Biomarkers, reagents, kits, and detection systems for the diagnosis of lung diseases.

Abstract

This invention relates to a biomarker, reagent, kit, and detection system for the diagnosis of lung diseases. The biomarker for detecting lung diseases is exosome-derived miRNA, including at least one of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, miR-192-5p, miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p. Lung diseases are diagnosed by detecting the expression of these miRNAs. The above-mentioned biomarker for detecting lung diseases has high accuracy in diagnosing benign and malignant pulmonary nodules.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biotechnology, in particular to a marker, reagent, kit and detection system for lung disease diagnosis. BACKGROUND

[0002] Early detection of lung cancer is the key to reducing mortality, and the wide application of CT has significantly increased the detection of lung nodules without clinical symptoms. The differentiation between benign and malignant nodules is a difficult and key point. However, it is very difficult to determine the benignity and malignancy of the nodules by imaging alone.

[0003] Liquid biopsy refers to the diagnosis of diseases such as cancer through body fluids, which has the advantages of simple sampling, repeatability, no radioactivity and trauma. In the era of precision medicine, liquid biopsy will be one of the methods for differentiating benign and malignant lung nodules. The main objects of liquid biopsy include circulating tumor DNA (ctDNA), circulating tumor cells (CTC) and exosomes. However, there are certain drawbacks due to the low content of CTC and ctDNA in blood and the complex purification method. Exosomes, as a new liquid biopsy target, are gradually becoming a new detection object.

[0004] Although exosomal miRNA has been used as a biomarker for disease diagnosis, the accuracy of exosomal miRNA for differentiating benign and malignant lung nodules is poor. SUMMARY

[0005] Therefore, it is necessary to provide a marker for diagnosing lung diseases, which has high accuracy in detecting benign and malignant lung nodules, in view of the problem that the traditional exosomal miRNA as a marker has poor accuracy in differentiating benign and malignant lung nodules.

[0006] In addition, the application of the above marker in the preparation of a lung disease detection product, a lung disease detection reagent, a lung disease detection kit and a lung disease detection system are also provided.

[0007] A marker for lung disease diagnosis, the marker is miRNA, the miRNA includes at least one of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, miR-192-5p, miR-20a-5p, miR-221-3p, miR-21-5p and miR-30e-5p, lung disease is diagnosed by detecting the expression of the miRNA.

[0008] In one embodiment, the miRNA is an exosomal miRNA.

[0009] In one embodiment, the marker satisfies at least one of the following characteristics:

[0010] (1) The biomarkers can be used for the auxiliary diagnosis of lung cancer to distinguish lung cancer from benign lung diseases, other cancers, other benign diseases, and healthy individuals;

[0011] (2) The markers can be used for differential diagnosis of benign and malignant pulmonary nodules to distinguish between malignant and benign pulmonary nodules;

[0012] (3) The biomarkers can be used to diagnose the efficacy of treatment in lung cancer patients and to assess the treatment effect;

[0013] (4) The biomarkers can be used for lung cancer recurrence monitoring and diagnosis, and to assess the risk of recurrence.

[0014] In one embodiment, the miRNA includes two or more of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, miR-192-5p, miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p.

[0015] The above-mentioned biomarkers for diagnosing lung diseases are used in the preparation of lung disease diagnostic products by means of the following steps: measuring the expression level of the above-mentioned biomarkers in a sample; and

[0016] Diagnose lung diseases based on the expression levels of the aforementioned biomarkers.

[0017] In one embodiment, the method further includes the steps of: enriching total exosomes of the sample; and / or enriching lung tissue-specific exosomes of clinical samples.

[0018] In one embodiment, the lung-specific exosomes are membrane-expressing EGFR proteins.

[0019] In one embodiment, the application includes any of the following:

[0020] 1) Application in the preparation of miRNA biomarker detection reagents for the diagnosis of lung diseases;

[0021] 2) Application in the preparation of reagent kits for the diagnosis of lung diseases;

[0022] 3) Application in the preparation of systems for the diagnosis of lung diseases.

[0023] In one embodiment, the lung disease diagnosis includes at least one of the following diagnoses:

[0024] 1) Auxiliary diagnosis of lung cancer, which is used to differentiate lung cancer from benign lung diseases, other cancers, other benign diseases, and healthy individuals;

[0025] 2) Differential diagnosis of benign and malignant pulmonary nodules, the aforementioned differential diagnosis of benign and malignant pulmonary nodules is used to distinguish between malignant and benign pulmonary nodules;

[0026] 3) Diagnosis of treatment efficacy in lung cancer patients, wherein the diagnosis of treatment efficacy in lung cancer patients is used to evaluate the treatment effect;

[0027] 4) Lung cancer recurrence monitoring and diagnosis, which is used to assess the risk of recurrence.

[0028] In one embodiment, the samples to which the method is applicable include bodily fluid samples and / or tissue samples.

[0029] A diagnostic reagent for lung diseases includes a reagent for detecting the expression of miRNAs in exosomes, said miRNAs including at least one of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, miR-192-5p, miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p.

[0030] In one embodiment, the reagent for detecting miRNA levels in exosomes includes a detection primer pair, which includes at least one of the following primer pairs: a miR-143-3p primer pair for detecting miR-143-3p expression; a miR-103a-3p primer pair for detecting miR-103a-3p expression; a miR-92a-3p primer pair for detecting miR-92a-3p expression; a miR-223-3p primer pair for detecting miR-223-3p expression; a miR-363-3p primer pair for detecting miR-363-3p expression; a miR-199b-3p primer pair for detecting miR-199b-3p expression; a miR-16-5p primer pair for detecting miR-16-5p expression; and a miR-155-5p primer pair for detecting miR-155-5p expression. Primer pairs for detecting miR-155-5p expression levels; primer pairs for detecting miR-122-5p expression levels; primer pairs for detecting miR-483-5p expression levels; primer pairs for detecting Let-7c-5p expression levels; primer pairs for detecting miR-486-5p expression levels; primer pairs for detecting miR-192-5p expression levels; primer pairs for detecting miR-20a-5p expression levels; primer pairs for detecting miR-221-3p expression levels; primer pairs for detecting miR-21-5p expression levels; and primer pairs for detecting miR-30e-5p expression levels.

