Plasma piRNA combination for early diagnosis of kidney cancer and application

Abstract

The invention discloses a plasma piRNA combination for early diagnosis of kidney cancer and application, the plasma piRNA combination for early diagnosis of kidney cancer is obtained on the basis of plasma piRNA expression profiles of Chinese population, and a kit for early diagnosis of kidney cancer is established on the basis of the plasma piRNA combination. The kit for early diagnosis of the kidney cancer can accurately predict the onset risk of early diagnosis of the kidney cancer and is beneficial to reduction of the detection cost, meanwhile, the Lasso Logistic regression model is used, the number of variables included in the model is greatly reduced, and application and popularization of the model are facilitated.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics technology, and particularly to a plasma piRNA combination for early diagnosis of renal cancer and its application. Background Art

[0002] Renal cancer is a malignant tumor originating from the urinary tubule epithelial system of the renal parenchyma, accounting for 80% - 90% of renal malignant tumors and being one of the most common malignant tumors in the urinary system. In recent years, the incidence of renal cancer in China has been gradually increasing, seriously threatening the lives and health of residents. Screening, early diagnosis, and early treatment of high-risk groups for renal cancer can effectively reduce the incidence and mortality of renal cancer.

[0003] In the early stage, renal cancer usually has no obvious symptoms, and symptoms such as hematuria, low back pain, and abdominal mass may only appear as the tumor grows. The traditional screening and diagnosis mode for renal cancer is as follows: Abdominal B-ultrasound is the most commonly used primary screening method, which can detect space-occupying lesions in the kidney and even detect small tumors with a diameter of 1 centimeter. In addition, laboratory tests can assist in the screening of renal cancer. CT examination is the "gold standard" for diagnosing renal cancer, which can clarify the nature and scope of the tumor, and then confirm renal cancer through renal puncture biopsy. Renal puncture biopsy has a certain degree of accuracy, but it is an invasive method, which may cause complications and sequelae such as bleeding and infection in severe cases. Compared with renal puncture biopsy, exploring and developing blood markers brings convenience to the early screening and diagnosis of renal cancer, and the results have characteristics such as stability and high patient compliance. The wide application of currently available detections such as ferritin, lipid-related sialic acid, alkaline fetal protein, and β2-microglobulin shows the potential of blood markers in the screening and diagnosis of renal cancer. Therefore, it is urgently necessary to explore more effective, accurate, and sensitive non-invasive clinical biomarkers for the early screening and diagnosis of renal cancer.

[0004] piRNA is a non-coding RNA with a length of 25 - 33 nt, which is closely related to genome stability maintenance, 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. Currently, the relevant content of piRNA in renal cancer patients still needs to be further studied. A better understanding of the role of piRNA in the occurrence and development of renal cancer can help to explore new biomarkers for early screening and diagnosis of renal cancer, which has an important role in the early screening and diagnosis of renal cancer patients. Summary of the Invention

[0005] The purpose of the present invention is to provide a plasma piRNA combination for early diagnosis of renal cancer and its application. The present invention provides a plasma piRNA combination for early diagnosis of renal cancer, and based on its results, an early diagnosis model for renal cancer is established and verified, which is convenient for early screening.

[0006] Technical solution of the present invention: A plasma piRNA combination for early diagnosis of renal cancer, wherein the plasma piRNAs in the plasma piRNA combination include: hsa-piR-59304, hsa-piR-9542, hsa-piR-18114, hsa-piR-7805, hsa-piR-69017, hsa-piR-49461.

[0007] Use of the above-mentioned plasma piRNA combination in the preparation of a kit for early diagnosis of renal cancer.

[0008] The aforementioned use, wherein the early renal cancer diagnosis model in the kit for early diagnosis of renal cancer is a Lasso Logistic regression model; the early renal cancer diagnosis model uses the above-mentioned plasma piRNA combination as a diagnostic marker.

[0009] The aforementioned use, wherein the mathematical expression of the Lasso Logistic regression model is as follows: Renal cancer incidence risk score = ∑(plasma piRNA expression value × corresponding regression coefficient).

[0010] The aforementioned use, wherein the regression coefficients are as follows: The regression coefficient of hsa-piR-59304 is -0.005; The regression coefficient of hsa-piR-9542 is -0.034; The regression coefficient of hsa-piR-18114 is -0.275; The regression coefficient of hsa-piR-7805 is 0.243; The regression coefficient of hsa-piR-69017 is -0.939; The regression coefficient of hsa-piR-49461 is 1.003.

