Application of reagents for preparing target genes of novel coronavirus in regulating tumorigenesis and prognosis

Abstract

The present invention provides the use of reagents for preparing target genes of novel coronavirus in regulating tumor occurrence and prognosis, and belongs to the field of genetic engineering technology. The present invention conducts a systematic multi-omics analysis of the target genes of SARS-CoV-2 in pan-cancer. At the genomic level, changes such as SNV, CNV and methylation of STGs are of great significance in pan-cancer. At the expression level, STGs are differentially expressed in a variety of tumors, which is closely related to the prognosis of tumors and related signaling pathways. STGs are closely related to the tumor microenvironment, and their abnormal expression promotes the immune infiltration of tumors. STGs participate in the formation of anticancer drug resistance, indicating its potential for targeted therapy. The present invention reveals the genomic and clinical characteristics of STGs, explores its relationship with tumor immunity, explains the potential molecular mechanism of action between tumors and novel coronavirus, deepens the understanding of novel coronavirus and tumors, and provides new ideas for viral therapy of tumors.

Description

Technical Field

[0001] The present invention relates to the field of genetic engineering, and in particular to the use of reagents for preparing target genes of novel coronavirus in regulating tumor occurrence and prognosis. Background Art

[0002] The ongoing pandemic of COVID-19 infection caused by the novel coronavirus SARS-CoV-2 is affecting global health. In 2020, there were 19.3 million new cases of cancer and nearly 10 million deaths worldwide. Faced with these two major threats to human health, it is urgent to explore new treatments. Based on previous experience, there is a close relationship between viruses and tumors, and it is estimated that 15-20% of cancers are caused by viral infections. A large number of studies have found a variety of recognized human tumor viruses, such as human hepatitis B (HBV) and C (HCV) viruses, human papillomavirus (HPV), Epstein-Barr virus (EBV), and Kaposi's sarcoma herpes virus (KSHV).

[0003] The latest research shows that SARS-CoV-2 can inhibit the development of Hodgkin lymphoma. RNA vaccines against SARS-CoV-2 can stimulate the host immune system and inhibit the development of tumors, but the relationship between tumors and SARS-CoV-2 has not yet been clarified. Summary of the invention

[0004] In order to solve the above problems, the present invention provides the use of reagents for preparing target genes of the new coronavirus in regulating tumor occurrence and prognosis. The present invention reveals the relationship between the target genes of the new coronavirus and tumors. Targeting these target genes may be an important method for treating cancer, providing new ideas for targeted treatment strategies for tumors.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides the use of a reagent for preparing a target gene of a novel coronavirus in regulating tumor occurrence and prognosis.

[0007] The present invention also provides the use of reagents for preparing target genes of the novel coronavirus in regulating the tumor immune microenvironment and influencing tumor progression.

[0008] The present invention also provides the use of a reagent for preparing abnormal expression of a target gene of a novel coronavirus in mediating tumor resistance to chemotherapy and targeted drug therapy.

[0009] The present invention also provides the use of reagents for preparing target genes of novel coronavirus in promoting tumor immune pathways to influence tumor progression.

[0010] Preferably, the novel coronavirus includes the novel coronavirus SARS-CoV-2.

[0011] Preferably, the target gene includes one or more of the ACE2 gene, the NRP1 gene, the SCARB1 gene, the AXL gene and the TMPRSS2 gene.

[0012] Preferably, the tumor comprises one or more of bladder urothelial carcinoma, acute myeloid leukemia, adrenocortical carcinoma, invasive breast cancer, renal chromophobe cell carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, esophageal carcinoma, glioblastoma multiforme, head and neck squamous cell carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, low-grade glioma, pheochromocytoma, paraganglioma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoid tumor diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, uveal melanoma, gastric adenocarcinoma, pancreatic adenocarcinoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine corpus endometrial carcinoma, uterine carcinosarcoma, and skin melanoma.

[0013] The present invention comprehensively evaluates the genomic and clinical characteristics of SARS-CoV-2 target genes (STGs) in 33 solid tumors. The present invention found that genomic and epigenetic changes led to abnormal expression of STGs, which was significantly correlated with activation of tumor-related pathways, clinical survival, and immune infiltration. The present invention reveals the relationship between STGs and tumors. Targeting these STGs may be an important method for treating cancer, providing new ideas for targeted treatment strategies for tumors. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments are briefly introduced below.

