DeNovix Inc.) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). Among the tested methods, the TRIzol-based approach (TRI) yielded the highest cfRNA concentrations. A Friedman test was performed to compare cfRNA concentrations across different isolation methods, yielding a statistically significant result (p\u0026thinsp;=\u0026thinsp;0.0199). Post-hoc multiple comparisons using the Dunn\u0026rsquo;s test revealed a significant difference between Method NOR and Method TRI (p\u0026thinsp;=\u0026thinsp;0.0270) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e). Our dPCR results demonstrate that plasma samples from different individuals exhibit varying expression levels, as evidenced by the number of copies/\u0026micro;L measured for each marker (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e expression were identified in cfRNA samples by digital PCR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026amp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). \u003cem\u003eGAPDH\u003c/em\u003e expression ranged from 0.663 to 11.442 copies/\u0026micro;L across the tested samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In order to compare \u003cem\u003eGAPDH\u003c/em\u003e expression in the same samples across different isolation methods, a Friedman test was performed, yielding a statistically significant result (p-value of 0.0366); however, the differences in \u003cem\u003eGAPDH\u003c/em\u003e expression among the groups were not statistically significant by Dunn\u0026rsquo;s test (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn order to further assess the potential impact of isolation methods, we also analyzed the expression of another widely used cfRNA housekeeping gene, \u003cem\u003eB2M\u003c/em\u003e, by digital PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). \u003cem\u003eB2M\u003c/em\u003e expression ranged from 0.178 to 207.385 copies/\u0026micro;L across the tested samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A Friedman test performed on the same samples across different methods, yielding a statistically significant result (p\u0026thinsp;=\u0026thinsp;0.0299). Post-hoc multiple comparisons using the Dunn\u0026rsquo;s test revealed a significant difference between Method ZYM and TRI (p\u0026thinsp;=\u0026thinsp;0.0137 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo determine whether the differences observed in digital PCR could also be detected by real-time PCR, we repeated the experiments using the same cfRNA samples. All samples were derived from healthy individuals and analyzed with the housekeeping genes \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e; Ct values were assessed at least in dublicate for each sample, and Friedman analysis was applied to the qRT-PCR data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similar to dPCR results, for \u003cem\u003eGAPDH\u003c/em\u003e, the Friedman test yielded a p-value of 0.0279, but the differences in \u003cem\u003eGAPDH\u003c/em\u003e expression among methods were not statistically significant by Dunn\u0026rsquo;s test (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For \u003cem\u003eB2M\u003c/em\u003e, the Friedman test produced a p-value of 0.0199, with a significant difference in the expression levels of \u003cem\u003eB2M\u003c/em\u003e between Method ZYM and Method TRI according to Dunn\u0026rsquo;s test (p\u0026thinsp;=\u0026thinsp;0.0137) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the consistency between qRT-PCR and digital PCR, correlation and agreement analyses were performed for the reference genes \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e. Ct values of qRT-PCR for \u003cem\u003eGAPDH\u003c/em\u003e showed a strong and significant negative correlation with copy numbers determined by dPCR (y = \u0026minus;\u0026thinsp;0.54x\u0026thinsp;+\u0026thinsp;31.25, Pearson r = \u0026minus;\u0026thinsp;0.76, R\u0026sup2; = 0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Similarly, analysis of \u003cem\u003eB2M\u003c/em\u003e revealed a negative correlation between qRT-PCR Ct values and dPCR copy numbers (y = \u0026minus;\u0026thinsp;0.039x\u0026thinsp;+\u0026thinsp;31.00, Pearson r = \u0026minus;\u0026thinsp;0.72, R\u0026sup2; = 0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Systematic deviations between the two methods for both reference genes were further assessed using Bland-Altman plots (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eLiquid biopsy-based diagnostic platforms using plasma or serum as input sources are increasingly gaining acceptance in clinical practice, generating substantial interest in circulating cfDNA and cfRNA as clinically relevant biomarkers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Due