[0031] In one embodiment, the nucleotide sequences of the miR-143-3p primer pair are shown in SEQ ID NO: 1-2; the nucleotide sequences of the miR-103a-3p primer pair are shown in SEQ ID NO: 3-4; the nucleotide sequences of the miR-92a-3p primer pair are shown in SEQ ID NO: 5-6; the nucleotide sequences of the miR-223-3p primer pair are shown in SEQ ID NO: 7-8; the nucleotide sequences of the miR-363-3p primer pair are shown in SEQ ID NO: 9-10; the nucleotide sequences of the miR-199b-3p primer pair are shown in SEQ ID NO: 11-12; the nucleotide sequences of the miR-16-5p primer pair are shown in SEQ ID NO: 13-14; the nucleotide sequences of the miR-155-5p primer pair are shown in SEQ ID NO: 15-16; and the nucleotide sequences of the miR-122-5p primer pair are shown in SEQ ID NO: 1-2. The nucleotide sequences of the miR-483-5p primer pair are shown in SEQ ID NO: 17-18; the nucleotide sequences of the Let-7c-5p primer pair are shown in SEQ ID NO: 21-22; the nucleotide sequences of the miR-486-5p primer pair are shown in SEQ ID NO: 23-24; the nucleotide sequences of the miR-143-3p primer pair are shown in SEQ ID NO: 25-26; the nucleotide sequences of the miR-20a-5p primer pair are shown in SEQ ID NO: 27-28; the nucleotide sequences of the miR-221-3p primer pair are shown in SEQ ID NO: 29-30; the nucleotide sequences of the miR-21-5p primer pair are shown in SEQ ID NO: 31-32; and the nucleotide sequences of the miR-30e-5p primer pair are shown in SEQ ID NO: 33-34.

[0032] A kit for detecting lung diseases, comprising the aforementioned lung disease detection reagents.

[0033] In one embodiment, the kit further includes at least one of RNA extraction reagents, quality control materials, and PCR reaction reagents.

[0034] A detection system for lung diseases includes a detection module, the detection module including the aforementioned lung disease detection reagents.

[0035] In one embodiment, a preprocessing module is also included for enriching miRNA from the sample.

[0036] In one embodiment, the pretreatment module further includes an exosome enrichment reagent for enriching total exosomes and / or lung tissue-specific exosomes from the sample.

[0037] In one embodiment, the lung tissue-specific exosomes are exosomes whose exosome membranes express EGFR protein.

[0038] In one embodiment, a data processing module is also included, which is used to convert the miRNA expression level of the sample into a diagnostic result. Attached Figure Description

[0039] Figure 1 The results of nanoparticle tracking analysis of the exosomes enriched in Example 1;

[0040] Figure 2 Electron micrograph of the exosomes enriched in Example 1;

[0041] Figure 3 This is a partial quality assessment result of the exosomal miRNA in Example 1;

[0042] Figure 4 This is a volcano diagram showing the differential expression of exosomal miRNAs in Example 1;

[0043] Figure 5 This is a scatter plot showing the differential expression of miR-143-3p, miR-103a-3p, miR-92a-3p, and miR-223-3p in Example 1.

[0044] Figure 6 This is a scatter plot showing the differential expression of miR-363-3p, miR-199b-3p, miR-20a-5p, and miR-16-5p in Example 1.

[0045] Figure 7 This is a scatter plot showing the differential expression of miR-221-3p, miR-21-5p, and miR-155-5p in Example 1.

[0046] Figure 8 This is a scatter plot showing the differential expression of miR-122-5p, miR-483-5p, Let-7c-5p, and miR-30e-5p in Example 1.

[0047] Figure 9 This is a scatter plot showing the differential expression of miR-486-5p and miR-192-5p in Example 1.

[0048] Figure 10 The ROC curve of the upregulated marker in Example 1;

[0049] Figure 11 The ROC curve of the down-regulation marker in Example 1;

[0050] Figure 12 This is a scatter plot showing the differential expression of miR-143-3p, miR-103a-3p, miR-363-3p, and miR-20a-5p in Example 2.

[0051] Figure 13 This is a scatter plot showing the differential expression of miR-483-5p, miR-122-5p, and miR-192-5p in Example 2.

[0052] Figure 14 The ROC curves of the markers miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p, miR-483-5p, miR-122-5p, and miR-192-5p in Example 2 are shown.

[0053] Figures 15-17 The expression levels and differences of exosomal miRNA markers miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p, miR-483-5p, miR-122-5p, and miR-192-5p in the untreated phase (week 0 of treatment), week 2 of treatment, and week 4 of treatment in Example 3 are shown.

[0054] Figures 18-20 The expression levels and differences of exosomal miRNA markers miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p, miR-483-5p, miR-122-5p, and miR-192-5p at 0 months and 6 months post-surgery in Example 4 are shown.

[0055] Figure 21 Scatter plot showing the distribution of score values ​​for the 17 miRNA biomarker combinations in Example 6 as biomarkers;

[0056] Figure 22 The ROC curves for the 17 miRNA biomarker combinations in Example 6 are shown.

[0057] Figure 23 A scatter plot of the 14 miRNA biomarker combinations from Example 7 as biomarkers;

[0058] Figure 24 The ROC curves for the 14 miRNA biomarker combinations in Example 7 are shown.

[0059] Figure 25 A scatter plot of the 12 miRNA biomarker combinations from Example 8 as biomarkers;

[0060] Figure 26 The ROC curves for the 12 miRNA biomarker combinations in Example 8 are shown.

[0061] Figure 27 The expression levels and differences of five miRNA markers (miR-143-3p, miR-103a-3p, miR-20a-5p, miR-122-5p, and miR-192-5p) in plasma exosomes and plasma exosomes expressing EGFR protein in a targeted capture membrane were investigated in Example 9.

[0062] Figure 28 The ROC curves for five miRNA markers in plasma exosomes and plasma exosomes expressing EGFR protein in targeted capture membranes, as shown in Example 9. Detailed Implementation

[0063] To facilitate understanding of the present invention, a more complete description will be provided below. The invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be more thorough and complete.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0065] Exosomes are tiny vesicles, ranging in size from 30 nm to 150 nm, secreted by living cells. They are widely distributed in bodily fluids such as saliva, blood, and urine. They contain biomolecules such as proteins, lipids, mRNA, and miRNA, and can induce cell migration, regulate immune responses, influence tumor microenvironment formation, and promote metastasis. Exosomes exhibit high biological stability and carry a large number of biomolecules (especially miRNAs), mediating intercellular material transport and communication. miRNAs are RNA molecules 21–25 nt in length that can specifically recognize target mRNAs and regulate the expression of their encoding genes.

[0066] In this document, unless otherwise specified, lung diseases include, but are not limited to, benign lung nodules, malignant lung nodules, and benign lung diseases (such as lung inflammation, benign lung tumors, bullae, and emphysema). In this document, unless otherwise specified, diagnosis includes auxiliary diagnosis, recurrence risk assessment, assessment of cancer risk and degree, and prognostic judgment. Further, in some embodiments, lung disease diagnosis includes at least one of the following diagnoses: auxiliary diagnosis of lung cancer, differential diagnosis of benign and malignant lung nodules, diagnosis for evaluating treatment efficacy in lung cancer patients, and diagnosis for monitoring lung cancer recurrence. Auxiliary diagnosis of lung cancer is used to differentiate lung cancer from benign lung diseases, other cancers, other benign diseases, and healthy individuals; differential diagnosis of benign and malignant lung nodules is used to differentiate between malignant and benign lung nodules; diagnosis for evaluating treatment efficacy in lung cancer patients is used to assess treatment effectiveness; and diagnosis for monitoring lung cancer recurrence is used to assess the risk of recurrence.