[0015] Figure 1 , (A) STGs expression differences between normal samples and tumor samples, (B) STGs survival analysis, the size of the point represents the significance of the gene's effect on the survival of each cancer type, and the p value is obtained by Kaplan-Meier analysis. Red points indicate that the expression has a poorer survival rate in the cancer type, and blue points indicate that the expression has a better survival rate in the cancer type;

[0016] Figure 2, (A) Mutation frequency of STGs. The numbers represent the number of samples with the corresponding mutated gene for a particular cancer, "0" means there is no mutation in the coding region of the gene, and no number means there is no mutation in any region of the gene, (B) A tumor distribution waterfall chart showing the mutation distribution of STGs and the classification of SNV types, (C) The relationship between SNVs in STGs and survival, showing the hazard ratio and Cox p value by bubble color and size, bubble color from blue to red represents the hazard ratio from low to high, and bubble size is positively correlated with Cox P value significance. Black outline border indicates Cox P value ≤ 0.05;

[0017] Figure 3 , (A) Distribution of CNVs in 33 cancers. The CNV pie chart shows the heterozygous or homozygous CNVs of each gene in each cancer. The pie chart shows the proportion of different types of CNVs of a gene in a cancer. Different colors represent different types of CNVs. (B) The bubble chart shows the spectrum between STGs expression and CNV levels. Blue bubbles represent negative correlations, and red bubbles represent positive correlations. The darker the color, the higher the correlation. The bubble size is positively correlated with FDR significance. The black outline border indicates FDR≤0.05. (C) The relationship between CNVs of STGs in cancer and survival rate. The Log-rank p value is displayed by bubble color and size. The bubble color from blue to red indicates the significance of Log-rank p value from low to high. The bubble size is positively correlated with the significance of Log-rank p value. The black outline border indicates Log-rank p value≤0.05.

[0018] Figure 4 , (A) Correlation between methylation and STGs expression, blue dots indicate negative correlation, red dots indicate positive correlation, where the darker the color, the higher the correlation, (B) Survival difference between high-methylation and low-methylation STGs samples, hazard ratio and CoxP value are displayed by bubble color and size, bubble color from blue to red indicates hazard ratio from low to high, bubble size is positively correlated with CoxP value, black outline border indicates CoxP value ≤ 0.05;

[0019] Figure 5 , (A) The percentage of cancers in which STGs expression has a potential impact on pathway activity, and in cancer types, the percentage of cancers in which genes have an impact on pathways (FDR <= 0.05). The numbers in each cell represent the percentage (the percentage of cancers in which a specific gene shows a significant association with a specific pathway out of the total number of analyzed cancers) (B) The bubble chart summarizes the correlation between STGs expression and drug IC50 (tumor resistance). Blue bubbles indicate negative correlation, and red bubbles indicate positive correlation. The darker the color, the higher the correlation. The bubble size is positively correlated with FDR significance. The black outline border indicates FDR ≤ 0.05.

[0020] Figure 6 , (A) The association between immune cell infiltration and STGs. The heat map summarizes the significance of P value and FDR for Pearman correlation analysis between input gene set GSA score and immune cell infiltration. Blue dots indicate negative correlation, and red dots indicate positive correlation. The darker the color, the higher the correlation. *: P value ≤ 0.05; #: FDR ≤ 0.05. (B) The correlation between STGs and immunostimulant immune pathways. R represents correlation, and R>0 indicates positive correlation. (C) The correlation between STGs and MHC immune pathways. R represents correlation, and R>0 indicates positive correlation. (D) The correlation between STGs and chemokine immune pathways. R represents correlation, and R>0 indicates positive correlation.

[0021] Figure 7 Prognostic risk model of STG in LUAD and KIRC. AC: LUAD, DF: KIRC, (A, D) Sample distribution for risk score analysis based on STGs prognostic risk model, different survival status and time patterns of low-risk and high-risk clusters, cluster analysis heat map showing the expression of STGs in each patient based on the risk model, (B, E) Kaplan-Meier survival curves of OS in patients in low-risk and high-risk groups, HR>1 suggests the use of risk model, while HR<1 suggests the use of protection model; 95% CL indicates HR confidence interval; median survival time indicates the time when the survival rate of low-risk and high-risk groups is 50% based, (C, F) ROC curves of risk models at different times, the larger the AUC value, the stronger the predictive ability;

[0022] Figure 8 , Bar graphs of STG and clinical factors used to predict LUAD and KIRC; (A, B, C, D) LUAD, (C, D, F, H) KIRC. (A, C) Forest plots of univariate survival analysis. (B, D) Forest plots of multivariate survival analysis, p value < 0.05 indicates a significant relationship with OS, (E, F) A nomogram for KIRC patients was prepared, and the nomogram was developed using NRP1, pT-stage and grade, (G, H) Calibration of the nomogram, calibration curve of the nomogram;

