CN112094910B

CN112094910B - miRNA marker for prostate cancer risk assessment

Info

Publication number: CN112094910B
Application number: CN202011020216.6A
Authority: CN
Inventors: 陈健; 吴佳伟
Original assignee: Wuxi Hospital of Traditional Chinese Medicine
Current assignee: Wuxi Hospital of Traditional Chinese Medicine
Priority date: 2020-09-25
Filing date: 2020-09-25
Publication date: 2023-03-24
Anticipated expiration: 2040-09-25
Also published as: CN112094910A

Abstract

The invention discloses a miRNA marker for assessing prostate cancer risk, wherein the marker is selected from one or more of miR-191, miR-6131 and miR-1246.

Description

miRNA marker for prostate cancer risk assessment

Technical Field

The invention belongs to the field of biomedicine, and relates to a miRNA marker for prostate cancer risk assessment.

Background

Prostate cancer (PCa) is the most common cancer among men in the united states and europe, and ranks the second and third causes of cancer death. Although the incidence of clinical prostate cancer is currently much lower in our country than in western countries, the incidence of prostate cancer has also risen year by year in recent years (Acloque H, thiery J P, nieto M A. The physiology and pathology of the EMT. Meeting on the epithelial-sensory transition [ J ]. EMBO Rep,2008,9 (4): 322-326.). Since the application of the detection of plasma Prostate Specific Antigen (PSA) in the clinical screening of prostate cancer, a great number of early asymptomatic prostate cancer patients are diagnosed and treated. However, in recent years, most of the early-diagnosed prostate cancers belong to the indolent prostate cancers, and patients do not die of the prostate cancers even if not receiving treatment, so that excessive treatment can be avoided by adopting an active monitoring mode. If aggressive prostate cancer cannot be completely removed by radical resection in the early stage, distant metastasis (e.g., bone) or progression to castration resistant prostate cancer (lirika, zhang ying, tragchin, research progress on novel drugs against castration resistant prostate cancer [ J ] medical diet and health, 2018 (7): 206, 208.) will eventually occur even after androgen deprivation therapy. Once the disease has progressed to this stage, there is no effective cure, and chemotherapy only benefits a subset of patients and only prolongs survival for several months. Therefore, actively screening early markers of prostate cancer and exploring the predictive value and a suitable disease progression monitoring mode of the early markers are hot problems in the current prostate cancer research field, and have important significance for diagnosing early prostate cancer patients.

With the development of gene sequencing, transcriptomics and bioinformatics, more and more non-coding RNAs have been found to be involved in the development process of tumors, including micro RNAs (miRNAs) and long non-coding RNAs (1 ncRNA). This provides a solid foundation for screening new tumor diagnosis markers and therapeutic targets. mirnas are an endogenous class of small RNAs of about 20-24 nucleotides in length that have a variety of important regulatory roles within the cell. The gene expression is mainly regulated at the post-transcriptional level (approximately 1/3 of the protein coding gene is regulated), so that the apoptosis, proliferation, differentiation and metabolism of cells and the development of individuals and the occurrence, development and drug resistance of tumors are controlled. The miRNA not only has potential as a prostate cancer diagnosis marker, but also is closely related to the prognosis of prostate cancer and the curative effect of medicaments. MicroRNA exists in various forms, the most original is pri-miRNA, the length of which is about 300-1000 bases; pri-miRNA becomes pre-miRNA, namely microRNA precursor after being processed for one time, and the length of the pre-miRNA is about 70-90 bases; the pre-miRNA is cut by Dicer enzyme to become mature miRNA with length of about 20-24 nt. The miRNA differential expression profile is very likely to provide a powerful weapon for early diagnosis, prognosis analysis and curative effect judgment of the prostate cancer.

Disclosure of Invention

In view of the problems in the prior art, the invention aims to provide a marker related to the occurrence and development of prostate cancer, and the prostate cancer marker provided by the invention has higher sensitivity and specificity when being used for diagnosing prostate cancer, and is beneficial to realizing early diagnosis of prostate cancer.

