CN115595369A - Liquid biopsy prediction model based on esophageal precancerous lesion or esophageal cancer miRNAs, diagnosis kit and application - Google Patents

Liquid biopsy prediction model based on esophageal precancerous lesion or esophageal cancer miRNAs, diagnosis kit and application Download PDF

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CN115595369A
CN115595369A CN202211350607.3A CN202211350607A CN115595369A CN 115595369 A CN115595369 A CN 115595369A CN 202211350607 A CN202211350607 A CN 202211350607A CN 115595369 A CN115595369 A CN 115595369A
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刘芝华
骆爱萍
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Cancer Hospital and Institute of CAMS and PUMC
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Abstract

The invention discloses a liquid biopsy prediction model based on esophageal precancerous lesions or esophageal cancer miRNAs, a diagnosis kit and application, relates to the field of biological medicine, and provides an esophageal cancer miRNAs liquid biopsy marker combination with high diagnosis value, which comprises at least two of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421.

Description

Liquid biopsy prediction model based on esophageal precancerous lesion or esophageal cancer miRNAs, diagnosis kit and application
Technical Field
The invention relates to the field of biological medicine, in particular to a liquid biopsy prediction model based on esophageal precancerous lesions or esophageal cancer miRNAs, a diagnosis kit and application.
Background
The esophagus is hidden in disease due to deep anatomical position, and lacks early warning specific markers, so that the overall survival rate of five years is lower than 30%. Clinical diagnosis and screening mainly depend on endoscopic examination, the compliance of people is poor, the cost is relatively high, and the requirement of early screening of high incidence areas of esophageal cancer cannot be met.
Therefore, the method needs to screen specific liquid biopsy targets, independently develops a noninvasive or minimally invasive real-time rapid evaluation method, is used for diagnosing esophageal precancerous lesion and esophageal cancer, improves the detection rate and saves the treatment cost.
In view of this, the invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a liquid biopsy prediction model based on esophageal precancerous lesions or esophageal cancer miRNAs, a diagnostic kit and application.
The invention is realized in the following way:
in a first aspect, embodiments of the present invention provide use of agents that detect expression levels of target miRNAs in the manufacture of products for predicting esophageal precancerous lesions or esophageal cancer, the target miRNAs comprising: has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421.
In a second aspect, embodiments of the present invention provide a reagent or a kit for predicting esophageal precancerous lesion or esophageal cancer, comprising: the reagents for detecting the expression levels of miRNAs of interest as described in the previous examples.
In a third aspect, an embodiment of the present invention provides a method for training a prediction model of esophageal precancerous lesion or esophageal cancer, including: obtaining a detection result of the expression level of the target miRNAs in the training sample and a corresponding labeling result thereof; wherein the labeling result is a label representing whether or not esophageal cancer or esophageal precancerous lesion occurs or occurs degree, and the target miRNAs are the target miRNAs described in the previous embodiment; inputting the detection result into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is a machine learning model capable of predicting whether the sample is esophageal cancer or esophageal cancer according to the expression level of the target miRNAs, or predicting whether the sample is esophageal cancer or esophageal cancer precancerous lesion degree; and updating parameters of the prediction model based on the labeling result and the prediction result.
In a fourth aspect, an embodiment of the present invention provides a device for predicting esophageal cancer or esophageal precancerous lesion, which includes an obtaining module and a predicting module. The acquisition module is used for acquiring the detection result of the expression level of the target miRNAs of the sample to be detected; wherein the target miRNAs are the target miRNAs of the previous embodiment; and the prediction module is used for inputting the detection result of the expression level of the target miRNAs into the prediction model obtained by training the training method in the embodiment to obtain the prediction result of the sample to be tested.
In a fifth aspect, an embodiment of the present invention provides an electronic device, which includes: a processor and a memory; the electronic device includes a processor and a memory; the memory is used for storing a program which, when executed by the processor, causes the processor to implement a training method for a predictive model of esophageal precancerous lesion or esophageal cancer or prediction of esophageal cancer as described in the previous embodiments; the prediction of esophageal cancer or esophageal precancerous lesions comprises: obtaining a detection result of the expression level of target miRNAs of a sample to be detected; wherein the target miRNAs are the target miRNAs of the previous embodiment; and inputting the detection result of the expression level of the target miRNAs into a prediction model obtained by training the training method in the embodiment to obtain the prediction result of the sample to be tested.
