A microbial marker composition for predicting the prognosis of hepatocellular carcinoma and its application

Abstract

The present invention relates to the field of medical testing. Specifically, the present invention provides a microbial marker composition for predicting the prognosis of hepatocellular carcinoma and its application. The present invention determines the correlation between specific microorganisms and clinical outcomes of HCC patients by utilizing advanced bioinformatics analysis, including linear discriminant analysis effect size and Cox proportional hazard regression, and further explores the relationship between microbial profiles and gene expression in tumor tissue to clarify the potential pathways by which microorganisms may affect HCC progression. The development of microbial prognostic feature models using these analyses aims to improve the prediction of patient survival. The present invention also explores the impact of HCC tumor-associated microbial communities on immune status, which can provide information for the formulation of immunotherapy strategies. The present invention enhances the understanding of the HCC tumor microenvironment by comprehensively analyzing the microbial properties and prognosis in HCC, thereby promoting the formulation of more precise and personalized cancer management strategies.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a microbial marker composition for predicting the prognosis of hepatocellular carcinoma and its application. Background Art

[0002] Hepatocellular carcinoma (HCC) is one of the leading malignancies worldwide and the leading cause of cancer-related death. As the third leading cause of cancer death worldwide, HCC has a low 5-year relative survival rate of approximately 18%. Despite significant advances in treatment options such as surgery, radiotherapy, chemotherapy, immunotherapy, and targeted therapies, the long-term prognosis for HCC patients remains dismal, primarily due to the high rates of metastasis and recurrence. Recent studies have highlighted the predominance of bacteria as the dominant microbiome in the human gastrointestinal tract, elucidating their critical role in regulating the liver microenvironment. This regulation plays a crucial role in the prevention, diagnosis, and treatment of liver cancer. Furthermore, clinical trials have provided compelling evidence that short-term probiotic supplementation can effectively improve markers of liver injury by restoring the gut microbiota. Recent evidence also suggests that the microbiome of the liver cancer microenvironment can serve as an independent biomarker for predicting postoperative outcomes in patients with liver cancer. However, limited research has been conducted on the microbiome within HCC, leaving this area of research urgently awaiting exploration. Summary of the Invention

[0003] Purpose of the invention: In order to solve the problems existing in the above-mentioned prior art, the present invention provides a microbial marker composition for predicting the prognosis of hepatocellular carcinoma and its application.

[0004] Technical solution: The present invention provides a microbial marker composition for predicting the prognosis of hepatocellular carcinoma, wherein the microbial marker composition includes: Candidatus_Glomeribacter, Sicinivirus, Shuttleworthia, Leucothrix, Schleiferia, Dichelobacter, Salinimicrobium, T4likevirus, Ictalurivirus, Sphaerotilus, Kitasatospora, and Succinimonas.

[0005] The present invention also provides a detection reagent, which takes the abundance of the above microbial markers as the detection purpose.

[0006] The present invention also provides a detection product, comprising the above detection reagent and an acceptable auxiliary agent, carrier or device.

[0007] The present invention also provides an evaluation method for predicting the prognosis of hepatocellular carcinoma. The abundance of the above-mentioned microorganisms is weighted and summed to construct a risk model to obtain a risk score; then the median of the risk model is used as the critical value. If the risk score is greater than or equal to the critical value, it is considered high risk, otherwise it is considered low risk.

[0008] Furthermore, the risk model is expressed as:

[0009] RiskScore=Candidatus_Glomeribacter*(-0.1772)+Sicinivirus*(-0.1386)+Shuttleworthia*(-0.1212)+Leucothrix*(-0.0817)+Schleiferia*(-0.0791)+Dichelobac ter*(-0.0460)+Salinimicrobium*(-0.0334)+T4likevirus*(-0.0332)+Ictalurivirus*(-0.0035)+Sphaerotilus*0.0003+Kitasatospora*0.0269+Succinimonas*0.0412

[0010] Among them, RiskScore is the risk score.

[0011] The present invention also provides a system for evaluating a method for predicting the prognosis of hepatocellular carcinoma, comprising a data collection module, a risk assessment module, and an output module;

[0012] The data collection module is used to collect samples from patients and measure the abundance of each microorganism in the sample, wherein the microorganism is a microorganism in the aforementioned microbial marker combination;

[0013] The risk assessment module is used to calculate the risk score according to the risk model in claim 5;

[0014] The output module is used to output risk scores.