[0067] One embodiment of this application provides a biomarker for the diagnosis of lung diseases. The biomarker is a miRNA, comprising at least one of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, and miR-192-5p, miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p. Further, the miRNA is an exosomal miRNA.

[0068] Furthermore, the miRNA includes two or more combinations of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, miR-192-5p, miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p.

[0069] In some embodiments, the miRNA includes at least one of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, and miR-192-5p, and lung diseases are diagnosed by detecting the expression of miRNA. Specifically, this invention has found that the expression of exosomal miRNAs miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, and miR-155-5p is significantly upregulated in patients with malignant pulmonary nodules (early-stage lung cancer), while the expression of miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, and miR-192-5p is significantly downregulated in the same patients. Therefore, at least one of the above miRNAs can be used to detect whether a patient has malignant pulmonary nodules (early-stage lung cancer). Optionally, the lung disease is lung cancer. In one optional specific embodiment, the lung disease is a malignant nodule (or early-stage lung cancer). Furthermore, the miRNAs used to detect lung diseases also include at least one of miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p from exosomes. Specifically, this invention has found that in patients with malignant pulmonary nodules (early-stage lung cancer), the expression of miR-20a-5p, miR-221-3p, and miR-21-5p is significantly upregulated, while the expression of miR-30e-5p is significantly downregulated. Therefore, at least one of miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p can be used for the diagnosis of malignant pulmonary nodules. Furthermore, at least one of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, and miR-192-5p, combined with at least one of miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p, can be used for the diagnosis of malignant pulmonary nodules. Validation showed that the combination of 17 miRNA biomarkers achieved a sensitivity of 93.0% and a specificity of 93.7% in differentiating early-stage lung cancer (malignant pulmonary nodules) from benign pulmonary nodules.A combination of 17 miRNA biomarkers showed a sensitivity of 85.7% and a specificity of 95.8% when used to differentiate between early-stage lung cancer (malignant pulmonary nodules) and benign lung diseases (such as lung inflammation, benign lung tumors, bullae, and emphysema). This combination also demonstrated a sensitivity of 85.7% and a specificity of 97.7% when used to differentiate between early-stage lung cancer (malignant pulmonary nodules), common cancers (such as stomach cancer, colorectal cancer, liver cancer, and breast cancer), and common diseases (such as hypertension, diabetes, heart disease, and bronchitis).

[0070] In some embodiments, an EGFR aptamer with biotin-terminated ends and a carrier (SA-resin or magnetic beads) with streptavidin-modified surface are used as a reaction system to separate and enrich exosomes expressing EGFR protein in plasma. At least one of miR-143-3p, miR-103a-3p, miR-20a-5p, miR-122-5p, and miR-192-5p from exosomes is used to more significantly distinguish and detect early-stage lung cancer from healthy individuals. Specifically, this invention has found that after targeted capture by EGFR aptamers, the area under the ROC curve of miR-143-3p, miR-103a-3p, miR-20a-5p, miR-122-5p, and miR-192-5p as markers in exosomes used to distinguish early-stage lung cancer from healthy individuals is significantly increased, thus improving predictive accuracy. The expression of miR-20a-5p and miR-192-5p differed significantly between lung cancer patients and healthy individuals.

[0071] This application also provides the application of a biomarker for diagnosing lung disease according to any of the above embodiments in the preparation of a product for diagnosing lung disease by a method comprising the steps of: measuring the expression level of the biomarker according to any of the above embodiments in a sample (e.g., a clinical sample); and diagnosing lung disease based on the expression level of the biomarker. Further, the method further comprises the steps of: enriching total exosomes of the sample; and / or enriching lung tissue-specific exosomes of the sample. Even further, the lung tissue-specific exosomes are membrane-expressing EGFR protein exosomes.

[0072] Based on the above, one embodiment of this application also provides a detection method for improving the differentiation between early-stage lung cancer and healthy individuals. This method includes the following steps: extracting exosomes from a sample to be tested; detecting the content of miRNAs in the exosomes; and distinguishing whether the sample to be tested is an early-stage lung cancer sample or a healthy individual sample based on the miRNA content of the sample. The above method can be used for diagnosing early-stage lung cancer, evaluating the efficacy of lung cancer surgery, and monitoring postoperative recurrence.

[0073] Optionally, the sample to be tested is a bodily fluid sample. In some embodiments, the sample to be tested is a blood sample (e.g., a plasma sample), a bronchoalveolar lavage fluid sample, or a sputum sample. It is understood that the sample to be tested is not limited to the above, and may also be other samples containing the above-mentioned markers (e.g., tissue samples). Further, in some embodiments, the step of extracting exosomes from the sample to be tested includes: isolating EGFR-positive exosomes from the sample to be tested using nucleic acid aptamers.

[0074] Based on the above, one embodiment of this application also provides the application of any of the above embodiments in the preparation of biomarkers for diagnosing lung diseases in diagnostic products (e.g., reagents or kits) for lung diseases.

[0075] Based on the above, one embodiment of this application also provides a detection reagent for lung diseases, which includes a reagent for detecting the content of miRNA in exosomes.

[0076] In some embodiments, the reagents for detecting the content of miRNAs in exosomes include reagents for detecting the content of at least one of miR-143-3p, miR-103a-3p, miR-92a-3p, miR-223-3p, miR-363-3p, miR-199b-3p, miR-16-5p, miR-155-5p, miR-122-5p, miR-483-5p, Let-7c-5p, miR-486-5p, and miR-192-5p in exosomes.

[0077] Optionally, the reagents used to detect the miRNA content in exosomes include detection primer pairs, which include at least one set of the following primer pairs:

[0078] The miR-143-3p primer pairs used for detecting miR-143-3p expression levels have nucleotide sequences as shown in SEQ ID NO: 1-2; the miR-103a-3p primer pairs used for detecting miR-103a-3p expression levels have nucleotide sequences as shown in SEQ ID NO: 3-4; the miR-92a-3p primer pairs used for detecting miR-92a-3p expression levels have nucleotide sequences as shown in SEQ ID NO: 5-6; and the miR-223-3p primer pairs used for detecting miR-223-3p expression levels have nucleotide sequences as shown in SEQ ID NO: 1-2. Primers for detecting miR-363-3p expression are shown in SEQ ID NO: 7-8; the nucleotide sequences of the miR-363-3p primer pairs are shown in SEQ ID NO: 9-10; the nucleotide sequences of the miR-199b-3p primer pairs for detecting miR-199b-3p expression are shown in SEQ ID NO: 11-12; the nucleotide sequences of the miR-16-5p primer pairs for detecting miR-16-5p expression are shown in SEQ ID NO: 13-14; and the nucleotide sequences of the miR-155-5p primer pairs for detecting miR-155-5p expression are shown in SEQ ID NO: 9-10; As shown in SEQ ID NO: 15-16; the miR-122-5p primer pair used to detect miR-122-5p expression level, the nucleotide sequence of the miR-122-5p primer pair is shown in SEQ ID NO: 17-18; the miR-483-5p primer pair used to detect miR-483-5p expression level, the nucleotide sequence of the miR-483-5p primer pair is shown in SEQ ID NO: 19-20; the Let-7c-5p primer pair used to detect Let-7c-5p expression level, the nucleotide sequence of the Let-7c-5p primer pair is shown in SEQ ID NO: 21-22; the miR-486-5p primer pair used to detect miR-486-5p expression level, the nucleotide sequence of the miR-486-5p primer pair is shown in SEQ ID NO: 19-20; As shown in NO: 23-24; the nucleotide sequences of the miR-192-5p primer pair and the miR-143-3p primer pair used for detecting miR-192-5p expression are shown in SEQ ID NO: 25-26. It is understood that the specific primer sequences for detecting each marker are not limited to those described above, and other primer sequences can be designed according to the specific miRNA to be detected.

