Analysis method and application of pan cancer immunotherapy

Abstract

The invention provides an analysis method and application of pan cancer immunotherapy, and belongs to the technical field of pan cancer immunotherapy. The analysis method provided by the invention can be used for screening to obtain the target gene for immunotherapy of the pan cancer. The invention provides a new theoretical basis for gene targeting immunotherapy of cancer, and promotes the development of precise and effective therapy, thereby improving the therapeutic effect of cancer patients.

Description

Technical Field

[0001] The present invention belongs to the technical field of pan-cancer immunotherapy, and in particular relates to an analysis method and application of pan-cancer immunotherapy. Background Art

[0002] Cancer is a major challenge facing global health. Its high incidence and mortality make it urgent to find effective treatment options. Traditional treatments for cancer are often ineffective, which brings huge mental and economic burdens to patients and their families. According to statistics from the World Health Organization (WHO), the number of new cancer cases worldwide has reached 20 million each year, and the number of deaths caused by cancer has reached 9 million each year. Although modern science and technology and medicine have made some important breakthroughs and have made some progress in the treatment and prevention of cancer, there are still many problems that need to be solved in the effective treatment of cancer.

[0003] Pan-Cancer reveals the molecular commonalities and heterogeneity of cancer by integrating large-scale genomic data from multiple cancer types, providing a comprehensive perspective on the biological mechanisms of cancer. Bioinformatics analysis provides a powerful tool for revealing the complex mechanisms of cancer by integrating and parsing massive amounts of cancer biology data (such as genomic, transcriptomic, proteomic and metabolomic data). Pan-Cancer and bioinformatics complement each other, bringing a new perspective to revealing the nature of cancer. By analyzing the commonalities and heterogeneity of different cancer types, it has promoted the transformation of cancer research from organ classification to molecular typing, which not only deepens the understanding of cancer biology, but also opens up a new path for clinical diagnosis and treatment and new drug development, marking that oncology has entered an era of integration and precision.

[0004] With the development of medical technology, there are more and more cancer treatment methods. Among them, immunotherapy has received much attention in recent years, and many patients have achieved significant clinical effects through immunotherapy. Immunotherapy uses the body's own immune system to attack cancer cells to achieve the purpose of treating cancer. The immune system is an important defense system of the human body. It can eliminate viruses, bacteria and other foreign bodies by producing antibodies or T cells. However, the immune system is not always effective. Sometimes it is bypassed by cancer cells, resulting in tumor cells not being eliminated and eventually forming cancer. The principle of immunotherapy is to activate the immune system so that it can successfully identify and attack cancer cells. Immunotherapy is one of the most popular cancer treatments and is widely used in the treatment of many cancers. The development of immunotherapy has provided cancer patients with new treatment ideas and new hopes, showing good clinical effects, but it still faces the problem of tumor cells escaping immune surveillance. Summary of the invention

[0005] In view of this, an object of the present invention is to provide an analytical method for pan-cancer immunotherapy.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] A method for analyzing pan-cancer immunotherapy, comprising the following steps:

[0008] S1. Screening for differential gene sets that exist in the TCGA database, HPA database, and GEPIA2 database and are related to cancer occurrence;

[0009] S2. Substitute the genes in the differential gene set in S1 into the TCGA database to screen genes with differential expression;

[0010] S3. Substitute the differentially expressed genes screened in S2 into the HPA database to screen for differentially expressed genes whose encoded proteins have significantly increased expression levels;

[0011] S4. Bring the differentially expressed genes screened in S3 into the TCGA database to screen for genes with cancer diagnostic value;

[0012] S5. Bring the genes screened in S4 into the TCGA database to screen genes related to the prognosis of cancer patients;

[0013] S6. Perform mutation analysis on the genes screened in S5 in the cBioPortal database to screen genes with low mutation probability;

[0014] S7. The genes screened in S6 were brought into the TCGA database for GO functional enrichment analysis and KEGG pathway enrichment analysis;

[0015] S8. In the TCGA database, analyze the relationship between the genes screened in S7 and the immune infiltration of cancer, and screen genes that inhibit infiltrating immune cells, which are the target genes for pan-cancer immunotherapy.

