A method for constructing a plasma or serum exosome RNA quality assessment model suitable for transcriptome sequencing and application thereof

Abstract

The present application relates to a kind of methods for constructing plasma or serum exosome RNA quality evaluation model suitable for transcriptome sequencing and its application, by screening stable expression RNA candidate factor, using verified primer probe to carry out qPCR quantification to exosome RNA, obtain Ct value;Exosome RNA Ct value is associated with RNA-seq data, and reasonable Ct value is screened as reference threshold value, and the quality of exosome RNA is graded, as the measurement index of whether RNA-seq data is qualified, both exosome miRNA or mRNA quality can be evaluated independently, also can evaluate the quality of two kinds of RNA.This application can effectively evaluate whether upstream sample collection, exosome separation, RNA extraction and other methods are appropriate in time, and can reduce the flow of low-quality samples to downstream high-throughput library construction experiment link, reduce the cost of manpower, sample, reagent consumables and other losses.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing and its application. Background Technology

[0002] Exosomes are cellular secretory products with a lipid bilayer and vesicle-like structure, carrying contents such as DNA, RNA, proteins, and lipids. They are key mediators for intercellular signal transduction, substance transport, and physiological metabolism. Exosomes are found in various body fluids and tissues, with plasma being the primary sample type for exosome research due to its ease of access and stability. Increasing research indicates that exosomes have become important carriers for discovering diagnostic biomarkers for various diseases and for targeted drug delivery research, and plasma exosome RNAomics research is one of the most common approaches to discovering disease diagnostic biomarkers.

[0003] Because exosomal RNA content is much lower than that of cellular RNA, it presents a greater challenge compared to conventional RNAomics research. The content and purity of exosomal RNA reflect the suitability of RNA extraction methods and also affect the quality of downstream transcriptome library construction and high-throughput sequencing.

[0004] Currently, the main methods for detecting and quantifying exosomal RNA include Nanodrop, qubit, Agilent 2100, and qPCR. Nanodrop quantifies RNA by detecting the absorbance of the sample at 260 nm, offering advantages such as low sample volume, simple operation, low cost, and the ability to determine the presence of protein contamination. However, it has low quantitative sensitivity and cannot display RNA fragment distribution. Qubit uses a specific fluorescent dye to bind to the target RNA, placing it in a fluorometer for RNA quantification and integrity detection. This method is convenient, fast, and offers high accuracy, sensitivity, and flexibility, but it cannot reflect RNA purity, fragment distribution, or PCR efficiency. Agilent 2100 is based on capillary electrophoresis and fluorescent staining technology. It separates RNA molecules of different sizes in the sample by electrophoresis and converts the fluorescence signal into an electrophoretic pattern, providing information on RNA concentration, fragment distribution, and integrity. This method enables automated, high-throughput, highly reproducible, and visualized RNA detection. Its drawbacks include high cost, relatively low quantitative accuracy, and the inability to reflect PCR efficiency. qPCR detection uses specific primers to reverse transcribe RNA from a sample. Using the reverse transcription product as a template, the intensity of the fluorescence signal generated in each PCR cycle is detected by fluorescent dyes or probes. The Ct value during the exponential growth phase is positively correlated with the copy number of RNA in the sample, thereby quantifying the RNA. This method can accurately reflect the efficiency of RNA reverse transcription and PCR, and has high quantitative accuracy. However, it is costly and involves many steps. In addition, it is crucial to select suitable qPCR analytes.

[0005] CN117721186A discloses a method for detecting exosomal lncRNAs. Researchers improved and optimized RT-qPCR, using Takara's SMARTScribe™ reverse transcriptase and template conversion technology to reverse transcribe low-abundance exosomal lncRNAs. RT-qPCR detection was then performed based on designed low-abundance lncRNA primers, enabling the detection of two low-abundance lncRNAs in plasma exosomes. This method has high sensitivity, but it is mainly used for clinical diagnosis, and studies on transcriptome library sequencing are lacking.

[0006] CN118506874A discloses a method for mRNA quality assessment in next-generation sequencing (NGS). This method uses NGS technology and a computer program to obtain the clean read positions and abundance information of mRNA in a sample, generating an mRNA center position index curve. Samples whose highest sequencing abundance peak falls within 20-85% of the total read position distribution range are considered to meet NGS requirements. It is argued that this invention is more suitable for NGS RNA quality assessment than RIN and DV200. While this method provides a novel perspective on RNA quality assessment and expands the possibilities for RNA-seq detection, it requires NGS library preparation, sequencing, and bioinformatics analysis to determine whether each sample meets the requirements, resulting in high costs. It also demands strong computer analysis skills from the experimenters and is only applicable to plasma mRNA, limiting its applicability to other sample types or other types of RNA.

[0007] Existing methods cannot accurately assess RNA quality, leading to low-quality samples being transferred to downstream high-throughput library construction experiments, which reduces the success rate of RNA library construction. Currently, there is a lack of accurate methods for quantifying exosomal RNA and assessing the correlation between exosomal RNA and the sequencing quality of downstream transcriptome library construction. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing and its application. By collecting plasma or serum samples of a certain disease type, exosomal miRNA-seq and mRNA-seq related data of the samples are obtained, candidate factors with stable expression levels are screened, and the candidate factors are quantified by qPCR to obtain Ct values. The quality of exosomal RNA is graded according to the Ct values.

[0009] To achieve this objective, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides a method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing, the method comprising the following steps:

[0011] (1) Collect plasma or serum samples and extract exosome RNA;

[0012] (2) Screening stable expressed RNA candidate factors, designing and validating qPCR primers and probes for the RNA candidate factors, and using the validated primers and probes to quantify exosomal RNA by qPCR to obtain Ct values;

[0013] (3) The Ct value of exosomal RNA was correlated with the transcriptome library inspection results and sequencing data quality control results. Reasonable Ct values ​​were selected and set as reference thresholds to classify the quality of exosomal RNA.

[0014] This invention provides a method for quantifying exosomal RNA and assessing the correlation between exosomal RNA and the sequencing quality of downstream transcriptome library construction. It can evaluate either short-chain RNA (miRNA-dominant) or long-chain RNA (mRNA-dominant) separately, or both simultaneously, to classify the quality of exosomal RNA. This allows for timely and effective evaluation of the appropriateness of upstream sample collection, exosomal isolation, and RNA extraction methods. Furthermore, it can reduce the transfer of low-quality samples to downstream high-throughput library construction experiments, thereby reducing costs associated with manpower, samples, reagents, and consumables.

