CN111718997A - Characteristic mRNA expression profile combination and early gastric cancer prediction method - Google Patents

Characteristic mRNA expression profile combination and early gastric cancer prediction method Download PDF

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CN111718997A
CN111718997A CN202010776571.XA CN202010776571A CN111718997A CN 111718997 A CN111718997 A CN 111718997A CN 202010776571 A CN202010776571 A CN 202010776571A CN 111718997 A CN111718997 A CN 111718997A
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向国安
李文兴
黄许森
陈小勋
贺轲
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Abstract

The invention discloses a characteristic mRNA expression profile combination and an early gastric cancer prediction method, wherein the mRNA nucleotide probe sequence is shown as SEQ ID NO. 1-20. The method for evaluating the early risk of the gastric cancer based on the mRNA expression profile combination characteristics has high precision and accuracy (AUC (area under ROC curve) ═ 0.985). The relative expression of the 20 mRNAs is only required to be obtained, and the early gastric cancer morbidity is calculated by a support vector machine model and can be used as a reference basis for early gastric cancer prediction.

Description

Characteristic mRNA expression profile combination and early gastric cancer prediction method
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 gastric cancer.
Background
Gastric cancer (gastric cancer) is a malignant tumor that originates in the epithelial cells of the gastric mucosa. Among them, gastric adenocarcinoma and gastric epithelial cell carcinoma account for about 95% of all gastric cancers, with the remaining 5% including adenosquamous carcinoma, squamous cell carcinoma and undifferentiated carcinoma. The onset age of gastric cancer is generally over 50 years, and the ratio of the incidence rates of men and women is 2: 1. the early stage of gastric cancer has no obvious symptoms, is often similar to the symptoms of chronic diseases of the stomach, such as gastritis, gastric ulcer and the like, and is easy to ignore. Therefore, the early diagnosis rate of gastric cancer is still low at present. Global Burden of Disease (GBD) data shows that over 280 million people with gastric cancer in 2017 worldwide, with over 140 million people in china. The number of deaths with gastric cancer in 2017 is about 86 ten thousand, accounting for 1.55% of the total deaths. The number of the death patients in 2017 in China is about 36 thousands, and accounts for 3.40 percent of the total death number. Statistics show that the prevalence rate of gastric cancer is continuously increased and the mortality rate is slowly increased globally from 1990 to 2017. In recent ten years, the prevalence rate of gastric cancer in China is rapidly increased, and the mortality rate is always kept at a high level.
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 and prognosis of tumors have 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. MessengerRNA (mRNA) is a single-stranded ribonucleic acid (RNA) that is transcribed from a DNA strand 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 prediction model of a combination of differential mRNA expression characteristics that contributes to early prediction of gastric cancer.
Disclosure of Invention
In view of the above, the present invention provides a combination of characteristic mRNA expression profiles and a method for early prediction of gastric cancer.
In order to solve the technical problems, the invention discloses a characteristic mRNA expression profile combination, which comprises ALG8, ATAD2, CENPL, CGAS, CHEK1, DCAF13, DKC1, FEN1, HMGB3, HSPE1, KNOP1, KPNA2, MCM2, MCM7, NDC1, PCNA, PPAT, RAB3IP, SOX4 and TOMM34, and the nucleotide probe sequences of the characteristic mRNA expression profile combination are shown in SEQ ID NO. 1-20.
The invention also discloses a gastric cancer early prediction method based on the characteristic mRNA expression profile combination, which comprises the following steps:
step 1, obtaining characteristic mRNA stably and differentially expressed by a patient with early gastric cancer;
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 method is useful for non-disease diagnostic and therapeutic purposes.
Optionally, the characteristic mRNA obtained by the step 1 and stably and differentially expressed by the patient with early gastric cancer is specifically:
step 1.1, downloading tumor tissue and precancerous tissue transcription Data and clinical Data of a gastric cancer patient from a Genomic Data common Data Portal database to obtain tumor tissue gene expression profile read counts values of the gastric cancer patient, namely sequencing read values, 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＝log2vij，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 gastric cancer patients with disease stages of I stage and II stage, recording the patients as early gastric cancer patients, and recording the total number of the early gastric cancer 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:
Figure BDA0002617841580000031
wherein j is the mRNA number, cvIs the coefficient of variation, cvjIs the variation of the j sampleCoefficient, σjIs 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:
Figure BDA0002617841580000032
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:
Figure BDA0002617841580000041
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:
Figure BDA0002617841580000042
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,
Figure BDA0002617841580000043
the variance of the mRNA in the tumor sample is obtained,
Figure BDA0002617841580000044
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:
Figure BDA0002617841580000045
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)。
optionally, the characteristic mRNA expression data in step 2 is selected, and data normalization is performed on each sample, where the formula is:
Figure BDA0002617841580000051
where 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.
Optionally, the constructing an early prediction model for the normalized data by using the support vector machine in step 3 specifically includes:
and 3.1, grouping all samples. Dividing 80% of all samples into a training set and a verification set, and dividing the rest 20% of all samples into a test set; the training set and the verification set are used for 5-fold cross verification, 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; parameters are given, a training set is used for constructing a model, and a verification set is used for checking the accuracy of the model;
step 3.2, screening optimal parameters; 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; the evaluation indexes include accuracy (accuracy), accuracy (precision), recall (call), specificity (specificity), F1 score (F1 score), Mathews 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:
Figure BDA0002617841580000061
Figure BDA0002617841580000062
Figure BDA0002617841580000063
Figure BDA0002617841580000064
Figure BDA0002617841580000065
