Method for constructing plasma or serum exosome RNA quality evaluation model suitable for transcriptome sequencing and application thereof

Abstract

The invention relates to a method for constructing a plasma or serum exosome RNA quality evaluation model suitable for transcriptome sequencing and application of the method, stably expressed RNA candidate factors are screened, a verified primer probe is used for conducting qPCR quantification on exosome RNA, and a Ct value is obtained; an RNACt value of the exosome is associated with RNA-seq data, a reasonable Ct value is screened and set as a reference threshold value, and the RNA quality of the exosome is graded as a measurement index for judging whether the RNA-seq data is qualified or not, so that the quality of miRNA or mRNA of the exosome can be independently evaluated, and the quality of the two RNAs can also be evaluated at the same time. The method can timely and effectively evaluate whether upstream sample collection, exosome separation, RNA extraction and other methods are appropriate or not, can reduce the link of transferring low-quality samples to a downstream high-throughput library construction experiment, and reduces the cost loss of manpower, samples, reagent consumables and the like.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and particularly relates to a method for constructing a quality assessment model of plasma or serum exosome RNA suitable for transcriptome sequencing and its application. Background Art

[0002] Exosomes are cell-secreted products with a lipid bilayer membrane and a vesicular structure, carrying contents such as DNA, RNA, proteins, and lipids, and are key mediators of intercellular signal transduction, material transport, and physiological metabolism. Exosomes are present in various body fluids and tissues, and due to the easy accessibility and stability of plasma, it has become the main sample type for exosome research. More and more studies have shown that exosomes have become an important carrier for the discovery of diagnostic markers for various diseases and targeted drug delivery research, and plasma exosome RNAomics research is one of the most common ways to discover disease diagnostic markers.

[0003] Since the RNA content in exosomes is much lower than that in cells, compared with conventional RNAomics, the research is more difficult. The content and purity of exosome RNA can reflect whether the RNA extraction method is appropriate, and at the same time will affect the quality of downstream transcriptome library construction and high-throughput sequencing.

[0004] At present, the main methods for detecting and quantifying exosomal RNA are Nanodrop, qubit, Agilent 2100, and qPCR detection. Nanodrop quantifies RNA by detecting the absorbance value of the sample at 260nm. It has the characteristics of small dosage, simple operation, low cost, and the ability to determine whether the sample is contaminated by protein, but the quantitative sensitivity is low and the distribution of RNA fragments cannot be displayed. Qubit is a method that uses specific fluorescent dyes to bind to target RNA and is placed in a fluorometer to quantify and detect the integrity of RNA. This method is convenient and fast, with high accuracy, sensitivity and flexibility, but it cannot reflect RNA purity, fragment distribution and PCR efficiency. Agilent 2100 is based on capillary electrophoresis and fluorescent staining technology to perform electrophoretic separation of RNA of different molecular sizes in the sample, and finally converts the fluorescent signal into an electrophoresis spectrum, which can obtain RNA concentration, fragment distribution and integrity information. This method can realize automated, high-throughput, high-repeat, and visual RNA detection. The disadvantages are high cost, low quantitative accuracy, and inability to reflect PCR efficiency. qPCR detection uses specific primers to reverse transcribe sample RNA, uses the reverse transcription product as a template, and detects the fluorescence signal intensity generated by each PCR cycle through fluorescent dyes or probes. The Ct value in the exponential growth period is positively correlated with the sample RNA copy number, thereby quantifying the RNA. This method can truly reflect the reverse transcription and PCR efficiency of RNA and has high quantitative accuracy. The disadvantages are that it is costly and has more operating steps. At the same time, how to screen out suitable qPCR test factors is also crucial.

[0005] CN117721186A discloses a method for detecting exosome lncRNA. The researchers improved and optimized RT-qPCR, used Takara's SMARTScribeTM reverse transcriptase and template switching technology to reverse transcribe low-abundance exosome lncRNA, and completed RT-qPCR detection based on the designed low-abundance lncRNA primers, which can detect two low-abundance lncRNAs in plasma exosomes. This method has high sensitivity, but is mainly used for clinical diagnosis, and there is no research on transcriptome library sequencing.

[0006] CN118506874A discloses a method for mRNA quality assessment for next-generation sequencing. By using next-generation sequencing technology and computer programs, the pure read positions and abundance information of mRNA in a sample are obtained, an mRNA central position index curve is generated, and samples in which the read position of the highest peak of sequencing abundance falls within the range of 20 - 85% of the total distribution range of read positions are determined to meet the requirements of next-generation sequencing. It is considered that this invention is more suitable for RNA quality assessment of NGS compared to RIN and DV200. This method provides a novel perspective for RNA quality assessment, expanding the possibility of RNA-seq detection for samples. However, it is necessary to perform next-generation library construction sequencing and bioinformatics analysis before determining whether each sample meets the requirements, which is costly. At the same time, it has high requirements for the computer analysis ability of experimental personnel, and it only targets plasma mRNA, with low applicability to other sample types or other types of RNA.

[0007] Existing methods cannot accurately judge RNA quality, resulting in the transfer of low-quality samples to downstream high-throughput library construction experimental links, reducing the qualification rate of RNA library construction. Currently, there is a lack of accurate assessment methods for the quantification of exosomal RNA and the correlation between exosomal RNA and the quality of downstream transcriptome library construction sequencing. Summary of the Invention

[0008] To solve the above technical problems, the present invention provides a method for constructing a quality assessment model for plasma or serum exosomal RNA applicable to 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 are screened, qPCR quantification is performed on the candidate factors to obtain Ct values, and the quality of exosomal RNA is graded according to the Ct values.

