CN113930506B - Glutamine metabolism gene label scoring system for predicting hepatocellular carcinoma prognosis and treatment resistance - Google Patents
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Abstract
The invention discloses a glutamine metabolism gene label scoring system for predicting hepatocellular carcinoma prognosis and treatment resistance, belonging to the technical field of biological medicines. The model can be used for judging the total life cycle of a patient by detecting the expression levels of 7 specific glutamine metabolism related genes of a hepatocellular carcinoma patient, evaluating the treatment resistance of the patient subjected to postoperative transcatheter arterial chemoembolization and evaluating the infiltration degree of immune cells in a tumor and the potential of immune escape of the tumor cells, and improving the prediction capability of the response to liver cancer immunotherapy. Meanwhile, the model provided by the invention can improve the accuracy of predicting the 3-year total survival time of hepatocellular carcinoma, and compared with the method for predicting the prognosis of a patient by directly adopting the conventional next-generation sequencing technology, the method has the advantages of reducing the number of genes to be detected, improving the efficiency and reducing the cost. The invention can be used as a molecular marker for objectively and accurately evaluating hepatocellular carcinoma treatment resistance and tumor immune state, and can realize accurate prediction and accurate implementation of hepatocellular carcinoma treatment prognosis.
Description
Technical Field
The invention particularly relates to a glutamine metabolism gene label scoring system for predicting hepatocellular carcinoma prognosis and treatment resistance, belonging to the technical field of biological medicines.
Background
In the last decade, advances have been made in the treatment of HCC. In contrast, HCC-associated biomarkers did not progress significantly in prognostic prediction and treatment response. Although there is increasing evidence that novel biomarkers are closely related to the clinical prognosis of HCC, especially biomarkers developed by NGS, these biomarkers still take some time to be clinically applied.
The liver is a metabolic organ and abnormally active Glutamine Metabolism (GM) is a key factor in HCC. After glutaminase 1 (GLS 1) is knocked out, the tumorigenicity of HCC stem cells is inhibited, and the high expression of GLS1 indicates that the prognosis is poor. In addition, the expression of the glutamine transporter ASCT2 is significantly up-regulated in HCC, an independent prognostic risk factor. In contrast, ketoglutarate-like dehydrogenase, which is highly expressed in limiting glutamine metabolism, is associated with a good prognosis in HCC patients and sensitizes HCC cells to sorafenib treatment. The expression levels of genes associated with glutamine metabolism reflect the molecular heterogeneity of HCC, which determines the different clinical outcomes of HCC patients. An increasing number of basic studies have shown that cell metabolism is inseparable from tumor progression. The glutamine metabolism related gene is a series of genes which are involved in the glutamine metabolism process in organisms, and comprises a series of processes such as synthesis, decomposition and transformation of glutamine. However, there is still a lack of comprehensive methods to quantify glutamine metabolic activity in the tumor microenvironment.
Disclosure of Invention
Considering the preferential glutamine utilization of tumor cells over immune cells, a glutamine metabolism gene signature scoring system was constructed to predict overall survival of hepatocellular carcinoma patients, to evaluate the treatment resistance of patients who have undergone post-operative transcatheter arterial chemoembolization, and to predict the potential for tumor immune escape and the efficacy of immune checkpoint inhibitors.
The invention aims to solve the technical problems of reflecting and predicting the total survival time of a hepatocellular carcinoma patient, evaluating the treatment resistance of a postoperative transcatheter arterial chemoembolization patient and predicting the tumor immune escape potential and the curative effect of an immune check point inhibitor by detecting tumor tissues.
The invention aims at providing a prognosis model which is based on glutamine metabolism related genes and can reflect and evaluate the overall survival period of hepatocellular carcinoma patients through tumor tissue detection. The GMSCore and the existing prognosis algorithm are used for complementary evaluation of patient age, tumor TNM stage, histology grading, alpha fetoprotein level and vascular invasion, so that the accuracy of predicting the 3-year survival time of the hepatocellular carcinoma patient is improved, and the application value of the existing prognosis prediction system is increased.
A second object of the invention is to evaluate the treatment resistance of post-operative transcatheter arterial chemoembolized patients. The growth advantage and genetic stability of the cells were assessed using biological characteristics. Patients with poor prognosis are then screened in time for additional interventions based on GMScore to improve patient prognosis.
The third purpose of the invention is to predict the tumor immune escape potential and the curative effect of the immune checkpoint inhibitor, improve the accuracy of the current screening of the patients suitable for immunotherapy, and reduce the incidence of adverse events, thereby improving the curative effect of immunotherapy.
