Single gene analysis system

Abstract

The invention relates to the technical field of bioinformatics, and discloses a single gene analysis system, which comprises an integrated database for integrating and storing data resources of a TCGA database, a UCSC database, a GSEA database and a TCIA database; the data interaction module is used for analyzing a gene analysis function and selecting a target cancer type and a target gene which need to be analyzed by analysts; the gene analysis module integrates a plurality of gene analysis functions and is used for acquiring target data required by the selected gene analysis function from the integrated database according to the target cancer type and the target gene; analyzing the target data according to the selected gene analysis function to obtain an analysis result, and sending the analysis result to a data interaction module for charting display; wherein the target data comprises gene expression data and related analysis data except the gene expression data. The efficiency of biological information data analysis can be improved, and the technical threshold of raw information data analysis is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of bioinformatics, and in particular to a single gene analysis system. Background Art

[0002] In the field of bioinformatics, single gene analysis is a basic and key technology that focuses on studying the genetic variation of a single gene and its association with biological phenotypes. With the surge in the amount of genomic data, the importance of single gene analysis in revealing disease mechanisms, discovering drug targets, and promoting personalized medicine has become increasingly prominent.

[0003] Traditional single-gene analysis methods rely on professional biostatistical techniques and complex computer algorithms, which not only requires researchers to have deep professional knowledge, but also limits the participation of researchers from non-professional fields. And when researchers need to adjust or optimize the analysis results, if they lack a bioinformatics background, they often have to rely on professional bioinformatics analysts to meet personalized data analysis needs. This process not only increases the workload, but is also time-consuming and labor-intensive, especially when faced with massive sequencing data, which significantly reduces the work efficiency of bioinformatics analysts.

[0004] Existing analysis tools and software are in urgent need of improvement in terms of user-friendliness and comprehensive functionality. The complex operating interface and cumbersome data processing procedures are a considerable challenge for beginners, making it difficult for non-professionals to quickly master and effectively use these tools for scientific research. Current database functions are often not comprehensive enough. When users want to conduct systematic bioinformatics analysis, they often need to conduct collaborative analysis of multiple databases to complete it. This not only increases the workload of users, but also the heterogeneity of data between different databases may introduce potential uncertainty in the results of bioinformatics analysis, which poses a certain risk to the accuracy and reliability of the research. Summary of the invention

[0005] The present application provides a single gene analysis system that integrates data acquisition, processing, analysis and result display, which solves the shortcomings of the existing technology in single gene analysis, improves the efficiency of bioinformatics data analysis, and lowers the technical threshold of bioinformatics data analysis.

[0006] To achieve the above objectives, the present application provides a single gene analysis system, comprising: Integrate databases, at least integrating data resources stored in the TCGA database, UCSC database, GSEA database, and TCIA database; The data interaction module is used by analysts to perform gene analysis functions, select target cancer types and target genes to be analyzed; The gene analysis module integrates multiple gene analysis functions and is used to obtain the target data required for the selected gene analysis function from the integrated database according to the target cancer type and the target gene; analyze the target data according to the selected gene analysis function to obtain analysis results, and send the analysis results to the data interaction module for graphical display, wherein the target data includes gene expression data and related analysis data other than the gene expression data.

[0007] In an optional embodiment of the present application, the gene analysis module includes a gene expression analysis module, a clinical correlation analysis module, a tumor microenvironment and mutation load analysis module and an immune analysis module.

[0008] In an optional embodiment of the present application, the gene expression analysis module includes a single gene difference analysis module, a survivability analysis module, a gene co-expression analysis module and a gene set enrichment analysis module.

[0009] In an optional embodiment of the present application, the clinical correlation analysis module includes a clinical trait correlation analysis module, a clinical trait correlation heat map analysis module and an independent prognostic factor analysis module.

[0010] In an optional embodiment of the present application, the independent prognostic factor analysis module includes a unifactor prognostic analysis module, a multifactor prognostic analysis module and an independent prognostic factor determination module.

[0011] In an optional embodiment of the present application, the tumor microenvironment and mutation load analysis module includes a tumor microenvironment analysis module and a tumor mutation load correlation analysis module.

[0012] In an optional embodiment of the present application, the immune analysis module includes an immune cell infiltration analysis module and an immunotherapy analysis module.

[0013] In an optional embodiment of the present application, the immune cell infiltration analysis module includes an immune cell infiltration correlation analysis module and an immune cell difference module.