[0009] To achieve this purpose, the present invention adopts the following technical solutions:

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

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

[0012] (2) Screen RNA candidate factors with stable expression, design and verify qPCR primers and probes for the RNA candidate factors, and perform qPCR quantification on exosomal RNA using the verified primers and probes to obtain Ct values;

[0013] (3) Correlate the Ct values of exosomal RNA with the results of transcriptome library inspection and the quality control results of sequencing data, screen and set reasonable Ct values as reference thresholds, and classify the quality of exosomal RNA into grades.

[0014] The present invention provides a method for quantifying exosomal RNA and evaluating the correlation between exosomal RNA and the quality of downstream transcriptome library construction and sequencing. It can separately evaluate short-chain RNAs mainly composed of miRNAs or long-chain RNAs mainly composed of mRNAs, or simultaneously evaluate the combination of the two, realizing the classification of the quality of exosomal RNA into grades, so as to timely and effectively evaluate whether the methods of upstream sample collection, exosome isolation, RNA extraction, etc. are appropriate, and can reduce the transfer of low-quality samples to the downstream high-throughput library construction experimental links, reducing the cost losses of manpower, samples, reagents and consumables, etc.

[0015] The present invention collects plasma or serum samples of a certain disease type, screens the top 10 candidate factors with the most stable expression levels, designs and verifies miRNA-qPCR and mRNA-qPCR primers for the candidate factors ranked at the top respectively, and uses the verified primers to perform miRNA-qPCR and mRNA-qPCR quantification on exosomal RNA respectively to obtain the final Ct values.

[0016] Correlate the Ct values of exosomal RNA with the results of transcriptome library inspection and the quality control results of sequencing data, screen out reasonable Ct value reference thresholds, and classify the quality of exosomal RNA into grades, that is, the Ct values within a certain range can be used as a measure of whether the RNA-seq data is qualified. The quality of exosomal miRNAs or mRNAs can be evaluated separately, or the quality of the two RNAs can be evaluated simultaneously. Finally, samples of multiple disease types containing multiple biological replicates are used to verify this quality assessment method to confirm the applicability of this method.

[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 plasma samples of diseased populations, plasma samples of healthy populations, serum samples of diseased populations or serum samples of healthy populations.

[0019] Preferably, the diseases suffered by the diseased populations include any one or a combination of at least two of lung cancer, colon cancer or liver cancer.

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

[0021] Preferably, the disease of the diseased population is lung cancer, and the plasma or serum samples include plasma or serum samples of healthy controls, benign nodules, carcinoma in situ, minimally invasive carcinoma, and invasive carcinoma populations.

[0022] Preferably, the disease of the diseased population is colon cancer, and the plasma or serum samples include plasma or serum samples of healthy controls, colon cancer, and colon cancer with liver metastasis populations.

[0023] Preferably, the disease of the diseased population is liver cancer, and the plasma or serum samples include plasma or serum samples of healthy controls, liver cancer, and adjacent cancer populations.

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

[0025] Removing adapters and low-quality sequences, quantifying the RNA of each sample, grouping the samples according to the disease development process, and based on the grouping information, counting the number of transcripts per million transcripts (TPM) of all identified RNAs. Calculate the CV2 value from the grouping information and TPM values, and further screen the 10 RNAs with the lowest CV2 values to obtain RNA candidate factors.

[0026] Preferably, the calculation method of the CV2 value includes:

[0027] Ave is the mean of the TPM values of each group of samples, SD is the standard deviation of the TPM values of each group of samples, the coefficient of variation CV of the TPM values within each group of samples = SD / Ave, and CV Sum is the sum of the CV values of each group of samples;

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

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

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

[0031] Preferably, the plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing includes a detection object, qPCR primer probes, and a Ct value - RNA quality grade relationship.

[0032] Preferably, the detection object includes plasma exosome miRNA of lung cancer patients. The nucleic acid sequences of the primer probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.3. The Ct value - RNA quality grade relationship includes: when Ct value ≤ 25, the RNA quality grade is qualified; when 25 < Ct value ≤ 26, the RNA quality grade is at risk; when Ct value > 26, the RNA quality grade is unqualified.

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

[0034] SEQ ID NO.2: CGCCAGTGCAATGATGAAA.

[0035] SEQ ID NO.3: TCGCACTGGATACGACATGCCCT.

[0036] Preferably, the detection object includes serum exosome miRNA of liver cancer patients. The nucleic acid sequences of the primer probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.3. The Ct value - RNA quality grade relationship includes: when Ct value ≤ 25, the RNA quality grade is qualified; when Ct value > 25, the RNA quality grade is unqualified.

[0037] Preferably, the detection object includes plasma exosome mRNA of colon cancer patients. The nucleic acid sequences of the primer probes include the sequences shown in SEQ ID NO.4 to SEQ ID NO.6. The Ct value - RNA quality grade relationship includes: when Ct value ≤ 24, the RNA quality grade is qualified; when 24 < Ct value ≤ 28, the RNA quality grade is at risk; when Ct value > 28, the RNA quality grade is unqualified.

[0038] SEQ ID NO.4: GAAGGAGCTGAACTACTTTGCAA.

[0039] SEQ ID NO.5: TTTGTCCAGCATATTATTGATGAGC.

[0040] SEQ ID NO.6: CAACTTTAGCTCCGCCCAGGATG.

[0041] Preferably, the detection object includes plasma exosome mRNA of lung cancer patients, the nucleic acid sequences of the primer probes include the sequences shown in SEQ ID NO.4 to SEQ ID NO.6, and the Ct value - RNA quality grade relationship includes: when Ct value ≤ 24, the RNA quality grade is qualified; when 24 < Ct value ≤ 28, the RNA quality grade is at risk; when Ct value > 28, the RNA quality grade is unqualified.