Figure BDA0002617841580000066
Figure BDA0002617841580000067
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;
step 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.
Optionally, the early prediction according to the expression level of the mRNA characteristic to the patient in the step 4 specifically comprises:
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:
Figure BDA0002617841580000071
wherein j is the characteristic mRNA number, uj' is normalized mRNAA numerical value;
step 4.2, substituting the mRNA value after the prediction sample is standardized into the final prediction for prediction; a prediction result of 1 indicates the presence of gastric cancer, and a prediction result of 0 indicates the normality.
Compared with the prior art, the invention can obtain the following technical effects:
1) the prediction speed is high: 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 accuracy is high: 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 0.985.
3) Platform heterogeneity impact is minor: 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.
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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 gastric cancer early prediction method based on characteristic mRNA expression profile combination, which can accurately predict the I/II stage of gastric cancer and comprises the following steps:
step 1, obtaining mRNA (characteristic mRNA) stably and differentially expressed by a patient with early gastric cancer, specifically:
step 1.1, downloading tumor tissue and precancerous tissue transcription Data and clinical Data of a gastric cancer patient from a Genomic Data common Data Portal database to obtain tumor tissue gene expression profile read counts values of the gastric cancer patient, namely sequencing read values, 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＝log2vij，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 the ith sample, jth mRNA number.
Step 1.3, selecting gastric cancer patients with disease stages of I stage and II stage, recording the patients as early gastric cancer patients, and recording the total number of the early gastric cancer 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:
Figure BDA0002617841580000091
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, μjNumbering the jth mRNAThe mean expression value of mRNA of (1) is set as m1For the total number of stably expressed mrnas, there are:
Figure BDA0002617841580000092
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:
Figure BDA0002617841580000093
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:
Figure BDA0002617841580000094
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,
Figure BDA0002617841580000095
the variance of the mRNA in the tumor sample is obtained,
Figure BDA0002617841580000096
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:
Figure BDA0002617841580000101
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:
Figure BDA0002617841580000102
where 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 diagnosis model for the standardized data by using a support vector machine, which specifically comprises the following steps:
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 index includes accuracy (accuracy), accuracy (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:
Figure BDA0002617841580000111
Figure BDA0002617841580000112
Figure BDA0002617841580000113
Figure BDA0002617841580000114
Figure BDA0002617841580000121
Figure BDA0002617841580000122
Figure BDA0002617841580000123
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:
Figure BDA0002617841580000124
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 result of 1 indicates the presence of gastric cancer, and a prediction result of 0 indicates the normality.
Example 1
A gastric cancer early prediction method based on characteristic mRNA expression profile combination comprises the following steps:
step 1, mRNA (characteristic mRNA) stably and differentially expressed by a patient with early gastric cancer is obtained, and the detailed flow is shown in a figure 1.
Step 1.1, downloading transcriptome Data and clinical Data of tumor tissues and tissues beside the gastric cancer patient from a Genomic Data common Data Portal database, obtaining tumor tissue gene expression profile read counts values of the gastric cancer patient, 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 gastric cancer patients with disease stages of I stage and II stage, wherein the gastric cancer patients are detailed in formulas (2) to (3), and recording the patients as early gastric cancer 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 gastric cancer characteristic mRNAs (sorted from small to large according to the P value after FDR correction) were selected for model construction, as shown in Table 1. The nucleotide probe sequences of 20 gastric cancer characteristic mRNAs are shown in Table 2.
TABLE 1 gastric cancer characteristic mRNA
Figure BDA0002617841580000131
Figure BDA0002617841580000141
TABLE 2 nucleotide probe sequence of gastric cancer characteristic mRNA
Figure BDA0002617841580000142
And 2, carrying out data standardization on each sample, wherein the details are shown in a formula (8).
And 3, constructing an early prediction model for the normalized 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 0.1 and the parameter C is 100: 0.899. the optimal parameters of the model are therefore: gamma is 0.1 and C is 100.
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 index includes accuracy (accuracy), accuracy (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 of which 6 indexes are greater than 0.91; FIG. 4 shows the ROC curve and AUC, with an AUC of 0.985 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
Figure BDA0002617841580000151
Figure BDA0002617841580000161
And 4.2, substituting the mRNA value after the prediction sample is normalized into the final prediction for prediction. A prediction result of 1 indicates the presence of gastric cancer, and a prediction result of 0 indicates the normality. The sample numbers of 10 cases, corresponding TCGA numbers, actual states and predicted results are shown in Table 4. The prediction results of 9 of 10 samples completely accord with the actual state, which shows that the invention can accurately predict the gastric cancer in early stage.
TABLE 4.10 sample numbers, corresponding TCGA numbers, actual and predicted states
Figure BDA0002617841580000162
In conclusion, the present invention has high prediction accuracy based on the combination of characteristic mRNA expression profiles, and can effectively predict gastric cancer at an 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.
Figure BDA0002617841580000181
Figure BDA0002617841580000191
Figure BDA0002617841580000201
Figure BDA0002617841580000211
Figure BDA0002617841580000221
SEQUENCE LISTING
<110> second people hospital of Guangdong province
<120> combination of characteristic mRNA expression profiles and early prediction method of gastric cancer
<130>2020
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Claims (6)