[0016] Preferably, the screening criteria for the differentially expressed genes in S2 are log2FoldChange>2, Pvalue<0.0001.

[0017] Preferably, the screening criteria for differentially expressed genes in S3 are that the expression level of the protein encoded by the differentially expressed genes in cancer tissues is increased, log2FoldChange>2, Pvalue<0.0001.

[0018] Preferably, the gene with cancer diagnostic value described in S4 has an AUC area > 0.55 when the cancer diagnostic value of the gene is analyzed using the ROC curve, and the patient's survival rate within 1 year is predicted to be > 65%, the survival rate within 3 years is > 50%, and the survival rate within 5 years is > 40%.

[0019] Preferably, the gene in S5 is negatively correlated with the overall survival of cancer patients, HR>1.2, P<0.05.

[0020] Preferably, the probability of gene mutation in S6 is ≤2%.

[0021] Preferably, the increased expression level of the target gene in S8 is positively correlated with the infiltration degree of suppressive immune cells in the tumor microenvironment.

[0022] Another object of the present invention is to provide target genes for immunotherapy of pan-cancer obtained by the analysis method, wherein the target genes are GSC2, NTN4, REM1, TBL1Y, GABRP, PCDH11Y, ANGPT4, FERMT1, ZC3H12B, GPR50, PCDH11X, CBLN1, SLC1A6, VIPR2, GLP1R, KHDRBS2, AGXT2, CNTN3, TACR1, OPRD1, PRLHR, OBP2A, BHLHE23, BMP2, LRRN4, MYOD1, FOXA1G Any one of ADD45G, SLC14A2, HMGCS2, RAX, ACRV1, MSTN, SSTR1, CDH18, SLC25A48, IL9, GSDMC, VAX1, TRIM48, PCDH15, MIPOL1, EPHB1, OR4K1, AFF2, TMSB15A, FGF17, ZNF208, LCN1, SSTR5, SMCP, DAPL1, IGFN1, TLX3, TSHR, OR4K2, LRTM2, SYNPO2L, NETO1, and KLK4GSC2.

[0023] Another object of the present invention is to provide the use of the analysis method or the target gene in the preparation of a drug for treating pan-cancer, wherein the drug can reduce the abnormal expression of key target genes of cancer cells in pan-cancer, inhibit the activity of the protein receptor expressed by the target gene, block the immune escape of cancer cells, and enable the body's immune system to recognize cancer cells, thereby achieving the purpose of treating cancer.

[0024] Preferably, the reduction of abnormal expression of the target gene includes target gene silencing and interference with target gene expression protein.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention provides an analytical method for pan-cancer immunotherapy, which can be used to screen and obtain target genes for pan-cancer immunotherapy, and can provide a new theoretical basis and treatment plan for the immunotherapy of many cancer patients, promote the development of precise and effective treatment for cancer patients, and thus improve the treatment effect of cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is the expression of GABRP mRNA in normal tissues (N=13084) in the HPA database;

[0028] Figure 2 The expression difference of GABRP in pan-cancer and corresponding adjacent normal samples in the TCGA database, where *p<0.05, **p<0.01, ***p<0.001, ns no correlation;

[0029] Figure 3 Online analysis of GABRP expression in various types of common clinical cancers and adjacent normal tissues for the GEPIA2 database;

[0030] Figure 4 The expression level of GABRP and the expression difference in the corresponding adjacent normal samples (scatter plot); the left picture is in low-grade glioma; the right picture is in high-grade glioma;

[0031] Figure 5 The protein expression levels of GABRP in normal tissues and glioma tissues of different grades, 100um, where the left picture is normal tissue; the middle picture is low-grade glioma; the right picture is high-grade glioma;

[0032] Figure 6 The ROC curve is used to analyze the value of GABRP in the diagnosis of different grades of gliomas. The left picture is a low-grade glioma, and the right picture is a high-grade glioma.