[0015] Beneficial effects: By utilizing advanced bioinformatics analyses, including linear discriminant analysis effect size (LEfSe) and Cox proportional hazard regression, the present invention determined the correlation between specific microorganisms and clinical outcomes of HCC patients, and further explored the relationship between microbial profiles and gene expression in tumor tissues to elucidate potential pathways by which microorganisms may affect HCC progression. In addition, the development of a microbial prognostic signature model based on these analyses aims to improve the prediction of patient survival and divide patients into different prognostic categories. This model combines microbial data with traditional clinical parameters to provide a more comprehensive prognostic assessment, which can more effectively formulate treatment strategies. In addition, the present invention also explores the impact of HCC tumor-associated microbiota on immune status, which can further provide information for the formulation of immunotherapy strategies. By comprehensively analyzing the microbial properties in HCC and its prognosis, the understanding of the HCC tumor microenvironment is enhanced, thereby promoting the formulation of more precise and personalized cancer management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Figure 1 shows the distribution of the kingdom, phylum, class, order, family, and genus levels in HCC samples and adjacent adjacent samples. Figure A shows the distribution of samples at the kingdom level, Figure B shows the distribution of samples at the phylum level, Figure C shows the distribution of samples at the class level, Figure D shows the distribution of samples at the order level, Figure E shows the distribution of samples at the family level, and Figure F shows the distribution of samples at the genus level.

[0017] Figure 2 Figures 1 and 2 show the results of LEfSe analysis, where Figure A shows the statistical display of the characteristic microbial LDAscore of the LEfSe analysis results, and Figure B shows the evolutionary branch diagram of the microorganisms analyzed by LEfSe;

[0018] Figure 3 Figure A is a volcano plot of the prognostic abundance of differential microbial markers and a forest plot of the univariate Cox regression results; Figure A is a volcano plot of the prognostic abundance of differential microbial markers, and Figure B is a forest plot of the univariate Cox regression results;

[0019] Figure 4 Figure 1 is a survival curve diagram of hepatocellular carcinoma microbial markers with significant P values, wherein Figure A is a survival curve diagram of the microbial marker g_Dichelobacter; Figure B is a survival curve diagram of the microbial marker g_Sicinivirus; Figure C is a survival curve diagram of the microbial marker g_Microvirgula; Figure D is a survival curve diagram of the microbial marker g_Trabulsiella; Figure E is a survival curve diagram of the microbial marker g_Methylophaga; and Figure F is a survival curve diagram of the microbial marker g_Candidatus_Glomeribacter.

[0020] Figure 5 Figure 1. Diagram of the process of constructing a risk scoring model using LASSO regression. Figure A shows the dynamic process of LASSO variable selection, and Figure B shows the process of selecting different combinations of the cross-validation parameter λ.

[0021] Figure 6 Figure A is the survival curve and ROC curve of the high-risk and low-risk groups in the training set, and Figure B is the ROC curve of the high-risk and low-risk groups in the training set.

[0022] Figure 7 Survival curves and ROC curves of the high- and low-risk groups in the validation set; Figure A is the survival curve of the high- and low-risk groups in the validation set, and Figure B is the ROC curve of the high- and low-risk groups in the validation set;

[0023] Figure 8 Figure A is the clinical nomogram of the training set, Figure B is the clinical nomogram of the validation set, Figure C is the ROC curve of the clinical nomogram of the training set; Figure D is the ROC curve of the clinical nomogram of the validation set;

[0024] Figure 9 The C-Index diagrams of clinical nomograms and Risk scores in the training and validation sets are shown;

[0025] Figure 10 Performance prediction graph for validating the risk scoring model using an external validation dataset;

[0026] Figure 11 A graph is presented for the correlation between risk score and clinical characteristics;

[0027] Figure 12 A graph showing the correlation between hepatocellular carcinoma gene expression and the signature model including microbial species abundance;

[0028] Figure 13 Figure 1 is a functional enrichment analysis diagram of significantly correlated genes; Figure A is a biological process diagram of GO enrichment analysis; Figure B is a cellular component diagram of GO enrichment analysis; Figure C is a molecular function diagram of GO enrichment analysis; Figure D is a KEGG enrichment analysis diagram;

