CN111560467B - Application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B - Google Patents
Application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer and hepatitis B Download PDFInfo
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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 diagnosed efficiently, and CHB and HCC can be distinguished in HBV infected people. Moreover, the diagnosis efficacy is high, the AUC reaches 0.88, the loading quantity is low, and only 200 mu L of serum is needed. The efficacy of diagnosing CHB and diagnosing HBV-related HCC is higher, and can be distinguished from healthy people, and the AUC reaches more than 0.97. The loading was low, and only 200. Mu.L of serum was required.
Description
Technical Field
The invention belongs to the technical field of biology, and in particular relates to application of miR-21 and miR-92a as markers for detecting and distinguishing HBV-related liver cancer from hepatitis B.
Background
Exosomes are extracellular vesicles (extracellular vesicle, EV) secreted by cells, about 30-150nm in size, containing functional nucleic acids and proteins, an important ring in intercellular communication. Tumor cells can specifically secrete cancer-related biomolecules through exosomes, thereby promoting the development and progression of cancer. Furthermore, the exosomes can be secreted into peripheral blood, have traceability, and can be detected from tumors by means of blood drawing. Therefore, exosomes are an important source of disease biomarkers.
In exosome-related studies, more and more studies have tended to replace "exosomes" by the word "small extracellular vesicles" (small extracellular vesicle, sEV) because exosome-specific biomarkers are not yet available and it is difficult to track whether isolated exosomes originate from an inclusion body (endosome). Such sEV refers to an EV of less than 200 nm.
Micrornas (mirnas) are small single-stranded RNAs that are involved in regulating biological processes. In many cancers, the miRNA profile of tumor cells is altered. There is a great deal of literature showing that mirnas in serum or plasma can serve as potential biomarkers for tumors. In peripheral blood, mirnas exist mainly in 3 forms: free miRNA, protein-bound miRNA, and miRNA present in EVs. However, free mirnas are easily degraded by in vivo rnases, protein-bound mirnas are difficult to separate and detect, and only mirnas present in EVs are the most stable and easily detected. Thus, direct detection of mirnas in serum or plasma is relatively disturbed much, and there may be cases where some trace amounts of mirnas are difficult to detect, or where the amounts of the same miRNA differ greatly among different people. Indeed, the trend of variation reported in different documents for the same amount of serum or plasma miRNA may be opposite. For example, there is literature that miR-92a in serum is significantly up-regulated in liver cancer, while there is literature that this 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 miRNA biomarkers in the serum/plasma sEV has more important significance.
In addition, the amount of certain mirnas in serum/plasma sEV is difficult to predict and requires validation with a large number of clinical samples. In the study of serum/plasma sEV miRNAs, conflicting conclusions may occur in different literature. For example, miR-21 and miR-92a in serum/plasma sEV are widely reported colorectal cancer biomarkers, but a large number of clinical samples are used for proving that the diagnosis 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 (chronic hepatitis B, CHB) is a chronic inflammatory disease of the liver caused by persistent infection with hepatitis b virus (hepatitis B virus, HBV). The HBV infected population is very huge, about 20 hundred million people worldwide have infected HBV, and 2.4 hundred million people are chronic HBV infected people. CHB and HBV-related HCC are the most prevalent, most health-affecting, and most interesting diseases compared to other liver diseases. How to monitor CHB and screen for HCC from the early, and to avoid degradation of CHB to HCC as much as possible, is a significant problem in the field.
At present, serological detection of HBV infection or CHB mainly comprises 'two halves of HBV', namely detection of HBV related antigen and antibody. Serological detection of HCC is mainly based on alpha-fetoprotein (AFP). Such detection based on the immunological method is difficult to detect very small amounts of proteins, and also very early lesions, so that diagnosis and treatment are delayed, missing the optimal period. It is important to find more sensitive and efficient serum biomarkers.
The miRNA in the serum EV has great HBV related HCC diagnosis prospect, and various documents show that the miRNA in the serum EV has potential diagnosis efficacy. However, these documents have problems such as insufficient sample size, failure to design experimental groups and control groups in consideration of HBV infection, ambiguous diagnostic efficacy (no ROC curve), and low AUC.
