Blood extracellular vesicle miRNA panel and its application in early diagnosis of lung cancer

Abstract

The present invention discloses blood extracellular vesicle miRNAs. The miRNAs hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, and hsa-miR-4732-5p can be used as biomarkers for the early diagnosis of lung cancer, thereby being able to be used to prepare chips, detection reagents, or detection kits for early diagnosis of lung cancer. The blood extracellular vesicle miRNA biomarkers of the present invention have high sensitivity and high specificity for the early diagnosis of lung cancer, thereby reducing the false positives caused by traditional diagnostic methods and having potential good clinical application value.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine. Specifically, this invention relates to the diagnosis of lung cancer, particularly the combination of extracellular vesicle miRNAs in blood and their application in the early diagnosis of lung cancer. Background Technology

[0002] Lung cancer is a malignant tumor originating from the bronchial mucosal epithelium or alveolar epithelium. It is the second most common type of cancer worldwide and a leading cause of cancer-related deaths, imposing a significant burden on society. Based on histopathological subtypes, lung cancer can be broadly classified into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC is the dominant type, accounting for approximately 80%-85% of all cases, and includes adenocarcinoma, squamous cell carcinoma, and large cell undifferentiated carcinoma. Early-stage lung cancer often presents with subtle clinical symptoms, and effective screening methods for early clinical diagnosis are lacking. Most lung cancer patients (75%) are diagnosed with locally advanced or metastatic disease at the time of diagnosis, missing the optimal window for radical surgical treatment. Despite significant advancements in treatment methods such as surgery, radiotherapy, chemotherapy, and targeted therapy over the past few decades, the 5-year survival rate for advanced lung cancer remains low (10%-20%), while the 5-year survival rate for early-stage lung cancer patients can reach 77%-92% after surgery. Therefore, early diagnosis of lung cancer is of great significance for reducing the mortality rate of lung cancer patients, improving survival prognosis, and alleviating the medical and economic burden on society.

[0003] Currently, early diagnosis of lung cancer mainly relies on imaging examinations (CT, PET-CT, etc.), laboratory tests, and pathological examinations. However, these methods all have limitations. Low-dose computed tomography (LDCT), the most commonly used imaging examination, suffers from high false-positive rates, overdiagnosis, and radiation exposure. Laboratory tests, such as sputum cytology and serum tumor antigen markers (e.g., CEA, CYFRA21-1, NSE, ProGRP), are limited in their sensitivity and specificity for lung cancer diagnosis, especially in early detection. Pathological examinations, such as bronchoscopy and CT-guided percutaneous lung biopsy, are invasive procedures with risks of pneumothorax and bleeding.

[0004] Therefore, the development of non-invasive / minimally invasive techniques based on liquid biopsy to assist in the differential diagnosis of early-stage lung cancer is an urgent clinical need. Summary of the Invention

[0005] This invention aims to reduce lung cancer mortality by discovering and validating a highly specific and sensitive blood extracellular vesicle miRNA biomarker model for early diagnosis of lung cancer.

[0006] Therefore, the present invention provides a biomarker that can be used clinically as a biomarker for the early diagnosis of lung cancer.

[0007] The present invention also provides a chip and a reagent kit that can be used for the early diagnosis of lung cancer.

[0008] The present invention also provides a method for early diagnosis of lung cancer using the aforementioned biomarkers.

[0009] In a first aspect, the present invention provides the use of blood extracellular vesicle miRNA in the preparation of chips, detection reagents or detection kits for early diagnosis of lung cancer.

[0010] In a specific implementation, the blood extracellular vesicle miRNA is:

[0011] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6; or

[0012] (ii) miRNAs that are complementary to the miRNA sequences described in (i).

[0013] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0014] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and SEQ ID NO:6.

[0015] In a preferred embodiment, the miRNA is a miRNA isolated from humans.

[0016] In a second aspect, the present invention provides a miRNA chip, the miRNA chip comprising:

[0017] Solid support; and

[0018] An oligonucleotide probe is ordered and immobilized on the solid support, and the oligonucleotide probe specifically binds to miRNA;

[0019] The miRNA mentioned above is:

[0020] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6; or

[0021] (ii) miRNAs that are complementary to the miRNA sequences described in (i).

[0022] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0023] (i) a combination of miRNAs with the sequence shown in SEQ ID NO:1, miRNAs with the sequence shown in SEQ ID NO:2, miRNAs with the sequence shown in SEQ ID NO:3, miRNAs with the sequence shown in SEQ ID NO:4, miRNAs with the sequence shown in SEQ ID NO:5, and miRNAs with the sequence shown in SEQ ID NO:6.

[0024] In a preferred embodiment, the oligonucleotide probe contains:

[0025] Complementary bonding region; and / or

[0026] The connection zone that is connected to the solid support.

[0027] In a preferred embodiment, the miRNA chip is used for the early diagnosis of lung cancer.

[0028] In a preferred embodiment, the miRNA is a miRNA isolated from humans.

[0029] In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.

[0030] In a third aspect, the present invention provides the use of the miRNA chip described in the second aspect for the preparation of an early diagnostic kit for lung cancer.

[0031] In a fourth aspect, the present invention provides a detection kit containing a detection reagent for detecting miRNA;

[0032] The miRNA mentioned above is:

[0033] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6; or

[0034] (ii) miRNAs complementary to the miRNA sequence described in (i)

[0035] Alternatively, the detection kit may contain the aforementioned miRNA chip.

[0036] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0037] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and SEQ ID NO:6.