In a sixth aspect, the present invention provides a computer readable medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the training method of the prediction model of the esophageal cancer or the esophageal cancer according to the foregoing embodiments or the prediction of the esophageal cancer or the esophageal cancer according to the foregoing embodiments.
The invention has the following beneficial effects:
the invention provides a marker combination of miRNAs liquid biopsy with extremely high diagnosis value, which can be used for constructing an early screening model of esophageal cancer, so as to be used as an auxiliary diagnosis means for screening high-incidence regions of esophageal cancer and clinical esophageal cancer, improve prognosis of esophageal cancer patients, and have the advantages of convenient and fast detection, low detection cost, high detection accuracy and the like.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a screen of differentially expressed miRNAs in esophageal cancer tissues/blood;
FIG. 2 is a graph showing the altered expression of 28 miRNAs in blood;
FIG. 3 is a training set for establishing a liquid biopsy prediction model of 7miRNAs for esophageal cancer;
FIG. 4 is a graph showing that 7miRNAs marker combinations can distinguish healthy populations from esophageal precancerous lesions and esophageal cancer;
fig. 5 is a validation set to determine the diagnostic performance of different miRNAs marker combinations in esophageal cancer.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below. The examples, in which specific conditions are not specified, were carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available commercially.
The embodiment of the invention provides application of a reagent for detecting the expression level of target miRNAs in preparing a product for predicting esophageal precancerous lesion or esophageal cancer, wherein the target miRNAs comprise: at least two of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421.
As used herein, "predicting esophageal precancerous lesions or esophageal cancer" is to be understood as diagnosing or aiding in the diagnosis of the occurrence or degree of occurrence of esophageal cancer or esophageal precancerous lesions.
Through a series of creative works, based on the constructed 155 paired esophageal squamous cell carcinoma adjacent cancer tissue transcriptome data and two public data sets (GSE 43732 and GSE 55856), combined with data of a large sample esophageal carcinoma serum miRNAs expression profile, the tumor heterogeneity is well avoided or reduced, and the problems of small size and poor reproducibility of a previous research queue are solved; the target miRNAs combination with extremely high diagnostic value is screened out, and the coverage area is wider and is better than other miRNAs combinations which are single or multiple. The MiRNAs have good stability, are not easy to degrade in blood, can be detected by quantitative PCR, can be used as markers for detecting esophageal cancer or esophageal precancerous lesions, can establish a good prediction model, and has high prediction accuracy, good sensitivity and good specificity; the endoscope is the main means for clinically diagnosing esophageal cancer at present, and is assisted with CT examination and serological examination. But the cost of endoscope and activity detection is high, and the detection cost is high and the period is relatively long when various indexes such as blood type, blood coagulation function, virus and the like are detected; the miRNAs liquid biopsy has small wound, can complete detection only by 100-200 mul of serum samples, has short period and can predict whether the samples to be detected have esophageal cancer or esophageal precancerous lesion or predict the disease process of the esophageal cancer or esophageal precancerous lesion of the samples at low cost.
Marker combinations consisting of at least two of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421 can achieve the same or similar prediction effects.
Optionally, the miRNAs of interest comprise: at least three of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421 can achieve the same or similar prediction effect.
Optionally, the miRNAs of interest comprise: at least four of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421 can achieve the same or similar prediction effect.
Optionally, the miRNAs of interest comprise: at least five of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421 can achieve the same or similar prediction effect.
Optionally, the miRNAs of interest comprise: at least six of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421 can achieve the same or similar prediction effect.
In some embodiments, the target miRNAs further comprise: at least one of has-miR-21-5p, has-miR-25-3p, hsa-miR-106b-3p, has-miR-146b-5p, has-miR-181b-5p, hsa-miR-421, has-miR-425 and has-miR-182-5 p.
It should be noted that "has" before miRNA represents the corresponding species, has represents human, and "5p" or "3p" after miR is used to distinguish which arm miRNA is formed from.
