Plasma piRNA combination and application for early diagnosis of acute lymphoblastic leukemia

Abstract

The present invention discloses a plasma piRNA combination for early diagnosis of acute lymphoblastic leukemia and its application. Based on the plasma piRNA expression spectrum of the Chinese population, the present invention obtains the plasma piRNA combination for early diagnosis of acute lymphoblastic leukemia, and establishes an acute lymphoblastic leukemia early diagnosis model based on the plasma piRNA combination. The model can more accurately predict the risk of early diagnosis of acute lymphoblastic leukemia, which helps to reduce the cost of detection. At the same time, the present invention uses a Lasso Logistic regression model, which greatly reduces the number of variables included in the model, and facilitates the application and promotion of the model.

Description

Technical Field

[0001] The present invention relates to the field of bioinformation technology, and in particular to a plasma piRNA combination and application for early diagnosis of acute lymphoblastic leukemia. Background Art

[0002] Acute lymphoblastic leukemia is a malignant tumor disease that originates from the abnormal proliferation of B-lineage or T-lineage lymphocytes in the bone marrow. In recent years, the incidence of acute lymphoblastic leukemia has been increasing year by year. Screening, early diagnosis and early treatment of people at high risk of acute lymphoblastic leukemia can effectively reduce the incidence and mortality of acute lymphoblastic leukemia.

[0003] The traditional screening and diagnosis model for acute lymphoblastic leukemia is: immunophenotyping analysis. Although immunophenotyping is an important means of diagnosing acute lymphoblastic leukemia, traditional flow cytometry testing requires obtaining a bone marrow sample from the patient before the diagnosis and evaluation of acute lymphoblastic leukemia can be performed, which delays the patient's clinical treatment. Bone marrow examination includes bone marrow aspiration and bone marrow biopsy, which is a key step in diagnosing acute lymphoblastic leukemia. The diagnosis is made by observing the proportion of primitive cells in the bone marrow. Usually, acute lymphoblastic leukemia can be diagnosed only when the proportion of primitive / immature lymphocytes in the bone marrow is ≥20%. In addition, this method is somewhat subjective and may not be sensitive enough for early or atypical cases. Some patients also have psychological factors that resist bone puncture. Therefore, it is urgent to explore more effective, accurate, and sensitive non-invasive clinical biomarkers for the early screening and diagnosis of acute lymphoblastic leukemia.

[0004] piRNA is a non-coding RNA with a length of 25 to 33 nt, which is closely related to the maintenance of genome stability, epigenetic regulation, germ stem cell differentiation, embryonic development and the occurrence and development of various diseases. Therefore, piRNA has the potential to be used as a biomarker. At present, the relevant content of piRNA in patients with acute lymphoblastic leukemia still needs further research. A better understanding of the role of piRNA in the occurrence and development of acute lymphoblastic leukemia can explore new biomarkers for early screening and diagnosis of acute lymphoblastic leukemia, which is important for the early screening and diagnosis of patients with acute lymphoblastic leukemia. Summary of the invention

[0005] The purpose of the present invention is to provide a plasma piRNA combination and application for early diagnosis of acute lymphoblastic leukemia. The present invention provides a plasma piRNA combination for early diagnosis of acute lymphoblastic leukemia, and based on the results, establishes and verifies an early diagnosis model for acute lymphoblastic leukemia, which is convenient for early screening.

[0006] The technical solution of the present invention: a plasma piRNA combination for early diagnosis of acute lymphoblastic leukemia, wherein the plasma piRNAs in the plasma piRNA combination include: has-piR-33168, has-piR-33026, has-piR-28398 and has-piR-27080.

[0007] Application of the above-mentioned plasma piRNA combination for early diagnosis of acute lymphoblastic leukemia in constructing an early diagnosis model for acute lymphoblastic leukemia.

[0008] In the aforementioned application, the early diagnosis model for acute lymphoblastic leukemia is a Lasso Logistic regression model.

[0009] For the aforementioned application, the mathematical expression of the Lasso Logistic regression model is as follows:

[0010] Acute lymphoblastic leukemia risk score = ∑ (plasma piRNA expression value × regression coefficient).

