Plasma piRNA combination for liver fibrosis diagnosis of hepatitis B patients and application

Abstract

The invention discloses a plasma piRNA combination for liver fibrosis diagnosis of hepatitis B patients and application. The plasma piRNA combination is composed of the following plasma piRNAs: hsa-piR-13530, hsa-piR-14581, hsa-piR-5114, hsa-piR-29030, and hsa-piR-2129, and is characterized in that the hsa-piR-13530, the hsa-piR-14581, the hsa-piR-5114, the hsa-piR-29030, the hsa-piR-2129, the hsa-piR-14581, the According to the present invention, the plasma piRNA expression profile of Chinese population is adopted as the research basis, the plasma piRNA combination suitable for early diagnosis of hepatitis B hepatic fibrosis is obtained through screening, and the hepatitis B hepatic fibrosis early diagnosis kit is established based on the plasma piRNA combination. The kit can be used for accurately predicting the onset risk of hepatitis B hepatic fibrosis, and is beneficial to reducing the detection cost. Meanwhile, a Lasso Logistic regression model is adopted for analysis, the number of variables included in the model is remarkably reduced, and convenience is provided for follow-up application and popularization of the model.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, and in particular to a plasma piRNA combination and its application for the diagnosis of liver fibrosis in patients with hepatitis B. Background Technology

[0002] Hepatitis B is one of the most common infectious liver diseases in my country, and its course is often accompanied by liver fibrosis, which poses a serious threat to the liver health and overall quality of life of residents. Early diagnosis and intervention of liver fibrosis in hepatitis B patients can effectively slow the progression of fibrosis, which is of great significance for protecting the liver health of residents and improving their quality of life.

[0003] The traditional diagnostic model for liver fibrosis in hepatitis B patients includes physical examination, liver function tests, and imaging examinations, but the gold standard remains liver biopsy. While traditional pathological examination has a certain degree of accuracy, its invasive nature causes considerable inconvenience to patients and may even lead to complications and sequelae such as bleeding and infection. Compared to pathological examination, the development of plasma biomarkers has facilitated the diagnosis of liver fibrosis in hepatitis B patients, and the results are more stable. The widespread use of existing biomarkers such as HA, PⅢNP, and LN demonstrates their potential in the diagnosis of liver fibrosis in hepatitis B patients. Therefore, exploring more effective, accurate, and sensitive non-invasive clinical biomarkers is urgently needed for the diagnosis of liver fibrosis in hepatitis B patients.

[0004] PiRNAs are a class of non-coding RNAs with a length of 25–33 nt. They are closely related to the maintenance of genome stability, epigenetic regulation, germline stem cell differentiation, embryonic development, and the progression of various diseases, thus possessing the potential to become biomarkers. Currently, the role of piRNAs in liver fibrosis in hepatitis B patients requires further research. Clarifying their role in the development and progression of this disease could lead to the discovery of novel diagnostic biomarkers, which is crucial for the diagnosis of liver fibrosis in hepatitis B patients. Summary of the Invention

[0005] The purpose of this invention is to provide a plasma piRNA combination and its application for the diagnosis of liver fibrosis in patients with hepatitis B. The plasma piRNA combination of this invention can be used for the diagnosis of liver fibrosis in patients with hepatitis B. Its application in related diagnostic products facilitates early screening and diagnosis of liver fibrosis in hepatitis B patients, offering the advantage of accurate results.

[0006] The technical solution of the present invention is a plasma piRNA combination for the diagnosis of liver fibrosis in patients with hepatitis B, wherein the plasma piRNA combination consists of the following plasma piRNAs: hsa-piR-13530, hsa-piR-14581, hsa-piR-5114, hsa-piR-29030 and hsa-piR-2129.

[0007] The above-mentioned plasma piRNA combination is used in the preparation of diagnostic products for liver fibrosis in patients with hepatitis B.

[0008] The aforementioned application refers to a reagent kit.

[0009] In the aforementioned application, the kit includes a diagnostic model for liver fibrosis in hepatitis B patients, which is a Lasso Logistic regression model; the diagnostic model for liver fibrosis in hepatitis B patients uses the plasma piRNA combination as a diagnostic biomarker.

[0010] The mathematical expression of the Lasso Logistic regression model, as described above, is as follows: The risk score for liver fibrosis in patients with hepatitis B is calculated as ∑(plasma piRNA expression value × regression coefficient of corresponding plasma piRNA).