In some embodiments, the target miRNAs comprise any one of combinations 1-9 or other combinations:
the combination 1 consists of has-miR-17-5p and has-miR-22-3 p.
The combination 2 consists of has-miR-17-5p, has-miR-22-3p and hsa-miR-223-3p.
The combination 3 consists of has-miR-17-5p, has-miR-22-3p, has-miR-130b and hsa-miR-223-3p.
The combination 4 consists of has-miR-17-5p, has-miR-22-3p, has-miR-128-3p, has-miR-130b and hsa-miR-223-3p.
The combination 5 consists of has-miR-17-5p, has-miR-22-3p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421.
The combination 6 consists of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and has-miR-421.
The combination 7 consists of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p, hsa-miR-421 and has-miR-25-3p.
The combination 8 consists of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p, hsa-miR-421, has-miR-25-3p and has-miR-106b-3p.
The combination 9 consists of has-miR-17-5p, has-miR-21-5p, has-miR-22-3p, has-miR-25-3p, hsa-miR-93-5p, hsa-miR-106b-3p, has-miR-128-3p, has-miR-130b, has-miR-146b-5p, has-miR-181b-5p, hsa-miR-421, has-miR-425, has-miR-182-5p and hsa-miR-223-3p.
In some embodiments, the target miRNAs further comprise, in addition to the marker combinations described above: and (4) internal reference miRNA.
Optionally, the internal reference miRNA comprises any one of miR-423, miR-149-3p, miR-2861 and miR-4463.
In some embodiments, the agent for detecting miRNAs comprises any one of a primer pair and a probe.
The main invention of the invention is to find out miRNAs markers which can distinguish whether or not the esophageal precancerous lesion or the esophageal cancer patient occurs or occurs to a certain extent. In the case of the disclosed miRNAs, the method and reagents for detecting the miRNAs can be implemented based on the existing detection methods, and are not described in detail. Under the condition of a certain detection method, whether the esophageal precancerous lesion or the esophageal cancer patient occurs or occurs degree can be better predicted by adopting the marker provided by the invention.
Optionally, the primer pair comprises a specific primer designed based on a stem-loop method. Specifically, a stem-loop primer and a qPCR primer pair for reverse transcription can be included.
Optionally, the probe comprises a Taqman probe.
Optionally, the reagent for detecting miRNAs of interest further comprises: at least one of a total RNA extraction reagent, a reverse transcription reagent and a fluorescent quantitative PCR detection reagent.
Optionally, the fluorescent quantitative PCR detection reagent comprises: detection reagent of SYBR Green dye method.
As used herein, "predicting esophageal precancerous lesions or esophageal cancer" or "diagnosis of esophageal precancerous lesions or esophageal cancer" includes: predicting or diagnosing the occurrence and/or degree of the esophageal precancerous lesion or esophageal cancer.
Optionally, the esophageal cancer comprises at least one of early esophageal cancer, mid-stage esophageal cancer, and late-stage esophageal cancer.
In some embodiments, the product comprises any of a reagent, a kit, and a predictive model.
In another aspect, an embodiment of the present invention provides a reagent or a kit for predicting esophageal precancerous lesion or esophageal cancer, including: the reagents for detecting the expression levels of miRNAs of interest as described in the previous examples.
In another aspect, an embodiment of the present invention provides a method for training a prediction model of esophageal precancerous lesion or esophageal cancer, including:
obtaining a detection result of the expression level of the target miRNAs in the training sample and a corresponding labeling result thereof; wherein the labeled result is a label representing whether or not esophageal precancerous lesion or esophageal cancer occurs or occurs degree, and the target miRNAs are the target miRNAs in any of the preceding embodiments;
inputting the detection result into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is a machine learning model capable of predicting whether the sample has esophageal cancer or esophageal cancer according to the expression level of the target miRNAs, or predicting whether the sample has esophageal cancer or esophageal cancer precancerous lesion degree;
and updating parameters of the prediction model based on the labeling result and the prediction result.
Optionally, the label is a character or a string of characters.