[0079] In some embodiments, the reagent for detecting the content of miRNA in exosomes further includes a reagent for detecting the content of at least one of miR-20a-5p, miR-221-3p, miR-21-5p, and miR-30e-5p in exosomes. Optionally, the detection primer pair further includes at least one set of the following primer pairs:

[0080] The nucleotide sequences of the miR-20a-5p primer pairs used for detecting miR-20a-5p expression are shown in SEQ ID NO: 27-28; the nucleotide sequences of the miR-221-3p primer pairs used for detecting miR-221-3p expression are shown in SEQ ID NO: 29-30; the nucleotide sequences of the miR-21-5p primer pairs used for detecting miR-21-5p expression are shown in SEQ ID NO: 31-32; and the nucleotide sequences of the miR-30e-5p primer pairs used for detecting miR-30e-5p expression are shown in SEQ ID NO: 33-34.

[0081] In some embodiments, the reagent for detecting miRNA content in exosomes further includes a detection probe corresponding to the detection primer pair. Specifically, the detection probe is attached with a fluorescent group and a quencher group. Optionally, the fluorescent group is located at the 5' end of the probe, and the quencher group is located at the 3' end of the probe. Optionally, the fluorescent group attached to the detection probe is selected from one of FAM, HEX, VIC, CY5, ROX, Texsa Red, JOE, and Quasar 705. Of course, when there are more than two probes in the same reaction system, the fluorescent groups attached to the different probes are different. It is understood that the fluorescent group attached to the detection probe is not limited to the above and can also be other fluorescent groups.

[0082] Based on the above, one embodiment of this application also provides a kit for detecting lung diseases, the kit including the lung disease detection reagent of any of the above embodiments.

[0083] In some embodiments, the kit further includes at least one of an RNA extraction reagent, a quality control sample, and a PCR reaction reagent. The RNA extraction reagent is used for the extraction of exosomal miRNA; the quality control sample is used for quality control; and the PCR reaction reagent is used to construct the PCR amplification reaction system.

[0084] Furthermore, one embodiment of this application also provides a detection system for lung diseases. This detection system includes a detection module, and the detection template includes the lung disease detection reagents of any of the above embodiments. Optionally, the detection module further includes a detection device. The content of the marker in the test sample is determined by detecting the reaction product (e.g., amplification product) between the test sample and the detection reagent using the detection device. Optionally, the detection device is a real-time PCR instrument.

[0085] Furthermore, the aforementioned detection system also includes a preprocessing module. Optionally, the preprocessing module includes an exosome enrichment reagent. The exosome enrichment reagent is used to enrich total exosomes and / or lung tissue-specific exosomes from the sample. Further, the lung tissue-specific exosomes are exosomes expressing EGFR protein on their membranes. Validation has shown that targeting and enriching plasma exosomes expressing EGFR protein in the test sample can significantly improve the correlation between the biomarker and early lung cancer. Optionally, the preprocessing module includes reagents and equipment related to nucleic acid aptamer-coated magnetic bead capture technology.

[0086] In some embodiments, the detection system further includes a data processing module. The data processing module is used to convert the miRNA expression levels of the sample to be tested into diagnostic results.

[0087] In addition, one embodiment of this application also provides a method for diagnosing lung diseases, the method comprising the following steps: measuring the expression level of the biomarkers of any of the above embodiments in a sample; and diagnosing lung diseases based on the expression levels of the biomarkers. It is understood that in some embodiments, the method further includes the step of collecting clinical patient samples. Specific Implementation

[0089] The following detailed description is provided with reference to specific embodiments. Unless otherwise specified, the embodiments do not include components other than unavoidable impurities. Unless otherwise specified, the reagents and instruments used in the embodiments are conventionally selected in the art. Experimental methods not specifying specific conditions in the embodiments are implemented according to conventional conditions, such as those described in literature, books, or methods recommended by the manufacturer.

[0090] Example 1

[0091] Peripheral venous blood was collected from 25 patients with untreated malignant pulmonary nodules (early-stage lung cancer) and 22 patients with benign nodules (as controls), and the following procedures were performed:

[0092] 1. Enrichment, purification and identification of exosomes

[0093] (1) Enrichment and purification of plasma exosomes (molecular chromatography size exclusion (SEC) method):

[0094] 1) For the composite chromatography ion exchange column, add 5 ml of PBS to wash the column until it reaches equilibrium;

[0095] 2) Discard the effluent, add serum / plasma sample (5ml), and discard the effluent;

[0096] 3) Then centrifuge at 500g for 3 minutes and collect the effluent, which is the high-purity total exosomes.

[0097] (2) The size and morphology of exosomes were identified using nanoparticle tracking analyzer (NTA) and transmission electron microscopy (TEM). The results are as follows: Figure 1 and Figure 2 As shown.

[0098] Depend on Figure 1 and Figure 2 It can be seen that the exosomes obtained in step (1) have an average particle size of 91.3 nm and an actual concentration of 6.5E+10 (Particles / mL). Under electron microscopy, the vesicle structure is round and has a double membrane structure, which is a typical exosome morphology.

[0099] 2. Extraction and identification of plasma exosome mRNA

[0100] Exosomal mRNA was extracted and purified using the commercially available miRNeasy mini kit (Qiagen, 217004). The concentration and purity of the extracted RNA were measured using QUBIT, and the total RNA quality and DV were analyzed using an Agilent 2100 bioanalyzer system. 200 Value. DV 200 The quality metric represents the percentage of RNA fragments exceeding 200 nt.

[0101] 3. RNA library construction

[0102] (1) Adopt The external library was constructed using the Multiplex Small RNA Library Prep Set for Illumina (NEBE7580S), with a starting total RNA amount of 100 ng.

[0103] (2) Purify the cDNA product after PCR amplification using the Monarch PCR & DNA Kit;

[0104] (3) Use AMPure XP magnetic beads to filter segments by size;

[0105] (4) Perform 6% polyacrylamide gel electrophoresis on the amplified cDNA product from step (2), cut out the target DNA fragment (corresponding to miRNA) and recover it to obtain the prepared miRNA sequencing library.

[0106] (5) Quality control: Agilent 2100 Bioanalyzer was used to detect fragment length range and Invitrogen Qubit was used for concentration quantification.

