Methods of revealing biological links between TP53 mutations and intratumoral microorganisms and TME

Abstract

The invention discloses a method for revealing biological relation among TP53 mutation, microorganisms in tumor and TME, which comprises the following steps: acquiring tumor sample data, and calculating mutation frequency of each mutant gene based on the tumor sample data; the method comprises the following steps: selecting a TP53 mutant gene for analysis, dividing tumor sample data into a TP53 mutation group and a TP53 wild type group, and performing difference analysis on the TP53 mutation group and the TP53 wild type group on microorganisms in tumors; and comparing the relationship of different cancer species among genomes, transcriptomes and microorganisms in tumors. According to the method disclosed by the invention, interaction among TP53 gene mutation, microorganisms in tumors and TME is analyzed, and a new thought and a new method are expected to be provided for precise treatment and individualized treatment of cancers.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics technology, and specifically to a method for revealing the biological relationship between TP53 mutations, intratumoral microorganisms, and the tumor microenvironment (TME). Background Art

[0002] As a complex disease, the occurrence and development of cancer involve the interaction of multiple factors. In recent years, more and more studies have shown that TP53 gene mutations, intratumoral microbiota, and the tumor microenvironment (TME) are closely related to the occurrence and development of cancer. However, the potential biological relationship among the three remains unclear.

[0003] TP53 is a key tumor suppressor gene, which plays a crucial role in maintaining genomic stability and preventing the occurrence of cancer. In various cancers, the TP53 gene often mutates, and these mutations are related to immune escape, increased mutation burden, and changes in the composition of the microbiota. In addition, certain intratumoral microbiota can directly interact with TP53, leading to its inactivation or further mutation. TP53 mutations not only affect the biological characteristics of cancer cells but also may promote cancer progression by affecting the TME.

[0004] The TME is a complex ecosystem, including various components such as tumor cells, stromal cells, immune cells, and microbiota. The various factors in this environment interact with each other, jointly affecting the growth, invasion, and metastasis ability of tumors. TP53 mutations have been proven to affect the functions of immune cells in the TME, such as T cells, B cells, and natural killer (NK) cells, thereby changing the immune response within the TME and providing favorable conditions for the growth and metastasis of tumor cells.

[0005] In recent years, with the continuous in-depth study of microbiomics, more and more evidence has shown that intratumoral microbiota plays an important role in the occurrence, development, and treatment of cancer. These microbiota can not only directly affect the biological behavior of tumor cells but also may affect the treatment effect and prognosis of tumors by regulating the TME.

[0006] Although there are currently some studies on the relationship between TP53 gene mutations, the intratumoral microbiota, and the TME, these studies mainly focus on descriptive observations and preliminary mechanism explorations. For example, existing studies have revealed the high frequency of TP53 mutations in certain cancer types and the association between these mutations and microbiota diversity. At the same time, there are also studies exploring the role of the TME in cancer progression and the impact of TP53 mutations on the function of immune cells in the TME. However, there is currently no study that tightly combines these three factors and deeply explores their interaction mechanisms and potential therapeutic targets, especially the lack of in-depth analysis and innovative treatment options for specific cancer types (such as liver cancer and endometrial cancer). Summary of the Invention

[0007] To address the deficiencies in the prior art, the present invention provides a method for revealing the biological connection between TP53 mutations, intratumoral microorganisms, and the TME. The method of the present invention analyzes the interaction mechanism among TP53 gene mutations, the intratumoral microbiota, and the TME, provides ideas for innovative cancer treatment methods based on the interaction mechanism among the three, and through in-depth study of the interaction among the three, the present invention is expected to provide new ideas and methods for the precision treatment and individualized treatment of cancer.

[0008] To achieve the above object, the present invention is realized through the following technical solutions:

[0009] A method for revealing the biological connection between TP53 mutations, intratumoral microorganisms, and the TME, comprising the following steps:

[0010] S1. Obtain tumor sample data and calculate the mutation frequency of each mutant gene based on the tumor sample data;

[0011] S2. Select the TP53 mutant gene for analysis, divide the tumor sample data into a TP53 mutant group and a TP53 wild-type group, and perform differential analysis on the intratumoral microbiota between the TP53 mutant group and the TP53 wild-type group;

[0012] S3. Compare the relationships among different cancer types in terms of genome, transcriptome, and intratumoral microbiota.

