A prognostic scoring system for hepatocellular carcinoma based on PUS family genes and its application

Abstract

The present invention provides a prognostic scoring system for hepatocellular carcinoma based on PUS family genes and its application. The input variables of the prognostic scoring system for hepatocellular carcinoma based on PUS family genes include the expression status coefficient, Gini coefficient and integrated risk coefficient of the PUS family genes in the dataset; the expression status coefficient is determined according to the relationship between the mRNA expression level of the PUS family genes and the average measured value. The prognostic scoring system for hepatocellular carcinoma based on PUS family genes associates the importance of 5 HCC-related PUS family genes with the prognosis and clinical characteristics of HCC patients. In different HCC datasets, there are significant differences in overall survival rates among patients with different scores. The higher the score, the lower the overall survival rate. The prognostic scoring system for hepatocellular carcinoma can be effectively used for the evaluation of the prognosis of HCC patients and has certain specificity and sensitivity in clinical practice.

Description

Technical Field

[0001] The present invention belongs to the technical field of oncology, and particularly relates to a prognostic scoring system for hepatocellular carcinoma based on PUS family genes and its application. Background Art

[0002] Liver cancer is the sixth most common malignancy globally and the fourth leading cause of cancer-related deaths. 85%-95% of primary liver cancers are hepatocellular carcinoma (HCC). Due to its insidious onset and imperfect early diagnostic measures, 80% of HCC patients are diagnosed at the middle or advanced stage, thus losing the opportunity for surgery. The mortality rate of patients with middle and advanced HCC is as high as 80%, the median survival time is less than 1 year, and the 5-year survival rate is less than 20%. Although progress has been made in surgical techniques, chemoradiotherapy techniques, targeted therapy drugs, and immunotherapy techniques in recent years, and these progress have brought new hope to patients with middle and advanced HCC, it is undeniable that the current efficacy of middle and advanced HCC is still disappointing.

[0003] Prognostic evaluation is a key step in the treatment of HCC patients. Several staging systems have been proposed in the medical community, including the Barcelona Clinic Liver Cancer (BCLC) system, the TNM staging system, the Japanese Comprehensive Staging System, etc. These staging systems all have their limitations in clinical use. In order to more accurately predict the prognosis (survival) of liver cancer patients, in addition to considering the patient's liver function, tumor stage, and physical condition, the molecular biological characteristics of the patient must also be considered simultaneously. A new prognostic evaluation system based on molecular biological characteristics will contribute to the individualized treatment and precision medicine of HCC patients.

[0004] Pseudouridine is an important RNA modification on rRNA, snRNA, and tRNA, and has important regulatory functions for RNA processing, translation, and splicing. Pseudouridine modification plays an important role in the occurrence and development of malignant tumors. The process of pseudouridylation modification is catalyzed by pseudouridine synthases (PUS), which changes the chemical structure of uridine nucleotides (U) to form pseudouridine nucleosides. Therefore, the related genes of the PUS family have guiding significance in the diagnosis, monitoring, and efficacy evaluation of malignant tumors.

[0005] Currently, it is still unclear about the change characteristics and clinical significance of PUS-related genes in liver cancer, and there are no related technologies and kits based on PUS family genes and other products for the diagnosis and prognostic evaluation of liver cancer patients. Therefore, creating a prognostic scoring system for hepatocellular carcinoma based on PUS family genes has important clinical application value. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a hepatocellular carcinoma prognosis scoring system based on PUS family genes and its application. Based on the data cohorts of multiple liver cancer patients and their mRNA expression data, the inventors screened out 5 PUS family genes related to HCC by using big data mining. Using these 5 PUS family genes, on the basis of an artificial intelligence algorithm (random forest algorithm), an integral system named "PUS-score" was created. The integral system can be effectively used for the prognosis evaluation of HCC patients, has certain specificity and sensitivity in clinical practice, and has important application value.

[0007] To achieve the object of the present invention, the following technical solutions are adopted:

[0008] In the first aspect, the present invention provides a hepatocellular carcinoma prognosis scoring system based on PUS family genes. The input variables of the hepatocellular carcinoma prognosis scoring system based on PUS family genes include the expression status coefficient (Expression), Gini coefficient (Importance), and integrated risk coefficient (Integrated HR) of the PUS family genes in the dataset.