[0033] Figure 7 It is the Nomogram survival prediction map;

[0034] Figure 8 To analyze the effect of GABRP expression difference in glioma tissue on the prognosis of cancer patients for Kaplan-Meier survival curve;

[0035] Fig. 9 is the frequency of GABRP mutations in different cancer types in Pan-Cancer;

[0036] Fig.10 The distribution of GABRP genomic changes in pan-cancer patients;

[0037] Fig.11 The figure below is the functional clustering and interaction network analysis of related genes. The left figure is a heat map showing the top 30 genes that are positively or negatively correlated with GABRP in glioma (GBM). Red represents positively correlated genes, and blue represents negatively correlated genes. The right figure is the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of GABRP-related genes in GBM.

[0038] Fig.12 The figure shows the correlation between GABRP expression and immune infiltration in glioma (GBM), where the upper figure shows dendritic cells (aDC), B cells, CD8T cells, CD8+T cells, etc. in the tumor microenvironment activated by high GABRP expression; the middle figure shows eosinophils, macrophages, mast cells, effective memory T cells (Tem), etc. in the tumor microenvironment activated by high GABRP expression; the lower figure shows follicular helper T cells (TFH), regulatory T cells (Treg), pDC cells, etc. in the tumor microenvironment activated by high GABRP expression; *p<0.05, **p<0.01, ***p<0.001, ns no correlation;

[0039] Fig.13 The relationship between GABRP expression and immune cell infiltration in glioma;

[0040] Fig.14 The relationship between GABRP gene copy number and immune cell infiltration in glioma. DETAILED DESCRIPTION

[0041] The present invention provides an analytical method for pan-cancer immunotherapy, comprising the following steps:

[0042] S1. Screen for differential gene sets that exist in the TCGA database, HPA database, and GEPIA2 database and are related to cancer occurrence.

[0043] S2. Substitute the genes in the differential gene set in S1 into the TCGA database to screen for differentially expressed genes; the screening criteria for differentially expressed genes are: log2FoldChange>2, Pvalue<0.00001. log2FoldChang represents the change in gene expression levels between the cancer group and the normal group, usually expressed in the form of logarithm (base 2), logFC>0 means that the expression level of the gene in the cancer group is higher than that in the normal group (upregulated gene), and logFC<0 means that the expression level of the gene in the cancer group is lower than that in the normal group (downregulated gene). Pvalue is the significance level of the statistical test results, which is used to measure whether the observed gene expression differences may be caused by random factors. P<0.05 (usual threshold) indicates that the gene expression difference is statistically significant, and P≥0.05 indicates that the gene expression difference may not be significant.

[0044] S3. Substitute the differentially expressed genes screened in S2 into the HPA database to screen differentially expressed genes whose encoded protein expression levels are significantly increased; the screening criteria for the differentially expressed genes is that the protein expression levels encoded by the differentially expressed genes in cancer tissues are increased, and the screening criteria for the differentially expressed genes are: log2FoldChange>2, Pvalue<0.00001.

[0045] S4. Bring the differentially expressed genes screened in S3 into the TCGA database to screen genes with cancer diagnostic value; the genes with cancer diagnostic value are those with an AUC area > 0.55 when the cancer diagnostic value of the genes is analyzed using the ROC curve, and the predicted survival rate of the patient within 1 year is > 65%, the survival rate within 3 years is > 50%, and the survival rate within 5 years is > 40%. As an implementable method, a nomogram model is established by combining differentially expressed genes with clinical variables to predict the survival rate of patients within 1 year, 3 years, and 5 years; the clinical variables include the patient's age, gender, and tumor WHO grade.

[0046] S5. Bring the genes screened in S4 into the TCGA database to screen genes related to the prognosis of cancer patients; the genes are negatively correlated with the overall survival of cancer patients, HR>1.2, P<0.05. Among them, HR (hazard ratio) represents the effect of the gene expression level on the patient's prognosis survival risk, HR=1 means that the risk of events between the two groups is the same, HR>1 means that the event risk of the experimental group is higher than that of the control group, and HR<1 means that the event risk of the experimental group is lower than that of the control group; P indicates whether the difference between the two survival curves is statistically significant, P<0.05 means that the survival difference between the two groups is statistically significant, and it can be considered that this difference is not accidental, and P≥0.05 means that the survival difference between the two groups is not statistically significant.