[0011] In the aforementioned application, the regression coefficients are as follows: The regression coefficient of hsa-piR-13530 is 0.007007783; The regression coefficient of hsa-piR-14581 is 0.06959053; The regression coefficient of hsa-piR-5114 is -0.02564205; The regression coefficient of hsa-piR-29030 is -0.02442593; The regression coefficient of hsa-piR-2129 is -0.008692064.

[0012] The aforementioned application describes the method for constructing the Lasso Logistic regression model as follows: (1) Collect plasma from patients with hepatitis B liver fibrosis and healthy controls, and extract cell-free piRNA from the plasma; (2) Obtain plasma piRNA expression profiles using piRNA transcriptome sequencing technology; (3) Randomly divide the two groups into training and test sets, construct a Lasso Logistic regression model in the training set, and obtain the regression coefficients of the piRNAs included in the model; (4) Based on the Lasso Logistic regression model established in the training set, evaluate the predictive accuracy of the model in the test set using ROC curves, sensitivity and specificity indicators.

[0013] In the aforementioned applications, the kit includes piRNA detection reagents for hsa-piR-13530, hsa-piR-14581, hsa-piR-5114, hsa-piR-29030, and hsa-piR-2129, as well as analytical tools for calculating the risk score of liver fibrosis in hepatitis B patients.

[0014] In the aforementioned applications, the detection reagent includes specific primer pairs for each piRNA, and the analytical tool includes regression coefficients corresponding to plasma piRNA expression values.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The specific combination of five piRNAs, including hsa-piR-13530 and hsa-piR-14581, screened in this invention, was validated by the Lasso Logistic regression model. The AUC on the training set reached 0.988 and the AUC on the test set reached 0.970. The sensitivity and specificity both exceeded 91%, indicating high diagnostic accuracy.

[0016] 2. The diagnostic method of this invention uses plasma as the test sample, eliminating the need for liver tissue biopsy, thus avoiding complications such as bleeding and infection caused by invasive procedures, greatly improving patient compliance, and is especially suitable for early screening and long-term follow-up.

[0017] 3. This invention uses Lasso regularization to screen variables, retaining only 5 core piRNA biomarkers, which simplifies the detection process and reduces the difficulty of data analysis. The accompanying kit contains specific primer pairs and risk scoring analysis tools, enabling rapid clinical application. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the technical solution of the present invention.

[0019] Figure 2 This is a graph showing the relationship between the regularization parameter λ and the partial likelihood estimation bias in the Lasso Logistic regression model.

[0020] Figure 3 This is a schematic diagram of the ROC curve of the model on the training set.

[0021] Figure 4 This is a schematic diagram of the ROC curve of the model on the test set. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0023] Example 1: Construction and validation of an early diagnostic model for hepatitis B liver fibrosis based on plasma piRNA expression profile; This embodiment consists of four parts: collecting plasma samples, extracting small RNAs from plasma using the Qiagen miRNeasy plasma kit, small RNA transcriptome sequencing, and establishing a Lasso Logistic regression model based on the plasma piRNA expression matrix. The procedure of this embodiment is as follows: Figure 1 As shown.

[0024] (1) Collecting plasma samples: 5 ml of whole blood was collected from 200 patients with hepatitis B and liver fibrosis and 100 healthy controls. The blood was placed in blood collection tubes containing EDTA anticoagulant, and the tubes were repeatedly inverted to ensure thorough mixing of the EDTA anticoagulant with the blood. The mixed blood collection tubes were then placed in a centrifuge and centrifuged at 3000 rpm and 4°C for 10 minutes. The supernatant after centrifugation was the plasma.

[0025] Use a sterile pipette to draw 2 ml of plasma, transfer it into an EP tube, and store it frozen at -80°C. (2) Extraction of small RNA from plasma using the Qiagen miRNeasy plasma kit: 1. Cell lysis and small RNA extraction: Add 1 ml of QIAzol lysis reagent to 200 μl of sample, vortex or invert to mix thoroughly, and incubate at room temperature (15-25℃) for 5 min. Add 200 μl of chloroform to the mixture, shake vigorously for 15 seconds, and incubate at room temperature for 2-3 min. After incubation, centrifuge at 4℃ and 12000g for 15 min, and transfer the upper aqueous phase to a new EP tube. Add 1.5 times the volume of 100% ethanol to the transferred aqueous phase, and invert to mix thoroughly with the ethanol.

