A miRNA biomarker, kit, and method for detecting EGFR mutations in lung adenocarcinoma.

Abstract

This invention discloses a miRNA biomarker, kit, and method for detecting EGFR mutations in lung adenocarcinoma. The miRNA biomarker consists of miR-10a-5p, miR-10b-5p, miR-34a-5p, miR-181a-5p, miR-203a-3p, miR-30e-3p, miR-542-3p, and miR-4772-3p. This invention uses the limma package to screen for differentially expressed miRNAs closely related to EGFR mutations in lung adenocarcinoma, and constructs a score for the above miRNA composition using Lasso regression and logistic regression, which can be used for the detection of EGFR mutations in lung adenocarcinoma. The detection method of this invention has high sensitivity and specificity, and is cost-effective, providing simple and rapid scientific guidance for clinical EGFR-TKI treatment.

Description

Technical Field

[0001] This invention relates to a miRNA biomarker, kit, and method for detecting EGFR mutations in lung adenocarcinoma, belonging to the field of molecular diagnostic technology. Background Technology

[0002] Lung adenocarcinoma is currently the most prevalent pathological subtype of lung cancer, accounting for nearly two-thirds of all new lung cancer cases annually. In recent years, advancements in basic sciences such as miRNA and histology have led to a deeper understanding of the molecular mechanisms underlying lung adenocarcinoma. Epidermal growth factor receptor (EGFR) mutations have been identified as one of the major driving factors for lung adenocarcinoma. The EGFR mutation rate is approximately 50% in lung adenocarcinoma patients, reaching as high as 60%-70% in non-smoking patients. EGFR gene mutations typically occur in exons 18-21, with exon 19 DEL deletion (approximately 45%) and exon 21 L858R point mutation (approximately 40%) being the most common.

[0003] Currently, EGFR tyrosine kinase inhibitors (EGFR-TKIs), such as gefitinib, afatinib, and osimertinib, have become the standard treatment for patients with advanced lung adenocarcinoma and EGFR mutations. Detecting EGFR mutations to determine a patient's suitability for EGFR-TKI treatment is now routine clinical practice.

[0004] Current methods for detecting EGFR mutations suffer from drawbacks such as high cost and cumbersome procedures. For example, the standard method for detecting EGFR mutations, direct sequencing, is somewhat complicated and time-consuming, and often produces false negatives in samples with a mutation rate below 10%. NGS, or Sequencing by Synthesis (SBS), offers high throughput and depth, but is also expensive. Summary of the Invention

[0005] The purpose of this invention is to provide a miRNA biomarker, kit, and method for detecting EGFR mutations in lung adenocarcinoma. By detecting the expression levels of multiple miRNA biomarkers, these biomarkers can be used as biomarkers to determine the EGFR mutation status of patients, providing guidance for clinical EGFR-TKI treatment.

[0006] To achieve the above objectives, the present invention provides an application of a detection reagent in the preparation of a kit for detecting EGFR mutations in lung adenocarcinoma. The detection reagent consists of reagents for detecting the expression levels of the following miRNAs: miR-10A-5P, miR-10B-5P, miR-34A-5P, miR-181A-5P, miR-203A-3P, miR-30E-3P, miR-542-3P, and miR-4772-3P. The detection reagent is the sole key component of the kit for detecting EGFR mutations in lung adenocarcinoma.

[0007] Preferably, the kit also includes an instruction manual, which describes the following scoring model:

[0008] Score=0.558×miR-10a-5p+0.446×miR-10b-5p+0.635×miR-34a-5p+0.921×miR-181a-5p- 0.346×miR-203a-3p+0.984×miR-30e-3p+0.503×miR-542-3p-0.74×miR-4772-3p-39.893;

[0009] In the scoring model above, each gene represents the Log2(TPM+1) conversion value of miRNA expression level, that is, the TPM is used to calculate the metric. The TPM formula is: (Number of reads aligned to each miRNA) / (Total number of aligned reads in the sample) × 10 6 The sample is converted into Log2(TPM+1) and then used in the scoring model to calculate the score. When the score of the test sample is greater than 0, the test sample has an EGFR mutation; when the score of the test sample is equal to or less than 0, the test sample does not have an EGFR mutation; the test sample is a fresh tissue tumor sample.