In order to solve the problems, the technical scheme adopted by the invention is to provide a prognosis model for predicting the total survival time of a hepatocellular carcinoma patient, evaluating the treatment resistance of a postoperative transcatheter arterial chemoembolization patient and predicting the tumor immune escape potential and the curative effect of an immune checkpoint inhibitor; the GMSCore of the prognostic model is obtained by calculating the expression level of glutamine metabolism-related genes through the sum of the expression levels after corresponding coefficient weights.
The invention provides a glutamine metabolism gene label scoring system for predicting hepatocellular carcinoma prognosis and treatment resistance, which comprises an extraction and division module, a data analysis module, a differential expression module, a gene screening module, a grouping module, an evaluation prognosis module, a treatment resistance module, an immune scoring module, an immune treatment module and a verification module which are sequentially connected; for predicting overall survival of hepatocellular carcinoma patients, assessing treatment resistance of post-operative transcatheter arterial chemoembolization patients, and predicting tumor immune escape potential and efficacy of immune checkpoint inhibitors.
1. An extraction and division module: extracting RNA components of hepatocellular carcinoma tumor tissues and paracancerous normal tissues, purifying and sequencing, randomly dividing patients into training sets and verification sets, and downloading from a Gene Ontology (GO) website and collating related literature related to glutamine metabolism to obtain a glutamine metabolism related gene set.
2. A data analysis module: relevant data and plots were analyzed by R software (version 4.0.3, http:// www.r-project. Org) or IBM SPSS Statistics ver.20 (IBM Corp., armonk, NY, USA). Student's t-test, chi-square test, fisher's exact probability test, or Wilcoxon rank-sum test were used to compare differences between groups. Statistical significance was set at p <0.05 and all p values were two-tailed.
3. A differential expression module: differentially expressed genes were determined between tumor tissue and paracancerous normal tissue by limma R package in liver cancer patients (false discovery rate < 0.05). Subsequently, genes associated with overall survival were selected using a univariate Cox regression model. The optimal cut-off value between high and low risk subgroups was determined based on the correlation of gene expression with overall survival using the surfminer R package. Assigning gene expression to 0 or 1 according to the optimal cut-off value; when gene expression is below the corresponding optimal cut-off value, a value of 0 is assigned, otherwise a value of 1 is assigned.
4. A gene screening module: identifying glutamine metabolism related genes which are statistically related to prognosis, and further determining the most valuable genes and corresponding coefficients thereof by adopting LASSO regression analysis to construct a glutamine metabolism gene prognosis model GMSCore. GMSCore is the sum of the mRNA expression levels of the glutamine metabolism-related genes incorporated into the model weighted by the corresponding coefficients.
5. And (3) scoring by an immune scoring module:
(1) Immune, matrix and ESTIMATE scores: the ESTIMATE algorithm was used to ESTIMATE the relative proportion of stromal cells and immune cells in the tumor microenvironment and was shown in the form of stromal score, immuneScore and ESTIMATEScore. (2) Tumor Mutation Burden (TMB) calculation: the total number of somatic non-synonymous mutations in the coding region includes missense, nonsense, splice sites, and frameshift mutations.
(3) Estimation of immune infiltration: based on transcriptome data, the CIBERSORT algorithm was used to quantify the infiltration abundance of immune cells in the tumor immune microenvironment.
(4) Tumor immune dysfunction and rejection score: TIDE is a transcriptome-based computational method for calculating T-cell dysfunction and rejection scores to predict tumor immune escape potential and tumor efficacy against immune checkpoint inhibitors.
6. A grouping module: dividing the patients into a high-risk group and a low-risk group according to the GMSCore optimal cut-off value, and comparing the two groups by using a Kaplan-Meier method of a logarithmic rank test by an evaluation prognosis module to predict the total survival time of the hepatocellular carcinoma patients; calculating the risk ratio of the prognostic factors and 95% confidence interval thereof by using univariate and multivariate Cox proportional risk models; assessing treatment resistance of post-operative transcatheter arterial chemoembolized patients and predicting differences in tumor immune escape potential and efficacy of immune checkpoint inhibitors; the prognostic value of the model was assessed by survival analysis, receiver operating characteristic curve (ROC), time-dependent ROC curve, area under ROC curve (AUC), and multifactorial COX proportional hazards regression analysis.