[0009] The expression status coefficient is determined according to the relationship between the mRNA expression level of the PUS family gene and the average measured value.

[0010] In the present invention, based on the results of COX survival analysis and random forest model, a hepatocellular carcinoma prognosis scoring system PUS-score was constructed using the mRNA expression values of 5 key HCC-related PUS family genes. PUS-score links the importance of 5 HCC-related PUS family genes with the prognosis and clinical characteristics of HCC patients. In different HCC datasets, there are significant differences in the overall survival rate among patients with different PUS-scores. The higher the PUS-score, the lower the overall survival rate. The hepatocellular carcinoma prognosis scoring system PUS-score can be effectively used for the prognosis evaluation of HCC patients and has certain specificity and sensitivity in clinical practice.

[0011] Preferably, the PUS family genes include PUS1, PUS3, PUS7, PUS7L, and RPUSD2.

[0012] Preferably, the dataset includes the Gene Expression Omnibus of Hepatocellular Carcinoma Cohorts, The Cancer Genome Atlas of Hepatocellular Carcinoma, International Cancer Genome Consortium Japan Liver Cancer Data, and CNHPP.

[0013] Preferably, the comprehensive database of gene expression of the hepatocellular carcinoma cohort includes any one or a combination of at least two of GSE14520, GSE22058, GSE25097, GSE36376, GSE45436, GSE54236, GSE63898, GSE64041, GSE76427, GSE102079, GSE104310, GSE107170, GSE11819, GSE14323, GSE15654, GSE17548, GSE17856, GSE19665, GSE22405, GSE29721, GSE31370, GSE33006, GSE33294, GSE36411, GSE38226, GSE39791, GSE41160, GSE41804, GSE45050, GSE45267, GSE46408, GSE51401, GSE54238, GSE55048, GSE56545, GSE57555, GSE57957, GSE62232, GSE63863, GSE65484, GSE65485, GSE67764, GSE69164, GSE7473, GSE77314, GSE84402, GSE84598, GSE87630, GSE89377, GSE94660, GSE95698 or GSE98383.

[0014] In the present invention, mRNA data of 52 HCC cohorts (comprehensive database of gene expression of the hepatocellular carcinoma cohort) are obtained from the Gene Expression Omnibus (https: / / www.ncbi.nlm.nih.gov / geo / ) database; RNA expression data of the HCC cohorts of TCGA-LIHC (The Cancer Genome Atlas Liver Hepatocellular Carcinoma), ICGC-LIRI-JP (International Cancer Genome Consortium Japan Liver Cancer Data) and CNHPP are obtained from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) data collection (https: / / www.cancer.gov / about-nci / organization / ccg / research / structural-genomics / tcga), International Cancer Genome Consortium (https: / / dcc.icgc.org / ) and CNHPP Data Portal (cnhpp.ncpsb.org.cn).

[0015] Preferably, the dataset includes GSE14520, the Liver Hepatocellular Carcinoma Genome Atlas, and the International Cancer Genome Consortium Japan Liver Cancer Data.

[0016] Preferably, the expression status coefficient of the PUS family genes in the dataset is 0 or 1. If the mRNA expression level of the PUS family genes is greater than the average measured value, the expression status coefficient is recorded as 1; otherwise, it is recorded as 0.

[0017] Preferably, the Gini coefficient represents an important coefficient for the PUS family genes to evaluate the prognosis of hepatocellular carcinoma patients, and the Gini coefficient is determined by the random forest algorithm; the Gini coefficient of PUS1 is 9.12, the Gini coefficient of PUS3 is 8.63, the Gini coefficient of PUS7 is 7.56, the Gini coefficient of PUS7L is 7.26, and the Gini coefficient of RPUSD2 is 6.58.

[0018] In the present invention, the random forest algorithm is used to analyze the importance of 5 HCC-related PUS family genes for evaluating the prognosis of HCC patients. The random forest model is constructed using the R statistical software (version 3.6.1) and the randomForest package. The Liver Hepatocellular Carcinoma Genome Atlas (TCGA-LIHC) dataset is used as the training set, and the GSE14520 cohort is used as the validation set. Finally, the important coefficients (Gini coefficients) for evaluating the prognosis of HCC patients of 5 PUS family genes are obtained.