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

[0043] In the third aspect, the present invention provides a method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing. The method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing includes the step of using the plasma or serum exosome RNA quality evaluation model applicable to transcriptome sequencing described in the second aspect.

[0044] Preferably, the method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing specifically includes:

[0045] (a) Separating exosomes, extracting RNA and performing RNA library construction and sequencing on the collected plasma or serum samples;

[0046] (b) Amplifying the RNA sample according to the detection object and qPCR primer probe information provided in the plasma or serum exosome RNA quality evaluation model applicable to transcriptome sequencing described in the second aspect to obtain the Ct value;

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

[0048] Preferably, the method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing specifically includes:

[0049] (a) Separating plasma exosomes of lung cancer patients, extracting miRNA and performing miRNA library construction and sequencing;

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

[0051] (c) Compare the Ct values 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 sequencing data obtained in step (a). The Ct value - RNA quality grade relationship includes: when Ct value ≤ 25, the RNA quality grade is qualified; when 25 < Ct value ≤ 26, the RNA quality grade is at risk; when Ct value > 26, the RNA quality grade is unqualified;

[0052] Alternatively, (a) Isolate, extract miRNA, and perform miRNA library sequencing on the serum exosomes collected from liver cancer patients;

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

[0054] (c) Compare the Ct values 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 sequencing data obtained in step (a). The Ct value - RNA quality grade relationship includes: when Ct value ≤ 25, the RNA quality grade is qualified; when Ct value > 25, the RNA quality grade is unqualified;

[0055] Alternatively, (a) Isolate, extract mRNA, and perform mRNA library sequencing on the plasma exosomes collected from colon cancer patients;

[0056] (b) Amplify the mRNA sample to obtain Ct values. The nucleic acid sequences of the primer probes used for the amplification include the sequences shown in SEQ ID NO.4 to SEQ ID NO.6;

[0057] (c) Compare the Ct values 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 sequencing data obtained in step (a). The Ct value - RNA quality grade relationship includes: when Ct value ≤ 24, the RNA quality grade is qualified; when 24 < Ct value ≤ 28, the RNA quality grade is at risk; when Ct value > 28, the RNA quality grade is unqualified;

[0058] Alternatively, (a) Isolate, extract mRNA, and perform mRNA library sequencing on the plasma exosomes collected from lung cancer patients;

[0059] (b) Amplify the mRNA sample to obtain the Ct value. The nucleic acid sequences of the primer-probe used for the 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 sequencing data obtained in step (a). The Ct value-RNA quality grade relationship includes: when the Ct value ≤ 24, the RNA quality grade is qualified; when 24 < Ct value ≤ 28, the RNA quality grade is at risk; when the Ct value > 28, the RNA quality grade is unqualified;

[0061] Alternatively, (a) Isolate, extract mRNA and perform mRNA library sequencing on the serum exosomes of liver cancer patients collected;

[0062] (b) Amplify the mRNA sample to obtain the Ct value. The nucleic acid sequences of the primer-probe used for the 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 RNA quality grade and evaluate the quality of the mRNA library sequencing data obtained in step (a). The Ct value-RNA quality grade relationship includes: when the Ct value ≤ 24, the RNA quality grade is qualified; when the Ct value > 24, the RNA quality grade is unqualified.

[0064] Fourthly, the present invention provides a device for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing. The device for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing is used to execute the method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing described in the third aspect.

[0065] Preferably, the device for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing includes a sample processing module, a qPCR module and a result analysis module;

[0066] The sample processing module is used to perform: isolate exosomes, extract RNA and perform RNA library sequencing on the collected plasma or serum samples;

[0067] The qPCR module is used to perform: according to the detection object and qPCR primer-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 to obtain the Ct value;

[0068] The result analysis module is used to perform the following steps: comparing the Ct values 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 described in the second aspect, determining the RNA quality grade, and thereby evaluating the quality of the RNA library sequencing data obtained in the sample processing module.

[0069] In a fifth aspect, the present invention provides a plasma or serum exosome miRNA quality assessment biomarker applicable to transcriptome sequencing. The plasma or serum exosome miRNA quality assessment biomarker is: an RNA candidate factor screened by the method for constructing a plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing described in the first aspect. The plasma or serum exosome miRNA quality assessment biomarker includes hsa-miR-130b-3p.

[0070] Preferably, the plasma or serum exosome miRNA quality assessment biomarker further includes 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 exosome 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 biomarker for the quality assessment of plasma or serum exosomal miRNAs is 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 miRNAs includes populations suffering from any one or a combination of at least two of lung cancer, colon cancer, or liver cancer.

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

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

[0081] Preferably, the nucleic acid sequences of the qPCR primer-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 miRNAs includes populations suffering from any one or a combination of at least two of lung cancer, colon cancer, or liver cancer.

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

[0084] The assessment of exosomal RNA quality provided by the present invention has high sensitivity, high throughput, and high accuracy, truly reflecting the reverse transcription and PCR efficiency of RNA, establishing an association between the exosomal RNA quality and the directly downstream transcriptome library construction sequencing data, enabling efficient assessment of exosomal RNA quality, timely and effectively reducing the transfer of low-quality samples to the downstream high-throughput library construction experimental links, reducing cost losses such as manpower, samples, reagent consumables, etc., and at the same time helping to discover problems in the upstream experiments and make corresponding adjustments and optimizations in a timely manner, including sample collection, exosome isolation, or exosomal RNA extraction. The present invention does not require expensive equipment, has a simple operation, a wide adaptation range, high throughput, and low requirements for the level of experimental personnel. The present invention can be extended to the assessment and detection of exogenous contamination in RNA, such as DNA contamination and microbial contamination, and can be popularized and applied to other species such as rats and mice to fill in the gaps in exosomal transcriptomics research in other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 It is a design flow chart of the technical solution of the present invention.

