CN111883209A - Method for screening immune infiltration related prognostic genes in breast cancer tumor microenvironment - Google Patents
Method for screening immune infiltration related prognostic genes in breast cancer tumor microenvironment Download PDFInfo
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Abstract
The invention belongs to the technical field of medicines, and particularly relates to a method for screening immune infiltration related prognostic genes in a breast cancer tumor microenvironment, which comprises the steps of carrying out tumor immune related gene analysis on TCGA-BRCA data sets and 3 GEO transcription group data sets subjected to batch correction by an estimate algorithm, grouping the obtained immune related gene scoring data according to high and low scores, and then carrying out differential expression analysis; taking intersection from differential expression analysis results obtained by the TCGA-BRCA data set and the GEO transcriptome data set; and taking intersection results of the two and combining clinical data to perform single-factor cox regression analysis, then using lasso regression analysis, then using multi-factor cox regression analysis, and finally performing survival analysis to obtain the breast cancer immunoinfiltration related prognostic gene. The breast cancer immune infiltration prognosis related gene with high credibility screened based on the method can provide effective information for diagnosis and treatment of breast cancer.
Description
Technical Field
The invention belongs to the technical field of medicines, and particularly relates to a method for screening a prognosis gene related to immune infiltration in a breast cancer tumor microenvironment.
Background
The breast cancer is one of the most common malignant tumors in the world, is a malignant tumor which occurs in mammary gland epithelial tissues, has an increasing trend of morbidity and mortality, and is a common tumor which threatens the physical and psychological health of women.
In recent years, there is increasing evidence that tumor immunity-related genes play an important role in a variety of human cancers. In some cases, immune cells may not only fail to function as anti-tumor effectors, but may also promote tumor growth. Therefore, it becomes very important to screen key genes related to immunity and perform targeted research. Although the study of genes associated with tumor immune microenvironment plays an important role in a variety of cancers, less is known about how to screen genes associated with tumor immune prognosis.
The research mainly researches related prognostic genes in tumor immunity, and provides a method for screening the genes related to the immunological prognosis.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for screening the breast cancer immune infiltration related prognostic gene, and the breast cancer immune infiltration prognostic related gene which is screened based on the method and has higher reliability can provide effective information for diagnosis and treatment of breast cancer.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method of screening for a prognostic gene associated with immune infiltration in a breast cancer tumor microenvironment, comprising the steps of:
carrying out tumor immunity related gene analysis on the TCGA-BRCA data set by an estimate algorithm, grouping the obtained immunity related gene score data according to high and low scores, and then carrying out differential expression analysis;
performing batch correction on the 3 GEO transcriptome data sets, merging the data into one batch of data, performing tumor immune related gene analysis by an estimate algorithm, grouping the obtained immune related gene scoring data according to high and low scores, and performing differential expression analysis;
taking intersection from differential expression analysis results obtained by the TCGA-BRCA data set and the GEO transcriptome data set; and taking intersection results of the two and combining clinical data to perform single-factor cox regression analysis, then using lasso regression analysis, then using multi-factor cox regression analysis, and finally performing survival analysis to obtain the breast cancer immunoinfiltration related prognostic gene.
Further, tumor immunity related gene analysis is carried out on a data set obtained from TCGA through an estimate algorithm to obtain immunity related gene scores, immunity score data are divided into a high score group and a low score group according to a sample score mean value, differential expression analysis of the two groups is carried out by utilizing a limma package, and the screening standard is | log2FC | >1 and FDR <0.05 to obtain the immunity differential expression genes.
Further, after 3 GEO data sets are corrected in batches, analyzing tumor immune infiltration related genes by using an estimate algorithm, so as to obtain immune related gene scores; and (3) dividing the immune score data into a high score group and a low score group according to the average value of the sample score, then performing differential expression analysis by using a limma package, and obtaining the immune differential expression gene with the screening standard of | log2FC | >1 and the FDR < 0.05.
Further, batch corrections were made to the 3 GEO data using the combat function of the svg package.
Further, the candidate genes were further screened using lasso regression, and at the same time, the expression of the candidate genes in cancer patients was analyzed.
Further, the survival analysis adopts a Kaplan-Meier method.
Further, the lasso regression analysis includes: first, the single-factor regression results are read, all gene expression levels are used as independent variables X, survival time and survival state are used as dependent variables Y, lasso regression analysis is performed by using a glmnet function, and candidate genes are further screened.
Furthermore, after the intersection result is combined with clinical data to carry out single-factor cox regression analysis, candidate genes are obtained preliminarily and used as input of multi-factor cox regression analysis, and finally the candidate genes are screened.
Furthermore, after obtaining the breast cancer immune infiltration related prognostic gene, the expression pattern of the candidate gene in the patient is further analyzed.
Further, if the gene is significantly overexpressed in breast cancer patients, a possible role in the development of cancer is suggested.