[0047] S6. In the cBioPortal database, mutation analysis is performed on the genes screened in S5 to screen genes with low mutation probability; the gene mutation probability is less than 2%. The present invention screens genes with low mutation probability, and the obtained genes have high stability, making small molecule inhibitors and antibody drugs more suitable for cancer patients with low-frequency mutations, and subsequent immunotherapy for cancer patients is more accurate and effective.

[0048] S7. The genes screened in S6 were brought into the TCGA database for GO functional enrichment analysis and KEGG pathway enrichment analysis to reveal the function and biological significance of the differential genes, as well as the metabolic and signaling pathways in which the differential genes were involved.

[0049] S8. In the TCGA database, the relationship between the genes screened in S7 and the immune infiltration of cancer was analyzed, and genes that inhibit infiltrating immune cells were screened, which are target genes for pan-cancer immunotherapy; the increased expression level of the target gene is positively correlated with the degree of infiltration of suppressive immune cells in the tumor microenvironment, mainly including regulatory T cells, T2 macrophages, myeloid-derived suppressor cells (MDSCs) and some B cells, that is, the higher the expression level of the target gene of the cancer, the easier it is for cancer cells to escape immune system in the tumor microenvironment, making it more difficult to treat cancer with drugs.

[0050] The present invention also provides target genes for immunotherapy of pan-cancer obtained by the analysis method, wherein the target genes are GSC2, NTN4, REM1, TBL1Y, GABRP, PCDH11Y, ANGPT4, FERMT1, ZC3H12B, GPR50, PCDH11X, CBLN1, SLC1A6, VIPR2, GLP1R, KHDRBS2, AGXT2, CNTN3, TACR1, OPRD1, PRLHR, OBP2A, BHLHE23, BMP2, LRRN4, MYOD1, FOXA1GAD Any one of D45G, SLC14A2, HMGCS2, RAX, ACRV1, MSTN, SSTR1, CDH18, SLC25A48, IL9, GSDMC, VAX1, TRIM48, PCDH15, MIPOL1, EPHB1, OR4K1, AFF2, TMSB15A, FGF17, ZNF208, LCN1, SSTR5, SMCP, DAPL1, IGFN1, TLX3, TSHR, OR4K2, LRTM2, SYNPO2L, NETO1, and KLK4GSC2.

[0051] The present invention also provides the use of the analysis method or the target gene in the preparation of a drug for treating pan-cancer, wherein the drug reduces abnormal expression of the target gene, thereby achieving the purpose of immunotherapy for a variety of cancers. Preferably, reducing abnormal expression of the target gene includes silencing the target gene and interfering with the target gene expression protein.

[0052] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0053] Example 1

[0054] A method for analyzing pan-cancer immunotherapy, comprising the following steps:

[0055] S1. Screen for differential gene sets that exist in the TCGA database, HPA database, and GEPIA2 database and are related to cancer occurrence.

[0056] S2. Substitute the genes in the differential gene set in S1 into the TCGA database to screen for genes with differential expression; the screening criteria for the differentially expressed genes are log2FoldChange>2, P value<0.0001.

[0057] S3. Substitute the differentially expressed genes screened in S2 into the HPA database to screen differentially expressed genes whose encoded proteins have significantly increased expression levels; the screening criteria for the differentially expressed genes are that the expression levels of the proteins encoded by the differentially expressed genes in cancer tissues are increased, log2FoldChange>2, and P value<0.0001.

[0058] S4. The differential genes screened in S3 were brought into the TCGA database to screen genes with cancer diagnostic value. The differential gene screened in accordance with the above conditions was NTN4. The gene with cancer diagnostic value was a gene with an AUC area > 0.55 when the cancer diagnostic value of the gene was analyzed using the ROC curve. The nomogram model was established by combining the differential genes with clinical variables, and the patient's survival rate within 1 year was predicted to be > 65%, the survival rate within 3 years was > 50%, and the survival rate within 5 years was > 40%.

