CN111560467A

CN111560467A - Application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B

Info

Publication number: CN111560467A
Application number: CN202010184874.2A
Authority: CN
Inventors: 王通; 罗彦彰; 林欣仪; 崔毅峙
Original assignee: Guangzhou Supbio Bio Technology And Science Co ltd; Jinan University
Current assignee: Guangzhou Supbio Bio Technology And Science Co ltd; Jinan University
Priority date: 2020-03-17
Filing date: 2020-03-17
Publication date: 2020-08-21
Anticipated expiration: 2040-03-17
Also published as: CN111560467B

Abstract

The invention discloses application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B for the first time. By jointly detecting miR-21 and miR-92a in serum sEV, CHB and HBV-related HCC can be efficiently diagnosed, and CHB and HCC can be distinguished in HBV-infected people. Moreover, the diagnostic efficacy is high, the AUC reaches 0.88, and the loading amount is low, and only 200 mu L of serum is needed. The CHB diagnosis and HBV related HCC diagnosis have higher efficacy, can be distinguished from healthy people, and the AUC reaches more than 0.97. The loading was low and only 200. mu.L serum was required.

Description

Application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B

Technical Field

The invention belongs to the technical field of biology, and particularly relates to application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B.

Background

Exosomes are Extracellular Vesicles (EVs) secreted by cells, are approximately 30-150nm in size, contain functional nucleic acids and proteins, and are an important loop in intercellular communication. Tumor cells can specifically secrete cancer-related biomolecules through exosomes, thereby promoting the occurrence and development of cancer. Moreover, the exosome can be secreted into peripheral blood, has traceability, and can detect the exosome derived from the tumor by a blood drawing means. Therefore, exosomes are an important source of disease biomarkers.

In exosome-related studies, since there are no exosome-specific biomarkers yet and it is difficult to track whether isolated exosomes originate from inclusion bodies (endosomes), more and more studies are preferred to replace "exosomes" with the word "small extracellular vesicles (sEV). This sEV refers to an EV of less than 200 nm.

Micro RNA (miRNA) is a small single-stranded segment RNA that participates in the regulation of biological processes. In many cancers, the miRNA profile of tumor cells is altered. There is a large body of literature that suggests mirnas in serum or plasma can be potential biomarkers for tumors. In peripheral blood, mirnas exist mainly in 3 forms: free mirnas, mirnas bound to proteins, and mirnas present in EVs. However, free mirnas are easily degraded by rnases in vivo, mirnas bound to proteins are difficult to detect by isolation, and only mirnas present in EVs are the most stable and easily detected. Therefore, direct detection of miRNA in serum or plasma is relatively interfered, and there may be cases where some trace amount of miRNA is difficult to detect, or the amount of the same miRNA varies greatly among different persons. In fact, the trend of the variation reported in different literatures for the amount of miRNA of the same serum or plasma may be opposite. For example, there is a literature that miR-92a in serum is significantly up-regulated in liver cancer, and there is a literature that miRNA is significantly down-regulated. Therefore, the detection of the miRNA in the serum/plasma sEV is more accurate and reliable than the direct detection of the miRNA in the serum/plasma, so that the search of the miRNA biomarker in the serum/plasma sEV has more important significance.

Furthermore, the amount of a certain miRNA in serum/plasma sEV is difficult to predict and needs to be validated with large clinical samples. In the study of serum/plasma sov mirnas, conflicting conclusions may appear in different literature. For example, miR-21 and miR-92a in serum/plasma sEV are widely reported biomarkers for colorectal cancer, but our application of a large number of clinical samples demonstrates that the diagnostic efficacy of miR-21 and miR-92a in serum sEV is low (see example 2 for details). Therefore, conclusions drawn from a few documents or samples cannot be easily generalized to all clinical samples.

Chronic Hepatitis B (CHB) is a chronic inflammatory disease of the liver caused by persistent infection with Hepatitis B Virus (HBV). HBV infected people are huge, about 20 million people are infected with HBV all over the world, and 2.4 million people are chronic HBV infected people. Compared to other liver diseases, CHB and HBV-associated HCC are the most prevalent and most health-affecting diseases. How to monitor CHB and screen for HCC early in the process to avoid CHB from deteriorating into HCC is a great problem in the field.

