CN114672569A - Tryptophan metabolism gene-based liver cancer prognosis evaluation method - Google Patents
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Abstract
The invention discloses a tryptophan metabolism gene-based liver cancer prognosis evaluation method, which is characterized in that the risk index of a liver cancer patient is calculated based on a formula (1); dividing RiskScore high-risk and low-risk groups according to the threshold value of 0, drawing a survival curve by adopting a Kaplan-Meier method, and evaluating the prognosis effect of the liver cancer; the invention has the advantages that a liver cancer clinical prognosis evaluation model is constructed according to 10 key genes of tryptophan metabolism phenotype, has stronger robustness, is independent of clinical pathological characteristics, can independently evaluate the prognosis condition of a liver cancer treatment means, and provides data support for effectively analyzing treatment data, optimizing a liver cancer clinical treatment scheme and formulating a personalized treatment scheme.
Description
Technical Field
The invention relates to the field of liver cancer research, in particular to a tryptophan metabolism gene-based liver cancer prognosis evaluation method.
Background
Liver cancer is a refractory malignant tumor with high heterogeneity and the highest mortality rate, and the mortality rate of the liver cancer is second place and is only second to that of lung cancer. The treatment means for liver cancer are many, but the five-year survival rate after treatment is very low, only about 12%, and part of the reasons are that the prognosis condition of the liver cancer treatment means cannot be evaluated by an effective means at present, so that the clinical treatment scheme for liver cancer has certain blindness, personalized treatment cannot be performed on liver cancer patients, treatment data cannot be effectively analyzed, and data support is provided for optimizing the clinical treatment scheme for liver cancer.
Disclosure of Invention
The invention aims to provide a liver cancer prognosis evaluation method based on tryptophan metabolism genes.
In order to realize the purpose, the invention adopts the following technical scheme:
the liver cancer prognosis evaluation method based on the tryptophan metabolism genes comprises the following steps: calculating a risk index for the liver cancer patient based on formula (1); dividing RiskScore high-risk and low-risk groups according to the threshold value of 0, drawing a survival curve by adopting a Kaplan-Meier method, and evaluating the prognosis effect of the liver cancer;
RiskScore=Σβi×Expi formula (1);
wherein, i represents the ith gene which is obviously related to the prognosis of the liver cancer based on the tryptophan metabolism gene, beta is a Cox regression coefficient of the gene expression level, and Exp is the gene expression level.
Further, the genes significantly related to liver cancer prognosis based on tryptophan metabolism genes include CDK1, TROAP, G6PD, MMP1, BAIAP2L2, PTTG1, LCAT, CYP2C9, CFHR3, SLC22a 10.
Further, the assessment includes prognostic signatures, clinical signatures, relative abundance and infiltration of immune cells, likelihood of immune escape, differential expression of immune checkpoint genes, degree of response to traditional chemotherapeutic drugs, gene metabolism pathway variability.
The specific principle of the method is as follows:
the inventors downloaded mutation data and copy number variation data for TCGA-LIHC, as well as RNA-Seq data for TCGA-LIHC, from TCGA GDC API. RNA-Seq data for TCGA-LIHC with no clinical follow-up information removed; no survival time; samples without Status were screened to contain 360 primary tumor samples and 50 normal samples.
Expression profile data of GSE14520, GSE76427 and HCCDB18 data are obtained through a GEO database, and 242, 115 and 389 liver cancer samples are obtained through screening. The screening principle is as follows: and downloading annotation information corresponding to the chip platform, mapping the probes to the genes according to the annotation information, and removing the probes matched with the genes by one probe. Taking the median as the gene expression value when a plurality of probes are matched with one gene.
TRYPTOPHAN METABOLISM genes were obtained by the MSigDB database TRYPTOPHAN METABOLISM pathway "KEGG _ TRYPTOPHAN _ METABOLISM".
First, the correlation between tryptophan metabolism genes and the survival prognosis of liver cancer patients was verified through three aspects.
(1) Aiming at 40 genes related to tryptophan metabolism, gene set mutation analysis is carried out by using a hallmark gene set to compare a tryptophan metabolism gene mutation group with a non-mutation group, and the tryptophan metabolism gene mutation is found to cause function change, thereby prompting that the tryptophan metabolism gene mutation affects the survival prognosis of a liver cancer patient.
(2) Through comparison of Copy Number Variation (CNV) of tryptophan metabolism genes of primary liver cancer patients, the fact that the patients with increased CNV show higher expression level of the tryptophan metabolism genes than the patients with CNV loss is determined, and the fact that the expression level of the tryptophan metabolism genes influences survival prognosis of the liver cancer patients is suggested.
(3) By comparing the mRNA (messenger RNA) changes of the tryptophan metabolism genes in the primary tumor tissue and the paracancer normal tissue, the significant difference of the tryptophan metabolism gene expression levels is determined, and the tryptophan metabolism gene expression level is also suggested to influence the survival prognosis of the liver cancer patient.
Therefore, the inventor provides a liver cancer prognosis evaluation method based on tryptophan metabolism genes, which is used for evaluating the prognosis condition of a liver cancer treatment means and providing data support for effectively analyzing treatment data, optimizing a liver cancer clinical treatment scheme and formulating a personalized treatment scheme.
The inventor determines that 8 genes in 40 tryptophan metabolism genes are related to liver cancer prognosis through univariate Cox regression analysis, and finds that the 8 different tryptophan metabolism genes have significant correlation after calculating the pairwise correlation between the expression of the tryptophan metabolism genes related to the liver cancer prognosis in liver cancer. Then, based on the consistent clustering of the 8 tryptophan metabolism gene expression profiles related to the prognosis of liver cancer, the patients are classified, and the number of the best clusters is determined to be 2 according to the cumulative distribution function (abbreviated as CDF in English), which is marked as cluster 1 and cluster 2.