[0086] Figure 2Transmission electron microscopy detection results of plasma exosomes in Example 2.

[0087] Figure 3 Western Blot detection results of plasma exosomes in Example 2.

[0088] Figure 4 Results of the correlation between different miRNA candidate factors and Ct values in Example 2.

[0089] Figure 5 Results of the success rate of miRNAseq library construction for different miRNA candidate factor combinations in Example 2.

[0090] Figure 6 Results of the correlation between miRNA-qPCR of plasma exosomes and library quality at level A in Example 2. Among them, Figure a is the box plot of samples with a Ct value of has-miR-130b-3p below 25, Figure b is the library inspection map of the miRNA library of the samples corresponding to level A in Figure a, Figure c is the box plot of the alignment rate of miRNA sequencing data of the samples corresponding to level A in Figure a with the reference genome, and Figure d is 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 Results of the correlation between miRNA-qPCR of plasma exosomes and library quality at level B in Example 2. Among them, Figure a is the box plot of samples with a Ct value of has-miR-130b-3p between 25 and 26, Figure b is the library inspection map of the miRNA library of the samples corresponding to level B in Figure a, Figure c is the box plot of the alignment rate of miRNA sequencing data of the samples corresponding to level B in Figure a with the reference genome, and Figure d is the box plot of the number of miRNAs identified in the miRNA sequencing data of the samples corresponding to level B in Figure a.

[0092] Figure 8 Results of the correlation between miRNA-qPCR of plasma exosomes and library quality at level C in Example 2. Among them, Figure a is the box plot of samples with a Ct value of has-miR-130b-3p above 26, Figure b is the library inspection map of the miRNA library of the samples corresponding to level C in Figure a, Figure c is the box plot of the alignment rate of miRNA sequencing data of the samples corresponding to level C in Figure a with the reference genome, and Figure d is the box plot of the number of miRNAs identified in the miRNA sequencing data of the samples corresponding to level C in Figure a.

[0093] Figure 9 Results of the success rate of mRNAseq library construction for different mRNA candidate factor combinations in Example 3.

[0094] Figure 10Result graph of the correlation between plasma exosome mRNA-qPCR and library quality at level A in Example 3. In the figure, a is the box plot of samples with a Ct value of PGK1 below 24, b is the inspection graph of the mRNA library of the samples corresponding to level A in figure a, and c is the box plot of the alignment rate of the mRNA sequencing data of the samples corresponding to level A in figure a with the reference genome.

[0095] Figure 11 Result graph of the correlation between plasma exosome mRNA-qPCR and library quality at level B in Example 3. In the figure, a is the box plot of samples with a Ct value of PGK1 between 24 and 28, b is the inspection graph of the mRNA library of the samples corresponding to level B in figure a, and c is the box plot of the alignment rate of the mRNA sequencing data of the samples corresponding to level B in figure a with the reference genome.

[0096] Figure 12 Result graph of the correlation between plasma exosome mRNA-qPCR and library quality at level C in Example 3. In the figure, a is the box plot of samples with a Ct value of PGK1 above 28, b is the inspection graph of the mRNA library of the samples corresponding to level C in figure a, and c is the box plot of the alignment rate of the mRNA sequencing data of the samples corresponding to level C in figure a with the reference genome.

[0097] Figure 13 Result graph of the correlation between plasma exosome mRNA-qPCR and sequencing data in colon cancer patients in Example 4.

[0098] Figure 14 Result graph of the library construction and sequencing success rate of different exosome RNA quality assessment methods in Example 5. Detailed implementation manners

[0099] To further elaborate on the technical means and effects adopted by the present invention, the present invention will be further described below in conjunction with examples and drawings. It can be understood that the specific implementation manners described herein are only used to explain the present invention, rather than limiting the present invention.

[0100] For those not specifying specific techniques or conditions in the examples, follow the techniques or conditions described in the literature in this field or according to the product specifications. For reagents or instruments not indicating the manufacturer, they are all conventional products that can be obtained through regular channels.

[0101] Example 1

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

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

[0104] The plasma or serum samples include plasma or serum samples from diseased and healthy populations. The diseases of the diseased population in the plasma or serum samples include any one or a combination of at least two of lung cancer, colon cancer, or liver cancer. The sample selection method for the plasma or serum samples includes: taking plasma or serum samples covering all development processes of the disease and healthy people.

[0105] (2)Screen for stably expressed RNA candidate factors, design and verify qPCR primers and probes for the RNA candidate factors, and perform qPCR quantification on exosomal RNA using the verified primers and probes to obtain Ct values;

[0106] The specific steps for screening stably expressed RNA candidate factors include:

[0107] Remove adapters and low-quality sequences, quantify the RNA of each sample, group the samples according to the development process of the disease, and based on the grouping information, count the number of transcripts per million transcripts (TPM) of all identified RNAs. Calculate the CV2 value from the grouping information and TPM values, and further screen the 10 RNAs with the lowest CV2 values to obtain RNA candidate factors;

[0108] The calculation method of the CV2 value includes:

[0109] Ave is the mean of the TPM values of each group of samples, SD is the standard deviation of the TPM values of each group of samples, the coefficient of variation CV of the TPM values within each group of samples = SD / Ave, and CV Sum is the sum of the CV values of each group of samples;

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

[0111] (3)Associate the exosomal RNA Ct values with the results of library inspection of the transcriptome library and the results of sequencing data quality control, screen for a reasonable Ct value as the reference threshold, and classify the quality of exosomal RNA.

