CN111471764A

CN111471764A - Biomarker combination for screening or auxiliary diagnosis of gastric cancer, kit and application thereof

Info

Publication number: CN111471764A
Application number: CN201911380514.3A
Authority: CN
Inventors: 陈锦飞; 高峰; 陈玥彤
Original assignee: Shenzhen Anke Weiyuan Medical Technology Co ltd
Current assignee: Shenzhen Anke Weiyuan Medical Technology Co ltd
Priority date: 2019-12-27
Filing date: 2019-12-27
Publication date: 2020-07-31
Anticipated expiration: 2039-12-27
Also published as: CN111471764B

Abstract

The invention discloses a biomarker combination for screening or auxiliary diagnosis of gastric cancer, a kit and application thereof, and relates to the technical field of gastric cancer detection, wherein the biomarker combination comprises the combination of any three or more miRNAs selected from miR18a, miR181b, miR335, miR93 and miR146 b; the biomarker combination can effectively improve the effectiveness and/or accuracy of gastric cancer screening or diagnosis, and provides a new way for gastric cancer detection.

Description

Biomarker combination for screening or auxiliary diagnosis of gastric cancer, kit and application thereof

Technical Field

The invention relates to the technical field of gastric cancer detection, in particular to a biomarker combination for screening or auxiliary diagnosis of gastric cancer, a kit and application thereof.

Background

Gastric cancer (gastric cancer) is a malignant tumor originated from gastric mucosal epithelium and is one of the most prevalent malignant tumors in the world.

Despite the continual improvement and renewal of surgical, chemotherapeutic and radiotherapeutic treatments, the prognosis of patients with advanced gastric cancer is still not ideal. Patients with early gastric cancer often fail to find the symptoms, which is not obvious, resulting in missing the optimal treatment time.

Therefore, the method for effectively screening or assisting in diagnosing the gastric cancer has important significance for improving the long-term survival rate of the gastric cancer patient.

In view of this, the invention is particularly proposed.

Disclosure of Invention

The invention aims to provide a biomarker combination for screening or auxiliary diagnosis of gastric cancer, a kit for screening or auxiliary diagnosis of gastric cancer, application of a reagent for detecting target miRNA in a sample in preparation of the kit for screening or auxiliary diagnosis of gastric cancer, a method for detecting target miRNA in a sample and a method for calculating a gastric cancer risk score.

The invention is realized by the following steps:

in a first aspect, embodiments provide a biomarker combination for screening or aiding diagnosis of gastric cancer, the biomarker combination comprising a combination of any three or more mirnas selected from: miR18a, miR181b, miR335, miR93, and miR146 b;

the sequences of miR18a, miR181b, miR335, miR93 and miR146b are sequentially shown in SEQ ID Nos. 1-5.

In a second aspect, embodiments provide a kit for screening or aiding diagnosis of gastric cancer, the kit comprising a primer pair and/or a probe for detecting a biomarker combination as described in the previous embodiments.

In a third aspect, the embodiments provide the use of a reagent for detecting a miRNA of interest in a sample, said miRNA of interest comprising a biomarker combination as described in the preceding embodiments, in the manufacture of a kit for screening or aiding in the diagnosis of gastric cancer.

In a fourth aspect, embodiments provide a method for detecting a miRNA of interest in a sample, comprising obtaining an expression level of the miRNA of interest in the sample, the miRNA of interest comprising a biomarker combination according to the preceding embodiments.

In a fifth aspect, embodiments provide a method, application and electronic device for calculating a gastric cancer risk score, the method comprising:

obtaining an expression amount of a target miRNA in a sample to be detected, wherein the target miRNA comprises the biomarker combination according to the previous embodiment;

calculating the risk score of the sample to be tested according to the biomarker combination;

the invention has the following beneficial effects:

the embodiment of the invention provides a biomarker combination for screening or auxiliary diagnosis of gastric cancer, a kit thereof and application thereof, wherein the biomarker combination comprises a combination of any three or more miRNAs selected from miR18a, miR181b, miR335, miR93 and miR146 b; the biomarker combination can effectively improve the effectiveness and/or accuracy of gastric cancer screening or diagnosis, and provides a new way for gastric cancer detection.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used 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 for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