[0015] This invention involves collecting plasma or serum samples of a specific disease type, screening out the top 10 candidate factors with the most stable expression levels, designing and validating miRNA-qPCR and mRNA-qPCR primers for the top-ranked candidate factors, and using the validated primers to quantify exosomal RNA using miRNA-qPCR and mRNA-qPCR to obtain the final Ct value.

[0016] The Ct values ​​of exosomal RNA were correlated with transcriptome library analysis results and sequencing data quality control results to screen reasonable Ct value reference thresholds. This allowed for the grading of exosomal RNA quality, meaning that Ct values ​​within a certain range could be used as a measure of the quality of RNA-seq data. This method can assess the quality of exosomal miRNA or mRNA individually, or simultaneously. Finally, the method was validated using samples from multiple disease types containing multiple biological replicates to confirm its applicability.

[0017] Preferably, the RNA includes miRNA and / or mRNA.

[0018] Preferably, the plasma or serum sample in step (1) includes any one or a combination of at least two of the following: plasma sample from a diseased person, plasma sample from a healthy person, serum sample from a diseased person, or serum sample from a healthy person.

[0019] Preferably, the disease suffered by the patient population includes any one or a combination of at least two of lung cancer, colon cancer, or liver cancer.

[0020] Preferably, the method for selecting plasma or serum samples in step (1) includes: taking plasma or serum samples covering all stages of disease development and healthy individuals.

[0021] Preferably, the disease suffered by the patient population is lung cancer, and the plasma or serum samples include plasma or serum samples from healthy controls, benign nodules, carcinoma in situ, microinvasive carcinoma, and invasive carcinoma populations.

[0022] Preferably, the disease suffered by the patient population is colon cancer, and the plasma or serum samples include plasma or serum samples from healthy controls, colon cancer patients, and colon cancer patients with liver metastases.

[0023] Preferably, the disease suffered by the patient population is liver cancer, and the plasma or serum samples include plasma or serum samples from healthy controls, liver cancer patients, and adjacent cancer patients.

[0024] Preferably, step (2) of screening for stably expressed RNA candidate factors specifically includes:

[0025] Linkers and low-quality sequences were removed, and RNA in each sample was quantified. The samples were grouped according to the disease progression. Based on the grouping information, the transcript count (TPM) per million transcripts of all identified RNAs was calculated. The CV2 value was calculated from the grouping information and the TPM value. The 10 RNAs with the lowest CV2 values ​​were further screened to obtain RNA candidate factors.

[0026] Preferably, the method for calculating the CV2 value includes:

[0027] Ave is the mean TPM value for each sample group, SD is the standard deviation of the TPM value for each sample group, and the coefficient of variation (CV) of the TPM value within each sample group is calculated as SD / Ave. Sum This represents the sum of the CV values ​​for all sample groups.

[0028] GP Ave GP is the mean of all sample TPM values. SD The standard deviation of TPM values ​​for all samples, and the coefficient of variation (GP) of TPM values ​​for all samples. CV =GP SD / GP Ave CV2 = CV Sum ×GP CV .

[0029] The CV2 value provided by this invention can be used to evaluate the stability of data. The smaller the value, the more stable the data. It also takes into account both intra-group stability and inter-group stability.

[0030] In a second aspect, the present invention provides a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing, wherein the plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing is constructed by the method described in the first aspect for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing.

[0031] Preferably, the plasma or serum exosome RNA quality assessment model suitable for transcriptome sequencing includes the detection target, qPCR primers and probes, and the Ct value-RNA quality grade relationship.

[0032] Preferably, the detection target includes exosomal miRNA from the plasma of lung cancer patients, the nucleic acid sequence of the primer probe includes the sequence shown in SEQ ID NO.1 to SEQ ID NO.3, and the Ct value-RNA quality grade relationship includes: Ct value ≤ 25, RNA quality grade is qualified; 25 < Ct value ≤ 26, RNA quality grade is risky; Ct value > 26, RNA quality grade is unqualified.

[0033] SEQ ID NO. 1: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAT GCCC.

[0034] SEQ ID NO. 2: CGCCAGTGCAAATGATGAAA.

[0035] SEQ ID NO. 3: TCGCACTGGATACGACATGCCCT.

[0036] Preferably, the detection target includes serum exosomal miRNA from liver cancer patients, the nucleic acid sequence of the primer probe includes the sequence shown in SEQ ID NO.1 to SEQ ID NO.3, and the Ct value-RNA quality grade relationship includes: Ct value ≤ 25, RNA quality grade is qualified; Ct value > 25, RNA quality grade is unqualified.

[0037] Preferably, the detection target includes exosome mRNA from the plasma of colorectal cancer patients, the nucleic acid sequence of the primer probe includes the sequence shown in SEQ ID NO.4 to SEQ ID NO.6, and the Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; 24 < Ct value ≤ 28, RNA quality grade is risky; Ct value > 28, RNA quality grade is unqualified.

[0038] SEQ ID NO. 4: GAAGGAGCTGAACTACTTTGCAA.

[0039] SEQ ID NO. 5: TTTTGTCCAGCATATTATTGATGAGC.

[0040] SEQ ID NO. 6: CAACTTTAGCTCCGCCCAGGATG.

[0041] Preferably, the detection target includes exosome mRNA from the plasma of lung cancer patients, the nucleic acid sequence of the primer probe includes the sequence shown in SEQ ID NO.4 to SEQ ID NO.6, and the Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; 24 < Ct value ≤ 28, RNA quality grade is risky; Ct value > 28, RNA quality grade is unqualified.

[0042] Preferably, the detection target includes serum exosome mRNA from liver cancer patients, the nucleic acid sequence of the primer probe includes the sequence shown in SEQ ID NO.4 to SEQ ID NO.6, and the Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; Ct value > 24, RNA quality grade is unqualified.

[0043] Thirdly, the present invention provides a method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing, the method comprising the step of using the plasma or serum exosomal RNA quality assessment model for transcriptome sequencing described in the second aspect.

[0044] Preferably, the method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing specifically includes:

[0045] (a) Collected plasma or serum samples were subjected to exosome isolation, RNA extraction, and RNA library construction and sequencing;

[0046] (b) Based on the detection target and qPCR primer and probe information provided in the plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing described in the second aspect, amplify the RNA sample and obtain the Ct value;

[0047] (c) The Ct value obtained in step (b) is compared with the Ct value-RNA quality grade relationship provided in the plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing described in the second aspect to determine the quality grade of RNA and assess the quality of RNA library construction and sequencing data obtained in step (a).