Has the advantages that: the method for screening the tumor immunity-related prognostic genes in the breast cancer is obtained by integrating, analyzing and screening bioinformatics based on high-throughput sequencing data, deeply studies the effect of tumor immunity candidate genes in human cancers, and provides a new thought and a new research direction for early diagnosis, gene target treatment and candidate of the breast cancer.
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FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 shows the prognostic genes significantly related to breast cancer tumor immunity according to an embodiment of the present invention.
Detailed Description
Example 1
As shown in fig. 1, a method of screening for a prognostic gene associated with immune infiltration in a breast cancer tumor microenvironment, comprising:
step 1) downloading gene expression data (TCGA-BRCA) of breast cancer and related clinical information in a TCGA database, and downloading 3 GEO data sets related to the breast cancer from GEO;
step 2) performing estimate analysis on the TCGA-BRCA data set, grouping the obtained immune related gene scoring data according to high and low scores, and then performing differential expression analysis by using a limma package;
step 3) carrying out batch correction on the 3 GEO data sets, merging the data into one batch of data, and expanding the analysis sample size;
and 4) performing estimate analysis according to the batch results obtained in the step 3), grouping the obtained immune related gene scoring data according to high and low scores, and then performing differential expression analysis by using a limma package.
Step 5) taking intersection from the difference results obtained in the two steps according to the steps 2 and 4);
step 6) carrying out single-factor cox regression analysis according to the intersection result obtained in the step 5) and clinical data to preliminarily obtain a prognostic gene;
step 7) according to the result obtained in the step 6), analyzing by using lasso regression to prevent overfitting caused by overlarge data amount and further screening candidate genes;
step 8) according to the result obtained in the step 7), using multi-factor cox regression analysis to finally determine candidate genes;
and 9) performing survival analysis by using a Kaplan-Meier method according to the result obtained in the step 8) to obtain BRCA immune-related prognostic genes, and analyzing the expression mode of the genes in the patient.
In some embodiments, the method for screening tumor immune-related prognostic genes in breast cancer, step 1), the data set comprises: 3 breast cancer related GEO data sets, TCGA breast cancer gene expression data, related clinical information and the like;
in some embodiments, the method for screening tumor immune-related prognostic genes in breast cancer, step 2), comprises performing an estimate analysis on the TCGA-BRCA data set, grouping the obtained immune-related gene score data according to high and low scores, and performing differential expression analysis by using limma package. The process comprises the following steps:
analyzing the TCGA-BRCA gene expression data by using an estimate package to obtain an immune gene related score data set;
the threshold conditions for the differential gene expression analysis were set as follows: | log2FC|>1 and FDR<0.05, the genes meeting the threshold condition are differentially expressed genes;
in some embodiments, in the method for screening tumor immune-related prognostic genes in breast cancer, step 3) the 3 GEO data sets are subjected to batch correction according to step 2), combined into one batch of data, the analysis sample size is expanded, the obtained batch results are subjected to estimate analysis, the obtained immune-related gene score data are grouped according to high and low scores, and then a limma package is used for differential expression analysis. The process comprises the following steps:
using a combat function of the svg packet to perform batch correction on the 3 GEO data, and performing estimate algorithm analysis on the course result;
the threshold conditions for the differential gene expression analysis were set as follows: | log2FC|>1 and FDR<0.05；
In some embodiments, in the method for screening tumor immunity-related prognostic genes in breast cancer, step 4) is to take intersection of the difference results obtained in the two steps according to steps 2 and 3), and the obtained result is combined with clinical data to perform single-factor cox regression analysis to obtain a prognostic gene preliminarily;
in some embodiments, the method of screening for tumor immune-related prognostic genes in breast cancer, step 5)) performs a lasso regression analysis based on the results obtained in step 4). The method comprises the following steps: firstly, reading in a single-factor regression result, taking all gene expression quantities as independent variables X and survival time and survival state as dependent variables Y, carrying out lasso regression analysis by using a glmnet function, and further screening candidate genes;
in some embodiments, the method for screening tumor immune-related prognostic genes in breast cancer, step 6), is to use the candidate gene obtained in step 5) as an input of the multifactor cox regression analysis, and finally screen the candidate gene ELOVL2 with significantly high expression (P ═ 0.0217).
In some embodiments, the method for screening tumor immunity-related prognostic genes in breast cancer, step 7), based on the results obtained in step 6), uses Kaplan-Meier method to perform survival analysis, and obtains a survival analysis curve of ELOVL2 gene in breast cancer patients (FIG. 2), wherein the gene is significantly over-expressed in the breast cancer patients, indicating a possible role in the development of cancer.
Example 2
A method for screening an immune infiltration related prognostic gene in a breast cancer tumor microenvironment comprises the following specific contents:
1. a data set to be analyzed is prepared. First, Breast Cancer (BRCA) transcriptome data (1,109 cases of Cancer tissues and 113 cases of normal tissues) and corresponding clinical data sets were downloaded from tcga (the Cancer Genome atlas) database, and then, Breast Cancer transcriptome data sets including GSE38959 (30 cases of Cancer tissues and 13 cases of normal tissues), GSE45827 (130 cases of Cancer tissues and 11 cases of normal tissues) and GSE65194 (153 cases of Cancer tissues and 11 cases of normal tissues) were screened and collected from geo (Expression Omnibus database) database.