[0059] S5. The differential genes screened in S4 were brought into the TCGA database to screen genes related to the prognosis of cancer patients; the genes were negatively correlated with the overall survival of the patients, HR>1.2, P<0.05.

[0060] S6. In the cBioPortal database, mutation analysis is performed on the genes screened in S5 to screen genes with low mutation probability; the gene mutation probability is ≤2%.

[0061] S7. The NTN4 gene screened in S6 was brought into the TCGA database for GO functional enrichment analysis and KEGG pathway enrichment analysis to screen genes related to cell migration function, as well as genes related to tumor-related pathways, cell proliferation and migration key pathways.

[0062] S8. In the TCGA database, the relationship between the genes screened in S7 and the immune infiltration of cancer was analyzed, and genes that inhibit infiltrating immune cells were screened, which are target genes for pan-cancer immunotherapy; the increased expression level of the target gene is positively correlated with the infiltration degree of inhibitory immune cells in the tumor microenvironment.

[0063] Example 2

[0064] 1.GABRP mRNA is present in TCGA database, HPA database and GEPIA2 database;

[0065] The expression of GABRP mRNA in normal tissues (N=13084) was detected using the Human Protein Atlas (HPA, https: / / www.proteinatlas.org / ) and Genotype-Tissue Expression (GTEx https: / / gtexportal.org / ). Figure 1 The data used were downloaded from the TCGA database (https: / / portal.gdc.cancer.gov) to detect the expression of GABRP mRNA in normal tissues (N=2922) ( Figure 2 The GEPIA2 database was used to analyze the expression of GABRP in various types of common clinical cancers and adjacent normal tissues. Figure 3 ), Figure 2 , Figure 3 The results showed that the expression level of GABRP in pan-cancer tissues was significantly higher than that in normal tissues.

[0066] 2. Expression of GABRP gene in gliomas of different grades

[0067] The expression level of GABRP gene in gliomas of different grades was further analyzed. The TCGA database was used to screen the GABRP gene according to the gene expression difference criteria log2FoldChange>2, P value<0.0001, and the GABRP gene that met the screening criteria was obtained. GBMLGG (low-grade glioma) included 706 glioma tissues and 50 adjacent normal tissues; GBM (high-grade glioma) included 174 glioma tissues and 5 adjacent normal tissues. The results showed that the expression level of GABRP in glioma was significantly higher than that in adjacent normal tissues ( Figure 4 ).

[0068] 3. Protein expression level of GABRP gene in gliomas of different grades

[0069] The human protein atlas (HPA) was used to study the protein expression level of GABRP in glioma tissues of different grades. Figure 5 ). The TCGA database was used to screen the GABRP gene according to the gene expression difference screening conditions log2FoldChange>2, P value<0.0001, and the GABRP gene that met the screening conditions was obtained. Compared with normal tissues, the protein expression level of the GABRP gene in glioma tissues was significantly increased.

[0070] 4. The diagnostic value of GABRP gene for glioma

[0071] The pROC package was used to perform ROC analysis on the data from the TCGA database, and the results were visualized using ggplot2. The ROC curve analysis showed the value of GABRP in the diagnosis of different grades of gliomas; in GBMLGG, the AUC was 0.584, and the confidence interval CI was 0.313-0.856; in GBM, the AUC was 0.587, and the confidence interval CI was 0.317-0.857. This shows that GABRP has a certain value in the diagnosis of gliomas ( Figure 6 ).

[0072] The nomogram model was established by combining the expression level of the GABRP gene with clinical variables to predict the patient's survival rate within 1 year > 65%, the survival rate within 3 years > 50%, and the survival rate within 5 years > 40%. The results of the nomogram model showed that compared with the traditional clinical variables of age and gender, the expression level of the GABRP gene was more accurate in predicting the patient's prognosis ( Figure 7 ).