[0107] After evaluation (partial results as follows) Figure 3 As shown in the figure, the plasma exosome miRNA library 2100 fragment analysis of each sample was qualified and can be used for subsequent sequencing.

[0108] 4. Perform sequencing to obtain data.

[0109] (1) The above miRNA library was subjected to second-generation sequencing. The sequencing platform was Illumina HiSeq sequencing platform, the sequencing read length was between 50bp and 150bp, and the sequencing mode was single-end sequencing or paired-end sequencing.

[0110] (2) Obtain offline data

[0111] 5. Analysis of data from the assay and screening of miRNA biomarkers

[0112] Analysis of exosome miRNA sequencing data includes: sequence alignment, whole-genome reads distribution map, miRNA classification and annotation, miRNA expression analysis, new miRNA prediction, miRNA expression difference analysis and cluster analysis among samples (groups), and finally, correlation analysis among samples.

[0113] (1) The data after the machine is subjected to data quality control and preprocessing by quality control tools to obtain effective data with low-quality sequences and sequencing adapters removed. The data is then compared with the human reference genome sequence to obtain the location information of the human reference genome sequence.

[0114] (2) Using location information and corresponding random tag sequences, PCR repeat sequences are removed, and the positions of the obtained sequences with removed PCR repeats are compared with the miRNA positions in the human reference genome (miRNA position information is taken from the miRBase database; when the 5' end of a sequence is consistent with the 5' end of a miRNA, this sequence is recorded as the sequencing sequence of this miRNA), thereby determining the expression levels of all miRNAs in the sample.

[0115] (3) Based on miRNA expression data, the edgeR package of R language was used to compare samples of patients with malignant lung nodules (early lung cancer) with samples of benign lung nodules (control group) to screen out miRNAs that were significantly highly or poorly expressed in the former.

[0116] After screening, 23 miRNAs were found to be significantly overexpressed in samples from patients with malignant pulmonary nodules (early-stage lung cancer), and 13 miRNAs were found to be significantly underexpressed (with a fold change greater than 2) (the differential expression volcano plot is shown below). Figure 4 (As shown).

[0117] (4) The expression level of miRNA was used as the independent variable. Logistic regression modeling was performed using the stats package of R language. The backward elimination method was used to select the independent variable. Finally, miRNAs with statistically significant differences in expression level were determined and sorted according to the fold change in expression level. The results are shown in Table 1.

[0118] Table 1

[0119]

[0120] Therefore, further confirmation revealed that among the 13 significantly overexpressed miRNAs, 11 miRNAs showed statistically significant differences in regression coefficients (P<0.05), and 6 miRNAs showed statistically significant differences in low expression (P<0.05). Thus, these 17 miRNAs can serve as miRNA biomarkers for early detection of lung cancer. Specifically, up-regulation: hsa-miR-143-3p, hsa-miR-92a-3p, hsa-miR-21-5p, hsa-miR-103a-3p, hsa-miR-363-3p, hsa-miR-223-3p, hsa-miR-20a-5p, hsa-miR-199b-3p, hsa-mi R-221-3p, hsa-miR-16-5p, hsa-miR-155-5p; down-regulation: hsa-miR-192-5p, hsa-miR-122-5p, hsa-miR-30e-5p, hsa-miR-Let-7c-5p, hsa-miR-483-5p, hsa-miR-486-5p.

[0121] 6. Clinical efficacy validation of candidate miRNA biomarkers

[0122] (1) Extract exosomes and total miRNA from plasma of different types of lung diseases (method as described above).

[0123] (2) Real-time quantitative RT-qPCR (primers and probes are shown in Table 2) was used to detect the expression levels of the above-mentioned candidate plasma exosomal miRNAs: A reverse transcription system was established using a RevertAid First Strand cDNA Synthesis Kit (Catalog No.: K1622, Fermentas) (20 μL system reaction conditions: 42℃, 60 min; 85℃, 5 min; 4℃, storage). Thermal cycling parameters were (37℃, 5 min; 94℃, 5 min pre-denaturation; denaturation at 94℃, 15 s and annealing at 60℃, 30 s, extension at 72℃, 15 s for a total of 50 cycles; final at 50℃, 30 s). The samples were extracted and quality controlled according to the CP value of the exogenous gene miR-39, and samples that failed the quality control were removed. The miRNA marker standard was serially diluted 10-fold to (1×10⁻⁶) 2 The expression level (copy / μL) was measured to assign values ​​to the expression level of the sample and to evaluate the PCR amplification efficiency.

[0124] Table 2

[0125]

[0126]

[0127] (3) Statistical methods

[0128] The CP value and relative expression abundance of miRNA were obtained from PCR data. The expression level of the miRNA target was calculated using the relative quantification formula: ΔCP = CP (miRNA marker) - CP (miRNA internal reference), and the detection result (relative quantification) = 2. -△CP The test results were compared and statistically analyzed with the pathological diagnoses of pulmonary nodules. Graphpad Prism 7.0 software was used to plot scatter plots of differences; SPSS 22.0 software and MedCalc V20.0 software were used to generate ROC curves, obtain cutoff values, and calculate the concordance rate. P < 0.05 was considered statistically significant. The results are shown in Table 3 and... Figures 5-11 As shown. Among them, Figure 5 This is a scatter plot of differential expression of miR-143-3p, miR-103a-3p, miR-92a-3p, and miR-223-3p; Figure 6 This is a scatter plot of differential expression of miR-363-3p, miR-199b-3p, miR-20a-5p, and miR-16-5p. Figure 7 This is a scatter plot showing the differential expression of miR-221-3p, miR-21-5p, and miR-155-5p. Figure 8This is a scatter plot of differential expression of miR-122-5p, miR-483-5p, Let-7c-5p, and miR-30e-5p; Figure 9 This is a scatter plot of differential expression of miR-486-5p and miR-192-5p; Figure 10 It is the ROC curve of the upregulation marker. Figure 11 It is the ROC curve of the down-regulation marker.

[0129] Table 3

[0130]

[0131]

[0132] From Table 3 and Figures 5-11 It can be seen that, using real-time quantitative PCR detection, the biomarkers hsa-miR-143-3p, hsa-miR-92a-3p, hsa-miR-21-5p, hsa-miR-103a-3p, hsa-miR-363-3p, hsa-miR-223-3p, hsa-miR-20a-5p, hsa-miR-199b-3p, hsa-miR-221-3p, and h The markers hsa-miR-16-5p, hsa-miR-155-5p, and downregulated markers hsa-miR-192-5p, hsa-miR-122-5p, hsa-miR-30e-5p, hsa-miR-Let-7c-5p, hsa-miR-483-5p, and hsa-miR-486-5p have shown good differentiation between benign and malignant pulmonary nodules in clinical samples and can be used to differentiate between benign and malignant pulmonary nodules.

[0133] Example 2

[0134] Forty-two bronchoalveolar lavage fluid samples (25 malignant nodules and 17 benign nodules) and 51 sputum samples (31 malignant pulmonary nodules and 20 benign nodules) from untreated patients with pulmonary nodules were included in the study.