[0026] 2. Small RNA column purification: Take 700 μL of the above mixture and add it to an RNeasy MinElute spin column. Centrifuge at 8000 g for 15 s at room temperature and discard the waste liquid in the collection tube. Repeat the above operation once for the remaining liquid, and pass the remaining mixture through the column.

[0027] Add 700 μl of Buffer RWT to the column, centrifuge at 8000g for 15 seconds, and discard the waste liquid; Add 500 μl of buffer RPE to the column, centrifuge at 8000g for 15s, and discard the waste liquid; Add 500 μl of 80% ethanol to the column, centrifuge at 8000g for 2 min, and discard the waste liquid; Transfer the RNeasy MinElute spin column to a new 2ml EP tube, open the rotating column cap, centrifuge at maximum speed for 5 minutes, and discard the residual liquid and collection tube. 3. Small RNA elution: Place the RNeasy MinElute spin column into a new 1.5 ml collection tube, add 15 μL of RNase-free water to the center of the filter membrane, gently cap the tube, and let it stand at room temperature for 2 minutes. Centrifuge at maximum speed for 1 minute, and collect the liquid at the bottom of the tube; this is the extracted small RNA. 4. RNA concentration and RNA integrity assessment: Take 1 μL of RNA sample and perform Aglient 2100 RNA Pico chip detection. The RNA fragment peak is generally below 200 nt.

[0028] The RNA quality standards were set as follows: RIN ≥ 7, > 50 ng / μL, and OD260 / 280 between 1.8 and 2.2. Only RNA samples meeting these standards were used for subsequent sequencing library construction. (3) Small RNA transcriptome sequencing: 1. Small RNA quantification: The qualified small RNA samples were quantified using a library quantification kit, and 1 μg of small RNA was used as the starting template for library construction.

[0029] 2. Connector connection: Specific adapter sequences were ligated to the 3' and 5' ends of the small RNA, respectively. 3. cDNA synthesis: Using the small RNA following the ligation of the adapter as a template, random primers and MMLV-derived PrimeScript reverse transcriptase (RT) were added to synthesize one-stranded cDNA via reverse transcription, followed by two-stranded cDNA synthesis to form a stable double-stranded cDNA structure.

[0030] 4. Rich library: PCR amplification was performed using sequencing primers, with 11-12 amplification cycles set to enrich the library concentration.

[0031] 5. Library purification: Gel electrophoresis was performed using 6% Novex TBE PAGE gel (1.0 mm thick, 10 wells). Based on the length distribution characteristics of small RNAs, the target fragments were excised and recovered to obtain purified sequencing libraries. 6. Sequencing and data analysis: The purified libraries were quantified using a Qubit 4.0 fluorescence quantitative analyzer, and multiple libraries were mixed according to a preset data ratio. The mixed library was transferred to cBot for bridge PCR amplification to generate clusters. The amplified sequencing chip was then placed on an Illumina NovaSeq 6000 platform for sequencing.

[0032] (4) Bioinformatics Analysis Section: 1. Statistics of raw sequence data: Illumina next-generation sequencing technology was used to sequence the samples. This technology can generate billions of reads in a single run. Due to the massive amount of data, it is impossible to display the quality of each read individually. Therefore, statistical methods were used to analyze the base distribution and quality fluctuation of each cycle of all sequencing reads, providing a macroscopic and intuitive reflection of the sequencing quality and library construction quality of the samples. Specific statistical indicators for the raw sequencing data of each sample included: A / T / G / C base content distribution statistics, base quality distribution statistics, and base error rate distribution statistics.

[0033] 2. Quality control of raw sequencing data: To ensure the accuracy of subsequent bioinformatics analysis, the raw sequencing data is filtered to obtain high-quality sequencing data. The specific steps and order are as follows: 1) Remove the 3' connector sequence from the reads, and also remove the reads that have no inserted segments due to connector self-connection or other reasons; 2) Cut the 3' end to sequence low-quality bases (quality value less than 20); 3) Remove reads containing the unknown base N; 4) Remove reads that are too short (<18nt); 5) Remove reads that are too long (>32nt); After quality control was completed, the length of clean reads was analyzed. Based on the characteristics of small RNAs, reads with a length of 18-32 nt were selected as useful reads for subsequent analysis.

[0034] 3. Alignment with reference genome: Bowtie was used to align the quality-controlled useful reads with a specified human reference genome, and then the reads were aligned to the piRBase database to obtain the plasma piRNA expression matrix.