Currently, serological detection of HBV infection or CHB mainly comprises two halves of hepatitis B, namely detection of HBV-associated antigen antibodies. Serological detection of HCC is mainly based on alpha-fetoprotein (AFP). Such immunoassay-based detection is difficult to detect a trace amount of protein and also difficult to detect a very early lesion, so that diagnosis and treatment are delayed and the optimal period is missed. The method is very important for searching more sensitive and efficient serum biomarkers.

miRNA in serum EV has great diagnosis prospect for HBV-related HCC, and a plurality of documents show that miRNA in serum EV has potential diagnosis efficacy. However, these documents have problems such as insufficient sample size, no consideration of HBV infection in designing experimental and control groups, unclear diagnostic efficacy (no ROC curve), and low AUC.

Disclosure of Invention

The first aspect of the invention aims at providing a detection index (serum EV miR-21 and miR-92a) which is easy to realize clinically aiming at the high-efficiency index that the hepatitis B has no early warning about the conversion into the liver cancer, and screening patients who will be deteriorated into the liver cancer in HBV infected people.

The second aspect of the invention aims to provide the application of the primer and the probe of miR-21 and the primer and the probe of miR-92a in preparing a reagent for detecting and distinguishing HBV-related liver cancer and hepatitis B.

The third aspect of the invention aims to provide

The technical scheme adopted by the invention is as follows:

the first aspect of the invention is to provide the application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B.

In the second aspect of the invention, the application of the primer and the probe of miR-21 and the primer and the probe of miR-92a in preparing a reagent for detecting and distinguishing HBV-related liver cancer and hepatitis B is provided.

In the third aspect of the invention, the application of the primer and the probe of miR-21 and the primer and the probe of miR-92a in the preparation of a kit for detecting and distinguishing HBV-related liver cancer and hepatitis B.

According to the use of any one of the first to third aspects of the invention, miR-21 and miR-92a are miR-21 and miR-92a in serum.

According to the use of any one of the first to third aspects of the invention, miR-21 and miR-92a are miR-21 and miR-92a in small extracellular vesicles in serum.

According to the application of the second or third aspect of the invention, the sequences of the primer and the probe of the miR-21 are as follows:

reverse transcription primer miR-21-RT:

GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCAACA(SEQ ID NO:1)，

an upstream primer miR-21-F: GCCCGCTAGCTTATCAGACTGATG (SEQ ID NO:2),

a downstream primer miR-21-R: GTGCAGGGTCCGAGGT (SEQ ID NO:3),

and probe miR-21-P: CTGGATACGACTCAACA (SEQ ID NO: 4).

According to the application of the second or third aspect of the invention, the primer and probe sequences of miR-92a are as follows:

reverse transcription primer miR-92 a-RT:

GTCGTATCCAGTGCTGGGTCCGAGTGATTCGCGCTGGATACGACACAGGCCG(SEQ ID NO:5)，

an upstream primer miR-92 a-F: CCCTGTATTGCACTTGTCC (SEQ ID NO:6),

the downstream primer miR-92 a-R: CAGTGCTGGGTCCGAGTGA (SEQ ID NO:7),

and probe miR-92 a-P: CGGCCTGTGTCGTATCCA (SEQ ID NO: 8).

In a fourth aspect of the present invention, there is provided a method for detecting and differentiating HBV-associated liver cancer from hepatitis B for non-disease diagnostic purposes, comprising the steps of:

s1, extracting small extracellular vesicles from a serum sample;

s2, extracting miRNA in the small extracellular vesicles in the step S1;

s3, carrying out reverse transcription on miR-21 and miR-92a in miRNA in the step S2 to obtain cDNA;

s4, carrying out qPCR detection on miR-21 and miR-92a on cDNA in S3 by adopting the primers and the probes in

claims

6 and 7, and distinguishing HBV-related liver cancer from hepatitis B according to the detection result.

According to the method of the fourth aspect of the present invention, the method for differentiating HBV-related liver cancer from hepatitis B in step S4 is:

when the quantitative result of miR-21 and the quantitative result of miR-92a in the sample are not obviously different from the quantitative result of a healthy person, the source of the sample is proved not to suffer from HBV-related liver cancer and hepatitis B;

when the quantitative result of miR-21 and the quantitative result of miR-92a in the sample are obviously different from the quantitative result of a healthy person, and the quantitative result of miR-21 in the sample is obviously different from the quantitative result of the hepatitis B group, the source of the sample is proved to have HBV-related liver cancer;

when the quantitative result of the miR-21 and the quantitative result of the miR-92a in the sample are obviously different from the quantitative result of a healthy person, and the quantitative result of the miR-21 in the sample is not obviously different from the quantitative result of the HBV-related liver cancer group, the source of the sample is indicated to suffer from hepatitis B.