[0014] In an optional embodiment of the present application, the data interaction module is also used for the analyst to set analysis parameters; The gene analysis module is also used to analyze the target data according to the selected gene analysis function and the analysis parameters to obtain analysis results.

[0015] In an optional embodiment of the present application, the gene analysis module also includes a customized analysis module for providing customized data analysis services.

[0016] The single gene analysis system of this application brings together data resources from multiple databases such as TCGA, UCSC, TCIA and GSEA on a single platform, providing analysts with a comprehensive analysis environment. This integrated design significantly reduces the need for analysts to frequently switch between multiple databases, thereby greatly improving research efficiency and convenience. Through this innovative platform, analysts can seamlessly access and analyze rich data sets across databases without worrying about the diversity and compatibility of data sources, ensuring the continuity of the analysis process and the consistency of results, and reducing the risk of data heterogeneity.

[0017] The single gene analysis system of this application integrates numerous bioinformatics analysis tools, providing comprehensive services from data query, sequence alignment to gene expression analysis, and providing analysts with a one-stop solution.

[0018] The single gene analysis system of the present application provides a simple and intuitive human-computer interaction interface and optimizes data processing and automation processes, which greatly reduces the burden on analysts, improves analysis efficiency and accuracy, makes complex bioinformatics data analysis simple and efficient, and lowers the technical threshold for bioinformatics data analysis.

[0019] The single gene analysis system of this application also provides customized data analysis services, allowing users to flexibly adjust and optimize results according to research needs without the assistance of external bioinformatics experts. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A functional module diagram of a single gene analysis system provided in one embodiment of the present application; Figure 2 A functional module diagram of a gene analysis module provided in one embodiment of the present application; Figure 3 A functional module diagram of a gene expression analysis module provided in one embodiment of the present application; Figure 4 A functional module diagram of a clinical correlation analysis module provided in an embodiment of the present application; Figure 5 A functional module diagram of a tumor microenvironment and mutation load analysis module provided in an embodiment of the present application; Figure 6 A functional module diagram of an immunoassay module provided in one embodiment of the present application.

[0021] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0023] It should be noted that the illustrations provided in the following embodiments are only used to illustrate the basic concept of the present application in a schematic manner, and therefore the illustrations only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0024] The present application provides a single gene analysis system 100, which is a comprehensive single gene online analysis system that integrates multiple cutting-edge analysis technologies: covering single gene difference analysis, survival analysis, clinical correlation analysis, independent prognostic factor exploration, gene co-expression analysis, tumor microenvironment assessment, GSEA enrichment analysis (Gene Set Enrichment Analysis), immune cell infiltration behavior research, tumor mutation load correlation analysis, immunotherapy response prediction and other analysis tools. Figure 1 The functional module diagram of a single gene analysis system 100 provided in an embodiment of the present application is shown. The single gene analysis system can integrate a database 20, a data interaction module 30 and a gene analysis module 10.

[0025] like Figure 1 As shown, the integrated database 20 integrates and stores data resources of multiple public databases such as TCGA database, UCSC database, GSEA database, TCIA database, etc. Developers can download relevant data resources from databases such as TCGA database, UCSC database, GSEA database, TCIA database, etc. in advance and store them in the integrated database 20 of the backend server as a data source for gene analysis.

[0026] Among them, the TCGA database (The Cancer Genome Atlas) contains a large amount of genomic data of cancer types, including gene expression, DNA methylation, copy number variation, mutation, etc. It is mainly used for cancer genomics research to help scientists understand the pathogenesis of cancer, find new therapeutic targets and develop personalized treatment plans.

[0027] The UCSC database (University of California Santa Cruz Genome Browser) provides genome browsers for a variety of organisms, including human genome data. It contains gene annotations, gene expression data, epigenetic data, etc., and is widely used for browsing, analyzing and visualizing genome data, supporting all aspects of genomic research.

[0028] The GSEA database (Gene Set Enrichment Analysis) focuses on gene set enrichment analysis and provides predefined gene sets and analysis tools to help researchers identify gene sets that are significantly enriched under specific conditions. It is mainly used to discover disease-related biological processes and signaling pathways, especially in cancer research, to help understand gene expression patterns in disease states.