[0011] The aforementioned use, wherein the method for constructing the Lasso Logistic regression model is as follows: (1) Collect plasma from renal cancer patients and healthy control subjects, and extract plasma-free piRNAs respectively; (2) Obtain the plasma piRNA expression profile by piRNA transcriptome sequencing; (3) Randomly divide renal cancer patients and healthy control subjects into a training set and a test set, establish a Lasso Logistic regression model in the training set, and obtain the piRNA regression coefficients included in the model; (4) Based on the Lasso Logistic regression model established in the training set, evaluate the prediction accuracy of the model using ROC curve, sensitivity and specificity indexes in the test set.

[0012] A kit for early diagnosis of renal cancer, comprising piRNA detection reagents for hsa-piR-59304, hsa-piR-9542, hsa-piR-18114, hsa-piR-7805, hsa-piR-69017, hsa-piR-49461, and an analysis tool for calculating the incidence risk score of renal cancer.

[0013] For the above kit, the detection reagent includes specific primer pairs for each piRNA, and the analysis tool includes the regression coefficients corresponding to the plasma piRNA expression values.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The innovation of the present invention lies in the first discovery of the application of plasma piRNA combinations in the early screening and diagnosis of renal cancer, and the construction of an early screening and diagnosis model for renal cancer based on plasma piRNA combinations. The area under the ROC curve of this model for predicting renal cancer is 0.943, the sensitivity is 91%, and the specificity is 92%. It can effectively distinguish renal cancer 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, and will help reduce the detection cost and the application and promotion of the model.

[0015] 2. The present invention is carried out by blood detection, which is convenient, fast, has a small trauma area, is easy to use and has good stability. Only 200 μl of plasma is required each time, avoiding the risk of physical damage to patients caused by multiple tissue biopsies to obtain tumor tissues for pathological identification, and greatly reducing the detection cost. Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the technical solution process of the present invention.

[0017] 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.

[0018] Figure 3 It is a schematic diagram of the ROC curve of the model in the training set.

[0019] Figure 4 It is a schematic diagram of the ROC curve of the model in the test set. Detailed Embodiments

[0020] The present invention will be further described below in conjunction with the drawings and embodiments, but it shall not be used as a basis for limiting the present invention.

[0021] Embodiment: Construction and verification of an early diagnosis model for renal cancer based on plasma piRNA expression profiles.

[0022] This example is divided into four parts: collecting plasma samples, extracting plasma small RNAs 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 process of this example is as Figure 1 shown.

[0023] (1) Collect plasma samples; Collect 5 ml of whole blood from 200 renal cancer patients and 100 healthy controls, and place it in a blood collection tube containing EDTA anticoagulant. After collection, invert the blood collection tube repeatedly to fully mix the EDTA anticoagulant with the blood. Centrifuge at 3000 rpm at 4°C for 10 min, 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.

[0024] (2) Extract plasma small RNAs using the Qiagen miRNeasy Plasma Kit; Cell lysis and small RNA extraction: Add 1 ml of QIAzol lysis reagent to 200 μl of the sample, vortex or invert the liquid up and down, and incubate at room temperature (15 - 25°C) for 5 min. Add 200 μl of chloroform, shake vigorously for 15 seconds, and incubate at room temperature for 2 - 3 min. After incubation, centrifuge at 12000g at 4°C for 15 min. After centrifugation, transfer the upper aqueous phase to a new EP tube, add 1.5 times the volume of the aqueous phase of 100% ethanol, and thoroughly mix the two by inverting up and down. Pipette 700 μL of the liquid onto an RNeasy MinElute spin column, centrifuge at 8000g at room temperature for 15 s, and discard the waste liquid in the collection tube. Repeat the operation with the remaining liquid from the above steps. Add 700 μl of Buffer RWT to the RNeasy MinElute spin column, centrifuge at 8000g for 15 s, and discard the waste liquid in the collection tube. Pipette 500 μl of buffer RPE into the RNeasy MinElute spin column, centrifuge at 8000g for 15 s, and discard the waste liquid in the collection tube. Add 500 μl of 80% ethanol to the RNeasy MinElute spin column, centrifuge at 8000g for 2 min, and discard the waste liquid in the collection tube. Place the RNeasy MinElute spin column in a new 2 ml EP tube, open the spin column cap, adjust the centrifuge speed to the maximum, centrifuge for 5 min, and discard the waste liquid and the collection tube in the collection tube. Place the RNeasy MinElute spin column in a new 1.5 ml collection tube.