[0019] Preferably, the integrated risk coefficient is determined by integrating the risk coefficients of the PUS family genes in the dataset based on the univariate COX proportional model.

[0020] Preferably, the PUS family genes are divided into risk factors and protective factors. PUS1, PUS7, PUS7L, and RPUSD2 in the PUS family genes are risk factors, and PUS3 in the PUS family genes is a protective factor; the integrated risk coefficient of the risk factors in the PUS family genes is 1, and the integrated risk coefficient of the protective factors in the PUS family genes is -1; the dataset includes GSE14520, the Liver Hepatocellular Carcinoma Genome Atlas, and the International Cancer Genome Consortium Japan Liver Cancer Data.

[0021] In the present invention, in three datasets of GSE14520, the Hepatocellular Carcinoma Genome Atlas, and the International Cancer Genome Consortium Japan Hepatocellular Carcinoma Data, COX proportional models were respectively constructed based on 5 PUS family genes, and a total of 3 models were obtained. The risk coefficients of each gene in the 3 models in each dataset were integrated to obtain the Integrated HR of the gene. The Integrated HR finally takes two values, 1 or -1, indicating that the gene is a risk factor or a protective factor, respectively.

[0022] The Importance and Integrated HR parameters of each gene are shown as follows:

[0023]

[0024] Preferably, the output variable of the hepatocellular carcinoma prognosis scoring system based on the PUS family genes is the sum of the products of the expression status coefficient, Gini coefficient, and integrated risk coefficient of the PUS family genes.

[0025] Preferably, the output variable of the hepatocellular carcinoma prognosis scoring system based on the PUS family genes is PUS-score, and the calculation formula of the PUS-score is:

[0026] PUS-score = Expression(PUS1) × Importance(PUS1) × Integrated HR(PUS1) + Expression(PUS3) × Importance(PUS3) × Integrated HR(PUS3) + Expression(PUS7) × Importance(PUS7) × Integrated HR(PUS7) + Expression(PUS7L) × Importance(PUS7L) × Integrated HR(PUS7L) + Expression(RPUSD2) × Importance(RPUSD2) × Integrated HR(RPUSD2);

[0027] Among them, Expression represents the expression status coefficient of the gene, and the value is 1 or 0;

[0028] Importance represents the Gini coefficient of the gene;

[0029] Integrated HR represents the integrated risk coefficient of the gene, and the value is 1 or -1.

[0030] In a second aspect, the present invention provides a combination of PUS family gene markers related to hepatocellular carcinoma, and the combination of PUS family gene markers related to hepatocellular carcinoma includes PUS1, PUS3, PUS7, PUS7L, and RPUSD2.

[0031] In a third aspect, the present invention provides a screening method for the combination of PUS family gene markers related to hepatocellular carcinoma described in the second aspect, and the screening method includes:

[0032] (1) Collect mRNA data of hepatocellular carcinoma and normal control tissues and perform normalization processing;

[0033] (2) Analyze the expression differences of PUS family genes in hepatocellular carcinoma and normal control tissues, and define the absolute value of the expression fold > 1.2 times and the P value < 0.05 as having differential expression;

[0034] (3) Screen out PUS family genes with consistent expression differences to obtain a combination of PUS family gene markers related to hepatocellular carcinoma.

[0035] In a fourth aspect, the present invention provides a hepatocellular carcinoma prognosis assessment kit, and the hepatocellular carcinoma prognosis assessment kit contains reagents for detecting the mRNA level or protein level of each gene in the combination of PUS family gene markers related to hepatocellular carcinoma described in the second aspect.

[0036] In a fifth aspect, the present invention provides the application of the hepatocellular carcinoma prognosis scoring system based on PUS family genes described in the first aspect in the preparation of hepatocellular carcinoma prognosis monitoring products.

[0037] Preferably, the hepatocellular carcinoma prognosis monitoring products include hepatocellular carcinoma prognosis monitoring kits and / or hepatocellular carcinoma prognosis monitoring medical devices.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) The hepatocellular carcinoma prognosis scoring system based on PUS family genes can be effectively used for the prognosis assessment of HCC patients, and has certain specificity and sensitivity in clinical practice. The COX proportional hazards model constructed by combining the PUS-score described in the present invention with the existing clinical TNM stage can effectively predict the overall survival probabilities of HCC patients at 1, 3, and 5 years.