[0029] The TCIA database (The Cancer Imaging Archive) mainly stores cancer-related medical imaging data, including CT, MRI, PET and other image data, as well as related clinical and genomic data. It is used for cancer imaging research, supporting the development and verification of new imaging diagnostic tools, and evaluating treatment effects.

[0030] This application provides analysts with a comprehensive analysis environment by bringing together data resources from multiple databases such as TCGA, UCSC, TCIA, and GSEA on a single platform. This integrated design significantly reduces the need for analysts to frequently switch between multiple databases, thereby greatly improving research efficiency and convenience. Through this innovative platform, analysts can seamlessly access and analyze rich data sets across databases without having to worry about the diversity and compatibility of data sources, ensuring the continuity of the analysis process and the consistency of results, and reducing the risk of data heterogeneity.

[0031] like Figure 1 As shown, the data interaction module 30 provides a simple and intuitive human-computer interaction interface, which is used by analysts to perform gene analysis functions, target cancer types to be analyzed, and target gene selection; it is also used by analysts to set relevant analysis parameters, and to display the analysis results of the gene analysis module in a graphical manner. The simple and intuitive human-computer interaction interface makes bioinformatics analysis within reach, and even non-professionals can easily master it, which reduces the technical threshold for bioinformatics data analysis.

[0032] like Figure 1As shown, the gene analysis module 10 integrates multiple gene analysis functions such as gene expression analysis, clinical correlation analysis, tumor microenvironment and mutation load analysis, and immune analysis, and is used to obtain the target data required for the selected gene analysis function from the integrated database 20 according to the target cancer type and the target gene; analyze the target data according to the selected gene analysis function to obtain the analysis result, and send the analysis result to the data interaction module 30 for graphical display; wherein the target data includes gene expression data and related analysis data other than the gene expression data.

[0033] Furthermore, when the analyst sets relevant analysis parameters through the data interaction module 20, the gene analysis module 10 is also used to analyze the target data according to the selected gene analysis function and the analysis parameters to obtain analysis results, and send the analysis results to the data interaction module 30 for graphical display.

[0034] like Figure 2 As shown, the gene analysis module 10 may further include a gene expression analysis module 101, a clinical correlation analysis module 102, a tumor microenvironment and mutation load analysis module 103 and an immune analysis module 104, which are used to perform gene expression analysis, clinical correlation analysis, tumor microenvironment and mutation load analysis and immune analysis, respectively.

[0035] See also Figure 3 As shown, the gene expression analysis module 101 may further include a single gene difference analysis module 1011 , a survivability analysis module 1012 , a gene co-expression analysis module 1013 and a gene set enrichment analysis module 1014 .

[0036] The single gene difference analysis module 1011 is used to perform single gene difference analysis of the target gene to analyze whether the expression amount of the target gene in the tumor sample and the normal sample corresponding to the target cancer type is significantly different, and the judgment standard is the significance level Pvalue<0.05 (configurable).

[0037] Furthermore, the single gene difference analysis module 1011 first obtains the target data required for the single gene difference analysis function from the integrated database 20 according to the target cancer type and the target gene, that is, the expression level of the target gene in the tumor sample and the normal sample of the selected target cancer type; then, the Wilcoxon rank sum test algorithm is used to perform a difference analysis on the expression level of the target gene in the normal sample and the expression level of the target gene in the tumor sample to obtain the difference result of the target gene; finally, the difference result of the target gene is sent to the data interaction module 30 for graphical display.

[0038] The Wilcoxon rank sum test algorithm first combines the expression data of the target gene in tumor samples and normal samples, then sorts them by size and assigns them a rank, where the ranks are given as the average of the ranks. The test statistic W can be expressed by the following formula:

[0039] Where R1 is the rank sum of the tumor sample group or the rank sum of the normal sample group, n1 and n2 are the sample sizes of the tumor sample data and the normal sample data respectively, and VR is the variance of the rank. After obtaining the test statistic W, the significance level pValue is obtained by using the normal approximation or table lookup method according to W and the size of the sample. When pValue < 0.05, it is considered that the target gene has a significant difference between the tumor sample and the normal sample of the target cancer type of interest.

[0040] The survival analysis module 1012 is used to perform survival analysis of the target gene to evaluate the relationship between the patient's survival time or disease progression time and the target gene.