[0025] Small RNA elution: Add 15 μL of RNase-free water in the middle of the filter membrane, gently cover the tube cap, let it stand at room temperature for 2 min, and centrifuge at maximum speed for 1 min. The RNA separated is at the bottom of the tube.

[0026] RNA concentration and RNA integrity assessment: Take 1 μL for Aglient 2100 RNA Pico chip detection. The peak is generally below 200 nt. Only use high-quality RNA samples (RIN≥7, >50 ng / μL, OD260 / 280 between 1.8 and 2.2) to construct the sequencing library.

[0027] (3) Small RNA transcriptome sequencing; Small RNA quantification: The small RNA samples used for library construction are first quantified with a library quantification kit. Use 1 μg as the starting material to generate the sequencing library.

[0028] Ligate adapter sequences: Ligate adapter sequences at the 3' and 5' ends respectively.

[0029] cDNA synthesis: Under the action of MMLV-derived PrimeScript reverse transcriptase (RT), using random primers, reverse transcribe the RNA after ligating the adapter into the first-strand cDNA, and then perform the second-strand synthesis to form a stable double-stranded structure.

[0030] Library enrichment: Use sequencing primers for PCR amplification (11 - 12 cycles) to enrich the library concentration.

[0031] Library purification: According to the length distribution characteristics of small RNAs, recover the target fragments by cutting the gel (6% Novex TBE PAGE gel, 1.0 mm, 10 well).

[0032] Sequencing on the machine and data analysis: Quantify with Qubit 4.0, mix and load onto the machine according to the data ratio; perform bridge PCR amplification on cBot to generate clusters; sequence on the Illumina NovaSeq 6000 platform.

[0033] (4) Bioinformatics analysis part; Statistics of raw sequence data: Illumina sequencing belongs to the second-generation sequencing technology. Each run can generate billions of reads. It is impossible to display the quality of each read one by one for such a huge amount of data. By using statistical methods to statistically analyze the base distribution and quality fluctuations of each cycle of all sequencing reads, the sequencing quality of the sample and the quality of library construction can be intuitively reflected macroscopically. Sequencing-related quality assessment is performed on the raw sequencing data of each sample, including: statistical analysis of the content distribution of A / T / G / C bases, statistical analysis of base quality distribution, and statistical analysis of base error rate distribution.

[0034] 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 sequences in the reads and remove the reads without insert fragments caused by adapter self-ligation, etc.; 2) Clip the bases with lower sequencing quality (quality value less than 20) at the 3' end; 3) Remove the reads containing unknown bases N; 4) Remove the reads with too short length (<18nt); 5) Remove the reads with too long length (>32nt). After quality control, analyze the length of clean reads. According to the characteristics of small RNAs, select the reads with a length of 18-32nt as useful reads for subsequent analysis.

[0035] Alignment with the reference genome: Use Bowtie to align the quality-controlled useful reads with the specified reference genome (human genome), and then align the reads to the piRBase database to calculate and obtain the plasma piRNA expression matrix.

[0036] Establish a Lasso Logistic regression model based on the plasma piRNA expression matrix; After obtaining the plasma piRNA expression matrix, randomly divide all samples into a training set and a test set at a ratio of 50% and 50%. Then construct a Lasso Logistic regression model in the training set, and then evaluate the prediction accuracy of the model using indicators such as AUC, sensitivity, and specificity in the training set and the test set. The software used is the glmnet package of the R language program.

[0037] Compared with the traditional Logistic regression model, the biggest difference of the Lasso Logistic regression model is that the Lasso Logistic regression model introduces the regularization parameter λ of the regression coefficient. By adjusting the value of the parameter λ, the regression coefficients of some variables can be made equal to 0 (making the regression coefficients of other piRNAs except the piRNAs shown in Table 1 equal to 0), achieving the purpose of variable screening and facilitating the application and promotion of the model.

[0038] The optimal value of λ is determined by using the 20-fold cross-validation method in the training set. At this value of λ, the partial likelihood estimation bias of the Lasso Logistic regression model is the smallest, as shown in Figure 2 , and it is obtained that at this value of λ, the regression coefficients of 69 piRNAs are equal to 0, and the regression coefficients of 6 piRNAs are not equal to 0. The sequences and regression coefficients of these 6 piRNAs are shown in Table 1.