[0038] In a preferred embodiment, the test kit is used for the early diagnosis of lung cancer.

[0039] In a preferred embodiment, the miRNA is a miRNA isolated from humans.

[0040] In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.

[0041] In a fifth aspect, the present invention provides a miRNA isolated from extracellular vesicles in blood for the early diagnosis of lung cancer;

[0042] The miRNA mentioned above is:

[0043] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6; or

[0044] (ii) miRNAs that are complementary to the miRNA sequences described in (i).

[0045] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0046] (i) a combination of miRNAs with the sequence shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6.

[0047] In a preferred embodiment, the miRNA is a miRNA isolated from humans.

[0048] In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.

[0049] In a sixth aspect, the present invention provides an isolated or artificially constructed precursor miRNA, said precursor miRNA being cleaved and expressed in human cells as the miRNA described in the fifth aspect.

[0050] In a seventh aspect, the present invention provides an isolated polynucleotide that can be transcribed into a precursor miRNA by human cells, the precursor miRNA being cleaved and expressed in human cells as the miRNA described in the fifth aspect.

[0051] In a preferred embodiment, the polynucleotide has the structure shown in Formula I:

[0052] Seq 正向 -X-Seq 反向 Formula I

[0053] In formula I,

[0054] Seq 正向 The nucleotide sequence is such that the miRNA can be expressed in human cells.

[0055] Seq 反向 These are nucleotide sequences that are substantially or completely complementary to the forward direction of the Seq sequence;

[0056] X is located at Seq 正向 and Seq 反向 The interval sequence between, and the interval sequence with Seq 正向 and Seq 反向 Not complementary;

[0057] The structure shown in Formula I, after being transferred into human cells, forms the secondary structure shown in Formula II:

[0058]

[0059] In Equation II, Seq 正向 Seq 反向 The definitions of X and X are as described above.

[0060] || indicates that in Seq 正向 and Seq 反向 The complementary base pairing relationship formed between them.

[0061] In an eighth aspect, the present invention provides a vector containing the miRNA described in the fifth aspect or the polynucleotide described in the seventh aspect.

[0062] In a ninth aspect, the present invention provides a method for early diagnosis of lung cancer, comprising the steps of:

[0063] (a) Detecting the expression level of miRNA in the sample of the subject to be tested;

[0064] (b) Input the expression value of the miRNA obtained in step (a) into the risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient;

[0065] (c) Based on the probability obtained in step (b), determine whether the object is a healthy object or a lung cancer patient;

[0066] The miRNA mentioned above is:

[0067] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6; or

[0068] (ii) miRNAs that are complementary to the miRNA sequences described in (i).

[0069] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0070] (i) a combination of miRNAs with the sequence shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6.

[0071] In a preferred embodiment, the miRNA is a miRNA isolated from humans.

[0072] In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.

[0073] In a preferred embodiment, the model formula for the risk prediction model in step (b) is as follows:

[0074]

[0075] Where, x i P(x) represents the numerical value of the marker for sample i. i ) represents the probability of the category corresponding to sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient; the category with the highest probability is taken as the final prediction result for sample i.

[0076] In a preferred embodiment, the risk prediction model is constructed using a machine learning algorithm; preferably, the machine learning algorithm is a gradient boosting machine (GBM), an extremely randomized tree, a linear model, a neural network, a random forest, and / or ensemble methods.

[0077] In a preferred embodiment, the method has a sensitivity of >95% and a specificity of >87% for the diagnosis of early lung cancer.

[0078] In a tenth aspect, the present invention provides an apparatus for early diagnosis of lung cancer, the apparatus having a storage device storing instructions for performing the method for early diagnosis of lung cancer as described in the ninth aspect.

[0079] In a preferred embodiment, the device includes the following means:

[0080] (a) A detection device that detects the expression level of miRNA in a sample of a test subject;

[0081] (b) A computing device that inputs the expression value of the miRNA detected by device (a) into a risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient; and

[0082] (c) An output device that outputs a conclusion on whether the object is a healthy object or a lung cancer patient based on the probability obtained by device (b);

[0083] The miRNA mentioned above is:

[0084] (i) miRNAs with sequences as shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6; or

[0085] (ii) miRNAs that are complementary to the miRNA sequences described in (i).

[0086] In a preferred embodiment, the blood extracellular vesicle miRNA is:

[0087] (i) a combination of miRNAs with the sequence shown in SEQ ID NO:1, SEQ ID NO:2, SEQ ID NO:3, SEQ ID NO:4, SEQ ID NO:5, and / or SEQ ID NO:6.

[0088] In a preferred embodiment, the miRNA is a miRNA isolated from humans.

[0089] In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.

[0090] In a preferred embodiment, the model formula for the risk prediction model in device (b) is as follows:

[0091]

[0092] Where, x i P(x) represents the numerical value of the marker for sample i. i ) represents the probability of the category corresponding to sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient; the category with the highest probability is taken as the final prediction result for sample i.

[0093] In a preferred embodiment, the risk prediction model is constructed using a machine learning algorithm; preferably, the machine learning algorithm is a gradient boosting machine (GBM), an extremely randomized tree, a linear model, a neural network, a random forest, and / or ensemble methods.