[0023] Fig. 9 , qRT-PCR experimental analysis of STG in KIRC (n=3), P<0.05 was considered to be significantly different, *: p<0.05, **: p<0.01, ***: p<0.001, ****: p<0.001, ns: not significant;

[0024] Fig.10, Two-dimensional map (heat map) of the correlation of gene-based related factors, red represents upregulation, positive correlation or presence, blue represents downregulation, negative correlation, and white represents no significant correlation, absence or missing correlation, (A) ACE2, (B) NRP1, (C) SCARB1, (D) AXL, (E) TMPRSS2. DETAILED DESCRIPTION

[0025] The present invention provides the use of a reagent for preparing a target gene of a novel coronavirus in regulating tumor occurrence and prognosis.

[0026] The present invention also provides the use of reagents for preparing target genes of the novel coronavirus in regulating the tumor immune microenvironment to influence tumor progression.

[0027] The present invention also provides the use of a reagent for preparing abnormal expression of a target gene of a novel coronavirus in mediating tumor resistance to chemotherapy and targeted drug therapy.

[0028] The present invention also provides the use of reagents for preparing target genes of novel coronavirus in promoting tumor immune pathways to influence tumor progression.

[0029] In the present invention, the novel coronavirus preferably includes the novel coronavirus SARS-CoV-2. In the present invention, the target gene preferably includes one or more of the ACE2 gene, the NRP1 gene, the SCARB1 gene, the AXL gene and the TMPRSS2 gene.

[0030] In the present invention, the tumor preferably includes one or more of bladder urothelial carcinoma, acute myeloid leukemia, adrenocortical carcinoma, invasive breast cancer, renal chromophobe cell carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, bile duct carcinoma, colon adenocarcinoma, esophageal cancer, glioblastoma multiforme, head and neck squamous cell carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, low-grade glioma, pheochromocytoma, paraganglioma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, lymphoid tumor diffuse large B-cell lymphoma, mesothelioma, ovarian serous cystadenocarcinoma, prostate adenocarcinoma, rectal adenocarcinoma, sarcoma, uveal melanoma, gastric adenocarcinoma, pancreatic adenocarcinoma, testicular germ cell tumor, thymoma, thyroid cancer, uterine corpus endometrial carcinoma, uterine carcinosarcoma, and skin melanoma.

[0031] In order to further illustrate the present invention, the present invention is described in detail below in conjunction with embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0032] Example 1

[0033] 1. Dataset download and processing

[0034] STGs were obtained from the VThunter database (https: / / db.cngb.org / VThunter) and literature. The GSCA database (http: / / bioinfo.life.hust.edu.cn / GSCA) collected tumor-related data from The Cancer Genome Atlas (TCGA) (https: / / portal.gdc.cancer.gov / ), including expression data (n=10471), clinical data (n=11,160), single nucleotide variation (SNV) data (n=10234), copy number variation (CNV) data (n=11461), and methylation data (n=10063). Reverse phase protein array (RPPA) data from The Cancer Proteome Atlas (TCPA) (https: / / tcpaportal.org / tcpa / index.html) were used for pathway studies. The correlation between gene expression and drug sensitivity was studied using the Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerrxgene.org).

[0035] Samples from 33 cancer types were included in the analysis: bladder urothelial carcinoma (BLCA, n = 411), acute myeloid leukemia (LAML, n = 173), adrenocortical carcinoma (ACC, n = 92), breast invasive carcinoma (BRCA, n = 1218), renal chromophobe cell tumor (KICH, n = 91), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC, n = 310), bile duct carcinoma (CHOL, n = 45), colon adenocarcinoma ( The patients were divided into two groups: COAD, n = 329), esophageal cancer (ESCA, n = 196), glioblastoma multiforme (GBM, n = 174), head and neck squamous cell carcinoma (HNSC, n = 566), renal clear cell carcinoma (KIRC, n = 606), renal papillary cell carcinoma (KIRP, n = 323), low-grade glioma (LGG, n = 534), pheochromocytoma and paraganglioma (PCPG, n = 187), hepatocellular carcinoma (L IHC, n = 359), lung adenocarcinoma (LUAD, n = 576), lung squamous cell carcinoma (LUSC, n = 554), lymphoid neoplasms diffuse large B-cell lymphoma (DLBC, n = 48), mesothelioma (MESO, n = 87), ovarian serous cystadenocarcinoma (OV, n = 309), prostate adenocarcinoma (PRAD, n = 550), rectal adenocarcinoma (READ, n = 105), sarcoma (SARC, n = 265), gravidarum Uveal melanoma (UVM, n = 80), gastric adenocarcinoma (STAD, n = 450), pancreatic adenocarcinoma (PAAD, n = 183), testicular germ cell tumor (TGCT, n = 156), thymoma (THYM, n = 122), thyroid cancer (THCA, n = 572), uterine corpus endometrial carcinoma (UCEC, n = 201), uterine carcinosarcoma (UCS, n = 57) and cutaneous melanoma (SKCM, n = 474).