[0040] (2) The HCC-related PUS family genes screened by the present invention can be used as new diagnostic and treatment targets for hepatocellular carcinoma for the prediction and assessment of HCC prognosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1AExpression characteristics of the PUS1 gene in 55 HCC cohorts in Example 1. * indicates that the level of the gene in cancer tissues is significantly different from that in normal control tissues.

[0042] Figure 1B Expression characteristics of the PUS3 gene in 55 HCC cohorts in Example 1. * indicates that the level of the gene in cancer tissues is significantly different from that in normal control tissues.

[0043] Figure 1C Expression characteristics of the PUS7 gene in 55 HCC cohorts in Example 1. * indicates that the level of the gene in cancer tissues is significantly different from that in normal control tissues.

[0044] Figure 1D Expression characteristics of the PUS7L gene in 55 HCC cohorts in Example 1. * indicates that the level of the gene in cancer tissues is significantly different from that in normal control tissues.

[0045] Figure 1E Expression characteristics of the RPUSD2 gene in 55 HCC cohorts in Example 1. * indicates that the level of the gene in cancer tissues is significantly different from that in normal control tissues.

[0046] Figure 2A It is the overall survival analysis result graph in the TCGA-LIHC cohort in Test Example 1.

[0047] Figure 2B It is the overall survival analysis result graph in the GSE14520 cohort in Test Example 1.

[0048] Figure 2C It is the overall survival analysis result graph in the ICGC-LIRI-JP cohort in Test Example 1.

[0049] Figure 3A It is the ROC curve of PUS-score for predicting the overall survival of HCC patients in the TCGA-LIHC cohort in Test Example 2.

[0050] Figure 3B It is the ROC curve of PUS-score for predicting the overall survival of HCC patients in the GSE14520 cohort in Test Example 2.

[0051] Figure 3C It is the ROC curve of PUS-score for predicting the overall survival of HCC patients in the ICGC-LIRI-J cohort in Test Example 2.

[0052] Figure 4It is a nomogram of the COX proportional hazard model constructed by combining the hepatocellular carcinoma prognostic scoring system based on the PUS family gene in test case 3 with the existing clinical TNM staging.

[0053] Figure 5A , Figure 5B and Figure 5C Calibration curves for PUS-score combined with TNM staging to predict 1-, 3-, and 5-year overall survival in HCC patients.

[0054] Figure 6A , Figure 6B and Figure 6C The clinical decision curves for predicting 1-, 3-, and 5-year overall survival of HCC patients using PUS-score combined with TNM staging. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is further described below by specific implementation methods. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0056] If no specific techniques or conditions are specified in the examples, the techniques or conditions described in the literature in the field or the product instructions are used. If no manufacturer is specified for the reagents or instruments used, they are all conventional products that can be purchased through regular channels.

[0057] Example 1

[0058] In this example, mRNA data of 52 HCC cohorts were obtained from the Gene Expression Omnibus (https: / / www.ncbi.nlm.nih.gov / geo / ) database (Hepatocellular Carcinoma Cohort Gene Expression Comprehensive Database). The access numbers of the above HCC cohorts in the Hepatocellular Carcinoma Cohort Gene Expression Comprehensive Database are: GSE14520, GSE22058, GSE25097, GSE36376, GSE45436, GSE54236, GSE63898, GSE64041, GSE76427, GSE102079, GSE104310, GSE107170, GSE11819, GSE14323, GSE15654, GSE17548, GSE17856, GSE19665, GSE22405, GSE29721, GSE31370, GSE33006, GSE33294, GSE36411, GSE38226, GSE39791, GSE41160, GSE41804, GSE45050, GSE45267, GSE46408, GSE51401, GSE54238, GSE55048, GSE56545, GSE57555, GSE57957, GSE62232, GSE63863, GSE65484, GSE65485, GSE67764, GSE69164, GSE7473, GSE77314, GSE84402, GSE84598, GSE87630, GSE89377, GSE94660, GSE95698 and GSE98383.

[0059] RNA expression data of TCGA-LIHC (The Cancer Genome Atlas Liver Hepatocellular Carcinoma), ICGC-LIRI-JP (International Cancer Genome Consortium Japan Liver Cancer Data) and HCC cohorts of CNHPP were also obtained from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) data collection (https: / / www.cancer.gov / about-nci / organization / ccg / research / structural-genomics / tcga), International Cancer Genome Consortium (https: / / dcc.icgc.org / ) and CNHPP Data Portal (cnhpp.ncpsb.org.cn).