[0029] Figure 14 Figure 2 is the difference in the enrichment scores of immune infiltrating cells between high-risk and low-risk groups;

[0030] Figure 15 This is a graph showing the differences in scores related to 13 immune functions between high-risk and low-risk groups;

[0031] Figure 16 This is a graph showing the difference in TIDE between high-risk and low-risk groups;

[0032] Figure 17Figure 2 shows the differences in immune checkpoint expression between high-risk and low-risk groups. DETAILED DESCRIPTION

[0033] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0034] Studies have shown that the microbiome in the liver cancer microenvironment can serve as an independent biomarker for predicting postoperative outcomes in patients with liver cancer. Notably, specific bacteria with known anti-tumor properties were significantly reduced in tumor tissue compared with adjacent non-tumor samples, and these differences were positively correlated with prognostic outcomes. The HCC intratumoral microbiome has emerged as a key factor in regulating HCC tumor behavior and host immune responses, providing new opportunities for prognostic and therapeutic intervention.

[0035] This example utilizes a large amount of data obtained from the Cancer Genome Atlas-Hepatocellular Carcinoma (TCGA-LIHC) project, which includes microbial abundance, clinical parameters, gene expression, survival indicators, and other phenotypic data. By using advanced bioinformatics analyses, including linear discriminant analysis effect size (LEfSe) and Cox proportional hazards regression, this example identifies the correlation between specific microorganisms and clinical outcomes in HCC patients and further explores the relationship between microbial profiles and gene expression within tumor tissue to elucidate potential pathways by which microorganisms may influence HCC progression. In addition, based on these analyses, a microbial prognostic signature model was developed to improve the prediction of patient survival and stratify patients into different prognostic categories. This model combines microbial data with traditional clinical parameters to provide a more comprehensive prognostic assessment, which can more effectively formulate treatment strategies. This example also explores the impact of HCC tumor-associated microbiota on immune status, which can further provide information for the formulation of immunotherapy strategies. By comprehensively analyzing the microbial properties and their prognostic effects in HCC, we have enhanced our understanding of the HCC tumor microenvironment, thereby promoting the development of more precise and personalized cancer management strategies.

[0036] This embodiment specifically includes the following steps:

[0037] 1. Obtain relevant samples:

[0038] Download TCGA hepatocellular carcinoma microbiome abundance data:

[0039] http: / / ftp.microbio.me / pub / cancer_microbiome_analysis / TCGA / Kraken / Krake n-TCGA-Voom-SNM-Plate-Center-Filtering-Data.csv;

[0040] Download TCGA hepatocellular carcinoma microbiome clinical data:

[0041] http: / / ftp.microbio.me / pub / cancer_microbiome_analysis / TCGA / Kraken / Metad ata-TCGA-Kraken-17625-Samples.csv;

[0042] The clinical data of TCGA hepatocellular carcinoma samples are shown in Table 1:

[0043] Table 1

[0044]

[0045]

[0046] Download TCGA hepatocellular carcinoma patient survival data:

[0047] https: / / tcga-xena-hub.s3.us-east-1.amazonaws.com / download / survival%2FLI HC_survival.txt;

[0048] Download TCGA hepatocellular carcinoma sample phenotype data:

[0049] https: / / tcga-xena-hub.s3.us-east-1.amazonaws.com / download / TCGA.LIHC.sampleMap%2FLIHC_clinicalMatrix.

[0050] Microbial community map display: Information related to the microbial abundance of each sample is obtained from the microbiome sample, and then the average population abundance is displayed based on the kingdom, phylum, class, order, family, and genus levels. Due to the large number of species within the phylum, class, order, family, and genus, the top 20 abundances in each level are taken to draw a box plot for display.

[0051] In this example, a microbiome map was displayed based on the sample microbiome information, and 420 HCC cancer samples and 104 adjacent cancer samples were obtained. Then, based on the kingdom, phylum, class, order, family, and genus levels, the average values were taken for merging, and the top 20 were selected for display. Figure 1 As shown in the figure, the proportions of each grade are evenly distributed in HCC cancer samples and adjacent adjacent samples.