[0094] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be described in detail here. Attached Figure Description

[0095] Figure 1 The results of TEM identification of extracellular vesicles are shown;

[0096] Figure 2 The results of NTA detection in extracellular vesicles are shown;

[0097] Figure 3 The ROC curve of the training queue is shown;

[0098] Figure 4 The ROC curve for the validation queue is shown. Detailed Implementation

[0099] Lung cancer is the second most common type of cancer worldwide and a leading cause of cancer-related deaths. Early detection of lung cancer significantly improves patient prognosis. Currently, early diagnosis of lung cancer in clinical practice mainly relies on low-dose computed tomography (LDCT). LDCT has high sensitivity but poor specificity, a high false-positive rate, and poses radiation hazards, bringing additional risks and expenses to patients. Liquid biopsy biomarkers hold promise for improving the accuracy of early lung cancer detection and reducing false-positive rates and overdiagnosis. However, the diagnostic efficiency of most current biomarkers is limited.

[0100] To address this technical problem, the inventors have explored a method for early diagnosis of lung cancer with higher specificity and sensitivity. Specifically, the research work of this invention consists of the following two parts:

[0101] (1) Research on blood extracellular vesicle microRNAs (miRNAs) biomarkers in patients with early lung cancer and healthy individuals, and construct a blood extracellular vesicle miRNA diagnostic model with high specificity and high sensitivity through statistical and machine learning methods;

[0102] (2) Evaluate the efficacy of miRNA early diagnostic models in distinguishing between lung cancer and healthy individuals using independent validation cohort data.

[0103] Through research work (1) and (2), the inventors discovered and validated a blood extracellular vesicle miRNA biomarker diagnostic model that can be used for the early diagnosis of lung cancer.

[0104] In this invention, extracellular vesicle miRNAs from blood cells are used to establish a biomarker model to improve the specificity of early lung cancer diagnosis. However, those skilled in the art will know that extracellular vesicles contain a variety of contents, such as proteins, lipids, small molecule metabolites, lncRNAs, circRNAs, and mRNAs, which can also be used to construct biomarker models to achieve similar effects to miRNAs. In addition, many components in a patient's blood, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), cell-free extracellular nucleic acids (cfRNA and cfDNA), proteins, and metabolites, can also serve as biomarkers for early lung cancer diagnosis and can be developed into independent liquid biopsy techniques.

[0105] definition

[0106] The scientific and technical terms used herein are consistent with the conventional understanding of those skilled in the art. For ease of understanding of this invention, the relevant terms are explained and defined as follows:

[0107] extracellular vesicles

[0108] As used herein, extracellular vesicles (EVs) have the meaning conventionally understood by those skilled in the art. Extracellular vesicles, also known as exosomes, are small, membrane-bound vesicles (30-150 nm) released into the extracellular matrix after the fusion of intracellular multivesicular bodies (MVBs) with the cell membrane. They are important mediators of intercellular communication. Exosomes can be produced by various cell types and are present in bodily fluids such as blood, urine, saliva, tears, and bile. Exosomes carry various substances, such as proteins, lipids, metabolites, and mRNA and microRNAs (miRNAs). Among these, miRNAs are the most abundant nucleic acid component in exosomes, giving them potential for early diagnosis of lung cancer.

[0109] Researchers have used miRNA-Seq technology to analyze the expression differences of plasma exosomal miRNAs in healthy individuals and stage I NSCLC patients, establishing an exosomal miRNA biomarker model that specifically distinguishes between lung adenocarcinoma and squamous cell carcinoma patients (PMID: 28606918). Other researchers have used miRNA microarray detection technology to identify specific exosomal miRNAs in the serum of small cell lung cancer patients. They identified that a 3-miRNA panel composed of serum exosomal miRNAs (miR-200b-3p, miR-3124-5p, and miR-92b-5p) significantly improves the diagnostic value of SCLC (AUC = 0.93), and also found that the 3-miRNA panel is significantly associated with poor prognosis (PMID: 37705067). These studies suggest that serum / plasma exosomal miRNAs may serve as potential biomarkers related to lung cancer diagnosis and prognosis.

[0110] The blood extracellular vesicle miRNA of the present invention

[0111] Currently, LDCT, widely used in clinical practice for early diagnosis of lung cancer, has high sensitivity but poor specificity and a high false-positive rate. Clinically, there is a lack of highly specific and sensitive biomarkers, especially blood biomarkers, for the early diagnosis of lung cancer. Furthermore, there are no biomarkers combined with LDCT to improve the diagnostic efficacy of lung cancer. Research on biomarkers for early lung cancer diagnosis exhibits the following characteristics: a. There are relatively few studies based on next-generation sequencing platforms; b. There are few studies using exosome contents as biomarkers for early lung cancer diagnosis that have been validated by another set of data; c. The research cohorts are mainly for NSCLC, SCLC, or subclasses such as lung adenocarcinoma and squamous cell carcinoma.

[0112] There are only a few reported studies on the use of extracellular vesicle miRNA biomarkers in the early diagnosis of lung cancer. For example, one researcher (PMID:35366907; CN 111218513 A) used small RNA sequencing technology to detect the expression profile of exosomal miRNAs in the plasma of patients with indeterminate pulmonary nodules (IPNs). They constructed a diagnostic model based on circulating exosomal miRNAs (CirsEV-miR) using the LASSO method, consisting of five miRNAs (let-7b-3p, miR-101-3p, miR-125b-5p, miR-150-5p, and miR-3168). This model can be used to differentiate between benign and malignant IPNs, achieving an AUC of 0.92. The model score correlated with the diameter of the IPNs, and it also showed good differentiation between benign and malignant IPNs with a diameter less than 1 cm. However, this study focuses on determining the benign or malignant nature of IPNs, which differs from the clinical application scenario of differentiating and diagnosing multiple types of lung cancer patients from healthy individuals.