Optionally, the machine learning model comprises a logistic regression model. In some embodiments, a predictive model for early esophageal squamous cell carcinoma screening can be established using multifactorial logistic regression by defining tumor patients in the esophageal squamous cell carcinoma miRNA seroset cohort as 1 and normal control populations as 0, as follows:
Log[P]＝β 0 +∑β i X i ；X i expression level of miRNAs in serum sample of esophageal squamous cell carcinoma patient, regression coefficient beta 0 Is an inherent contribution of the model to the risk ratio, β i Risk ratio predicted for serum miRNA expression level to early esophageal squamous cell carcinomaThe capacity is contributed. Confusion matrices and receiver operating characteristic curves (ROCs) are used to measure model performance and to find optimal classification criteria.
And selecting the optimal panel by using a cross-validation logistic regression optimization model.
It should be noted that the parameters of the prediction model can be obtained based on a specific sample training construction, and in the case that the marker is disclosed, the formula of the prediction model is available to those skilled in the art based on the conventional technical knowledge.
Alternatively, the number of training samples may be equal to or greater than any of 10, 50, 100, 200, 300, 400, 500.
In another aspect, an embodiment of the present invention provides a device for predicting esophageal precancerous lesion or esophageal cancer, including:
the acquisition module is used for acquiring the detection result of the expression level of the target miRNAs of the sample to be detected; wherein the target miRNAs are the target miRNAs of any of the preceding examples;
and the prediction module is used for inputting the detection result of the expression level of the target miRNAs into the prediction model obtained by training the training method in any embodiment to obtain the prediction result of the sample to be tested.
Alternatively, the modules described in the above embodiments may be stored in a memory in the form of software or Firmware (Firmware) or be fixed in an Operating System (OS) of the electronic device provided in the present application, and may be executed by a processor in the electronic device. Meanwhile, data, codes of programs, and the like required to execute the above modules may be stored in the memory.
In some embodiments, the test sample or the training sample is selected from the group consisting of: a plasma sample, a serum sample, a whole blood sample, a negative standard, or a positive standard. Optionally, the test sample or training sample may also be selected from: an environmental sample comprising at least one of a plasma sample or a serum sample.
In another aspect, an embodiment of the present invention provides an electronic device, which includes: a processor and a memory; the electronic device comprises a processor and a memory; the memory is used for storing a program which, when executed by the processor, causes the processor to implement a training method for a predictive model of esophageal precancerous lesion or esophageal cancer or a prediction of esophageal cancer or esophageal precancerous lesion as described in any of the preceding embodiments;
the prediction of esophageal precancerous lesions or esophageal cancer comprises: obtaining a detection result of the expression level of target miRNAs of a sample to be detected; wherein the target miRNAs are the target miRNAs of any of the preceding examples; inputting the detection result of the expression level of the target miRNAs into a prediction model obtained by training according to the training method described in any of the previous embodiments, and obtaining the prediction result of the sample to be tested.
The electronic device may include a memory, a processor, a bus, and a communication interface that are electrically connected, directly or indirectly, to each other to enable the transfer or interaction of data. For example, the components may be electrically connected to each other via one or more bus lines or signal lines.
The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like.
The processor may be an integrated circuit chip having signal processing capabilities. The Processor 120 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
The electronic device may be a server, a cloud platform, a mobile phone, a tablet computer, a notebook computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a Personal Digital Assistant (PDA), a wearable electronic device, a virtual reality device, or the like, and thus the embodiment of the present application does not limit the type of the electronic device.
Furthermore, an embodiment of the present invention provides a computer readable medium, on which a computer program is stored, and the computer program, when executed by a processor, implements a training method for a prediction model of esophageal precancerous lesion or esophageal cancer according to any of the foregoing embodiments or a prediction of esophageal precancerous lesion or esophageal cancer according to any of the foregoing embodiments.
The computer readable medium may be a general storage medium such as a removable disk, a hard disk, etc.
The features and properties of the present invention are described in further detail below with reference to examples.