[0036] 2. Differential expression and prognostic analysis

[0037] In the mRNA differential expression analysis, only 14 cancer types, COAD, ESCA, KIRC, HNSC, PRAD, BRCA, BLCA, THCA, STAD, KIRP, LUAD, LIHC, and KICH, were included in the analysis because they contained more than 10 pairs of tumor and normal samples. The expression values ​​of mRNA in TCGA were expressed as normalized RSEM values. The fold change was calculated by mean (tumor) / mean (normal), and the p value was determined by t-test, and the p value was adjusted by false discovery rate (FDR).

[0038] After the STGs expression data and corresponding clinical survival data in 33 types of cancer were merged, the tumor samples were divided into high and low groups according to the median value and expression survival analysis was performed.

[0039] 3. Single Nucleotide Variation Analysis

[0040] Single nucleotide variation (SNV) data of 10,234 samples of 33 types of cancer were collected from the TCGA database. The analysis included seven mutation types called harmful mutations: Missense_Mutation, Nonsense_Mutation, Frame_Shift_Ins, Splice_Site, Frame_Shift_Del, In_Frame_Del, and In_Frame_Ins.

[0041] 4. Copy Number Variation Analysis

[0042] The raw data of copy number (CNV) of 33 cancer types (n=11495) were collected from the TCGA database and processed using GISTICS2.0 to identify significantly altered regions of amplification or deletion in the patient group 10.1186 / gb-2011-12-4-r41. The mRNA expression data and CNV raw data were combined and Spearman correlation analysis was performed to obtain the correlation between gene mRNA expression and CNV. The P value was adjusted by FDR. The Log-rank test tested the survival difference between the groups.

[0043] 5. Methylation Analysis

[0044] Methylation data (n=10063) were collected from the TCGA database, and methylation analysis was based on these 14 cancer types with paired data. T-test was used to determine the methylation differences between tumor and normal samples, and the p-value was adjusted by FDR. Spearman analysis was used to obtain the correlation between gene mRNA expression and methylation levels. The p-value was adjusted by FDR. The median methylation was used to divide the tumor samples into high-methylation group and low-methylation group for survival analysis.

[0045] 6. Related pathway analysis

[0046] Pathway activity scores were calculated for 10 cancer-related pathways (TSC / mTOR, RTK, RAS / MAPK, PI3K / AKT, hormone ER, hormone AR, EMT, DNA damage response, cell cycle, and apoptosis pathways) for 33 tumor samples. The difference in gene expression between pathway activity groups (activated and inhibited) was defined by the median pathway score. The samples were divided into two groups (high and low) according to the median gene expression, and the difference in pathway activity scores (PAS) between the groups was defined by a T test, and the p-value was adjusted by FDR. For example, when PAS (Gene A High expression) > PAS (Gene A Low expression), the present invention believes that gene A may have an activating effect on the pathway, otherwise it has an inhibitory effect on the pathway.

[0047] 7. Drug Sensitivity Analysis

[0048] IC50 of 265 small molecules and their corresponding gene expression in 860 cell lines were collected from Genomics of Drug Sensitivity in Cancer (GDSC) and merged with expression data. Pearson correlation analysis was performed to obtain the correlation between gene mRNA expression and drug IC50. P value was adjusted by FDR. A positive correlation means that high expression of the gene indicates resistance to the drug.

[0049] 8. Analysis of the relationship between STGs and immunity

[0050] The ImmuCellAI algorithm was used to evaluate the infiltration of 24 immune cells, expressed as correlation coefficients. The association between immune cell infiltration and influenza A virus target gene GSVA scores was analyzed by Spearman correlation, and the P value was adjusted by FDR. The marker gene sets of three immune-related pathways, chemokines, MHC pathways, and immunostimulants, were obtained from TISIDB (http: / / cis.hku.hk / TISIDB / ). The relationship between STGs and the three immune-related pathways, chemokines, MHC pathways, and immunostimulants, was analyzed using the GEPIA2 database (peason coefficient).