Disclosure of Invention
The first aspect of the invention aims to provide a clinically easy-to-realize detection index (serum EV miR-21 and miR-92 a) for the high-efficiency index of early warning that the hepatitis B is converted into liver cancer, and the patients which are likely to deteriorate into liver cancer are screened out from HBV infected people.
The invention in a second aspect aims at providing a primer and a probe of miR-21 and an application of the primer and the probe of miR-92a in preparation of reagents for detecting and distinguishing HBV-related liver cancer from hepatitis B.
The third aspect of the present invention is directed to providing
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 from hepatitis B.
In a second aspect, the invention provides application of a primer and a probe of miR-21 and a primer and a probe of miR-92a in preparation of reagents for detecting and distinguishing HBV-related liver cancer from hepatitis B.
In the third aspect of the invention, the primer and the probe of miR-21 and the primer and the probe of miR-92a are applied to preparation of a kit for detecting and distinguishing HBV-related liver cancer from 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 sequence of the primer and the probe of the miR-21 is as follows:
reverse transcription primer miR-21-RT:
GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCAACA(SEQ ID NO:1)，
upstream primer miR-21-F: GCCCGCTAGCTTATCAGACTGATG (SEQ ID NO: 2),
downstream primer miR-21-R: GTGCAGGGTCCGAGGT (SEQ ID NO: 3),
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 the miR-92a are as follows:
reverse transcription primer miR-92a-RT:
GTCGTATCCAGTGCTGGGTCCGAGTGATTCGCGCTGGATACGACACAGGCCG(SEQ ID NO:5)，
upstream primer miR-92a-F: CCCTGTATTGCACTTGTCC (SEQ ID NO: 6),
downstream primer miR-92a-R: CAGTGCTGGGTCCGAGTGA (SEQ ID NO: 7),
probe miR-92a-P: CGGCCTGTGTCGTATCCA (SEQ ID NO: 8).
In a fourth aspect of the present invention, there is provided a method for detecting and distinguishing HBV-associated liver cancer from hepatitis B for non-disease diagnosis purposes, comprising the steps of:
s1, extracting small extracellular vesicles in a serum sample;
s2, extracting miRNA in small extracellular vesicles in the step S1;
s3, performing reverse transcription on miR-21 and miR-92a in the miRNA in the step S2 to obtain cDNA;
s4, qPCR detection of miR-21 and miR-92a is carried out on cDNA in S3 by using the primer and probe set in claims 5 and 6, and HBV related liver cancer and hepatitis B are distinguished according to detection results.
According to the method of the fourth aspect of the present invention, the method for distinguishing HBV-associated liver cancer from hepatitis B in step S4 comprises the following steps:
when the quantitative result of miR-21 and the quantitative result of miR-92a in the sample are not significantly different from the quantitative result of healthy people, the source of the sample is not affected by HBV-related liver cancer and hepatitis B;
when the quantitative results of miR-21 and miR-92a in the sample are obviously different from those of healthy people, and the quantitative results of miR-21 in the sample are obviously different from those of a hepatitis B group, the source of the sample is indicated to have HBV-related liver cancer;
when the quantitative results of miR-21 and miR-92a in the sample are significantly different from those of healthy people, and no significant difference exists between the quantitative results of miR-21 in the sample and that of HBV-related liver cancer groups, the source of the sample is indicated to suffer from hepatitis B.