[0011] For the aforementioned application, the regression coefficients are as follows:

[0012] The regression coefficient of has-piR-33168 is -0.0002121094;

[0013] The regression coefficient of has-piR-33026 is 0.01545394;

[0014] The regression coefficient of has-piR-28398 is 0.01136185;

[0015] The regression coefficient of has-piR-27080 is 0.03472638.

[0016] For the aforementioned application, the Lasso Logistic regression model construction method is as follows:

[0017] (1) Plasma was collected from ALL patients and healthy controls, and free piRNAs were extracted from plasma; (2) piRNA transcriptome sequencing was used to obtain the plasma piRNA expression profile; (3) ALL patients and healthy controls were randomly divided into a training set and a test set, and a Lasso Logistic regression model was established in the training set to obtain the piRNA regression coefficients included in the model; (4) Based on the Lasso Logistic regression model established in the training set, the ROC curve, sensitivity, and specificity indicators were used in the test set to evaluate the prediction accuracy of the model.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. The innovation of the present invention is that it first discovered the application of plasma piRNA combination in early screening and diagnosis of acute lymphoblastic leukemia, and constructed an early screening and diagnosis model for acute lymphoblastic leukemia based on the plasma piRNA combination. The model predicts that the area under the ROC curve of acute lymphoblastic leukemia is 0.995, the sensitivity is 99%, and the specificity is 96%. It can effectively distinguish acute lymphoblastic leukemia patients from healthy controls, and has good sensitivity. In addition, the present invention uses the Lasso Logistic regression model, which greatly reduces the number of variables included in the model, which will help reduce the cost of detection and the application and promotion of the model.

[0020] 2. The present invention is carried out by blood testing, which is convenient and fast, with small trauma area, easy to use and good stability. Only 200 μl of plasma is needed each time, which greatly reduces the cost of testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the technical solution flow of the present invention.

[0022] Figure 2 It is a graph showing the relationship between the regularization parameter λ and the partial likelihood estimation deviation in the Lasso Logistic regression model.

[0023] Figure 3 It is a schematic diagram of the ROC curve of the model in the training set.

[0024] Figure 4 It is a schematic diagram of the ROC curve of the model in the test set. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with the accompanying drawings and embodiments, but they are not intended to limit the present invention.

[0026] Example: Construction and validation of an early diagnosis model for acute lymphoblastic leukemia based on plasma piRNA expression profile.

[0027] This example is divided into four parts: collecting plasma samples, extracting plasma small RNA with Qiagen miRNeasy plasma kit, sequencing small RNA transcriptome, and establishing a Lasso Logistic regression model based on the plasma piRNA expression matrix. Figure 1 shown.

[0028] (1) Collect plasma samples;

[0029] Collect 5 ml of whole blood from 200 patients with acute lymphoblastic leukemia and 100 healthy controls and place them in a blood collection tube containing EDTA anticoagulant. After collection, repeatedly invert the blood collection tube to fully mix the EDTA anticoagulant with the blood. Centrifuge at 3000 rpm and 4°C for 10 minutes, and the supernatant is the plasma. Take 2 ml of plasma and place it in an EP tube and store it in a -80°C refrigerator.

[0030] (2) Qiagen miRNeasy plasma kit to extract plasma small RNA;

[0031] Cell lysis and small RNA extraction: Add 1 ml of QIAzol lysis reagent to 200 μl of sample, vortex or invert to mix the liquid, and incubate at room temperature (15-25°C) for 5 minutes. Add 200 μl of chloroform, shake vigorously for 15 seconds, and incubate at room temperature for 2-3 minutes. After incubation, centrifuge at 4°C, 12000g for 15 minutes. After centrifugation, transfer the upper aqueous phase to a new EP tube, add 1.5 times the amount of 100% ethanol in the aqueous phase, and mix the two thoroughly by inverting. Pipette 700 μl of liquid into RNeasy MinElute spin column, centrifuge at 8000g for 15 seconds at room temperature, and discard the waste liquid in the collection tube. Repeat the above steps for the remaining liquid. Add 700 μl of Buffer RWT to RNeasy MinElute spin column, centrifuge at 8000g for 15 seconds, and discard the waste liquid in the collection tube. Pipette 500μl of buffer RPE into the RNeasy MinElute spincolumn, centrifuge at 8000g for 15s, and discard the waste liquid in the collection tube. Add 500μl of 80% ethanol to the RNeasy MinElute spin column, centrifuge at 8000g for 2min, and discard the waste liquid in the collection tube. Place the RNeasy MinElute spin column in a new 2ml EP tube, open the spin column cover, adjust the centrifuge speed to the maximum, centrifuge for 5min, and discard the waste liquid and collection tube in the collection tube. Place the RNeasy MinElute spin column in a new 1.5ml collection tube.