[0041] Further, the survival analysis module 1012 first obtains the target data required for the survival analysis function from the integrated database 20 according to the target cancer type and the target gene, that is, the expression data of the target gene in the tumor sample of the selected target cancer type and the survival information of the case corresponding to the tumor sample of the selected target cancer type, wherein the survival information includes the patient's survival status (e.g., 1 indicates death, and 0 indicates survival) and survival time; the expression data of the target gene and the survival information of the case are merged according to the sample number to obtain a data matrix, which includes the case number in the tumor sample of the target cancer type, the expression level of the target gene corresponding to each case, and the survival information corresponding to each case; according to the median value of the target gene expression level, the sample data is divided into two groups, the data with a gene expression level greater than the median value of the target gene is classified as a high gene expression group, and the data with a gene expression level lower than the median value of the target gene is classified as a low gene expression group, and then a survival analysis is performed based on the Kaplan-Meier curve to obtain the survival analysis result of the target gene, and the survival analysis result of the target gene is sent to the data interaction module 30 for graphical display.

[0042] It should be noted that the difference between the survival curves of the two groups can be evaluated by using statistical methods (such as Log-Rank test) to determine whether the survival distribution between the two groups is significantly different, so as to obtain the survival analysis result of the target gene; the survival analysis result of the target gene is sent to the data interaction module 30 for graphical display. Finally, it is determined whether the survival distribution between the two groups is significantly different. When the significance level pValue is less than 0.05, it indicates that the target gene has a significant survival difference in the selected target cancer type, and it can be considered that the target gene has an impact on the patient's survival time.

[0043] The gene co-expression analysis module 1013 is used to perform gene co-expression analysis to study the common expression patterns among multiple genes. Through gene co-expression analysis, the interactions and functional associations among genes and the gene sets involved in similar biological processes can be revealed.

[0044] Further, the gene co-expression analysis module 1013 first obtains the target data required for the gene co-expression analysis function from the integrated database 20 according to the target cancer type and the target gene, that is, the expression data of each gene containing the target gene in the tumor sample of the selected target cancer type, and the expression data of each gene of all tumor samples is used as a separate set of genome data; the target gene and the genome data of other genes are subjected to Pearson correlation analysis to obtain the gene correlation analysis result of the target gene, and the survival analysis result of the target gene is sent to the data interaction module 30 for graphical display. By comparing the correlation between the target gene and other genes through the Pearson correlation coefficient algorithm, the correlation coefficient and significance level between the two genomes are obtained. Due to the huge amount of gene data, a large amount of redundant data will appear when all the results are output. Therefore, it can be screened according to the correlation coefficient filtering conditions and significance level filtering conditions pre-set in the data interaction module 30. The gene correlation results that meet the user's screening conditions are output as the gene correlation analysis results of the target gene, so that it can be intuitively seen which genes have significant correlation with the target gene.

[0045] It should be noted that the gene co-expression analysis module 1013 is also used to compare the genomic data of any two genes in pairs, so as to obtain the correlation between the genomic data of any two genes, and then generate a gene co-expression heat map, and send the gene co-expression heat map to the data interaction module 30 for display. The gene co-expression heat map will show the co-expression relationship between the target gene and other genes in the tumor sample, as well as the co-expression relationship between other genes.

[0046] The gene set enrichment analysis module 1014 is used to perform gene set enrichment analysis GSEA (Gene Set Enrichment Analysis, GSEA) of the target gene, and to reveal the enrichment of the gene set under different biological conditions. In the given gene expression data, GSEA can find gene sets associated with specific biological processes or pathways by comparing the expression of genes in the gene set under different conditions, calculating the enrichment score and related statistical information of the gene set, and thus deeply understanding the functions and regulatory mechanisms of genes in different biological processes.

