CN109097476B - Lung cancer bone metastasis gene detection and diagnosis kit - Google Patents
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Abstract
The invention relates to a lung cancer bone metastasis gene detection and diagnosis kit. Although hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p alone have only a certain or lower accuracy in distinguishing patients with bone metastasis of lung cancer from patients with non-metastasis of lung cancer, hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p in combination have a higher accuracy and a higher accuracy in distinguishing patients with bone metastasis of lung cancer from patients with non-metastasis of lung cancer. Therefore, a gene detection kit for diagnosing lung cancer bone metastasis can be manufactured based on the kit, and the kit contains real-time fluorescent quantitative PCR primers hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p (shown in Table 3), and also contains real-time fluorescent quantitative PCR primers of internal references, and enzymes and reagents required by real-time fluorescent quantitative PCR.
Description
Technical Field
The invention relates to a tumor detection kit, in particular to a lung cancer bone metastasis gene detection and diagnosis kit.
Background
Lung cancer is one of common malignant tumors in China, and seriously affects the life quality and life health of patients. The bone metastasis is one of the signs of the deterioration of the lung cancer, and if the accurate detection and diagnosis of whether the bone metastasis of a patient occurs can be carried out, the bone metastasis diagnostic kit has important significance for clinical symptomatic treatment and improvement of treatment effect. The tumor cells actively release or synthesize part of the tumor markers in the proliferation process, and the stimulation of the host cells by the tumor cells can also cause the host cells to generate the tumor markers. The tumor marker has important significance in auxiliary diagnosis of tumor and detection of disease change and prognosis of patients, but the research on the correlation between the level of the tumor marker and bone metastasis of lung cancer patients is less, and the requirement of a lung cancer bone metastasis detection and diagnosis kit is greatly notched.
Disclosure of Invention
The invention aims to provide a lung cancer bone metastasis gene detection and diagnosis kit.
In order to achieve the purpose, the invention provides the following technical scheme:
a gene detection kit for diagnosing bone metastasis of a patient with lung cancer comprises real-time fluorescent quantitative PCR primers of hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3 p; wherein, the real-time fluorescent quantitative PCR upstream primer of hsa _ circ _0068527 is shown as Sequence No.9, and the real-time fluorescent quantitative PCR downstream primer thereof is shown as Sequence No. 10; the real-time fluorescent quantitative PCR upstream primer of hsa-miR-591 is shown in Sequence NO.5, and the real-time fluorescent quantitative PCR downstream primer is shown in Sequence NO. 6; the real-time fluorescent quantitative PCR upstream primer of hsa-miR-365a-3p is shown in Sequence NO.7, and the real-time fluorescent quantitative PCR downstream primer is shown in Sequence NO. 8.
Preferably, the kit also contains real-time fluorescent quantitative PCR primers of internal reference GAPDH, wherein the real-time fluorescent quantitative PCR upstream primer is shown in Sequence NO.1, and the real-time fluorescent quantitative PCR downstream primer is shown in Sequence NO. 2.
Preferably, the kit further comprises enzymes, reagents and the like required for real-time fluorescent quantitative PCR.
Although hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p alone have only a certain or lower accuracy in distinguishing patients with bone metastasis of lung cancer from patients with non-metastasis of lung cancer, hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p in combination have a higher accuracy and a higher accuracy in distinguishing patients with bone metastasis of lung cancer from patients with non-metastasis of lung cancer. Therefore, a gene detection kit for diagnosing lung cancer bone metastasis can be manufactured based on the kit, and the kit contains real-time fluorescent quantitative PCR primers hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p (shown in Table 3), and also contains real-time fluorescent quantitative PCR primers of internal references, and enzymes and reagents required by real-time fluorescent quantitative PCR.
Drawings
FIG. 1 is a ROC curve for hsa-miR-6793-5p alone to distinguish training set bone metastasis groups from training set non-metastasis groups.
FIG. 2 is a ROC curve for hsa-miR-591 to distinguish training set bone metastasis group from training set non-metastasis group alone.
FIG. 3 is a ROC curve for hsa-miR-365a-3p alone to distinguish training set bone metastasis groups from training set non-metastasis groups.
FIG. 4 is a ROC curve for the combined differentiation of bone metastasis groups in the training set and non-metastasis groups in the training set by hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3 p.
FIG. 5 is a ROC curve for hsa _ circ _0068527 to distinguish training set bone metastasis alone from training set non-metastasis.
FIG. 6 is a ROC curve for hsa _ circ _0084169 alone to distinguish training set bone metastasis groups from training set non-metastasis groups.