to its fragile structure, cfRNA is highly susceptible to degradation, and both the isolation process and downstream analyses can be affected by pre-analytical factors such as blood collection, tube type, processing time, freeze\u0026ndash;thaw cycles, hemolysis, and cellular contamination. These challenges, combined with the inherently low yield of cfRNA, limit the efficiency and reliability of downstream analyses. Nevertheless, cfRNA offers unique advantages as it reflects real-time transcriptional activity and includes both coding and non-coding RNA species, enabling the detection of tissue- and disease-specific molecular signatures [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These characteristics make cfRNA a promising tool for early diagnosis, disease monitoring, and therapeutic stratification [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite technical challenges, various methods, including RNA sequencing, microarray, qPCR, and dPCR, are widely used for cfRNA validation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Among these, dPCR is particularly advantageous because its high sensitivity allows for the reliable detection and quantification of low abundance cfRNA species in plasma.\u003c/p\u003e\u003cp\u003eIn this study, we systematically compared five cfRNA isolation methods with respect to RNA quality and downstream performance using dPCR and qRT-PCR. In addition, the expression stability of potential reference genes was evaluated in the context of these isolation methods. The novelty of this work lies in the direct comparison of five distinct isolation strategies using fresh plasma samples, combined with reference gene\u0026ndash;based expression analyses, thereby providing a comprehensive evaluation of both methodological performance and normalization approaches in cfRNA studies. The protocols evaluated were selected based on practical considerations, including time efficiency, ease of use, and compatibility with small sample volumes. Since clinical specimens are often limited in availability, methods allowing multiple downstream analyses from minimal input material are particularly valuable for biomarker discovery and diagnostic applications.\u003c/p\u003e\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, both dPCR and qRT-PCR analyses demonstrated that Zymo Quick-cfDNA/cfRNA kit (Method ZYM) achieved the highest expression levels of \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e, while miRNeasy Serum/Plasma Advanced kit (Method MIR) and the QIAamp ccfDNA/RNA kit (Method QIA) yielded comparable outcomes. The higher yields observed with the Zymo Quick-cfDNA/cfRNA kit (Method ZYM) may be related to its use of a vacuum manifold during the isolation step, whereas the other kits relied solely on centrifugation. In the literature, further comparisons between the miRNeasy Serum/Plasma Advanced Kit (Method MIR), the Zymo Quick-cfDNA/cfRNA Kit (Method ZYM), and the QIAamp Circulating Nucleic Acids Kit have evaluated the abundance of hsa-miR-16-5p, a specific microRNA found in blood plasma, using qRT-PCR [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consistent with our findings, that study reported that the Zymo Quick-cfDNA/cfRNA Kit yielded approximately tenfold higher levels of hsa-miR-16-5p compared with the other kits. Similarly, a study evaluating six cfRNA isolation kits using ovine plasma reported that the Zymo Quick-cfDNA/cfRNA kit (ZYM) outperformed the miRNeasy Serum/Plasma Advanced kit (MIR) in fresh samples but exhibited reduced efficiency in frozen samples. However, the miRNeasy Serum/Plasma Advanced Kit (MIR) yielded more consistent results across both fresh and frozen samples and demonstrated higher reproducibility, whereas the Zymo Quick-cfDNA/cfRNA kit (ZYM) showed greater variability between replicates and sample types [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our analyses, no statistically significant differences were observed between Methods ZYM, MIR, and QIA; however, Method ZYM consistently yielded higher expression across blood plasma samples. Previous studies using animal plasma have reported that while the Zymo Quick-cfDNA/cfRNA Kit can achieve higher cfRNA recovery, its performance may vary depending on sample type and storage conditions, whereas the Qiagen kits generally provide more consistent outcomes across different conditions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These reports suggest that performance differences are primarily attributable to the distinct chemistries of the kits, with Zymo\u0026rsquo;s design favoring cfRNA recovery and Qiagen\u0026rsquo;s optimization being more suitable for miRNA enrichment.