[0073] 5. Correlation between GABRP and the prognosis of patients with different grades of glioma

[0074] The Kaplan-Meier survival curve was used to analyze the effect of GABRP on the prognosis of patients with different grades of gliomas, and the relationship curve between GABRP and the prognosis of patients with lung squamous cell carcinoma was obtained, such as Figure 8 As shown in the results, the expression level of GABRP is significantly negatively correlated with the overall survival of patients with lung squamous cell carcinoma, indicating that the GABRP gene is a tumor-promoting gene. High expression of GABRP in tumors can lead to a worse prognosis for patients with lung squamous cell carcinoma ( Figure 8 , HR=1.46, P=0.007).

[0075] 6. Mutation analysis of GABRP in pan-cancer

[0076] The cBioPortal database was used to study the mutation of GABRP. The TCGA database showed the frequency of GABRP mutations in different cancer types in Pan-cancer ( Fig. 9 ), 226 (2%) patients had gene mutations among 10,953 patients. Gene deletion was the most common mutation of GABRP. The specific details showed that the probability of GABRP gene mutation was 2%, and the main mutation was gene deletion ( Fig.10 ).

[0077] 7.GO functional enrichment analysis and KEGG pathway enrichment analysis

[0078] To understand the biological function of GABRP in glioma, we used the LinkFinder module of the LinkedOmics website to detect the co-expression pattern of GABRP in glioma in the TCGA database. The red dots represent the top 30 genes that are positively correlated with GABRP, and the green dots represent the bottom 30 genes that are negatively correlated with GABRP ( Fig.11 center left).

[0079] DAVID functional annotation bioinformatics microarray analysis was used to identify and screen the enriched GO functions and KEGG pathways of GABRP-related genes (top 50), and the results were visualized after correction. The GO functional enrichment analysis results included cell components (CC) and MF analysis results, which showed that the main functions of GABRP were to regulate GABA-A receptor complex, GABA receptor complex GABA-A receptor activity, GABA receptor activity, etc. ( Fig.11 center right).

[0080] In addition, as shown in Table 1, KEGG pathway enrichment analysis found that the co-expressed genes of GABRP in gliomas were mainly enriched in three pathways: nicotine addiction, GABAergic synapse, and morphine addiction.

[0081] Table 1 GO functional enrichment analysis and KEGG pathway enrichment analysis of GABRP

[0082]

[0083]

[0084] 8. Correlation between GABRP expression and immune infiltration in glioma

[0085] In order to explore the correlation between GABRP expression level and immune infiltration in glioma, ssGSEA was performed based on the TCGA dataset using the R-GSVA package in R-studioR software to analyze the relationship between glioma (GBM) with different GABRP expression levels and the immune infiltration of 24 immune cells.

[0086] The results showed that the infiltration levels of activated dendritic cells (aDC), B cells, CD8 T cells, CD8+ T cells, dendritic cells (DC), eosinophils, macrophages, mast cells, effective memory T cells (Tem), follicular helper T cells (TFH), Th1 cells, regulatory T cells (Treg), pDC cells, NK cells, NKCD56dim cells and NKCD56bright cells in GBM patients with high GABRP expression were significantly higher than those in GBM patients with low GABRP expression.

[0087] In addition, there was no significant difference in the infiltration levels of immature dendritic cells, neutrophils, helper T cells, central memory T cells (Tcm), γ-δ T cells (Tgd), Th2 cells, and Th17 cells between patients with high and low GABRP expression ( Fig.12 ).

[0088] We further analyzed the relationship between GABRP and six types of infiltrating immune cells (including B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells) by accessing the "Gene" section in the TIMER database ( Fig.13 ). The results showed that the expression level of GABRP was positively correlated with the infiltration degree of B cells (r=0.15, P<0.001), CD8+T cells (r=0.116, P=0.002), T cells (r=0.117, P=0.002), macrophages (r=0.069, P=0.067), neutrophils (r=0.063, P=0.094), and T helper cells (r=0.232, P<0.001). This may lead to worse clinical characteristics and outcomes of patients.