In order to achieve the purpose, the invention adopts the following technical scheme:

the invention provides a miRNA marker related to prostatic cancer, wherein the miRNA marker is selected from one or more of miR-191, miR-6131 and miR-1246.

As an alternative embodiment, the miRNA marker is selected from any one of miR-191, miR-6131 and miR-1246. Preferably, the miRNA marker is selected from miR-6131.

As a preferred embodiment, the miRNA markers are selected from any two of miR-191, miR-6131 and miR-1246.

As a more preferable embodiment, the miRNA marker is a combination of miR-191, miR-6131 and miR-1246.

In a second aspect of the invention, there is provided a reagent capable of detecting the expression level of a miRNA marker according to the first aspect of the invention.

As an alternative embodiment, the reagent is a reagent for detecting the expression level of miR-191.

As an alternative embodiment, the reagent is a reagent for detecting the expression level of miR-6131.

As an alternative embodiment, the reagent is a reagent for detecting the expression level of miR-1246.

As a preferred embodiment, the reagent comprises a reagent for detecting the expression level of miR-191 and miR-6131.

As a preferred embodiment, the agent comprises an agent for detecting the expression level of miR-191 and miR-1246.

As another preferred embodiment, the reagent comprises a reagent for detecting the expression level of miR-6131 and miR-1246.

As a more preferable embodiment, the reagent comprises a reagent for detecting the expression levels of the three miRNAs miR-191, miR-6131 and miR-1246.

Further, the agent is selected from:

an oligonucleotide probe that specifically recognizes the miRNA marker according to the first aspect of the present invention; or

A primer for specifically amplifying the miRNA marker of the first aspect of the invention.

In a third aspect, the present invention provides a product for diagnosing prostate cancer, which comprises a reagent for detecting the expression level of the miRNA marker according to the first aspect of the present invention.

Further, the product comprises: the reagent for detecting the expression level of the miRNA marker of the first aspect of the invention is RT-PCR, real-time quantitative PCR, in-situ hybridization, a chip or a high-throughput sequencing platform.

Further, the product comprises a chip or a kit.

In the present invention, the chip can be prepared by conventional methods for manufacturing biochips known in the art, for example, if a modified glass slide or silicon wafer is used as the solid support, and the 5' -end of the probe contains a poly-dT strand modified with an amino group, the oligonucleotide probe can be prepared into a solution, and then spotted on the modified glass slide or silicon wafer using a spotting apparatus, arranged into a predetermined sequence or array, and then fixed by standing overnight, so that the miRNA chip of the present invention can be obtained. If the nucleic acid does not contain amino modifications, the preparation can also be referred to: the "Gene diagnostic technique-non-Radioactive operation Manual" edited by Wangshen five; l.l.erisi, v.r.i.er, p.o.brown.expanding the metabolic and genetic control of gene expression a genetic scale, science,1997;278:680 and maris, jiang china major edition biochip, beijing: chemical industry Press, 2000,1-130.

The present invention provides a kit for diagnosing prostate cancer, as an alternative embodiment, the kit comprising one or more probes that specifically bind to one or more miRNA markers. As a further embodiment, the kit further comprises a wash solution. As further embodiments, the kit further comprises reagents for performing a hybridization assay, nucleic acid isolation or purification means, detection means, and positive and negative controls. As a further embodiment, the kit further comprises instructions for using the kit, wherein the instructions describe how to use the kit for detection, and how to use the detection results to determine tumor progression and select a treatment regimen. Such a kit may employ, for example, a test strip, membrane, chip, tray, test strip, filter, microsphere, slide, multiwell plate, or optical fiber. The solid support of the kit can be, for example, a plastic, a silicon wafer, a metal, a resin, a glass, a membrane, a particle, a precipitate, a gel, a polymer, a sheet, a sphere, a polysaccharide, a capillary, a film, a plate, or a slide.