\u003c/p\u003e\u003cp\u003eAfter Method ZYM, the highest levels of \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e were obtained with Methods MIR and QIA, which performed comparably in our analyses. In the literature, another comparative study assessed six commercial kits, including the miRNeasy Serum/Plasma Advanced Kit (MIR), using healthy human plasma samples. Comparisons were made between whole plasma and plasma-derived ultracentrifugation (UC) fractions. To evaluate overall cfRNA yield and concentration, four endogenous miRNAs (miR-451a, miR-21-1, miR-30d, and miR-122), present at varying levels in plasma, were quantified by qRT-PCR [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The results demonstrated that the miRNeasy Serum/Plasma Advanced Kit (MIR) and the Macherey-Nagel NucleoSpin miRNA Plasma Kit provided the highest miRNA yield and purity among the kits tested and further indicated that the incorporation of an ultracentrifugation step in Method MIR enhanced miRNA recovery [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In a related study, eight RNA purification methods, including the miRNeasy Serum/Plasma Advanced Kit (MIR) and the QIAamp ccfDNA/RNA Kit (QIA), were systematically evaluated for extracellular RNA (exRNA) isolation from plasma, generating 456 transcriptomic datasets that encompassed both plasma- and serum-derived microRNAs and mRNAs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Significant performance differences were observed across the evaluated methods in terms of RNA concentration, yield, and the number of detectable transcripts. The miRNeasy Serum/Plasma Advanced Kit (Method MIR) showed the highest number of detectable miRNAs with low replicate variability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, the QIAamp ccfDNA/RNA Kit (Method QIA) performed best with larger plasma input volumes (up to 4 mL), providing higher mRNA concentrations and recovery, but it was less efficient than the miRNeasy kit for miRNA detection [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In a complementary study, the expression of \u003cem\u003eCAVIN2\u003c/em\u003e, \u003cem\u003eNRGN\u003c/em\u003e, \u003cem\u003eAIF1\u003c/em\u003e, and \u003cem\u003eB2M\u003c/em\u003e genes from human plasma was analyzed using six co-purification kits for cfDNA and cfRNA, including Methods MIR and QIA [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Using an optimized framework, the authors compared the co-purification efficiency of two manual kits: miRNeasy Serum/Plasma Advanced Kit (MIR) and QIAamp ccfDNA/RNA Kit (QIA) along with two semi-automated platforms (MagNA Pure 24 Total NA Isolation Kit and iCatcher Circulating cfDNA/cfRNA 4000 Kit) across different plasma input volumes. All kits were able to successfully co-purify cfDNA and cfRNA, as confirmed by duplex assays, demonstrating their applicability for parallel nucleic acid recovery. Notably, the study highlighted that in some cases equivalent or even higher eluate concentrations could be achieved with smaller plasma input volumes\u0026mdash;for example, Method MIR with 0.6 mL yielded cfDNA and cfRNA concentrations comparable to or greater than those obtained with Method QIA using 1 mL input. These findings emphasize that plasma input requirements, in addition to kit chemistry, critically influence recovery efficiency and may account for performance variability observed between different platforms. In summary, consistent with numerous reports, Qiagen kits\u0026mdash;particularly Method MIR\u0026mdash;have been shown to achieve high efficiency and yield in cfRNA isolation from plasma samples. In our study, Method MIR was applied at its maximum input volume (600 \u0026micro;L), whereas the other methods were tested with 1 mL plasma, corresponding to their lowest recommended input volumes. Together with published evidence, this underscores that sample input volume is a critical factor influencing method performance. Since Method ZYM requires at least 1 mL of plasma, Method MIR emerges as a practical alternative when sample availability is limited, making it the second-best option in settings where input material is scarce.