after log10 conversion, the miR-21 quantitative values of the samples are respectively substituted into x1 positions in formula 1, formula 2 and formula 3, and after log10 conversion, the miR-92a quantitative values of the samples are respectively substituted into x2 positions in formula 1, formula 2 and formula 3;

when p1 in formula 1 is less than 0.1 and p2 in formula 2 is less than 0.12, it indicates that the sample source does not suffer from HBV-related liver cancer and hepatitis B;

when p1 in formula 1 is greater than 0.1 and p3 in formula 3 is greater than 0.31, it indicates that the sample is derived from HBV-associated liver cancer;

when p2 in formula 2 is greater than 0.12 and p3 in formula 3 is less than 0.31, the source of the sample is hepatitis B;

equation 1:

equation 2:

equation 3:

the invention has the beneficial effects that:

the invention discovers for the first time that the combination of miR-21 and miR-92a in serum sEV can efficiently diagnose CHB and HBV-related HCC, and can also distinguish CHB from HCC in HBV-infected people. The discovery has high diagnosis effect, has the potential of prewarning HCC in CHB people, and screens patients who will deteriorate into liver cancer in HBV infected people. Can distinguish CHB from HCC in HBV infected people, and has the potential of pre-warning HCC in CHB people. And the diagnosis efficacy is high, and the AUC reaches 0.88. The CHB diagnosis and HBV related HCC diagnosis have higher efficacy, can be distinguished from healthy people, and the AUC reaches more than 0.97. The loading was low, requiring only 200. mu.L of serum.

Drawings

FIG. 1 analysis of the difference of miRNA quantitative values of liver cancer, hepatitis and healthy group. Note: the quantitative values were analyzed after log10 transformation, wherein, HBV-associated liver cancer group (HCC) n was 47, chronic hepatitis b group (CHB) n was 77, and healthy group (Normal) n was 569. FIG. A, F: and (4) quantifying the distribution of the values. Represents P <0.05 compared to any other group, and the inter-group difference analysis was performed using Kruskal-Wallis test, and further multiple comparisons were performed using Dunn's test. FIG. B, G: bootstrap analysis of quantitative values. 10000 resampling were performed by the bias-corrected and averaged (BCa) bootstrap method and the 95% confidence interval of the mean was determined. Mean of the quantified values and line segment of 95% confidence interval of the mean after resampling. FIGS. C to E, H to J: ROC curve analysis of quantitative values. The red circle represents the threshold chosen for the maximum Youden's index of the index, which has been plotted along with its corresponding sensitivity and specificity. FIGS. K to M: ROC curve analysis combining miR-21 and miR-92a quantitative values.

Figure 2 quantitative miRNA value differential analysis for colorectal, adenoma and healthy groups. Note: the quantitative values were analyzed after log10 transformation, and the colorectal cancer group (CRC) n was 288, the Adenoma group (Adenoma) n was 135, and the healthy group (Normal) n was 569. FIG. A, E: and (4) quantifying the distribution of the values. Represents P <0.05 compared to any other group, and the inter-group difference analysis was performed using Kruskal-Wallis test, and further multiple comparisons were performed using Dunn's test. FIGS. B to D, F to H: ROC curve analysis of quantitative values. The red circle represents the threshold chosen for the maximum Youden's index of the index, which has been plotted along with its corresponding sensitivity and specificity.

FIG. 3 analysis of the differences in the quantitative values of miRNA in nasopharyngeal carcinoma, lung cancer, breast cancer, bladder cancer and healthy group. Note: the quantitative values were analyzed after log10 transformation, wherein the nasopharyngeal carcinoma group (NPC) n was 38, the lung cancer group (lung cancer) n was 28, the breast cancer group (breast cancer) n was 22, the bladder group (bladder cancer) n was 31, and the healthy group (Normal) n was 569. FIG. A, G: and (4) quantifying the distribution of the values. P <0.05, and multiple comparisons were performed using Kruskal-Wallis test for inter-cohort difference analysis, and further Dunn's test. FIG. B, H: bootstrap analysis of quantitative values. 10000 resampling were performed by the bias-corrected and estimated mean (BCa) bootstrap method and the 95% confidence interval of the mean was determined. Mean of the quantified values and line segment of 95% confidence interval of the mean after resampling. FIGS. C to F, I to L: ROC curve analysis of quantitative values.

FIG. 4 optimization of miR-92a primer sequence. FIG. A: amplification curves for the G1-G3 primer sets (SYBR method). FIGS. B to D: the melting curves of the G1-G3 primer sets (SYBR method). FIG. E: amplification curves of the G2 primer set and the miR-92a primer set (probe method). Positive indicates Positive samples, negative indicates negative samples, and NTC indicates blank control.

Detailed Description

The present invention will be described in further detail with reference to specific examples. The starting materials used in the examples are, unless otherwise specified, commercially available from conventional sources.