[0089] Genes with altered cell copy numbers can affect the occurrence, progression, and immunotherapy response of tumor cells. GABRP copy number changes are associated with the infiltration of many immune cells such as regulatory T cells, B cells, CD8+T cells, CD4+T cells, neutrophils, and dendritic cells in GABRP ( Fig.14 Among them, high expression of GABRP is positively correlated with the content of regulatory T cells, M2 macrophages and B cells, and suppressive immune cells mainly include regulatory T cells, myeloid-derived suppressor cells (MDSCs) and some B cells, so high expression of GABRP can suppress immune cells and cause pan-cancer tumor cells to escape immune system.

[0090] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for analyzing pan-cancer immunotherapy, characterized in that: The steps include: S1. Screening for differential gene sets that exist in the TCGA database, HPA database, and GEPIA2 database and are related to cancer occurrence; S2. Substitute the genes in the differential gene set in S1 into the TCGA database to screen genes with differential expression; S3. Substitute the differentially expressed genes screened in S2 into the HPA database to screen for differentially expressed genes whose encoded proteins have significantly increased expression levels; S4. Bring the differentially expressed genes screened in S3 into the TCGA database to screen for genes with cancer diagnostic value; S5. Bring the genes screened in S4 into the TCGA database to screen genes that are significantly correlated with the prognosis of cancer patients; S6. Perform mutation analysis on the genes screened in S5 in the cBioPortal database to screen genes with low mutation probability; S7. The genes screened in S6 were brought into the TCGA database for GO functional enrichment analysis and KEGG pathway enrichment analysis; S8. In the TCGA database, analyze the relationship between the genes screened in S7 and the immune infiltration of cancer, and screen genes that inhibit infiltrating immune cells, which are the target genes for pan-cancer immunotherapy.

2. The analysis method according to claim 1, characterized in that The screening criteria for differentially expressed genes described in S2 were: log2FoldChange>2, Pvalue<0.0001.

3. The analysis method according to claim 1, characterized in that The screening criteria for differentially expressed genes in S3 were that the expression level of proteins encoded by the differentially expressed genes in cancer tissues was increased, log2FoldChange>2, and Pvalue<0.0001.

4. The analysis method according to claim 1, characterized in that The gene with cancer diagnostic value described in S4 is a gene with an AUC area greater than 0.55 when the cancer diagnostic value of the gene is analyzed using the ROC curve, and the patient's survival rate within 1 year is predicted to be greater than 65%, the survival rate within 3 years is greater than 50%, and the survival rate within 5 years is greater than 40%.

5. The analysis method according to claim 1, characterized in that The genes described in S5 were negatively correlated with the overall survival of cancer patients, with HR>1.2, P<0.

05.

6. The analysis method according to claim 1, characterized in that The probability of gene mutation described in S6 is ≤ 2%.

7. The analysis method according to claim 6, characterized in that The increased expression level of the target genes described in S8 is positively correlated with the infiltration degree of suppressive immune cells in the tumor microenvironment.

8. The target gene for pan-cancer immunotherapy obtained by the analysis method according to any one of claims 1 to 7, characterized in that: The target genes are GSC2, NTN4, REM1, TBL1Y, GABRP, PCDH11Y, ANGPT4, FERMT1, ZC3H12B, GPR50, PCDH11X, CBLN1, SLC1A6, VIPR2, GLP1R, KHDRBS2, AGXT2, CNTN3, TACR1, OPRD1, PRLHR, OBP2A, BHLHE23, BMP2, LRRN4, MYOD1, FOXA1GADD45G, SLC14A2, HM Any one of GCS2, RAX, ACRV1, MSTN, SSTR1, CDH18, SLC25A48, IL9, GSDMC, VAX1, TRIM48, PCDH15, MIPOL1, EPHB1, OR4K1, AFF2, TMSB15A, FGF17, ZNF208, LCN1, SSTR5, SMCP, DAPL1, IGFN1, TLX3, TSHR, OR4K2, LRTM2, SYNPO2L, NETO1 and KLK4GSC2.

9. Use of the analysis method according to any one of claims 1 to 7 or the target gene according to claim 8 in the preparation of a drug for treating pan-cancer, characterized in that: The drug reduces abnormal expression of the target gene.

10. The use according to claim 9, characterized in that: The reduction of abnormal expression of the target gene includes target gene silencing and interference with target gene expression protein.

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