[0060] Convert the gene identification IDs of all datasets into the latest HUGO gene symbols, and perform log2 transformation normalization on the mRNA expression data. A total of 55 HCC cohorts were included, including 2,644 cases of normal control tissues and 3,308 cases of cancer tissues.

[0061] Analyze the expression differences of all PUS family genes in normal control tissues and cancer tissues in 55 cohorts respectively. Define the absolute value of the expression fold change > 1.2 and the P value < 0.05 as having differential expression. If a certain PUS family gene is a differentially expressed gene in more than 45 HCC cohorts and the expression trends are consistent, then the said gene is defined as an HCC-related PUS family gene.

[0062] According to this standard, a total of 5 PUS family genes were identified, namely PUS1, PUS3, PUS7, PUS7L, and RPUSD2. The expression characteristics of PUS1, PUS3, PUS7, PUS7L, and RPUSD2 in the HCC cohort are respectively as Figure 1A 、 Figure 1B 、 Figure 1C 、 Figure 1D and Figure 1E shown. There are differential expressions in the levels of PUS1, PUS3, PUS7, PUS7L, and RPUSD2 in cancer tissues compared with normal control tissues. The expression trends of these 5 PUS family genes in the HCC cohort are consistent, and the differences are statistically significant. The above experimental results prove that the expression of the said 5 PUS family genes is closely related to hepatocellular carcinoma.

[0063] Example 2

[0064] In this example, a prognostic scoring system for hepatocellular carcinoma based on PUS family genes was constructed. The output variable of the prognostic scoring system for hepatocellular carcinoma based on PUS family genes is PUS-score, and the calculation formula of the PUS-score is as follows:

[0065] PUS - score = Expression(PUS1)×Importance(PUS1)×Integrated HR(PUS1)+Expression(PUS3)×Importance(PUS3)×Integrated HR(PUS3)+Expression(PUS7)×Importance(PUS7)×Integrated HR(PUS7)+Expression(PUS7L)×Importance(PUS7L) ×Integrated HR(PUS7L)+Expression(RPUSD2)×Importance(RPUSD2)×Integrated HR(RPUSD2);

[0066] Among them, Expression represents the expression status coefficient of the gene, and the value is 1 or 0;

[0067] Importance represents the Gini coefficient of the gene;

[0068] Integrated HR represents the integrated risk coefficient of the gene, and the value is 1 or - 1.

[0069] The Importance is determined by the random forest algorithm, that is, using the random forest algorithm to analyze the importance of 5 HCC - related PUS family genes for evaluating the prognosis of HCC patients. The random forest model is constructed using R statistical software (version 3.6.1) and the randomForest package, with the TCGA - LIHC dataset as the training set and the GSE14520 cohort as the validation set. Finally, the importance coefficients (Gini coefficients) of 5 PUS family genes for evaluating the prognosis of HCC patients are obtained.

[0070] Determination of Integrated HR (integrated risk coefficient): COX proportional models are constructed based on 5 PUS family genes in the three datasets of TCGA - LIHC, GSE14520, and ICGC - LIRI - JP, and a total of 3 models are obtained; the risk coefficients of each gene in the 3 models in each dataset are integrated. Among the PUS family genes, PUS1, PUS7, PUS7L, and RPUSD2 are risk factors, and PUS3 in the PUS family genes is a protective factor. The integrated risk coefficient of the risk factors in the PUS family genes is 1, and the integrated risk coefficient of the protective factor in the PUS family genes is - 1.

[0071] The Importance and Integrated HR parameters of each gene are shown in Table 1:

[0072] Table 1

[0073] Gene Name Importance Integrated HR PUS1 9.12 1 PUS3 8.63 -1 PUS7 7.56 1 PUS7L 7.26 1 RPUSD2 6.58 1

[0074] Test Example 1

[0075] Application of the hepatocellular carcinoma prognosis scoring system based on the PUS family genes.