[0051] 9. Establishment and verification of risk model

[0052] The survival package of R software was used to perform multivariate Cox regression analysis and establish a risk prognostic model. The model is a RiskScore formula containing multiple genes, each gene has a weight, a negative number represents that the gene is a protective gene, and a positive number represents that the gene is a risk gene. Patients were divided into high-risk and low-risk groups according to the median value. Log rank was used to test KM survival analysis to compare the survival differences between the above two or more groups, perform the ROC method to evaluate the predictive efficiency of the model, and analyze the relationship between genes within the risk. For the Kaplan-Meier curve, the p value and the hazard ratio (HR) with 95% confidence interval (CI) were obtained by logrank test and univariate Cox regression.

[0053] 10. Establishment of Nomogram

[0054] Univariate and multivariate Cox regression analysis and forest plots were used by the "forestplot" package to display the P value, HR, and 95% CI for each variable. Based on the results of the multivariate Cox proportional hazard analysis, a nomogram was established using the "rms" package to predict 1, 3, and 5-year overall survival. The nomogram provides a graphical result of these factors, and the prognostic risk of a single patient can be calculated by the points associated with each risk factor.

[0055] 11. qRT-PCR experimental verification and analysis

[0056] HK-2, 786-O and Caki-1 cell lines were obtained from the American Type Culture Collection ( Virginia, USA). Cells were seeded at a density of 50,000 in 6-well plates. When cells reached confluence, total RNA was extracted using Trizol reagent (Invitrogen, NY, US). cDNA was synthesized using iScript cDNA synthesis kit (Bio Rad, Hercules, US). qPCR was performed using a PCR instrument ( Real-time quantitative PCR was performed using 480 (Roche Life Sciences), Power SYBR Green PCR Master Mix (Thermo Fisher Scientific, Massachusetts, USA) and gene-specific primers. β-actin served as an internal control. The efficiency was corrected using 2 -△△CT The fold changes of gene expression were calculated using the method.

[0057] 12. Statistical Analysis

[0058] All statistical analyses were performed using R software (v4.0.3) unless otherwise specified. Correlation analysis was performed using the Spearman correlation test. Survival risk and HR were calculated using the Cox proportional hazards model. The R package “survival” was used to analyze the survival time and survival status of the two groups. The log-rank test was used for comparison. The rank sum test detected two groups of data, and P values ​​< 0.05 or FDR ≤ 0.05 were considered statistically significant. Genes and cancer types with p values ​​less than 0.05 are shown in the results.

[0059] result:

[0060] 1. Determination of the target gene of the new coronavirus

[0061] Four STGs (ACE2, NRP1, SCARB1, and AXL) were retrieved from the VThunter database, and one STG (TMPRSS2) was found in the literature for the next analysis.

[0062] 2. STGs have significant expression differences between tumors and normal tissues

[0063] Figure 1 A shows that STGs were abnormally expressed in 13 solid tumors including KICH, LUSC, BRCA, LIHC, BLCA, COAD, PRAD, THCA, LUAD, HNSC, ESCA, KIRP, and KIRC (P<0.05, Figure 1In A), there was no significant difference in expression in STAD. In particular, the expression level of SCARB1 was significantly upregulated in tumors (P<0.05). In KIRC, all four STGs had significant expression differences and their expressions were upregulated (P<0.05).

[0064] Figure 1 Figure B shows that the expression of NRP1 is associated with low survival rate in STAD, CESC, READ, and UVM, and is a risk factor (HR>1); it is associated with high survival rate in LGG and KIRC, and is a protective factor (HR<1). SCARB1 is associated with low survival rate in HNSC and UVM, and is a risk factor (HR>1); it is associated with high survival rate in LGG, and is a protective factor (HR<1). AXL is a risk factor (HR>1) in STAD, LGG, MESO, and KIRC, and is a protective factor (HR<1) in SKCM and KIRP. ACE2 is a protective factor (HR<1) in UVM, MESO, KIRC, and LIHC. TMPRSS2 is a risk factor for BRCA (HR>1), but a protective factor (HR<1) in KICH, LUAD, and THYM.

[0065] These results suggest that abnormal expression of STGs may affect tumorigenesis and prognosis.

[0066] 3. Somatic mutations of STGs

[0067] The present invention analyzes STGs-related SNV data to detect the frequency and variation type of each cancer.

[0068] Figure 2 A shows that STGs are somatically mutated in other cancers except KICH. The mutation of STGs is more obvious in UCEC and SKCM, with the highest mutation rate exceeding 10%.