7. A verification module: the ability of the glutamine metabolism gene model to predict hepatocellular carcinoma prognosis is verified in a verification set: calculating GMSCore of each tumor sample in the verification set, dividing the patients into a high risk group and a low risk group according to the optimal boundary value in the training set, and comparing the two groups by using a Kaplan-Meier method of logarithmic rank test to predict the total survival time of the hepatocellular carcinoma patients; calculating the risk ratio of the prognostic factors and 95% confidence interval thereof by using univariate and multivariate Cox proportional risk models; assessing treatment resistance of post-operative transcatheter arterial chemoembolized patients and predicting differences in tumor immune escape potential and efficacy of immune checkpoint inhibitors; the prognostic value of the model was assessed by survival analysis, receiver operating characteristic curve (ROC), time-dependent ROC curve, area under ROC curve (AUC), and multifactorial COX proportional hazards regression analysis.
8. An immunotherapy module: the accuracy of GMScore for the prognostic evaluation of immunotherapy was assessed by calculating GMScore for each tumor sample in the immunotherapy cohort from the cohort treated with immune checkpoint inhibitors, dividing the patients into high risk and low risk groups with the best cut-off in the immunotherapy cohort, comparing the overall survival of two groups of patients with predicted hepatocellular carcinoma using the Kaplan-Meier method of the log rank test.
The treatment resistance module assesses the treatment resistance of post-operative transcatheter arterial chemoembolization patients through two sets of post-operative survival periods.
Compared with the prior art, the invention has the following beneficial effects:
(1) The overall survival of hepatocellular carcinoma patients can be objectively assessed by detecting the expression levels of 7 glutamine metabolism-related genes in hepatocellular carcinoma tumor tissue and calculating individualized GMSCore scores. GMScore is an independent risk factor for overall survival and is superior to current clinical indicators as well as other biomarkers such as tumor mutation burden, immune score, stromal score, etc.
(2) Meanwhile, the GMSCore score is clinically helpful for evaluating the treatment resistance of postoperative transcatheter arterial chemoembolization patients, perfecting treatment measures as early as possible and improving prognosis.
(3) In addition, the degree of immune cell infiltration and tumor immune escape potential in the tumor immune microenvironment are analyzed, as well as the efficacy of immune checkpoint inhibitors is predicted. High-scoring tumors have a relatively high probability of immune escape, thereby developing drug resistance and reducing the efficacy of immunotherapy. The GMScore score facilitates the administration of accurate treatments for hepatocellular carcinoma.
Drawings
FIG. 1 is a model building diagram; FIG. 1A is a graph showing differential expression of 30 genes selected from 41 glutamine metabolism-related genes between HCC and normal tissues; FIG. 1B is a graph showing the significant association of 20 genes with overall survival; FIGS. 1C and 1D are diagrams of the Lasso model construction;
FIG. 2 is a graph of model validation and accuracy; FIG. 2A is a graph of the ROC for 1,2,3 years; FIG. 2B is a survival diagram; FIG. 2C is a graph of a multifactorial COX assay;
FIG. 3 is a ROC graph; FIGS. 3A and 3B are ROC plots of GMSCore in the training and validation sets, respectively, compared to other indices for prediction accuracy;
FIG. 4 is a graph of treatment resistance survival; FIG. 4A is a graph of survival of transcatheter arterial chemoembolization treatments in a training set; FIG. 4B is a graph of survival of a salvage transcatheter arterial chemoembolization treatment after a recurrence in a training set; FIG. 4C is a graph demonstrating the survival of a focused transcatheter arterial chemoembolization treatment;
figure 5 is a plot of immune checkpoint expression, immune infiltrating cell abundance, immune dysfunction and escape box profiles and immune checkpoint inhibitor treatment cohort validation; FIG. 5A is a boxplot of the expression of immune checkpoint genes; FIG. 5B is a boxplot of the differences in immune cell infiltration; FIG. 5C is a plot of the TIDE algorithm calculated T cell dysfunction and exclusion score bins; FIG. 5D is a histogram of the prediction of the response rate to immunotherapy for the training and validation sets; FIG. 5E is a bar graph of the response rate prediction to immunotherapy for the immunotherapy cohort; fig. 5F is a graph of immunotherapy cohort survival.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention aims to solve the technical problems of reflecting and predicting the total survival time of a hepatocellular carcinoma patient, evaluating the treatment resistance of a postoperative transcatheter arterial chemoembolization patient and predicting the tumor immune escape potential and the curative effect of an immune check point inhibitor by detecting tumor tissues.
The invention aims to provide a prognosis model system which is based on glutamine metabolism related genes and can reflect and evaluate the overall survival time of hepatocellular carcinoma patients through tumor tissue detection. The GMSCore and the existing prognosis algorithm are used for complementary evaluation of patient age, tumor TNM stage, histology grade, alpha fetoprotein level and vascular invasion, so that the accuracy of predicting 3-year survival time of hepatocellular carcinoma patients is improved, and the application value of the existing prognosis prediction system is increased.