[0035] 4. Establish a Lasso Logistic Regression Model: Based on the obtained plasma piRNA expression matrix, a Lasso Logistic regression model was constructed, as follows: 1) Randomly divide all samples into training and test sets at a 50% to 50% ratio; 2) Construct a Lasso Logistic regression model in the training set using the glmnet package in the R language program; 3) Evaluate the model’s prediction accuracy using metrics such as AUC, sensitivity, and specificity in both the training and test sets.

[0036] Compared with the traditional Logistic regression model, the Lasso Logistic regression model is characterized by the introduction of a regularization parameter λ for the regression coefficients. By adjusting the value of parameter λ, the regression coefficients of certain variables can be made equal to 0 (the regression coefficients of other piRNAs besides those shown in Table 1 can be made equal to 0), thereby achieving variable screening and facilitating the application and promotion of the model.

[0037] The optimal value of λ was determined using 20-fold cross-validation on the training set. This value of λ minimizes the partial likelihood estimation bias of the Lasso Logistic Regression model. (See...) Figure 2 The results showed that when λ was set to a certain value, the regression coefficients of 528 piRNAs were equal to 0, and the regression coefficients of 5 piRNAs were not equal to 0. The sequences of these 5 piRNAs and their regression coefficients are shown in Table 1.

[0038] Table 1

[0039] The regression coefficient for each piRNA expression value represents the change in the subject's hepatitis B liver fibrosis risk score for every 1 unit change in piRNA expression. A positive regression coefficient indicates an increased risk of hepatitis B liver fibrosis when the piRNA expression level increases; similarly, a negative regression coefficient indicates a decreased risk when the piRNA expression level increases. The mathematical formula for calculating the hepatitis B liver fibrosis risk score is as follows: The risk score for hepatitis B liver fibrosis (Lasso_Logistic_Score) = ∑(plasma piRNA expression value × regression coefficient).

[0040] After constructing a risk prediction model for hepatitis B liver fibrosis using a Lasso Logistic regression model in the training set, the model achieved an AUC of 0.988, a sensitivity of 96.0%, and a specificity of 94.7% in the training set. Figure 3 As shown. Applying the above model to the test set, the model achieved an ATC of 0.970, a sensitivity of 96.1%, and a specificity of 91.9% on the test set. Figure 4 As shown above, the results demonstrate that the method and model of this invention can accurately predict the risk of developing liver fibrosis in hepatitis B.

[0041] In summary, this invention, based on the plasma piRNA expression profile of the Chinese population, yielded a plasma piRNA combination for the early diagnosis of hepatitis B liver fibrosis, and established an early diagnostic model for hepatitis B liver fibrosis based on the plasma piRNA combination. This model can accurately predict the risk of hepatitis B liver fibrosis, helping to reduce the cost of detection. Furthermore, this invention uses a Lasso Logistic regression model, which significantly reduces the number of variables included in the model, facilitating its application and promotion.

[0042] Example 2: piRNA kit for diagnosing liver fibrosis in patients with hepatitis B; The piRNA-specific detection reagent contains four specific primer pairs: hsa-piR-13530, hsa-piR-14581, hsa-piR-5114, hsa-piR-29030, and hsa-piR-2129. Each primer pair is supplied in 20 μl tubes, containing 10 μM of primer. The piRNA-specific primer pairs in this kit can be directly designed by those skilled in the art based on the complete base sequences of the five piRNAs disclosed in the specification, combined with conventional knowledge and general rules for quantitative PCR primer design.

[0043] The test reagents also include a reverse transcriptase mixture, 50 μl per tube, containing MMLV reverse transcriptase, dNTPs, and RNase inhibitors; a real-time PCR mixture, 100 μl per tube, containing Taq enzyme, SYBR Green fluorescent dye, and buffer; a positive control, a mixture containing 5 piRNA mimics at a concentration of 100 ng / μl, 10 μl per tube; and a negative control, RNase-free water, 50 μl per tube.

[0044] The analysis tools include risk scoring calculation software (loaded on a USB flash drive) and an instruction manual. The software has a built-in LassoLogistic regression model and a database of five piRNA regression coefficients. The instruction manual details the sample processing, testing procedures, and scoring interpretation criteria.

[0045] Reagent kit usage procedure Sample processing: Take 2 ml of plasma from the subject, add the cell lysis buffer provided with the kit, mix thoroughly according to the instructions, and extract free piRNA by centrifugation, column purification, elution, etc.; use a UV spectrophotometer to detect the purity, ensuring that the OD260 / 280 is between 1.8 and 2.2, and is ready for use after meeting the detection requirements.