[0010] The present invention also provides a kit for detecting EGFR mutations in lung adenocarcinoma, comprising at least reagents for specifically detecting the expression levels of the following miRNAs: miR-10A-5P, miR-10B-5P, miR-34A-5P, miR-181A-5P, miR-203A-3P, miR-30E-3P, miR-542-3P, and miR-4772-3P.

[0011] The present invention also provides a method for detecting EGFR mutations in lung adenocarcinoma for non-disease diagnosis and treatment purposes, comprising the following steps:

[0012] Step 1: Extract miRNA from tumor tissue samples using the miRcute miRNA Isolation Kit;

[0013] Step 2: miRNA library construction: This was completed using the TruSeq miRNA Sample Prep Kit v2.

[0014] Step 3: Cluster generation: This was accomplished using the TruSeq SR Cluster Kitv3-cBot–HS kit;

[0015] Step 4: Illumina Hiseq2000 sequencing: The sequencing results are converted from raw data to Fastq format using the Hiseq2000.

[0016] Step 5: Data Analysis and Calculation

[0017] After removing adapter sequences from the original Fastq file data, the quality and length of the sequencing fragments were examined, and reliable sequencing fragments were selected. The sequencing results were compared and filtered against the miRbase database to identify miRNAs in the test samples. Expression levels of the identified miRNAs were statistically analyzed using the Total Read Performance Measurement (TPM) metric. The TPM formula is: (Number of reads aligned to each miRNA) / (Total number of aligned reads in the sample) × 10. 6 The expression level of the target miRNA was converted to Log2(TPM+1) and then entered into the scoring model: Score = 0.558×miR-10a-5p + 0.446×miR-10b-5p + 0.635×miR-34a-5p + 0.921×miR-181a-5p - 0.346×miR-203a-3p + 0.984×miR-30e-3p + 0.503×miR-542-3p - 0.74×miR-4772-3p - 39.893 to calculate the Score.

[0018] Step 6: Judgment: When the test sample score is greater than 0, the test sample has an EGFR mutation; when the test sample score is equal to or less than 0, the test sample does not have an EGFR mutation; the test sample is a fresh tissue tumor sample.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0020] (1) In this invention, by detecting the expression levels of multiple miRNAs in patients as biomarkers to determine the EGFR mutation status, it will provide a more cost-effective guarantee for EGFR mutation detection, while having high sensitivity, specificity and application value, which will help to achieve precision medicine for patients.

[0021] (2) This invention is the first to propose that the miRNA composition can be used to evaluate EGFR mutations in lung adenocarcinoma. Compared with the currently commonly used methods, it has the advantages of convenient operation, quantitative analysis, high accuracy, good sensitivity and specificity, and high cost performance. Attached Figure Description

[0022] Figure 1 Volcano plot of miRNAs showing significant differences between EGFR mutant and wild-type patients;

[0023] Figure 2 Plot of LASSO regression results;

[0024] Figure 3 This is the ROC curve. Detailed Implementation

[0025] To make the present invention more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0026] Example 1: Screening and Validation of miRNA Sets

[0027] (I) Construction of a prognostic scoring model for lung adenocarcinoma

[0028] 1. Method

[0029] First, miRNA expression and mutation data from 448 lung adenocarcinoma samples were obtained from the TCGA database. Patients were divided into EGFR-mutant (51 cases) and wild-type (397 cases) based on EGFR mutation status. The limma package in R was used to screen for 38 significantly differentially expressed miRNAs between the two groups, including 36 significantly upregulated miRNAs and 2 significantly downregulated miRNAs. Figure 1 ).

[0030] Further Lasso regression identified 18 miRNAs: miR-101-3p, miR-10a-5p, miR-10b-5p, miR-34a-5p, miR-181a-5p, miR-203a-3p, miR-205-5p, miR-181a-3p, miR-224-5p, miR-30e-3p, miR-501-5p, miR-542-3p, miR-181a-2-3p, miR-339-3p, miR-146b-3p, miR-589-5p, miR-4772-3p, miR-664b-3p. Figure 2 ).