A second object of the invention is to evaluate the treatment resistance of post-operative transcatheter arterial chemoembolized patients. The growth advantage and genetic stability of the cells were assessed using biological characteristics. Patients with poor prognosis are then screened in time based on GMSCore to develop additional interventions to improve patient prognosis.
The third purpose of the invention is to predict the tumor immune escape potential and the curative effect of the immune checkpoint inhibitor, improve the accuracy of the current screening of suitable immunotherapy patients, and reduce the incidence of adverse events so as to improve the curative effect of immunotherapy.
In order to achieve the purpose of solving the problems, the technical scheme adopted by the invention is to provide a prognosis model for predicting the overall survival period of a hepatocellular carcinoma patient, evaluating the treatment resistance of a patient subjected to postoperative transcatheter arterial chemoembolization and predicting the tumor immune escape potential and the curative effect of an immune checkpoint inhibitor; the GMSCore of the prognostic model is obtained by calculating the expression level of glutamine metabolism-related genes through the sum of the expression levels after corresponding coefficient weights.
The invention provides a system for constructing and predicting the total survival period of a hepatocellular carcinoma patient, evaluating the treatment resistance of a postoperative transcatheter arterial chemoembolization patient, and predicting the tumor immune escape potential and the curative effect of an immune checkpoint inhibitor, which comprises the following steps:
1. extracting RNA components of the collected hepatocellular carcinoma tumor tissues, purifying and sequencing the RNA components, and randomly dividing patients into a training set and a verification set; the gene set related to glutamine metabolism is obtained by downloading from a Gene Ontology (GO) website and organizing related literature related to glutamine metabolism.
R software (version 4.0.3, http:// www.r-project. Org) or IBM SPSS Statistics ver.20 (IBM Corp., armonk, NY, USA) was used to analyze the relevant data and plots. Student's t-test, chi-square test, fisher's exact probability test, or Wilcoxon rank-sum test were used to compare differences between groups. Statistical significance was set at p <0.05 and all p values were two-tailed.
3. Differentially expressed genes were determined between tumor tissue and paracancerous normal tissue by limma R package in liver cancer patients (false discovery rate < 0.05). Subsequently, genes associated with overall survival were selected using a univariate Cox regression model. The optimal cut-off value between high and low risk subgroups was determined based on the correlation of gene expression with overall survival using the surfminer R package. Assigning the gene expression to 0 or 1 according to the optimal cut-off value; when gene expression is below the corresponding optimal cut-off value, a value of 0 is assigned, otherwise a value of 1 is assigned.
4. Identifying glutamine metabolism related genes which are statistically related to prognosis, and further adopting LASSO regression analysis to determine the most valuable genes and the corresponding coefficients thereof to construct a glutamine metabolism gene prognosis model GMSCore. GMSCore is the sum of the mRNA expression levels of the glutamine metabolism-related genes incorporated into the model weighted by the corresponding coefficients.
5. And (3) scoring:
(1) Immune, matrix and ESTIMATE scores: the ESTIMATE algorithm was used to ESTIMATE the relative proportion of stromal cells and immune cells in the tumor microenvironment and was displayed in the form of stromal score, immuneScore and ESTIMATEScore. (2) Tumor Mutation Burden (TMB) calculation: the total number of somatic non-synonymous mutations in the coding region includes missense, nonsense, splice sites and frameshift mutations.
(3) Estimation of immune infiltration: based on transcriptome data, the CIBERSORT algorithm was used to quantify the infiltration abundance of immune cells in the tumor immune microenvironment.
(4) Tumor immune dysfunction and rejection score: TIDE is a transcriptome-based computational method for calculating T-cell dysfunction and rejection scores to predict tumor immune escape potential and tumor efficacy against immune checkpoint inhibitors.
6. Dividing the patients into a high-risk group and a low-risk group by the GMSCore optimal cut-off value, and comparing the two groups by using a Kaplan-Meier method of a logarithmic rank test to predict the total survival time of the hepatocellular carcinoma patients; calculating the risk ratio of the prognostic factors and 95% confidence interval thereof by using univariate and multivariate Cox proportional risk models; assessing treatment resistance of post-operative transcatheter arterial chemoembolized patients and predicting differences in tumor immune escape potential and efficacy of immune checkpoint inhibitors; the prognostic value of the model was assessed by survival analysis, receiver operating characteristic curve (ROC), time-dependent ROC curve, area under ROC curve (AUC), and multifactorial COX proportional hazards regression analysis.