[0113] To address the shortcomings of current research, the inventors used blood samples from early-stage lung cancer patients as research samples and healthy individuals as the control group. Extracellular vesicles were extracted using a self-developed extracellular vesicle (exosome) extraction reagent, L3525 (3DMed, Shanghai). Next-generation sequencing technology (small RNA sequencing) was then used to detect the expression levels of extracellular vesicle miRNAs in the blood of lung cancer patients and healthy individuals. In two independent populations—a training cohort and a validation cohort—an extracellular vesicle miRNA biomarker diagnostic model suitable for early lung cancer diagnosis was discovered and validated. Ultimately, a blood extracellular vesicle miRNA biomarker with high specificity and sensitivity was obtained.

[0114] Specifically, the inventors used small RNA sequencing technology to detect the expression profiles of extracellular vesicle miRNAs in the blood of early-stage lung cancer patients and healthy individuals. Using statistical and machine learning methods, they identified six extracellular vesicle miRNAs—shsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, and hsa-miR-4732-5p—that can serve as biomarkers for early lung cancer diagnosis. Based on these six identified biomarkers, the inventors constructed a risk prediction model with high specificity and sensitivity. The model exhibits a sensitivity >95% and a specificity >87% for the diagnosis of early-stage lung cancer.

[0115] In specific embodiments, the blood extracellular vesicle miRNA of the present invention is a miRNA with the sequence shown in SEQ ID NO:1, a miRNA with the sequence shown in SEQ ID NO:2, a miRNA with the sequence shown in SEQ ID NO:3, a miRNA with the sequence shown in SEQ ID NO:4, a miRNA with the sequence shown in SEQ ID NO:5, and / or a miRNA with the sequence shown in SEQ ID NO:6; or a miRNA complementary to them.

[0116] In a preferred embodiment, the blood extracellular vesicle miRNA is a combination of the miRNA with the sequence shown in SEQ ID NO:1, the miRNA with the sequence shown in SEQ ID NO:2, the miRNA with the sequence shown in SEQ ID NO:3, the miRNA with the sequence shown in SEQ ID NO:4, the miRNA with the sequence shown in SEQ ID NO:5, and the miRNA with the sequence shown in SEQ ID NO:6.

[0117] Based on the teachings of this invention, those skilled in the art will understand that the extracellular vesicle miRNA of the present invention can be used for the early diagnosis of lung cancer, and can be used to prepare miRNA chips, detection reagents or detection kits for the early diagnosis of lung cancer.

[0118] In a specific embodiment, the miRNA chip includes: a solid support; and oligonucleotide probes ordered immobilized on the solid support, the oligonucleotide probes specifically binding to the miRNAs described above. The design of the oligonucleotide probes is conventionally possible in the art. For example, the oligonucleotide probes contain: complementary binding regions; and / or, linker regions connected to the solid support.

[0119] In a specific embodiment, the detection kit contains detection reagents for detecting the above-mentioned miRNAs, or contains the miRNA chip.

[0120] Based on the teachings of this invention, those skilled in the art will realize that the miRNA of this invention can be prepared as an isolated or artificially constructed precursor miRNA, which can be cleaved and expressed as the miRNA of this invention in human cells. Furthermore, those skilled in the art will also realize that an isolated polynucleotide can be transcribed by human cells into the precursor miRNA, which can then be cleaved and expressed as the miRNA of this invention in human cells.

[0121] In a specific embodiment, the polynucleotide has the structure shown in Formula I:

[0122] Seq 正向-X-Seq 反向 Formula I

[0123] In Equation I, Seq 正向 The nucleotide sequence that can be expressed as the miRNA in human cells; Seq 反向 X represents the nucleotide sequence that is substantially or completely complementary to the forward direction of Seq; X is the nucleotide sequence located in Seq. 正向 and Seq 反向 The interval sequence between, and the interval sequence with Seq 正向 and Seq 反向 Not complementary;

[0124] The structure shown in Formula I, after being transferred into human cells, forms the secondary structure shown in Formula II:

[0125]

[0126] In Equation II, Seq 正向 Seq 反向 The definition of X is as described above, and || represents the expression in Seq. 正向 and Seq 反向 The complementary base pairing relationship formed between them.

[0127] Based on the teachings of this invention, those skilled in the art can also conceive of vectors containing the above-mentioned miRNA or polynucleotides.

[0128] The method of the present invention and the apparatus for carrying out the method of the present invention

[0129] The extracellular vesicle miRNA in blood of the present invention can be used for the early diagnosis of lung cancer. Therefore, the inventors also provide a method for the early diagnosis of lung cancer using the said extracellular vesicle miRNA, comprising the following steps:

[0130] (a) Detecting the expression level of miRNA in the sample of the subject to be tested;

[0131] (b) Input the expression value of the miRNA obtained in step (a) into the risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient;

[0132] (c) Based on the probability obtained in step (b), determine whether the object is a healthy object or a lung cancer patient.

[0133] The early lung cancer diagnosis method of the present invention has a sensitivity of >95% and a specificity of >87% for the diagnosis of early lung cancer.

[0134] Based on the teachings of this invention, namely that specific extracellular vesicle miRNAs in blood can be used for the early diagnosis of lung cancer, those skilled in the art can employ various methods, such as various algorithms, to construct a risk prediction model for lung cancer. In a specific embodiment, the risk prediction model is constructed using machine learning algorithms; these machine learning algorithms include, but are not limited to, Gradient Boosting Machine (GBM), Extremely Randomized Trees, Linear Models, Neural Networks, Random Forests, and / or Ensemble Methods.

