Colorectal adenocarcinoma prognosis and treatment reaction risk prediction method based on epithelial-to-mesenchymal transformation characteristic related genes

Abstract

The invention discloses a colorectal adenocarcinoma prognosis and treatment reaction risk prediction method based on epithelium-to-mesenchymal transformation characteristic related genes, and is applied to the technical field of colorectal adenocarcinoma treatment. Comprising the following steps: detecting expression levels of mRNA of all genes in colorectal adenocarcinoma tumor tissues by using a high-throughput transcriptome sequencing method, then preprocessing and standardizing gene expression data, then calculating relative expression values of mRNA of three key genes, namely GPRC5B, LSAMP and PDGFRA, in the tissues, multiplying the relative expression values by respective risk coefficients, and finally calculating the expression levels of mRNA of the three key genes, namely GPRC5B, LSAMP and PDGFRA. And finally calculating a risk score for predicting the risk. The method has the advantages that multiple patient samples can be evaluated at the same time, calculation is fast, sensitivity is high, and prediction capacity is good.

Description

Technical Field

[0001] The present invention relates to the technical field of colorectal adenocarcinoma treatment, and more specifically to a method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial to mesenchymal transition characteristics. Background Art

[0002] For the treatment of colorectal adenocarcinoma, the prediction of patient prognosis and treatment response is very important. However, the existing colorectal adenocarcinoma diagnostic methods have limitations in predicting patient prognosis and treatment response. Therefore, how to provide a method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes related to epithelial to mesenchymal transition characteristics is an urgent problem that technicians in this field need to solve. Summary of the invention

[0003] In view of this, the present invention provides a method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes related to epithelial to mesenchymal transition characteristics, and detects the mRNA of three key genes GPRC5B, LSAMP and PDGFRA in the tissues of colorectal adenocarcinoma patients to predict the prognosis of colorectal adenocarcinoma patients and their response to drug treatment.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] A method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial-mesenchymal transition characteristics, comprising the following steps:

[0006] S1. Obtain tissue samples from patients with colorectal adenocarcinoma, evaluate the samples by hematoxylin-eosin staining, screen the samples, and perform mRNA extraction;

[0007] S2. Send samples to a gene sequencing company to obtain colorectal adenocarcinoma gene expression data;

[0008] S3. Preprocess all acquired colorectal adenocarcinoma gene expression data;

[0009] S4, transform the preprocessed gene expression data using log2(x+1);

[0010] S5. Use the prcomp function to perform dimensionality reduction analysis on the transformed gene expression data to obtain the principal component matrix and calculate the z-score of the expression profile;

[0011] S6. Calculate risk scores to assess the prognosis and treatment response risk of patients with colorectal adenocarcinoma.

[0012] Optionally, the screening samples in S1 specifically include: eliminating tissue samples of colorectal adenocarcinoma patients with tumor content less than 50%.

[0013] Optional, S3 is:

[0014] Delete the gene data with an expression ratio of 0 greater than 50% in all samples, and then use the "impute.KNN" function of the R package impute, and set the number of neighbors to 1. The KNN algorithm will infer the missing value based on the values ​​of the 10 neighbors most similar to the missing data points in the known data, thereby filling the missing part in the expression data and making the data complete.

[0015] Optionally, S5 is specifically:

[0016] Each gene expression value minus the mean expression value of all genes is divided by the standard deviation to measure the degree of deviation between the gene expression level and the average expression level. The specific implementation process of dimensionality reduction analysis to obtain the principal component matrix is ​​to project the high-dimensional data into a low-dimensional space through principal component analysis. The prcomp function calculates the covariance matrix of the data, and then performs eigenvalue decomposition on the covariance matrix to obtain the principal components.

[0017] Optionally, S6 is specifically:

[0018] S=0.974964442884743z1-1.30337826305244z2-0.639834133401197z3

[0019] Where S is the risk score, z1 is the mRNA expression level of the key gene GPRC5B, z2 is the mRNA expression level of the key gene LSAMP, and z3 is the mRNA expression level of the key gene PDGFRA.