[0112] Example 2

[0113] This example uses the method provided in Example 1 to screen miRNA candidate factors for the quality assessment of plasma exosome miRNAs in 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, they were divided into 5 groups, including healthy controls, benign nodules, carcinoma in situ, minimally invasive carcinoma, and invasive carcinoma. Each group contained 20 - 40 biological samples. The blood of patients was collected using EDTA anticoagulant tubes. The collected blood was centrifuged at 1500g for 20 min at 4°C to remove the cells in the blood. The supernatant was taken and centrifuged at 3000g for 15 min at 4°C. The supernatant was collected and filtered using a 0.45 μm filter membrane. The filtrate was collected, and the sampling volume of each plasma sample was 1 mL.

[0115] Table 1

[0116] Species type Sample type Group Disease type Sample quantity Human Plasma Group1 Healthy control 29 Human Plasma Group2 Benign nodule 30 Human Plasma Group3 Carcinoma in situ 23 Human Plasma Group4 Microinvasive carcinoma 35 Human Plasma Group5 Invasive carcinoma 29

[0117] (2) Exosome isolation: Size exclusion chromatography and ultrafiltration techniques were used to isolate plasma exosomes. The size exclusion chromatography column was pre - warmed to room temperature and washed with 10 mL of 0.1 M PBS solution. After washing, all 1 mL of plasma collected in step (1) was added to the chromatography column. Elution was performed using PBS solution as the mobile phase, with 0.5 mL as one fraction. Fractions 1 - 5 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: Transmission electron microscopy equipment was used to characterize and detect the exosome samples obtained in step (2). The required sample volume was 10 - 15 μL. As Figure 2 shown, clear cup - shaped exosomes could be observed. Western Blot detection was performed on the exosome samples using specific antibodies. As Figure 3 shown, the exosome positive protein markers Tsg101, Hsp70, Alix, and CD9 were significantly expressed, while the negative protein marker Calnexin was not expressed.

[0119] (4) Extraction and quality detection of exosomal RNA: Total exosomal RNA was extracted based on the Qiagen miRNeasy Serum / Plasma Advanced Kit. 200 μL was aspirated from each of the exosome samples in step (2), and 200 μL of QIAzol lysis buffer was added. After vortexing thoroughly, it was incubated at room temperature for 5 min. Then, 140 μL of chloroform was added, and it was mixed by inverting up and down for 15 s and incubated at room temperature for 2 min. It was centrifuged at 12,000 g for 15 min at 4 °C. The supernatant was aspirated and transferred to a new centrifuge tube. 1.5 times the volume of absolute ethanol was added, and after mixing for 15 s, it was transferred into an RNeasy Mini column and placed in a 2 mL collection tube. It was centrifuged at 8,000 g for 15 s at 25 °C, and the liquid in the collection tube was discarded. Washing solutions such as RWT and RPE were added successively and discarded after centrifugation. Finally, 35 μL of enzyme-free water was added, and it was allowed to stand at room temperature for 2 min and then centrifuged at 12,000 g for 1 min. The liquid at the bottom of the centrifuge tube was the exosomal RNA. 1 μL was taken for qubit fluorescence detection to obtain the concentration information of exosomal RNA.

[0120] (5) Construction and sequencing of exosome RNA library: Take 3 μL from each of the exosome RNA samples in 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 °C for 1 h, incubate at 65 °C for 20 min, store at 4 °C for at least 5 min, and immediately proceed to the next reaction 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 °C for 30 min, incubate at 65 °C for 20 min, store at 4 °C for at least 5 min, and immediately proceed to the next reaction 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, 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 stand and let stand for 5 min, discard the supernatant and retain the magnetic beads, add 200 μL of freshly prepared 80% ethanol and wash twice, add 9 μL of enzyme-free water, mix well and let stand at room temperature for 5 min, place on a magnetic stand and let stand for 5 min, transfer 7.5 μL of the 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 °C for 15 min, cycle 1 time; denature at 95 °C for 15 s, anneal at 60 °C for 30 s, extend at 72 °C for 15 s, cycle 22 times; react at 72 °C for 2 min, cycle 1 time; store at 4 °C for at least 5 min.Add 38 μL of QIAseq miRNA NGS Beads, incubate at room temperature for 5 min, transfer to a magnetic stand 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 and incubate at room temperature for 5 min. Place on the magnetic stand and let stand for 5 min. Discard the supernatant, add 200 μL of freshly prepared 80% ethanol and wash twice. Add 21 μL of nuclease-free water, mix well and let stand at room temperature for 5 min. Place on the magnetic stand and let stand for 5 min. Transfer 20 μL of the supernatant to a new centrifuge tube to complete the miRNA library construction. Detect each miRNA library based on the Agilent 2100 analyzer, and perform NGS high-throughput sequencing on all libraries.

[0121] (6) Screening of miRNA quality assessment factors: Perform quality control on the NGS high-throughput sequencing data in step (5), remove adapters and low-quality sequences, identify, predict and quantify the miRNAs in each sample, a total of 3,878 miRNAs. According to the grouping information in Table 1, statistically compare the TPM expression levels of all identified miRNAs within and between groups, and calculate the CV2 value. As shown in Table 2, screen out the top 10 miRNAs ranked by CV2.