A fourth aspect of the present invention provides a method of modeling a prostate cancer diagnosis, the method comprising the steps of:

1) Collecting samples, dividing the samples into a training set and a testing set, and detecting the expression level of genes in the samples;

2) Integrating the training set data and the test set data to carry out normalization processing;

3) Screening differential expression genes;

4) Processing data of the training set by adopting a one-dimensional neural convolution network to construct a risk scoring model;

5) And testing the risk scoring model by using the test set data, and detecting the prediction accuracy of the risk scoring model.

Further, the differentially expressed gene is the miRNA marker of the first aspect of the invention.

Further, the risk score model takes the expression level of miR-191, miR-6131 or miR-1246 as an input variable, and when the risk score is greater than 0.5, the risk of the subject suffering from the prostate cancer is high; when the risk score is less than 0.5, the subject is at low risk of having prostate cancer.

In a fifth aspect of the invention there is provided a system for predicting prostate cancer, the system comprising a diagnostic module that incorporates a risk scoring model constructed by the method of the fourth aspect of the invention.

In a specific embodiment of the invention, the system has the expression level of miR-191, miR-6131 or miR-1246 as an input variable, and the subject is at high risk of having prostate cancer when the risk score is greater than 0.5; when the risk score is less than 0.5, the subject is at low risk of having prostate cancer.

A sixth aspect of the invention provides the use of any one of:

1) The application of the miRNA marker in the first aspect of the invention in constructing a computational model for predicting prostate cancer and preparing a pharmaceutical composition for treating prostate cancer;

2) Use of a reagent according to the second aspect of the invention, a product according to the third aspect of the invention, in the manufacture of a means for diagnosing prostate cancer;

3) Use of a method according to the fourth aspect of the invention in the construction of a diagnostic model for prostate cancer or a system for predicting prostate cancer.

In the present invention, when a marker is employed to indicate or be a marker of an abnormal process, disease or other condition in an individual, the marker is generally described as being over-expressed or under-expressed as compared to the expression level or value of the marker which indicates or is a marker of a normal process, no disease or other condition in the individual. "upregulation," "upregulated," "overexpression," "overexpressed," and any variation thereof, are used interchangeably to refer to a value or level of a marker in a biological sample that is greater than the value or level (or range of values or levels) of the marker that is typically detected in a healthy or normal individual. The term can also refer to a value or level of a marker in a biological sample that is greater than the value or level (or range of values or levels) of the marker that is detectable at different stages of a particular disease.

"downregulated," "under-expressed," and any variation thereof, are used interchangeably to refer to a value or level of a marker in a biological sample that is less than the value or level (or range of values or levels) of the marker that is typically detected in a healthy or normal individual. The term may also refer to a value or level of a marker in a biological sample that is less than the value or level (or range of values or levels) of the marker that is detectable at different stages of a particular disease.

Furthermore, an over-expressed or under-expressed marker may also be referred to as "differentially expressed" or as having a "differential level" or a "differential value" as compared to a "normal" expression level or value for a marker indicative of, or as a marker for, normal progression or absence of a disease or other condition in an individual. Thus, "differential expression" of a marker may also be referred to as a variation in the "normal" expression level of the marker.

The terms "differential marker expression" and "differential expression" are used interchangeably to refer to a marker whose expression is activated to a higher or lower level in a subject with a particular disease, relative to its expression in a normal subject, or relative to its expression in a patient who responds differently to a particular treatment or has a different prognosis. The term also includes markers whose expression is activated to a higher or lower level at different stages of the same disease. It is also understood that differentially expressed markers may be activated or inhibited at the nucleic acid level or at the protein level, or may be subject to alternative splicing to produce a different polypeptide product. This difference can be evidenced by a variety of changes including mRNA levels, microrna levels, antisense transcript levels, or other divisions of protein surface expression, secretion, or polypeptides. Differential marker expression may include a comparison of expression between two or more genes or gene products thereof; or a comparison of the ratio of expression between two or more genes or gene products thereof; or even a comparison of two differently processed products of the same gene, which differ between normal and diseased subjects; or different at different stages of the same disease. Differential expression includes, for example, quantitative and qualitative differences in transient expression patterns or cellular expression patterns in markers between normal and diseased cells or between cells undergoing different disease events or disease stages.