FIG. 1 is a high throughput sequenced miRNA expression profile of example 1;

FIG. 2 is a miRNA expression profile of 11 miRNAs in example 1;

FIG. 3 is a comprehensive evaluation of ROC, -log 10P-value and log2 fold change for 11 miRNAs in example 1;

FIG. 4 is the diagnosis result calculated by the random forest classifier according to the detection values of 10 miRNAs in the discovery queue in example 1;

fig. 5 is the diagnosis result calculated by the random forest classifier according to the detection values of 10 mirnas in

validation cohorts

1 and 2 in example 1, wherein a and B in fig. 5 are the diagnosis result of validation cohort 1, and C and D in fig. 5 are the diagnosis result of validation cohort 2;

FIG. 6 shows the difference in expression levels of 5 miRNAs in example 1 between a gastric cancer patient and a healthy human;

FIG. 7 shows the results of calculation based on the expression levels of 3 miRNAs using a gastric cancer risk prediction model in example 1;

FIG. 8 shows the results of detection of 3-miR, CEA and CA19-9 in validation example 1;

fig. 9 is the risk scores at various stages of gastric cancer measured in validation example 2.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. The examples, in which specific conditions are not specified, were conducted under 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 a biomarker combination for screening or auxiliary diagnosis of gastric cancer, wherein the biomarker combination comprises any three or more miRNAs selected from the following miRNAs: miR18a, miR181b, miR335, miR93, and miR146 b;

It is to be noted that the phrase "biomarker combination including a combination of any three or more mirnas selected from the group consisting of: the biomarker combinations include: any three of the above 5 mirnas (miR18a, miR181b, miR335, miR93, and miR146b) in combination, any 4 of the mirnas in combination, or 5 of the mirnas in combination.

In alternative embodiments, the biomarker combination is miR18a, miR181b, and miR 335.

The embodiment of the invention also provides a kit for screening or auxiliary diagnosis of gastric cancer, which comprises a primer pair and/or a probe for detecting the biomarker combination of any one of the preceding embodiments.

In alternative embodiments, the primer pair comprises any one or a combination of primer pairs of:

the primer pairs 1-5 are used for sequentially detecting miR18a, miR181b, miR335, miR93 and miR146b in a sample to be detected.

In alternative embodiments, the probes include any one or combination of probes selected from the group consisting of:

and probes 1-5 for sequentially detecting miR18a, miR181b, miR335, miR93 and miR146b in a sample to be detected.

The embodiments of the present invention also provide the use of a reagent for detecting a miRNA of interest in a sample, the miRNA of interest including a biomarker combination as described in any one of the preceding embodiments, in the preparation of a kit for screening or assisted diagnosis of gastric cancer.

In an alternative embodiment, the reagent for detecting a miRNA of interest in a sample comprises a primer pair and/or a probe for detecting a miRNA of interest.

The primer pair and/or probe are as described in the previous embodiments and will not be described herein.

The present embodiments also provide a method for detecting a target miRNA in a sample, including obtaining an expression level of the target miRNA in the sample, where the target miRNA includes a biomarker combination according to any one of the preceding embodiments.

In alternative embodiments, the detection method is aimed at diagnosis and/or treatment of a non-disease.

In addition, the embodiment of the invention also provides a method for calculating the gastric cancer risk score, application thereof and electronic equipment, wherein the method comprises the following steps:

obtaining an expression level of a target miRNA in a sample to be tested, wherein the target miRNA comprises the biomarker combination according to any one of the preceding embodiments;

in an alternative embodiment, when the mirnas of interest are miR18a, miR181b, and miR335, the risk score is calculated by the formula:

logit(P)＝(-0.275+(0.596×E_miR18a)+(0.465×E_miR181b)+(0.216×E_miR335))；

wherein, location (P) is a score of risk value, E_miR18aIs the expression level of miR18a in the sample, E_miR181bIs the expression level of miR181b in the sample, E_miR335Is the expression level of miR335 in the sample.