[0020] It can be seen from the above technical solution that, compared with the prior art, the present invention provides a method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes related to epithelial to mesenchymal transition characteristics, which has the advantages of being able to simultaneously evaluate multiple patient samples, fast calculation, high sensitivity, and good predictive ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0022] Figure 1 The flowchart of the method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma of the present invention is as follows;

[0023] Figure 2Schematic diagram of the Kaplan-Meier all-cause survival (OS) curve of the TCGA-COAD data set in an embodiment of the present invention;

[0024] Figure 3 Schematic diagram of the Kaplan-Meier all-cause survival (OS) curve of the TCGA-READ data set in an embodiment of the present invention;

[0025] Figure 4 Schematic diagram of the Kaplan-Meier progression-free survival (PFS) curve of the GSE103479 data set in an embodiment of the present invention;

[0026] Figure 5 Schematic diagram of risk score results for liver metastases and colorectal cancer primary lesions in GSE81423, GSE81558, and GSE41258 datasets in the embodiments of the present invention;

[0027] Figure 6 This is a schematic diagram of the receiver operating characteristic curve for predicting liver metastasis in the GSE81423, GSE81558, and GSE41258 datasets in the embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of the drug sensitivity results of the high-risk and low-risk populations in the TCGA-COAD dataset estimated by the oncoCpredict algorithm in an embodiment of the present invention;

[0029] Figure 8 This is a schematic diagram of the drug sensitivity results of the high-risk group and the low-risk group estimated by the pRRophetic algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The present invention discloses a method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial-mesenchymal transition characteristics, such as Figure 1 As shown, the following steps are included:

[0032] S1. Obtain tissue samples from patients with colorectal adenocarcinoma, evaluate the samples by hematoxylin-eosin staining, screen the samples, and perform mRNA extraction;

[0033] S2. Send samples to a gene sequencing company to obtain colorectal adenocarcinoma gene expression data;

[0034] S3. Preprocess all acquired colorectal adenocarcinoma gene expression data;

[0035] S4, transform the preprocessed gene expression data using log2(x+1);

[0036] S5. Use the prcomp function to perform dimensionality reduction analysis on the transformed gene expression data to obtain the principal component matrix and calculate the z-score of the expression profile;

[0037] S6. Calculate risk scores to assess the prognosis and treatment response risk of patients with colorectal adenocarcinoma.

[0038] Furthermore, the screening samples in S1 are specifically: tissue samples of colorectal adenocarcinoma patients with tumor content less than 50% are eliminated.

[0039] Furthermore, S3 is specifically:

[0040] Delete the gene data with an expression ratio of 0 greater than 50% in all samples, and then use the "impute.KNN" function of the R package impute, and set the number of neighbors to 1. The KNN algorithm will infer the missing value based on the values ​​of the 10 neighbors most similar to the missing data points in the known data, thereby filling the missing part in the expression data and making the data complete.

[0041] Furthermore, S5 is specifically:

[0042] Each gene expression value minus the mean expression value of all genes is divided by the standard deviation to measure the degree of deviation between the gene expression level and the average expression level. The specific implementation process of dimensionality reduction analysis to obtain the principal component matrix is ​​to project the high-dimensional data into a low-dimensional space through principal component analysis. The prcomp function calculates the covariance matrix of the data, and then performs eigenvalue decomposition on the covariance matrix to obtain the principal components.

[0043] Furthermore, S6 is specifically:

[0044] S=0.974964442884743z1-1.30337826305244z2-0.639834133401197z3

[0045] Where S is the risk score, z1 is the mRNA expression level of the key gene GPRC5B, z2 is the mRNA expression level of the key gene LSAMP, and z3 is the mRNA expression level of the key gene PDGFRA.

[0046] In an embodiment of the present invention, the above prediction method is used to determine the prognosis of colorectal cancer patients in multiple databases. The results are as follows:

[0047] like Figure 2 As shown, the Kaplan-Meier all-cause survival (OS) curves of patients in the high-risk and low-risk groups determined by the above prediction method in the Cancer Genome Atlas Project (TCGA)-colon adenocarcinoma (COAD) data set indicate that the prognosis of patients in the high-risk score group is significantly worse than that of patients in the low-risk group;