[0039]

[0040] Table 1 The regression coefficient value of each piRNA expression value represents the change value of the renal cancer incidence risk score of the subject when the expression level of this piRNA changes by 1 unit. If the regression coefficient is positive, it means that the renal cancer incidence risk increases when the piRNA expression value increases; similarly, if the regression coefficient is negative, it means that the renal cancer incidence risk decreases when the piRNA expression value increases. The mathematical calculation formula of the renal cancer incidence risk score is: Renal cancer incidence risk score (Lasso_Logistic_Score) = ∑(plasma piRNA expression value * corresponding regression coefficient).

[0041] After constructing the renal cancer incidence risk prediction model using the Lasso Logistic regression model in the training set, the AUC of this model in the training set is 0.93, the sensitivity is 95%, and the specificity is 93%, as shown in Figure 3 . Applying the above model to the test set, the AUC of this model in the test set is 0.943, the sensitivity is 91%, and the specificity is 92%, as shown in Figure 4 . The above results show that the method and the constructed model of the present invention can accurately predict the renal cancer incidence risk.

[0042] 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 renal cancer, and established an early diagnosis model for renal cancer based on the plasma piRNA combination. This model can accurately predict the risk of renal cancer, helping to reduce the cost of detection. At the same time, the present invention uses the Lasso Logistic regression model, which greatly reduces the number of variables included in the model and facilitates the application and popularization of the model. In addition, the plasma piRNA combination of the present invention can be used to prepare a kit for the early diagnosis of renal cancer. The kit for the early diagnosis of renal cancer contains piRNA detection reagents for hsa-piR-59304, hsa-piR-9542, hsa-piR-18114, hsa-piR-7805, hsa-piR-69017, and hsa-piR-49461, as well as an analysis tool for calculating the risk score of renal cancer onset. The detection reagent includes specific primer pairs for each piRNA, and the analysis tool contains the regression coefficients corresponding to the plasma piRNA expression values.

[0043] The above describes an embodiment of the present invention. Those of ordinary skill in the art can understand that it should be pointed out that various changes, modifications, substitutions, and supplements can be made to these embodiments, methodologies, and models without departing from the principles and purposes of the present invention, and these changes, modifications, substitutions, and supplements should also be regarded as the protection scope of the present invention.

Claims

1. A plasma piRNA combination for early diagnosis of renal cancer, characterized in that: The plasma piRNAs in the plasma piRNA combination include: hsa-piR-59304, hsa-piR-9542, hsa-piR-18114, hsa-piR-7805, hsa-piR-69017, and hsa-piR-49461.

2. Use of the plasma piRNA combination according to claim 1 in preparing a kit for early diagnosis of renal cancer.

3. The use according to claim 2, characterized in that: The early diagnosis model for renal cancer in the early diagnosis kit for renal cancer is a Lasso Logistic regression model; the early diagnosis model for renal cancer uses the plasma piRNA combination as a diagnostic marker.

4. The use according to claim 3, characterized in that: The mathematical expression of the Lasso Logistic regression model is as follows: Renal cancer risk score = ∑ (plasma piRNA expression value × corresponding regression coefficient).

5. The use according to claim 4, characterized in that: The regression coefficients are as follows: The regression coefficient of hsa-piR-59304 was -0.005; The regression coefficient of hsa-piR-9542 was -0.034; The regression coefficient of hsa-piR-18114 was -0.275; The regression coefficient for hsa-piR-7805 was 0.243; The regression coefficient of hsa-piR-69017 was -0.939; The regression coefficient of hsa-piR-49461 was 1.

003.

6. The use according to claim 4, characterized in that: The Lasso Logistic regression model construction method is as follows: (1) Plasma was collected from patients with renal cancer and healthy controls, and free piRNAs were extracted from plasma. (2) piRNA transcriptome sequencing was used to obtain the expression profile of plasma piRNAs. (3) Renal cancer patients and healthy controls were randomly divided into a training set and a test set. A Lasso Logistic regression model was established in the training set to obtain the regression coefficients of piRNAs 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.

7. A kit for early diagnosis of renal cancer, characterized in that: Contains piRNA detection reagents for hsa-piR-59304, hsa-piR-9542, hsa-piR-18114, hsa-piR-7805, hsa-piR-69017, and hsa-piR-49461, as well as analysis tools for calculating kidney cancer risk scores.

8. The kit according to claim 7, characterized in that: The detection reagent includes a specific primer pair for each piRNA, and the analysis tool includes a regression coefficient corresponding to the plasma piRNA expression value.

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