Example 1
1. Differential expression miRNAs (micro ribonucleic acids) in esophageal cancer tissue transcriptome data analysis
155 matched transcriptome data (Baidu II) of esophageal squamous cell carcinoma and adjacent paracarcinoma tissues are constructed by using an RNA-seq method, and the transcriptome data set is the largest transcriptome data set of esophageal carcinoma tissues at present. To determine miRNAs differentially expressed in early esophageal cancer, 83 samples of clinical stage I/II were selected for analysis of miRNAs significantly highly expressed in esophageal cancer, and the conditions were selected: logFC >0.5, CPM >1, FDR < 0.05), a total of 254 miRNAs with significantly high expression in esophageal cancer tissue and 133 miRNAs with down-regulated expression (Baidu II I/II stage, A in FIG. 1) were obtained. Due to tumor heterogeneity, miRNAs with different patient sources and differential expressions are greatly different, and the miRNAs with common and significant high expression in esophageal cancer tissues are jointly analyzed by using GSE55856, GSE43732 and Baidu II I/II stage, and found that 314miRNAs are up-regulated in a GSE55856 data set; in the GSE43732 data set, 330miRNAs were upregulated (B, C in fig. 1).
2. miRNAs differentially expressed in esophageal cancer blood
In order to establish a liquid biopsy model for esophageal cancer early screening, firstly, miRNAs which can be secreted into blood, have high abundance and are remarkably highly expressed are analyzed and screened by using an esophageal cancer serum miRNAs data set GSE122497, and 1572miRNAs are found to be up-regulated while 461miRNAs are down-regulated (D in figure 1).
Then mutual analysis is carried out on the miRNAs with common differential expression in the esophageal cancer tissue and the miRNAs with serum differential expression, the miRNAs with common differential expression are screened, and the 56miRNAs with obvious high expression in the esophageal cancer tissue and blood are obtained (E in figure 1).
3. Establishment of liquid marker combinations of candidate miRNAs
Firstly, obtaining 28 miRNAs according to the abundance of the miRNAs; secondly, 23 miRNAs were obtained according to area under ROC curve, AUC >0.7 as standard. In order to further determine the expression level of the 23 miRNAs with differential expression in the plasma of the esophageal cancer patients, selecting serum samples of esophageal cancer patients and healthy people of Chinese population, detecting the expression level of the 23 miRNAs by utilizing a qRT-PCR method, selecting CT (computed tomography) to be less than or equal to 35, and obtaining 14 potential serum miRNAs markers with extremely high diagnostic value: has-miR-17-5p, has-miR-21-5p, has-miR-22-3p, has-miR-25-3p, hsa-miR-93-5p, hsa-miR-106b-3p, has-miR-128-3p, has-miR-130b, has-miR-146b-5p, has-miR-181b-5p, hsa-miR-421, has-miR-425, has-miR-182-5p and hsa-miR-223-3p.
4. Establishment of serum/plasma miRNAs detection system
In order to establish an esophageal cancer screening model, reverse transcription is performed by a stem-loop method, and the expression level of blood miRNAs is detected by a qRT-PCR method. External reference or internal reference is usually selected for the standardization of the expression level of the miRNAs, and the external reference usually selects nematode miRNAs, namely cel-miR-39, cel-miR-54 and cel-miR-238; due to the fact that different tumors and populations have larger differences in internal references, miR-149-3p which is rich in content and highly conservative is selected as the internal reference through experiments and corresponding algorithms in the early stage and is used for data standardization. Calculating the miRNA expression amount according to the qRT PCR result, wherein the specific formula is as follows: Δ CT = CT miRNA －CT Internal reference Selecting miRNAs with CT less than or equal to 35 and obvious high expression in esophageal cancer for establishing an early diagnosis model of esophageal cancer, wherein the specific scheme is as follows:
A. serum/plasma sample collection
Serum or plasma samples of healthy populations of subjects with good health and no tumor, and patients with esophageal precancerous lesions and esophageal cancer (population: chinese population) were collected. .
B. Serum/plasma RNA extraction
Serum/Plasma RNA was extracted from esophageal cancer patients and healthy people using the miRNeasy Serum/Plasma kit (Qiagen) as follows: 250 μ l plasma/serum sample 10,000RPM, centrifuged for 5mins, cell debris removed, 200 μ l supernatant transferred to a new RNase-free centrifuge tube; 1,000. Mu.l of QIAzol lysate was added, and the final RNA was dissolved in 30. Mu.l of RNase-free water according to the procedure of the kit, and frozen at 80 ℃ until use.