[0076] The PUS-score was calculated and analyzed for 367 HCC patients from TCGA-LIHC. The median value of the PUS-score of all patients was used as the cut-off value, and the 367 HCC patients were divided into a high PUS-score group and a low PUS-score group. For TCGA-LIHC (n = 367), the overall survival analysis results are as Figure 2A shown. Using Kaplan-Meier survival analysis, it was found that the overall survival of patients in the high PUS-score group was poor in the TCGA-LIHC dataset.

[0077] The PUS-score was calculated for 242 HCC patients from the GSE14520 cohort and 212 HCC patients from the ICGC-LIRI-JP cohort. The median value of the PUS-score of all patients was used as the cut-off value, and the PUS-score was divided into a high PUS-score group and a low PUS-score group. For GSE14520 (n = 242), the overall survival analysis results are as Figure 2B shown, and for ICGC-LIRI-JP (n = 212), the overall survival analysis results are as Figure 2C shown. Using Kaplan-Meier survival analysis, it was found that the overall survival of patients in the high PUS-score group was poor in both the GSE14520 and ICGC-LIRI-JP datasets.

[0078] Test Example 2

[0079] Clinical efficacy test of the hepatocellular carcinoma prognosis scoring system based on the PUS family genes.

[0080] The receiver operating characteristic (ROC) curve was used to test the clinical efficacy of the PUS-score in predicting patient prognosis, and the results are as Figure 3A 、 Figure 3B and Figure 3CAs shown in Figure 3A, the ROC curve of PUS-score for predicting the overall survival of HCC patients in the TCGA-LIHC cohort is presented. The AUC for 1-year overall survival is 75.19, the AUC for 3-year overall survival is 71.65, and the AUC for 5-year overall survival is 70.84. Figure 3B The ROC curve of PUS-score for predicting the overall survival of HCC patients in the GSE14520 cohort is shown. The AUC for 1-year overall survival is 70.13, the AUC for 3-year overall survival is 72.32, and the AUC for 5-year overall survival is 70.04. Figure 3C The ROC curve of PUS-score for predicting the overall survival of HCC patients in the ICGC-LIRI-J cohort is presented. The AUC for 1-year overall survival is 70.06, and the AUC for 3-year overall survival is 72.13.

[0081] Analysis found that in the TCGA-LIHC ( Figure 3A ), GSE14520 ( Figure 3B ), and ICGC-LIRI-JP cohorts ( Figure 3C ), PUS-score, as an indicator, has clinically acceptable sensitivity and specificity for predicting the 1-year, 3-year, and 5-year overall survival of patients (the area under the curve AUC is greater than 70).

[0082] Test Example 3

[0083] The prognostic scoring system for hepatocellular carcinoma based on PUS family genes is combined with the existing clinical TNM staging to construct a COX proportional hazards model.

[0084] The nomogram of the COX proportional hazards model constructed by combining the prognostic scoring system for hepatocellular carcinoma based on PUS family genes with the existing clinical TNM staging is as Figure 4 shown. The method of using the two in combination is as follows. For example, for a certain HCC patient with a TNM stage of advanced (stage Ⅲ-Ⅳ), the corresponding nomogram score is 30, and the PUS-score is 15 points, corresponding to a nomogram score of 28 points. Then the total nomogram score is 58 points. Draw a vertical line at the 58-point position on the total nomogram score axis and intersect it with the lower survival probability axis. It can be seen that the 1-year overall survival probability of this patient is about 50%, the 3-year overall survival probability is about 20%, and the 5-year overall survival probability is less than 10%.

[0085] The dataset GSE14520 is used to evaluate the accuracy and clinical efficacy of PUS-score combined with TNM staging in predicting the prognosis of HCC patients. Figure 5A 、 Figure 5B and Figure 5C are the calibration curves of PUS-score combined with TNM staging for predicting the 1-year, 3-year, and 5-year overall survival of HCC patients. It can be seen that the predicted overall survival is similar to the actual overall survival of patients.Figure 6A , Figure 6B and Figure 6C The clinical decision curves of PUS-score combined with TNM staging for predicting the 1-, 3-, and 5-year overall survival of HCC patients are shown. It can be seen that using PUS-score combined with TNM staging to predict the prognosis of HCC patients has the potential to benefit patients. Figure 5A , Figure 5B and Figure 5C as well as Figure 6A , Figure 6B and Figure 6C indicate that PUS-score combined with TNM staging has accuracy in predicting the prognosis of HCC patients.