[0013] The differential analysis of the intratumoral microbiota between the TP53 mutant group and the TP53 wild-type group in step S2 includes:

[0014] S21. Calculate the species richness and α-diversity of the intratumoral microbiota of each tumor sample in the TP53 mutant group and the TP53 wild-type group, and evaluate the observed integrity of the species in the tumor sample in combination with the Goods coverage rate; use statistical tests to compare the differences between the TP53 mutant group and the TP53 wild-type group;

[0015] S22. Calculate and compare the beta diversity of the intratumoral microbiota in the TP53 mutant group and the TP53 wild-type group, and perform PERMANOVA analysis using the adonis2 function to test the effect of TP53 mutation on the intratumoral microbiota community structure;

[0016] S23. Use LEfSe analysis to analyze the differences in the intratumoral microbiota of different cancer types between the TP53 mutant group and the TP53 wild-type group;

[0017] S24. Calculate the correlation between the intratumoral microbiota in the TP53 mutant group and the TP53 wild-type group of different cancer types using the Pearson correlation coefficient.

[0018] The alpha diversity in step S21 includes the Shannon index, Simpson index (species diversity index), Pielou index (species evenness index), and species richness index. The species richness index includes one or more of the Richness index, Chao1 index, Ace index, and obs index. Among them, the Pielou index is calculated based on the Shannon index and the Richness index;

[0019] Shannon index:

[0020] Simpson index:

[0021] Pielou index:

[0022] Where: i represents the i-th sample, and S ij represents the number of species of the j-th type within the i-th sample, and S i represents the total number of species within the i-th sample, and p ij is the relative abundance of the j-th species within the i-th sample, that is, the ratio of the number of species of the j-th type to the total number of species within the sample; H i ′ represents the Shannon index of the i-th sample; D i represents the Simpson index of the i-th sample; J i represents the Pielou index of the i-th sample.

[0023] In step S22, calculating and comparing the beta diversity of the intratumoral microbiota in the TP53 mutant group and the TP53 wild-type group includes:

[0024] Calculate the beta diversity of the intratumoral microbiota in the TP53 mutant group and the TP53 wild-type group respectively using the Bray-Curtis distance;

[0025] Visualize the Bray-Curtis distances of the TP53 mutant group and the TP53 wild-type group in three-dimensional space by PCoA.

[0026] The different cancer types in step S3 are EC and HCC respectively.

[0027] Step S3 includes:

[0028] S31. Compare the alpha diversity and beta diversity of the relative abundances of intratumoral microorganisms in different cancer types;

[0029] S32. Compare the differences in stromal scores, immune scores, and tumor purity in different cancer types;

[0030] S33. Use the TIDE algorithm to predict the immunotherapy response of patients, calculate the TIDE score, and visualize it;

[0031] S34. Compare the beta diversity of all immune cells and immune gene checkpoints in different cancer types;

[0032] S35. Analyze the differences in TMB between the TP53 mutant group and the wild-type group in different cancer types;

[0033] S36. Analyze the differences in the abundances of six stromal cell types between the TP53 mutant group and the wild-type group in different cancer types;

[0034] S37. Perform Cox proportional hazards analysis based on the intratumoral microorganisms with significant differences identified by LEfSe, generate the survival curves of tumor patients, and perform correlation analysis on intratumoral microorganisms and stromal cells using the Spearman correlation coefficient.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. The present invention mainly aims to reveal the complex relationship between the TP53 mutation status and the intratumoral microbiota, and how this relationship affects the TME. Specifically, the method of the present invention elucidates the impact of TP53 mutations on the diversity of intratumoral microorganisms: by comparing the microbial diversity indices (such as Shannon, Simpson, etc.) of the TP53 mutant group and the wild-type group, it reveals how TP53 mutations reduce microbial diversity.

[0037] 2. The present invention analyzes the relationship between TP53 mutations and the intratumoral microbial community structure: using analysis methods such as PCoA and PERMANOVA, it shows how the TP53 mutation status affects the distribution and composition of intratumoral microbiota in three-dimensional space.

[0038] 3. The present invention explores the interaction between TP53 mutations, the microbiota, and the TME: to study how TP53 mutations affect the growth, apoptosis of tumor cells, and the infiltration and function of immune cells by altering the intratumoral microbiota, thereby providing new insights and strategies for tumor treatment. By combining multi-omics data to study the occurrence and development of cancer, it is found that there are significant differences in TP53 mutations, and precision targeted therapy for cancer can be carried out using the microbiota and TP53. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] FIG. Figure 1 a is a mutation frequency graph of the 30 genes with the highest mutation frequencies in the HCC cancer type.