[0048] Preferably, the method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing specifically includes:

[0049] (a) Collected exosomes from lung cancer patients' plasma, extracted miRNAs, constructed a library of miRNAs, and sequenced them;

[0050] (b) Amplify the miRNA sample and obtain the Ct value. The nucleic acid sequences of the primers and probes used for amplification include the sequences shown in SEQ ID NO.1 to SEQ ID NO.3.

[0051] (c) Compare the Ct value obtained in step (b) with the Ct value-RNA quality grade relationship to determine the RNA quality grade and evaluate the quality of the miRNA library construction and sequencing data obtained in step (a); the Ct value-RNA quality grade relationship includes: Ct value ≤ 25, RNA quality grade is qualified; 25 < Ct value ≤ 26, RNA quality grade is risky; Ct value > 26, RNA quality grade is unqualified;

[0052] Alternatively, (a) the exosomes collected from the serum of liver cancer patients are isolated, miRNAs are extracted, and miRNA libraries are constructed and sequenced;

[0053] (b) Amplify the miRNA sample and obtain the Ct value. The nucleic acid sequences of the primers and probes used for amplification include the sequences shown in SEQ ID NO.1 to SEQ ID NO.3.

[0054] (c) Compare the Ct value obtained in step (b) with the Ct value-RNA quality grade relationship to determine the quality grade of the RNA and evaluate the quality of the miRNA library construction and sequencing data obtained in step (a); the Ct value-RNA quality grade relationship includes: Ct value ≤ 25, RNA quality grade is qualified; Ct value > 25, RNA quality grade is unqualified;

[0055] Alternatively, (a) the exosomes collected from the plasma of colorectal cancer patients are isolated, mRNA is extracted, and mRNA libraries are constructed and sequenced;

[0056] (b) Amplify the mRNA sample and obtain the Ct value, wherein the nucleic acid sequences of the primers and probes used for amplification include the sequences shown in SEQ ID NO.4 to SEQ ID NO.6;

[0057] (c) Compare the Ct value obtained in step (b) with the Ct value-RNA quality grade relationship to determine the RNA quality grade and evaluate the quality of the mRNA library construction and sequencing data obtained in step (a); the Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; 24 < Ct value ≤ 28, RNA quality grade is risky; Ct value > 28, RNA quality grade is unqualified;

[0058] Alternatively, (a) the exosomes collected from the plasma of lung cancer patients are isolated, mRNA is extracted, and mRNA libraries are constructed and sequenced;

[0059] (b) Amplify the mRNA sample and obtain the Ct value, wherein the nucleic acid sequences of the primers and probes used for amplification include the sequences shown in SEQ ID NO.4 to SEQ ID NO.6;

[0060] (c) Compare the Ct value obtained in step (b) with the Ct value-RNA quality grade relationship to determine the RNA quality grade and evaluate the quality of the mRNA library construction and sequencing data obtained in step (a); the Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; 24 < Ct value ≤ 28, RNA quality grade is risky; Ct value > 28, RNA quality grade is unqualified;

[0061] Alternatively, (a) the exosomes collected from the serum of liver cancer patients are isolated, mRNA is extracted, and mRNA libraries are constructed and sequenced;

[0062] (b) Amplify the mRNA sample and obtain the Ct value, wherein the nucleic acid sequences of the primers and probes used for amplification include the sequences shown in SEQ ID NO.4 to SEQ ID NO.6;

[0063] (c) Compare the Ct value obtained in step (b) with the Ct value-RNA quality grade relationship to determine the quality grade of RNA and evaluate the quality of the mRNA library construction and sequencing data obtained in step (a); the Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; Ct value > 24, RNA quality grade is unqualified.

[0064] Fourthly, the present invention provides an apparatus for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing, the apparatus being used to perform the method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing as described in the third aspect.

[0065] Preferably, the device for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing includes a sample processing module, a qPCR module, and a result analysis module;

[0066] The sample processing module is used to perform the following operations: separating exosomes from collected plasma or serum samples, extracting RNA, and constructing RNA libraries for sequencing.

[0067] The qPCR module is used to perform the following: amplifying RNA samples and obtaining Ct values ​​based on the detection target and qPCR primer and probe information provided in the plasma or serum exosome RNA quality assessment model suitable for transcriptome sequencing as described in the second aspect;

[0068] The result analysis module is used to perform the following: comparing the Ct value obtained by the qPCR module with the Ct value-RNA quality grade relationship provided in the plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing as described in the second aspect, determining the RNA quality grade, and thereby assessing the quality of the RNA library construction and sequencing data obtained by the sample processing module.

[0069] Fifthly, the present invention provides a plasma or serum exosomal miRNA quality assessment biomarker suitable for transcriptome sequencing, wherein the plasma or serum exosomal miRNA quality assessment biomarker is an RNA candidate factor screened by the method described in the first aspect for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing, and the plasma or serum exosomal miRNA quality assessment biomarker includes hsa-miR-130b-3p.

[0070] Preferably, the plasma or serum exosomal miRNA quality assessment biomarkers further include any one or a combination of at least two of hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p, or hsa-miR-22-5p.

[0071] Preferably, the plasma or serum exosomal miRNA quality assessment biomarker is any one of the following combinations:

[0072] Combination 1: hsa-miR-130b-3p and hsa-miR-378a-3p;

[0073] Combination 2: hsa-miR-130b-3p and hsa-miR-17-5p;

[0074] Combination 3: hsa-miR-130b-3p, hsa-miR-17-5p, and hsa-miR-103a-3p;

[0075] Combination 4: hsa-miR-130b-3p, hsa-miR-378a-3p, hsa-miR-103a-3p and hsa-miR-22-5p;

[0076] Combination 5: hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p and hsa-miR-22-5p.

[0077] Preferably, the plasma or serum exosomal miRNA quality assessment biomarkers are a combination of hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p and hsa-miR-22-5p.

[0078] Preferably, the source of the plasma or serum exosomal miRNA includes individuals suffering from any one or at least a combination of two of the following diseases: lung cancer, colon cancer, or liver cancer.

[0079] In a sixth aspect, the present invention provides the application of an hsa-miR-130b-3p expression level detection reagent in the quality assessment of plasma or serum exosomal miRNAs suitable for transcriptome sequencing.

[0080] Preferably, the expression level detection reagent includes qPCR primers and probes for amplifying hsa-miR-130b-3p.

[0081] Preferably, the nucleic acid sequences of the qPCR primers and probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.3.