Example 1 MiR-21 and miR-92a discrimination function in serum sEV

sEV in serum is firstly extracted, miRNA is then extracted, the miRNA is reversely transcribed into DNA, miR-21 and miR-92a are absolutely quantified by a qPCR method, and finally, the quantitative value is analyzed.

1) sEV extraction: sEV was extracted using an exosome isolation kit (Hirtet Biotech, Guangzhou). The operation steps are carried out according to the instruction. Specifically, 200. mu.L of serum was added to 100. mu.L of extract A and 5. mu.L of extract B, mixed well, and incubated at room temperature for 10 min. The mixture was transferred to the upper filter chamber of an Exo column, centrifuged at 3000 Xg for 10min and the last filter chamber discarded. The retentate from the middle filter chamber was collected as sEV.

2) And (3) miRNA extraction: the miRcute serum/plasma miRNA extraction and separation kit (Tiangen Biochemical technology (Beijing) Co., Ltd.) is adopted for extraction according to the instruction. Specifically, the extracted sEV was transferred to an EP tube, 900. mu.L of lysate MZA was added, and the mixture was shaken and mixed for 30 seconds. Standing at room temperature for 5min, adding 200 μ L chloroform, shaking vigorously for 15s, and standing at room temperature for 5 min. Centrifuge at 13400g, 4 ℃ for 15min and transfer the upper clear aqueous phase to a new EP tube. Slowly adding 2 times of anhydrous ethanol according to the volume of the transfer solution, mixing, transferring to adsorption column MiRelute, and standing at room temperature for 2 min. Centrifuging at 13400g for 30s, discarding the effluent and retaining the adsorption column miRelute. Add 700. mu.L deproteinized MRD solution to the adsorption column miRelute and let stand at room temperature for 2 min. Centrifuge at 13400g for 30s and discard the effluent. To the adsorption column miRelute was added 500. mu.L of the rinsing solution RW, and the mixture was allowed to stand at room temperature for 2 min. Centrifuge at 13400g for 30s and discard the effluent. Add 500. mu.L of the rinsing solution RW again, let stand at room temperature for 2min, centrifuge (13400g, 30s), and discard the effluent. Centrifuge at 13400g for 2min at room temperature and discard the collection tube. Transferring the adsorption column MiRelute into a new EP tube, adding 30 μ L RNase-Free ddH2O to the center of the adsorption membrane, and standing at room temperature for 2 min. Centrifuge at 13400g for 2min at room temperature. And collecting filtrate to obtain the miRNA.

3) Reverse transcription and qPCR: the miRNA is subjected to reverse transcription and qPCR quantification by using a quantitative detection kit (PCR-fluorescent probe method) for the micro ribonucleic acid (microRNA-21) and a quantitative detection kit (PCR-fluorescent probe method) for the micro ribonucleic acid (microRNA-92a) (Guangzhou Hirtet Biotech, Inc.). The method carries out qPCR quantification on the target miRNA by using a specific reverse transcription primer to carry out reverse transcription on the target miRNA and using an absolute quantification method based on a TaqMan MGB probe technology. The operation steps are carried out according to the instruction.

The sequences of the primers and the probes of the microRNA-21 and the microRNA-92a are shown as follows:

reverse transcription primer miR-21-RT:

GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCAACA(SEQ ID NO:1)，

an upstream primer miR-21-F: GCCCGCTAGCTTATCAGACTGATG (SEQ ID NO:2),

a downstream primer miR-21-R: GTGCAGGGTCCGAGGT (SEQ ID NO:3),

and probe miR-21-P: (6-FAM) CTGGATACGACTCAACA(MGB) (SEQ ID NO: 4).

Reverse transcription primer miR-92 a-RT:

GTCGTATCCAGTGCTGGGTCCGAGTGATTCGCGCTGGATACGACACAGGCCG(SEQ ID NO:5)，

an upstream primer miR-92 a-F: CCCTGTATTGCACTTGTCC (SEQ ID NO:6),

the downstream primer miR-92 a-R: CAGTGCTGGGTCCGAGTGA (SEQ ID NO:7),

and probe miR-92 a-P: VIC/FAM-CGGCCTGTGTCGTATCCA-MGB (SEQ ID NO: 8).