[0135] In a preferred embodiment, the model formula for the risk prediction model in step (b) is as follows:

[0136]

[0137] Where, x i P(x) represents the numerical value of the marker for sample i. i ) represents the probability of the category corresponding to sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient; the category with the highest probability is taken as the final prediction result for sample i.

[0138] To facilitate the implementation of the diagnostic method of the present invention, the inventors also provide an apparatus for early diagnosis of lung cancer, the apparatus having a storage device storing instructions for executing the method for early diagnosis of lung cancer of the present invention.

[0139] Based on the teachings of this invention, those skilled in the art will know how to construct a device for the early diagnosis of lung cancer. For example, the device may include a detection device for detecting the expression level of miRNA in a sample of a subject; a calculation device for inputting the miRNA expression value obtained by the detection device into a risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient; and an output device for outputting a conclusion on whether the subject is a healthy subject or a lung cancer patient based on the probability obtained by the calculation device.

[0140] Advantages of this invention:

[0141] The inventors incorporated samples from various types of lung cancer patients in the real world, extracted extracellular vesicles from blood cells using their self-developed extraction reagent L3525, and further used small RNA sequencing to detect the expression of extracellular vesicle miRNAs in blood cells. Ultimately, they discovered and validated extracellular vesicle miRNA biomarkers with high specificity and high sensitivity.

[0142] The extracellular vesicle miRNA biomarkers and early lung cancer diagnosis methods of the present invention have high sensitivity and high specificity for the early diagnosis of lung cancer.

[0143] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 2001), or as recommended by the manufacturer. Percentages and parts are by weight unless otherwise stated.

[0144] Example

[0145] Material

[0146] All materials used in the following examples are commercially available.

[0147] method

[0148] 1. Study cohort and clinical information

[0149] This study included 71 lung cancer patients. Blood samples were collected from these patients before surgery. Fifty patients were randomly selected as the training cohort to construct a highly specific and sensitive extracellular vesicle miRNA diagnostic model, while 21 patients served as the validation cohort to verify the efficacy of the early lung cancer diagnostic model. In addition, 52 healthy individuals were collected as the control group, with 36 assigned to the training cohort and 16 to the validation cohort.

[0150] 2. Extraction and characterization of extracellular vesicles

[0151] 1) Blood collection and extraction of extracellular vesicles

[0152] Blood samples were collected in 10 mL anticoagulated vacuum blood collection tubes (REF367525, BD, USA). After being slowly inverted several times, the tubes were placed upright, and plasma samples were then separated using a two-step centrifugation method. First, the samples were centrifuged at 1600g for 10 min at 4°C. After centrifugation, the hemolysis grade of the samples was determined according to standards, and samples with a hemolysis grade less than 4 were used for subsequent studies. Then, the supernatant was transferred to 1.5 mL EP tubes and centrifuged at 16000g for 15 min at 4°C to remove residual cell debris. The supernatant was then aliquoted into 1.5 mL EP tubes and stored at -80°C for later use.

[0153] Thaw the frozen plasma samples in a 37°C water bath and centrifuge at 12000g for 10 min at 4°C. The supernatant should be filtered sequentially through a 0.45μm filter column (CLS8163-100EA, Corning, USA) and a 0.22μm filter column (CLS8161-100EA, Corning, USA) at 4°C for 5 min. Collect the filtrate into a 2mL EP tube. Measure the volume of the filtrate, add 1 / 4 volume of L-type exosome precipitant (L3525, 3DMed, Shanghai), mix thoroughly, incubate at 4°C for 30 min, centrifuge at 4700g for 30 min at 4°C, discard the supernatant, and resuspend the extracellular vesicles in 200μL PBS.

[0154] In addition to using the independently developed extracellular vesicle (exosome) extraction reagent L3525 (3DMed, Shanghai), the following alternative methods can be used to extract extracellular vesicles from blood:

[0155] Centrifugation (differential centrifugation, density gradient centrifugation), precipitation (PEG precipitation, organic solvent precipitation), particle size separation (ultrafiltration, size exclusion chromatography), immunoaffinity chromatography, microfluidic technology, and the use of other commercially available exosome isolation kits are preferred. Direct use of commercially available exosome isolation kits is also preferred.

[0156] 2) Characteristics of extracellular vesicles

[0157] To detect the characteristics of extracellular vesicles in the blood of lung cancer patients and healthy individuals, this invention uses transmission electron microscopy (TEM) to detect the morphology of extracellular vesicles and nanoparticle tracking analysis (NTA) to detect the particle size distribution of extracellular vesicles.

[0158] TEM detection: Extracellular vesicles were resuspended in PBS and fixed with 4% paraformaldehyde. The fixed extracellular vesicles were then dropped onto a carbon-coated copper grid. After incubation at room temperature for 5 min, the grid was first rinsed twice with PBS, then washed with PBS containing 50 mM glycine for 3 min, incubated with PBS containing 0.5% BSA for 10 min, and negatively stained with 2% uranyl acetate for 10 min. Finally, the grid was observed and photographed using a transmission electron microscope (H-7650, Hitachi, Japan).