[0047] Furthermore, the gene set enrichment analysis module 1014 first obtains the target data required for the gene set enrichment analysis function from the integrated database 20 according to the target cancer type, the target gene, and the enrichment analysis type parameter (which can be set in the data interaction module 30), that is, the expression data of the target gene in the tumor sample of the selected target cancer type and the enrichment file corresponding to the number of enrichment analysis types adopted, wherein the enrichment analysis type parameter includes GO (Gene Ontology) enrichment analysis or KEGG (Kyoto Encyclopedia of Genes and Genomes, Kyoto Gene and Genome Database) enrichment analysis, the corresponding enrichment files include GO enrichment files or KEGG enrichment files; according to the median of the expression data, the expression data of the target gene in the tumor sample of the selected target cancer type is divided into a high gene expression group and a low gene expression group, and the expression difference FC of the target gene in different groups is calculated, and then the logFC value of the target gene is obtained; then the GSEA function can be used to perform enrichment analysis on the KEGG enrichment file or the GO enrichment file respectively to screen out significant enrichment pathways (GO) or functions (KEGG), so as to obtain enrichment statistics, and the corresponding enrichment result graph can also be generated according to the enrichment statistics to display the enrichment scores of the first few enrichment pathways / functions in the human-computer interaction interface of the data interaction module 30. Among them, in the result graph of the enrichment analysis, the peak is at the top, indicating that the GO function or KEGG pathway is active when the target gene is highly expressed, on the contrary, the peak is at the bottom, indicating that the GO function or KEGG pathway is active when the target gene is low expressed.

[0048] like Figure 4 As shown, the clinical correlation analysis module 102 may further include a clinical trait correlation analysis module 1021 , a clinical trait correlation heat map module 1022 and an independent prognostic factor analysis module 1023 .

[0049] The clinical trait correlation analysis module 1021 and the clinical trait correlation heat map module 1022 are both used to perform clinical trait correlation analysis of the target gene to explore whether there is a significant correlation between the target gene and the clinical trait. When the significance level pValue is less than 0.05, it indicates that there is a significant correlation between the target gene and the clinical trait.

[0050] Furthermore, the clinical trait correlation analysis module 1021 and the clinical trait correlation heat map module 1022 will first obtain the target data required for the clinical trait correlation analysis function from the integrated database 20 according to the target cancer type and the target gene, that is, the expression data of the target gene in the tumor sample of the selected target cancer type and the clinical traits corresponding to the tumor sample of the selected cancer type, wherein each tumor sample is a specific patient case, and the clinical traits include the patient's age, gender, tumor grade, stage, T stage, M stage and N stage, etc.; the target gene expression data and the clinical traits are merged and processed according to the sample number to obtain a first clinical trait data matrix, which includes the case number in the tumor sample of the target cancer type, the expression level of the target gene corresponding to each case, and the clinical trait information of each case; the correlation analysis of each clinical trait is performed according to the first clinical trait data matrix to obtain the clinical trait correlation analysis result of the target gene, and the clinical trait correlation analysis result of the target gene is sent to the data interaction module 30 for graphical display.

[0051] The difference is that, in the process of performing correlation analysis on each clinical trait separately to obtain the clinical trait correlation analysis results of the target gene, the clinical trait correlation analysis module 1021 will first group the tumor samples according to the staging of the target clinical trait, so as to obtain several groups. Taking the tumor stage as an example, the tumor samples are grouped according to the stage staging to obtain stageI group, stageII group, stageIII group and stageIV group; then based on the Wilcoxon rank sum test algorithm, the expression levels of the target genes between different groups are analyzed for differences, so as to obtain the significance level between different groups of the same clinical trait, and then the differences in the stages in different clinical traits are compared to determine the significance level, and finally the clinical trait stage with a significance level pValue<0.05 is determined as a clinical trait that may be related to the development of the tumor.

[0052] The difference is that, in the process of performing correlation analysis on each clinical trait separately to obtain the clinical trait correlation analysis results of the target gene, the clinical trait correlation heat map module 1022 will classify the tumor samples of each clinical trait according to the median value of the target gene expression data. The data with gene expression greater than the median value of the target gene are classified into a high gene expression group, and the data with gene expression lower than the median value of the target gene are classified into a low gene expression group; then, different stages within each clinical trait are compared to see whether there are significant differences between the high and low gene expression groups, so as to obtain a clinical correlation heat map.

[0053] The independent prognostic factor analysis module 1023 is used to perform independent prognostic factor analysis on the target gene to determine which factors have an independent predictive effect on the patient's survival or disease progression, that is, these factors can still independently affect the risk of survival or disease progression when other factors are taken into consideration. Prognosis refers to the expected condition of the patient after receiving treatment.