According to the method of the fourth aspect of the present invention, the method for distinguishing HBV-associated liver cancer from hepatitis B in step S4 comprises the following steps:
after log10 conversion, the quantitative value of miR-21 of the sample is substituted into x1 in formula 1, formula 2 and formula 3 respectively, and after log10 conversion, the quantitative value of miR-92a of the sample is substituted into x2 in formula 1, formula 2 and formula 3 respectively;
when p1<0.1 in formula 1 and p2<0.12 in formula 2, it is indicated that the source of the sample is not affected by HBV-associated liver cancer and hepatitis B;
when p1>0.1 in formula 1 and p3>0.31 in formula 3, it is indicated that the source of the sample has HBV-associated liver cancer;
when p2>0.12 in equation 2 and p3<0.31 in equation 3, it indicates that the source of the sample has hepatitis B;
equation 1:
equation 2:
equation 3:
the invention has the beneficial effects that:
the invention discovers miR-21 and miR-92a in the combined serum sEV for the first time, can diagnose CHB and HBV related HCC with high efficiency, and can distinguish CHB and HCC in HBV infected people. The diagnosis efficacy is higher, the potential of early warning HCC in CHB population is provided, and patients which are likely to deteriorate into liver cancer are screened in HBV infected population. Can distinguish CHB from HCC in HBV infected people, and has potential of pre-warning HCC in CHB people. And has high diagnosis effect, and AUC reaches 0.88. The efficacy of diagnosing CHB and diagnosing HBV-related HCC is higher, and can be distinguished from healthy people, and the AUC reaches more than 0.97. The loading was low, only 200. Mu.L serum was required.
Drawings
FIG. 1 differential analysis of quantitative miRNA values for liver cancer, hepatitis and healthy groups. And (3) injection: quantitative values were analyzed after log10 conversion, where HBV-related liver cancer group (HCC) n=47, chronic hepatitis b group (CHB) n=77, and healthy group (Normal) n=569. Fig. A, F: quantitative value distribution. * Representative P <0.05 compared to any other group, the inter-group variance analysis was performed using Kruskal-Wallis test, further using Dunn's test for multiple comparisons. Fig. B, G: boottrap analysis of quantitative values. 10000 resampling was performed by bias-corrected and accelerated (BCa) bootstrap method and the 95% confidence interval of the mean was determined. * The mean of the quantitative values is represented, and the line segment represents the 95% confidence interval of the mean after resampling. Graphs C-E, H-J: ROC curve analysis of quantitative values. The red circle represents the threshold value chosen when the index about index (Youden's index) is maximum, which threshold value and its corresponding sensitivity and specificity have been marked. Graphs K-M: ROC curve analysis combining miR-21 and miR-92a quantitative values.
Figure 2 analysis of the differences in quantitative miRNA values for colorectal, adenoma and healthy groups. And (3) injection: quantitative values were analyzed after log10 conversion, colorectal cancer group (CRC) n=288, adenoma group (Adenoma) n=135, healthy group (Normal) n=569. Fig. A, E: quantitative value distribution. * Representative P <0.05 compared to any other group, the inter-group variance analysis was performed using Kruskal-Wallis test, further using Dunn's test for multiple comparisons. Graphs B to D, F to H: ROC curve analysis of quantitative values. The red circle represents the threshold value chosen when the index about index (Youden's index) is maximum, which threshold value and its corresponding sensitivity and specificity have been marked.
FIG. 3 differential analysis of quantitative values of miRNAs for nasopharyngeal carcinoma, lung cancer, breast cancer, bladder cancer and healthy groups. And (3) injection: quantitative values were analyzed after log10 conversion, where nasopharyngeal carcinoma group (NPC) n=38, lung carcinoma group (lung cancer) n=28, breast carcinoma group (breast cancer) n=22, bladder group (bladder cancer) n=31, and healthy group (Normal) n=569. Fig. A, G: quantitative value distribution. * P <0.05, the difference analysis between the groups was performed using Kruskal-Wallis test, and further using Dunn's test for multiple comparisons. Fig. B, H: boottrap analysis of quantitative values. 10000 resampling was performed by bias-corrected and accelerated (BCa) bootstrap method and the 95% confidence interval of the mean was determined. * The mean of the quantitative values is represented, and the line segment represents the 95% confidence interval of the mean after resampling. Graphs C to F, I to L: ROC curve analysis of quantitative values.
FIG. 4 optimization of miR-92a primer sequences. Graph a: amplification curves of the G1 to G3 primer sets (SYBR method). Graphs B to D: dissolution profile of the G1 to G3 primer sets (SYBR method). Diagram E: amplification curves of the G2 primer set and the miR-92a primer set (probe method). Positive indicates Positive samples, negative samples, NTC indicates blank.
Detailed Description
The present invention will be described in further detail with reference to specific examples. The starting materials used in the examples were all commercially available from conventional sources unless otherwise specified.