[0082] Preferably, the source of the plasma or serum exosomal miRNA includes individuals suffering from any one or at least a combination of two of the following diseases: lung cancer, colon cancer, or liver cancer.

[0083] Compared with the prior art, the present invention has the following beneficial effects:

[0084] This invention provides a highly sensitive, high-throughput, and accurate method for assessing the quality of exosomal RNA, accurately reflecting the efficiency of RNA reverse transcription and PCR. It directly correlates exosomal RNA quality with downstream transcriptome library construction and sequencing data, enabling efficient assessment of exosomal RNA quality. This effectively reduces the need for low-quality samples to be transferred to downstream high-throughput library construction experiments, lowering costs associated with manpower, samples, and reagents. It also helps identify problems in upstream experiments and allows for timely adjustments and optimizations, including sample collection, exosome isolation, or exosomal RNA extraction. This invention requires no expensive equipment, is simple to operate, has a wide range of applications, high throughput, and low skill requirements for researchers. It can be extended to assess and detect exogenous contamination in RNA, such as DNA and microbial contamination, and can be applied to other species such as rats and mice, filling gaps in exosomal transcriptomics research in other fields. Attached Figure Description

[0085] Figure 1 This is a flowchart illustrating the design of the technical solution of the present invention.

[0086] Figure 2This is a transmission electron microscopy (TEM) image of plasma exosomes from Example 2.

[0087] Figure 3 This is a graph showing the results of Western blotting of plasma exosomes in Example 2.

[0088] Figure 4 This is a graph showing the correlation between different miRNA candidate factors and Ct values ​​in Example 2.

[0089] Figure 5 This is a graph showing the success rate of miRNAseq library construction and sequencing for different miRNA candidate factor combinations in Example 2.

[0090] Figure 6 The following is a box plot showing the correlation between plasma exosome miRNA-qPCR and library quality (Level A) results in Example 2. Figure a shows the box plot of samples with a Ct value of has-miR-130b-3p below 25. Figure b shows the miRNA library scan of the samples corresponding to Level A in Figure a. Figure c shows the box plot of the alignment rate between miRNA sequencing data and the reference genome of the samples corresponding to Level A in Figure a. Figure d shows the box plot of the number of miRNAs identified in the miRNA sequencing data of the samples corresponding to Level A in Figure a.

[0091] Figure 7 Figure 2 shows the results of plasma exosome miRNA-qPCR and library quality correlation level B. Figure a is a box plot of samples with Ct values ​​of has-miR-130b-3p between 25 and 26. Figure b is a library scan of miRNA libraries of samples corresponding to level B in Figure a. Figure c is a box plot of the alignment rate between miRNA sequencing data and reference genome of samples corresponding to level B in Figure a. Figure d is a box plot of the number of miRNAs identified in the miRNA sequencing data of samples corresponding to level B in Figure a.

[0092] Figure 8 The following is a box plot showing the correlation between plasma exosome miRNA-qPCR and library quality level C in Example 2. Figure a is a box plot of samples with a Ct value of has-miR-130b-3p of 26 or higher. Figure b is a library scan of miRNA libraries of samples corresponding to level C in Figure a. Figure c is a box plot of the alignment rate between miRNA sequencing data and the reference genome of samples corresponding to level C in Figure a. Figure d is a box plot of the number of miRNAs identified in the miRNA sequencing data of samples corresponding to level C in Figure a.

[0093] Figure 9 This is a graph showing the success rate of mRNAseq library construction and sequencing for different combinations of mRNA candidate factors in Example 3.

[0094] Figure 10The following is a box plot showing the correlation between plasma exosome mRNA-qPCR and library quality level A in Example 3. Figure a is a box plot of samples with a PGK1 Ct value below 24, Figure b is a library scan of the mRNA library of the corresponding level A sample in Figure a, and Figure c is a box plot of the alignment rate between the mRNA sequencing data of the corresponding level A sample in Figure a and the reference genome.

[0095] Figure 11 The results of plasma exosome mRNA-qPCR and library quality correlation level B in Example 3 are shown in Figure a. Figure a is a box plot of samples with PGK1 Ct values ​​between 24 and 28. Figure b is the mRNA library retrieval map of the samples corresponding to level B in Figure a. Figure c is a box plot of the alignment rate between the mRNA sequencing data of the samples corresponding to level B in Figure a and the reference genome.

[0096] Figure 12 The following is a box plot showing the correlation between plasma exosome mRNA-qPCR and library quality level C in Example 3. Figure a is a box plot of samples with a PGK1 Ct value of 28 or higher, Figure b is a library retrieval plot of mRNA library of the corresponding level C sample in Figure a, and Figure c is a box plot of the alignment rate between the mRNA sequencing data of the corresponding level C sample in Figure a and the reference genome.

[0097] Figure 13 This is a graph showing the correlation between plasma exosome mRNA-qPCR and sequencing data from colorectal cancer patients in Example 4.

[0098] Figure 14 This is a graph showing the library construction and sequencing success rates of different exosomal RNA quality assessment methods in Example 5. Detailed Implementation

[0099] To further illustrate the technical means and effects of this invention, the following description, in conjunction with embodiments and accompanying drawings, provides a further explanation of the invention. It is understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it.

[0100] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased from legitimate channels.

[0101] Example 1

[0102] This embodiment provides a method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing, wherein the RNA includes miRNA and / or mRNA, such as... Figure 1 As shown, it includes the following steps:

[0103] (1) Collect plasma or serum samples and extract exosome RNA;

[0104] The plasma or serum samples include plasma or serum samples from both diseased and healthy individuals. The diseased individuals in the plasma or serum samples are those suffering from lung cancer, colon cancer, or liver cancer, or a combination of at least two of these diseases. The method for selecting the plasma or serum samples includes taking plasma or serum samples that cover all stages of the disease and healthy individuals.

[0105] (2) Screening stable expressed RNA candidate factors, designing and validating qPCR primers and probes for the RNA candidate factors, and using the validated primers and probes to quantify exosomal RNA by qPCR to obtain Ct values;

[0106] The specific RNA candidate factors selected for stable expression include:

[0107] Linkers and low-quality sequences were removed, and RNA in each sample was quantified. The samples were grouped according to the disease progression. Based on the grouping information, the number of transcripts per million transcripts (TPM) of all identified RNAs was counted. The CV2 value was calculated from the grouping information and the TPM value. The 10 RNAs with the lowest CV2 values ​​were further screened to obtain RNA candidate factors.