[0052] LEfSe specific microbial maker identification: LEfSe analysis was performed based on cancer samples and adjacent adjacent samples using the R package microeco. Using alpha = 0.05 (significance threshold) as the threshold, cancer-related characteristic microbial markers were obtained. An LDA statistical graph of the characteristic microbial markers was then drawn based on the top 30 LDA values. An evolutionary branch diagram was then drawn based on the top 100 bacterial communities with the highest richness.

[0053] This example uses LEfSe analysis based on cancer samples and adjacent paracancerous samples. The results show that there are 581 microbial markers with significant differences between cancer samples and adjacent paracancerous samples, such as Figure 2 As shown in Figure A, 218 of them belong to the microbial markers with higher LDA in the hepatocellular carcinoma group, and the remaining 363 belong to the microbial markers with higher LDA in the para-cancer group. Figure 2 As shown in Figure B, most bacteria from the Bacteroidetes, Firmicutes, and Euryarchaeota phyla played a significant role in the tumor group, while most bacteria from the Spirochaetes and Acidobacteria phyla played a significant role in the adjacent normal tissue group. The 581 differential microbial markers were analyzed at the kingdom, phylum, class, order, family, and genus levels. After integration, a total of 381 differentially expressed microorganisms were identified at the genus level.

[0054] Construction and validation of the microbial prognostic feature model: The TCGA hepatocellular carcinoma samples were divided into 5:5 ratios, with 50% of the data used for modeling (training set) and 50% for validation (validation set). Furthermore, combined with the survival period data, the abundance values of the characteristic microbial markers of the training set samples were subjected to batch Cox univariate regression analysis using the R packages survival (v3.2-7) and survminer (v0.4.8). After the regression analysis, characteristic microbial markers significantly correlated with the survival period were screened with a threshold of P < 0.05 for subsequent analysis.

[0055] This example uses univariate Cox regression analysis based on the abundance of 381 genus-level differential microorganisms and combined with the data of overall survival to screen for microbial markers that are significantly correlated with survival; Figure 3 As shown in Figure A, a total of 62 microbial markers significantly associated with prognosis were obtained, such as Figure 3 As shown in Figure B, HR greater than 1.0 indicates that the increase in its abundance may cause poor clinical prognosis in patients with hepatocellular carcinoma, and HR less than 1.0 indicates that the increase in its abundance may improve the clinical prognosis of patients with hepatocellular carcinoma. The survival curve of the microbial markers is further displayed, as shown in Figure 2. Figure 4 As shown, the significant prognostic differences between high and low abundance of microbial markers indicate a unique clinical value for the prognosis of patients with hepatocellular carcinoma.

[0056] LASSOCox regression analysis: We further performed lasso regression dimensionality reduction on the prognostic-related characteristic microbial markers and constructed a risk score model. This process primarily relied on the R package glmnet (v4.0-2). To build a more accurate regression model, we first used cross-validation to filter lambda values. We then selected the model corresponding to lambda.min. We then extracted the abundance matrix of the relevant characteristic microbial markers in the model and calculated the risk score for each sample using the following formula:

[0057]

[0058] Where abundance represents the abundance of the corresponding microbial marker, β represents the regression coefficient (coef) of the corresponding marker in the lasso regression result, RScore represents the sum of the abundance of the significantly correlated marker in each sample multiplied by the coef of the corresponding marker, i represents the sample, and j represents the microbial marker.

[0059] In this example, microbial markers were incorporated into LASSOCox regression to establish a prognostic model to calculate the risk score. Specifically, LASSO regression was used to reduce the dimension of the univariate Cox regression results to obtain 12 microbial markers, such as Figure 5 The risk scoring model was constructed as shown, namely: Risk Score = Candidatus_Glomeribacter abundance*(-0.1772)+Sicinivirus*(-0.1386)+Shuttleworthia*(-0.1212)+Leucothrix*(-0.0817)+Schleiferia*(-0.0791)+Dichelobacter*(-0.0460)+Salinimicrobium*(-0.0334)+T4likevirus*(-0.0332)+Ictalurivirus*(-0.0035)+Sphaerotilus*0.0003+Kitasatospora*0.0269+Succinimonas*0.0412.