1. A combination of characteristic mRNA expression profiles comprising ALG8, ATAD2, CENPL, CGAS, CHEK1, DCAF13, DKC1, FEN1, HMGB3, HSPE1, KNOP1, KPNA2, MCM2, MCM7, NDC1, PCNA, PPAT, RAB3IP, SOX4 and TOMM34, the nucleotide probe sequences of which are shown in SEQ ID No. 1-20.
2. A method for early prediction of gastric cancer 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 early gastric cancer;
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 method is used for non-diagnostic and therapeutic purposes of the disease.
3. The method for predicting early gastric cancer according to claim 2, wherein the characteristic mRNAs for acquiring stable differential expression of early gastric cancer patients in step 1 are specifically:
step 1.1, downloading tumor tissue and precancerous tissue transcription Data and clinical Data of a gastric cancer patient from a Genomic Data common Data Portal database to obtain tumor tissue gene expression profile read counts values of the gastric cancer patient, namely sequencing read values, 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＝log2vij，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 gastric cancer patients with disease stages of I stage and II stage, recording the patients as early gastric cancer patients, and recording the total number of the early gastric cancer 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:
Figure FDA0002617841570000021
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:
Figure FDA0002617841570000022
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:
Figure FDA0002617841570000023
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:
Figure FDA0002617841570000024
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,
Figure FDA0002617841570000031
the variance of the mRNA in the tumor sample is obtained,
Figure FDA0002617841570000032
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:
Figure FDA0002617841570000033
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 method for early prediction of gastric cancer according to 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:
Figure FDA0002617841570000034
wherein i is the sample number, j is the characteristic mRNA number, μiMean, 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 method for early prediction of gastric cancer according to claim 2, wherein the construction of the early prediction model for the normalized data using the support vector machine in step 3 is specifically as follows:
and 3.1, grouping all samples. Dividing 80% of all samples into a training set and a verification set, and dividing the rest 20% of all samples into a test set; the training set and the verification set are used for 5-fold cross verification, 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; parameters are given, a training set is used for constructing a model, and a verification set is used for checking the accuracy of the model;
step 3.2, screening optimal parameters; 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; the evaluation indexes include accuracy (accuracy), accuracy (precision), recall (call), specificity (specificity), F1 score (F1 score), Mathews 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 as False Negative (FN), the tumor count as normal and predicted as true negative (tn); the above evaluation index calculation formula is:
Figure FDA0002617841570000041
Figure FDA0002617841570000042
Figure FDA0002617841570000043
Figure FDA0002617841570000051
Figure FDA0002617841570000052
Figure FDA0002617841570000053
Figure FDA0002617841570000054
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;
step 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.
6. The method for early prediction of gastric cancer according to claim 2, wherein the early prediction according to the expression level of the patient characteristic mRNA in the step 4 comprises:
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:
Figure FDA0002617841570000055
wherein j is the characteristic mRNA number, uj' is the normalized mRNA value;
step 4.2, substituting the mRNA value after the prediction sample is standardized into the final prediction for prediction; a prediction result of 1 indicates the presence of gastric cancer, and a prediction result of 0 indicates the normality.
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