2. Analyzing the data set, and screening tumor immune infiltration related prognostic genes in BRCA, wherein the method mainly comprises the following three steps: firstly, carrying out tumor immune related gene analysis on a data set obtained from TCGA by an estimate algorithm to obtain immune related gene scores, dividing immune score data into a high score group and a low score group according to a sample score mean value, then carrying out differential expression analysis on the two groups by using a limma package, and screening the data set with a standard of | log | (log)2FC|>1 and FDR<0.05, obtaining the immune differential expression gene. In the second step, 3 GEO data sets were batch corrected using the combat function of svg package and tumor immunoinfiltration related gene analysis was performed using the estimate algorithm to obtain immune related gene scores. Dividing the immune score data into a high score group and a low score group according to the average value of the sample score, then using a limma package to perform differential expression analysis, and obtaining a screening standard of | log2FC|>1 and FDR<0.05, obtaining the immune differential expression gene. And thirdly, taking intersection sets of the immune differential expression genes respectively obtained in the first step and the second step, and carrying out single-factor cox regression analysis to obtain candidate genes. To prevent overfitting, the candidate genes were further screened using lasso regression and simultaneously analyzed for expression in cancer patients. Finally, using multifactorial cox regression analysis, the most relevant immunoinfiltration candidate gene ELOVL2(P ═ 0.0217) was identified, and further survival analysis results showed that patients with significantly low expression of ELOVL2 had a poorer prognosis (P ═ 0.0033), and that this gene was significantly overexpressed in cancer-based patients. The breast cancer immune infiltration prognosis related gene with high credibility screened based on the method can provide effective information for diagnosis and treatment of breast cancer.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (10)
1. A method of screening for a prognostic gene associated with immune infiltration in a breast cancer tumor microenvironment, comprising the steps of:
carrying out tumor immunity related gene analysis on the TCGA-BRCA data set by an estimate algorithm, grouping the obtained immunity related gene score data according to high and low scores, and then carrying out differential expression analysis;
performing batch correction on the 3 GEO transcriptome data sets, merging the data into one batch of data, performing tumor immune related gene analysis by an estimate algorithm, grouping the obtained immune related gene scoring data according to high and low scores, and performing differential expression analysis;
taking intersection from differential expression analysis results obtained by the TCGA-BRCA data set and the GEO transcriptome data set; and taking intersection results of the two and combining clinical data to perform single-factor cox regression analysis, then using lasso regression analysis, then using multi-factor cox regression analysis, and finally performing survival analysis to obtain the breast cancer immunoinfiltration related prognostic gene.
2. The method of screening for a prognostic gene associated with immune infiltration in a breast cancer tumor microenvironment of claim 1,
analyzing tumor immunity related genes of a data set obtained from TCGA by an estimate algorithm to obtain immunity related gene scores, dividing immunity score data into a high score group and a low score group according to a sample score mean value, then performing differential expression analysis of the two groups by using a limma package, and screening the data set with a standard of | log |2FC|>1 and FDR<0.05, obtaining the immune differential expression gene.
3. The method of screening for a prognostic gene associated with immune infiltration in a breast cancer tumor microenvironment of claim 1,
after 3 GEO data sets are corrected in batches, analyzing tumor immune infiltration related genes by using an estimate algorithm, thereby obtaining immune related gene scores; dividing the immune score data into a high score group and a low score group according to the average value of the sample score, then using a limma package to perform differential expression analysis, and obtaining a screening standard of | log2FC|>1 and FDR<0.05, obtaining the immune differential expression gene.
4. The method of screening for a prognostic gene related to immune infiltration in a breast cancer tumor microenvironment, according to claim 3, characterized in that the 3 GEO data are batch corrected using the combat function of the svg package.
5. The method of screening for a prognostic gene associated with immune infiltration in a breast cancer tumor microenvironment of claim 1, wherein the candidate gene is further screened using lasso regression and simultaneously analyzed for expression in cancer patients.
6. The method of claim 1, wherein the survival analysis is performed by Kaplan-Meier method.
7. The method of screening for a prognostic gene associated with immune infiltration in a breast cancer tumor microenvironment of claim 1, wherein said lasso regression analysis includes: first, the single-factor regression results are read, all gene expression levels are used as independent variables X, survival time and survival state are used as dependent variables Y, lasso regression analysis is performed by using a glmnet function, and candidate genes are further screened.
8. The method of claim 1, wherein the candidate genes are initially obtained after single-factor cox regression analysis of the intersection results in combination with clinical data, and input as multifactor cox regression analysis, and the candidate genes are finally screened.
9. The method of claim 1, wherein the expression pattern of the candidate gene in the patient is further analyzed after the prognostic gene associated with breast cancer immunoinfiltration is obtained.
10. The method of claim 9, wherein the gene is significantly over-expressed in a breast cancer patient to indicate a possible role in the development of a cancer.
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