[0040] FIG. Figure 1 b is a mutation frequency graph of the 30 genes with the highest mutation frequencies in the EC cancer type.

[0041] FIG. Figure 2 a is a TP53 mutation frequency graph of ten cancer types.

[0042] FIG. Figure 2 b, c, d, e, f, g, h, i, j, k are heatmaps of the respective indices of the intratumoral microbial α-diversity of the ESCA, PAAD, COAD, LUAD, BLCA, STAD, EC, BRCA, HCC, PRAD cancer types.

[0043] FIG. Figure 3 is a box plot of the respective indices of the intratumoral microbial α-diversity of the HCC and EC cancer types.

[0044] FIG. Figure 4 is a PCoA graph of the Bray-Curtis dissimilarity matrix of each cancer type, where a, b, c, d, e, f, g, h, i, j are ESCA, PAAD, COAD, LUAD, BLCA, STAD, PRAD, BRCA, HCC, EC respectively.

[0045] Figure 5 is a box plot of the Bray-Curtis dissimilarity matrix of the HCC and EC cancer types.

[0046] Figure 6 is a bar graph of the LDAScore of the specific effect of TP53 mutations on the intratumoral microbial communities of HCC and EC patients.

[0047] Figure 7 is a network graph of the intratumoral microbiota of HCC and EC based on the correlation between microorganisms.

[0048] Figure 8a and b are the display charts of the number of samples in the TP53 mutant group and the number of samples in the TP53 wild-type group for the two cancer types of EC and HCC respectively.

[0049] Figure 8 c and d are the stacked bar charts of the relative abundances of the top five genera of intratumoral microorganisms in the two cancer types of HCC and EC.

[0050] Figure 8 e is the difference radar chart of intratumoral microorganisms in HCC and EC at the phylum level.

[0051] Figure 8 f is the box plot of the α-diversity of bacteria in HCC and EC.

[0052] Figure 8 g is the PcoA plot of the β-diversity of bacteria in HCC and EC.

[0053] Figure 9 a is the calculation flow chart of stromal score, immune score and tumor purity.

[0054] Figure 9 b, c and d are the corresponding box plots of the differences in stromal score, immune score and tumor purity of HCC and EC.

[0055] Figure 9 e is the violin plot of the TIDE scores of HCC and EC.

[0056] Figure 9 f and g are the PcoA plots of the β-diversity of all immune cells and immune gene checkpoints of HCC and EC.

[0057] Figure 9 h and i are the heat maps of the differences between all immune cells and immune checkpoint genes in HCC and EC.

[0058] Figure 10 a and b are the box plots of TMB of the TP53 mutant group and the TP53 wild-type group of EC and HCC.

[0059] Figure 11 a and b are the heat maps of the abundances of six stromal cell types between the TP53 mutant group and the TP53 wild-type group in HCC and EC.

[0060] Figure 12 a and b are the box plots of stromal cells with significant differences between the TP53 mutant group and the TP53 wild-type group of EC and HCC.

[0061] Figure 13 a is the survival curve of EC patients.

[0062] Figure 13b is a graph showing a significant association between specific intratumoral microorganisms and stromal cells.

[0063] Figure 13 c, d, and e are graphs showing potential links between TP53 mutations, intratumoral microorganisms, and the TME. Detailed implementation manners

[0064] To make the objectives, technical solutions, and advantages of the technical solutions of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of specific embodiments of the present invention. It should be noted that the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] The present invention provides a method for revealing the biological links between TP53 mutations, intratumoral microorganisms, and the TME, including the following steps:

[0066] S1. Obtain tumor sample data and calculate the mutation frequencies of each mutant gene based on the tumor sample data.

[0067] The tumor sample data can be obtained from the TCGA official website. Exemplarily, the data of the following ten cancer types can be obtained from the TCGA official website: esophageal cancer ESCA, pancreatic cancer PAAD, colon cancer COAD, lung adenocarcinoma LUAD, bladder cancer BLCA, gastric cancer STAD, endometrial cancer EC, breast cancer BRCA, liver cancer HCC, prostate cancer PRAD; then, based on the tumor sample data of each cancer type, calculate the mutation frequencies of each mutant gene of each cancer type.