[0032] Elution of small RNA: Add 15 μl RNase-free water in the middle of the filter membrane, gently cover the tube, let it stand at room temperature for 2 minutes, centrifuge at maximum speed for 1 minute, and the separated RNA is at the bottom of the tube.

[0033] RNA concentration and RNA integrity assessment: Take 1 μl for Aglient 2100 RNA Pico chip detection, the peak is generally below 200 nt. Only high-quality RNA samples (RIN ≥ 7, > 50 ng / μl, OD260 / 280 between 1.8 and 2.2) were used to construct sequencing libraries.

[0034] (3) Small RNA transcriptome sequencing;

[0035] Small RNA quantification: Small RNA samples used for library construction were first quantified using a library quantification kit. 1 μg was used as the starting point to generate sequencing libraries.

[0036] Ligate adapter sequences: Attach adapter sequences at the 3' and 5' ends, respectively.

[0037] cDNA synthesis: Under the action of MMLV-derived PrimeScript reverse transcriptase (RT), random primers are used to reversely synthesize the first-strand cDNA using the RNA connected to the adapter as a template, followed by second-strand synthesis to form a stable double-stranded structure.

[0038] Library enrichment: PCR amplification (11-12 cycles) was performed using sequencing primers to enrich the library concentration.

[0039] Library purification: According to the length distribution characteristics of small RNA, the target fragments were recovered by gel cutting (6% Novex TBE PAGE gel, 1.0 mm, 10 well).

[0040] Sequencing and data analysis: Qubit 4.0 quantification, mixing according to data ratio, bridge PCR amplification on cBot to generate clusters, and sequencing on Illumina NovaSeq 6000 platform.

[0041] (4) Bioinformatics analysis;

[0042] Statistics of raw sequence data: Illumina sequencing belongs to the second generation sequencing technology. A single run can generate billions of reads. Such a large amount of data cannot show the quality of each read one by one. Using statistical methods, the base distribution and quality fluctuation statistics of each cycle of all sequencing reads can intuitively reflect the sequencing quality and library construction quality of the sample from a macro perspective. Sequencing-related quality assessment is performed on the raw sequencing data of each sample, including: A / T / G / C base content distribution statistics, base quality distribution statistics, and base error rate distribution statistics.

[0043] Quality control of raw sequencing data: The raw sequencing data contains sequencing adapter sequences or low-quality reads. To ensure the accuracy of subsequent bioinformatics analysis, the raw sequencing data is first filtered to obtain high-quality sequencing data to ensure the smooth progress of subsequent analysis. The specific steps and order are as follows: 1) Remove the 3' adapter sequence in the reads, and remove reads without inserted fragments due to reasons such as adapter self-ligation; 2) Cut the bases with low sequencing quality at the 3' end (quality value less than 20); 3) Remove reads containing unknown base N; 4) Remove reads that are too short (<18nt); 5) Remove reads that are too long (>32nt); After quality control, analyze the length of clean reads, and select reads with a length of 18-32nt as useful reads for subsequent analysis based on the characteristics of small RNA.

[0044] Alignment with the reference genome: Use Bowtie to align the useful reads after quality control with the specified reference genome (human genome), and then align the reads to the piRBase database to calculate the plasma piRNA expression matrix.

[0045] A Lasso Logistic regression model was established based on the plasma piRNA expression matrix;

[0046] After obtaining the plasma piRNA expression matrix, all samples were randomly divided into a training set and a test set at a ratio of 50% and 50%, and a Lasso Logistic regression model was constructed in the training set. Then, the prediction accuracy of the model was evaluated using indicators such as ATC, sensitivity, and specificity in the training and test sets. The software used was the glmnet package of the R language program.