[0054] Furthermore, the independent prognostic factor analysis module 1023 first obtains the target data required for the independent prognostic factor analysis function from the integrated database 20 according to the target cancer type and the target gene, namely, the expression data of the target gene in the tumor sample of the selected target cancer type, the clinical traits corresponding to the tumor sample of the selected target cancer type, and the survival information of the case corresponding to the tumor sample of the selected target cancer type, wherein each tumor sample is a specific patient case, the clinical traits include the patient's age, gender, tumor grade, tumor stage, T stage, M stage and N stage, etc., and the survival information includes the patient's survival status (e.g. The target gene expression data, clinical traits and survival information are combined according to the sample number to obtain a second clinical trait data matrix, which includes the case number of the tumor sample of the target cancer type, the expression level of the target gene corresponding to each case, the clinical trait information of each case and the survival information corresponding to each case; the target gene expression data and different clinical traits are compared with the survival information respectively according to the second clinical trait data matrix through the Cox regression model to obtain the independent prognostic factor analysis results, and are presented in the form of a forest diagram (of course, other graphical representations are also possible) through the data interaction module 30.

[0055] Further, the independent prognostic factor analysis module 1023 may further include a univariate prognostic analysis module, a multivariate prognostic analysis module, and an independent prognostic factor determination module. The univariate analysis module 1023 is used to compare the expression of the target gene, different clinically relevant traits and other factors with the survival information of the case through the Cox regression model, so as to obtain the significance level of each factor as the first linear analysis result. The multivariate prognostic analysis module is used to input the expression of the target gene and all factors such as clinical traits through the Cox regression model, and compare them with the survival information of the case to obtain the significance level of each factor as the second linear analysis result. The independent prognostic factor determination module is used to determine the independent prognostic factor according to the first significance analysis result and the second significance analysis result. When the significance level pvalue<0.05, it can be shown that this factor is related to the prognosis of the case; if a factor is shown as pValue<0.05 in both the univariate independent prognostic analysis and the multivariate independent prognostic analysis, it is shown that this factor can be used as an independent prognostic factor for this cancer type independently of other factors.

[0056] like Figure 5 As shown, the tumor microenvironment and mutation load analysis module 103 includes a tumor microenvironment analysis module 1031 and a tumor mutation load correlation analysis module 1032 .

[0057] The tumor microenvironment analysis module 1031 is used to perform tumor microenvironment analysis to study the microenvironment surrounding tumor cells, including tumor-related immune cells, blood vessels, stromal cells, etc. Through tumor microenvironment analysis, the interaction between tumor cells and their surrounding environment can be revealed, thereby better understanding the occurrence, development and treatment mechanism of tumors.

[0058] Furthermore, the tumor microenvironment analysis module 1031 first obtains the target data required for the single gene difference analysis function from the integrated database 20 according to the target cancer type and the target gene, that is, the expression data of each gene in the tumor sample in the selected target cancer type, and lists the expression data of the target gene in the tumor sample separately as a group of data; then, the extracted tumor sample data is scored for the tumor microenvironment through the filterCommonGenes function and the estimateScore function, so as to obtain the microenvironment score data of each tumor sample, and the microenvironment score includes StromalScore (stromal score), ImmuneScore (immune score), ESTIMATEScore (ESTIMATE score) and TumorPurity (tumor purity), wherein ESTIMATEscore is obtained by adding the stromal score and the immune score. TumorPurity is obtained by using the following formula: , After obtaining these microenvironment score data, the microenvironment score data group and the target gene expression data of each tumor sample are merged and processed by case number to form a set of data; according to the median value of the target gene expression, the obtained set of data is divided into a high gene expression group and a low gene expression group, and the Wilcoxon rank sum test algorithm is also used to analyze the tumor microenvironment differences between the high and low target gene groups, and a violin plot (or other diagrams) is drawn and displayed through the data interaction module 30. When the significance level pValue is less than 0.05, it indicates that there is a significant correlation between the tumor microenvironment and the development or treatment of the tumor.

[0059] The tumor mutation load correlation analysis module 1032 is used for tumor mutation load correlation analysis to study the correlation between the expression level of the target gene and the tumor mutation load, providing important reference and data support for further tumor research.

[0060] The tumor mutation load correlation analysis module 1032 first obtains the target data required for the tumor mutation load correlation analysis function from the integrated database 20 according to the target cancer type, the target gene and the enrichment analysis type parameters, namely, the expression data of the target gene in the tumor sample of the selected target cancer type and the tumor mutation load data of the tumor sample of the selected target cancer type; then, according to the name of the tumor sample, the target gene expression data and the tumor mutation load data are intersected and merged to obtain comprehensive data including the expression of the target gene and the tumor mutation load information of the tumor sample of the cancer type; finally, based on the Spearman rank correlation coefficient, the expression of the target gene and the tumor mutation load data are correlated, so as to obtain the tumor mutation load correlation coefficient and the significance level pValue, and draw a tumor mutation load correlation scatter plot to be displayed in the data interaction module 30.