[0069] Figure 2 Figure B shows that missense mutations are the main type. SNV percentage analysis showed that the gene mutation rates were ranked as AXL (37%), NRP1 (33%), ACE2 (25%), TMPRSS2 (17%), and SARB1 (15%).

[0070] Figure 2 Middle C shows that the HR of ACE2 in UCEC is less than 1, which is a protective factor. The HR of AXL in LUAD and NRP1 in SKCM are greater than 1, which are risk factors.

[0071] These results indicate that there are significant differences in the mutated and non-mutated genes of STGs between tumors and normal tissues, and that mutations in some STGs can affect the prognosis of tumors.

[0072] 4. Changes in copy number of STGs

[0073] In order to determine the changes in CNV, the present invention analyzed the CNV data of STGs.

[0074] Figure 3 Figure A shows that the main CNV types of STGs are heterozygous amplification and deletion. (HeteAmp: heterozygous amplification; HeteDel: heterozygous deletion; HomoAmp: homozygous amplification; Homo Del: homozygous deletion;)

[0075] Figure 3 As shown in Figure B, most of the expressions of STGs were not significantly correlated with CNV, some were positively correlated, and only a very small number were negatively correlated.

[0076] Figure 3 Middle C shows that in some tumors, CNV changes in STGs are negatively correlated with tumor survival rate and are risk factors.

[0077] This result shows that the majority of CNV changes in STGs are heterozygous amplifications and deletions, which are correlated with STGs expression and tumor prognosis and may be associated with poor prognosis.

[0078] 5. Methylation analysis of STGs

[0079] The present invention explores the methylation status of STGs to determine epigenetic regulation.

[0080] Figure 4 Middle A shows that the correlation analysis between methylation and mRNA expression showed that in most tumors, the expression levels of the vast majority of STGs were negatively correlated with their methylation levels.

[0081] Figure 4 Figure B shows that high methylation of STGs in PRAD, MESO, LIHC, PRAD, THYM, SKCM, and ESCA is associated with low survival rate and is a risk factor (HR>1). In particular, ACE2 in ACC and UVM, AXL in ACC, and SCARB1 in LGG are risk factors.

[0082] 6. Analysis of the relationship between STGs and cancer-related pathways

[0083] The present invention also analyzes the relationship between STGs and cancer-related pathways.

[0084] Figure 5Middle A shows that all four STGs are significantly involved in cancer-related signaling pathways, including Apoptosis, Cell Cycle, DNA Damage, EMT, Hormone AR, Hormone ER, PI3K / AKT, RAS / MAPK, RTK, and TSC / mTOR. The numbers in each cell in the figure indicate the percentage of cancer types associated with a specific pathway in the total cancer types. In particular, AXL is deeply involved in the inhibition of the cell cycle pathway and the activation of the EMT pathway. These results indicate that STGs play an important role in regulating cancer-related pathways.

[0085] 7. Analysis of STGs and Tumor Drug Resistance

[0086] Genomic changes influence patients' clinical responses to chemotherapy and targeted therapies.

[0087] Figure 5 Figure B shows that the resistance (IC50) of tumors to 30 drugs is significantly correlated with the expression of STGs. These results suggest that abnormal expression of STGs may mediate tumor resistance to chemotherapy and targeted drug therapy.

[0088] 8. Relationship between STGs and immunity

[0089] Figure 6 Center A shows that the GSVA score of STGs in tumors is positively correlated with InfiltrationScore, and in most tumors, the GSVA score of STGs is positively correlated with the expression of multiple immune infiltrating cells (*: P value≤0.05; #: FDR≤0.05).

[0090] Figure 6 The BD showed that STGs were positively correlated with the immune stimulation pathway (FIG.6B, R=0.42), STGs were positively correlated with the MHC immune pathway (FIG.6C, R=0.40), and STGs were positively correlated with the chemokine immune pathway (FIG.6D, R=0.33). These results indicate that STGs are related to tumor immunity and may affect tumor progression through tumor immune pathways.