[0108] The method for calculating the CV2 value includes:

[0109] Ave is the mean TPM value for each sample group, SD is the standard deviation of the TPM value for each sample group, and the coefficient of variation (CV) of the TPM value within each sample group is calculated as SD / Ave. Sum This represents the sum of the CV values ​​for all sample groups.

[0110] GP Ave GP is the mean of all sample TPM values. SD The standard deviation of TPM values ​​for all samples, and the coefficient of variation (GP) of TPM values ​​for all samples. CV =GP SD / GP Ave CV2 = CV Sum ×GP CV .

[0111] (3) The Ct value of exosomal RNA was correlated with the transcriptome library inspection results and sequencing data quality control results. Reasonable Ct values ​​were selected and set as reference thresholds to classify the quality of exosomal RNA.

[0112] Example 2

[0113] This embodiment uses the method provided in Example 1 to screen candidate miRNAs for quality assessment of exosomal miRNAs in plasma samples from lung cancer patients and healthy individuals, including the following steps:

[0114] (1) Sample collection: Plasma from lung cancer patients and healthy individuals was used as test samples, as shown in Table 1. The samples were divided into 5 groups, including healthy controls, benign nodules, carcinoma in situ, microinvasive carcinoma, and invasive carcinoma. Each group contained 20-40 biological samples. Blood was collected from patients using EDTA anticoagulant tubes. The collected blood was centrifuged at 1500g at 4°C for 20 min to remove cells. The supernatant was collected and centrifuged at 3000g at 4°C for 15 min. The supernatant was collected and filtered using a 0.45μm filter membrane. The filtrate was collected. The sample volume of each plasma sample was 1 mL.

[0115] Table 1

[0116] Species type Sample type Group Disease type Sample quantity people plasma Group 1 Health comparison 29 people plasma Group2 benign nodules 30 people plasma Group3 Carcinoma in situ 23 people plasma Group4 Minimally invasive carcinoma 35 people plasma Group5 Invasive cancer 29

[0117] (2) Exosome separation: Plasma exosomes were separated using size exclusion chromatography and ultrafiltration. The size exclusion chromatography column was brought back to room temperature beforehand and 10 mL of 0.1 M PBS solution was added for washing. After washing, 1 mL of plasma collected in step (1) was added to the chromatography column and eluted with PBS solution as the mobile phase. 0.5 mL was one fraction, and 1-5 fractions were collected. 2.5 mL of the fraction was transferred to a 100 kDa ultrafiltration tube and concentrated to 250 μL.

[0118] (3) Exosome characterization and detection: The exosome samples obtained in step (2) were characterized and detected using a transmission electron microscope. The required sample volume was 10-15 μL. Figure 2 As shown, clearly visible cup-shaped exosomes can be observed. Western blotting of the exosome samples using specific antibodies yielded the following results: Figure 3 As shown, exosome positive protein markers Tsg101, Hsp70, Alix, and CD9 were significantly expressed, while the negative protein marker Calnexin was not expressed.

[0119] (4) Exosomal RNA extraction and quality control: Based on the Qiager miRNeasy Serum / Plasma Advanced kit. Kit was used to extract total RNA from exosomes. 200 μL of each exosome sample from step (2) was taken, and 200 μL of QIAzol lysis buffer was added. After vortexing and mixing, the mixture was incubated at room temperature for 5 min. 140 μL of chloroform was added, and the mixture was inverted and mixed for 15 s. The mixture was incubated at room temperature for 2 min. The mixture was centrifuged at 12000g for 15 min at 4℃. The supernatant was taken and transferred to a new centrifuge tube. 1.5 times the volume of anhydrous ethanol was added, and the mixture was mixed for 15 s. The mixture was then transferred to an RNeasyMini column and placed in a 2 mL collection tube. The mixture was centrifuged at 8000g for 15 s at 25℃. The liquid in the collection tube was discarded. RWT, RPE and other washing solutions were added in sequence. The mixture was centrifuged and discarded. Finally, 35 μL of enzyme-free water was added, and the mixture was allowed to stand at room temperature for 2 min. The mixture was centrifuged at 12000g for 1 min. The liquid at the bottom of the centrifuge tube was the exosome RNA. 1 μL of the mixture was taken for qubit fluorescence detection to obtain the exosome RNA concentration information.

[0120] (5) Exosomal RNA library construction and sequencing: Take 3 μL of each exosomal RNA sample from step (4), add 7 μL of 3' adapter ligation reaction mixture (QIAseq miRNA NGS 3' Adapter, QIAseq miRNA NGS RI, QIAseq miRNA NGS 3' Ligase, QIAseq miRNA NGS 3' Buffer, 2x miRNA Ligation Activator), mix well, and place in a PCR instrument. Incubate at 28℃ for 1 h, 65℃ for 20 min, and store at 4℃ for at least 5 min. Proceed to the next reaction immediately after completion. Add 10 μL of 5' adapter ligation reaction mixture (QIAseq miRNA NGS 5' Buffer, QIAseq miRNA NGS RI, QIAseq miRNA NGS 5' Ligase, QIAseq miRNA NGS 5' Adapter), mix well, incubate at 28℃ for 30 min, 65℃ for 20 min, and store at 4℃ for at least 5 min. Proceed to the next reaction immediately after completion. Add 10 μL of reverse transcription mixture (QIAseq miRNA NGS RT Primer, QIAseq miRNA NGS RT Initiator, QIAseq miRNA NGS RT Buffer, QIAseq miRNA NGS RI, QIAseq miRNA NGS RT Enzyme), incubate at 50°C for 1 h, incubate at 70°C for 15 min, and store at 4°C for at least 5 min. Add 72 μL of QIAseq miRNA NGS Beads, incubate at room temperature for 5 min, transfer to a magnetic rack and let stand for 5 min, discard the supernatant and retain the magnetic beads, wash twice with 200 μL of freshly prepared 80% ethanol, add 9 μL of enzyme-free water, mix well, let stand at room temperature for 5 min, place on a magnetic rack and let stand for 5 min, transfer 7.5 μL of supernatant to a new 0.2 mL EP tube. Add 12.5 μL of PCR reaction mixture (QIAseq miRNA NGS Library Buffer, HotStarTaq DNA Polymerase, index, Nuclease-free water), pre-denature at 95℃ for 15 min, cycle once; denature at 95℃ for 15 s, anneal at 60℃ for 30 s, extend at 72℃ for 15 s, cycle 22 times; react at 72℃ for 2 min, cycle once; store at 4℃ for at least 5 min.Add 38 μL of QIAseq miRNA NGS Beads, incubate at room temperature for 5 min, transfer to a magnetic rack and let stand for 5 min. Transfer the supernatant to a new 1.5 mL centrifuge tube, add 65 μL of QIAseq miRNA NGS Beads, vortex, incubate at room temperature for 5 min, let stand on a magnetic rack for 5 min, discard the supernatant, add 200 μL of freshly prepared 80% ethanol to wash twice, add 21 μL of enzyme-free water, mix well, let stand at room temperature for 5 min, let stand on a magnetic rack for 5 min, transfer 20 μL of supernatant to a new centrifuge tube to complete the miRNA library construction. Each miRNA library was analyzed using an Agilent 2100 analyzer, and all libraries were subjected to NGS high-throughput sequencing.