[0047] The biggest difference between the Lasso Logistic Regression Model and the traditional Logistic Regression Model is that the Lasso Logistic Regression Model introduces a regularization parameter λ of the regression coefficient. By adjusting the value of parameter λ, the regression coefficient of some variables can be made equal to 0 (making the regression coefficient of other piRNAs except the piRNAs shown in Table 2 equal to 0), achieving the purpose of variable screening and facilitating the application and promotion of the model.

[0048] The optimal λ value is determined by using a 20-fold cross validation method in the training set. When this λ value is taken, the partial likelihood estimation bias of the Lasso Logistic regression model is minimized. Figure 2 , and it was found that when λ was taken, the regression coefficients of 498 piRNAs were equal to 0, and the regression coefficients of 4 piRNAs were not 0. The sequences of these 4 piRNAs and their regression coefficients are shown in Table 1.

[0049]

[0050] The regression coefficient value of each piRNA expression value represents the change in the subject's acute lymphoblastic leukemia risk score for every 1 unit change in the expression of the piRNA. If the regression coefficient is a positive number, it means that the risk of acute lymphoblastic leukemia increases when the piRNA expression value increases; similarly, if the regression coefficient is a negative number, it means that the risk of acute lymphoblastic leukemia decreases when the piRNA expression value increases. The mathematical calculation formula for the acute lymphoblastic leukemia risk score is:

[0051] Acute lymphoblastic leukemia risk score (Lasso_Logistic_Score) = ∑ (plasma piRNA expression value * regression coefficient).

[0052] After the Lasso Logistic regression model was used to construct the risk prediction model for acute lymphoblastic leukemia in the training set, the ATC of the model in the training set was 0.97, the sensitivity was 100%, and the specificity was 90%. Figure 3 The above model is applied to the test set. The ATC of the model in the test set is 0.995, the sensitivity is 99%, and the specificity is 96%. Figure 4 The above results show that the method of the present invention and the constructed model can more accurately predict the risk of acute lymphoblastic leukemia.

[0053] In summary, based on the plasma piRNA expression profile of the Chinese population, the present invention obtained a plasma piRNA combination for the early diagnosis of acute lymphoblastic leukemia, and established an early diagnosis model for acute lymphoblastic leukemia based on the plasma piRNA combination. The model can more accurately predict the risk of acute lymphoblastic leukemia and help reduce the cost of detection. At the same time, the present invention uses a Lasso Logistic regression model to greatly reduce the number of variables included in the model, which is convenient for the application and promotion of the model.

[0054] An embodiment of the present invention is described above. Those skilled in the art will appreciate that various changes, modifications, substitutions and supplements may be made to these embodiments, methodologies and models without departing from the principles and purpose of the present invention, and these changes, modifications, substitutions and supplements should also be regarded as within the scope of protection of the present invention.

Claims

1. A plasma piRNA combination for early diagnosis of acute lymphoblastic leukemia, characterized in that: The plasma piRNAs in the plasma piRNA combination include: hsa-piR-33168, hsa-piR-33026, hsa-piR-28398 and hsa-piR-27080.

2. Use of a reagent for detecting the plasma piRNA combination according to claim 1 in the preparation of an early diagnosis kit for acute lymphoblastic leukemia.

3. The use according to claim 2, characterized in that: The acute lymphoblastic leukemia early diagnosis kit adopts a Lasso Logistic regression model; the mathematical expression of the Lasso Logistic regression model is as follows: Acute lymphoblastic leukemia risk score = ∑ (plasma piRNA expression value × regression coefficient); the regression coefficient is as follows: The regression coefficient of hsa-piR-33168 was -0.0002121094; The regression coefficient of hsa-piR-33026 was 0.01545394; The regression coefficient of hsa-piR-28398 was 0.01136185; The regression coefficient of hsa-piR-27080 is 0.03472638.

4. The use according to claim 3, characterized in that: The Lasso Logistic regression model construction method is as follows: (1) Plasma was collected from ALL patients and healthy controls, and free piRNAs were extracted from plasma; (2) piRNA transcriptome sequencing was used to obtain the plasma piRNA expression profile; (3) ALL patients and healthy controls were randomly divided into a training set and a test set, and a Lasso Logistic regression model was established in the training set to obtain the piRNA regression coefficients included in the model; (4) Based on the Lasso Logistic regression model established in the training set, the ROC curve, sensitivity, and specificity indicators were used in the test set to evaluate the prediction accuracy of the model.

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