[0159] NTA detection: First, plasma extracellular vesicles were diluted with PBS to 1*10^ 7 -1*10^ 9The sample was mixed at a concentration of / mL by pipetting. Then, the NTA instrument (NanoSight NS300, Malvern, UK) was turned on and the sample was injected into the sample chamber. Using the 488nm excitation module, the camera lens parameters were set as follows: shutter speed 890, gain 146, and detection threshold 7. At least 200 complete tracks were analyzed for each video. Finally, the nanoparticle tracking data of plasma extracellular vesicles were analyzed using NTA version 2.3 analysis software.

[0160] 3. Extraction of extracellular vesicle miRNA from plasma

[0161] Total RNA was extracted from extracellular vesicles in plasma using the miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai) according to the product instructions. RNA was then eluted with 15 μL of RNase-free water. The concentration and fragment distribution of miRNAs were detected using an Agilent 2100 bioanalyzer and its accompanying small RNA analysis kit (5067-1548, Agilent, USA).

[0162] 4. Detection of extracellular vesicle miRNA expression levels in plasma

[0163] Following the product instructions, miRNA libraries were prepared using the NEBNext Multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA). 6 μL of each RNA sample was loaded, followed by ligation of 3' adapters, hybridization reverse transcription primers, ligation of 5' adapters, reverse transcription, and PCR amplification for 18 cycles. The PCR-enriched products were purified using the NucleoSpin Geland PCR Clean-up Kit (740609.250, MN, Germany), and the library DNA was washed away with 30 μL of nuclease-free water. DNA concentration was quantified using an Invitrogen Qubit 4.0 fluorometer and the accompanying Qubit dsDNA HSAssay Kit (Q32854, Thermofisher, USA). The distribution of DNA fragments in the library was detected using an Agilent 2100 bioanalyzer and the accompanying chips and reagents, the Agilent High Sensitivity DNA Kit (5067-4626, Agilent, USA). Sequencing was performed using the Illumina NovaSeq platform with the PE150 sequencing strategy, and each library generated 6GB of sequencing data.

[0164] This invention uses small RNA sequencing technology to detect the expression level of extracellular vesicle miRNAs in blood cells. However, those skilled in the art are aware of various alternative methods, including but not limited to: qRT-PCR, microarray chip methods, and nanobiosensing technology.

[0165] 5. Sequencing data analysis workflow

[0166] The expression profiles of miRNAs in extracellular vesicles of blood cells from lung cancer patients and healthy individuals were obtained using small RNA sequencing technology. The analysis workflow for the sequencing data is as follows:

[0167] 1) Sequencing data alignment. After removing the sequencing adapters from the small RNA sequencing data, the sequencing data was aligned to the human reference genome hg19 using BWA software (version: 0.7.12-r1039) (genome download link: [link missing]). http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ), and count the number of reads aligned to miRNA.

[0168] 2) miRNA annotation. miRNAs were annotated using the Gencode v25 and miRBase v21 databases, retaining those annotated as known mature miRNAs for subsequent analysis.

[0169] 3) miRNA filtering. For the training cohort, mature miRNAs with a length of 30 nt or less and covering at least 10 reads in at least g samples in the training cohort data are retained for subsequent analysis, where g is the number of samples in the smallest group after cohort grouping; for the validation cohort, miRNAs selected from the training cohort are retained for subsequent analysis.

[0170] 4) miRNA expression level normalization. The original miRNA expression levels of the training cohort samples were normalized using the M-value weighted truncated mean (TMM) method, and the same parameters were used to process the validation cohort samples.

[0171] 6. Discovery of biomarkers

[0172] Based on the expression levels of miRNAs in the training cohort, samples were grouped according to pathological test results. Statistical and machine learning methods were used to discover miRNAs that can distinguish between lung cancer patients and healthy individuals as biomarkers. The process is as follows:

[0173] 1) Training cohort grouping. Based on the pathological test results of the samples, the samples in the training cohort were divided into two groups: healthy individuals and lung cancer patients;

[0174] 2) Statistical methods were used to screen biomarkers. First, the U-test and T-test were used to analyze the expression differences of all miRNAs between the two groups. Simultaneously, the mutual information (MI) between the expression of all miRNAs and the sample groups was calculated. miRNAs with a statistically significant difference between groups (P-value ≤ 0.1) and a MI score ranking in the top 10% of all biomarkers were selected. Then, the correlation coefficients between these miRNAs were calculated. Among the miRNAs with a correlation coefficient > 0.8, only the miRNA with the largest variance was retained as a candidate biomarker for subsequent analysis.

[0175] 3) Machine learning methods for biomarker screening. Using the miRNAs screened in step 2) as initial biomarkers, three machine learning feature screening methods were first applied. Method 1: The classification performance of each biomarker was evaluated, and biomarkers with a performance score <0.8 were marked as candidate biomarkers to be discarded. Method 2: All initial biomarkers were treated as a whole, and their classification performance was evaluated. Then, the expression levels of each biomarker in the samples were randomly shuffled, and the decrease in classification performance score after shuffling was calculated. Biomarkers with a performance score decrease <0.01 were marked as candidate biomarkers to be discarded. Method 3: All initial biomarkers were treated as a whole, and their classification performance was evaluated. Biomarkers were ranked according to their importance. Then, starting with the most important biomarker, one biomarker was added sequentially, and the improvement in model score was evaluated. Biomarkers with a score improvement <0.01 were marked as candidate biomarkers to be discarded. The intersection of the candidate biomarkers obtained from the above three methods was discarded, resulting in the remaining 7 biomarkers. Subsequently, six machine learning algorithms—Gradient Boosting Machine (GBM), Extremely Randomized Trees, Linear Model, Neural Network, Random Forest, and Ensemble methods—were used to train and evaluate the classification performance and feature weights on the training queue data, treating the seven markers as a whole. Markers with feature weights less than 5% were marked as candidate for discard. If a marker was marked as discarded by all algorithms, it was not retained. Based on the above methods, six biomarkers were obtained as candidate biomarkers, including hsa-miR-150-3p (SEQ ID NO:1, CUGGUACAGGCCUGGGGGACAG), hsa-miR-150-5p (SEQ ID NO:2, UUCCCAACCCUUGUACCAGUG), hsa-miR-335-3p (SEQ ID NO:3, UUUUUCAUUAUUGCUCCUGACC), hsa-miR-375 (SEQ ID NO:4, UUUGUUCGUUCGGCUCGCGUGA), hsa-miR-4433b-5p (SEQ ID NO:5, AUGUCCCACCCCCACUCCUGU), and hsa-miR-4732-5p (SEQ ID NO:6, UGUAGAGCAGGGAGCAGGAAGCU), for subsequent analysis;