By "differential expression increase" or "upregulation" is meant that gene expression (as measured by RNA expression or protein expression) exhibits an increase of at least 10% or more, e.g., 20%, 30%, 40% or 50%, 60%, 70%, 80%, 90% or more or 1.1-fold, 1.2-fold, 1.4-fold, 1.6-fold, 1.8-fold or more, of the gene relative to a control.

By "differential expression reduction" or "down-regulation" is meant a gene whose expression (as measured by RNA expression or protein expression) exhibits a reduction in gene expression relative to a control of at least 10% or more, e.g., 20%, 30%, 40% or 50%, 60%, 70%, 80%, 90% or less than 1.0-fold, 0.8-fold, 0.6-fold, 0.4-fold, 0.2-fold, 0.1-fold or less.

In the present invention, the area under the curve is used to judge the diagnostic efficacy of the marker, which is the area under the receiver operating characteristic curve (ROC) well known in the art. The area under the curve (AUC) measurements help compare the accuracy of the classifier via the overall data range. Classifiers with larger area under the curve (AUC) have greater ability to accurately classify an unknown between two groups of interest (e.g., prostate cancer samples and normal or control samples). In distinguishing between two populations (e.g., a group with prostate cancer and a non-prostate cancer control group), a subject operating characteristic curve (ROC) is useful for graphically representing the performance of a particular feature (e.g., any item of marker and/or additional biomedical information described in the present disclosure). Typically, the above feature data across the entire population (e.g., patient group and control group) is sorted in ascending order based on a single feature value. Then, for each value of the above-described features, a true positive rate (true positive rate) and a false positive rate (false positive rate) are calculated for the data. The true positive rate is determined by calculating the number of cases higher than or equal to a value for the characteristic thereof and dividing the number of cases by the total number of cases. The false positive rate is determined by counting the number of controls above the value for the characteristic and dividing by the total number of controls. Although the definition refers to the case where the characteristic of the patient group is high relative to the control group, the definition also applies to the case where the characteristic of the patient group is low relative to the control group (in this case, the number of samples whose values are lower than the above characteristic can be calculated). A receiver operating characteristic curve (ROC) may be generated for other single calculations, but also for a single characteristic, in order to provide a single sum value, e.g., more than two characteristics may be mathematically combined (e.g., added, subtracted, multiplied, etc.), which may be represented by the receiver operating characteristic curve (ROC). Additionally, combinations of multiple characteristics that can lead to a single calculated value can be plotted against a receiver operating characteristic curve (ROC). These combinations of characteristics may constitute a test. The receiver operating characteristic curve (ROC) is a graph showing the true positive rate (sensitivity) of the test relative to the false positive rate (1-specificity) of the test.

Conventionally, the AUC area is always ≧ 0.5 (if not, the decision rule can be reversed to do so). The range of values was between 1.0 (test values that perfectly separated the two groups) and 0.5 (no significant distribution difference between the test values of the two groups). The area depends not only on a particular part of the line graph, such as the point closest to the diagonal or the sensitivity at 90% specificity, but also on the entire line graph. This is a quantitative, descriptive representation of how ROC plots are close to perfect (area = 1.0).

Overall assay sensitivity will depend on the specificity required to carry out the methods disclosed herein. In certain preferred settings, a specificity of 75% may be sufficient, and statistical methods and resulting algorithms may be based on this specificity requirement. In a preferred embodiment, the method for assessing an individual at risk for prostate cancer is based on specificity of 80%, 85%, or also preferably 90% or 95%.