SYBR Green assay
The stem-loop method and the SYBR Green amplification method are adopted to amplify the target miRNAs, so that the detection cost is saved, and the social and economic efficiency is improved; the detection method is simple and easy to implement and noninvasive, and large-scale population screening and clinical auxiliary diagnosis are carried out in esophageal cancer high-incidence areas.
1) Reverse transcription-stem-loop method
Designing a primer:
reverse primer (reverse transcription primer): each reverse primer carries a fixed sequence which can form a stem loop, and the fixed sequence is as follows: 5-CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAG-3, eight bases are added after the sequence, and the eight bases are reverse complementary sequences of the microRNA (mature body) counting eight bases from the back; forward primer (quantitative PCR primer): each forward primer also carries an immobilized sequence of: ACACTCCAGCTGGG. This sequence is followed by the remaining base sequence in the mature body except the last six. URP (unified reverse PCR primers): fixed sequence tggtcgtggagtcg.
Reverse transcription of miRNA: reverse transcription was performed using specific stem-loop primers and Bulge-LoopTM miRNA qRT-PCR starter kit (Sharp Biotech, guangzhou), 10. Mu.l reaction: from 1.2. Mu.l of RNA sample, 0.5. Mu.l of Bulge-Loop TM miRNA RT primer (10 μm), 2 μ l Reverse transcription buffer, 2 μ l RTase Mix, and 4.3 μ l RNA-free H2O; reaction conditions are as follows: 4Reacting at 2 ℃ for 60 minutes and at 70 ℃ for 10 minutes, and after the reaction is finished, storing the reverse transcription product at-20 ℃ for later use.
2) Method for detecting miRNAs expression by qRT-PCR (quantitative reverse transcription-polymerase chain reaction)
Carrying out amplification by an SYBR Green dye method, and carrying out quantitative PCR (polymerase chain reaction) instrument: quantStudio DX instrument (ABI corporation), repeated 2 times per sample. 10 μ l qRT-PCR reaction: mu.l 2X SYBR Green mix, 4. Mu.l RT Product, 0.2. Mu.l Bulge-Loop TM miRNA Forward Primer(10μM)，0.2μl Bulge-Loop TM Reverse Primer (10. Mu.M) and 0.6. Mu.l RNase-free water. Reaction conditions are as follows: pre-denaturation: 95 ℃ for 10min,40 cycles of amplification reaction: 95 ℃ for 5sec,60 ℃ for 20sec,70 ℃ for 10sec, and 70 ℃ for 95 ℃.
4. Liquid biopsy diagnosis model of miRNAs for early diagnosis of esophageal cancer
The expression of 14miRNAs in the esophageal cancer blood dataset GSE122497 was first analyzed (fig. 2).
In order to establish an optimal early diagnosis model of esophageal cancer, 3 diagnosis models were established by using the esophageal cancer blood data set GSE122497 (race: japan), logistic regression, and setting a threshold value according to ROC. The diagnostic method of the diagnostic model is as follows: the 566 esophageal cancers contained a 50% seroset of 51 esophageal precancers (stage 0), 255 cases 1, 115 cases 2, 128 cases 3 and 17 cases 4 and 4965 healthy controls: dividing 50% of the test samples into two groups at random, and respectively using the two groups as a training set and a test set; constructing a logistic regression model in the training set, and measuring the performance of the model by using a confusion matrix and a subject working characteristic curve; model optimization was performed using k-fold cross-validation regression.
The target miRNAs for the diagnostic model were as follows:
experimental group 1 was 14miRNAs: has-miR-17-5p, has-miR-21-5p, has-miR-22-3p, has-miR-25-3p, hsa-miR-93-5p, hsa-miR-106b-3p, has-miR-128-3p, has-miR-130b, has-miR-146b-5p, has-miR-181b-5p, has-miR-182-5p, hsa-miR-421, has-miR-425 and hsa-miR-223-3p.
Experimental group 2 was 7miRNAs: has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421.
Experimental group 3 was 6miRNAs: based on a diagnostic model 7miRNAs, one miRNA is randomly removed, and the miRNA comprises has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b and hsa-miR-223-3p.
Experimental group 4 was 5miRNAs: two miRNAs are randomly removed on the basis of a diagnosis model 7miRNAs, including has-miR-17-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421.