7. The ability of the glutamine metabolism gene model to predict hepatocellular carcinoma prognosis was verified in a verification set: calculating GMSCore of each tumor sample in the verification set, dividing the patients into a high risk group and a low risk group according to the optimal boundary value in the training set, and comparing the two groups by using a Kaplan-Meier method of logarithmic rank test to predict the total survival time of the hepatocellular carcinoma patients; calculating the risk ratio of the prognostic factors and 95% confidence interval thereof by using univariate and multivariate Cox proportional risk models; assessing treatment resistance of post-operative transcatheter arterial chemoembolization patients and predicting differences in tumor immune escape potential and efficacy of immune checkpoint inhibitors; the prognostic value of the model was assessed by survival analysis, receiver operating characteristic curve (ROC), time-dependent ROC curve, area under ROC curve (AUC), and multifactorial COX proportional hazards regression analysis.
8. The accuracy of GMScore for the prognostic evaluation of immunotherapy was assessed by calculating GMScore for each tumor sample in the immunotherapy cohort from the cohort treated with immune checkpoint inhibitors, dividing the patients into high risk and low risk groups with the best cut-off in the immunotherapy cohort, comparing the overall survival of two groups of patients with predicted hepatocellular carcinoma using the Kaplan-Meier method of the log rank test.
Through the steps, the overall survival time of the high-risk hepatocellular carcinoma patient identified based on a glutamine metabolism gene prognosis model is found to be shorter, and GMSCore is closely related to clinical characteristics of the patient, including staging, histological grading, alpha-fetoprotein level and vascular invasion. High GMScore is an independent risk factor for overall survival, superior to current clinical indicators and other biomarkers. The biological characteristics of the high GMSCore indicate that the cells of this group of patients have a growth advantage and genetic stability, which is associated with poor overall survival in patients receiving Transcatheter Arterial Chemoembolization (TACE). High GMScore is also associated with high expression of immune checkpoint genes, increased regulatory T cell infiltration and decreased M1 macrophage infiltration. Thus, it is predicted that high GMScore may reduce the efficacy of tumors against immune checkpoint inhibitors, a conclusion that may be confirmed in the cohort of immune checkpoint inhibitor treatments. GMSCore is a powerful prognostic indicator that can be integrated into existing clinical algorithms. Based on transcriptome and immune profiles, patients with high GMScore may suggest resistance to transcatheter arterial chemoembolization and immune checkpoint inhibitors.
Example of the implementation
1. Extracting RNA component of collected hepatocellular carcinoma tumor tissue, purifying and sequencing,
study subjects: patients from the cancer genomic map (TCGA) were screened as a training set and patients from the International Cancer Genomic Consortium (ICGC) as a validation set. The following inclusion criteria were used:
(1) Sequencing data that can be used in GMScore calculations,
(2) The pathological diagnosis of HCC is carried out,
(3) There has been no previous history of radiotherapy, chemotherapy, targeted therapy, immunotherapy or other anti-cancer drugs, including neoadjuvant therapy.
And acquiring transcriptome data and corresponding clinical information data of the training set and the verification set, preprocessing the transcriptome data and the corresponding clinical information data, and adjusting the transcriptome data into transcriptome data in an FPKM format. The association of GMScore with immunotherapy outcome was verified using the melanoma external cohort treated with anti-PD-1 (GSE 78220).
2. The research method comprises the following steps:
1. the extraction and division module extracts and collects RNA components of hepatocellular carcinoma tumor tissues for purification and sequencing, and randomly divides patients into a training set and a verification set; the number of samples in the training set is 365, and the number of samples in the verification set is 221. The gene set related to glutamine metabolism is obtained by downloading from a Gene Ontology (GO) website and organizing related literature related to glutamine metabolism.
2. The data analysis module was used to analyze the relevant data and plots with R software (version 4.0.3, http:// www.r-project.org) or IBM SPSS Statistics ver.20 (IBM Corp., armonk, NY, USA). Student's t-test, chi-square test, fisher's exact probability test, or Wilcoxon rank-sum test were used to compare differences between groups. Statistical significance was set at p <0.05 and all p values were two-tailed.
3. Differential expression module differential expression genes between tumor tissue and paracancerous normal tissue were determined by limma R package in liver cancer patients (false discovery rate < 0.05). Subsequently, genes associated with overall survival were selected using a univariate Cox regression model. The optimal cut-off value between high and low risk subgroups was determined based on the correlation of gene expression with overall survival using the surfminer R package. Assigning the gene expression to 0 or 1 according to the optimal cut-off value; when gene expression is below the corresponding optimal cut-off value, a value of 0 is assigned, otherwise a value of 1 is assigned.