[0061] Among them, in the Spearman rank correlation coefficient algorithm, the correlation degree and direction between the target gene expression data group and the tumor mutation load data group are expressed by r s To reflect:

[0062] Where d is the rank difference of each group of observations, and n is the number of data pairs, which can be understood as the number of tumor samples.

[0063] When there are many identical ranks in X (target gene expression data set) or Y (tumor mutation load data set), it is necessary to s Calibrate, the calibration value is :

[0064] In the formula, , t i is the number of the same rank in X(Y), when T X =T Y =0, the above two equations are the same.

[0065] like Figure 6 As shown, the immune analysis module 104 includes an immune cell infiltration analysis module 1041 and an immunotherapy analysis module 1042 .

[0066] The immune cell infiltration analysis module 1041 is used for immune cell infiltration analysis to understand the mechanism of action of the immune system in the tumor microenvironment. By performing immune cell infiltration analysis on gene expression data, the number and activity of different types of immune cells in tumor tissue can be accurately evaluated. By comparing and analyzing the immune cell infiltration, the role of immune cells in the development of tumors can be better understood, and important support can be provided for individualized treatment. This comprehensive research method has broad application prospects in the fields of tumor immunology, bioinformatics, and clinical medicine, and will help promote the in-depth development of tumor immune microenvironment research.

[0067] Furthermore, the immune cell infiltration analysis module 1041 first obtains the target data required for the immune cell infiltration analysis function from the integrated database 20 according to the target cancer type, the target gene and the enrichment analysis type parameters (set in the data interaction module 30), that is, the gene expression data set of the tumor sample of the selected target cancer type, including the expression level of each gene of the tumor sample of the selected target cancer type, and the expression level data of the target gene in the tumor sample are separately listed as a group of data; then, the gene expression data set is subjected to immune cell infiltration analysis by using the CIBERSORT deconvolution algorithm, which includes 22 types of immune cells, thereby obtaining an immune cell infiltration data file; finally, according to the tumor sample number of the cancer type, the data group of the expression level of the target gene is intersected with the immune infiltration data file, and the data is merged to obtain a set of comprehensive data sets including the expression level of the target gene and the sample immune infiltration score, which is used for subsequent immune cell difference analysis or correlation analysis between immune cells and target genes, so as to obtain the corresponding analysis results, and display them through the data interaction module 30.

[0068] Furthermore, the immune cell infiltration analysis module 1041 may further include an immune cell infiltration correlation analysis module and an immune cell difference module.

[0069] Among them, the immune cell infiltration correlation analysis module is used to perform correlation analysis between immune cells and target genes. Specifically, when performing correlation analysis between immune cells and target genes, correlation analysis is performed on each group of immune cell data sets and the expression amount of the target gene, thereby obtaining the correlation between 22 types of immune cells and the target gene, so as to obtain the immune cell infiltration correlation analysis result.

[0070] The immune cell difference module is used for immune cell difference analysis. It can divide the comprehensive data set into a high gene expression group and a low gene expression group according to the median expression value of the target gene, and perform gene difference analysis of the high and low gene expression group data on each group of immune cells, thereby obtaining the immune cell difference analysis results.

[0071] The immunotherapy analysis module 1042 is used to perform immunotherapy analysis to study the response of tumor patients to immunotherapy. By analyzing the expression level of specific genes in tumor samples, patients can be divided into two groups: high expression and low expression, and then the relationship between gene expression and immunotherapy effect can be explored. Through immunotherapy analysis, the predictability and therapeutic responsiveness of target genes to immunotherapy can be evaluated, which can help reveal the immune characteristics of patients and evaluate the potential effect of immunotherapy.