[0068] Exemplarily, as Figure 1 shown in a and b, the 30 mutant genes with the highest mutation frequencies in the two cancer types of HCC and EC are respectively. As Figure 1 shown in a, the mutation frequencies of the mutant genes in HCC are 6% to 28%. Among them, the mutation frequency of TP53 is as high as 28%, which is the most common mutant gene; and the genes related to the Wnt pathway, such as CTNNB1 and AXIN1, also have relatively high mutation frequencies, indicating that TP53 inactivation and Wnt pathway aberration are important mechanisms for the development of HCC. As Figure 1 shown in b, the mutation frequencies of the mutant genes in EC are 20% to 57%; among them, the mutation frequency of TP53 reaches 36%. Different genes show different mutation patterns, highlighting the existence of multiple mutation mechanisms in cancer types.

[0069] Select genes with relatively high mutation frequencies for the next analysis. In the present invention, TP53 is selected for analysis, as shown in the appendix Figure 2As shown in a, the TP53 mutation frequencies of ten cancer types are presented, where MUT represents the mutant type and WT represents the wild type. The results show that ESCA and PAAD exhibit relatively high TP53 mutation rates, while PRAD shows a relatively low TP53 mutation rate. This indicates that TP53 mutations may play an important role in the occurrence and development of specific cancers.

[0070] S2. Select the TP53 mutant genes for analysis. Divide the tumor sample data into the TP53 mutant group and the TP53 wild-type group, and conduct differential analysis on the intratumoral microbiota between the TP53 mutant group and the TP53 wild-type group, specifically including:

[0071] S21. Calculate the α-diversity of the intratumoral microbiota of each tumor sample in the TP53 mutant group and the TP53 wild-type group, and evaluate the observed completeness of species in the tumor samples in combination with the Goods coverage rate; use statistical tests to compare the differences between the TP53 mutant group and the TP53 wild-type group.

[0072] S22. Calculate and compare the β-diversity of the intratumoral microbiota between the TP53 mutant group and the TP53 wild-type group, and perform PERMANOVA analysis using the adonis2 function to test the effect of TP53 mutations on the intratumoral microbiota community structure.

[0073] S23. Use LEfSe to analyze the differences in intratumoral microbiota of different cancer types between the TP53 mutant group and the TP53 wild-type group.

[0074] S24. Calculate the correlation between the intratumoral microbiota of the TP53 mutant group and the TP53 wild-type group of different cancer types using the Pearson correlation coefficient.

[0075] In step S21, the α-diversity includes the Shannon index, Simpson index, Pielou index, and species richness index. The species richness index includes one or more of the Richness index, Chao1 index, Ace index, and obs index. The species richness in this step can be the number of species at the genus level or the species level. The Pielou index is calculated based on the Shannon index and the Richness index.

[0076] Shannon index:

[0077] Simpson index:

[0078] Pielou index:

[0079] Where: i represents the i-th sample, S ijrepresents the number of species of the j-th type within the i-th sample, S i represents the total number of species within the i-th sample, which is also the Richness index; p ij is the relative abundance of the j-th species within the i-th sample, that is, the ratio of the number of species of the j-th type to the total number of species within this sample; H i ′ represents the Shannon index of the i-th sample; D i represents the Simpson index of the i-th sample; J i represents the Pielou index of the i-th sample.

[0080] Specifically, an R package for ecological and biodiversity analysis (vegan package) can be used. The diversity function is used to calculate the Shannon and Simpson diversity indices, which measure the richness and diversity of species in a community. The specnumber function is used to calculate the Richness index, that is, the number of different species in each sample; the estimateR function is used to estimate the Chao1 index and Ace index, which attempt to correct for unobserved species and estimate the actual number of species present in the community. The Pielou evenness index is calculated using the Shannon index and Richness index, which measures the evenness of species distribution. The Goods coverage rate is calculated, which estimates the proportion of species observed in the sample to the total number of species in the community and evaluates the observational integrity of species in the sample.