FIG. 7 is a ROC curve for hsa _ circ _0024887 to distinguish training set bone metastasis alone from training set non-metastasis.
FIG. 8 is a ROC curve for hsa _ circ _0068527, hsa _ circ _0084169, and hsa _ circ _0024887 to jointly distinguish between bone-metastasis groups in the training set and non-metastasis groups in the training set.
FIG. 9 is a ROC curve for the combined differentiation of bone metastasis groups in the training set from non-metastasis groups in the training set by hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3 p.
Detailed Description
Example 1: micro RNA-based kit
First, experiment sample
200 lung cancer patients who were treated in the tumor hospital of sichuan province from 6 months 2014 to 6 months 2018 were selected as study subjects. Inclusion criteria were: the estimated life time is more than or equal to 3 months; ② blood platelet > 8X 1010L; ③ stopping the radiotherapy and chemotherapy for more than 1 month; fourthly, stopping using the calcium preparation before treatment; exclusion criteria: the diseases of gravity center, liver and kidney are severe; ② there are other neoplastic diseases; ③ serious diabetes; fourthly, the disease of immune system. According to clinical diagnosis, imaging diagnosis, pathological diagnosis and the like, the patients with bone metastasis are used as a bone metastasis group, and the patients without bone metastasis are used as an untransferred group. 88 cases of bone metastasis groups and 112 cases of non-metastasis groups. 42 women in the bone metastasis group, aged 36-75 years, mean (52.15 ± 5.25) years; 58 women in the non-metastatic group, aged 38-72 years, averaged (54.23 ± 5.16) years; the bone metastasis group and the non-metastasis group have no significant difference in sex ratio and age.
The bone metastasis group and the non-metastasis group were further randomly divided in half as follows:
training set bone metastasis group: 44; verification set bone metastasis group: 44;
training set non-transferred group: 56; verification set no transfer group: 56.
second, Experimental methods
1. Peripheral blood sample collection and preservation
Collecting 5mL of fasting peripheral blood of a patient in the early morning by using an EDTA (ethylene diamine tetraacetic acid) anticoagulation tube, centrifuging for 10min at 2000r/min at 4 ℃, carefully absorbing supernatant clear plasma liquid into a sterile freeze-drying tube, marking, and storing in a refrigerator at-80 ℃ for later use.
2. Extraction and quality detection of total RNA in blood plasma
250 μ L of plasma was taken from each sample and total RNA was extracted from the plasma samples according to the TaKaRa RNAiSo Blood kit protocol. And measuring the optical density D (260) and D (280) values of the total RNA by using a spectrophotometer, calculating the concentration of the total RNA by using the D (260) value, calculating the D (260)/D (280) value, and detecting the purity of the total RNA, wherein the D (260)/D (280) value is in a range of 1.8-2.1 and is considered to be qualified.
3. Real-time fluorescent quantitative PCR detection of target micro RNA
The procedure was performed according to the instructions of the PrimeScript RTreagen Kit with gDNA Eraser Kit. The real-time fluorescent quantitative PCR primers for the target microRNA and internal reference GAPDH were synthesized by Ruibo, Guangzhou, and are shown in Table 1.
TABLE 1 real-time fluorescent quantitative PCR primers
2.1 removal of genomic DNA
The reaction system is as follows: 5 XgDNAeraser Buffer 2.0. mu.L, gDNAeraser 1.0. mu.L, Total RNA 2. mu.g, RNase Free dH2And O is supplemented to 10 mu L. The reaction conditions are as follows: 42 ℃ for 2min, 4 ℃.
2.2 reverse transcription reaction
Reaction solution of the previous step experiment: 10.0. mu.L, Prime Script RT Enzyme Mix I1.0. mu.L, RTPrimer Mix 1.0. mu.L, 5 XPrime Script Buffer 24.0. mu.L, RNase Free dH2O4.0. mu.L, 20. mu.L in total. The reaction conditions are as follows: 15min at 37 ℃, 5s at 85 ℃ and 4 ℃.
2.3RT-PCR reaction
The reaction was carried out using an Applied Biosystems Stepous fluorescent quantitative PCR analyzer, and a PCR reaction solution was prepared by the following components: SYBR Premix Ex Taq II 10. mu.L, PCR upstream primer (10. mu. mol/L) 0.8. mu.L, PCR downstream primer (10. mu. mol/L) 0.8. mu.L, ROX Reference Dye 0.4. mu.L, RT reaction solution (cDNA solution) 2.0. mu.L, dH2O (sterilized distilled water) 6. mu.L, a total of 20. mu.L. A two-step PCR reaction procedure was used. After the reaction, the amplification curve and the melting curve of PCR were confirmed. Application 2-ΔΔCtThe method calculates the relative expression quantity of the target micro RNA.