In order to quantitatively detect the content of the microRNA-21 and the content of the microRNA-92a more accurately, a research team of the inventor designs 4 sets of microRNA-92a primer combinations, and screens out a set of microRNA-92a primers and probes (the sequences) with the best detection effect. We first evaluated the amplification effect of the 3 primer sets G1-G3 by the SYBR method. Specifically, in the 3 primer group comparisons G1-G3, the G2 primer group has a strong amplification signal in the positive sample, a weakest amplification signal in the negative sample, a largest Ct value, and a largest difference in Ct values between the positive sample and the negative sample (fig. 4A); compared with the primer sets of G1 and G3 (FIG. 4B, D), the peak of the dissolution curve of the primer set of G2 (FIG. 4C) is more concentrated, and the signal of the negative sample is lower. Then, we compared the amplification effect of the above primer set (abbreviated as: miR-92a) with that of the G2 primer set by using a probe method. The specific expression is that the amplification signal of the miR-92a primer group is stronger than that of the G2 primer group, the Ct value is smaller, and the effect is better. Therefore, the microRNA-92a primer probe set has the best effect. The sequences of the remaining candidate primer sets (G1-G3) are as follows:

reverse transcription primer G1-RT:

GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACACAGGCCG (SEQ ID NO:9)，

the upstream primer G1-F: CGGGGTATTGCACTTGTCC (SEQ ID NO:10),

the downstream primer G1-R: GTATCCAGTGCGTGTCGTG (SEQ ID NO: 11);

reverse transcription primer G2-RT:

GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACACAGGCCG(SEQ ID NO:12)，

the upstream primer G2-F: CCCCGTATTGCACTTGTCC (SEQ ID NO:13),

the downstream primer G2-R: GTGCAGGGTCCGAGGTATT (SEQ ID NO: 14);

reverse transcription primer G3-RT:

CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGACAGGCCG(SEQ ID NO:15)，

the upstream primer G3-F: GCCCCTATTGCACTTGTCC (SEQ ID NO:16),

the downstream primer G3-R: CTCAACTGGTGTCGTGGAGT (SEQ ID NO: 17).

According to the kit specification, the qPCR quantification operation of the microRNA-21 and the microRNA-92a is specifically that components with corresponding volumes are taken according to the proportion (reverse transcription reaction liquid 13 muL/test + miR-21 or miR-92a RT primer 1 muL/test + reverse transcriptase system 1 muL/test), and the components are fully and uniformly mixed and then are subpackaged on a PCR reaction plate according to 15 muL/hole. Adding 5 mu L/hole nucleic acid sample (nucleic acid sample to be detected, miR-21 or miR-92a quantitative reference substance) into the reaction plate, mixing uniformly, covering the reaction plate with a thin film, and centrifuging at 1000rpm for 1 min. The reverse transcription reaction was immediately performed with the following parameters: multiplying at 25 ℃ for 5 min; 60min at 42 ℃; multiplying 15min at 70 ℃; infinity at 4 ℃. Thus, cDNA was obtained.

Then, taking the components with the corresponding volume according to the proportion (29.4 muL/test + 0.6 muL/test of PCR enzyme in miR-21 or miR-92a reaction solution), fully mixing the components uniformly, and subpackaging the mixture on a PCR reaction plate according to 30 muL/hole. The reverse transcribed cDNA was added to the reaction plate at 20. mu.L/well, mixed well, covered with the reaction plate membrane, and centrifuged at 1000rpm for 1 min. qPCR was immediately performed with the following parameters: multiplying by 10min at 95 ℃; cycle at 95 ℃ for 15s, 65 ℃ for 1min (FAM channel fluorescence collection) 40 times. Namely obtaining quantitative data of miR-21 and miR-92a of the target sample.

The diagnostic efficacy of the present invention for CHB and HBV-associated HCC, and for differentiating between the two, were determined by comparing 47 HBV-associated HCC samples, 77 CHB samples and 569 healthy human samples (Normal), respectively. A large-scale health control sample is used, so that the actual conditions in the population are reflected as much as possible, and the statistical efficacy and the reliability of the result are increased. As the number of samples of healthy people is more than 8 times of that of HCC or CHB samples, besides common difference analysis, botstrap analysis is added, the statistical efficacy is improved, and the difference condition is reflected intuitively. Specifically, 10000 resampling were performed by the bias-corrected and accessed (BCa) bootstrap method and 95% confidence intervals of the mean were determined. If the two sets of 95% confidence intervals overlap, the difference is not significant, and if the two sets do not overlap, the difference is significant. We finally determine the diagnostic efficacy of the index by using Receiver Operating Characteristic (ROC) curve, and expressed by AUC, the more the AUC approaches to 1, the higher the diagnostic efficacy. The results are shown in FIG. 1.

As can be seen from the results in fig. 1, we found that miR-21 in the CHB group was significantly higher than in the other groups, and that HCC group was significantly higher than in the Normal group, respectively (fig. 1A, P < 0.05). We found by bootstrap analysis that CHB group did not overlap with 95% confidence intervals of the mean values of miR-21 quantitative values of other groups, but HCC group did overlap with Normal group (FIG. 1B).