[0046] Reverse transcription reaction: Add 5 μl of piRNA sample, 10 μl of reverse transcriptase mixture, and 2 μl of primer mixture (equal amounts of 5 specific primer pairs) to a PCR tube in sequence, and add RNase-free water to make up to 20 μl; set the reaction conditions: incubate at 42℃ for 30 min to complete reverse transcription, heat at 85℃ for 5 min to inactivate reverse transcriptase, and obtain cDNA product.

[0047] Real-time PCR detection: Take 2 μl of cDNA product, 10 μl of real-time PCR mixture, 2 μl of the corresponding piRNA-specific primer pair, and add RNase-free water to 20 μl. Amplify 5 piRNAs separately. Reaction program: 95℃ pre-denaturation for 5 min; 95℃ denaturation for 15 s, 60℃ annealing and extension for 30 s, for a total of 40 cycles. Record Ct value in real time.

[0048] Risk score calculation and result determination: The Ct values ​​of the five piRNAs are input into the analysis software one by one. The software automatically converts the Ct values ​​into plasma piRNA expression values, substitutes them into the model formula (Hepatitis B liver fibrosis incidence risk score = ∑(plasma piRNA expression value × corresponding regression coefficient)), and outputs the risk score and diagnostic result within 10 seconds. A score ≥0.40 is considered high risk (suggesting the possibility of liver fibrosis), and <0.40 is considered low risk (suggesting a low risk of liver fibrosis). This threshold is determined based on the combined training and test set data to ensure diagnostic accuracy.

[0049] The embodiments of the present invention have been described above. Those skilled in the art will understand that various changes, modifications, substitutions, and additions can be made to these embodiments, methodologies, and models without departing from the principles and spirit of the present invention, and these changes, modifications, substitutions, and additions should also be considered within the scope of protection of the present invention.

Claims

1. A plasma piRNA combination for the diagnosis of liver fibrosis in hepatitis B patients, characterized by: The plasma piRNA combination consists of hsa-piR-13530, hsa-piR-14581, hsa-piR-5114, hsa-piR-29030 and hsa-piR-2129.

2. Use of the plasma piRNA combination according to claim 1 in the preparation of a product for diagnosing liver fibrosis in hepatitis B patients.

3. Use according to claim 2, characterized in that: The product is a kit.

4. Use according to claim 3, characterized in that: The kit is provided with a hepatitis B patient liver fibrosis diagnosis model, which is a Lasso Logistic regression model; the hepatitis B patient liver fibrosis diagnosis model takes the plasma piRNA combination as a diagnostic marker.

5. Use according to claim 4, characterized in that: The mathematical expression of the Lasso Logistic regression model is as follows: The risk score of hepatitis B patient liver fibrosis = ∑ (plasma piRNA expression value x regression coefficient of corresponding plasma piRNA).

6. Use according to claim 5, characterized in that: The regression coefficients are as follows: The regression coefficient of hsa-piR-13530 is 0.007007783; The regression coefficient of hsa-piR-14581 is 0.06959053; The regression coefficient of hsa-piR-5114 is -0.02564205; The regression coefficient of hsa-piR-29030 is -0.02442593; The regression coefficient of hsa-piR-2129 is -0.008692064.

7. Use according to claim 4, characterized in that: The Lasso Logistic regression model construction method is as follows: (1) Collect the plasma of hepatitis B liver fibrosis patients and healthy controls respectively, and extract the plasma free piRNA therefrom; (2) obtain the plasma piRNA expression profile by piRNA transcriptome sequencing technology; (3) randomly divide the above two types of people into training set and test set, construct Lasso Logistic regression model in the training set, and then obtain the regression coefficient of the piRNA included in the model; (4) based on the Lasso Logistic regression model established in the training set, the prediction accuracy of the model is evaluated by using ROC curve, sensitivity and specificity indexes in the test set.

8. Use according to claim 5, characterized in that: The kit is provided with piRNA detection reagents for hsa-piR-13530, hsa-piR-14581, hsa-piR-5114, hsa-piR-29030 and hsa-piR-2129, and analysis tools for calculating the risk score of hepatitis B patient liver fibrosis.

9. Use according to claim 8, characterized in that: The detection reagents include specific primer pairs for each piRNA, and the analysis tools include the regression coefficient corresponding to the plasma piRNA expression value.

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