[0135] Total miRNA from the sample was obtained by following the methods for enriching and purifying exosomes and extracting exosome miRNAs as described in Example 1.

[0136] The expression levels of exosomal miRNAs in the above samples were detected by RT-qPCR according to the method in Example 1, and the Cp values ​​and relative expression abundances of miRNAs were obtained. Differential scatter plots were plotted using Graphpad Prism 7.0 software; ROC curves were generated using SPSS 22.0 software and MedCalc V20.0 software. P < 0.05 was considered statistically significant.

[0137] The results are as follows Figures 12-14 As shown. Among them, Figure 12 This is a scatter plot showing the differential expression of miR-143-3p, miR-103a-3p, miR-363-3p, and miR-20a-5p. Figure 13 This is a scatter plot showing the differential expression of miR-122-5p, miR-483-5p, and miR-192-5p. Figure 14 These are the ROC curves for the seven miRNA markers mentioned above.

[0138] Example 3: miRNA biomarkers for efficacy evaluation in early-stage lung cancer patients

[0139] We longitudinally collected body fluid samples (peripheral venous blood, bronchoalveolar lavage fluid, and sputum) from hospital-diagnosed lung cancer patients during the untreated phase (week 0 of treatment), week 2 of treatment, and week 4 of treatment.

[0140] Exosomal miRNAs were extracted and purified using the commercially available miRNeasy mini kit. The expression levels (Cp values) of the miRNAs were detected by RT-qPCR, and the relative expression levels were calculated using the formula. The results are as follows: Figures 15-17 As shown, where, Figure 15 Scatter plot showing the differential expression of exosomal miRNA markers in venous peripheral blood before surgery, at week 2 during surgery, and at week 4 during surgery; Figure 16 Scatter plot showing the differential expression of exosomal miRNA markers in bronchoalveolar lavage fluid before surgery, at week 2 during surgery, and at week 4 during surgery; Figure 17 Scatter plot showing the differential expression of exosomal miRNA markers in sputum samples before surgery, at week 2 during surgery, and at week 4 during surgery.

[0141] like Figures 15-17 The exosomal miRNAs in patient samples at different treatment stages were detected. The expression levels of miRNA upregulation markers (miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p) showed statistically significant differences between the treatment and pre-treatment levels (p<0.05). In the expression level detection results of miR-122-5p, miR-483-5p, and miR-192-5p, the expression levels of miRNA target genes were upregulated, which was significantly correlated with therapeutic efficacy and can be used for treatment effect evaluation.

[0142] Example 4: miRNA biomarkers for relapse monitoring and detection results

[0143] Postoperative fluid samples (peripheral blood, bronchoalveolar lavage fluid, and sputum) were collected from 13 patients with early-stage lung cancer. Follow-up sampling and testing were conducted 6 months postoperatively. Seven patients showed no recurrence characteristics, while six patients experienced recurrence or lymph node metastasis within one year postoperatively. The expression of exosomal miRNAs at 0 and 6 months post-treatment were statistically analyzed to examine the correlation between miRNA expression levels and recurrence. Results are as follows: Figures 18-20 As shown, where: Figure 18 The expression levels and differences of exosomal miRNA markers in peripheral blood veins at 0 months and 6 months post-surgery were investigated. Figure 19 The expression levels and differences of exosomal miRNA markers in bronchoalveolar lavage fluid at 0 months and 6 months post-surgery; Figure 20 The expression levels and differences of exosomal miRNA markers in sputum samples at 0 months and 6 months post-surgery were investigated.

[0144] like Figure 18 As shown, in the 6 relapsed samples, there were statistically significant differences in plasma exosome miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p, miR-483-5p, miR-122-5p, and miR-192-5p between 0 months and 6 months post-surgery, while there were no significant differences in the corresponding miRNA targets in the non-relapsed samples. Figure 19 As shown, the differences in gene expression levels of exosomes miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p, miR-483-5p, miR-122-5p, and miR-192-5p in bronchoalveolar lavage fluid at 0 and 6 months post-surgery were statistically significant in 6 relapsed samples, but not significant in 7 non-relapsed samples. Figure 20 As shown, the gene expression levels of exosomes miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p, miR-483-5p, miR-122-5p, and miR-192-5p in sputum samples at 0 and 6 months post-surgery were statistically significant in 6 recurrence samples, but not in 7 non-recurrence samples. This indicates that exosomes miR-143-3p, miR-103a-3p, miR-363-3p, miR-20a-5p, miR-483-5p, miR-122-5p, and miR-192-5p can serve as biochemical biomarkers for post-operative lung cancer detection and for assessing recurrence risk.

[0145] Example 5: Two-Gene Joint Detection Modeling and Analysis

[0146] The expression levels of the nine miRNAs in Example 1 were analyzed by two-gene joint detection modeling to obtain formula S.

[0147] The formula for S is: S(Score)=C0+∑(Ci×miR-i)

[0148] Based on a specific clinical sample (47 patients with malignant nodules and 32 controls with benign nodules), specific constants C0 and regression coefficients Ci for miRNA markers were obtained, and a score was calculated for each sample. ROC curve analysis was then performed based on the score values, and the results are shown in Table 4.

[0149] miRNA upregulation target miRNA-16-5p, combined with upregulation targets miRNA-143-3p, miRNA-103a-3p, miRNA-363-3p, miRNA-20a-5p, and miRNA-223-3p, were used to detect exo-miRNA expression levels in clinical samples. Modeling analysis was performed to obtain the following formulas S1-S5.

[0150] S1(Score)=-2.471+0.434×miR-16-5p+0.151×miR-143-3p;

[0151] S2(Score)=-2.613+0.21×miR-16-5p+0.278×miR-103a-3p;

[0152] S3(Score)=-2.319+0.291×miR-16-5p+0.535×miR-363-3p;

[0153] S4(Score)=-1.910+0.381×miR-16-5p+1.058×miR-20a-5p;

[0154] S5(Score)=-2.260+0.361×miR-16-5p+0.886×miR-223-3p;

[0155] miRNA upregulation of target miRNA-16-5p was combined with downregulation of target miRNA-483-5p, miRNA-122-5p, and miRNA-192-5p. The expression level of exo-miRNA was detected in clinical samples, and modeling analysis was performed to obtain the following formulas S6-S8.

[0156] S6(Score)=-1.062-0.344×miR-16-5p+1.545×miR-483-5p;

[0157] S7(Score)=-1.033-0.237×miR-16-5p+0.410×miR-122-5p;

[0158] S8(Score)=-0.425-0.349×miR-16-5p+0.380×miR-192-5p;

[0159] miRNA upregulation target miRNA-223-3p was combined with upregulation targets miRNA-143-3p, miRNA-103a-3p, miRNA-363-3p, and miRNA-20a-5p to detect exo-miRNA expression levels in clinical samples, and modeling analysis was performed to obtain the following formulas S9-S12.