4. The gene screening module identifies glutamine metabolism related genes which are statistically related to prognosis, and further adopts LASSO regression analysis to determine the most valuable genes and the corresponding coefficients thereof to construct a glutamine metabolism gene prognosis model GMSCore. GMSCore is the sum of the mRNA expression levels of glutamine metabolism-associated genes incorporated into the model weighted by the respective coefficients. GMScore =0.374 × slc1a5 mRNA expression +0.359 × gapdh mRNA expression +0.264 × slc38a1 mRNA expression +0.112 × slc38a7 mRNA expression-0.049 × ftcd mRNA expression-0.113 × mthfs mRNA expression-0.157 got2 mRNA expression.
5. And (3) scoring by an immune scoring module:
(1) Immune, matrix and ESTIMATE scores: the ESTIMATE algorithm was used to ESTIMATE the relative proportion of stromal cells and immune cells in the tumor microenvironment and was displayed in the form of stromal score, immuneScore and ESTIMATEScore. (2) Tumor Mutation Burden (TMB) calculation: the total number of somatic non-synonymous mutations in the coding region includes missense, nonsense, splice sites and frameshift mutations.
(3) Estimation of immune infiltration: based on transcriptome data, the CIBERSORT algorithm was used to quantify the infiltration abundance of immune cells in the tumor immune microenvironment.
(4) Tumor immune dysfunction and rejection score: TIDE is a transcriptome-based computational method for calculating T cell dysfunction and rejection scores to predict tumor immune escape potential and tumor efficacy against immune checkpoint inhibitors.
6. The grouping module divides the patients into a high risk group and a low risk group according to the GMSCore optimal boundary value, and the evaluation and prognosis module compares the two groups by using a Kaplan-Meier method of logarithmic rank test to predict the total survival period of the hepatocellular carcinoma patients; calculating the risk ratio of the prognostic factors and 95% confidence interval thereof by using univariate and multivariate Cox proportional risk models; assessing treatment resistance of post-operative transcatheter arterial chemoembolized patients and predicting differences in tumor immune escape potential and efficacy of immune checkpoint inhibitors; the prognostic value of the model was assessed by survival analysis, receiver operating characteristic curve (ROC), time-dependent ROC curve, area under ROC curve (AUC), and multifactorial COX proportional hazards regression analysis.
7. The verification module verifies the ability of the glutamine metabolism gene model for predicting hepatocellular carcinoma prognosis in a verification set: calculating GMSCore of each tumor sample in the verification set, dividing the patients into a high risk group and a low risk group according to the optimal boundary value in the training set, and comparing the two groups by using a Kaplan-Meier method of logarithmic rank test to predict the total survival time of the hepatocellular carcinoma patients; calculating the risk ratio of the prognostic factors and 95% confidence intervals thereof using univariate and multivariate Cox proportional hazards models; assessing treatment resistance of post-operative transcatheter arterial chemoembolization patients and predicting differences in tumor immune escape potential and efficacy of immune checkpoint inhibitors; the prognostic value of the model was assessed by survival analysis, receiver operating characteristic curve (ROC), time-dependent ROC curve, area under ROC curve (AUC), and multifactorial COX proportional hazards regression analysis.
8. The immunotherapy module calculates GMSCore of each tumor sample in the immunotherapy cohort through the cohort treated by the immune checkpoint inhibitor, divides the patients into a high risk group and a low risk group with the optimal cut-off value in the immunotherapy cohort, compares the two groups to predict the overall survival of hepatocellular carcinoma patients using the Kaplan-Meier method of log rank test, and evaluates the accuracy of GMSCore in the prognosis evaluation of immunotherapy.
The above related algorithms such as univariate and multivariate Cox models, limma R package, survminer R package, LASSO regression analysis, kaplan-Meier method, ESTIMATE algorithm, CIBERSOR algorithm, TIDE transcriptome calculation method, and the like, and R language program package are the existing technologies and are realized by direct calling.