The ROC curve indicates that the AUC of miR-21 distinguishing HCC from Normal group is 0.68 (FIG. 1C), the AUC of miR-21 distinguishing CHB from Normal group is 0.89 (FIG. 1D), and the AUC of miR-21 distinguishing HCC from CHB is 0.73 (FIG. 1E). The results show that the quantitative value of miR-21 in serum sEV can distinguish CHB from healthy people, but the diagnostic efficacy of distinguishing HBV-related HCC from CHB or distinguishing CHB from healthy people is lower.

The quantitative values of serum sEV miR-92a of the HCC group and the CHB group are respectively obviously higher than that of the Normal group (FIG. 1F, P <0.05), and the values of the HCC group and the CHB group are not obviously different (FIG. 1F). After the analysis by the bootstrap method, the 95% confidence intervals of the HCC group and the CHB group are respectively non-overlapped with that of the Normal group, and the 95% confidence intervals of the HCC group and the CHB group are partially overlapped (FIG. 1G), and the results are consistent with the test results. The ROC curve indicates that the AUC of miR-92a distinguishing HCC group from Normal group is 0.96 (FIG. 1H), the AUC of miR-92a distinguishing CHB group from Normal group is 0.97 (FIG. 1I), and the AUC of miR-92a distinguishing HCC group from CHB group is 0.66 (FIG. 1J). The results show that miR-92a in serum sEV can distinguish HBV-related HCC patients from healthy people, can distinguish CHB patients from healthy people, and has lower diagnostic efficacy in distinguishing HBV-related HCC from CHB patients.

When the logistic regression method was used in combination with the two indices miR-21 and miR-92a, the ROC curve showed that the AUC for distinguishing HCC from Normal was 0.98 (FIG. 1K), the AUC for distinguishing CHB from Normal was 0.97 (FIG. 1L), and the AUC for distinguishing HCC from CHB was 0.88 (FIG. 1M). The results show that the diagnosis effect of distinguishing HBV related HCC patients from CHB patients can be improved by combining two indexes of miR-21 and miR-92a in serum sEV, the diagnosis effect is high, and the diagnosis effect of distinguishing HBV related HCC patients from healthy people and the diagnosis effect of distinguishing CHB patients from healthy people are achieved.

After combining the two indexes of miR-21 and miR-92a by a logistic regression method, comparing CWhen HB group and Normal group, formula 1 can be obtained; when comparing the CHB group with the Normal group, equation 2 can be obtained; in comparing the HCC group with the CHB group, equation 3 can be obtained. Log miR-21 quantitative values of the samples₁₀After conversion, x is substituted into formula 1, formula 2 and formula 3₁In the method, the miR-92a quantitative value of the sample is subjected to log₁₀After conversion, x is substituted into formula 1, formula 2 and formula 3₂To (3). We performed ROC curve analysis on the p values calculated by the respective formulas, and selected the p value at which the yoden's index (sensitivity + specificity) is the maximum as the cut-off value. The cut-off values of the formulas are respectively: p is a radical of₁＝0.1，p₂＝0.12，p_3＝0.31. Therefore, the miR-21 quantitative value and the miR-92a quantitative value of the sample to be detected are subjected to log₁₀After conversion, substitute x in 3 formulas respectively₁And x₂At least one of (1) and (b); when p in formula 1₁<0.1, and p in formula 2₂<0.12, the source of the sample does not suffer from HBV related liver cancer and hepatitis B; when p in formula 1₁>0.1, and p in formula 3₃>0.31, the sample is derived from HBV-related liver cancer; when p in formula 2₂>0.12, and p in equation 3₃<0.31, the source of the sample is hepatitis B.

Equation 1:

equation 2:

equation 3:

comparative example 1 miR-21 and miR-92a in serum sEV have no diagnostic efficacy for rectal cancer

Comparison of 288 colorectal cancer (CRC) samples, 135 Adenoma (Adenoma) samples and 569 healthy human samples (Normal) determined the diagnostic efficacy of the present invention for CRC and Adenoma, respectively, and to differentiate the diagnostic efficacy of the two. The analytical method is similar to example 1. The analysis results are shown in figure 2.