In the calculation of the gastric cancer risk score, the gastric cancer risk score can be calculated from the expression levels of the mirnas in the biomarker combinations without using the internal reference miRNA to calculate the relative expression levels of the mirnas in the biomarker combinations.

The features and properties of the present invention are described in further detail below with reference to examples.

Example 1

For information on patients and healthy participants in the present example, refer to table 1.

TABLE 1 statistics of patients and healthy participants

Remarking: CEA: is a carcinoembryonic antigen; CA 19-9: is cancer antigen 19-9; positive and negative values: is the mean. + -. standard error.

The biomarker discovery cohort was from the TCGA database, consisting of small RNA sequencing data from 477 gastric cancer patients, for screening miRNAs for early diagnosis of gastric cancer. From the high-throughput sequenced miRNA expression profiles (from 436 GC tissues and 41 normal tissues), 117 differentially expressed (FDR-adjusted P <0.05, absolute log2 fold change >1) mirnas were found among all the expressed mirnas, see fig. 1.

Among 117 miRNAs, according to FDR-adjusted P<1×10^-5,AUC>0.9, screening conditions for up-regulation of expression in GC, screening 9 mirnas: miR21, miR196a-1, miR146b, miR196 b, miR135b, miR181b, miR181a, miR93 and miR335, on the basis of which 2 mirnas, namely miR196a-2 and miR18a, were additionally incorporated, although their expression differences (between patients and healthy persons) were small (AUC ═ 0.899 and 0.865), miR196a-2 and miR18a was significantly highly expressed (log2 fold change) in GC (gastric cancer) patients>2， FDR-adjusted P<1×10^-5) Please refer to fig. 2 and fig. 3.

After cross-platform cross validation, 11 miRNAs are combined into 10, miR196a-1 is equivalent to miR196a-2, and 10 miRNAs are miR18a, miR21, miR196a, miR146b, miR196 b, miR135b, miR181b, miR181a, miR93 and miR 335.

To confirm the predictive ability of the above 10 mirnas in diagnosing GC patients, a Random classifier was trained from the Z-normalized miRNA expression levels of 41 paired gastric and paracancer normal tissues in the cohort by analyzing two independent datasets (validation datasets 1 and validation datasets 2,

validation cohorts

1 and 2, where validation cohort 1 included 40 tumor tissue samples and 40 non-tumor tissue samples, which were miRNA detection data obtained from Agilent-019118 Human mixed gastric tissue platform, and validation cohort 2 included 37 primary gastric cancers and 4 gastric mucosal tissues from miRNA detection data of the above mirnas in mirnas _ Human _ V2 miRNA microarrays (National DNA-Microarray Facility).

The 10 mirnas showed stable diagnostic values in both the entire discovery cohort (AUC 0.984, please refer to fig. 4) and the two validation cohorts (AUC values 0.939 and 1.000, respectively; please refer to fig. 5) calculated by the random forest classifier.

The expression level of the 10 miRNAs in 50 pairs of fresh frozen GC tissues and matched normal tissues is detected by qRT-PCR, and 9 miRNAs in 10 tissues are more highly expressed in gastric cancer tissues compared with the normal tissues. 9 mirnas are miR21, miR196a, miR146b, miR196 b, miR181b, miR181a, miR93, miR335 and miR18 a;

by analyzing the circulating miRNA expression profiles of 115 gastric cancer patients and 115 healthy people (randomly drawn from 2732 healthy people) in the common serum cohort (GSE106817), 5 of the above 9 mirnas (miR18a, miR93, miR146b, miR181b and miR335) were found to be significantly up-regulated in GC patients, please refer to fig. 6. See table 2 for sequences of 5 mirnas.

TABLE 2 MiRNA sequences

3 combinations of mirnas (miR18a, miR181b and miR335) were selected from the above 5 mirnas by logistic regression analysis (logistic regression with elastic netregularization, alpha 0.55 and lambda 0.052, optimized by 25 bootstrapping analysis).

The gastric cancer risk prediction model is obtained by a multiple regression model, namely logit (P) ═ (-0.275+ (0.596 × E)_miR18a)+(0.465×E_miR181b)+(0.216×E_miR335) ); wherein, location (P) is a score of risk value, E_miR18aIs the expression level of miR18a in the sample, E_miR181bIs the expression level of miR181b in the sample, E_miR335Is the expression level of miR335 in the sample.