[0081] Statistical tests (Wilcoxon test) are used to compare the differences in these indices between the TP53 mutant group and the TP53 wild-type group. When p < 0.05, * is used to indicate its significance, when p < 0.01, ** is used to indicate its significance, when p < 0.001, *** is used to indicate its significance, and when p < 0.00001, **** is used to indicate its significance. Heatmaps of each index of the intratumoral microbial α-diversity in the TP53 mutant group and the TP53 wild-type group for the corresponding ten cancer types are drawn (as shown in b-k of Figure 2 , and the specific significance is marked. It can be found that there are significant differences in cancer types such as sub-EC, HCC, and LUAD. Among them, box plots of each index of the intratumoral microbial α-diversity in the HCC and EC cancer types are as shown in Figure 3 .

[0082] Analysis of intratumoral microbial α-diversity shows that: in HCC, compared with the wild-type group, the Shannon index and Simpson index in the TP53 mutant group are significantly reduced ( Figure 3.a); In EC, compared with the wild-type group, the Richness index and Chao1 index of the TP53 mutant group were significantly reduced, which also indicated a decrease in community diversity ( Figure 3 .b).

[0083] In step S22, the Bray-Curtis distance can be used to calculate the β-diversity of the intratumoral microorganisms in the TP53 mutant group and the TP53 wild-type group respectively; the Bray-Curtis distance of the TP53 mutant group and the TP53 wild-type group is visualized in three-dimensional space by PCoA.

[0084] Specifically, analyze the β-diversity of the intratumoral microorganisms in ten cancer types, that is, the relationship between the bacterial community data and the TP53 mutant group and the TP53 wild-type group, and display this relationship through PCoA and PERMANOVA analysis and visualization means. The specific method is to use the vegdist function to calculate the Bray-Curtis distance matrix between the bacterial data of the TP53 mutant group and the TP53 wild-type group, apply the cmdscale function to perform principal coordinate analysis (PCoA) on the distance matrix to visualize the similarity between samples in three-dimensional space, and use the adonis2 function (from the vegan package) to perform permutational multivariate analysis of variance (PERMANOVA) to test the effect of TP53 mutation on the intratumoral microbial community structure, and output the corresponding analysis results, including R 2 value and P value.

[0085] Appendix Figure 4 is the PCoA plot of the Bray-Curtis dissimilarity matrix for each cancer type, where a, b, c, d, e, f, g, h, i, j, k are ESCA, PAAD, COAD, LUAD, BLCA, STAD, PRAD, BRCA, HCC, EC respectively. Figure 5 is the box plot of the Bray-Curtis dissimilarity matrix for the HCC and EC cancer types. The results showed that: in HCC, compared with the wild-type group, the dissimilarity of the TP53 mutant group was lower ( Figure 5 .a); while in EC, compared with the wild-type group, the dissimilarity of the TP53 mutant group was higher ( Figure 5 .b). By comparison, it was found that there were significant differences in both α and β diversities between HCC and EC, and there were significant differences in the intratumoral microbiota between the TP53 mutant group and the wild-type group. Next, focus on studying these two cancer types.

[0086] S23. Analyze the differences in intratumoral microorganisms between TP53 mutant groups and TP53 wild-type groups in different cancer types using LEfSe. Specifically, first use the ruskal-Wallis test to identify significant differences between the TP53 mutant group and the TP53 wild-type group, and use a linear discriminant analysis (LDA) score of 2 as the threshold to evaluate the impact size of differential microorganisms; draw a bar chart of the LEfSe results to show the intratumoral microorganisms with significant differences and their LDA Score.

[0087] As Figure 6 shown, it is a bar chart of the LDA Score of the specific impact of TP53 mutation on the intratumoral microbial community in HCC and EC patients. Figure 6 .a shows that the differential microorganisms in HCC: Burkholderia are more abundant in the TP53 mutant group, while Lactobacillus, Mycobacterium, Bacteroides, Streptomyces, and Proteobacteria are more abundant in the wild-type group. Figure 6 .b shows that the differential microorganisms in EC: the TP53 mutant group is rich in Xanthomonas, Neisseria, Lactobacillus, Staphylococcus, Bordetella, and Shigella, while in the TP53 wild-type group, the relative abundances of Streptococcus, Escherichia, Terrabacter, and Aeromonas increase ( Figure 6 .b).

[0088] In summary, changes in the microbial community may be involved in the occurrence and development of tumors, and TP53 gene mutation is one of the important regulatory factors in this process. It is necessary to further study the complex mechanism between the TP53 gene and the tumor microecology to provide new targets and strategies for cancer prevention and treatment.