[0121] (6) Screening of miRNA quality assessment factors: The NGS high-throughput sequencing data from step (5) were subjected to quality control to remove adapters and low-quality sequences. The miRNAs of each sample were identified, predicted, and quantified, totaling 3878 miRNAs. Based on the grouping information in Table 1, the TPM expression levels of all identified miRNAs within and between groups were statistically analyzed and compared, and the CV2 value was calculated. As shown in Table 2, the top 10 miRNAs with the highest CV2 ranking were selected.

[0122] Table 2

[0123]

[0124]

[0125] qPCR primers were designed for five miRNAs (hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p, and hsa-miR-22-5p). Then, 55 samples were randomly selected from the samples in step (4), and their Ct values ​​were obtained. By comparing the correlation between the next-generation sequencing data (UMI reads) of the candidate factors and the Ct values ​​of the qPCR, it was found that hsa-miR-130b-3p was superior to hsa-miR-103a-3p and hsa-miR-22-5p. The results are shown in [see attached table]. Figure 4 In addition, the library construction and sequencing success rates of has-miR-130b-3p were compared with those of the other four miRNA combinations. The miRNA combinations are shown in Table 3, and the library construction and sequencing success rate results are as follows: Figure 5 As shown, combination 5 yields the highest accuracy. If both accuracy and efficiency are considered, has-miR-130b-3p alone can meet the needs of most samples. Therefore, has-miR-130b-3p is selected as a candidate factor for subsequent validation.

[0126] Table 3

[0127] Panel miRNA types Combination 1 hsa-miR-130b-3p, hsa-miR-378a-3p Combination 2 hsa-miR-130b-3p, hsa-miR-17-5p Combination 3 hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-103a-3p Combination 4 hsa-miR-130b-3p, hsa-miR-378a-3p, hsa-miR-103a-3p, hsa-miR-22-5p Combination 5 hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p, hsa-miR-22-5p

[0128] (7) Establishment of plasma exosome RNA quality assessment method: The candidate factor has-miR-130b-3p in step (6) was further verified, and the primer information is shown in Table 4.

[0129] Table 4

[0130]

[0131] Using the primers in Table 4, miRNA-qPCR was performed on the exosomal RNA of all samples in step (4) to obtain the Ct value of the miRNA, and the correlation between the Ct value and the library scan and sequencing data was analyzed.

[0132] like Figure 6 , Figure 7 and Figure 8 As shown, the success rate of downstream miRNA library construction and sequencing for exosomal RNA with a Ct value < 25 was 96%, with a clear main peak in the library scan, a small proportion of adapter sequence peaks, a sequencing data alignment rate of over 70% with the reference database, and more than 400 miRNAs identified, indicating high data reliability. For exosomal RNA with a Ct value ≤ 26, the success rate of downstream miRNA library construction and sequencing was 46%, with sequencing data alignment rates between 35-50% and the number of identified miRNAs between 300-400. For exosomal RNA with a Ct value > 26, the success rate of downstream miRNA library construction and sequencing was 18%, with sequencing data alignment rates below 35% and the number of identified miRNAs below 200, indicating significantly lower data reliability.

[0133] The final grading results of plasma exosomal miRNA quality are shown in Table 5. This method offers a higher level of discrimination for exosomal RNA quality assessment. The sample loss rate was reduced from 22% to 4%.

[0134] Table 5

[0135]

[0136] Example 3

[0137] Following the method provided in Example 1 and the specific steps provided in Example 2, candidate mRNA factors for quality assessment of exosomal mRNA in plasma samples from lung cancer patients and healthy individuals were screened. Ten candidate mRNA factors were identified: PGK1, HBB, RPLP2, ACTB, TMSB4X, FTL, TMSB10, RPL13A, RPL26, and EEF1A1. Five of these factors—PGK1, HBB, RPLP2, ACTB, and TMSB4X—were combined, and the library construction and sequencing success rates of PGK1 with each combination listed in Table 6 were compared. Figure 9 As shown, the success rates from highest to lowest are: combination 5, combination 4, combination 3, combination 1, combination 2, and PGK1.

[0138] Table 6

[0139] Panel mRNA type Combination 1 PGK1, RPLP2 Combination 2 PGK1, HBB Combination 3 PGK1, HBB, RPLP2 Combination 4 PGK1, RPLP2, ACTB, TMSB4X Combination 5 PGK1, HBB, RPLP2, ACTB, TMSB4X

[0140] Further validation of the candidate factor PGK1 was performed, and the primer information is shown in Table 7.

[0141] Table 7

[0142] SEQ ID NO. Primer name Primer sequence 5' Modification 3' Modification 4 PGK1-F GAAGGAGCTGAACTACTTTGCAA none none 5 PGK1-R TTTGTCCAGCATATTATTGATGAGC none none 6 PGK1-PROBE CAACTTTAGCTCCGCCCAGGATG 5'FAM 3'BHQ-1

[0143] Using the primers in Table 7, mRNA-qPCR was performed on the exosomal RNA of all samples in step (4) of Example 2 to obtain the Ct value of the mRNA, and the correlation between the Ct value and the library scan and sequencing data was analyzed.

[0144] like Figure 10 , Figure 11 and Figure 12 As shown, the success rate of downstream mRNA library construction and sequencing for exosomal RNA with a Ct value ≤ 24 was 93%. The library scans showed a significant dominant fluorescence peak, a small proportion of adapter sequence peaks, and a sequencing data alignment rate exceeding 60%, indicating high data reliability. For exosomal RNA with a Ct value ≤ 28, the success rate was 52%. The library scans showed a significant short fragment peak around 150 bp, a low dominant fluorescence value, and a sequencing data alignment rate between 20% and 60%. For exosomal RNA with a Ct value < 28, the success rate was 17%. The library scans showed a significant short fragment peak around 137 bp, a very low dominant fluorescence value, and a sequencing data alignment rate below 35%, indicating significantly lower data reliability. The final results of the quality classification of plasma exosomal mRNA are shown in Table 8. This method reduced the sample loss rate from 20% to 7% for exosomal RNA quality assessment.