The gastric cancer risk score calculated by this formula showed good diagnostic ability (AUC 0.84, 95% CI 0.79-0.89; OR 14.09, 95% CI 7.43-26.72), see fig. 7.

Example 2

A method of calculating a gastric cancer risk score comprising the steps of:

obtaining the expression quantity of target miRNA in a sample to be detected, wherein the target miRNA comprises the biomarker combination (miR18a, miR181b and miR335) as described in example 1;

the target miRNAs are miR18a, miR181b and miR335, and the risk score is calculated by the formula of logit (P) ═ 0.275+ (0.596 × E)_miR18a)+(0.465×E_miR181b)+(0.216 ×E_miR335))；

Wherein, logic (P) is a risk score value, E in the formula_miR18a"is of miR18a in the sampleExpression amount, E_miR181bAnd E_miR335And so on.

Example 3

A method for detecting a target miRNA in a sample is provided.

(1) Collecting serum samples

Whole blood was collected in Sarstedt containing a coagulant

A Serum-Gel 9m L collection tube (cat. No.02.1388, note that in other embodiments, other collection tubes without coagulant or EDTA may be used) is completely coagulated, and the tube containing the coagulant is left at room temperature (20 ℃) for 30min (note that the tube containing the coagulant is left for 10min, and the tube without the coagulant is left for at least 30 min).

Centrifuging the test tube at 1900 × g and 3000rpm at 4 ℃ for 10min, transferring the supernatant serum into a new test tube with a conical bottom (3-5 ml serum can be obtained from 10ml whole blood), centrifuging the plasma in the test tube with the conical bottom at 16000 × g and 4 ℃ for 10min, transferring the supernatant into a new test tube, and storing at 2 ℃ or storing the serum at minus 80 ℃ for a long time after subpackaging.

Prior to use, the serum was dissolved at 20 ℃ C. (15 ℃ C. about.25 ℃ C.) to remove the condensed protein, the thawed serum sample was centrifuged at 16000 × g, 4 ℃ C. for 5min, and the supernatant was transferred to a new tube to start nucleic acid extraction.

(2) Nucleic acid extraction

RNA extraction was performed using a nucleic acid extraction Kit, and QIAGEN miRNeasy Serum/Plasma Kit (Cat No. ID:217184) was used in this example.

Adding 1ml of the serum obtained In step (1) into a nontoxic and enzyme-free EP tube, adding 5 times of QIAzol L ysisReagent, vortex mixing to obtain a mixture, standing the mixture at room temperature (25 deg.C) for 5min, adding 3.5 μ l of mirneasysSerum/Plasma Spike-In Control (1.6 × 10)⁸copies/mul working solution), adding chloroform with the same volume as the original serum, closing the cover, vortexing for 15s, placing the test tube at room temperature for 3min, 12000 × g, centrifuging at 4 deg.C for 15min, and separating the sample into upper, middle and lower layers, the upper layerThe layer is colorless, water-like phase and contains RNA; the middle layer is white; the lower layer is a red organic layer. Transferring the upper water sample layer (600 μ l) to a new collection tube, adding 100% ethanol (900 μ l) with 1.5 times of volume, and mixing to obtain a mixed solution.

The mixture was transferred to RNeasy MinElute spin column in a 2ml collection tube, at most 700. mu.l each transfer, closed at room temperature > 8000 g 8000 × g > 10000rpm, centrifuged for 15s, the filtrate removed and the procedure repeated until the sample was used up.

Adding 700 μ l buffer RWT into RNeasy MinElute spin column, closing the lid, not less than 8000 × g, not less than 10000rpm, centrifuging at room temperature for 15s, removing the filtrate, adding 500 μ l buffer RPE into RNeasy MinElute spin column, closing the lid, not less than 8000 × g (not less than 10,000rpm), centrifuging at room temperature for 15s, removing the filtrate, adding 500 μ l 80% ethanol (80% ethanol is prepared by 100% ethanol and RNase-freewater), closing the lid, 8000 × g (not less than 10,000rpm), centrifuging at room temperature for 2min, and removing the filtrate.