\u003c/p\u003e\u003cp\u003eIn this study, TRIzol-based extraction (Method TRI) produced the highest cfRNA concentration measurements (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e); however, the apparent recovery of cfRNA was the lowest (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This discrepancy is most likely attributable to residual phenol from the isolation procedure, which can artificially inflate spectrophotometric RNA measurements while simultaneously inhibiting downstream PCR applications by interfering with polymerase and reverse transcriptase activity in a concentration-dependent manner [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In agreement with previous reports, commercial column-based kits provided higher cfRNA yield and purity from small plasma volumes compared with guanidinium thiocyanate/phenol\u0026ndash;chloroform methods [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Phenol\u0026ndash;chloroform approaches such as TRIzol generally result in lower dPCR/qRT-PCR signals from plasma-derived cfRNA, likely due to their limited ability to enrich fragmented RNAs and to efficiently remove inhibitory contaminants [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nonetheless, these methods remain widely used for total RNA isolation from cells and tissues, and hybrid protocols that integrate column- and phenol-based chemistries may improve the consistency and reliability of cfRNA analyses.\u003c/p\u003e\u003cp\u003eAnother critical aspect of the comparison involved the selection of candidate reference genes, as this choice is essential for reliable relative quantification in cfRNA studies. In our study, both \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e were evaluated as potential reference genes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Following cfRNA extraction, expression levels of \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e were assessed by dPCR and qRT-PCR. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a stronger linear correlation was observed between dPCR and qRT-PCR results for \u003cem\u003eGAPDH\u003c/em\u003e compared with \u003cem\u003eB2M\u003c/em\u003e. \u003cem\u003eGAPDH\u003c/em\u003e also displayed lower variation across plasma cfRNA samples, suggesting greater stability as a housekeeping gene, although its abundance still varied among individuals. Importantly, the stability of reference genes is context-dependent, influenced by factors such as tissue type, disease state, and sample handling, and no single universal reference gene has yet been established. For instance, Wagner et al. reported significant inter-individual variation in \u003cem\u003eGAPDH\u003c/em\u003e transcript levels in plasma-derived cfRNA, likely reflecting donor-specific basal expression [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Likewise, other studies emphasize that pre-analytical and biological variables\u0026mdash;including storage, handling, and processing\u0026mdash;strongly affect reference gene stability, underscoring the need for careful validation in cfRNA biomarker discovery [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Supporting this, a study conducted with umbilical cord blood and healthy adult samples evaluated the suitability of eight reference genes, including \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eB2M\u003c/em\u003e, using algorithms such as BestKeeper, GeNorm, and NormFinder [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The results indicated that \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003ePPIB\u003c/em\u003e were among the most stable reference genes across both pediatric and adult cohorts, suggesting broad applicability and reliable performance in RNA expression datasets. Taken together, these observations underscore the importance of systematically assessing cfRNA normalization transcripts across time and individuals as a prerequisite for robust cfRNA diagnostic applications. Ultimately, successful translation of cfRNA analyses into clinical practice will require standardized Standard Operating Procedures (SOPs) spanning both pre-analytical and analytical stages. Moreover, integration with complementary biomarkers (cfDNA, proteins, imaging) and the adoption of advanced computational normalization strategies, including machine learning, are expected to further enhance robustness and diagnostic accuracy.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn summary, our comparative evaluation of five cfRNA isolation methods demonstrated that commercial kits generally outperformed phenol\u0026ndash;chloroform\u0026ndash;based approaches. When assessed for qRT-PCR and dPCR performance, Method ZYM produced the most consistent results, followed by Methods MIR and QIA, underscoring that protocol selection can markedly influence cfRNA recovery and detectability. Beyond cfRNA quality, additional factors such as processing time, cost, and ease of application should also be considered when evaluating commercial kits. In our reference gene analysis, \u003cem\u003eGAPDH\u003c/em\u003e exhibited greater stability than \u003cem\u003eB2M\u003c/em\u003e in plasma-derived cfRNA; however, the observed interindividual variability highlights the need for careful validation of normalization strategies in future studies. Taken together, these findings emphasize the