[0072] Furthermore, the immunotherapy analysis module 1042 first obtains the target data required for the immunotherapy analysis function from the integrated database 20 according to the target cancer type and the target gene, namely, the expression data of the target gene in the tumor sample of the selected target cancer type, and the immunophenotype score data set in the TCIA database, wherein the immunophenotype score data set includes the "ctla4_neg_pd1_neg" immunophenotype score, the "ctla4_neg_pd1_pos" immunophenotype score, the "ctla4_pos_pd1_neg" immunophenotype score and the "ctla4_pos_pd1_pos" immunophenotype score; then, the expression data of the target gene and the immunophenotype score data set are intersected according to the name of the tumor sample, so as to obtain a comprehensive data set including the expression data of the target gene and the immunophenotype score data; then, according to the median expression value of the target gene, the comprehensive data set is divided into a high gene expression group and a low gene expression group. Finally, based on the Wilcoxon rank sum test algorithm, the immunotherapy differences of the four groups of immune phenotype score data were analyzed respectively, and violin plots were drawn according to the difference analysis results and displayed through the data interaction module 30.

[0073] It should be noted that the gene analysis module of the present application also includes a customized analysis module, which is used to provide customized data analysis services, allowing analysts to flexibly adjust and optimize results according to research needs without the assistance of external bioinformatics experts.

[0074] The single gene analysis system of this application brings together valuable data resources from multiple databases such as TCGA, UCSC, TCIA and GSEA on a single platform, providing analysts with a comprehensive analysis environment. This integrated design significantly reduces the need for analysts to frequently switch between multiple databases, thereby greatly improving research efficiency and convenience. Through this innovative platform, analysts can seamlessly access and analyze rich data sets across databases without worrying about the diversity and compatibility of data sources, ensuring the coherence of the analysis process and the consistency of results, and reducing the risk of data heterogeneity.

[0075] The single gene analysis system of this application integrates numerous bioinformatics analysis tools, providing comprehensive services from data query, sequence alignment to gene expression analysis, and providing analysts with a one-stop solution.

[0076] The single gene analysis system of the present application provides a simple and intuitive human-computer interaction interface and optimizes data processing and automation processes, which greatly reduces the burden on analysts, improves analysis efficiency and accuracy, makes complex bioinformatics data analysis simple and efficient, and lowers the technical threshold for bioinformatics data analysis.

[0077] The single gene analysis system of this application also provides customized data analysis services, allowing users to flexibly adjust and optimize results according to research needs without the assistance of external bioinformatics experts.

[0078] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application.

[0079] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A single gene analysis system, characterized in that: The system comprises: Integrate databases, at least integrating data resources stored in the TCGA database, UCSC database, GSEA database, and TCIA database; The data interaction module is used by analysts to perform gene analysis functions, select target cancer types and target genes to be analyzed; The gene analysis module integrates multiple gene analysis functions and is used to obtain the target data required for the selected gene analysis function from the integrated database according to the target cancer type and the target gene; analyze the target data according to the selected gene analysis function to obtain analysis results, and send the analysis results to the data interaction module for graphical display; wherein the target data includes gene expression data and related analysis data other than the gene expression data.

2. The single gene analysis system according to claim 1, characterized in that: The gene analysis module includes a gene expression analysis module, a clinical correlation analysis module, a tumor microenvironment and mutation load analysis module and an immune analysis module.

3. The single gene analysis system according to claim 2, characterized in that: The gene expression analysis module includes a single gene difference analysis module, a viability analysis module, a gene co-expression analysis module and a gene set enrichment analysis module.

4. The single gene analysis system according to claim 2, characterized in that: The clinical correlation analysis module includes a clinical trait correlation analysis module, a clinical trait correlation heat map module and an independent prognostic factor analysis module.

5. The single gene analysis system according to claim 4, characterized in that: The independent prognostic factor analysis module includes a univariate prognostic analysis module, a multivariate prognostic analysis module and an independent prognostic factor determination module.

6. The single gene analysis system according to claim 2, characterized in that: The tumor microenvironment and mutation load analysis module includes a tumor microenvironment analysis module and a tumor mutation load correlation analysis module.

7. The single gene analysis system according to claim 2, characterized in that: The immune analysis module includes an immune cell infiltration analysis module and an immunotherapy analysis module.

8. The single gene analysis system according to claim 7, characterized in that: The immune cell infiltration analysis module includes an immune cell infiltration correlation analysis module and an immune cell difference module.

9. The single gene analysis system according to claim 1, characterized in that: The data interaction module is also used for analysts to set analysis parameters; The gene analysis module is also used to analyze the target data according to the selected gene analysis function and the analysis parameters to obtain analysis results.

10. The single gene analysis system according to claim 1, characterized in that: The gene analysis module also includes a customized analysis module for providing customized data analysis services.

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