Compared with the prior art, the invention has the following beneficial effects:

according to the invention, miRNA with significant difference in prostate cancer is screened firstly, and the miRNA with differential expression has higher diagnosis efficiency.

The risk score model of the prostate cancer is constructed, the expression levels of miR-191, miR-6131 and miR-1246 are used as input variables of the risk score model, whether a subject suffers from the prostate cancer is judged according to the risk score, and the risk score model has high accuracy, specificity and sensitivity when being used for diagnosing the prostate cancer.

Drawings

FIG. 1 is a graph of diagnostic efficacy of miR-6131 in a training set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 2 is a graph of the diagnostic efficacy of miR-191 in the training set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 3 is a graph of the diagnostic efficacy of miR-1246 in the training set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 4 is a graph of the combined diagnostic efficacy of miR-191 and miR-6131 in a training set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 5 is a graph of the combined diagnostic efficacy of miR-6131 and miR-1246 in the training set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 6 is a graph of the combined diagnostic efficacy of miR-1246 and miR-191 in a training set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 7 is a graph of the combined diagnostic efficacy of miR-191, miR-6131 and miR-1246 in the training set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 8 is a graph of diagnostic efficacy of miR-6131 in a test set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 9 is a graph of the diagnostic efficacy of miR-191 in the test set, wherein Panel A is a ROC plot and Panel B is a confusion matrix graph;

FIG. 10 is a graph of the diagnostic efficacy of miR-1246 in a test set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 11 is a graph of the combined diagnostic efficacy of miR-191 and miR-6131 in a test set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 12 is a graph of the combined diagnostic efficacy of miR-6131 and miR-1246 in a test set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 13 is a graph of the combined diagnostic efficacy of miR-1246 and miR-191 in a test set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot;

FIG. 14 is a graph of the combined diagnostic efficacy of miR-191, miR-6131 and miR-1246 in the test set, wherein Panel A is a ROC plot and Panel B is a confusion matrix plot.

Detailed Description

The invention is described in detail below with reference to the drawings and examples, which are only preferred embodiments of the invention, and it should be noted that a person skilled in the art may make several modifications and additions without departing from the method of the invention, and these modifications and additions should also be regarded as the scope of protection of the invention.

Example 1 screening for prostate cancer-associated mirnas

1. Sample(s)

Prostate cancer patients and corresponding normal persons were selected from the GEO database as study subjects, and a total of 849 prostate cancer samples and 10475 normal person samples were included. Randomly selecting 300 prostate cancer samples from 849 prostate cancer samples as a test set, and selecting the rest 549 prostate cancer samples as a training set; 300 of 10475 normal persons were randomly selected as a test set, and the remaining 10175 persons were selected as a training set.

2. Data normalization processing

And (3) carrying out normalization processing on the test set data and the training set data: a) Normalizing the data to the (0, 1) interval or the (-1, 1) interval; b) And changing the dimensional expression into a dimensionless expression.

3) Screening for differentially expressed molecules

Screening out differential expression miRNA by using an edgeR package according to the test set data and the training set data; the screening standard is that p-value is less than or equal to 0.05 ₂ FC ≥ 2 or log ₂ FC≤-2，FDR≤0.05。

4) As a result, the

The results show that 3 mirnas were significantly up-regulated in prostate cancer patients by analytical screening, and the related information is shown in table 1.

TABLE 1 differentially expressed genes

Example 2 Risk scoring model construction

And constructing a risk scoring model by using a one-dimensional convolutional neural network model. The dimension of the input tensor of the one-dimensional convolutional neural network model is (length, 1), wherein the length represents the number of the selected characteristic miRNA.