[0086] The COX proportional hazards model constructed by combining the described hepatocellular carcinoma prognosis scoring system based on PUS family genes with the existing clinical TNM staging can effectively predict the 1-, 3-, and 5-year overall survival probabilities of HCC patients.

[0087] In summary, the hepatocellular carcinoma prognosis scoring system based on PUS family genes described in the present invention can be effectively used for the evaluation of the prognosis of HCC patients and has certain specificity and sensitivity in clinical practice. The COX proportional hazards model constructed by combining the PUS-score described in the present invention with the existing clinical TNM staging can effectively predict the 1-, 3-, and 5-year overall survival probabilities of HCC patients.

[0088] The applicant declares that the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention fall within the protection scope and the disclosure scope of the present invention.

Claims

1. A prognostic scoring system for hepatocellular carcinoma based on the PUS family of genes, characterized in that, The input variables of the hepatocellular carcinoma prognosis scoring system based on the PUS family genes include the expression status coefficient, Gini coefficient, and integrated risk coefficient of the PUS family genes in the dataset; The expression status coefficient is determined according to the magnitude relationship between the mRNA expression level of the PUS family genes and the average measured value; The PUS family genes include PUS1, PUS3, PUS7, PUS7L, and RPUSD2; The output variable of the hepatocellular carcinoma prognosis scoring system based on the PUS family genes is PUS-score, and the calculation formula of the PUS-score is: PUS-score = Expression(PUS1) × Importance(PUS1) × Integrated HR(PUS1) + Expression(PUS3) × Importance(PUS3) × Integrated HR(PUS3) + Expression(PUS7) × Importance(PUS7) × Integrated HR(PUS7) + Expression(PUS7L) × Importance(PUS7L) × Integrated HR(PUS7L) + Expression(RPUSD2) × Importance(RPUSD2) × Integrated HR(RPUSD2); Among them, Expression represents the expression status coefficient of the gene, and the value is 1 or 0; Importance represents the Gini coefficient of the gene; Integrated HR represents the integrated risk coefficient of the gene, and the value is 1 or -1; The expression status coefficient of the PUS family genes in the dataset is 0 or 1. If the mRNA expression level of the PUS family genes is greater than the average measured value, the expression status coefficient is recorded as 1; otherwise, it is recorded as 0; The Gini coefficient is determined by the random forest algorithm; the Gini coefficient of PUS1 is 9.12, the Gini coefficient of PUS3 is 8.63, the Gini coefficient of PUS7 is 7.56, the Gini coefficient of PUS7L is 7.26, and the Gini coefficient of RPUSD2 is 6.58; The integrated risk coefficient is determined by integrating the risk coefficients of the PUS family genes based on the univariate COX proportional model in the dataset; the PUS family genes are divided into risk factors and protective factors. Among the PUS family genes, PUS1, PUS7, PUS7L, and RPUSD2 are risk factors, and PUS3 among the PUS family genes is a protective factor; the integrated risk coefficient of the risk factors in the PUS family genes is 1, and the integrated risk coefficient of the protective factors in the PUS family genes is -1.

2. The prognostic scoring system for hepatocellular carcinoma based on the PUS family genes according to claim 1, wherein The dataset includes the Comprehensive Oncogenomic Resource of hepatocellular carcinoma cohort, The Cancer Genome Atlas of hepatocellular carcinoma, the International Cancer Genome Consortium Japan Hepatocellular Carcinoma Data, and CNHPP.

3. A combination of PUS family gene markers related to hepatocellular carcinoma, characterized in that, The hepatocellular carcinoma-related PUS family gene marker combination is PUS1, PUS3, PUS7, PUS7L, and RPUSD2.

4. A prognostic assessment kit for hepatocellular carcinoma, characterized in that, The hepatocellular carcinoma prognosis assessment kit contains reagents for detecting the mRNA level or protein level of each gene in the hepatocellular carcinoma-related PUS family gene marker combination described in claim 3.

5. Use of the hepatocellular carcinoma prognosis scoring system based on the PUS family gene according to any one of claims 1-2 in the preparation of a hepatocellular carcinoma prognosis monitoring product.

6. According to the use described in claim 5, the hepatocellular carcinoma prognosis monitoring product includes a hepatocellular carcinoma prognosis monitoring kit and / or a hepatocellular carcinoma prognosis monitoring medical device.

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