importance of method optimization and reference gene selection to improve the reliability of cfRNA-based biomarker discovery. Large-scale validation, rigorous control of pre-analytical variables, and integration with complementary analytes will be essential to facilitate the translation of cfRNA analyses into routine clinical diagnostics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ecfRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecell-free RNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003edPCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDigital PCR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eERCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExtracellular RNA Communication Consortium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: G.I.Y (Equal) and Z.O.U (Equal), Methodology: G.I.Y (Equal), Z.O.U (Equal) and D.T (Lead), Validation: G.I.Y (Lead), \u0026nbsp;Investigation: G.I.Y (Lead), Formal analysis: G.I.Y (Equal) and Z.O.U (Equal), Writing - Original Draft Preparation: G.I.Y (Lead), Z.O.U (Equal), F.S (Equal) and D.T (Equal), Writing - Review \u0026amp; Editing: G.I.Y (Equal), Z.O.U (Equal), F.S (Equal) and D.T (Equal), Visualization: G.I.Y (Equal) and Z.O.U (Equal), Supervision: F.S (Equal) and D.T (Lead), Project administration: D.T (Lead), Funding acquisition: F.S (Lead).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is provided by the Scientific and Technological Research Council of T\u0026uuml;rkiye (TUBITAK) TUBİTAK-1004, Project No. 23AG009.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing financial and/or non-financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are included within the article. No datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the decision of Yeditepe University Clinical Research Ethics Committee (Date 10.11.2022/No:1678) and all requirements of the World Medical Association Helsinki Declaration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIslam MN, Masud MK, Haque MH, Hossain MSA, Yamauchi Y, Nguyen NT, Shiddiky MJ (2017) RNA biomarkers: diagnostic and prognostic potentials and recent developments of electrochemical biosensors. Small Methods 1(7):1700131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/smtd.201700131\u003c/span\u003e\u003cspan address=\"10.1002/smtd.201700131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzil\u0026aacute;gyi M, P\u0026ouml;s O, M\u0026aacute;rton \u0026Eacute;, Bugly\u0026oacute; G, Solt\u0026eacute;sz B, Keserű J, Nagy B (2020) Circulating cell-free nucleic acids: main characteristics and clinical application. Int J Mol Sci 21(18):6827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms21186827\u003c/span\u003e\u003cspan address=\"10.3390/ijms21186827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDrag MH, Kilpel\u0026auml;inen TO (2021) Cell-free DNA and RNA\u0026mdash;measurement and applications in clinical diagnostics with focus on metabolic disorders. Physiol Genom 53(1):33\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/physiolgenomics.00086.2020\u003c/span\u003e\u003cspan address=\"10.1152/physiolgenomics.00086.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai X, Janku F, Zhan Q, Fan JB (2015) Accessing genetic information with liquid biopsies. Trends Genet 31(10):564\u0026ndash;575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tig.2015.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tig.2015.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMareboina M, Deng E, Mouratidis I, Yee NS, Pitteloud N, Georgakopoulos-Soares I, Chartoumpekis DV (2024) A review on cell-free RNA profiling: Insights into metabolic diseases and predictive value for bariatric surgery outcomes. Mol metabolism 87:101987. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molmet.2024.101987\u003c/span\u003e\u003cspan address=\"10.1016/j.molmet.2024.101987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eP\u0026ouml;s O, Bir\u0026oacute; O, Szemes T, Nagy B (2018) Circulating cell-free nucleic acids: characteristics and applications. Eur J Hum Genet 26(7):937\u0026ndash;945. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41431-018-0132-4\u003c/span\u003e\u003cspan address=\"10.1038/s41431-018-0132-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCab\u0026uacute;s L, Lagarde J, Curado J, Lizano E, P\u0026eacute;rez-Boza J (2022) Current challenges and best practices for cell-free long RNA biomarker discovery. Biomark Res 10(1):62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40364-022-00409-w\u003c/span\u003e\u003cspan address=\"10.1186/s40364-022-00409-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKan CM, Pei XM, Yeung MHY, Jin N, Ng SSM, Tsang HF, Wong SCC (2023) Exploring the role of circulating cell-free RNA in the development of colorectal cancer. Int J Mol Sci 24(13):11026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms241311026\u003c/span\u003e\u003cspan address=\"10.3390/ijms241311026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoy C, Ahmann L, De Vlaminck I, Gu W (2024) Liquid biopsy based on cell-free DNA and RNA. Annu Rev Biomed Eng 26:169\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-bioeng-110222-111259\u003c/span\u003e\u003cspan address=\"10.1146/annurev-bioeng-110222-111259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmolegbe SM, Johnston NC, Ambrosi A, Ganguly A, Howcroft TK, Kuo LS, Happel C (2025) Extracellular RNA communication: A decade of NIH common fund support illuminates exRNA biology. J Extracell Vesicles 14(1):e70016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jev2.70016\u003c/span\u003e\u003cspan address=\"10.1002/jev2.70016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou D, Tang W, Liu X, An HX, Zhang Y (2017) Clinical verification of plasma messenger RNA as novel noninvasive biomarker identified through bioinformatics analysis for lung cancer. Oncotarget 8(27):43978\u0026ndash;43989. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/oncotarget.16701\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.16701\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheung KWE, Choi SYR, Lee LTC, Lee NLE, Tsang HF, Cheng YT, Wong SCC (2019) The potential of circulating cell free RNA as a biomarker in cancer. Expert Rev Mol Diagn 19(7):579\u0026ndash;590. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14737159.2019.1633307\u003c/span\u003e\u003cspan address=\"10.1080/14737159.2019.1633307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim H, Wu G, Kemp R, Okobi Q, Barberan-Solder S, Jia XY Assessment of Cell-Free Nucleic Acid Isolation Efficiencies for Liquid Biopsy Research. Zymo Res, 1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright K, de Silva K, Purdie AC, Plain KM (2020) Comparison of methods for miRNA isolation and quantification from ovine plasma. Sci Rep 10(1):825. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-57659-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-57659-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeerson A, Ploug T (2016) Assessment of six commercial plasma small RNA isolation kits using qRT-PCR and electrophoretic separation: higher recovery of microRNA following ultracentrifugation. Biology Methods Protocols 1(1):bpw003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/biomethods/bpw003\u003c/span\u003e\u003cspan address=\"10.1093/biomethods/bpw003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThe exRNAQC Consortium (2025) Blood collection tube and RNA purification method recommendations for extracellular RNA transcriptome profiling. \u003cem\u003eNature communications\u003c/em\u003e, 2025, 16.1: 4513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-025-58607-7\u003c/span\u003e\u003cspan address=\"10.1038/s41467-025-58607-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeleu, J., Schoofs, K., Decock, A., Verniers, K., Roelandt, S., Denolf, A., \u0026hellip; Vandesompele,J. (2022). Digital PCR-based evaluation of nucleic acid extraction kit performance for the co-purification of cell-free DNA and RNA. Human Genomics, 16(1), 73. https://doi.org/10.1186/s40246-022-00446-4\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUnger C, Lokmer N, Lehmann D, Axmann IM (2019) Detection of phenol contamination in RNA samples and its impact on qRT-PCR results. Anal Biochem 571:49\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ab.2019.02.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ab.2019.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe APH, Tran TT, Cao THM, Le TM, Le PT, Huong HTT (2020), July Evaluate and Optimize Cell-Free RNA Extraction Methods to Apply for Alzheimer\u0026rsquo;s Disease Biomarkers Detection. In \u003cem\u003eInternational Conference on the Development of Biomedical Engineering in Vietnam\u003c/em\u003e (pp. 591\u0026ndash;609). Cham: Springer International Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-75506-5_50\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-75506-5_50\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEldh M, L\u0026ouml;tvall J, Malmh\u0026auml;ll C, Ekstr\u0026ouml;m K (2012) Importance of RNA isolation methods for analysis of exosomal RNA: evaluation of different methods. Mol Immunol 50(4):278\u0026ndash;286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molimm.2012.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.molimm.2012.