Example 1 function of discriminating miR-21 and miR-92a in serum sEV
Firstly extracting sEV in serum, then extracting miRNA, performing reverse transcription to DNA, then absolute quantification of miR-21 and miR-92a is performed by a qPCR method, and finally quantitative values are analyzed.
1) sEV extraction: sEV was extracted using an exosome isolation kit (Highway Biotechnology Co., ltd.). The operation steps are carried out according to the specification. Specifically, 200. Mu.L of serum was taken, 100. Mu.L of extract A and 5. Mu.L of extract B were added, and mixed well, and incubated at room temperature for 10min. The mixture was transferred to the upper filter chamber of the Exo column and centrifuged at 3000 Xg for 10min, and the last filter chamber was discarded. The intermediate layer filter cavity retentate was collected as sEV.
2) miRNA extraction: the extraction was performed according to the instructions of the miRcute serum/plasma miRNA extraction separation kit (tiangen biochemical technology (beijing) limited). Specifically, sEV after extraction was transferred to an EP tube, 900. Mu.L of lysate MZA was added, and mixed by shaking for 30s. Standing at room temperature for 5min, adding 200 μl chloroform, shaking vigorously for 15s, and standing at room temperature for 5min. The upper clear aqueous phase was transferred to a fresh EP tube by centrifugation at 13400g for 15min at 4 ℃. According to the volume of the transfer liquid, slowly adding 2 times of absolute ethyl alcohol of the transfer liquid, uniformly mixing, transferring into an adsorption column mirinlite, and standing at room temperature for 2min. The effluent was discarded and the column mirilute was retained by centrifugation at 13400g for 30s. 700. Mu.L of deproteinized solution MRD was added to the column mirilute, and the mixture was allowed to stand at room temperature for 2 minutes. Centrifuge at 13400g for 30s and discard the effluent. 500. Mu.L of rinse RW was added to the column mirilute and allowed to stand at room temperature for 2min. Centrifuge at 13400g for 30s and discard the effluent. 500. Mu.L of rinse RW was added again, left standing at room temperature for 2min, centrifuged (13400 g,30 s), and the effluent was discarded. Centrifuge at 13400g for 2min at room temperature and discard the collection tube. The column mirilute was transferred to a new EP tube, 30. Mu.L RNase-Free ddH2O was added to the center of the adsorption membrane, and the mixture was left at room temperature for 2min. Centrifuge at 13400g for 2min at room temperature. And collecting filtrate, namely miRNA.
3) Reverse transcription and qPCR: reverse transcription and qPCR quantification of the above-mentioned miRNA were performed using a micro ribonucleic acid (microRNA-21) quantitative detection kit (PCR-fluorescent probe method) and a micro ribonucleic acid (microRNA-92 a) quantitative detection kit (PCR-fluorescent probe method) (Highway Biotechnology Co., ltd.). The method comprises the steps of reversely transcribing target miRNA by using a specific reverse transcription primer, and then carrying out qPCR quantification on the target miRNA by using an absolute quantification method based on a TaqMan MGB probe technology. The operation steps are carried out according to instructions.
The sequences of the primer and probe of microRNA-21 and microRNA-92a are as follows:
reverse transcription primer miR-21-RT:
GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCAACA(SEQ ID NO:1)，
upstream primer miR-21-F: GCCCGCTAGCTTATCAGACTGATG (SEQ ID NO: 2),
downstream primer miR-21-R: GTGCAGGGTCCGAGGT (SEQ ID NO: 3),
probe miR-21-P: (6-FAM) CTGGATACGACTCAACA (MGB) (SEQ ID NO: 4).
Reverse transcription primer miR-92a-RT:
GTCGTATCCAGTGCTGGGTCCGAGTGATTCGCGCTGGATACGACACAGGCCG(SEQ ID NO:5)，
upstream primer miR-92a-F: CCCTGTATTGCACTTGTCC (SEQ ID NO: 6),
downstream primer miR-92a-R: CAGTGCTGGGTCCGAGTGA (SEQ ID NO: 7),
probe miR-92a-P: VIC/FAM-CGGCCTGTGTCGTATCCA-MGB (SEQ ID NO: 8).