Experimental group 5 was 8miRNAs: based on a diagnostic model 7miRNAs, one miRNA is randomly added, and comprises has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p, hsa-miR-421 and has-miR-25-3p.
Experimental group 6 was 9miRNAs: two miRNAs are randomly added on the basis of a diagnosis model 7miRNAs, including has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p, hsa-miR-421, has-miR-25-3p and has-miR-106b-3p.
The results are shown in fig. 3, and 3 prediction models can achieve good diagnosis effect, wherein 7miRNAs marker combinations can achieve diagnosis performance similar to 14miRNAs marker combinations, and the specificity and the sensitivity are high. Wherein, 7miRNAs marker combinations: has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and has-miR-421, and the same diagnostic efficacy as that of the 14miRNAs marker combination can be realized (figure 3).
The results are shown in FIG. 4, and the Risk score (Risk probability) of healthy population, patients with esophageal precancerous lesions (cstage 0) and patients in clinical stages of esophageal cancer (cstage 1-4) was calculated using a training set logistic regression model. 7miRNAs marker combinations: has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and has-miR-421 can well distinguish normal healthy people from patients with esophageal precancerous lesion and early cancer (figure 4).
The results are shown in fig. 5, where the diagnostic performance of the model was tested in a validation set of 32 chinese populations of esophageal squamous cell carcinoma versus controls including 6 cases of stage 1, 13 cases of stage 2, 6 cases of stage 3, and 1 case of stage 4 versus 16 normal healthy populations, 4miRNAs marker combinations: has-miR-17-5p, has-miR-22-3p, has-miR-130b, hsa-miR-223-3p can realize the diagnostic performance of 7miRNAs marker combinations (has-miR-17-5 p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and has-miR-421) (figure 5).
According to the information shown in fig. 5, the specificity of the miRNA marker combination of the present invention for esophageal cancer diagnosis is 0.875, the sensitivity is 0.906, and the accuracy is 0.936. The prediction performance of the training set and the verification set has certain difference, probably because of the race (Japan/China) difference and the size of the sample size, the GSE122497 data set comprises 4965 healthy population controls, and 566 esophageal cancer patients are not matched completely; the validation set serum sample size was relatively small. Subsequent retrospective, multicenter and prospective studies will further optimize the model to develop liquid biopsy products based on esophageal cancer miRNAs.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The application of a reagent for detecting the combined expression level of target miRNAs markers in preparing a reagent or a kit for diagnosing esophageal precancerous lesion or esophageal cancer is characterized in that the target miRNAs comprise: at least two of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421.
2. The use as claimed in claim 1 wherein the target miRNAs comprise: at least three of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421;
preferably, the miRNAs of interest comprise: at least four of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421;
preferably, the miRNAs of interest comprise: at least five of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421;
preferably, the miRNAs of interest comprise: at least six of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421;
preferably, the miRNAs of interest further comprise: at least one of has-miR-21-5p, has-miR-25-3p, hsa-miR-106b-3p, has-miR-146b-5p, has-miR-181b-5p, has-miR-425 and has-miR-182-5 p.
3. The use according to claim 1 wherein the target miRNAs comprise any one of combinations 1 to 9;
the combination 1 consists of has-miR-17-5p and has-miR-22-3 p;
the combination 2 consists of has-miR-17-5p, has-miR-22-3p and hsa-miR-223-3 p;
the combination 3 consists of has-miR-17-5p, has-miR-22-3p, has-miR-130b and hsa-miR-223-3 p;
the combination 4 consists of has-miR-17-5p, has-miR-22-3p, has-miR-128-3p, has-miR-130b and hsa-miR-223-3 p;
the combination 5 consists of has-miR-17-5p, has-miR-22-3p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and hsa-miR-421;
the combination 6 consists of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p and has-miR-421;
the combination 7 consists of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p, hsa-miR-421 and has-miR-25-3 p;
the combination 8 consists of has-miR-17-5p, has-miR-22-3p, hsa-miR-93-5p, has-miR-128-3p, has-miR-130b, hsa-miR-223-3p, hsa-miR-421, has-miR-25-3p and has-miR-106b-3 p;
combination 9 consists of has-miR-17-5p, has-miR-21-5p, has-miR-22-3p, has-miR-25-3p, hsa-miR-93-5p, hsa-miR-106b-3p, has-miR-128-3p, has-miR-130b, has-miR-146b-5p, has-miR-181b-5p, hsa-miR-421, has-miR-425, has-miR-182-5p and hsa-miR-223-3p.