[0176] In addition to the model building methods described above, those skilled in the art may employ other alternatives, including but not limited to: complex feature engineering, support vector machines, and deep learning.

[0177] 7. Risk Scoring Model Construction

[0178] Using healthy individuals and lung cancer patients as classification and prediction targets, six biomarkers discovered in (6) were employed. Six machine learning algorithms were used: Gradient Boosting Machine (GBM), Neural Network, Extremely Randomized Trees, Ensemble Methods, Random Forest, and Linear Model. Different hyperparameters were preset for each algorithm, and multiple diagnostic models using different machine learning algorithms were trained using training queue sample data. The model formulas are as follows:

[0179]

[0180] x i P(x) represents the numerical value of the marker for sample i. i Let be the probability of the class corresponding to sample i predicted by the diagnostic model, where 0 represents a healthy individual and 1 represents a lung cancer patient. The class with the highest probability is taken as the final prediction result for sample i. The trained model is saved as a file on the hard drive. When calling the model, inputting the sample marker expression value will yield the model's prediction result.

[0181] 8. Evaluation and validation of the performance of the risk scoring model

[0182] In the training queue, 5-fold cross-validation was used to evaluate the model's classification performance and feature weights, and the same method was used in the validation queue. Model evaluation and validation metrics included the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1), accuracy (ranging from 0 to 1), positive predictive value (ranging from 0 to 1), and negative predictive value (ranging from 0 to 1). The model's specificity (ranging from 0 to 1) and sensitivity (ranging from 0 to 1) were also evaluated; higher values ​​indicated better model classification performance.

[0183] 9. Application of Risk Scoring Model

[0184] 1) Collect peripheral blood from patients with suspected lung cancer based on clinical diagnosis, obtain extracellular vesicle miRNAs from peripheral blood cells, and use small RNA sequencing to obtain the expression levels of biomarkers;

[0185] 2) Input the expression values ​​of the obtained blood extracellular vesicles hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, and hsa-miR-4732-5p into the trained model formula to calculate the probability of each patient being predicted as healthy or having lung cancer;

[0186] 3) Select the output result with the highest probability and give the prediction result of the risk of lung cancer for each patient.

[0187] Example 1.

[0188] In this embodiment, a total of 71 lung cancer patients were included. Blood samples were collected from the patients before surgery, and 50 patients were randomly selected as the training cohort to construct a highly specific and sensitive blood extracellular vesicle miRNA diagnostic model. 21 patients served as the validation cohort to verify the efficacy of the early lung cancer diagnostic model. In addition, 52 healthy individuals were collected as the control group, of whom 36 were assigned to the training cohort and 16 to the validation cohort.

[0189] See Table 1 for specific group information.

[0190] Table 1

[0191]

[0192] Example 2. Extraction and characterization of extracellular vesicles in blood cells

[0193] In this embodiment, extracellular vesicles (exosomes) were extracted from the plasma of lung cancer patients and healthy individuals using L3525 (3DMed, Shanghai), an extracellular vesicle (exosome) extraction reagent independently developed by Shanghai 3DMed Biomedical Technology Co., Ltd. To detect the characteristics of the isolated extracellular vesicles, TEM was used to detect their morphology. The results showed that the extracellular vesicles exhibited a typical "horseshoe" shape (see...). Figure 1 The particle size distribution of extracellular vesicles was detected using NTA. The results showed that the average particle size of extracellular vesicles was 114.2 nm, which is consistent with the particle size distribution of extracellular vesicles (see...). Figure 2 ).

[0194] Example 3. Discovery of Biomarkers

[0195] In this embodiment, small RNA sequencing was used to detect the expression levels of extracellular vesicle miRNAs in the blood of lung cancer patients and healthy individuals. Statistical methods (U-test and T-test) were used to analyze the expression differences of all miRNAs between lung cancer patients and healthy individuals, as well as the correlation coefficients between miRNAs. Simultaneously, three machine learning feature screening methods were used to screen miRNAs that could distinguish between lung cancer patients and healthy individuals as biomarkers. The candidate molecular markers included hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, hsa-miR-4732-5p, and hsa-miR-1908-5p, a total of seven, for subsequent analysis. Subsequently, six machine learning algorithms were used, including Gradient Boosting Machine (GBM), Neural Network, Extremely Randomized Trees, Ensemble methods, Random Forest, and Linear Model, to mark markers with feature weights <5% as candidate discard markers. If a marker was marked as discarded by all algorithms, it was not retained, resulting in six candidate markers: hsa-miR-150-3p, hsa-miR-150-5p, hsa-miR-335-3p, hsa-miR-375, hsa-miR-4433b-5p, and hsa-miR-4732-5p, which were used to construct risk scoring models for lung cancer and healthy individuals.