[0091] 9. Predictive characteristics of STGs in LUAD and KIRC

[0092] Since all five STGs were aberrantly expressed in KIRC, this study considered KIRC to evaluate the STG-based risk prognostic model. Meanwhile, since SARS-CoV-2 is a respiratory virus, we also considered LUAD. Five STGs were identified by multivariate Cox regression analysis and used to create a predictive signature in LUAD and KIRC. In LUAD, risk core = (0.135)*ACE2+(0.001)*NRP1+(0.1469)*SCARB1+(0.1219)*AXL+(-0.1506)*TMPRSS2. In KIRC, risk core = (-0.2469)*ACE2+(-0.3409)*NRP1+(-0.0526)*SCARB1+(0.2364)*AXL+(-0.1833)*TMPRSS2. According to the median cutoff point of the risk score, all patients were divided into high-risk group and low-risk group according to the prognostic score. Each STGs expression value in the formula associated with the risk score was displayed on the heat map. In LUAD and KIRC, when the risk score increased, the survival rate decreased and the cancer-related mortality increased ( Figure 7 A, D). Patients with low risk scores are considered to have a greater chance of achieving the same survival time as those with high risk scores ( Figure 7 B, E). The AUC value of the prognostic feature of ROC analysis for one-year survival was 0.646 in LUAD, while the AUC value of the prognostic feature of ROC analysis for one-year survival was 0.733 in KIRC ( Figure 7 C, F). This result demonstrates the effectiveness of STGs in tumor prediction and, more importantly, the important correlation between STGs and tumors.

[0093] 10. Tumor prognostic nomogram of STGs and clinical factors

[0094] In order to develop a clinically applicable method to assess patients' chances of survival, a prediction model considering clinicopathological parameters was constructed using a nomogram. Figure 8 A, B) and KIRC ( Figure 8 Univariate and multivariate analysis of OS rates in the C and D groups was performed, and a nomogram model was established. The Cox regression algorithm was used to predict 1-, 3-, and 5-year OS in the discovery group, and predictors included factors such as Pt-stage. The C index for LUAD was found to be 0.713 ( Figure 8 E), KIRC is 0.73 ( Figure 8 Compared with the ideal model in the entire cohort, LUAD ( Figure 8 Middle G) and KIRC( Figure 8 (H) Calibration plot of 1-, 3-, and 5-year OS rates shows correct predictions.

[0095] 11. qRT-PCR Analysis of STG in KIRC

[0096] The results showed that the transcriptional levels of all STGs (ACE2, NRB1, SCARB1, AXL) were upregulated in KIRC. TMPRSS2 was downregulated in KIRC. In addition, the basal expression of TMPRSS2 was very low in KIRC cell lines ( Fig. 9 ). This result experimentally verified that STG is abnormally expressed in tumors.

[0097] 12. Summary of STGs performance in pan-cancer

[0098] like Fig.10 As shown, TMPRSS2 and AXL are frequently downregulated in cancer, but SCARB1 shows the opposite trend, while all three are expressed more frequently than ACE2 and NRP1, which may be due to their limited cases and cannot provide expression patterns. Interestingly, three STGs, including NRP1, SCARB1, and AXL, were shown to be more associated with poor prognosis in cancer patients than with good prognosis, while downregulation of ACE2 and TMPRSS2 was more often associated with poor prognosis. In addition, CNVs of the five STGs were all associated with their expression in many tumors (SCARB1 was the most in 18 cancers, TMPRSS2 was the most in 13 cancers, followed by AXL in 10 cancers, ACE2 in 7 cancers, and NRP1 in 6 cancers), and most of the associations were positive, indicating that CNVs of STGs are often associated with upregulation of STGs expression and are associated with promoting tumor growth. In addition, methylation of STGs is also associated with their expression in tumors. In fact, their associations (SCARB1 was the most in all 33 cancers investigated, followed by NRP1 in 31 cancers, TMPRSS2 in 27 cancers, AXL in 25 cancers, and ACE2 in 13 cancers) appeared in more cancer types than CNVs with the same trend in the five STGs. However, most of the associations were negative, indicating that abnormal hypermethylation of STGs promotes the downregulation of STGs expression in cancer and participates in controlling tumor growth. Finally, although the association of methylation with tumor prognosis was shown in no more than six cancer types, 5 / 6, 3 / 4, 2 / 3, 1 / 2, and 2 / 5 were positive in NRP1, AXL, SCARB1 ACE2, and TMPRSS2, respectively, indicating that methylation of these STGs has at least 40% of favorable outcomes in malignant tumors. Based on these findings, we hypothesize that genetic and epigenetic modifications in STGs will regulate the occurrence and development of multiple malignancies.

[0099] The interaction between the new coronavirus and tumors is not clear yet and needs further study. By mining multi-omics analysis data, the present invention comprehensively characterized the target genes of the new coronavirus in multiple samples of 33 types of cancer. The results not only revealed the potential relationship between the target genes of the new coronavirus and tumors, but also clarified the overall picture of the relationship between new coronavirus infection and tumors.