3. The experimental results are as follows:
(1) Association of GMSCore with clinical features
In the training set, 30 genes were first screened from 41 glutamine metabolism-related genes (table 1) for differential expression between HCC and normal tissues by univariate Cox regression analysis (fig. 1A). Of these, 20 genes were significantly associated with overall survival (fig. 1B). Subsequently, 7 most valuable genes were finally selected by LASSO Cox regression analysis to construct a glutamine metabolism gene prognostic model (fig. 1C and 1D). The specific genes and their corresponding coefficients are shown in table 2. And the risk score of each sample is the sum of the glutamine related genes weighted by corresponding coefficients, and the scores of the samples in the training set and the verification set are calculated. GMScore =0.374 × SLC1a5 expression level +0.359 × gapdh expression level +0.264 × SLC38A1 expression level +0.112 × SLC38a7-0.049 expression level FTCD expression level-0.113 × MTHFS expression level-0.157 × got2 expression level.
TABLE 1 41 genes associated with glutamine metabolism
7 glutamine metabolism-related genes contained in molecular tags constructed in Table 2 and coefficients thereof
(2) GMScore is an independent prognostic factor:
patients were divided into high and low GMScore subgroups to establish a prognostic prediction model in TCGA and validated using ICGC. ROC curve analysis shows that the constructed glutamine prognosis model has higher prediction efficiency for 1,2,3-year prognosis, and the prediction efficiency of GMSCore for 3-year prognosis is higher than that of the existing hepatocellular carcinoma prognosis prediction system (training set: area =0.715 under the curve, validation set: area =0.745 under the curve). The results of ROC curve analysis confirmed the high predictive value of the established risk model in TCGA and ICGC (fig. 2A). Kaplan-Meier survival analysis confirmed the difference in survival between the high and low GMSCore groups (FIG. 2B). Multifactorial COX analyses of TCGA and ICGC showed that high GMScore was an independent predictor of poor overall survival (training set: risk ratio =4.20, 95% confidence interval =2.38-7.40, p <0.001; validation set: risk ratio =3.91, 95% confidence interval =1.92-7.97, p < 0.001), even better than staged risk ratio (fig. 2C).
(3) GMSCore is superior to other biomarkers in prognosis prediction
ROC curve analysis showed that GMScore was significantly superior to other biomarkers in predicting 3-year overall survival (fig. 3A and 3B).
(4) High GMSCore resistance to TACE treatment
We investigated the effect of high GMScore on Transcatheter Arterial Chemoembolization (TACE) results. In the TCGA HCC cohort, high GMSCore significantly reduced the overall survival of patients receiving adjuvant TACE (p =0.003; FIG. 4A) or rescue TACE after relapse (p <0.001; FIG. 4B). Similar results were found by analysis of patients treated with TACE in ICGC HCC cohort (fig. 4C).
(5) GMSCore is associated with immune profiles and can predict response to ICI
TMB has been identified as a predictor of the efficacy of ICI in a variety of cancers. In this study, we found that high GMSCore significantly increased the expression of immune checkpoint genes, including PD-1, CTLA-4, TIM-3 and TIGIT in TCGA and ICGC, compared to low GMSCore (FIG. 5A). For immune cell infiltration in tumors, the high GMScore significantly increased the abundance of regulatory T cells (Tregs), but significantly decreased the abundance of M1 macrophages (fig. 5B). These findings suggest that high GMScore may lead to a "cold" tumor immune microenvironment. We used the TIDE algorithm to calculate T cell dysfunction and exclusion scores and predict ICI response by pooled TIDE scores. We observed that high GMScore significantly increased the exclusion score and the TIDE score compared to low GMScore (fig. 5C). Thus, for the predicted response rates, the high GMSCore HCC was significantly lower in TCGA and ICGC than the low GMSCore HCC (FIG. 5D).
(6) Verification of constructed prognostic model in existing immune checkpoint inhibitor treatment data
Using the melanoma cohort for ICI treatment to validate the effect of GMSCore on immunotherapy outcomes, it was found that the objective remission rate for the high GMSCore subgroups was significantly lower than for the low GMscore subgroups (14.3% vs 66.7%, p =0.016; FIG. 5E). More importantly, the overall lifetime was significantly reduced for the high GMSCore compared to the low GMSCore (p =0.039; FIG. 5F).