[0196] Example 4. Lung Cancer and Healthy Individual Risk Scoring Model

[0197] To construct a risk classification model for lung cancer patients and healthy individuals, miRNA expression data from the training cohort were used. A 5-fold cross-validation method was employed in the training cohort to evaluate the model's classification performance and feature weights. Combined with pathological examination results, a risk scoring model for lung cancer and healthy individuals was constructed, providing a predicted risk of lung cancer for each patient based on the model formula and procedure. Model evaluation metrics included the area under the receiver operating characteristic curve (AUC, ranging from 0 to 1), accuracy (ranging from 0 to 1), positive predictive value (ranging from 0 to 1), negative predictive value (ranging from 0 to 1), specificity (ranging from 0 to 1), and sensitivity (ranging from 0 to 1), which were respectively 1 (…). Figure 3The values ​​were 0.9675, 0.9818, 0.9556, 0.975, and 0.96 (Table 2). The results indicate that in the training cohort, this risk prediction model exhibits high AUC, accuracy, positive predictive value, negative predictive value, specificity, and sensitivity, demonstrating superior predictive performance (see Table 2).

[0198] Table 2. Results of cross-validation evaluation of biomarkers in the training cohort

[0199]

[0200] Example 5. Performance Validation of Lung Cancer and Healthy Individual Risk Scoring Model

[0201] To validate the performance of the risk scoring model in predicting lung cancer, an independent cohort was selected as the validation cohort to evaluate the model's classification effectiveness and feature weights. Using pathological examination results as the true value, the model evaluation metrics included AUC (range 0–1), accuracy (range 0–1), positive predictive value (range 0–1), negative predictive value (range 0–1), specificity (range 0–1), and sensitivity (range 0–1), which were 0.994 (...). Figure 4 The values ​​were 0.9137, 0.9091, 0.9333, 0.875, and 0.9524 (Table 3). The results indicate that in the validation cohort, this risk prediction model exhibits high AUC, accuracy, positive predictive value, negative predictive value, specificity, and sensitivity, demonstrating superior predictive performance (see Table 3).

[0202] Table 3. Results of self-sampling evaluation of biomarkers in the validation cohort

[0203]

[0204] All documents mentioned in this invention are incorporated herein by reference as if each document were individually incorporated by reference. Furthermore, it should be understood that after reading the foregoing teachings of this invention, those skilled in the art can make various alterations or modifications to this invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. Use of a reagent for detecting a combination of extracellular vesicle miRNAs in blood for preparing a chip, a detection reagent, or a detection kit for early diagnosis of lung cancer, wherein the combination of extracellular vesicle miRNAs in blood is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, and a miRNA having a sequence as shown in SEQ ID NO: 6; or (ii) a miRNA combination complementary to the miRNA sequence described in (i).

2. A miRNA chip, comprising: solid phase carrier; as well as Oligonucleotide probes are sequentially fixed on the solid support, wherein the oligonucleotide probes specifically bind to the miRNA combination; Wherein, the miRNA combination is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, and a miRNA having a sequence as shown in SEQ ID NO: 6; or (ii) a miRNA combination complementary to the miRNA sequence described in (i).

3. Use of the miRNA chip according to claim 2 in preparing a kit for early diagnosis of lung cancer.

4. A detection kit containing detection reagents for detecting miRNA combinations; in, The miRNA combination is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, and a miRNA having a sequence as shown in SEQ ID NO: 6; or (ii) a miRNA combination complementary to the miRNA sequence described in (i); Alternatively, the detection kit is equipped with the miRNA chip according to claim 2.

5. A miRNA combination isolated from extracellular vesicles of blood, characterized in that: For early diagnosis of lung cancer; Wherein, the miRNA combination is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, and a miRNA having a sequence as shown in SEQ ID NO: 6; or (ii) a miRNA combination complementary to the miRNA sequence described in (i).

6. An isolated or artificially constructed precursor miRNA combination, which can be cleaved and expressed in human cells to form the miRNA combination according to claim 5.

7. An isolated polynucleotide, wherein the polynucleotide can be transcribed into a precursor miRNA combination by human cells, and the precursor miRNA combination can be cleaved and expressed into the miRNA combination according to claim 5 in human cells. A vector comprising the miRNA combination according to claim 5 or the polynucleotide according to claim 7.

9. A device for early diagnosis of lung cancer, the device comprising a storage device storing instructions for executing a method for early diagnosis of lung cancer, the method comprising the steps of: (a) detecting the expression level of a miRNA combination in a sample of a subject; (b) inputting the expression value of the miRNA combination obtained in step (a) into the risk prediction model to obtain the probability that the subject is a healthy subject or a lung cancer patient; (c) determining whether the subject is a healthy subject or a lung cancer patient based on the probability obtained in step (b); in, The miRNA combination is: (i) a miRNA having a sequence as shown in SEQ ID NO: 1, a miRNA having a sequence as shown in SEQ ID NO: 2, a miRNA having a sequence as shown in SEQ ID NO: 3, a miRNA having a sequence as shown in SEQ ID NO: 4, a miRNA having a sequence as shown in SEQ ID NO: 5, and a miRNA having a sequence as shown in SEQ ID NO: 6; or (ii) a miRNA combination complementary to the miRNA sequence described in (i).

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