The model main body sequentially comprises an initial convolutional layer (init _ conv), eight residual convolutional modules (res _ block), a global pooling layer (globalaveragePooling), a fully connected layer (Dense) and an active output layer (Sigmoid). Wherein conv is a one-dimensional convolution operation, k represents the size of a convolution kernel, and filters represents the number of the convolution kernels. The BatchNorm is a batch normalization layer and is used for normalizing the output tensor of the upper layer to be standard normal distribution with the mean value of 0 and the variance of 1 so as to relieve gradient dispersion and gradient explosion in network training and accelerate the training speed of the model. The ReLU is a Linear rectification function (Rectified Linear Unit), also called as a modified Linear Unit, and is a commonly used activation function in a neural network.

The initial convolutional layer consists of conv (k =2, filters = 64), batchNorm, reLU. The convolution module is composed of BatchNorm, reLU, conv (k, filters). The residual convolution module consists of a conv _ block (k =1, filters 1), a conv _ block (k =2, filters 2) and a conv _ block (k =1, filters 3), wherein the filters1, the filters2 and the filters3 represent that three numbers of convolution kernels are selected. Experiments show that whether the expression quantity of the input miRNA is the prostatic cancer can be accurately judged by using the designed CNN classification model.

Substituting the three differential expression miRNAs into the risk scoring model constructed by the one-dimensional convolutional neural network model as follows: risk score = model (expression level of miR-191, expression level of miR-6131, expression level of miR-1246). When the risk score is greater than 0.5, the subject is at high risk for having prostate cancer; when the risk score is less than 0.5, the subject is at low risk of having prostate cancer.

Example 3 detection of diagnostic efficacy of Risk Scoring model

In the training set, the results of diagnosing the risk of prostate cancer in a subject using the risk scoring model of the present invention show that a single miRNA or a combination of several mirnas can be used as an independent factor for diagnosing the risk of prostate cancer, and the area under the curve (AUC) formed by the combination of mirnas is the highest, and has higher specificity and sensitivity (confusion matrix), as shown in table 2 and fig. 1-7,

TABLE 2 AUC and ACC (accuracy) for diagnosis of different miRNA markers

miRNA	AUC	ACC
			miR-6131	0.906	0.785
miR-191	0.914	0.724
			miR-1246	0.891	0.779
miR-191+miR-6131	0.979	0.807
			miR-6131+miR-1246	0.978	0.812
miR-191+miR-1246	0.974	0.864
			miR-191+miR-6131+miR-1246	0.974	0.888

In the test set, the results of diagnosing the risk of prostate cancer in a subject using the risk scoring model of the present invention show that a single miRNA or a combination of several mirnas can be used as an independent factor for diagnosing the risk of prostate cancer, and the area under the curve (AUC) formed by the combination of 3 mirnas is the highest, and also has higher specificity and sensitivity (confusion matrix) as shown in table 3 and fig. 8-14.

TABLE 3 AUC and ACC (accuracy) for diagnosis of different miRNA markers

miRNA	AUC	ACC
			miR-6131	0.930	0.817
miR-191	0.913	0.724
			miR-1246	0.915	0.802
miR-191+miR-6131	0.971	0.81
			miR-6131+miR-1246	0.969	0.785
miR-191+miR-1246	0.968	0.853
			miR-191+miR-6131+miR-1246	0.975	0.903

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims

1. The application of a reagent for detecting the expression level of a miRNA marker in the preparation of products for diagnosing prostatic cancer is characterized in that the miRNA is selected from miR-6131; or miR-6131 in combination with the following mirnas: miR-191 and/or miR-1246.

2. The use according to claim 1, wherein the agent is selected from the group consisting of:

an oligonucleotide probe that specifically recognizes the miRNA marker; or

And (3) a primer for specifically amplifying the miRNA marker.

3. Use according to claim 1, characterized in that the product comprises: the reagent for detecting the expression level of the miRNA marker through RT-PCR, real-time quantitative PCR, in-situ hybridization, a chip or a high-throughput sequencing platform.

4. The use of claim 1, wherein the product comprises a chip or a kit.