3. Statistical method
Data were analyzed using SPSS 19.0. The measurement data are expressed as mean + -deviation by t-test, the count data are expressed as percentage by χ2And (4) testing, wherein P is less than 0.05, the difference has statistical significance, an ROC curve is established, and the area under the curve (AUC) and a 95% confidence interval are calculated. And screening variables by using Logistic regression, establishing a regression equation and generating a group of new variables Y. And carrying out ROC curve analysis on the new variables and each single index.
Third, experimental results
1. Plasma total RNA quality detection
The D (260)/D (280) value of the total RNA of each sample detected by the ultraviolet spectrophotometer is 1.8-2.1, the total RNA of each sample has good quality, and real-time quantitative PCR detection can be carried out.
2. Comparing relative expression quantity of target microRNAs of training set bone metastasis group and training set non-metastasis group
Compared with the non-transferred group, the relative expression level of the target micro RNA in the bone transferred group is totally up-regulated (P < 0.05).
3. ROC curve for individually distinguishing training set bone metastasis group from training set non-metastasis group by target microRNA
The ROC curve is a characteristic curve of the work of a subject, is a comprehensive index reflecting continuous variables of sensitivity and specificity, reveals the correlation of the sensitivity and the specificity by a composition method, calculates a series of sensitivity and specificity by setting the continuous variables to be different critical values, and then draws a curve by taking the sensitivity as a vertical coordinate and (1-specificity) as a horizontal coordinate, wherein the larger the area under the curve is, the higher the diagnosis accuracy is. The area under the ROC curve is between 1.0 and 0.5. When AUC >0.5, the closer the AUC is to 1, the better the diagnostic effect. AUC has lower accuracy when being 0.5-0.7, AUC has certain accuracy when being 0.7-0.9, and AUC has higher accuracy when being more than 0.9.
ROC curves for distinguishing a training set bone metastasis group and a training set non-metastasis group by using hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p separately are respectively shown in figures 1-3. The area under the ROC curve for distinguishing the training set bone metastasis group from the training set non-metastasis group by hsa-miR-6793-5p is 0.784, and certain accuracy is achieved; the areas under the ROC curves of the training set bone metastasis group and the training set non-metastasis group which are separately distinguished by hsa-miR-591 and hsa-miR-365a-3p are respectively 0.632 and 0.641, and the accuracy is low.
4. ROC curve for target micro RNA combined distinguishing training set bone metastasis group and training set non-metastasis group
Firstly, acquiring a logistic regression model of hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p for jointly distinguishing a training set bone metastasis group and a training set non-metastasis group by adopting SPSS software, and then drawing an ROC curve of hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p for jointly distinguishing the training set bone metastasis group and the training set non-metastasis group according to the logistic regression model. The specific operation method comprises the following steps:
relative expression amounts of hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p in plasma samples of patients in a training set bone metastasis group and a training set non-metastasis group are used as independent variables (X is set)1Relative expression amount of hsa-miR-6793-5p, X2Relative expression amount of hsa-miR-591, X3Taking the group (bone metastasis and non-metastasis) as a dependent variable, and performing binary logistic regression on the relative expression quantities of hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p in plasma samples of patients in a bone metastasis group of a training set and a non-metastasis group of the training set to obtain a binary logistic regression equation: y ═ 0.203+5.294X1+4.779X2+6.051X3(ii) a Then, relative table of hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p in plasma samples of each patient is shownSubstituting the amount of expression into the binary logistic regression equation to obtain the regression value Y of each sample, calculating the sensitivity and specificity by taking the possible regression value Y as a diagnosis point, drawing an ROC curve (shown in figure 4) according to the sensitivity and specificity, wherein the AUC is 0.953, and the accuracy is higher. And calculating the Viden index which is specificity + sensitivity-1 according to the coordinates of the ROC curve, wherein the corresponding Y value at the maximum value of the Viden index is the optimal cut-off value 3.913, namely the diagnosis critical value, which can be used for diagnosing and distinguishing patients in a bone metastasis group of the training set and patients in a non-metastasis group of the training set.
5. Accuracy verification for distinguishing bone metastasis and non-metastasis by target micro RNA combined diagnosis
And (3) respectively substituting the relative expression quantities of hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p in the blood plasma of the patients in the verification set bone metastasis group and the verification set non-metastasis group into the binary logistic regression equation, predicting bone metastasis when the Y value is higher than the diagnosis critical value, predicting non-metastasis when the Y value is lower than the diagnosis critical value, counting the number of the patients with correct prediction, and dividing the number of the patients with correct prediction by the total number of the patients in the verification set bone metastasis group and the verification set non-metastasis group to obtain the accuracy of distinguishing the bone metastasis and the non-metastasis by the target micro RNA joint diagnosis, wherein the accuracy is 93% (93 cases/100 cases) and the accuracy is high.
In conclusion, although hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p are used for distinguishing patients with lung cancer bone metastasis from patients with lung cancer non-metastasis only with certain or lower accuracy, hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p are combined for distinguishing patients with lung cancer bone metastasis from patients with lung cancer non-metastasis with higher accuracy and higher accuracy. Therefore, a gene detection kit for diagnosing lung cancer bone metastasis can be manufactured based on the kit, and the kit contains real-time fluorescent quantitative PCR primers of hsa-miR-6793-5p, hsa-miR-591 and hsa-miR-365a-3p (shown in Table 1), and also contains real-time fluorescent quantitative PCR primers of internal references, and enzymes and reagents required by real-time fluorescent quantitative PCR.
Example 2: circular RNA-based kit
First, experiment sample
The same as in example 1.
Second, Experimental methods
1. Peripheral blood sample collection and preservation
The same as in example 1.
2. Extraction and quality detection of total RNA in blood plasma
The same as in example 1.
3. Real-time fluorescent quantitative PCR detection of target circular RNA
The procedure was performed according to the instructions of the PrimeScript RTreagen Kit with gDNA Eraser Kit. Real-time fluorescent quantitative PCR primers for target circular RNA and internal reference GAPDH were synthesized by the cantonella biosignature, see table 2.
TABLE 2 real-time fluorescent quantitative PCR primers
2.1 removal of genomic DNA
The reaction system is as follows: 5 XgDNAeraser Buffer 2.0. mu.L, gDNAeraser 1.0. mu.L, Total RNA 2. mu.g, RNase Free dH2And O is supplemented to 10 mu L. The reaction conditions are as follows: 42 ℃ for 2min, 4 ℃.
2.2 reverse transcription reaction
Reaction solution of the previous step experiment: 10.0. mu.L, Prime Script RT Enzyme Mix I1.0. mu.L, RTPrimer Mix 1.0. mu.L, 5 XPrime Script Buffer 24.0. mu.L, RNase Free dH2O4.0. mu.L, 20. mu.L in total. The reaction conditions are as follows: 15min at 37 ℃, 5s at 85 ℃ and 4 ℃.
2.3RT-PCR reaction
The reaction was carried out using an Applied Biosystems Stepous fluorescent quantitative PCR analyzer, and a PCR reaction solution was prepared by the following components: SYBR Premix Ex Taq II 10. mu.L, PCR upstream primer (10. mu. mol/L) 0.8. mu.L, PCR downstream primer (10. mu. mol/L) 0.8. mu.L, ROX Reference Dye 0.4. mu.L, RT reaction solution (cDNA solution) 2.0. mu.L, dH2O (sterilized distilled water) 6. mu.L, a total of 20. mu.L. A two-step PCR reaction procedure was used. After the reaction, the amplification curve and the melting curve of PCR were confirmed. Application 2-ΔΔCtThe relative expression level of the target circular RNA is calculated by the method.
3. Statistical method
Data adoption SPSS19.0 for analysis. The measurement data are expressed as mean + -deviation by t-test, the count data are expressed as percentage by χ2And (4) testing, wherein P is less than 0.05, the difference has statistical significance, an ROC curve is established, and the area under the curve (AUC) and a 95% confidence interval are calculated. And screening variables by using Logistic regression, establishing a regression equation and generating a group of new variables Y. And carrying out ROC curve analysis on the new variables and each single index.
Third, experimental results
1. Plasma total RNA quality detection
The D (260)/D (280) value of the total RNA of each sample detected by the ultraviolet spectrophotometer is 1.8-2.1, the total RNA of each sample has good quality, and real-time quantitative PCR detection can be carried out.
2. Comparing relative expression quantity of target circular RNA of training set bone metastasis group and training set non-metastasis group
Compared with the non-transferred group, the relative expression level of the target circular RNA in the bone transferred group is totally up-regulated (P < 0.05).
3. ROC curve for target circular RNA to distinguish training set bone metastasis group from training set non-metastasis group separately
The ROC curve is a characteristic curve of the work of a subject, is a comprehensive index reflecting continuous variables of sensitivity and specificity, reveals the correlation of the sensitivity and the specificity by a composition method, calculates a series of sensitivity and specificity by setting the continuous variables to be different critical values, and then draws a curve by taking the sensitivity as a vertical coordinate and (1-specificity) as a horizontal coordinate, wherein the larger the area under the curve is, the higher the diagnosis accuracy is. The area under the ROC curve is between 1.0 and 0.5. When AUC >0.5, the closer the AUC is to 1, the better the diagnostic effect. AUC has lower accuracy when being 0.5-0.7, AUC has certain accuracy when being 0.7-0.9, and AUC has higher accuracy when being more than 0.9.
ROC curves for distinguishing a training set bone metastasis group from a training set non-metastasis group individually by hsa _ circ _0068527, hsa _ circ _0084169 and hsa _ circ _0024887 are shown in FIGS. 5-7. The area under the ROC curve for distinguishing the training set bone metastasis group from the training set non-metastasis group is 0.775 by hsa _ circ _0068527, and certain accuracy is achieved; the areas under ROC curves for distinguishing the bone metastasis group of the training set and the non-metastasis group of the training set separately are 0.609 and 0.625, so that the accuracy is low.
4. ROC curve for target circular RNA combined distinguishing training set bone metastasis group and training set non-metastasis group
Firstly, SPSS software is adopted to obtain a logistic regression model which jointly distinguishes a bone metastasis group of a training set from a non-metastasis group of the training set from hsa _ circ _0068527, hsa _ circ _0084169 and hsa _ circ _0024887, and then an ROC curve which jointly distinguishes a bone metastasis group of the training set from a non-metastasis group of the training set is drawn according to the logistic regression model. The specific operation method comprises the following steps:
relative expression levels of hsa _ circ _0068527, hsa _ circ _0084169 and hsa _ circ _0024887 in plasma samples of patients in the bone metastasis group and the non-metastasis group of the training set are used as independent variables (X is set)1Relative expression amount of hsa _ circ _0068527, X2Relative expression amount of hsa _ circ _0084169, X3Hsa _ circ _0024887 relative expression quantity), taking the group (bone metastasis and non-metastasis) as a dependent variable, and performing binary logistic regression on the relative expression quantities of hsa _ circ _0068527, hsa _ circ _0084169 and hsa _ circ _0024887 in plasma samples of patients in a bone metastasis group and a non-metastasis group of a training set to obtain a binary logistic regression equation: y ═ 0.282+7.106X1+5.223X2+5.155X3(ii) a Then substituting the relative expression of hsa _ circ _0068527, hsa _ circ _0084169 and hsa _ circ _0024887 in the plasma sample of each patient into the binary logistic regression equation to obtain the regression value Y of each sample, taking the possible regression value Y as a diagnosis point, calculating the sensitivity and specificity, and accordingly drawing an ROC curve (as shown in figure 8), wherein the AUC is 0.955, and the accuracy is higher. And calculating a Viden index which is specificity + sensitivity-1 according to the coordinates of the ROC curve, wherein the corresponding Y value at the maximum value of the Viden index is the optimal cut-off value 3.775 which can be used for diagnosing and distinguishing patients in a bone metastasis group of the training set and patients in a non-metastasis group of the training set, namely a diagnosis critical value.
5. Accuracy verification of target circular RNA joint diagnosis for distinguishing bone metastasis from non-metastasis
And (3) respectively substituting relative expression quantities of hsa _ circ _0068527, hsa _ circ _0084169 and hsa _ circ _0024887 in the blood plasma of patients in the verification set bone metastasis group and the verification set non-metastasis group into the binary logistic regression equation, wherein the prediction with the Y value higher than the diagnosis critical value is bone metastasis, the prediction with the Y value lower than the diagnosis critical value is non-metastasis, the number of patients with correct prediction is counted, and the accuracy of distinguishing the bone metastasis and the non-metastasis of the target circular RNA joint diagnosis is obtained by dividing the number of the patients with correct prediction by the total number of the patients in the verification set bone metastasis group and the verification set non-metastasis group, wherein the accuracy is 91% (91 cases/100 cases) and is higher.
In summary, although hsa _ circ _0068527, hsa _ circ _0084169, and hsa _ circ _0024887 alone have some or less accuracy in distinguishing between patients with bone metastases and non-metastases of lung cancer, hsa _ circ _0068527, hsa _ circ _0084169, and hsa _ circ _0024887 in combination have greater accuracy and higher accuracy in distinguishing between patients with bone metastases and non-metastases of lung cancer. Therefore, a gene detection kit for diagnosing bone metastasis of lung cancer can be manufactured based on the method, and the kit contains real-time fluorescent quantitative PCR primers hsa _ circ _0068527, hsa _ circ _0084169 and hsa _ circ _0024887 (shown in Table 2), internal reference real-time fluorescent quantitative PCR primers, and enzymes and reagents required by real-time fluorescent quantitative PCR.
Example 3: kit based on miRNA and circular RNA
First, experiment sample
The same as in example 1.
Second, Experimental methods
1. Peripheral blood sample collection and preservation
The same as in example 1.
2. Extraction and quality detection of total RNA in blood plasma
The same as in example 1.
3. Real-time fluorescent quantitative PCR detection of target RNA
The procedure was performed according to the instructions of the PrimeScript RT reagent Kit with gDNA Eraser Kit. Real-time fluorescent quantitative PCR primers for target RNA and internal reference GAPDH were synthesized by the cantonella biosignature, table 3.
TABLE 3 real-time fluorescent quantitative PCR primers
2.1 removal of genomic DNA
The reaction system is as follows: 5 XgDNAeraser Buffer 2.0. mu.L, gDNAeraser 1.0. mu.L, Total RNA 2. mu.g, RNase Free dH2And O is supplemented to 10 mu L. The reaction conditions are as follows: 42 ℃ for 2min, 4 ℃.
2.2 reverse transcription reaction
Reaction solution of the previous step experiment: 10.0. mu.L, Prime Script RT Enzyme Mix I1.0. mu.L, RTPrimer Mix 1.0. mu.L, 5 XPrime Script Buffer 24.0. mu.L, RNase Free dH2O4.0. mu.L, 20. mu.L in total. The reaction conditions are as follows: 15min at 37 ℃, 5s at 85 ℃ and 4 ℃.
2.3RT-PCR reaction
The reaction was carried out using an Applied Biosystems Stepous fluorescent quantitative PCR analyzer, and a PCR reaction solution was prepared by the following components: SYBR Premix Ex Taq II 10. mu.L, PCR upstream primer (10. mu. mol/L) 0.8. mu.L, PCR downstream primer (10. mu. mol/L) 0.8. mu.L, ROX Reference Dye 0.4. mu.L, RT reaction solution (cDNA solution) 2.0. mu.L, dH2O (sterilized distilled water) 6. mu.L, a total of 20. mu.L. A two-step PCR reaction procedure was used. After the reaction, the amplification curve and the melting curve of PCR were confirmed. Application 2-ΔΔCtThe method calculates the relative expression quantity of the target RNA.
3. Statistical method
Data were analyzed using SPSS 19.0. The measurement data are expressed as mean + -deviation by t-test, the count data are expressed as percentage by χ2And (4) testing, wherein P is less than 0.05, the difference has statistical significance, an ROC curve is established, and the area under the curve (AUC) and a 95% confidence interval are calculated. And screening variables by using Logistic regression, establishing a regression equation and generating a group of new variables Y. And carrying out ROC curve analysis on the new variables and each single index.
Third, experimental results
1. Plasma total RNA quality detection
The D (260)/D (280) value of the total RNA of each sample detected by the ultraviolet spectrophotometer is 1.8-2.1, the total RNA of each sample has good quality, and real-time quantitative PCR detection can be carried out.
2. Comparing the relative expression of target RNA in the training set bone metastasis group and the training set non-metastasis group
Compared with the non-transferred group, the relative expression level of the target RNA in the bone transferred group is totally up-regulated (P < 0.05).
3. ROC curve for target RNA to distinguish training set bone metastasis group from training set non-metastasis group separately
The ROC curve is a characteristic curve of the work of a subject, is a comprehensive index reflecting continuous variables of sensitivity and specificity, reveals the correlation of the sensitivity and the specificity by a composition method, calculates a series of sensitivity and specificity by setting the continuous variables to be different critical values, and then draws a curve by taking the sensitivity as a vertical coordinate and (1-specificity) as a horizontal coordinate, wherein the larger the area under the curve is, the higher the diagnosis accuracy is. The area under the ROC curve is between 1.0 and 0.5. When AUC >0.5, the closer the AUC is to 1, the better the diagnostic effect. AUC has lower accuracy when being 0.5-0.7, AUC has certain accuracy when being 0.7-0.9, and AUC has higher accuracy when being more than 0.9.
ROC curves for separately distinguishing the training set bone metastasis group from the training set non-metastasis group by hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p are respectively shown in FIG. 5, FIG. 2 and FIG. 3. The area under the ROC curve for distinguishing the training set bone metastasis group from the training set non-metastasis group is 0.775 by hsa _ circ _0068527, and certain accuracy is achieved; the areas under the ROC curves of the training set bone metastasis group and the training set non-metastasis group which are separately distinguished by hsa-miR-591 and hsa-miR-365a-3p are respectively 0.632 and 0.641, and the accuracy is low.
4. ROC curve for target RNA combined distinguishing training set bone metastasis group from training set non-metastasis group
Firstly, acquiring a logistic regression model for jointly distinguishing a training set bone metastasis group and a training set non-metastasis group by using SPSS software, and then drawing an ROC curve for jointly distinguishing a training set bone metastasis group and a training set non-metastasis group by using hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p according to the logistic regression model. The specific operation method comprises the following steps:
relative expression amounts of hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p in plasma samples of patients in a training set bone metastasis group and a training set non-metastasis group are taken as independent variables (X is set)1Relative expression amount of hsa _ circ _0068527, X2Relative expression amount of hsa-miR-591, X3Taking the group (bone metastasis and non-metastasis) as a dependent variable, and performing binary logistic regression on the relative expression quantities of hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p in plasma samples of patients in a bone metastasis group of a training set and a non-metastasis group of the training set to obtain a binary logistic regression equation: y ═ 0.197+6.303X1+5.085X2+6.227X3(ii) a And substituting the relative expression amounts of hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p in the plasma samples of the patients into the binary logistic regression equation to obtain the regression value Y of each sample, calculating the sensitivity and specificity by taking the possible regression value Y as a diagnosis point, drawing an ROC curve (shown in figure 9) according to the sensitivity and specificity, wherein the AUC is 0.949, and the accuracy is high. And calculating a Viden index which is specificity + sensitivity-1 according to the coordinates of the ROC curve, wherein the corresponding Y value at the maximum value of the Viden index is the optimal cut-off value 3.892 which can be used for diagnosing and distinguishing patients in a bone metastasis group of the training set and patients in a non-metastasis group of the training set, namely a diagnosis critical value.
5. Accuracy verification for distinguishing bone metastasis and non-metastasis by target RNA joint diagnosis
And (3) respectively substituting the relative expression quantities of hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p in the blood plasma of the patients in the verification set bone metastasis group and the verification set non-metastasis group into the binary logistic regression equation, wherein the prediction that the Y value is higher than the diagnosis critical value is bone metastasis, the prediction that the Y value is lower than the diagnosis critical value is non-metastasis, the number of the patients with correct prediction is counted, and the accuracy of distinguishing the bone metastasis and the non-metastasis by the target RNA joint diagnosis is obtained by dividing the number of the patients with correct prediction by the total number of the patients in the verification set bone metastasis group and the verification set non-metastasis group, wherein the accuracy is 94% (94 cases/100 cases) and the accuracy is high.
In conclusion, although hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p only have certain or lower accuracy when used for distinguishing patients with lung cancer bone metastasis from patients with lung cancer non-metastasis, hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p are combined to distinguish patients with lung cancer bone metastasis from patients with lung cancer non-metastasis with higher accuracy and higher accuracy. Therefore, a gene detection kit for diagnosing lung cancer bone metastasis can be manufactured based on the kit, and the kit contains real-time fluorescent quantitative PCR primers hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3p (shown in Table 3), and also contains real-time fluorescent quantitative PCR primers of internal references, and enzymes and reagents required by real-time fluorescent quantitative PCR.
Researchers also studied the diagnostic efficacy of hsa-miR-6793-5p, hsa _ circ _0084169 and hsa _ circ _0024887 in combination to distinguish the bone-metastasis group in the training set from the non-metastasis group in the training set, the study method was as above, the AUC was 0.796, and there was no significant difference in diagnostic efficacy of hsa-miR-6793-5p alone in distinguishing the bone-metastasis group in the training set from the non-metastasis group in the training set, because hsa _ circ _ 00169 or hsa _ circ _0024887 had no obvious tonic effect on the diagnostic efficacy of hsa-miR-6793-5 p.
Sequence listing
<110> Qinghai seven-color peanut science and technology Co., Ltd
<120> lung cancer bone metastasis gene detection and diagnosis kit
<160> 14
<170> SIPOSequenceListing 1.0
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<212> DNA/RNA
<213> Artificial Sequence (Artificial Sequence)
<400> 3
gattgtgtga caatatatgt agg 23
<210> 4
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<212> DNA/RNA
<213> Artificial Sequence (Artificial Sequence)
<400> 4
gtggcagtga ctcgtggtat ttca 24
<210> 5
<211> 20
<212> DNA/RNA
<213> Artificial Sequence (Artificial Sequence)
<400> 5
tggccgtgga gacaatgtgc 20
<210> 6
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<213> Artificial Sequence (Artificial Sequence)
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<213> Artificial Sequence (Artificial Sequence)
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<213> Artificial Sequence (Artificial Sequence)
<400> 9
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<210> 10
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<213> Artificial Sequence (Artificial Sequence)
<400> 10
tggttgttgg tcaacgacaa cctaa 25
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<213> Artificial Sequence (Artificial Sequence)
<400> 11
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<213> Artificial Sequence (Artificial Sequence)
<400> 12
agggttatgg tagccagcct 20
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<213> Artificial Sequence (Artificial Sequence)
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cactctggca gccctgcttt a 21
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<211> 20
<212> DNA/RNA
<213> Artificial Sequence (Artificial Sequence)
<400> 14
aatgcgcttg ccacattgat 20
Claims (3)
1. A gene detection kit for diagnosing bone metastasis of a patient with lung cancer, which is characterized in that: real-time fluorescent quantitative PCR primers containing hsa _ circ _0068527, hsa-miR-591 and hsa-miR-365a-3 p; wherein, the real-time fluorescent quantitative PCR upstream primer of hsa _ circ _0068527 is shown as Sequence No.9, and the real-time fluorescent quantitative PCR downstream primer thereof is shown as Sequence No. 10; the real-time fluorescent quantitative PCR upstream primer of hsa-miR-591 is shown in Sequence NO.5, and the real-time fluorescent quantitative PCR downstream primer is shown in Sequence NO. 6; the real-time fluorescent quantitative PCR upstream primer of hsa-miR-365a-3p is shown in Sequence NO.7, and the real-time fluorescent quantitative PCR downstream primer is shown in Sequence NO. 8.
2. The gene assaying kit according to claim 1, characterized in that: and the real-time fluorescent quantitative PCR primer also contains internal reference GAPDH, wherein the real-time fluorescent quantitative PCR upstream primer is shown in Sequence NO.1, and the real-time fluorescent quantitative PCR downstream primer is shown in Sequence NO. 2.
3. The gene assaying kit according to claim 1 or 2, characterized in that: and also contains enzymes and reagents required by real-time fluorescence quantitative PCR.
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| CN102876788A (en) * | 2012-09-19 | 2013-01-16 | 杨祚璋 | Fluorescent quantitative polymerase chain reaction (PCR) assay kit for bone metastasis of lung cancer |
| CN102876789A (en) * | 2012-09-19 | 2013-01-16 | 杨祚璋 | Kit for detecting bone metastasis of lung cancer |
| CN102899405A (en) * | 2012-09-19 | 2013-01-30 | 杨祚璋 | Kit for detecting changes of lung cancer cell RNA |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102876788A (en) * | 2012-09-19 | 2013-01-16 | 杨祚璋 | Fluorescent quantitative polymerase chain reaction (PCR) assay kit for bone metastasis of lung cancer |
| CN102876789A (en) * | 2012-09-19 | 2013-01-16 | 杨祚璋 | Kit for detecting bone metastasis of lung cancer |
| CN102899405A (en) * | 2012-09-19 | 2013-01-30 | 杨祚璋 | Kit for detecting changes of lung cancer cell RNA |
Non-Patent Citations (4)
| Title |
|---|
| Cell-Type Specific Features of Circular RNA Expression;Julia Salzman et al;《PLOS Genetics》;20130930;第9卷(第9期);全文 * |
| Dysregulation of miR-106a and miR-591 confers paclitaxel resistance to ovarian confers paclitaxel resistance to ovarian;J H Huh et al;《British Journal of Cancer》;20131231;第109卷;全文 * |
| MicroRNA‑365 promotes lungcarcinogenesis by downregulating the USP33/SLIT2/ROBO1 signalling pathway;Yuhuan Wang et al;《Cancer Cell International》;20180501;全文 * |
| MicroRNA-365a-3p promotes tumor growth and metastasis in laryngeal squamous cell carcinoma;JIANGQIAO GENG et al;《ONCOLOGY REPORTS》;20161231;第35卷;全文 * |
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