In order to more accurately and quantitatively detect the content of the microRNA-21 and the microRNA-92a, a research team of the inventor designs 4 groups of microRNA-92a primer combinations in total, and screens a group of microRNA-92a primers and probes (the sequences) with the best detection effect from the primer combinations. We first evaluated the amplification effect of the 3 sets of primers G1 to G3 by SYBR method. Specifically, in the comparison of the 3 primer sets of G1-G3, the G2 primer set has stronger amplification signal in the positive sample, weakest amplification signal in the negative sample, maximum Ct value and maximum Ct value difference between the positive sample and the negative sample (FIG. 4A); compared with the G1 and G3 primer sets (FIG. 4B, D), the dissolution profile peak of the G2 primer set (FIG. 4C) was more concentrated and the signal of the negative sample was lower. Then, we compared the amplification effect of the above primer set (abbreviated as miR-92 a) with that of the G2 primer set by using a probe method. The miR-92a primer group has stronger amplification signals than the G2 primer group, smaller Ct value and better effect. Therefore, the microRNA-92a primer probe set has the best effect. The remaining candidate primer sets (G1-G3) were as follows:
reverse transcription primer G1-RT:
GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACACAGGCCG(SEQ ID NO:9)，
upstream primer G1-F: CGGGGTATTGCACTTGTCC (SEQ ID NO: 10),
downstream primer G1-R: GTATCCAGTGCGTGTCGTG (SEQ ID NO: 11);
reverse transcription primer G2-RT:
GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACACAGGCCG(SEQ ID NO:12)，
upstream primer G2-F: CCCCGTATTGCACTTGTCC (SEQ ID NO: 13),
downstream primer G2-R: GTGCAGGGTCCGAGGTATT (SEQ ID NO: 14);
reverse transcription primer G3-RT:
CTCAACTGGTGTCGTGGAGTCGGCAATTCAGTTGAGACAGGCCG(SEQ ID NO:15)，
upstream primer G3-F: GCCCCTATTGCACTTGTCC (SEQ ID NO: 16),
downstream primer G3-R: CTCAACTGGTGTCGTGGAGT (SEQ ID NO: 17).
According to the instruction of the kit, the quantitative operation of qPCR on the microRNA-21 and the microRNA-92a is specifically that corresponding volume components are taken according to a proportion (13 mu L of reverse transcription reaction liquid/test+miR-21 or miR-92a RT primer 1 mu L/test+reverse transcriptase system 1 mu L/test), fully and uniformly mixed, and then split-packed on a PCR reaction plate according to 15 mu L/hole. Adding 5 mu L/hole nucleic acid sample (nucleic acid sample to be detected, miR-21 or miR-92a quantitative reference product) into the reaction plate, uniformly mixing, covering the reaction plate film, and centrifuging at 1000rpm for 1min. The reverse transcription reaction was immediately performed according to the following parameters: 25 ℃ for 5min;42 ℃ for 60min;70 ℃ for 15min;4 ℃ is infinity. Thus, cDNA was obtained.
Then, the corresponding volume components are taken according to the proportion (miR-21 or miR-92a reaction liquid 29.4 mu L/test+PCR enzyme 0.6 mu L/test), fully and uniformly mixed, and then split-packed on a PCR reaction plate according to 30 mu L/hole. Add 20. Mu.L/well of reverse transcribed cDNA to the reaction plate, mix well, cover the reaction plate membrane and centrifuge at 1000rpm for 1min. qPCR was performed immediately with the following parameters: 95 ℃ for 10min;95 ℃ C..times.15 s,65 ℃ C..times.1 min (FAM channel fluorescence acquisition), and the cycle was 40 times. And obtaining quantitative data of miR-21 and miR-92a of the target sample.