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLak NSM, Seijger A, van Zogchel LMJ, Gelineau NU, Javadi A, Zappeij-Kannegieter L, Bongiovanni L, Andriessen A, Stutterheim J, van der Schoot CE, de Bruin A, Tytgat GAM (2023) Cell-Free RNA from Plasma in Patients with Neuroblastoma: Exploring the Technical and Clinical Potential. Cancers 15(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers15072108\u003c/span\u003e\u003cspan address=\"10.3390/cancers15072108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWagner JT, Kim HJ, Johnson-Camacho KC, Kelley T, Newell LF, Spellman PT, Ngo TT (2020) Diurnal stability of cell-free DNA and cell-free RNA in human plasma samples. Sci Rep 10(1):16456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-73350-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-73350-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHieronymus, K., Dorschner, B., Schulze, F., Vora, N. L., Parker, J. S., Winkler, J.L., \u0026hellip; Winkler, S. (2021). Validation of reference genes for whole blood gene expression analysis in cord blood of preterm and full-term neonates and peripheral blood of healthy adults. BMC genomics, 22(1), 489. https://doi.org/10.1186/s12864-021-07801-0\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"liquid biopsy, plasma, cell-free RNA, RNA derivatives, digital PCR","lastPublishedDoi":"10.21203/rs.3.rs-7848426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7848426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: RNA biomarker research has grown significantly in recent years, with a focus on blood-based biomarker studies using cell-free nucleic acids (cf-NAs). These RNA derivatives are useful in diagnostic procedures for genetic changes and can be found in a variety of physiological fluids, such as plasma, brain fluid, and urine. RNA biomarkers are composed of different coding and non-coding transcripts. Among these, cfRNAs extracted from blood provide a minimally invasive source for disease diagnosis and monitoring. However, cfRNA isolation is challenging due to degradation, instability, lack of standardization, and contamination by microbial, environmental, and intrasample DNA. Therefore, it is crucial to obtain cell-free RNAs in high concentration using appropriate methods and process them using downstream PCR systems.\u003c/p\u003e\n\u003cp\u003e​​Methods and Results: To compare the purity and quality of cfRNAs isolated from plasma, five different isolation methods using commercial cfRNA kits were evaluated by digital PCR (dPCR) and qRT-PCR. RNA quality was assessed across kits, and expression levels were measured using cfRNA reference genes such as GAPDH and B2M. The results of analysis revealed that the GAPDH housekeeping gene showed greater consistency than B2M, and both genes exhibited statistically significant expression.\u003c/p\u003e\n\u003cp\u003eConclusions: Our comparative analysis of five cfRNA isolation methods demonstrated that commercial kits outperformed phenol–chloroform–based procedures. These results underscore the need for optimized isolation strategies and careful reference gene selection to enhance the reliability of cfRNA-based biomarker discovery.\u003c/p\u003e","manuscriptTitle":"Evaluation of Plasma-Derived Cell-Free RNA Isolation Methods Using PCR- Based Quantification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 18:41:23","doi":"10.21203/rs.3.rs-7848426/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T10:15:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T17:39:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T17:03:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339885306730101771574269413598675673322","date":"2025-10-20T19:00:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34093514934760082050538259640285641453","date":"2025-10-17T18:11:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T15:52:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T15:24:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T15:20:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2025-10-13T11:15:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d87c4d76-a598-451b-8891-ebef044e29f9","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:03:57+00:00","versionOfRecord":{"articleIdentity":"rs-7848426","link":"https://doi.org/10.1007/s11033-025-11410-5","journal":{"identity":"molecular-biology-reports","isVorOnly":false,"title":"Molecular Biology Reports"},"publishedOn":"2026-01-03 15:57:25","publishedOnDateReadable":"January 3rd, 2026"},"versionCreatedAt":"2025-10-29 18:41:23","video":"","vorDoi":"10.1007/s11033-025-11410-5","vorDoiUrl":"https://doi.org/10.1007/s11033-025-11410-5","workflowStages":[]},"version":"v1","identity":"rs-7848426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7848426","identity":"rs-7848426","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}