[0048] like Figure 3 As shown, the Kaplan-Meier all-cause survival (OS) curves of the high-risk score group and the low-risk score group in the TCGA rectal adenocarcinoma (READ) data set indicate that the prognosis of patients in the high-risk score group is significantly worse than that of patients in the low-risk group;

[0049] like Figure 4 As shown, the Kaplan-Meier curves of progression-free survival (PFS) of high-risk score group and low-risk score group in GSE103479 dataset, which included cancer (CRC) patients receiving adjuvant 5-fluorouracil (5FU) chemotherapy, showed that the PFS of patients in the high-risk score group was significantly lower than that of patients in the low-risk group;

[0050] like Figure 5 As shown in the figure, the risk scores of liver metastases and primary colorectal cancer samples from three patient datasets, GSE81423, GSE81558, and GSE41258, were significantly increased in liver metastasis samples. The data are presented in the form of box plots (Wilcoxon test was used to calculate the significance of the difference). Figure 6 As shown, the receiver operating characteristic (ROC) curve of predicting liver metastasis by the risk scoring model in three patient data sets of GSE81423, GSE81558 and GSE41258 shows that the area under the curve (AUC) reaches 0.697. It can be seen that the prediction method of this embodiment can reliably predict the risk of liver metastasis in patients with colorectal cancer.

[0051] like Figure 7 As shown in Figure 2, the oncoCpredict algorithm was used to estimate the drug sensitivity of high-risk and low-risk populations in the TCGA-COAD dataset. The data are presented in the form of box plots (Wilcoxon test was used to calculate the significance of the difference). Figure 8 As shown, the pRRophetic algorithm is used to estimate the drug sensitivity of the high-risk group and the low-risk group of the TCGA-COAD data set. The data are presented in the form of a box plot (Wilcoxon test calculates the significance of the difference). It can be seen that the prediction method of this embodiment has a good value in predicting drug sensitivity.

[0052] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0053] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial-to-mesenchymal transition characteristics, characterized in that: The following steps are involved: S1. Obtain tissue samples from patients with colorectal adenocarcinoma, evaluate the samples by hematoxylin-eosin staining, screen the samples, and perform mRNA extraction; S2. Send samples to a gene sequencing company to obtain colorectal adenocarcinoma gene expression data; S3. Preprocess all acquired colorectal adenocarcinoma gene expression data; S4, transform the preprocessed gene expression data using log2(x+1); S5. Use the prcomp function to perform dimensionality reduction analysis on the transformed gene expression data to obtain the principal component matrix and calculate the z-score of the expression profile; S6. Calculate risk scores to assess the prognosis and treatment response risk of patients with colorectal adenocarcinoma.

2. The method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial-to-mesenchymal transition characteristics according to claim 1, characterized in that: The screening samples in S1 are specifically: tissue samples of colorectal adenocarcinoma patients with tumor content less than 50% are eliminated.

3. The method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial-to-mesenchymal transition characteristics according to claim 1, characterized in that: S3 is specifically: Delete the gene data with an expression ratio of 0 greater than 50% in all samples, and then use the "impute.KNN" function of the R package impute, and set the number of neighbors to 1. The KNN algorithm will infer the missing value based on the values ​​of the 10 neighbors most similar to the missing data points in the known data, thereby filling the missing part in the expression data and making the data complete.

4. The method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial-to-mesenchymal transition characteristics according to claim 1, characterized in that: S5 is as follows: Each gene expression value minus the mean expression value of all genes is divided by the standard deviation to measure the degree of deviation between the gene expression level and the average expression level. The specific implementation process of dimensionality reduction analysis to obtain the principal component matrix is ​​to project the high-dimensional data into a low-dimensional space through principal component analysis. The prcomp function calculates the covariance matrix of the data, and then performs eigenvalue decomposition on the covariance matrix to obtain the principal components.

5. The method for predicting the prognosis and treatment response risk of colorectal adenocarcinoma based on genes associated with epithelial-to-mesenchymal transition characteristics according to claim 1, characterized in that: S6 is specifically: S=0.974964442884743z1-1.30337826305244z2-0.639834133401197z3 Where S is the risk score, z1 is the mRNA expression level of the key gene GPRC5B, z2 is the mRNA expression level of the key gene LSAMP, and z3 is the mRNA expression level of the key gene PDGFRA.

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