[0060] In order to verify the effectiveness of the model, based on the risk scores of the training set samples, the median was used as the node to divide the high-risk and low-risk groups. The survival curve was drawn in combination with the survival period data, and the P value was calculated. If the P-value < 0.05, it was judged that the difference between the high-risk and low-risk groups was significant. Then, the sample risk score was further used as the model prediction result, and the AUC value of the model was calculated in combination with the survival data, and the ROC curve was drawn. Figure 6The survival curves of the two groups shown in Figure A were significantly different (P<0.0001). Figure 6 As shown in Figure B, the sample risk score is used as the prediction result of the risk prognosis model, and the 3-year, 4-year, and 5-year AUC values of the model calculated in combination with survival data are all greater than 0.6, indicating that the model has good performance.

[0061] The model performance was further verified in the validation set. The sample risk score was calculated according to the formula. Based on the sample risk score, the high and low risk groups were divided with the median as the node, such as Figure 7 As shown in A in Figure 1, the survival curve was drawn by combining the overall survival data. The difference between the two groups was significant (P = 0.031). Figure 7 As shown in Figure B, the sample risk score is used as the model prediction result, and the AUC of the model calculated based on the survival data for 3 years, 4 years, and 5 years are all greater than 0.6, indicating that the model has good performance.

[0062] A clinical nomogram was constructed to guide clinical decision-making. The probability of patient outcomes was calculated based on the scoring indicators. The nomogram was drawn mainly using the R packages rms (v6.1-0) and survival. First, the cox proportional hazard regression model was constructed using the cph() function in combination with age, grade, and clinical_stage. Then, the survival() function was used to calculate the survival probability. Finally, the nomogram object was constructed using the nomogram() function and displayed using plot(). In addition, the C-index of the training and validation set data was calculated, and statistical graphs were drawn for display.

[0063] Risk score, age, gender, grade, and stage in the training and validation data were used to draw a nomogram to guide clinical decision-making, and a ROC curve based on the nomogram was also drawn. Figure 8 As shown in Figure 2, the AUCs for 3, 4, and 5 years are all greater than 0.65 for both the training set and the validation set, indicating that the model is effective. Finally, the C-Index (C index) of the nomogram and Risk score in the training set and the validation set were counted. Figure 9 As shown in the figure, the C-Index of both models is greater than 0.5, indicating positive predictive efficacy. However, the C-Index of the nomogram in the training set is slightly lower than the Risk score, while the C-Index of the nomogram in the validation set is significantly higher than the Risk score. The prognostic efficacy of the validation set nomogram is significantly better than that of the single Riskscore, indicating that the prediction of the multifactor clinical nomogram has a certain clinical guiding significance for the screening and treatment of high-risk groups for hepatocellular carcinoma.

[0064] In order to evaluate the predictive performance of the risk scoring model, this example obtained an external validation dataset (PRJNA714196) from the NCBI-SRA database and performed ROC analysis using selected overlapping microorganisms, as shown in Figure 2. Figure 10 As shown in Figure 3, the AUC of the external validation set PRJNA714196 was 0.68, so the results of external validation showed that the risk score model constructed by 12 microbial markers had good performance.

[0065] The feature model of this embodiment is related to the patient's prognosis and clinical characteristics: This embodiment combines the validation set and training set data, and counts the differences in risk scores among age, gender, grade, pathologic_M, pathologic_N, pathologic_T, and stage groups in the overall data. Figure 11 As shown in the figure, there were significant differences in risk scores among age, gender, pathologic_N, pathologic_T, and stage groups.

[0066] Correlation between hepatocellular carcinoma gene expression and the abundance of microbial species included in the characteristic model: This example statistically analyzed the correlation between the abundance of microbial markers in the model and the expression of protein-coding genes in the overall sample. First, 19,597 protein-coding genes were obtained. Then, based on the correlation threshold, 50 groups with significant correlations were obtained, including 49 protein-coding genes. Figure 12 A correlation heat map is given. In the model, Salinimicrobium, Candidatus_Glomeribacter, and Succinimonas have a higher proportion of positive correlation with related genes, while the remaining other microorganisms have a higher proportion of negative correlation with related genes.

[0067] Functional enrichment analysis of hepatocellular carcinoma genes: This example performs functional enrichment analysis on 49 protein-coding genes. GO enrichment analysis is divided into three parts: biological process (BP), cellular component (CC), and molecular function (MF). Figure 13As shown in the figure, for genes, the BP-enriched pathways are mainly nucleotide metabolic process, nucleosidephosphate metabolic process, purine ribonucleotide metabolic process, etc.; the CC-enriched pathways are mainly nucleoid, mitochondrial nucleoid, etc.; the MF-enriched pathways are mainly lyase activity, carbon-oxygen lyase activity, etc.; the KEGG-enriched pathways are mainly HIF-1signalingpathway, Glycolysis / Gluconeogenesis, Carbon metabolism, etc.