[0089] S24. Calculate the correlations between intratumoral microorganisms in the TP53 mutant groups and TP53 wild-type groups of different cancer types using the Pearson correlation coefficient, and draw the corresponding intratumoral microorganism network diagram.

[0090] Specifically, use the Pearson correlation coefficient to calculate the correlations between intratumoral microorganisms in the TP53 mutant groups and TP53 wild-type groups of HCC and EC, and draw the corresponding intratumoral microorganism network diagram, as Figure 7As shown, the results show that there are more nodes in the mutant group in both HCC and EC cancer types, and the interaction between bacteria is relatively more complex. The correlations between the microbiota in the TP53 mutant group and the wild-type group in both cancer types are mostly positive correlations.

[0091] The diagrams showing the number of samples in the TP53 mutant group and the TP53 wild-type group for both EC and HCC cancer types are as Figure 8 shown in a and b.

[0092] Step S3: Compare the relationships between different cancer types in terms of genome, transcriptome, and intratumoral microbiota. Specifically, the relationships between HCC and EC cancer types in terms of genome, transcriptome, and intratumoral microbiota can be compared. The specific steps include:

[0093] S31: Compare the α-diversity and β-diversity of intratumoral microbiota in different cancer types;

[0094] S32: Compare the differences in stromal scores, immune scores, and tumor purity among different cancer types;

[0095] S33: Use the TIDE algorithm to predict the immune therapy response of patients, calculate the TIDE score, and visualize it;

[0096] S34: Compare the β-diversity of all immune cells and immune gene checkpoints among different cancer types;

[0097] S35: Analyze the difference in TMB between the TP53 mutant group and the wild-type group in different cancer types;

[0098] S36: Analyze the difference in the abundance of six stromal cell types between the TP53 mutant group and the wild-type group in different cancer types;

[0099] S37: Use LEfSe to analyze the differences in specific genus-level bacteria among different cancer types in the TP53 grouping, obtain the abundance data of genus-level bacteria with differences, perform Cox proportional hazards analysis based on the data, generate the survival curve of tumor patients, and perform correlation analysis between intratumoral microbiota and stromal cells using the Spearman correlation coefficient.

[0100] In step S31, first, screen out the relative abundances of intratumoral microbiota at the genus level for both HCC and EC cancer types, and calculate their relative abundances; then screen out the top five genera in terms of relative abundance at the genus level, and represent other genera as other; draw a stacked bar chart of the relative abundances of these top five genera in different cancer types ( Figure 8 .c and Figure 8 .d).

[0101] Calculate the top four specific phyla of the microorganisms within the tumors of these two cancer types. Use "other" to represent all the other phyla except the top four. Compare the differences in the microorganisms within the tumors of these two cancer types at the phylum level. Use a statistical test (Wilcoxon test) to compare the differences in the microorganisms within the tumors of the two cancer types at the phylum level. When p < 0.05, represent its significance with *; when p < 0.01, represent its significance with **; when p < 0.001, represent its significance with ***; when p < 0.00001, represent its significance with ****. As Figure 8 .e shows, it is a radar chart of the differences in the microorganisms within the tumors of HCC and EC at the phylum level. The differences between the specific phyla of different cancer types are marked with *.

[0102] Compare the α-diversity and β-diversity of bacteria in these two cancer types, HCC and EC. The results show that there are significant differences in their diversities ( Figure 8 .f and Figure 8 .g).

[0103] Step S32: Compare the differences in the stromal score, immune score, and tumor purity of cancer types, specifically including: First, screen out the gene sets related to stromal cells and immune cells from public databases (such as TCGA). These gene sets are obtained through bioinformatics analysis, literature investigation, experimental verification, etc.; then, for each tumor sample, calculate the expression levels of each gene in these gene sets, and transform these expression levels into stromal scores and immune scores through the ssGSEA method; finally, add the stromal score and immune score, and use the cosine function to obtain an estimated score for inferring tumor purity. The abundances of stromal cells and immune cells are calculated for immune cells through methods such as TIMER, CIBERSORT, CIBERSORT.ABS, MCPCOUNTER, XCELL, and EPIC. The flowchart of obtaining these data is Figure 9 .a.

[0104] Compare the differences in the stromal score, immune score, and tumor purity of HCC and EC, and draw the corresponding box plots ( Figure 9 .b and Figure 9 .c and Figure 9 .d). The results show that the stromal score and immune score of HCC are significantly higher than those of EC, but the tumor purity of EC is significantly higher than that of HCC. Using immunotherapy strategies to treat cancer has extremely high clinical value, but only a part of the patients are responders to immunotherapy.