[0100] The present invention evaluates the genomic characteristics of STGs in 33 tumors. The present invention finds that STGs are involved in the occurrence of tumors, and their expression is abnormally expressed in a variety of tumors, and also affects the prognosis of tumors. Genetic and epigenetic analysis shows that the frequency of SNV, CNV and methylation of STGs is very high, and it will affect the expression of STGs in tumors, and will affect the prognosis of tumors. For example, angiotensin-converting enzyme 2 (ACE2) may be a potential prognostic biomarker and target of BRCA. Targeting NRP1 to inhibit NRP1 expression can promote apoptosis to treat tumors. Upregulated SCARB1 expression can promote the malignant progression of clear cell renal cell carcinoma. In addition, CNV is positively correlated with STGs expression in a variety of tumors, and the CNV of STGs is associated with tumor prognosis, indicating that the CNV of STGs is associated with the upregulation of STGs expression and is involved in the regulation of tumor progression. Further epigenetic analysis shows that in all tumors of the present invention, abnormal high methylation of STGs promotes the downregulation of STGs expression and is associated with a good prognosis of a variety of cancers. Based on these results, the present invention speculates that genetic and epigenetic changes of STGs may regulate the occurrence and development of certain tumors.

[0101] In pathway analysis, STGs were identified as key regulators of cancer-related signaling pathways. Different STGs are associated with different cancer-related signaling pathways and produce inconsistent activation or inhibition effects. Therefore, these results indicate that STGs constitute an interactive network of cancer-related signaling pathways and may be involved in promoting tumor progression. The results of further immune analysis revealed the mechanism by which the new coronavirus may regulate the tumor immune microenvironment through STGs and thus affect tumor progression. This also demonstrates the important role of STGs in tumor immunotherapy and provides a potential solution for the future development of immunotherapy. At the same time, the drug resistance analysis of the present invention determined that STGs are associated with tumor resistance to multiple drugs. Therefore, targeting STGs may be an ideal method for cancer treatment and new coronavirus infection treatment.

[0102] In order to further verify the relationship between the new coronavirus and tumors, the present invention explores the prognostic value of STGs in tumor patients, and the present invention constructs a KIRC prognostic risk model based on STGs. The four genes used to establish the risk model in the present invention have been shown to be closely related to the development and progression of tumors. The survival analysis of the present invention shows that the established risk model exhibits effective predictive performance for the survival of KIRC patients. Moreover, through the construction of the prognostic diagnostic nomogram of KIRC, the present invention obtains a nomogram with good predictive performance that includes STGs and clinical factors, and further clarifies the potential of NRP1 in STGs as a key gene for prognostic diagnostic decision-making. The present invention found that these results all confirmed the prognostic prediction effect of STGs in tumors.

[0103] The main limitation of this study is that there are few studies on the effects of the new coronavirus on tumors through STGs, and there are relatively few existing research results used as references. The effects of the new coronavirus on tumors still need further verification. However, the findings of the present invention are still of great significance and provide new insights into the exploration of the interaction mechanism between tumors and the new coronavirus. Secondly, changes in STGs exist at all regulatory levels, including genetic and epigenetic changes, immune infiltration microenvironment, and pathway correlations. These changes may in turn lead to differences in drug effects, treatment responses, and patient survival. Moreover, these results of the present invention all show the genomic and clinical characteristics of STGs in tumors, revealing the close relationship between the new coronavirus and pan-cancer. The new coronavirus may be able to affect the occurrence and prognosis of tumors through STGs, and is often related to immunity, which will provide a potential resource with good reference for viral therapy and immunotherapy of tumors.

[0104] In summary, the multi-omics analysis of the present invention clarifies the genomic and clinical characteristics of the SARS-CoV-2 target genes in pan-cancer. The results show that the expression of SARS-CoV-2 target genes is associated with tumor prognosis, immune metabolism, and drug sensitivity. In addition, there are many interesting mechanisms between SARS-CoV-2 target genes and cancer-related pathways. There is a potential close connection between SARS-CoV-2 and tumors, and a novel and critical cancer therapy may be developed based on SARS-CoV-2 and immunity.

[0105] Although the above embodiment describes the present invention in detail, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the protection scope of the present invention.

Claims

1. Application of reagents for detecting the expression level of target genes of novel coronavirus in the preparation of tumor prognosis products; The novel coronavirus is the novel coronavirus SARS-CoV-2; The target gene is a combination of ACE2 gene, NRP1 gene, SCARB1 gene, AXL gene and TMPRSS2 gene; The tumor is renal clear cell carcinoma or lung adenocarcinoma.

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