From the experimental results, it was found that miR-21 (fig. 2A, P <0.05) was significantly lower in both the colorectal cancer and adenoma groups than in the healthy group, respectively, with no significant difference between the colorectal and adenoma groups. The ROC curve indicates that the miR-21 quantification had an area under the curve (AUC) of 0.68 for the colorectal cancer group versus the healthy group (fig. 2B), an AUC of 0.63 for the adenoma group versus the healthy group (fig. 2C), and an AUC of 0.53 for the colorectal cancer group versus the adenoma group (fig. 2D). There was no significant difference in miR-92a quantification comparisons between any 2 groups in colorectal cancer, adenoma, and healthy groups (fig. 2E). The ROC curve indicates that the miR-92a quantification value has an AUC of 0.51 for the colorectal cancer group and the healthy group (fig. 2F), an AUC of 0.54 for the adenoma group and the healthy group (fig. 2G), and an AUC of 0.54 for the colorectal cancer group and the adenoma group (fig. 2H). The results show that in the serum sEV, no matter miR-21 or miR-92a, the diagnosis efficacy for distinguishing colorectal cancer patients, adenoma patients and healthy people is low, and the results are inconsistent with the results reported in the literature.

Comparative example 2 diagnostic efficacy not possessed by miR-21 and miR-92a in serum sEV

The inventors' research team compared 38 nasopharyngeal cancer samples (NPC), 28 lung cancer samples (lung cancer), 22 breast cancer samples (breast cancer), 31 bladder cancer samples (bladdercancer) with 569 healthy human samples (Normal) using the similar analysis method as in example 1, and determined the diagnosis efficacy of the present invention on the above 4 cancers, respectively. The results are shown in FIG. 3.

From the experimental results, it was found that there was a significant difference between the miR-21 quantitative values of the lung cancer group and the nasopharyngeal cancer group and the healthy group (FIG. 3A, P <0.05), and there was no significant difference between the other groups (FIG. 3A). We analyzed by the bootstrap method to find that the 95% confidence intervals of the miR-21 quantitative value mean values of the lung cancer group and the nasopharyngeal cancer group and the healthy group do not overlap, and the other groups are partially overlapped (FIG. 3B). The ROC curve indicates that AUC for the nasopharyngeal carcinoma group and the healthy group was 0.56 (fig. 3C), AUC for the pneumonia group and the healthy group was 0.68 (fig. 3D), AUC for the breast cancer group and the healthy group was 0.52 (fig. 3E), and AUC for the bladder cancer group and the healthy group was 0.54 (fig. 3F). The results show that the diagnostic efficacy of miR-21 in serum sEV in distinguishing the 4 cancer patients from healthy people is low.

The quantitative values of miR-92a of the nasopharyngeal carcinoma group and the lung cancer group and the healthy group are respectively and obviously different, and the quantitative values of miR-92a of the other groups are not obviously different (figure 3G). After the analysis by the bootstrap method, the 95% confidence intervals of the nasopharyngeal carcinoma group and the lung carcinoma group are not overlapped, and the 95% confidence intervals of other groups are partially overlapped (FIG. 3H). The ROC curves indicate that AUC for nasopharyngeal carcinoma group versus healthy group was 0.64 (fig. 3I), AUC for pneumonia group versus healthy group was 0.60 (fig. 3J), AUC for breast cancer group versus healthy group was 0.54 (fig. 3K), and AUC for bladder cancer group versus healthy group was 0.57 (fig. 3L). The results show that miR-92a in serum sEV has low diagnosis efficacy in distinguishing healthy people from nasopharyngeal carcinoma, lung cancer, breast cancer or bladder cancer patients.

In conclusion, example 1 shows that miR-21 and miR-92a in serum sEV have high diagnosis efficacy on CHB and HBV-related HCC, and have diagnosis efficacy of distinguishing CHB from HCC in HBV-infected people, and the diagnosis efficacy is high. The results of comparative example 1 show that the amount of miRNA in serum EV is not consistent with literature reports that cannot be expanded in general to all clinical samples. The results of comparative example 1 and comparative example 2 together show that the invention has a certain disease specificity, and miR-21 and miR-92a in serum EV have no diagnostic efficacy in other 5 cancers.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.

SEQUENCE LISTING

<110> river-south university

Guangzhou Supbio Bio-technology and Science Co.,Ltd.