RNeasy MinElute spin column was placed in a new 2ml collection tube. Spin column was uncapped and centrifuged at full speed for 5 min. And removing the filtrate and the collecting pipe. The RNeasy MinElute spin column was placed in a new 1.5ml collection tube. Mu.l of RNase-free water was added directly to the center of the filter membrane, the lid was closed, and the RNA was eluted by centrifugation at full speed for 1 min.

(3)RT-PCR

And (3) carrying out reverse transcription and RT-PCR on the RNA extracted in the step (2) by adopting an RNA reverse transcription kit. Biomarker combinations for screening or aiding diagnosis of gastric cancer are selected from: miR18a, miR181b, miR335, miR93 and miR146b, the kit TaqMan mirnas assay ID used for reverse transcription is shown in table 3 (it is to be noted that any primer and/or probe combination for detecting the MiRNA biomarker combinations provided herein is within the protection range claimed herein), and the RNA reverse transcription system is shown in table 4.

TABLE 3 TaqMan MiRNA Assays ID

miR-18a	002422
		miR-146b	001097
miR-181b	001098
		miR-335	000546
miR-93	001090

TABLE 4 reverse transcription System

	Sample(s)
		100mM dNTPs	0.06μl
MultiScribe^TMReverse transcriptase	0.42μl
		10 × RT buffer	0.6μl
RNase inhibitors	0.06μl
		20 × RT primer	0.11μl
Nuclease-free water	3.55μl
		RNA	1.2μl
Total volume	6μl

RNA reverse transcription reaction conditions: 30min at 16 ℃; 30min at 42 ℃; 5min at 85 ℃; the reaction was terminated at 4 ℃.

The Taqman qPCR system is shown in table 5.

TABLE 5 Taqman qPCR System

Taqman qPCR reaction conditions: at 50 ℃ for 2 min; at 95 ℃ for 10 min; denaturation at 95 ℃ for 15 s; 60 ℃ extension for 60s, denaturation-extension for 40 cycles.

Verification example 1

The detection ability of the 3 miRNA marker combinations provided in example 1 was verified.

176 gastric cancer patients (including GC patients at four stages of Stage I, II, III and IV) and 173 healthy people were prospectively collected.

The expression quantity of 3-miR (miR18a, miR181b and miR335) is detected on the collected prospective sample by adopting the qPCR method provided by the embodiment 3, the risk coefficient is calculated by the calculation method provided by the embodiment 2, two groups of control examples are set in a control mode, CEA and CA19-9 are set in the control mode, and the control examples are detected by adopting the existing detection methods of CEA and CA19-9 respectively in the 2 groups of control examples.

Please refer to fig. 8, fig. 9 and table 6 for the detection results.

TABLE 6 test results

	3-miR signature	3-miR signature(stage I)	CEA	CA19-9
					AUC	0.84(0.80-0.88)	0.85(0.79-0.90)	0.65(0.57-0.73)	0.67(0.59-0.76)
Odds Ratio	10.12(6.14-16.66)	14.94(7.21-30.93)	2.83(1.57-5.12)	0.14(0.07-0.27)
					Specificity	0.81(0.61-0.92)	0.87(0.60-0.94)	0.68(0.33-0.96)	0.85(0.80-0.96)
Sensitivity	0.70(0.60-0.91)	0.69(0.59-0.94)	0.57(0.27-0.90)	0.56(0.42-0.68)
					Accuracy	0.76(0.73-0.81)	0.83(0.67-0.88)	0.63(0.56-0.71)	0.73(0.69-0.80)
NPV	0.73(0.69-0.88)	0.90(0.88-0.98)	0.68(0.63-0.83)	0.73(0.69-0.79)
					PPV	0.79(0.70-0.90)	0.63(0.41-0.77)	0.57(0.49-0.84)	0.73(0.67-0.89)

As can be seen from FIG. 8, the biomarker combination consisting of miR18a, miR181b and miR335 is shown to exhibit stable and effective detection capability, and when the combination is detected by all GC patients, the AUC value is 0.84, the 95% CI is 0.80-0.88, the sensitivity (sensitivity) is 70%, the specificity (specificity) is 82%, and the combination is significantly better than CEA and CA 19-9.