[0122] Table 2

[0123]

[0124]

[0125] Design qPCR primers for 5 of these miRNAs (hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p, hsa-miR-22-5p). Then, randomly select 55 samples from the samples in step (4) to obtain the Ct values of the samples. By comparing the correlation between the UMI reads of the second-generation sequencing data of the candidate factors and the Ct values of qPCR, it is found that hsa-miR-130b-3p is superior to hsa-miR-103a-3p and hsa-miR-22-5p. The results are shown in Figure 4 . In addition, compare the library construction and sequencing success rates of the combination of has-miR-130b-3p and the other 4 miRNAs. The miRNA combinations are shown in Table 3, and the library construction and sequencing success rate results are shown in Figure 5 As shown. Selecting combination 5 can obtain the highest accuracy. If both accuracy and efficiency are considered, using only has-miR-130b-3p can also meet the needs of most samples. Therefore, select has-miR-130b-3p as the candidate factor for subsequent verification.

[0126] Table 3

[0127] Panel miRNA type 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 a method for evaluating the quality of plasma exosome RNA: Further verification was carried out on the candidate factor has-miR-130b-3p in step (6), and the primer information is shown in Table 4.

[0129] Table 4

[0130]

[0131] Using the primers in Table 4, miRNA-qPCR detection was performed on the exosome RNA of all samples in step (4) to obtain the Ct values of miRNAs, and the correlation between the Ct values and the library inspection map and sequencing data was analyzed.

[0132] As Figure 6 、 Figure 7 and Figure 8 shown, the success rate of constructing and sequencing the downstream miRNA library for exosome RNA with Ct value < 25 was 96%, the main peak was obvious in the library inspection map, the proportion of adapter sequence peaks was very small, the alignment rate of sequencing data with the reference database reached over 70%, the number of identified miRNAs was over 400, and the data credibility was high; the success rate of constructing and sequencing the downstream miRNA library for exosome RNA with 25 < Ct value ≤ 26 was 46%, the alignment rate of sequencing data with the reference database was mainly between 35 - 50%, and the number of identified miRNAs was between 300 - 400; the success rate of constructing and sequencing the downstream miRNA library for exosome RNA with Ct value > 26 was 18%, the alignment rate of sequencing data with the reference database was below 35%, the number of identified miRNAs was below 200, and the data credibility decreased significantly.

[0133] Finally, the results of grading the quality of plasma exosome miRNAs are shown in Table 5. For the evaluation of exosome RNA quality, this method has a stronger discrimination level. The sample loss rate can be reduced from 22% to 4%.

[0134] Table 5

[0135]

[0136] Example 3

[0137] Refer to the method provided in Example 1 and the specific steps provided in Example 2 to screen the mRNA candidate factors for the quality assessment of plasma exosome mRNA in lung cancer patients and healthy individuals. Ten mRNA candidate factors, namely PGK1, HBB, RPLP2, ACTB, TMSB4X, FTL, TMSB10, RPL13A, RPL26, and EEF1A1, were screened. Five of these factors, PGK1, HBB, RPLP2, ACTB, and TMSB4X, were combined, and the library construction and sequencing success rates of PGK1 and each combination in Table 6 were compared. As Figure 9 shown, the success rates from high to low 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] Candidate factor PGK1 was further verified, 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] Use the primers in Table 7 to perform mRNA-qPCR detection on the exosome RNA of all samples in step (4) of Example 2 to obtain the Ct values of mRNA, and analyze the correlation between the Ct values, the library inspection graph, and the sequencing data.

[0144] As Figure 10 , Figure 11 and Figure 12 shown, the success rate of downstream mRNA library construction and sequencing for exosome RNA with Ct values ≤ 24 is 93%. The main peak fluorescence value in the library inspection graph is significantly dominant, and the proportion of adapter sequence peaks is very small. The alignment rate of the sequencing data with the reference database reaches over 60%, and the data credibility is high. The success rate of downstream mRNA library construction and sequencing for exosome RNA with 24 < Ct values ≤ 28 is 52%. The library inspection graph shows obvious short fragment peaks at around 150 bp, and the main peak fluorescence value is low. The alignment rate of the sequencing data with the reference database is mainly between 20 - 60%. The success rate of downstream mRNA library construction and sequencing for exosome RNA with Ct values > 28 is 17%. The library inspection graph shows obvious short fragment peaks at 137 bp, and the main peak fluorescence value is very low. The alignment rate of the sequencing data with the reference database is below 35%, and the data credibility significantly decreases. The final results of the grade classification of plasma exosome mRNA quality are shown in Table 8. For the quality assessment of exosome RNA, this method can reduce the sample loss rate from 20% to 7%.

[0145] Table 8

[0146]

[0147] Example 4

[0148] In this example, the plasma of colon cancer patients was used as the test sample, and the sample information is shown in Table 9. Referring to the method provided in Example 1 and the specific steps provided in Example 3, the collected samples were subjected to exosome isolation, RNA extraction, mRNA quality assessment, mRNA library construction and sequencing, etc., and the obtained Ct values and sequencing data were analyzed.

[0149] Table 9

[0150]

[0151]

[0152] The analysis results are as Figure 13 shown. The mapping% of the mRNAseq data of samples S1-S6 is above 60%, and the corresponding Ct-mRNA is below 24; among samples S7-S15, the mapping% of the mRNAseq data of 3 samples is above 50%, and the mapping% of the mRNAseq data of the remaining 6 samples is below 40%. The Ct-mRNA of these 9 samples is between 24-28; the mapping% of the mRNAseq data of samples S16-S30 is below 40%, and the corresponding Ct-mRNA is above 28.

[0153] Example 5

[0154] In this example, 42 serum samples of liver cancer patients were collected, and the sampling volume of each sample was 1 mL. The information is shown in Table 10. Referring to Example 2, exosome isolation, RNA extraction and quality control were performed on the samples. Two methods were used to perform quality control on RNA. One was qubit quantitative detection, and the other was qPCR quantitative detection provided by the present invention.