Comparison of 47 HBV-associated HCC samples, 77 CHB samples and 569 healthy human samples (Normal) was performed to determine the diagnostic efficacy of the present invention on CHB and HBV-associated HCC, and to distinguish between the two. The large-scale healthy control sample is used, so that the actual situation in the crowd is reflected as much as possible, and the statistical efficacy and the credibility of the result are improved. Since the number of healthy people is more than 8 times of that of HCC or CHB, besides the common difference analysis, bootstrap analysis is added, the statistical efficacy is improved, and the difference condition is intuitively reflected. Specifically, 10000 resampling was performed with bias-corrected and accelerated (BCa) boottrap method and 95% confidence intervals of the mean were determined. If the two groups of 95% confidence intervals overlap, the difference is not obvious, and if the two groups of 95% confidence intervals do not overlap, the difference is obvious. We finally determined the diagnostic efficacy of the index using the receiver operating characteristics (receiver operating characteristic, ROC) curve, expressed as AUC, with the closer the AUC is 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 both miR-21 was significantly higher in the CHB group than in the other groups, respectively, and that in the HCC group was significantly higher than in the Normal group (FIG. 1A, P < 0.05). We found by boottrap analysis that the CHB groups did not overlap with the 95% confidence intervals of the mean of the miR-21 quantitative values of the other groups, respectively, but that the HCC groups overlapped partially with the Normal groups (fig. 1B).
The ROC curve showed that the AUC of miR-21 distinguishing HCC group from Normal group was 0.68 (FIG. 1C), the AUC of miR-21 distinguishing CHB group from Normal group was 0.89 (FIG. 1D), and the AUC of miR-21 distinguishing HCC group from CHB group was 0.73 (FIG. 1E). The above results indicate 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.
Serum sEV miR-92a quantification values were significantly higher for HCC and CHB groups, respectively (FIG. 1F, P < 0.05), than for Normal group, with no significant difference between HCC and CHB groups (FIG. 1F). After analysis by bootstrap method, the 95% confidence intervals of HCC group and CHB group were found to be non-overlapping with Normal group, and the 95% confidence intervals of HCC group and CHB group were partially overlapping (fig. 1G), and the results were consistent with the above test results. The ROC curve shows that the AUC of miR-92a, which distinguishes HCC group from Normal group, is 0.96 (FIG. 1H), that of miR-92a, which distinguishes CHB group from Normal group is 0.97 (FIG. 1I), and that of miR-92a, which distinguishes HCC group from CHB group is 0.66 (FIG. 1J). The above results indicate that miR-92a in serum sEV can distinguish HBV-associated HCC patients from healthy persons, and can distinguish CHB patients from healthy persons, and that the diagnostic efficacy of distinguishing HBV-associated HCC from CHB patients is lower.
When both miR-21 and miR-92a indicators were combined by using a logistic regression method, the ROC curve showed that the AUC distinguishing the HCC group from the Normal group was 0.98 (FIG. 1K), the AUC distinguishing the CHB group from the Normal group was 0.97 (FIG. 1L), and the AUC distinguishing the HCC group from the CHB group was 0.88 (FIG. 1M). The results show that the diagnosis efficacy of distinguishing HBV-related HCC patients from CHB patients can be improved by combining the miR-21 index and the miR-92a index in serum sEV, the diagnosis efficacy is higher, the diagnosis efficacy of distinguishing HBV-related HCC patients from healthy people is achieved, and the diagnosis efficacy of distinguishing CHB patients from healthy people is achieved.
After the miR-21 and miR-92a indexes are combined by a logistic regression method, when the CHB group and Normal group are compared, a formula 1 can be obtained; when comparing CHB group with Normal group, equation 2 can be obtained; in comparing HCC group with CHB group, equation 3 can be obtained. Log miR-21 quantification value of sample 10 After conversion, x in equation 1, equation 2 and equation 3 are substituted respectively 1 Here, the miR-92a quantitative value of the sample is subjected to log 10 After conversion, x in equation 1, equation 2 and equation 3 are substituted respectively 2 Where it is located. We performed ROC curve analysis on the p-values calculated by the formulas, respectively, and selected the p-value at which the about index (Youden's index=sensitivity+specificity) is maximum as the cut-off value. The cut-off values of the formulas are respectively: p is p 1 ＝0.1，p 2 ＝0.12，p 3 =0.31. Therefore, the miR-21 quantitative value and miR-92a quantitative value of the sample to be detected are subjected to log 10 After conversion, x in 3 formulas is substituted respectively 1 Department and x 2 A place; when p in formula 1 1 <0.1, and p in formula 2 2 <0.12, indicating that the source of the sample is not affected by HBV-associated liver cancer and hepatitis B; when p in formula 1 1 >0.1, and p in equation 3 3 >0.31, indicating that the source of the sample has HBV-associated liver cancer; when p in formula 2 2 >0.12, and p in equation 3 3 <0.31, the origin of the sample is indicated to have hepatitis B.