[0031] Further logistic regression analysis was performed on the above 18 miRNAs, and 8 miRNAs were finally selected (Table 1), and a scoring model was constructed:

[0032] Score=0.558×miR-10a-5p+0.446×miR-10b-5p+0.635×miR-34a-5p+0.921×miR-181a-5p- 0.346×miR-203a-3p+0.984×miR-30e-3p+0.503×miR-542-3p-0.74×miR-4772-3p-39.893;

[0033] Table 1: Logistic Regression Results

[0034]

[0035] Finally, based on the expression of this miRNA, we calculated the score for each sample and analyzed it using ROC curves. Figure 3 The sensitivity and specificity of TCGA samples were analyzed, and the samples were divided into two groups: those with a score greater than 0 were classified as EGFR mutants, and those with a score equal to or less than 0 were classified as EGFR wild-types. Compared with the actual test results, the calculated sensitivity was 41 / 51 = 80.4%, and the specificity was 371 / 444 = 83.1% (Table 2).

[0036] Table 2. Sensitivity and specificity of this method in predicting lung adenocarcinoma in the TCGA database.

[0037]

[0038] (II) Effect Verification

[0039] We performed miRNA sequencing on 47 lung adenocarcinoma samples collected from the Department of Thoracic Surgery at Zhongshan Hospital affiliated with Fudan University. The specific steps are as follows:

[0040] ① Use the miRcute miRNA Isolation Kit to extract miRNA from tumor tissue samples;

[0041] ② miRNA library construction: Completed using the TruSeq miRNA Sample Prep Kitv2;

[0042] ③ Cluster generation: Completed using the TruSeq SR Cluster Kitv3-cBot–HS kit;

[0043] ④ Illumina Hiseq2000 sequencing: The Hiseq2000 was used to convert the raw sequencing data into Fastq format;

[0044] ⑤ Data Analysis:

[0045] After removing adapter sequences from the original Fastq file data, the quality and length of the sequencing fragments were examined, and reliable sequencing fragments were selected. The sequencing results were compared and filtered against the miRbase database to identify known human miRNAs. Expression levels of the identified miRNAs were statistically analyzed using TPM (transcript per million), calculated as follows: TPM = (number of reads aligned to each miRNA) / (total number of aligned reads in the sample) × 10-1 6 The expression level of the target miRNA was converted to Log2(TPM+1). Log2(TPM+1) was used to calculate the expression level of the target miRNA, and the score was calculated according to the scoring model: Score = 0.558×miR-10a-5p + 0.446×miR-10b-5p + 0.635×miR-34a-5p + 0.921×miR-181a-5p - 0.346×miR-203a-3p + 0.984×miR-30e-3p + 0.503×miR-542-3p - 0.74×miR-4772-3p - 39.893.

[0046] ⑥ Based on the calculation results, the results were divided into two groups: those with a value greater than 0 were EGFR mutants, and those with a value equal to or less than 0 were EGFR wild-types. Compared with the actual detection results, the calculated sensitivity was 20 / 26 = 76.9%, and the specificity was 16 / 21 = 71.4%.

[0047] Table 3. Sensitivity and specificity of this method in predicting lung adenocarcinoma patients at Zhongshan Hospital.

[0048]

[0049] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention in any form or substance. It should be noted that those skilled in the art can make several improvements and additions without departing from the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention.

Claims

1. The application of a detection reagent in the preparation of a kit for detecting EGFR mutations in lung adenocarcinoma, characterized in that, The detection reagent consists of reagents for detecting the expression levels of the following miRNAs: miR-10A-5P, miR-10B-5P, miR-34A-5P, miR-181A-5P, miR-203A-3P, miR-30E-3P, miR-542-3P, and miR-4772-3P. This detection reagent is the only key component of the kit for detecting EGFR mutations in lung adenocarcinoma.

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