[0105] In step S33, the TIDE algorithm was used to predict the immunotherapy response of patients. After calculating the TIDE score using the corresponding data, the scores of two cancer types, HCC and EC, were visualized using a violin plot, and the significant difference was calculated ( Figure 9 .e). The results showed that EC was significantly higher than HCC, indicating that the response of EC patients to immunotherapy was worse than that of the HCC patient group; this shows that relatively speaking, the immunotherapy response effect of HCC may be better.

[0106] In step S34, the beta diversity of all immune cells and immune gene checkpoints of different cancer types was compared. For example, the beta diversity of all immune cells and immune gene checkpoints of two cancer types, HCC and EC, was compared, and a significant difference was found between the two cancer types ( Figure 9 .f and Figure 9 .g).

[0107] Next, the differences between all immune cells (All Immune Cells) and immune checkpoint genes (ImmuneCheckpoint Genes) were compared, as shown in Figure 9 .h and Figure 9 .i respectively. Each row represents a cell and each column represents a sample. The specific calculation method of each immune cell was also marked, and the difference between each cell was calculated. It was found that there were significant differences in most cells between HCC and EC.

[0108] In step S35, the difference in TMB between the TP53 mutant group and the wild-type group of different cancer types was analyzed.

[0109] As shown in Figure 10 , the difference in TMB between the TP53 mutant group and the TP53 wild-type group of HCC ( Figure 10 .a) and EC ( Figure 10 .b) was analyzed. The results showed that the TMB value of the TP53 mutant group was significantly higher than that of the TP53 wild-type group.

[0110] S36: Analyze the difference in the abundance of six stromal cells between the TP53 mutant group and the wild-type group of different cancer types.

[0111] As shown in Figure 11 , the difference in the abundance of six stromal cells between the TP53 mutant group and the TP53 wild-type group of HCC ( Figure 11 .a) and EC ( Figure 11 .b) was analyzed. The results showed a significant difference, indicating that TP53 mutation may lead to a TME with a higher degree of mesenchymal and angiogenesis inhibition. Specifically, in HCC, compared with the TP53 wild-type group, the abundances of endothelial cells and cancer-associated fibroblasts in the TP53 mutant group were significantly reduced ( Figure 12.b), similar results were detected in EC ( Figure 12 .a). In summary, TP53 gene mutations may increase TMB and lead to a shift in the TME towards increased anti-angiogenesis and mesenchymal characteristics, thereby affecting the progression and treatment outcomes of HCC and EC. This provides important clues for further studying the role of TP53 in tumor development and immune regulation.

[0112] S37. For the significantly different intratumoral microorganisms identified by LEfSe, Cox proportional hazards analysis was performed, and survival curves of tumor patients were generated. The Spearman correlation coefficient was used to analyze the correlation between intratumoral microorganisms and stromal cells.

[0113] Specifically, Cox proportional hazards analysis was performed on the significantly different microorganisms identified by LEfSe; subsequently, we generated survival curves for EC patients ( Figure 13 .a). Using the median risk score as the cut-off value, individuals were divided into high-risk and low-risk subgroups. The results showed a statistically significant difference in overall survival between the two risk groups in EC, highlighting the prognostic value of these intratumoral microbial characteristics. The survival time can be affected by adjusting the content of microorganisms.

[0114] We found that the mutation status of TP53 was related to the diversity of intratumoral microorganisms and stromal cells in the TME. Therefore, we used the Spearman correlation coefficient to analyze the correlation between the intratumoral microbiota and stromal cells. The results revealed a significant association between specific intratumoral microorganisms and stromal cells ( Figure 13 .b). In addition, we found that the intratumoral microorganisms significantly correlated with cancer-associated fibroblasts included Yersinia, Nocardioides, Klebsiella, Pantoea, and Roseomonas. This indicates that these intratumoral microorganisms may play an important role in regulating the TME. In addition, we observed some potential links between TP53 mutations and these two factors, intratumoral microorganisms and the TME ( Figure 13 .c, d, e). Specifically, it can be said that microorganisms affect TP53 mutations, and TP53 mutations in turn affect the TME and thus affect the occurrence and development of tumors.