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. A method for constructing a glutamine metabolism gene label scoring model for predicting prognosis and treatment resistance of hepatocellular carcinoma is characterized by comprising the following steps: 1) Extracting and collecting RNA components of hepatocellular carcinoma tumor tissues by adopting an extraction and division module, purifying and sequencing the RNA components, and randomly dividing patients into a training set and a verification set;
2) Analyzing the related data and the drawing by adopting a data analysis module;
3) Determining a glutamine metabolic gene differentially expressed between a tumor tissue and a paracancerous normal tissue by using a differential expression module;
4) Identifying glutamine metabolism related genes which are statistically related to liver cancer prognosis by using a gene screening module, and establishing a glutamine metabolism gene prognosis model GMScore, wherein the GMScore =0.374 × SLC1A5 mRNA expression quantity +0.359 × GAPDH mRNA expression quantity +0.264 × SLC38A1 mRNA expression quantity +0.112 × SLC38A7 mRNA expression quantity-0.049 FTCD mRNA expression quantity-0.113 × MTHFS mRNA expression quantity-0.157 × TGO2 mRNA expression quantity, the gene screening module identifies the glutamine metabolism related genes which are statistically related to prognosis, and further, determining the most valuable genes and corresponding prognosis coefficients thereof by using LASSO regression analysis to construct a glutamine metabolism gene model, and the GMScore is the sum of the mRNA expression levels of the glutamine metabolism related genes which are included in the model and weighted by the corresponding coefficients;
5) Dividing patients in a selected training set into a high-risk group and a low-risk group by adopting a grouping module and a GMSCore optimal boundary value, comparing the overall life cycles of the two groups of patients by adopting an assessment prognosis module, assessing the treatment resistance of the postoperative transcatheter arterial chemoembolization patients by adopting a treatment resistance module through the two groups of postoperative life cycles, and predicting the difference between the tumor immune escape potential and the curative effect of an immune checkpoint inhibitor by adopting an immune scoring module, wherein the grouping module defines the patients as the high-risk group by the GMSCore higher than the optimal boundary value, and defines the patients as the low-risk group by the GMSCore lower than the optimal boundary value; determining an optimal cut-off value between high and low risk subgroups based on gene expression and overall survival using the surfminer R package; the assessment prognosis module compares the overall survival of two groups of patients with predicted hepatocellular carcinoma using the Kaplan-Meier method of the log rank test; evaluating the prognosis value of the model through survival analysis, a receiver operating characteristic curve ROC, a time dependence ROC curve, an area AUC under the ROC curve and multi-factor COX proportional risk regression analysis; calculating the risk ratio of the prognostic factors and 95% confidence interval thereof by using univariate and multivariate Cox proportional risk models;
6) And performing prognosis verification on the patients in the verification set by adopting a verification module.
2. The method of claim 1, wherein the data analysis module is used to analyze relevant data and plots, including student's t-test, chi-square test, fisher's exact probability test, or Wilcoxon rank sum test for comparison of differences between groups, statistical significance is set to p <0.05, and all p values are two-tailed.
3. The method of claim 1, wherein the differential expression module identifies a glutamine metabolism gene that is differentially expressed between tumor tissue and paracancerous normal tissue by limma R package; selecting genes associated with overall survival using univariate Cox regression, determining an optimal cut-off value between high and low risk subgroups based on association of gene expression with overall survival using the surfminer R package, assigning gene expression to 0 or 1 according to the optimal cut-off value; when gene expression is below the corresponding optimal cut-off value, a value of 0 is assigned, otherwise a value of 1 is assigned.
4. The method of claim 1, wherein the immune scoring module comprises:
immune, matrix and ESTIMATE scores: the ESTIMATE algorithm was used to ESTIMATE the relative proportion of stromal and immune cells in the tumor microenvironment and was shown in the form of stromal score, immuneScore and ESTIMATEScore;
tumor mutation burden TMB calculation: the total number of somatic non-synonymous mutations in the coding region, including missense, nonsense, splice sites, and frameshift mutations;
estimation of immune infiltration: based on transcriptome data, the CIBERSORT algorithm was used to quantify the infiltration abundance of immune cells in the tumor immune microenvironment;
tumor immune dysfunction and rejection score: TIDE is a transcriptome-based computational method for calculating T cell dysfunction and rejection scores to predict tumor immune escape potential and tumor efficacy against immune checkpoint inhibitors.
5. The method of claim 1, wherein the authentication module authenticates in an authentication set: calculating and verifying GMSCore of each tumor sample in a set, dividing patients into a high risk group and a low risk group according to an optimal boundary value in a training set, and evaluating the prognosis value of the model through survival analysis, a receiver operating characteristic curve (ROC), a time-dependent ROC curve, an area AUC under the ROC curve and multi-factor COX proportional risk regression analysis; comparing the overall survival of two groups of predicted hepatocellular carcinoma patients using the Kaplan-Meier method of log rank test; calculating the risk ratio of the prognostic factors and 95% confidence interval thereof by using univariate and multivariate Cox proportional risk models; comparing treatment resistance of postoperative transcatheter arterial chemoembolization patients and predicting tumor immune escape potential and differences in efficacy of immune checkpoint inhibitors.
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