[0155] Table 10

[0156] Species type Sample type Group Disease type Sample quantity Human Serum Group1 Control 14 Human Serum Group2 Liver cancer 14 Human Serum Group3 Adjacent to cancer 14

[0157] Qubit quantitative detection: Take 3 μL of RNA from each sample, and use Qubit TM microRNA quantitative kit (ThermoFisher, product number: Q32880) for miRNA quantification. If the RNA concentration is higher than 0.33 ng / μL, it is defined as qualified, otherwise it is defined as unqualified. Take 4 μL of RNA from each sample and use Qubit TMFor mRNA quantification using the RNA High Sensitivity (HS) Quantification Kit, if the RNA concentration is higher than 0.25 ng / μL, it is defined as qualified; otherwise, it is defined as unqualified.

[0158] Quality control detection of the present invention: Take 3 μL of RNA from each sample, and use has-miR-130b-3p in Example 2 to perform miRNA quantification on the sample. If the Ct value ≤ 25, it is defined as qualified; otherwise, it is defined as unqualified. Take 4 μL of RNA from each sample, and use PGK1 in Example 3 to perform mRNA quantification on the sample. If the Ct value ≤ 24, it 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 with qualified miRNA and mRNA detections are all subjected to corresponding transcriptome library construction sequencing and sequencing data analysis, and the statistical results Figure 14 show that the miRNA library construction sequencing success rate of the method of the present invention is 92%, significantly higher than 62% of the qubit detection method. At the same time, the mRNA library construction sequencing success rate of the method of the present invention has obvious advantages. It shows that the method of the present invention has high sensitivity in the quality assessment of exosomal RNA, can effectively reduce the transfer of low-quality RNA samples into transcriptome library construction sequencing, thereby realizing the adjustment and optimization of the upstream experimental links and reducing cost waste in the downstream experiments.

[0160] Table 11

[0161]

[0162]

[0163] In summary, the present invention realizes the grading of the quality of exosomal RNA, thereby timely and effectively evaluating whether the methods of upstream sample collection, exosome isolation, RNA extraction, etc. are appropriate, and can reduce the transfer of low-quality samples to the downstream high-throughput library construction experimental link, reducing cost losses such as manpower, samples, reagents and consumables.

[0164] The applicant declares that the above description is only the specific implementation manner 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 thought of by those skilled in the art within the technical scope disclosed by the present invention fall within the protection scope and the disclosure scope of the present invention.

Claims

1. A method for constructing a quality assessment model of plasma or serum exosomal RNA suitable for transcriptome sequencing, characterized in that, The method for constructing a quality assessment model for plasma or serum exosomal RNA applicable to transcriptome sequencing comprises the following steps: (1) Collect plasma or serum samples and extract exosomal RNA; (2) Screen stable-expressing RNA candidate factors, design and validate qPCR primers and probes for the RNA candidate factors, and perform qPCR quantification on exosomal RNA using the validated primers and probes to obtain Ct values; (3) Correlate the exosomal RNA Ct values with the results of transcriptome library inspection and sequencing data quality control, screen reasonable Ct values as reference thresholds, and classify the quality of exosomal RNA.

2. The method for constructing a plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing according to claim 1, characterized in that The RNA includes miRNA and / or mRNA; Preferably, the plasma or serum sample in step (1) includes any one or a combination of at least two of plasma samples from diseased populations, plasma samples from healthy populations, serum samples from diseased populations, or serum samples from healthy populations; Preferably, the diseases suffered by the diseased populations include any one or a combination of at least two of lung cancer, colon cancer, or liver cancer; Preferably, the method for selecting the plasma or serum sample in step (1) includes: taking plasma or serum samples covering all development processes of the disease and healthy people; Preferably, the disease suffered by the diseased population is lung cancer, and the plasma or serum sample includes plasma or serum samples from healthy controls, benign nodules, carcinoma in situ, minimally invasive carcinoma, and invasive carcinoma populations; Preferably, the disease suffered by the diseased population is colon cancer, and the plasma or serum sample includes plasma or serum samples from healthy controls, colon cancer, and colon cancer with liver metastasis populations; Preferably, the disease suffered by the diseased population is liver cancer, and the plasma or serum sample includes plasma or serum samples from healthy controls, liver cancer, and adjacent cancer populations.

3. The method for constructing a plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing according to claim 1 or 2, characterized in that, The specific process of screening stable-expressing RNA candidate factors in step (2) includes: Removing adapters and low-quality sequences, quantifying the RNA of each sample, grouping the samples according to the development process of the disease, and based on the grouping information, counting the number of transcripts per million transcripts TPM of all identified RNAs. Calculate the CV2 value from the grouping information and TPM values, and further screen the 10 RNAs with the lowest CV2 values to obtain RNA candidate factors; Preferably, the calculation method of the CV2 value includes: Ave is the mean of the TPM values of each group of samples, SD is the standard deviation of the TPM values of each group of samples, and the coefficient of variation CV of the TPM values within each group of samples = SD / Ave, and CV Sum is the sum of the CV values of each group of samples; GP Ave is the mean of the TPM values of all samples, GP SD is the standard deviation of the TPM values of all samples, and the coefficient of variation of the TPM values of all samples GP CV = GP SD / GP Ave , CV2 = CV Sum × GP CV .

4. A plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing, characterized in that, The quality assessment model for plasma or serum exosomal RNA applicable to transcriptome sequencing is constructed by the method for constructing a quality assessment model for plasma or serum exosomal RNA applicable to transcriptome sequencing according to any one of claims 1 to 3.

5. The plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing according to claim 4, characterized in that The quality assessment model for plasma or serum exosomal RNA applicable to transcriptome sequencing includes a detection object, qPCR primers and probes, and a Ct value - RNA quality grade relationship; Preferably, the detection object includes plasma exosomal miRNA 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, and the Ct value - RNA quality grade relationship includes: when Ct value ≤ 25, the RNA quality grade is qualified; when 25 < Ct value ≤ 26, the RNA quality grade is at risk; when Ct value > 26, the RNA quality grade is unqualified; Preferably, the detection object includes miRNA of serum exosomes from liver cancer patients. The nucleic acid sequences of the primer-probes include the sequences shown in SEQ ID NO.1 to SEQ ID NO.

3. The relationship between Ct value and RNA quality grade is as follows: when Ct value ≤ 25, the RNA quality grade is qualified; when Ct value > 25, the RNA quality grade is unqualified. Preferably, the detection object includes mRNA of plasma exosomes from colon cancer patients. The nucleic acid sequences of the primer-probes include the sequences shown in SEQ ID NO.4 to SEQ ID NO.

6. The relationship between Ct value and RNA quality grade is as follows: when Ct value ≤ 24, the RNA quality grade is qualified; when 24 < Ct value ≤ 28, the RNA quality grade is at risk; when Ct value > 28, the RNA quality grade is unqualified. Preferably, the detection object includes mRNA of plasma exosomes from lung cancer patients. The nucleic acid sequences of the primer-probes include the sequences shown in SEQ ID NO.4 to SEQ ID NO.

6. The relationship between Ct value and RNA quality grade is as follows: when Ct value ≤ 24, the RNA quality grade is qualified; when 24 < Ct value ≤ 28, the RNA quality grade is at risk; when Ct value > 28, the RNA quality grade is unqualified. Preferably, the detection object includes mRNA of serum exosomes from liver cancer patients. The nucleic acid sequences of the primer-probes include the sequences shown in SEQ ID NO.4 to SEQ ID NO.

6. The relationship between Ct value and RNA quality grade is as follows: when Ct value ≤ 24, the RNA quality grade is qualified; when Ct value > 24, the RNA quality grade is unqualified.

6. A method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing, characterized in that, The method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing includes the steps of using the plasma or serum exosome RNA quality evaluation model applicable to transcriptome sequencing as claimed in claim 4 or 5. Preferably, the method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing specifically includes: (a) Separating exosomes, extracting RNA and performing RNA library sequencing on the collected plasma or serum samples; (b) Amplifying the RNA samples according to the detection object and qPCR primer-probe information provided in the plasma or serum exosome RNA quality evaluation model applicable to transcriptome sequencing as claimed in claim 4 or 5 to obtain Ct values; (c) Comparing the Ct values obtained in step (b) with the Ct value - RNA quality grade relationship provided in the plasma or serum exosome RNA quality evaluation model applicable to transcriptome sequencing as claimed in claim 4 or 5 to determine the RNA quality grade and evaluate the quality of the RNA library sequencing data obtained in step (a).

7. An apparatus for evaluating the quality of plasma or serum exosomal RNA applicable to transcriptome sequencing, characterized in that, The device for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing is used to execute the method for evaluating the quality of plasma or serum exosome RNA applicable to transcriptome sequencing as claimed in claim 6.

8. The device for evaluating the quality of plasma or serum exosomal RNA applicable to transcriptome sequencing according to claim 7, characterized in that, The device for evaluating the quality of plasma or serum exosome RNA applicable to 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, extracting RNA, and constructing an RNA library for sequencing from the collected plasma or serum samples; The qPCR module is used to perform the following operations: amplifying the RNA sample according to the detection targets and qPCR primer-probe information provided in the plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing as described in claim 4 or 5 to obtain Ct values; The result analysis module is used to perform the following operations: comparing the Ct values 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 claim 4 or 5 to determine the RNA quality grade, thereby evaluating the quality of the RNA library sequencing data obtained in the sample processing module.

9. A plasma or serum exosome miRNA quality assessment biomarker applicable to transcriptome sequencing, characterized in that, The plasma or serum exosome miRNA quality assessment biomarker is: an RNA candidate factor screened by the method for constructing a plasma or serum exosome RNA quality assessment model applicable to transcriptome sequencing as described in any one of claims 1 to 3, and the plasma or serum exosome miRNA quality assessment biomarker includes hsa-miR-130b-3p; Preferably, the plasma or serum exosome miRNA quality assessment biomarker further includes 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; Preferably, the plasma or serum exosome 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; Preferably, the plasma or serum exosome miRNA quality assessment biomarker is a combination of hsa-miR-130b-3p, hsa-miR-17-5p, hsa-miR-378a-3p, hsa-miR-103a-3p, and hsa-miR-22-5p; Preferably, the source of the plasma or serum exosome miRNA includes populations suffering from any one or a combination of at least two of the diseases of lung cancer, colon cancer, or liver cancer. Application of the reagent for detecting the expression level of 10.hsa-miR-130b-3p in the quality assessment of plasma or serum exosomal miRNA applicable to transcriptome sequencing; Preferably, the reagent for detecting the expression level comprises qPCR primer probes for amplifying hsa-miR-130b-3p; Preferably, the nucleic acid sequences of the qPCR primer probes comprise the sequences shown in SEQ ID NO.1 to SEQ ID NO.3; Preferably, the source of the plasma or serum exosomal miRNA includes populations suffering from any one or a combination of at least two of the diseases of lung cancer, colon cancer or liver cancer.

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