[0145] Table 8

[0146]

[0147] Example 4

[0148] This embodiment uses plasma from colorectal cancer patients as the test sample, and the sample information is shown in Table 9. Following the methods provided in Example 1 and the specific steps provided in Example 3, the collected samples underwent exosome isolation, RNA extraction, mRNA quality assessment, mRNA library construction, and sequencing. The obtained Ct values ​​and sequencing data were then analyzed.

[0149] Table 9

[0150]

[0151]

[0152] Analysis results as follows Figure 13 As shown, the mRNA-seq data mapping% of samples S1-S6 is above 60%, corresponding to Ct-mRNA levels below 24; among samples S7-S15, 3 samples have mRNA-seq data mapping% above 50%, and the remaining 6 samples have mRNA-seq data mapping% below 40%, with Ct-mRNA levels between 24 and 28 for these 9 samples; the mRNA-seq data mapping% of samples S16-S30 is below 40%, corresponding to Ct-mRNA levels above 28.

[0153] Example 5

[0154] This embodiment collected serum samples from 42 liver cancer patients, with each sample having a volume of 1 mL. Information is shown in Table 10. Exosome isolation, RNA extraction, and quality control were performed on the samples according to Example 2. Two methods were used for RNA quality control: qubit quantification and the qPCR quantification provided by this invention.

[0155] Table 10

[0156] Species type Sample type Group Disease type Sample quantity people serum Group 1 Comparison 14 people serum Group2 liver cancer 14 people serum Group3 Next to cancer 14

[0157] Qubit quantification: 3 μL of RNA was taken from each sample and analyzed using Qubit. TM The microRNA quantification kit (ThermoFisher, catalog number: Q32880) was used for miRNA quantification. A concentration higher than 0.33 ng / μL was considered acceptable; otherwise, it was considered unacceptable. 4 μL of RNA was taken from each sample and analyzed using a Qubit analyzer. TMThe RNA High Sensitivity (HS) Quantitative Kit is used for mRNA quantification. If the RNA concentration is higher than 0.25 ng / μL, it is defined as qualified; otherwise, it is defined as unqualified.

[0158] The quality control test of this invention is as follows: 3 μL of RNA is taken from each sample, and the miRNA of the sample is quantified using has-miR-130b-3p in Example 2. A Ct value ≤ 25 is defined as qualified, otherwise it is defined as unqualified; 4 μL of RNA is taken from each sample, and the mRNA of the sample is quantified using PGK1 in Example 3. A Ct value ≤ 24 is defined as qualified, otherwise it is defined as unqualified.

[0159] The detection results of the two RNA quality control methods are shown in Table 11. Samples that passed both miRNA and mRNA detection were subjected to corresponding transcriptome library sequencing and sequencing data analysis. Statistical results were then presented. Figure 14 The results show that the miRNA library construction and sequencing success rate of the method of this invention is 92%, significantly higher than the 62% of the qubit detection method. Furthermore, the method of this invention demonstrates a clear advantage in mRNA library construction and sequencing success rate. This indicates that the method of this invention has high sensitivity for quality assessment of exosomal RNA, effectively reducing the number of low-quality RNA samples transferred to transcription and library construction for sequencing, thereby enabling adjustments and optimizations to upstream experimental steps and reducing cost waste in downstream experiments.

[0160] Table 11

[0161]

[0162]

[0163] In summary, this invention enables the classification of exosomal RNA quality levels, thereby allowing for timely and effective evaluation of the appropriateness of upstream sample collection, exosomal isolation, and RNA extraction methods. Furthermore, it can reduce the transfer of low-quality samples to downstream high-throughput library construction experiments, thereby reducing costs associated with manpower, samples, reagents, and other consumables.

[0164] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing, characterized in that, The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing includes the following steps: (1) Collect plasma or serum samples and extract exosome RNA; (2) Screening for stable expressed RNA candidate factors, designing and validating qPCR primers and probes for the RNA candidate factors, and using the validated primers and probes to quantify exosomal RNA by qPCR to obtain Ct values; (3) Correlate the Ct value of exosomal RNA with the transcriptome library inspection results and sequencing data quality control results, screen and set reasonable Ct values ​​as reference thresholds, and classify the quality of exosomal RNA. Step (2) of screening for stably expressed RNA candidate factors specifically includes: Linkers and low-quality sequences were removed, and RNA in each sample was quantified. The samples were grouped according to the disease progression. Based on the grouping information, the number of transcripts per million transcripts (TPM) of all identified RNAs was counted. The CV2 value was calculated from the grouping information and the TPM value. The 10 RNAs with the lowest CV2 values ​​were further screened to obtain RNA candidate factors. The method for calculating the CV2 value includes: Ave is the mean TPM value for each sample group, SD is the standard deviation of the TPM value for each sample group, and the coefficient of variation (CV) of the TPM value within each sample group is calculated as SD / Ave. Sum This represents the sum of the CV values ​​for all sample groups. GP Ave GP is the mean of all sample TPM values. SD The standard deviation of TPM values ​​for all samples, and the coefficient of variation (GP) of TPM values ​​for all samples. CV =GP SD / GP Ave CV2=CV Sum ×GP CV .

2. The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing according to claim 1, characterized in that, The RNA includes miRNA and / or mRNA.

3. The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing according to claim 1, characterized in that, The plasma or serum sample in step (1) includes any one or a combination of at least two of the following: plasma sample from a diseased person, plasma sample from a healthy person, serum sample from a diseased person, or serum sample from a healthy person.

4. The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing according to claim 3, characterized in that, The diseases suffered by the patient population include any one or a combination of at least two of lung cancer, colon cancer, or liver cancer.

5. The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing according to claim 3, characterized in that, The sample selection method for plasma or serum samples in step (1) includes: taking plasma or serum samples covering all stages of disease development and healthy individuals.

6. The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing according to claim 3, characterized in that, The disease in the patient population is lung cancer, and the plasma or serum samples include plasma or serum samples from healthy controls, benign nodules, carcinoma in situ, microinvasive carcinoma, and invasive carcinoma.

7. The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing according to claim 3, characterized in that, The disease in the patient population is colorectal cancer, and the plasma or serum samples include plasma or serum samples from healthy controls, colorectal cancer patients, and patients with colorectal cancer liver metastases.