Equation 1:
equation 2:
equation 3:
comparative example 1 MiR-21 and miR-92a in serum sEV did not have the efficacy of diagnosing rectal cancer
The diagnostic efficacy of the invention on colorectal cancer (CRC), 135 Adenoma (Adenoma) and 569 healthy human samples (Normal) were determined by comparing 288 samples, 135 samples, and distinguishing between them, respectively. The analytical method was 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 colorectal and adenoma groups than in the healthy group, respectively, with no significant difference between the colorectal and adenoma groups. ROC curve shows that the area under the curve (AUC) for the miR-21 quantitative value to distinguish colorectal cancer group from healthy group is 0.68 (fig. 2B), the AUC to distinguish adenoma group from healthy group is 0.63 (fig. 2C), and the AUC to distinguish colorectal cancer group from adenoma group is 0.53 (fig. 2D). There were no significant differences in comparison of miR-92a quantification values between any 2 of colorectal cancer, adenoma and healthy groups (fig. 2E). The ROC curve shows that the quantitative value of miR-92a distinguishes colorectal cancer group from healthy group with AUC of 0.51 (FIG. 2F), colorectal cancer group from healthy group with AUC of 0.54 (FIG. 2G), and colorectal cancer group from adenoma group with AUC of 0.54 (FIG. 2H). The results show that in serum sEV, whether miR-21 or miR-92a is adopted, the diagnosis efficacy of distinguishing colorectal cancer patients, adenoma patients and healthy people is low, and is inconsistent with the report of the literature.
Comparative example 2 diagnostic efficacy not possessed by miR-21 and miR-92a in serum sEV
The inventors' study team compared 38 Nasopharyngeal Cancer Samples (NPCs), 28 lung cancer samples (lung cancer), 22 breast cancer samples (breast cancer), 31 bladder cancer samples (blade cancer) with 569 healthy human samples (Normal) using a similar analysis method as in example 1, and determined the diagnostic efficacy of the invention for the 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, respectively (FIG. 3A, P < 0.05), and there was no significant difference between the other groups, respectively (FIG. 3A). The analysis by using the bootstrap method shows that the lung cancer group and the nasopharyngeal cancer group are not overlapped with each other in the 95% confidence interval of the average value of the miR-21 quantitative values of the healthy group, and the other groups are partially overlapped (figure 3B). ROC curves indicated an AUC of 0.56 (fig. 3C) for the nasopharyngeal carcinoma group versus the healthy group, 0.68 (fig. 3D) for the pneumonia group versus the healthy group, 0.52 (fig. 3E) for the breast cancer group versus the healthy group, and 0.54 (fig. 3F) for the bladder cancer group versus the healthy group. The above results indicate that miR-21 in serum sEV has lower diagnostic efficacy in distinguishing the 4 cancer patients from healthy people.
There were significant differences between the miR-92a quantification values of the nasopharyngeal carcinoma group and the lung cancer group and the healthy group, respectively, and no significant differences between the other groups (fig. 3G). After analysis by bootstrap method, it was found that there was no overlap between the 95% confidence intervals of the nasopharyngeal carcinoma group and the lung carcinoma group, and that there was a partial overlap between the 95% confidence intervals of the other groups (fig. 3H). ROC curves indicated that AUC for distinguishing nasopharyngeal carcinoma group from healthy group was 0.64 (fig. 3I), AUC for distinguishing pneumonia group from healthy group was 0.60 (fig. 3J), AUC for distinguishing breast cancer group from healthy group was 0.54 (fig. 3K), and AUC for distinguishing bladder cancer group from 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.