[0160] S9(Score)=-2.280+0.672×miR-223-3p+0.113×miR-143-3p;

[0161] S10(Score)=-2.677+0.591×miR-223-3p+0218×miR-103a-3p;

[0162] S11(Score)=-2.604+0.724×miR-223-3p+0.433×miR-363-3p;

[0163] S12(Score)=-2.389+0.859×miR-223-3p+0.896×miR-20a-5p;

[0164] The expression levels of exo-miRNA were detected in clinical samples by combining upregulated target miRNA-223-3p with downregulated targets miRNA-483-5p, miRNA-122-5p, and miRNA-192-5p, and modeling analysis was performed to obtain the following formulas S13-S15.

[0165] S13(Score)=-0.302-0670×miR-223-3p+1.239×miR-483-5p;

[0166] S14(Score)=-0.296-0.547×miR-223-3p+0.327×miR-122-5p;

[0167] S15(Score)=-0.511+0.786×miR-223-3p-0.268×miR-192-5p;

[0168] The miRNA upregulated target miRNA-143-3p and the co-upregulated target miRNA-103a-3p were used to detect the expression level of exo-miRNA in clinical samples, and a modeling analysis was performed to obtain the following formula S16.

[0169] S16(Score) = -2.706 + 0.102×miR-143 + 0.217×miR-103a-3p.

[0170] Table 4

[0171]

[0172] Example 6 Modeling analysis of combined detection of multiple genes

[0173] The expression levels of 17 miRNAs in Example 1 were subjected to a modeling analysis of combined detection of multiple genes to obtain formula S.

[0174] Formula S is: S(Score) = C0 + ∑(Ci×miR-i)

[0175] Based on specific clinical samples (47 patients with malignant nodules and a control group of 32 patients with benign nodules), the specific constant C0 and the regression coefficients Ci of 17 miRNA markers were obtained. Thus, the Score value of each sample was calculated. Then, a ROC curve analysis was performed based on the Score value to obtain the cutoff value. According to the cutoff value, the cancer risk of the subject was judged: Score > cutoff value indicates high risk; Score < cutoff value indicates low risk.

[0176] Result: S(Score) = -24.375 + 0.214×miR-143-3p + 0.357×miR-103a-3p + 1.242×miR-92a-3p + 0.856×miR-223-3p + 0.421×miR-363-3p + 0.779×miR-199b-3p + 2.134×miR-20a-5p + 0.827×miR-16-5p + 0.353×miR-221-3p + 3.662×miR-21-5p + 7.931×miR-155-5p - 0.847×miR-122-5p - 4.356×miR-483-5p - 0.231×Let-7c-5p - 0.867×miR-30e-5p - 2.374×miR-486-5p - 0.327×miR-192-5p.

[0177] For 47 patients with malignant nodules and a control group of 32 patients with benign nodules, the scatter plot and ROC curve of the 17 miRNAs marker combination as a marker are as Figures 21-22 shown. ByFigures 21-22 It can be seen that the Score value based on the combination of 17 miRNAs markers has a good discriminatory effect on benign and malignant lung nodules, with an AUC of 0.959 (95% CI: 0.914 - 1.000, P < 0.0001). The cutoff value is 35.70, the sensitivity is 93.0%, and the specificity is 93.7%.

[0178] Example 7 Modeling Analysis of Combined Detection of Multiple Genes

[0179] Perform modeling analysis according to the same method as in Example 6 to obtain Formula S.

[0180] Formula S is: S(Score) = C0 + ∑(Ci × miR - i);

[0181] Based on 35 untreated early lung cancer patients, 46 patients with benign lung diseases (such as pulmonary inflammation, benign lung tumors, pulmonary bullae, and pulmonary emphysema), and 25 healthy controls, peripheral venous blood was collected to isolate and purify plasma exosome - miRNA, and then real - time fluorescence quantitative PCR was performed. The specific constant C0 and the regression coefficients Ci of 14 miRNA markers were obtained, and thus the Score value of each sample was calculated. Then, ROC curve analysis was performed based on the Score value to obtain the cutoff value, and the cancer risk of the subjects was judged according to the cutoff value: Score > cutoff value is high risk; Score < cutoff value is low risk.

[0182] Result: S(Score) = 13.694 + 0.86 × miR - 143 - 3p + 0.172 × miR - 103a - 3p + 0.338 × miR - 92a - 3p + 1.357 × miR - 223 - 3p + 0.834 × miR - 363 - 3p + 0.932 × miR - 199b - 3p + 1.135 × miR - 20a - 5p + 1.11 × miR - 16 - 5p + 0.204 × miR - 21 - 5p - 4.736 × miR - 122 - 5p - 0.631 × miR - 483 - 5p - 0.798 × Let - 7c - 5p - 5.391 × miR - 30e - 5p - 0.401 × miR - 192 - 5p.

[0183] For 35 early lung cancer patients, 46 patients with benign lung diseases (such as pulmonary inflammation, benign lung tumors, pulmonary bullae, and pulmonary emphysema), and 25 healthy controls, the scatter plot and ROC curve of the 14 miRNAs markers as markers are as Figures 23-24 shown. As Figures 23-24It can be seen that the Score value based on the combination of 14 miRNAs markers has a good discrimination effect on lung cancer and lung diseases, and can effectively distinguish cancer and healthy groups. The AUC is 0.950 (95% CI: 0.907 - 0.994, P < 0.0001), the cutoff value is 44.46, the sensitivity is 85.7%, and the specificity is 95.8%.

[0184] Example 8 Modeling Analysis of Combined Detection of Multiple Genes

[0185] Modeling analysis was carried out according to the same method as in Example 6 to obtain Formula S.

[0186] Formula S is: S(Score) = C0 + ∑(Ci × miR - i);

[0187] Based on 35 patients with untreated pulmonary malignant nodules (early lung cancer), 54 patients with common cancers (gastric cancer, colorectal cancer, liver cancer, breast cancer, etc.), and 38 patients with common diseases (hypertension, diabetes, heart disease, bronchitis, etc.), peripheral venous blood was collected to isolate and purify plasma exosome - miRNA, and then real - time fluorescence quantitative PCR was performed to obtain the specific constant C0 and the regression coefficients Ci of 12 miRNA markers. Thus, the Score value of each sample was calculated. Then, ROC curve analysis was performed according to the Score value to obtain the cutoff value, and the cancer risk of the subjects was judged according to the cutoff value: Score > cutoff value is high risk; Score < cutoff value is low risk.

[0188] Result: S(Score) = - 8.898 + 0.501×miR - 143 - 3p + 2.663×miR - 103a - 3p + 1.376×miR - 199b - 3p + 0.484×miR - 16 - 5p + 0.128×miR - 221 - 3p + 3.431×miR - 21 - 5p + 0.638×miR - 155 - 5p - 1.899×miR - 122 - 5p - 0.659×Let - 7c - 5p - 0.555×miR - 30e - 5p - 1.586×miR - 486 - 5p - 4.479×miR - 192 - 5p.