The biomarker combination also has good detectability in distinguishing Stage I GC patients from healthy populations, with AUC values of 0.85, 95% CI of 0.79-0.90, sensitivity (sensitivity) of 69%, and specificity of 87%.

As can be seen in FIG. 9, there was a significant increase in the risk score for GC patients in all four stages compared to the healthy control group (all P <0.0001, one-side Student's t-test).

One-factor and multi-factor analysis

Non-invasive detection of 3-miR, CEA and CA19-9 on GC patients in a prospective validation cohort and GC patients in Stage I was performed for single-factor and multi-factor analysis, and the results are shown in Table 7.

TABLE 7 Single and Multi-factor analysis

As can be seen from Table 7, the 3-miR marker combination is a very effective prediction index in detecting gastric cancer at each stage.

Cost effectiveness analysis (cost effectiveness analysis, CEA)

The cost benefit of the miRNA marker combination provided by the embodiment 1 of the invention in gastric cancer screening is evaluated by verifying the sensitivity and specificity obtained by the cohort and performing cost effect analysis under the clinical practical background of continental China, the target high-risk population is male 50-75 years old in China, Markov model-based CEA shows that compared with the current screening means, the miRNA marker (3-miR) provided by the embodiment of the invention is cost-effective in large-scale application [ ICER (CNY 22114.5/QA L Y ], please refer to Table 8.

TABLE 8 CEA results

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.

SEQUENCE LISTING

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SHENZHEN ANKE WEIYUAN MEDICAL TECHNOLOGY Co.,Ltd.

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Claims

1. A biomarker combination for screening or aiding diagnosis of gastric cancer, comprising a combination of any three or more mirnas selected from: miR18a, miR181b, miR335, miR93, and miR146 b;

2. The biomarker combination for screening or aiding diagnosis of gastric cancer according to claim 1, comprising miR18a, miR181b and miR 335.

3. A kit for screening or aiding diagnosis of gastric cancer, comprising primer pairs and/or probes for detecting the biomarker combinations according to claim 1 or 2.

4. The kit for screening or aiding diagnosis of gastric cancer according to claim 3, wherein the primer pair comprises any one or more of the following primer pairs in combination:

the primer pair 1-5 is used for sequentially detecting miR18a, miR181b, miR335, miR93 and miR146b in a sample to be detected.

5. The kit for screening or aiding diagnosis of gastric cancer according to claim 3 or 4, wherein the probes comprise any one or more of the following probes in combination:

6. Use of a reagent for detecting a miRNA of interest in a sample for the manufacture of a kit for screening or assisted diagnosis of gastric cancer, wherein the miRNA of interest comprises a biomarker combination according to claim 1 or 2.

7. Use of the reagent for detecting the miRNA of interest in the sample for preparing the kit for screening or assisting in diagnosing gastric cancer according to claim 6, wherein the reagent for detecting the miRNA of interest in the sample comprises a primer pair and/or a probe for detecting the miRNA of interest.

8. A method for detecting a miRNA of interest in a sample, comprising obtaining an expression level of the miRNA of interest in the sample, wherein the miRNA of interest comprises the biomarker combination of claim 1 or 2.

9. The method of claim 8, wherein the method is for the purpose of diagnosis and/or treatment of a non-disease condition.

10. A method for calculating a gastric cancer risk score, application thereof and an electronic device, wherein the method comprises the following steps:

obtaining the expression amount of target miRNA in a sample to be tested, wherein the target miRNA comprises the biomarker combination as claimed in claim 1 or 2;

preferably, when the miRNA of interest is the biomarker combination of claim 2, the risk score is calculated by the formula of logit (P) ═ 0.275+ (0.596 × E)_miR18a)+(0.465×E_miR181b)+(0.216×E_miR335))；

Wherein, location (P) is a score of risk value, E_miR18aOf miR18a in the sampleExpression amount, E_miR181bIs the expression level of miR181b in the sample, E_miR335Is the expression level of miR335 in the sample.