4. The use according to any one of claims 1 to 3 wherein the target miRNAs further comprise: an internal reference miRNA;
preferably, the reagent for detecting target miRNAs comprises at least one of a primer pair, a probe and a chip for detecting target miRNAs;
preferably, the primer pair comprises: a specific primer designed based on a stem-loop method;
preferably, the probe comprises: taqman fluorescent probe;
preferably, the agent for detecting miRNAs of interest further comprises: a total RNA extraction reagent, a reverse transcription reagent and a fluorescent quantitative PCR detection reagent;
preferably, the fluorescent quantitative PCR detection reagent comprises: detection reagent of SYBR Green dye method.
5. Use according to any one of claims 1 to 3, wherein the diagnosis of esophageal precancerous lesions or esophageal cancer comprises: predicting or diagnosing a precancerous esophageal lesion or esophageal cancer occurrence and/or degree of occurrence;
preferably, the esophageal cancer includes at least one of early esophageal cancer, mid-stage esophageal cancer, and late esophageal cancer;
preferably, the product comprises any one of a reagent, a kit and a predictive model.
6. A reagent or kit for predicting esophageal cancer or a precancerous lesion of esophagus, comprising: the agent for detecting the expression levels of miRNAs of interest according to any one of claims 1 to 5.
7. A method for training a predictive model of esophageal precancerous lesions or esophageal cancer, comprising:
obtaining a detection result of the expression level of the target miRNAs in the training sample and a corresponding labeling result thereof; wherein the labeled result is a label representing whether or not esophageal precancerous lesion or esophageal cancer occurs or occurs in a sample, and the target miRNAs are the target miRNAs as claimed in any one of claims 1 to 3;
inputting the detection result into a pre-constructed prediction model to obtain a prediction result; wherein the prediction model is a machine learning model which can predict whether the sample is the esophageal precancerous lesion or the esophageal cancer or whether the sample is the esophageal cancer or the esophageal cancer degree according to the expression level of the target miRNAs;
and updating parameters of the prediction model based on the labeling result and the prediction result.
8. A device for predicting esophageal precancerous lesion or esophageal cancer, comprising:
the acquisition module is used for acquiring the detection result of the expression level of the target miRNAs of the sample to be detected; wherein the target miRNAs are the target miRNAs of any one of claims 1 to 3;
a prediction module for inputting the detection result of the expression level of the target miRNAs into the prediction model obtained by the training method as claimed in claim 6 to obtain the prediction result of the sample to be tested.
9. An electronic device, comprising: a processor and a memory; the electronic device comprises a processor and a memory; the memory for storing a program which, when executed by the processor, causes the processor to implement a training method for a predictive model of esophageal precancerous lesions or esophageal cancer or a prediction of esophageal cancer according to claim 7;
the prediction of esophageal precancerous lesions or esophageal cancer comprises: obtaining a detection result of the expression level of target miRNAs of a sample to be detected; wherein the target miRNAs are the target miRNAs as set forth in any one of claims 1 to 3; inputting the detection result of the expression level of the target miRNAs into a prediction model obtained by training the training method according to claim 6, and obtaining the prediction result of the sample to be tested.
10. A computer-readable medium, characterized in that the computer-readable medium has stored thereon a computer program which, when being executed by a processor, implements a method of training a predictive model of esophageal cancer or a pre-esophageal cancer lesion according to claim 7 or a prediction of esophageal cancer or a pre-esophageal cancer according to claim 9.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012135091A2 (en) * 2011-03-28 2012-10-04 The Johns Hopkins University Serum-based mirna microarray and its use in diagnosis and treatment of barrett's esophagus (be) and esophageal adenocarcinoma (eac)
CN105176983A (en) * 2015-09-23 2015-12-23 河南师范大学 Kit for detecting esophageal squamous carcinoma associated serum miRNAs genes
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