<130>

<160>17

<170>PatentIn version 3.5

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gtcgtatcca gtgcagggtc cgaggtattc gcactggata cgactcaaca 50

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gcccgctagc ttatcagact gatg 24

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gtgcagggtc cgaggt 16

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ctggatacga ctcaaca 17

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gtcgtatcca gtgctgggtc cgagtgattc gcgctggata cgacacaggc cg 52

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ccctgtattg cacttgtcc 19

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cagtgctggg tccgagtga 19

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cggcctgtgt cgtatcca 18

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gtcgtatcca gtgcgtgtcg tggagtcggc aattgcactg gatacgacac aggccg 56

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cggggtattg cacttgtcc 19

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gtatccagtg cgtgtcgtg 19

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gtcgtatcca gtgcagggtc cgaggtattc gcactggata cgacacaggc cg 52

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ccccgtattg cacttgtcc 19

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gtgcagggtc cgaggtatt 19

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<212>DNA

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ctcaactggt gtcgtggagt cggcaattca gttgagacag gccg 44

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gcccctattg cacttgtcc 19

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ctcaactggt gtcgtggagt 20

Claims

Application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B.
2, application of miR-21 primers and probes and miR-92a primers and probes in preparation of reagents for detecting and distinguishing HBV-related liver cancer and hepatitis B.
3, application of the primer and the probe of miR-21 and the primer and the probe of miR-92a in preparation of a kit for detecting and distinguishing HBV-related liver cancer and hepatitis B.
4. The use of any one of claims 1 to 3, wherein miR-21 and miR-92a are miR-21 and miR-92a in serum.
5. The use of claim 4, wherein miR-21 and miR-92a are miR-21 and miR-92a in small extracellular vesicles in serum.
6. The use of claim 2 or 3, wherein the primer and probe of miR-21 have the sequences as follows:

reverse transcription primer miR-21-RT:

GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCAACA(SEQ ID NO:1)，

an upstream primer miR-21-F: GCCCGCTAGCTTATCAGACTGATG (SEQ ID NO:2),

a downstream primer miR-21-R: GTGCAGGGTCCGAGGT (SEQ ID NO:3),

and probe miR-21-P: CTGGATACGACTCAACA (SEQ ID NO: 4).
7. The use of claim 2 or 3, wherein the primer and probe sequences of miR-92a are as follows:

reverse transcription primer miR-92 a-RT:

GTCGTATCCAGTGCTGGGTCCGAGTGATTCGCGCTGGATACGACACAGGCCG(SEQ ID NO:5)，

an upstream primer miR-92 a-F: CCCTGTATTGCACTTGTCC (SEQ ID NO:6),

the downstream primer miR-92 a-R: CAGTGCTGGGTCCGAGTGA (SEQ ID NO:7),

and probe miR-92 a-P: CGGCCTGTGTCGTATCCA (SEQ ID NO: 8).
8. A method for detecting and differentiating HBV-related liver cancer from hepatitis B for non-disease diagnostic purposes, comprising the steps of:

s1, extracting small extracellular vesicles from a serum sample;

s2, extracting miRNA in the small extracellular vesicles in the step S1;

s3, carrying out reverse transcription on miR-21 and miR-92a in miRNA in the step S2 to obtain cDNA;

s4, carrying out qPCR detection on miR-21 and miR-92a on cDNA in S3 by adopting the primers and the probes in claims 6 and 7, and distinguishing HBV-related liver cancer from hepatitis B according to the detection result.
9. The method of claim 8, wherein the method for differentiating HBV-related liver cancer from hepatitis B in step S4 comprises:

when the quantitative result of miR-21 and the quantitative result of miR-92a in the sample are not obviously different from the quantitative result of a healthy person, the source of the sample is proved not to suffer from HBV-related liver cancer and hepatitis B;

when the quantitative result of miR-21 and the quantitative result of miR-92a in the sample are obviously different from the quantitative result of a healthy person, and the quantitative result of miR-21 in the sample is obviously different from the quantitative result of the hepatitis B group, the source of the sample is proved to have HBV-related liver cancer;

when the quantitative result of the miR-21 and the quantitative result of the miR-92a in the sample are obviously different from the quantitative result of a healthy person, and the quantitative result of the miR-21 in the sample is not obviously different from the quantitative result of the HBV-related liver cancer group, the source of the sample is indicated to suffer from hepatitis B.
10. The method of claim 8, wherein the method for differentiating HBV-related liver cancer from hepatitis B in step S4 comprises:

log miR-21 quantitative values of the samples₁₀After conversion, x is substituted into formula 1, formula 2 and formula 3₁At the position of the air compressor, the air compressor is started,

log miR-92a quantitative value of the sample₁₀After conversion, x is substituted into formula 1, formula 2 and formula 3₂At least one of (1) and (b);

when p in formula 1₁<0.1，And p in formula 2₂<0.12, the source of the sample does not suffer from HBV related liver cancer and hepatitis B;

when p in formula 1₁>0.1, and p in formula 3₃>0.31, the sample is derived from HBV-related liver cancer;

when p in formula 2₂>0.12, and p in equation 3₃<0.31, the source of the sample is indicated to have hepatitis B;

equation 1:

equation 2:

equation 3: