CN111944898A - Characteristic mRNA expression profile combination and renal clear cell carcinoma early prediction method - Google Patents
Characteristic mRNA expression profile combination and renal clear cell carcinoma early prediction method Download PDFInfo
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Abstract
The invention discloses a characteristic mRNA expression profile combination and a renal clear cell carcinoma early prediction method, wherein the mRNA comprises AC074143.1, ANXA9, ASAP3, CCND1, CDH16, CDK18, CTNNAL1, DGKD, EPB41L5, GABARAPL1, GPD1L, KRBA1, PAQR7, PCCB, RIOX2, SAP30, SCARB1, TNFRSF14, VEGFA and ZNF395, and the nucleotide probe sequence is shown as SEQ ID NO. 1-20. The method has high accuracy and precision in assessing the early risk of renal clear cell carcinoma (AUC (area under ROC curve) ═ 1.000). Only the relative expression quantity of the 20 mRNAs is obtained, and the early stage prevalence probability of the renal clear cell carcinoma is calculated through a support vector machine model.
Description
Technical Field
The invention belongs to the technical field of biotechnology and medicine, and particularly relates to a characteristic mRNA expression profile combination and an early prediction method of renal clear cell carcinoma.
Background
Renal clear cell carcinoma (kidney renal clear cell carcinoma) accounts for approximately 70% -80% of renal cell carcinoma. Renal clear cell carcinoma often has no symptoms in the early stage, or has general symptoms such as fever and hypodynamia, and the early diagnosis is difficult. Global Burden of Disease (GBD) data shows that over 210 million people with renal cancer worldwide in 2017, with about 27 million people in china. The number of deaths of kidney cancer in 2017 worldwide is about 14 ten thousand, accounting for 0.25% of the total deaths. The number of the death patients in 2017 years in China is about 1.7 ten thousand, and accounts for 0.16 percent of the total death number. Statistics show that the prevalence and mortality of kidney cancer continues to increase worldwide from 1990 to 2017. In China, the prevalence rate and the death rate of the renal cancer are relatively stable in the last decade.
A Support Vector Machine (SVM) is a generalized linear classifier that performs binary classification on data in a supervised learning manner, and a decision boundary of the SVM is a maximum edge distance hyperplane for solving a learning sample. The SVM model represents instances as points in space, so that the mapping is such that instances of the individual classes are separated by as wide an apparent interval as possible. The new instances are then mapped to the same space and the categories are predicted based on which side of the interval they fall on. When the training data is linearly separable, the SVM is classified by hard interval maximization learning. When the training data is linearly non-separable, the SVM is classified by using a kernel technique and soft interval maximization learning. SVMs are powerful for medium-sized data sets with similar meaning of features and are also suitable for small data sets. In general, the prediction effect is good for the SVM data set with the sample size less than 1 ten thousand. SVM has a wide range of applications in disease diagnosis, tumor classification, tumor gene recognition, and the like.
Early diagnosis of tumors has been a difficult problem in the medical community. The existing early diagnosis methods mostly observe the expression level of a certain marker or a class of markers, and the ideal diagnosis effect is difficult to achieve. Since the expression profiles of these markers in tumor patients and normal populations partially overlap, it is difficult to define a cut-off for the markers that better separates tumor patients from normal populations. Therefore, the use of multiple marker expression signature combinations may be an effective method for early diagnosis of tumors. Messenger RNA (mRNA) is a single-stranded ribonucleic acid that is transcribed from a single strand of DNA as a template and carries genetic information that directs protein synthesis. Tumor tissues often show a large number of mRNA disorders compared to normal tissues, and studies have shown that these mRNA disorders are closely related to tumor occurrence, pathological mechanisms and prognosis status. However, it is difficult to define the critical value for early diagnosis due to the overlapping distribution of single mRNA molecules expressed in tumor and normal human populations.
Therefore, there is a need to establish a more stable predictive model of a combination of multiple differential mRNA expression profiles that contributes to the early prediction of renal clear cell carcinoma.
Disclosure of Invention
In view of the above, the present invention provides a combination of characteristic mRNA expression profiles and a method for early stage renal clear cell carcinoma prediction, which can accurately predict the stage I/II renal clear cell carcinoma.
In order to solve the technical problem, the invention discloses a characteristic mRNA expression profile combination, which comprises AC074143.1, ANXA9, ASAP3, CCND1, CDH16, CDK18, CTNNAL1, DGKD, EPB41L5, GABARAPL1, GPD1L, KRBA1, PAQR7, PCCB, RIOX2, SAP30, SCARB1, TNFRSF14, VEGFA and ZNF395, and the nucleotide probe sequences of the characteristic mRNA expression profile combination are shown in SEQ ID NO. 1-20.
The invention also discloses an early renal clear cell carcinoma prediction method based on the characteristic mRNA expression profile combination, which comprises the following steps:
step 2, selecting characteristic mRNA expression data, and carrying out data standardization on each sample;
step 3, constructing an early prediction model for the standardized data by using a support vector machine;
step 4, early prediction is carried out according to the expression level of the mRNA which is characteristic of the patient;
the methods are for non-disease diagnostic and therapeutic purposes.
Optionally, the step 1 of obtaining characteristic mrnas stably and differentially expressed by the patient with early renal clear cell carcinoma includes:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and para-carcinoma tissues of patients with renal clear cell carcinoma from a Genomic Data common Data Portal database to obtain a tumor tissue gene expression profile read counts value of the patients with renal clear cell carcinoma, namely a sequencing read value, and carrying out logarithmic conversion;
step 1.2, selecting mRNA with certain expression abundance, namely, reading counts of the mRNA in all samples are more than or equal to 10; taking logarithm of read counts of all mRNA, setting the total number of samples as n, taking the total number of screened mRNA as m, v as the read counts of the mRNA, and u as an expression value after taking logarithm, and then obtaining the result;
uij＝log2 vij，i∈(1，n)，j∈(1，m) (1)
wherein i is the sample number, j is the mRNA number, uijThe expression value after taking the logarithm of the ith sample and the jth mRNA number, vijRead counts values for sample i, mRNA j number;
step 1.3, selecting renal clear cell carcinoma patients with disease stages of I and II, recording the patients as renal clear cell carcinoma early-stage patients, and recording the total number of the renal clear cell carcinoma early-stage patients as n';
step 1.4, selecting mRNA stably expressed in the tumor sample and the normal sample, namely mRNA with the variation coefficient smaller than 0.1 in the tumor sample and the normal sample, setting mu as the expression mean value of the mRNA in all samples, setting sigma as standard deviation, and calculating the variation coefficient according to the formula:
wherein j is the mRNA number, cvIs the coefficient of variation, cvjCoefficient of variation, σ, for the j-th samplejIs the standard deviation of the jth mRNA number, μjmR numbering the jth mRNAMean expression of NA, m1For the total number of stably expressed mrnas, there are:
step 1.5, mRNA which is differentially expressed in a tumor sample and a normal sample is selected; the logarithmized expression values were used to calculate the log-oriented fold change f of the tumor and normal sample mrnas, and the formula is:
wherein j is the mRNA number, fjFold change for jth mRNA numbering,. mu.1jExpression mean, μ, of tumor samples numbered for the jth mRNA2jExpression mean of the normal sample numbered for the jth mRNA;
the expression difference of mRNA in tumor and normal samples was then compared using independent sample t-test, which was formulated as:
wherein n is1Is the number of tumor samples, n2Is a normal number of samples, mu1Mean tumor sample mRNA expression, μ2Is the mean value of the mRNA expression of a normal sample,
the variance of the mRNA in the tumor sample is obtained,
mRNA variance for normal samples;
correcting the p values obtained by all t tests by using a False Discovery Rate (FDR), wherein q is a value corrected by the FDR, and r is a p value in m1The sequenced positions in the individual mRNAs are:
wherein j is the mRNA number, qjRepresents the FDR corrected value of the jth mRNA number, pjP-value, r, from t-test representing the jth mRNA numberjP representing the jth mRNA number
Value in m1The sequenced position in the individual mRNA;
finally selecting mRNA with the multiple change f of more than 1 and the FDR corrected q value of less than or equal to 0.05, marking as characteristic mRNA, and setting the total number of the characteristic mRNA as m2Then, there are:
m2＝m1{|fj|≥1，qj≤0.05}，j∈(1，m1) (7)
。
optionally, the characteristic mRNA expression data in step 2 is selected, and data normalization is performed on each sample, where the formula is:
wherein i is the sample number and j is the feature mRNA number; mu.siMean, σ, of all characteristic mRNA expressions of the ith sampleiFor all characteristic mRNA standard deviations, u, of the ith sampleijFor logarithmic characteristic mRNA expression values,. mu.ij' is the normalized mRNA value.
Optionally, the step 3 of constructing an early prediction model for the normalized data by using a support vector machine specifically includes:
step 3.1, grouping all samples, dividing 80% of all samples into a training set and a verification set, dividing the rest 20% of all samples into a test set, wherein the training set and the verification set are used for 5-fold cross verification, namely, dividing the training set and the verification set into 5 equal groups, taking one group as the verification set and the rest 4 groups as the training set in sequence, giving parameters, wherein the training set is used for constructing a model, and the verification set is used for checking the accuracy of the model;
step 3.2, optimal parameter screening, wherein the parameter gamma in the SVM controls the width of a Gaussian kernel, and C is a regularization parameter and limits the importance of each point; the parameter grid is set as:
gamma＝[0.001，0.01，0.1，1，10，100] (9)
C＝[0.001，0.01，0.1，1，10，100] (10)
in the cross validation, a model is constructed by sequentially using the combination of every two parameters gamma and C, and then the accuracy of the model is checked by using a validation set; for each parameter combination, each validation of 5-fold cross-validation yielded 1 accuracy, and a total of 5 validations yielded 5 accuracies. Selecting a parameter combination with the highest average accuracy of 5 times of verification as an optimal parameter;
3.3, constructing a model by using the optimal parameters and the data of the training set and the verification set, and finally evaluating the model by using the test set; assessment indicators include accuracy (accuracy), precision (precision), recall (call), specificity (specificity), F1 score (F1 score), Mathematic Correlation Coefficient (MCC), and area under the subject operating curve (ROC) (AUC); in the test set, defining the tumor count as True Positive (TP), the tumor count as normal but predicted as False Positive (FP), the tumor count as true but predicted as normal False Negative (FN), the tumor count as normal but predicted as True Negative (TN); the above evaluation index calculation formula is:
the accuracy, recall, specificity, F1 score and AUC of the above assessment indices returned values between (0, 1); the higher the accuracy is, the higher the overall prediction efficiency of the model is; higher accuracy indicates that the class I error is smaller; higher recall indicates that a class II error is being made smaller; the high specificity indicates that few negative examples are mixed in the samples predicted to be positive examples; the F1 score is a comprehensive index and is a harmonic average of the accuracy rate and the recall rate; MCC is the correlation coefficient between observed and predicted binary classifications, returning a value between (-1, 1), where 1 represents perfect prediction, 0 represents no better than random prediction, -1 represents a complete disparity between prediction and observation; a higher AUC indicates a higher probability of a positive instance being predicted by the classifier. Therefore, the closer the above index is to 1, the better the overall prediction effect of the model is;
and 3.4, if the evaluation indexes are all larger than 0.9, the model has a better prediction effect, and then all data are used and the optimal parameter combination is used for constructing a final prediction model.
Optionally, the early prediction in step 4 is performed according to the expression level of mRNA characteristic of the patient, specifically:
step 4.1, standardizing the characteristic mRNA expression data of the prediction sample, setting u as the characteristic mRNA expression value of the prediction sample, setting mu as the characteristic mRNA expression mean value of the prediction sample, setting sigma as the standard deviation of the characteristic mRNA of the prediction sample, and adopting the following formula:
wherein j is the characteristic mRNA number, μj' is the normalized mRNA value;
and 4.2, substituting the mRNA value after the prediction sample is normalized into the final prediction to predict, wherein the prediction result is 1 to indicate that the patient has renal clear cell carcinoma, and the prediction result is 0 to indicate that the patient is normal.
Compared with the prior art, the invention can obtain the following technical effects:
1) the prediction speed of the invention is fast: the prediction model constructed by the invention can be used for rapidly predicting large-scale samples, and the prediction time of 100 samples only needs a few seconds.
2) The invention has high accuracy: the prediction model constructed by the method has high prediction accuracy and accuracy which are both more than 90%, and the AUC of the area under the ROC curve can reach 1.000.
3) The platform heterogeneity of the invention has little influence: because mRNA expression values measured by different analysis platforms have large difference, the invention predicts and uses normalized characteristic mRNA expression values, and is less influenced by platform heterogeneity.
Of course, it is not necessary for any one product in which the invention is practiced to achieve all of the above-described technical effects simultaneously.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of data screening and model building according to the present invention;
FIG. 2 is a cross-validation parameter optimization process for a support vector machine model according to the present invention;
FIG. 3 is a diagram of a test set evaluation index for a support vector machine model according to the present invention;
FIG. 4 is a support vector machine model test set ROC curve of the present invention.
Detailed Description
The following embodiments are described in detail with reference to the accompanying drawings, so that how to implement the technical features of the present invention to solve the technical problems and achieve the technical effects can be fully understood and implemented.
The invention discloses a renal clear cell carcinoma early stage prediction method based on characteristic mRNA expression profile combination, which is used for the purpose of non-disease diagnosis and treatment and comprises the following steps:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and para-carcinoma tissues of patients with renal clear cell carcinoma from a Genomic Data common Data Portal database to obtain a tumor tissue gene expression profile read counts value of the patients with renal clear cell carcinoma, namely a sequencing read value, and carrying out logarithmic conversion;
step 1.2, selecting mRNA with certain expression abundance, namely the read counts of the mRNA in all samples are more than or equal to 10. Taking logarithm of read counts of all mRNA, setting the total number of samples as n, taking the total number of screened mRNA as m, v as the read counts of the mRNA, and u as an expression value after taking logarithm, and then obtaining the result;
uij＝log2，vij，j∈(1，n)，j∈(1，m) (1)
wherein i is the sample number, j is the mRNA number, uijThe expression value after taking the logarithm of the ith sample and the jth mRNA number, vijRead counts values for the ith sample, jth mRNA number.
Step 1.3, selecting renal clear cell carcinoma patients with disease stages of I and II, recording the patients as renal clear cell carcinoma early-stage patients, and recording the total number of the renal clear cell carcinoma early-stage patients as n';
step 1.4, selecting mRNA stably expressed in the tumor sample and the normal sample, namely mRNA with the variation coefficient smaller than 0.1 in the tumor sample and the normal sample, setting mu as the expression mean value of the mRNA in all samples, setting sigma as standard deviation, and calculating the variation coefficient according to the formula:
wherein j is the mRNA number, cvIs the coefficient of variation, cvjCoefficient of variation, σ, for the j-th samplejIs the standard deviation of the jth mRNA number, μjThe expression average of the mRNA numbered by the jth mRNA is set as m1For the total number of stably expressed mrnas, there are:
step 1.5, mRNA which is differentially expressed in tumor samples and normal samples is selected. The logarithmized expression values were used to calculate the log-oriented fold change f of the tumor and normal sample mrnas, and the formula is:
wherein j is the mRNA number, fjFold change for jth mRNA numbering,. mu.1jExpression mean, μ, of tumor samples numbered for the jth mRNA2jThe expression mean of the j-th mRNA-numbered normal samples.
The expression difference of mRNA in tumor and normal samples was then compared using independent sample t-test, which was formulated as:
wherein n is1Is the number of tumor samples, n2Is a normal number of samples, mu1Mean tumor sample mRNA expression, μ2Is the mean value of the mRNA expression of a normal sample,
is a tumorThe variance of the mRNA of the sample,
is the normal sample mRNA variance.
Correcting the p values obtained by all t tests by using a False Discovery Rate (FDR), wherein q is a value corrected by the FDR, and r is a p value in m1The sequenced positions in the individual mRNAs are:
wherein j is the mRNA number, qjRepresents the FDR corrected value of the jth mRNA number, pjP-value, r, from t-test representing the jth mRNA numberjP-value at m representing the jth mRNA number1The sequenced position in individual mRNAs.
Finally selecting mRNA with the multiple change f of more than 1 and the FDR corrected q value of less than or equal to 0.05, marking as characteristic mRNA, and setting the total number of the characteristic mRNA as m2Then, there are:
m2＝m1{|fj|≥1，qj≤0.05}，j∈(1，m1) (7)
。
step 2, selecting characteristic mRNA expression data, and carrying out data standardization on each sample, wherein the formula is as follows:
wherein i is the sample number and j is the characteristic mRNA number. Mu.siMean, σ, of all characteristic mRNA expressions of the ith sampleiFor all characteristic mRNA standard deviations, u, of the ith sampleijFor logarithmic characteristic mRNA expression values, uij' is the normalized mRNA value.
Step 3, constructing an early prediction model for the standardized data by using a support vector machine, specifically:
and 3.1, grouping all samples. 80% of all samples are divided into training set + validation set, and the remaining 20% are divided into test set. The training set and the verification set are used for 5-fold cross validation, namely the training set and the verification set are divided into 5 groups which are equal, one group is used as the verification set in sequence, and the other 4 groups are used as the training set. Given the parameters, the training set is used to construct the model, and the validation set is used to verify the accuracy of the model.
And 3.2, screening the optimal parameters. The parameter gamma in the SVM controls the width of the Gaussian kernel, and C is a regularization parameter, limiting the importance of each point. The parameter grid is set as:
gamma＝[0.001，0.01，0.1，1，10，100] (9)
C＝[0.001，0.01，0.1，1，10，100] (10)
in cross-validation, the model is constructed using a combination of every two parameters gamma and C in turn, and then the validation set is used to verify the model accuracy. For each parameter combination, each validation of 5-fold cross-validation yielded 1 accuracy, and a total of 5 validations yielded 5 accuracies. And selecting the parameter combination with the highest average accuracy of 5 times of verification as the optimal parameter.
And 3.3, constructing a model by using the optimal parameters and the data of the training set and the verification set, and finally evaluating the model by using the test set. The evaluation indices include accuracy (accuracy), precision (precision), recall (call), specificity (specificity), F1 score (F1 score), Mathematic Correlation Coefficient (MCC), and area under the subject operating curve (ROC) (AUC). In the test set, the tumor counts are defined as True Positive (TP), normal but predicted tumor counts as False Positive (FP), tumor counts as False Negative (FN), and normal and predicted as True Negative (TN). The above evaluation index calculation formula is:
the accuracy, recall, specificity, F1 score and AUC returned values between (0, 1) in the above evaluation indices. The higher the accuracy is, the higher the overall prediction efficiency of the model is; higher accuracy indicates that the class I error is smaller; higher recall indicates that a class II error is being made smaller; the high specificity indicates that few negative examples are mixed in the samples predicted to be positive examples; the F1 score is a comprehensive index and is a harmonic average of the accuracy rate and the recall rate; MCC is the correlation coefficient between observed and predicted binary classifications, returning a value between (-1, 1), where 1 represents perfect prediction, 0 represents no better than random prediction, -1 represents a complete disparity between prediction and observation; a higher AUC indicates a higher probability of a positive instance being predicted by the classifier. Therefore, the closer the above index is to 1, the better the prediction effect of the entire model is.
And 3.4, if the evaluation indexes are all larger than 0.9, the model has a better prediction effect. The final prediction model is constructed with the optimal parameter combinations using all the data.
And 4, carrying out early prediction according to the expression level of the mRNA characteristic of the patient, specifically comprising the following steps:
step 4.1, standardizing the characteristic mRNA expression data of the prediction sample, setting u as the characteristic mRNA expression value of the prediction sample, setting mu as the characteristic mRNA expression mean value of the prediction sample, setting sigma as the standard deviation of the characteristic mRNA of the prediction sample, and adopting the following formula:
wherein j is the characteristic mRNA number, uj' is the normalized mRNA value.
And 4.2, substituting the mRNA value after the prediction sample is normalized into the final prediction for prediction. A prediction of 1 indicates the presence of clear cell renal carcinoma, and a prediction of 0 indicates normal.
Example 1
A renal clear cell carcinoma early prediction method based on characteristic mRNA expression profile combination comprises the following steps:
Step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and tissues beside renal clear cell carcinoma patients from a Genomic Data common Data Portal database, obtaining tumor tissue gene expression profile read counts values of the renal clear cell carcinoma patients, and carrying out logarithmic conversion.
Step 1.2, selecting mRNA with certain expression abundance, namely the read counts of the mRNA in all samples are more than or equal to 10, and the detailed description is shown in a formula (1).
And 1.3, selecting patients with renal clear cell carcinoma with disease stages of I and II, wherein the patients are described in formulas (2) to (3) in detail, and recording the patients as early-stage renal clear cell carcinoma patients.
And step 1.4, selecting mRNA stably expressed in the tumor sample and the normal sample, namely mRNA with the variation coefficient smaller than 0.1 in the tumor sample and the normal sample.
Step 1.5, mRNA differentially expressed in tumor and normal samples is selected, and see formulas (4) - (7) for details. The signature mRNA is recorded. In this example, the first 20 clear cell carcinoma characteristic mRNAs (sorted from small to large according to FDR corrected P values) were selected for model construction, as shown in Table 1. The nucleotide probe sequences of 20 renal clear cell carcinoma-characteristic mRNAs are shown in Table 2.
TABLE 1 renal clear cell carcinoma signature mRNA
TABLE 2 nucleotide probe sequences for renal clear cell carcinoma signature mRNAs
And 2, carrying out data standardization on each sample, wherein the details are shown in a formula (8).
And 3, constructing an early diagnosis model for the standardized data by using a support vector machine.
And 3.1, grouping all samples. 80% of all samples are divided into training set + validation set, and the remaining 20% are divided into test set. The training set and the verification set are used for 5-fold cross validation, namely the training set and the verification set are divided into 5 groups which are equal, one group is used as the verification set in sequence, and the other 4 groups are used as the training set. Given the parameters, the training set is used to construct the model, and the validation set is used to verify the accuracy of the model. See figure 1 for details.
And 3.2, screening the optimal parameters. The SVM parameter grid is set by formulas (9) - (10). In cross-validation, the model is constructed using a combination of every two parameters gamma and C in turn, and then the validation set is used to verify the model accuracy. For each parameter combination, each validation of 5-fold cross-validation yielded 1 accuracy, and a total of 5 validations yielded 5 accuracies. And selecting the parameter combination with the highest average accuracy of 5 times of verification as the optimal parameter. Fig. 2 shows the cross-validation parameter optimization process, where the model cross-validation accuracy is highest when the parameter gamma is 0.01 and the parameter C is 10: 0.997. the optimal parameters of the model are therefore: gamma is 0.01 and C is 10.
And 3.3, constructing a model by using the optimal parameters and the data of the training set and the verification set, and finally evaluating the model by using the test set. The evaluation indices include accuracy (accuracy), precision (precision), recall (call), specificity (specificity), F1 score (F1 score), Mathematic Correlation Coefficient (MCC), and area under the subject operating curve (ROC) (AUC). The evaluation index is described in detail in formulas (11) to (17).
Step 3.4, fig. 3 shows accuracy, recall, specificity, F1 score and MCC in the above evaluation indexes, all 6 indexes being 1.000; FIG. 4 shows the ROC curve and AUC, with an AUC of 1.000 in the test set. The evaluation indexes show that the model has good prediction effect. Thus, using all the data, the final prediction model is constructed with the optimal parameter combinations.
And 4, early prediction is carried out according to the expression level of the mRNA which is characterized by the patient:
and 4.1, standardizing the characteristic mRNA expression data of the prediction sample, wherein the details are shown in a formula (18). The method randomly selects 10 samples for prediction, and eliminates the 10 samples when a final prediction model is constructed. The numbers of 10 samples taken and the normalized characteristic mRNA values are shown in Table 3.
TABLE 3.10 sample numbers and values normalized for characteristic mRNA
And 4.2, substituting the mRNA value after the prediction sample is normalized into the final prediction for prediction. A prediction of 1 indicates the presence of clear cell renal carcinoma, and a prediction of 0 indicates normal. The sample numbers of 10 cases, corresponding TCGA numbers, actual states and predicted results are shown in Table 4. The prediction results of 10 samples completely accord with the actual state, which shows that the invention can accurately predict the renal clear cell carcinoma in early stage.
TABLE 4.10 sample numbers, corresponding TCGA numbers, actual and predicted states
In conclusion, the characteristic mRNA expression profile combination has high prediction accuracy, and can effectively predict renal clear cell carcinoma at early stage. In addition, the method has no platform dependency, and can predict data from various sources.
While the foregoing description shows and describes several preferred embodiments of the invention, it is to be understood, as noted above, that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
SEQUENCE LISTING
<110> institute of Buddha science and technology
<120> a characteristic mRNA expression profile combination and early prediction method of renal clear cell carcinoma
<130> 2020
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<400> 5
gggagtgctc caaatgtcag ggtgtttgcc 30
<210> 6
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 6
ccacacacgt ttcatctttt tcccctctga 30
<210> 7
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 7
ggatagtaaa acttgagaag cttttggggt 30
<210> 8
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 8
caccgtggaa acaaacgcct cttgtattgt 30
<210> 9
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 9
agtggtcaga agaaagttgg aaacttttcc 30
<210> 10
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 10
ggattggctt tgatagagga atggggatga 30
<210> 11
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 11
cagccaatct gtgaatgtaa aaactacact 30
<210> 12
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 12
atttgagaga actcagcccc ctggtctaag 30
<210> 13
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 13
agatacaaat gggtcccatc cagtgcttca 30
<210> 14
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 14
gagatcatgc ccaattatgc caagaacatc 30
<210> 15
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 15
gtatctttta aaactaggat tgactctctc 30
<210> 16
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 16
ggagacgtgg aattgagact aataacttgg 30
<210> 17
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 17
tcctggaccc tggggcaaac ctgtgaccct 30
<210> 18
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 18
ttaggccaag tgtggagaaa tcaatgatgt 30
<210> 19
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 19
ccagcacata ggagagatga gcttcctaca 30
<210> 20
<211> 30
<212> DNA
<213> Artificial sequence (Artificial sequence)
<400> 20
ttcagagagt gttttgagcc atggccacgt 30
Claims (6)
1. A combination of signature mRNA expression profiles comprising AC074143.1, ANXA9, ASAP3, CCND1, CDH16, CDK18, CTNNAL1, DGKD, EPB41L5, GABARAPL1, GPD1L, KRBA1, PAQR7, PCCB, RIOX2, SAP30, SCARB1, TNFRSF14, VEGFA and ZNF395, the nucleotide probe sequences of which are set forth in SEQ ID nos. 1-20.
2. A method for the early prediction of clear cell renal carcinoma based on the combination of characteristic mRNA expression profiles according to claim 1, comprising the steps of:
step 1, obtaining characteristic mRNA stably and differentially expressed by a patient with renal clear cell carcinoma at an early stage;
step 2, selecting characteristic mRNA expression data, and carrying out data standardization on each sample;
step 3, constructing an early prediction model for the standardized data by using a support vector machine;
step 4, early prediction is carried out according to the expression level of the mRNA which is characteristic of the patient;
the methods are for non-disease diagnostic and therapeutic purposes.
3. The early stage prediction method according to claim 2, wherein the step 1 of obtaining the characteristic mRNA stably and differentially expressed by the patient in the early stage of renal clear cell carcinoma comprises:
step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and para-carcinoma tissues of patients with renal clear cell carcinoma from a Genomic Data common Data Portal database to obtain a tumor tissue gene expression profile read counts value of the patients with renal clear cell carcinoma, namely a sequencing read value, and carrying out logarithmic conversion;
step 1.2, selecting mRNA with certain expression abundance, namely, reading counts of the mRNA in all samples are more than or equal to 10; taking logarithm of read counts of all mRNA, setting the total number of samples as n, taking the total number of screened mRNA as m, v as the read counts of the mRNA, and u as an expression value after taking logarithm, and then obtaining the result;
uij＝log2 vij，i∈(1，n)，j∈(1，m) (1)
wherein i is the sample number, j is the mRNA number, uijIs the ith sampleExpression value, v, of the jth mRNA after taking the logarithm of the numberijRead counts values for sample i, mRNA j number;
step 1.3, selecting renal clear cell carcinoma patients with disease stages of I and II, recording the patients as renal clear cell carcinoma early-stage patients, and recording the total number of the renal clear cell carcinoma early-stage patients as n';
step 1.4, selecting mRNA stably expressed in the tumor sample and the normal sample, namely mRNA with the variation coefficient smaller than 0.1 in the tumor sample and the normal sample, setting mu as the expression mean value of the mRNA in all samples, setting sigma as standard deviation, and calculating the variation coefficient according to the formula:
wherein j is the mRNA number, cvIs the coefficient of variation, cvjCoefficient of variation, σ, for the j-th samplejIs the standard deviation of the jth mRNA number, μjThe expression average of the mRNA numbered by the jth mRNA is set as m1For the total number of stably expressed mrnas, there are:
step 1.5, mRNA which is differentially expressed in a tumor sample and a normal sample is selected; the logarithmized expression values were used to calculate the log-oriented fold change f of the tumor and normal sample mrnas, and the formula is:
wherein j is the mRNA number, fjFold change for jth mRNA numbering,. mu.1jExpression mean, μ, of tumor samples numbered for the jth mRNA2jExpression mean of the normal sample numbered for the jth mRNA;
the expression difference of mRNA in tumor and normal samples was then compared using independent sample t-test, which was formulated as:
wherein n is1Is the number of tumor samples, n2Is a normal number of samples, mu1Mean tumor sample mRNA expression, μ2Is the mean value of the mRNA expression of a normal sample,
the variance of the mRNA in the tumor sample is obtained,
mRNA variance for normal samples;
correcting the p values obtained by all t tests by using a False Discovery Rate (FDR), wherein q is a value corrected by the FDR, and r is a p value in miThe sequenced positions in the individual mRNAs are:
wherein j is the mRNA number, qjRepresents the FDR corrected value of the jth mRNA number, pjP-value, r, from t-test representing the jth mRNA numberjP-value at m representing the jth mRNA number1The sequenced position in the individual mRNA;
finally selecting mRNA with the multiple change f of more than 1 and the FDR corrected q value of less than or equal to 0.05, marking as characteristic mRNA, and setting the total number of the characteristic mRNA as m2Then, there are:
m2＝m1{|fj|≥1，qj≤0.05}，j∈(1，m1) (7)。
4. the early prediction method of claim 2, wherein the characteristic mRNA expression data is selected in step 2, and the data is normalized for each sample according to the formula:
wherein i is the sample number and j is the feature mRNA number; mu.siMean, σ, of all characteristic mRNA expressions of the ith sampleiFor all characteristic mRNA standard deviations, u, of the ith sampleijFor logarithmic characteristic mRNA expression values, uij' is the normalized mRNA value.
5. The early prediction method according to claim 2, wherein the step 3 of constructing the early prediction model on the normalized data by using a support vector machine comprises:
step 3.1, grouping all samples, dividing 80% of all samples into a training set and a verification set, dividing the rest 20% of all samples into a test set, wherein the training set and the verification set are used for 5-fold cross verification, namely, dividing the training set and the verification set into 5 equal groups, taking one group as the verification set and the rest 4 groups as the training set in sequence, giving parameters, wherein the training set is used for constructing a model, and the verification set is used for checking the accuracy of the model;
step 3.2, optimal parameter screening, wherein the parameter gamma in the SVM controls the width of a Gaussian kernel, and C is a regularization parameter and limits the importance of each point; the parameter grid is set as:
gamma＝[0.001，0.01，0.1，1，10，100] (9)
C＝[0.001，0.01，0.1，1，10，100] (10)
in the cross validation, a model is constructed by sequentially using the combination of every two parameters gamma and C, and then the accuracy of the model is checked by using a validation set; for each parameter combination, each verification of 5-fold cross verification generates 1 precision, and 5 times of verification is performed to generate 5 precisions; selecting a parameter combination with the highest average accuracy of 5 times of verification as an optimal parameter;
3.3, constructing a model by using the optimal parameters and the data of the training set and the verification set, and finally evaluating the model by using the test set; assessment indicators include accuracy (accuracy), precision (precision), recall (call), specificity (specificity), F1 score (F1 score), Mathematic Correlation Coefficient (MCC), and area under the subject operating curve (ROC) (AUC); in the test set, defining the tumor count as True Positive (TP), the tumor count as normal but predicted as False Positive (FP), the tumor count as true but predicted as normal False Negative (FN), the tumor count as normal but predicted as True Negative (TN); the above evaluation index calculation formula is:
the accuracy, recall, specificity, F1 score and AUC of the above assessment indices returned values between (0, 1); the higher the accuracy is, the higher the overall prediction efficiency of the model is; higher accuracy indicates that the class I error is smaller; higher recall indicates that a class II error is being made smaller; the high specificity indicates that few negative examples are mixed in the samples predicted to be positive examples; the F1 score is a comprehensive index and is a harmonic average of the accuracy rate and the recall rate; MCC is the correlation coefficient between observed and predicted binary classifications, returning a value between (-1, 1), where 1 represents perfect prediction, 0 represents no better than random prediction, -1 represents a complete disparity between prediction and observation; a higher AUC indicates a higher probability of a positive instance being predicted by the classifier; therefore, the closer the above index is to 1, the better the overall prediction effect of the model is;
and 3.4, if the evaluation indexes are all larger than 0.9, the model has a better prediction effect, and then all data are used and the optimal parameter combination is used for constructing a final prediction model.
6. The early prediction method according to claim 2, wherein the early prediction in step 4 is performed according to the expression level of mRNA characteristic to the patient, specifically:
step 4.1, standardizing the characteristic mRNA expression data of the prediction sample, setting u as the characteristic mRNA expression value of the prediction sample, setting mu as the characteristic mRNA expression mean value of the prediction sample, setting sigma as the standard deviation of the characteristic mRNA of the prediction sample, and adopting the following formula:
wherein j is the characteristic mRNA number, uj' is the normalized mRNA value;
and 4.2, substituting the mRNA value after the prediction sample is normalized into the final prediction to predict, wherein the prediction result is 1 to indicate that the patient has renal clear cell carcinoma, and the prediction result is 0 to indicate that the patient is normal.
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