[0068] Demonstration of immune cell infiltration and immune function differences: This example shows the GSVA calculation results of immune cells in high-risk and low-risk groups. Figure 14 As shown ( Figure 14 * indicates P < 0.05; ** indicates P < 0.01). There were differences in the proportions of the four immune infiltrating cells between the high-risk and low-risk groups. The proportion of Plasmacytoid dendritic cells in the high-risk group was significantly higher than that in the low-risk group. The proportions of the remaining activated CD8 T cells, Eosinophils, and Gamma delta T cells in the low-risk group were significantly higher than those in the high-risk group. Further statistical analysis of the differences in 13 immune function-related scores between the high-risk and low-risk groups was performed, as shown in Figure 3. Figure 15 As shown ( Figure 15 * indicates P < 0.05; ** indicates P < 0.01). Among them, three immune function-related scores showed significant differences between the high-risk and low-risk groups, including cytolytic activity, inflammation-promoting, and type II IFN response, all of which had higher enrichment scores in the low-risk group.

[0069] Differences in the expression of immune indicators and checkpoints: Differences in the expression of immune indicators and immune checkpoints between high and low risk groups were statistically analyzed. Figure 16 As shown ( Figure 16 * indicates P < 0.05). The tumor immune dysfunction and exclusion (TIDE) score of the low-risk group was significantly higher than that of the high-risk group, suggesting that the effect of immune checkpoint inhibitor treatment in the low-risk group was worse than that in the high-risk group, and was associated with the poor clinical prognosis of the low-risk group samples. Figure 17 As shown ( Figure 17In the figure, * indicates P < 0.05; ** indicates P < 0.01; *** indicates P < 0.001). For the 64 immune checkpoints, 8 showed significant differences between the high-risk and low-risk components, of which 1 was significantly overexpressed in the high-risk group, and the remaining 7 were significantly overexpressed in the low-risk group.

[0070] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. To avoid unnecessary repetition, the present invention will not further describe various possible combinations.

Claims

1. A microbial marker composition for predicting the prognosis of hepatocellular carcinoma, characterized in that: The microbial marker composition consists of Candidatus_Glomeribacter, Sicinivirus, Shuttleworthia, Leucothrix, Schleiferia, Dichelobacter, Salinimicrobium, T4likevirus, Ictalurivirus, Sphaerotilus, Kitasatospora and Succinimonas.

2. A detection reagent, characterized in that The detection reagent is used to detect the abundance of the microbial marker composition according to claim 1.

3. Detection product, characterized in that, The detection product comprises the detection reagent for detecting the abundance of the microbial marker composition as claimed in claim 2, and an acceptable auxiliary agent, carrier or device.

4. A method for constructing a risk model for predicting the prognosis of hepatocellular carcinoma, characterized in that: The abundance of the microbial marker composition according to claim 1 is weighted and summed to construct a risk model, wherein the risk model is expressed as follows: RiskScore = Candidatus_Glomeribacter * (-0.1772) + Sicinivirus * (-0.1386) + Shuttleworthia * (-0.1212) + Leucothrix * (-0.0817) + Schleiferia *(-0.0791) + Dichelobacter * (-0.0460) + Salinimicrobium * (-0.0334) +T4likevirus * (-0.0332) + Ictalurivirus * (-0.0035) + Sphaerotilus * 0.0003 +Kitasatospora * 0.0269 + Succinimonas * 0.0412 Among them, RiskScore is the risk score.

5. A hepatocellular carcinoma prognosis evaluation system based on the method for constructing a risk model for predicting the prognosis of hepatocellular carcinoma according to claim 4, characterized in that: Includes data collection module, risk assessment module and output module; The data collection module is used to collect samples from patients and measure the abundance of each microorganism in the samples, wherein the microorganisms are the microorganisms in the microbial marker combination according to claim 1; The risk assessment module is used to calculate the risk score according to the risk model in claim 4; The output module is used to output risk scores.

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