[0189] For 35 patients with untreated pulmonary malignant nodules (early lung cancer), 54 patients with common cancers (gastric cancer, colorectal cancer, liver cancer, breast cancer, etc.), and 38 patients with common diseases (hypertension, diabetes, heart disease, bronchitis, etc.), the scatter plots and ROC curves of the 12 - miRNA markers combination as markers are as Figures 25-26 shown. By Figures 25-26The results show that the combined score of 12 miRNA biomarkers can effectively differentiate lung cancer from other common tumors, with an AUC of 0.930 (95% CI: 0.871–0.988, P<0.0001), a cutoff value of 57.81, a sensitivity of 85.7%, and a specificity of 97.7%. This indicates that the combined score of the 12 miRNA biomarkers is not affected by other tumors and common diseases, and can be effectively used for the differential diagnosis of lung diseases (benign / malignant nodules, cancer, and other diseases).

[0190] Example 9: Targeted capture of exo combined with miRNA detection for early lung cancer screening

[0191] Peripheral venous blood was collected from 28 untreated early-stage lung cancer patients and 28 healthy individuals. Total exosomes in the plasma were directly enriched (as a control), and exosomes expressing EGFR protein in the plasma were isolated and enriched using nucleic acid aptamer-coated magnetic bead capture technology (as the experimental group). Total exosomal miRNAs and EGFR protein-targeted capture exosomal miRNAs were obtained from the samples according to the miRNA extraction method described in Example 1.

[0192] The expression levels of exosomal miRNAs in the above samples were detected using RT-qPCR, and the Cp values ​​and relative expression abundances of miRNAs were obtained. Graphpad Prism 7.0 software was used to plot differential scatter plots; SPSS 22.0 software and MedCalc V20.0 software were used to generate ROC curves. P < 0.05 was considered statistically significant, indicated by *, ** for P < 0.01, *** for P < 0.001, and **** for P < 0.0001.

[0193] like Figures 27-28 As shown, direct detection of plasma exosomal miRNA-143-3p markers showed a statistically significant difference in expression levels between early-stage lung cancer patients and healthy individuals (AUC: 0.8380, 95% CI: 0.7347–0.9414, P<0.0001). Targeted capture and enrichment of plasma exosomal miRNA-143-3p markers also effectively differentiated between early-stage lung cancer and healthy individuals, with a statistically significant difference in expression levels (AUC: 0.9018, 95% CI: 0.8239–0.9797, P<0.0001). Compared with direct detection of plasma exosomal miRNA-143-3p marker expression levels, targeted capture and enrichment of exosomes improved the detection discrimination between early-stage lung cancer and healthy individuals.

[0194] like Figures 27-28As shown, direct detection of plasma exosomal miRNA-103a-3p markers showed a statistically significant difference in expression levels between early-stage lung cancer patients and healthy individuals (AUC 0.9258, 95% CI: 0.8589–0.9927, P < 0.0001). Targeted capture and enrichment of plasma exosomal miRNA-103a-3p markers effectively differentiated between early-stage lung cancer and healthy individuals, with a statistically significant difference in expression levels (AUC 0.9478, 95% CI: 0.8935–1.000, P < 0.0001). Compared to direct detection of plasma exosomal miRNA-103a-3p marker expression levels, targeted capture and enrichment of exosomes improved the detection discrimination between early-stage lung cancer and healthy individuals.

[0195] like Figures 27-28 As shown, direct detection of plasma exosomal miRNA-20a-5p markers showed a statistically significant difference in expression levels between early-stage lung cancer patients and healthy individuals (AUC 0.7054, 95% CI: 0.5534–0.8573, P = 0.0083). Targeted capture and enrichment of plasma exosomal miRNA-20a-5p markers effectively differentiated between early-stage lung cancer and healthy individuals, with a statistically significant difference in expression levels (AUC 0.9279, 95% CI: 0.8653–0.9906, P < 0.0001). Compared to direct detection of plasma exosomal miRNA-20a-5p marker expression levels, targeted capture and enrichment of exosomes improved the detection discrimination between early-stage lung cancer and healthy individuals.

[0196] like Figures 27-28 As shown, direct detection of plasma exosomal miRNA-122-5p markers showed a statistically significant difference in expression levels between early-stage lung cancer patients and healthy individuals (AUC: 0.8776, 95% CI: 0.7822–0.9729, P<0.0001). Targeted capture and enrichment of plasma exosomal miRNA-122-5p markers also effectively differentiated between early-stage lung cancer and healthy individuals, with a statistically significant difference in expression levels (AUC: 0.8827, 95% CI: 0.7929–0.9724, P<0.0001). Compared with direct detection of plasma exosomal miRNA-122-5p marker expression levels, targeted capture and enrichment of exosomes improved the detection discrimination between early-stage lung cancer and healthy individuals.

[0197] like Figures 27-28As shown, direct detection of plasma exosomal miRNA-192-5p markers showed a statistically significant difference in expression levels between early-stage lung cancer patients and healthy individuals (AUC 0.7615, 95% CI: 0.6371–0.8859, P = 0.0008). Targeted capture and enrichment of plasma exosomal miRNA-192-5p markers effectively differentiated between early-stage lung cancer and healthy individuals, with a statistically significant difference in expression levels (AUC 0.8472, 95% CI: 0.7449–0.9495, P < 0.0001). Compared to direct detection of plasma exosomal miRNA-192-5p marker expression levels, targeted capture and enrichment of exosomes improved the detection discrimination between early-stage lung cancer and healthy individuals.

[0198] like Figures 27-28 As shown in the figure, compared with direct detection of plasma exosomal miRNA biomarkers, targeted capture and enrichment of exosomals expressing EGFR protein in plasma, followed by extraction and detection of miRNA biomarker expression levels, can improve the correlation between biomarkers and early lung cancer, which has significant advantages.

[0199] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] The embodiments described above are merely illustrative of several implementations of the present invention, facilitating a detailed understanding of the technical solutions of the present invention, but should not be construed as limiting the scope of protection of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. It should be understood that technical solutions obtained by those skilled in the art based on the technical solutions provided by the present invention through logical analysis, reasoning, or limited experimentation are all within the scope of protection of the appended claims. Therefore, the scope of protection of this invention patent should be determined by the content of the appended claims, and the specification and drawings can be used to interpret the content of the claims.

Claims

1. Use of a marker for lung cancer diagnosis in the manufacture of a product for diagnosing lung cancer by a method characterized in that, The method comprises the following steps: enriching exosomes with lung tissue specificity in a sample; measuring the expression level of a marker in the exosomes; and diagnosing lung cancer according to the expression level of the marker; the marker is miRNA and is exosomal miRNA, the miRNA comprises at least one of miR-143-3p, miR-103a-3p, miR-122-5p, miR-192-5p and miR-20a-5p, and lung cancer is diagnosed by detecting the expression of the miRNA; the sample suitable for the method is plasma; and the exosomes with lung tissue specificity are exosomes expressing EGFR protein on the membrane.

2. Use according to claim 1, characterized in that, The application comprises any one of the following applications: 1) application in preparing an miRNA marker detection reagent for diagnosing lung cancer; 2) application in preparing a kit for diagnosing lung cancer; 3) application in preparing a system for diagnosing lung cancer.

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