[0115] Those skilled in the art should understand that the above specific embodiments are merely examples and not limitations. Various modifications, combinations, partial combinations, and substitutions of the embodiments of the present invention can be made according to design requirements and other factors, as long as they are within the scope of the appended claims or their equivalents, that is, within the scope of the rights to be protected by the present invention.

Claims

1. A method for revealing the biological connection between TP53 mutations and intratumoral microorganisms and TME, characterized in that: The following steps are involved: S1. Obtaining tumor sample data, and calculating the mutation frequency of each mutant gene based on the tumor sample data; S2. Select TP53 mutant genes for analysis, divide tumor sample data into TP53 mutant group and TP53 wild-type group, and perform differential analysis on intratumor microorganisms between TP53 mutant group and TP53 wild-type group; S3. Compare the relationships among the genome, transcriptome, and intratumor microorganisms of different cancer types.

2. The method of revealing the biological connection between TP53 mutations and intratumoral microorganisms and TME according to claim 1, characterized in that In step S2, differential analysis of intratumor microorganisms between the TP53 mutant group and the TP53 wild-type group is performed, including: S21. Calculate the α diversity of intratumoral microorganisms in each tumor sample of the TP53 mutant group and the TP53 wild-type group, and evaluate the observational completeness of species in the tumor samples in combination with Goods coverage; use statistical tests to compare the differences between the TP53 mutant group and the TP53 wild-type group; S22. Calculate and compare the β-diversity of intratumoral microorganisms in the TP53 mutant group and the TP53 wild-type group, and perform PERMANOVA analysis using the adonis2 function to examine the effect of TP53 mutation on the structure of the intratumoral microbial community. S23. LEfSe was used to analyze the differences in intratumoral microorganisms in TP53 mutant and TP53 wild-type groups of different cancer types. S24. Use the Pearson correlation coefficient to calculate the correlation between the intratumor microorganisms of the TP53 mutation group and the TP53 wild-type group in different cancer types.

3. The method of revealing the biological connection between TP53 mutations and intratumoral microorganisms and TME according to claim 2, characterized in that: The alpha diversity in step S21 includes one or more of the Shannon index, Simpson index, Pielou uniformity, Richness index, Chao1 index, Ace index, and obs index, and the Pielou uniformity is calculated based on the Shannon index and the Richness index; Shannon Index: Simpson Index: Pielou Index: in: i represents the i-th sample, S ij represents the number of species of the jth species in the i-th sample, S i represents the total number of species in the i-th sample, p ij is the relative abundance of the jth species in the i-th sample, that is, the ratio of the number of species of the j-th species to the total number of species in the sample; H i ′ represents the Shannon index of the i-th sample; D i represents the Simpson index of the i-th sample; J i Represents the Pielou index of the i-th sample.

4. The method of revealing the biological connection between TP53 mutations and intratumoral microorganisms and TME according to claim 2, characterized in that In step S22, the beta diversity of intratumor microorganisms in the TP53 mutant group and the TP53 wild-type group is calculated and compared, including: The Bray-Curtis distance was used to calculate the beta diversity of intratumoral microorganisms in the TP53 mutant group and the TP53 wild-type group; The Bray-Curtis distances of the TP53 mutant group and the TP53 wild-type group were visualized in three-dimensional space by PCoA.

5. The method of revealing the biological connection between TP53 mutations and intratumoral microorganisms and TME according to claim 1, characterized in that The different cancer types in step S3 are EC and HCC.

6. The method of revealing the biological connection between TP53 mutations and intratumoral microorganisms and TME according to claim 1, characterized in that Step S3 includes: S31. Compare the α-diversity and β-diversity of microorganisms in tumors of different cancer types; S32. Compare the differences in stromal scores, immune scores, and tumor purity among different cancer types; S33. Use the TIDE algorithm to predict patients’ immunotherapy responses, calculate TIDE scores, and visualize them. S34. Comparison of the beta diversity of all immune cells and immune gene checkpoints in different cancer types; S35. Analyze the difference in TMB between TP53 mutant and wild-type groups in different cancer types; S36, analyze the differences in the abundance of six stromal cell types between TP53 mutant and wild-type groups in different cancer types; S37. Based on the significantly different intratumoral microorganisms identified by LEfSe, Cox proportional hazard analysis was performed and survival curves of tumor patients were generated. The Spearman correlation coefficient was used to perform correlation analysis between intratumoral microorganisms and stromal cells.

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