8. The method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing according to claim 3, characterized in that, The disease in the patient population is liver cancer, and the plasma or serum samples include plasma or serum samples from healthy controls, liver cancer patients, and adjacent cancer patients.

9. A plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing, characterized in that, The plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing is constructed by the method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing as described in any one of claims 1 to 8.

10. The plasma or serum exosomal RNA quality assessment model for transcriptome sequencing according to claim 9, characterized in that, The plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing includes the detection subject, qPCR primers and probes, and the Ct value-RNA quality grade relationship.

11. The plasma or serum exosomal RNA quality assessment model for transcriptome sequencing according to claim 10, characterized in that, The detection targets include exosomal miRNA from the plasma of lung cancer patients. The nucleic acid sequences of the primers and probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.

3. The Ct value-RNA quality grade relationship includes: Ct value ≤ 25, RNA quality grade is qualified; 25 < Ct value ≤ 26, RNA quality grade is risky; Ct value > 26, RNA quality grade is unqualified.

12. The plasma or serum exosomal RNA quality assessment model for transcriptome sequencing according to claim 10, characterized in that, The detection targets include serum exosomal miRNA from liver cancer patients, and the nucleic acid sequences of the primers and probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.

3. The Ct value-RNA quality grade relationship includes: Ct value ≤ 25, RNA quality grade is qualified; Ct value > 25, RNA quality grade is unqualified.

13. The plasma or serum exosomal RNA quality assessment model for transcriptome sequencing according to claim 10, characterized in that, The detection target includes exosome mRNA from the plasma of colorectal cancer patients. The nucleic acid sequence of the primer probe includes the sequence shown in SEQ ID NO.4~SEQ ID NO.

6. The Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; 24 < Ct value ≤ 28, RNA quality grade is risky; Ct value > 28, RNA quality grade is unqualified.

14. The plasma or serum exosomal RNA quality assessment model for transcriptome sequencing according to claim 10, characterized in that, The detection target includes exosome mRNA from the plasma of lung cancer patients. The nucleic acid sequence of the primer probe includes the sequence shown in SEQ ID NO.4~SEQ ID NO.

6. The Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; 24 < Ct value ≤ 28, RNA quality grade is risky; Ct value > 28, RNA quality grade is unqualified.

15. The plasma or serum exosomal RNA quality assessment model for transcriptome sequencing according to claim 10, characterized in that, The detection target includes serum exosome mRNA from liver cancer patients, and the nucleic acid sequence of the primer probe includes the sequences shown in SEQ ID NO.4~SEQ ID NO.

6. The Ct value-RNA quality grade relationship includes: Ct value ≤ 24, RNA quality grade is qualified; Ct value > 24, RNA quality grade is unqualified.

16. A method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing, characterized in that, The method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing includes the step of using the plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing as described in any one of claims 9-15.

17. A method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing according to claim 16, characterized in that, The methods for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing specifically include: (a) Collected plasma or serum samples were subjected to exosome isolation, RNA extraction, and RNA library construction and sequencing; (b) Using the detection target and qPCR primer and probe information provided in the plasma or serum exosome RNA quality assessment model for transcriptome sequencing according to any one of claims 9-15, amplify the RNA sample and obtain the Ct value; (c) The Ct value obtained in step (b) is compared with the Ct value-RNA quality grade relationship provided in the plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing as described in any one of claims 9-15, to determine the quality grade of RNA and assess the quality of RNA library construction and sequencing data obtained in step (a).

18. An apparatus for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing, characterized in that, The apparatus for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing is used to perform the method for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing as described in claim 16.

19. The apparatus for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing according to claim 18, characterized in that, The device for assessing the quality of plasma or serum exosomal RNA suitable for transcriptome sequencing includes a sample processing module, a qPCR module, and a result analysis module. The sample processing module is used to perform the following operations: separating exosomes from collected plasma or serum samples, extracting RNA, and constructing RNA libraries for sequencing. The qPCR module is used to perform the following: amplifying RNA samples and obtaining Ct values ​​by providing the detection target and qPCR primer and probe information provided in the plasma or serum exosome RNA quality assessment model suitable for transcriptome sequencing according to any one of claims 9-15; The result analysis module is used to perform the following: comparing the Ct value obtained by the qPCR module with the Ct value-RNA quality grade relationship provided in the plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing as described in any one of claims 9-15, determining the RNA quality grade, thereby assessing the quality of RNA library construction and sequencing data obtained by the sample processing module.

20. The application of a plasma or serum exosomal miRNA quality assessment biomarker in transcriptome sequencing, characterized in that, The plasma or serum exosomal miRNA quality assessment biomarker is: an RNA candidate factor screened by the method for constructing a plasma or serum exosomal RNA quality assessment model suitable for transcriptome sequencing as described in any one of claims 1-8, wherein the plasma or serum exosomal miRNA quality assessment biomarker includes hsa-miR-130b-3p.

21. The application according to claim 20, characterized in that, The plasma or serum exosomal miRNA quality assessment biomarker is any one of the following combinations: Combination 1: hsa-miR-130b-3p and hsa-miR-378a-3p; Combination 2: hsa-miR-130b-3p and hsa-miR-17-5p; Combination 3: hsa-miR-130b-3p, hsa-miR-17-5p, and hsa-miR-103a-3p; Combination 4: hsa-miR-130b-3p, hsa-miR-378a-3p, hsa-miR-103a-3p and hsa-miR-22-5p; Combination 5: hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p and hsa-miR-22-5p.

22. The application according to claim 21, characterized in that, The plasma or serum exosomal miRNA quality assessment biomarkers are a combination of hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p, and hsa-miR-22-5p.

23. The application according to claim 20, characterized in that, The sources of the plasma or serum exosomal miRNAs include individuals with any one or at least a combination of two of the following diseases: lung cancer, colon cancer, or liver cancer.

24. Application of hsa-miR-130b-3p expression level detection reagent in quality assessment of plasma or serum exosomal miRNAs suitable for transcriptome sequencing.

25. The application according to claim 24, characterized in that, The expression level detection reagent includes qPCR primers and probes for amplifying hsa-miR-130b-3p.

26. The application according to claim 25, characterized in that, The nucleic acid sequences of the qPCR primers and probes include those shown in SEQ ID NO.1 to SEQ ID NO.

3.

27. The application according to claim 24, characterized in that, The sources of the plasma or serum exosomal miRNAs include individuals with any one or at least a combination of two of the following diseases: lung cancer, colon cancer, or liver cancer.

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