Taken together, example 1 shows that miR-21 and miR-92a in serum sEV have higher diagnostic efficacy on CHB and HBV-associated HCC, and have diagnostic efficacy in HBV infected people to distinguish CHB from HCC, and have higher diagnostic efficacy. The results of comparative example 1 show that the amount of miRNA in serum EV is inconsistent with that reported in the literature, which cannot be extended to all clinical samples in a generalized manner. The results of comparative example 1 and comparative example 2 together demonstrate that the invention has certain disease specificity, and miR-21 and miR-92a in serum EV have no diagnostic efficacy in other 5 cancers.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
SEQUENCE LISTING
<110> and university of south China
Guangzhou Supbio Bio-technology and Science Co.,Ltd.
Application of <120> miR-21 and miR-92a as markers for detecting and distinguishing HBV related liver cancer and hepatitis B
<130>
<160> 17
<170> PatentIn version 3.5
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Claims (6)
1. Application of substances for detecting expression levels of miR-21 and miR-92a in preparation of products for detecting and distinguishing HBV-related liver cancer from hepatitis B, wherein miR-21 and miR-92a are miR-21 and miR-92a in small extracellular vesicles in serum.
2. The use according to claim 1, characterized in that: the product is a reagent.
3. The use according to claim 1, characterized in that: the product is a kit.
4. A use according to any one of claims 1 to 3, characterized in that:
the substances are a primer and a probe of miR-21 and a primer and a probe of miR-92a.
5. The use of claim 4, wherein the primer, probe sequence of miR-21 is:
reverse transcription primer miR-21-RT:
GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTCAACA（SEQ ID NO:1），
upstream primer miR-21-F: GCCCGCTAGCTTATCAGACTGATG (SEQ ID NO: 2),
downstream primer miR-21-R: GTGCAGGGTCCGAGGT (SEQ ID NO: 3),
probe miR-21-P: CTGGATACGACTCAACA (SEQ ID NO: 4).
6. The use of claim 4, wherein the primer, probe sequence of miR-92a is:
reverse transcription primer miR-92a-RT:
GTCGTATCCAGTGCTGGGTCCGAGTGATTCGCGCTGGATACGACACAGGCCG（SEQ ID NO:5），
upstream primer miR-92a-F: CCCTGTATTGCACTTGTCC (SEQ ID NO: 6),
downstream primer miR-92a-R: CAGTGCTGGGTCCGAGTGA (SEQ ID NO: 7),
probe miR-92a-P: CGGCCTGTGTCGTATCCA (SEQ ID NO: 8).
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| CN107012241A (en) * | 2017-05-09 | 2017-08-04 | 广州海力特生物科技有限公司 | A kind of U6, miR 92a and miR 21 triple RT qPCR detection methods and kit |
| CN110872628A (en) * | 2018-08-30 | 2020-03-10 | 杨昆德 | Use of extracellular vesicles as biomarkers for the preparation of kits |
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| CN101641440A (en) * | 2006-11-23 | 2010-02-03 | 米尔克斯治疗有限责任公司 | Oligonucleotides for modulating target RNA activity |
| CN101368213A (en) * | 2008-10-13 | 2009-02-18 | 南京大学 | Blood serum minuteness ribonucleic acid reagent kit and application in early diagnosis of hepatitis B |
| US20130178383A1 (en) * | 2008-11-12 | 2013-07-11 | David Spetzler | Vesicle isolation methods |
| CN102021167B (en) * | 2010-11-03 | 2013-02-27 | 南京大学 | Serum miRNA composition for detecting occult hepatitis B and applications thereof |
| CN103028121B (en) * | 2012-12-30 | 2014-06-11 | 中国人民解放军第三军医大学第二附属医院 | miR-21 compound of target microglial cell and preparation method and application of miR-21 compound |
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| CN102443642A (en) * | 2011-12-20 | 2012-05-09 | 苏州福英基因科技有限公司 | In-situ hybridization detection kit for MICRORNA (MICRO Ribonucleic Acid)-92A level at pathologic evolution early stage of colon cancer as well as microRNA-92A in-situ hybridization detection method and application of microRNA-92a to preparation of in-situ hybridization detection kit for colon cancer |
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