Then, the inventor further confirms that the prognosis of cluster 2 is better and the prognosis of cluster 1 is worse through single-factor analysis including prognosis characteristics, clinical characteristics, relative abundance and infiltration degree of immune cells, possibility of immune escape, differential expression of immune checkpoint genes, response degree to traditional chemotherapeutic drugs and difference of gene metabolic pathways.
Therefore, 739 differential genes were found by analyzing the differential genes between cluster 1 and cluster 2, and a single-factor cox analysis was performed by a coxph function of the subvalval package to identify 189 genes having a large influence on the prognosis of liver cancer, and these 189 differentially expressed genes were further compressed by lasso regression, and finally 10 genes with lambda = 0.066 were selected as target monitor genes. The 10 genes are used for constructing a liver cancer prognosis evaluation model. Calculating a risk index for the liver cancer patient based on formula (1); the RiskScore high-risk and low-risk groups are divided according to the threshold value of 0, namely, the RiskScore score calculated by the formula (1) is divided into the high-risk group and the low-risk group by taking the threshold value of 0 as a demarcation point. And a survival curve (abbreviated as KM in English) is drawn by adopting a Kaplan-Meier method, and the method can be used for evaluating the prognosis effect of the liver cancer.
The invention has the advantages that a liver cancer clinical prognosis evaluation model is constructed according to 10 key genes of tryptophan metabolism phenotype, has stronger robustness, is independent of clinical pathological characteristics, can independently evaluate the prognosis condition of a liver cancer treatment means, and provides data support for effectively analyzing treatment data, optimizing a liver cancer clinical treatment scheme and formulating a personalized treatment scheme.
Drawings
FIG. 1 is a graph of a KM curve and a ROC curve plotted by the method of the present invention.
FIG. 2 is a graph comparing the results of the assessment with clinical grading according to the method of the invention.
FIG. 3 is a graph comparing the high/low risk groups with the immune cell scores according to the methods of the present invention.
FIG. 4 is a graph of the difference in expression of the partial immune checkpoint genes in the high/low risk groups according to the methods of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
According to the visualized interactive display method for the concrete dam cracks, 10 tryptophan metabolism-based genes in TCGA data set samples and gene expression levels obviously related to liver cancer prognosis are used, the risk score of each sample is calculated according to a formula (1), a patient is evaluated to be a high risk group or a low risk group, and a survival curve is drawn.
(1) Verifying the efficiency of 1,3 and 5-year prognosis prediction evaluation of the method
ROC analysis is carried out on the evaluation result of RiskScore by using R software package timeROC, 1,3 and 5 years of prognostic prediction evaluation efficiency is respectively analyzed, Zscore is carried out on RiskScore, samples with RiskScore larger than zero after Zscore transformation are divided into high risk groups and low risk groups, and a KM curve is drawn to show that the high risk groups or the low risk groups have extremely significant difference p < 0.0001 (shown in figure 1), which indicates that 1,3 and 5 years of prognostic prediction evaluation efficiency of the method is higher.
(2) Comparison of the results of the methods described in this application with the consistency of clinical grading
Comparing the TNM grade and Stage clinical rating to the risksscore calculated by the method described herein, the risksscore increased with increasing clinical rating. Samples with higher clinical grade had higher RiskScore scores (as shown in figure 2). The method is consistent with clinical judgment standards and accurate in evaluation.
(3) Comparing the results of the evaluation with the patient's actual immune profile using the methods described herein
As shown in fig. 3, the relative abundance of 22 immune cells was significantly different between the high risk group and the low risk group determined by the methods described herein. The evaluation of immune cell infiltration by using ESTIMATE also shows that the low risk group determined by the method provided by the application has slightly higher ImmuneScore than the high risk group and has higher immune cell infiltration. The method can accurately reflect the prognostic immune characteristics of the liver cancer patients.
(4) Comparison of the results of the methods described herein with the results of actual immunotherapy for patients
As shown in fig. 4, there was a difference in partial immune checkpoint gene expression in the high risk group and the low risk group determined by the methods described herein. Evaluation of immunotherapy using the TIDE (http:// TIDE. dfci. harvard. edu /) software shows that the high risk groups identified by the methods described herein have a higher likelihood of immune escape and a lower likelihood of benefit from immunotherapy.
Meanwhile, the risk score calculated by the method has positive correlation with TIDE, IFNG, MDSC and exception scores and negative correlation with Dysfunction. The method can accurately reflect the immunotherapy effect of the liver cancer patient.
(5) Comparison of results of the methods described herein with immunotherapy
Patients with high risk scores in the method of the present application show significant clinical benefit in the anti-PD-L1 cohort (IMvigor210 cohort) and significantly prolonged overall survival, indicating that the method of the present application has good predictive performance in clinical applications.
Claims (3)
1. A liver cancer prognosis evaluation method based on tryptophan metabolism genes is characterized in that: calculating a risk index for the liver cancer patient based on formula (1); dividing RiskScore high-risk and low-risk groups according to the threshold value of 0, drawing a survival curve by adopting a Kaplan-Meier method, and evaluating the prognosis effect of the liver cancer;
RiskScore=Σβi×Expiformula (1);
wherein i represents the ith gene which is obviously related to the prognosis of the liver cancer based on the tryptophan metabolism gene, beta is a Cox regression coefficient of the gene expression level, and Exp is the gene expression level.
2. The method for prognosis evaluation of liver cancer based on tryptophan metabolism gene according to claim 1, wherein: the genes which are obviously related to the prognosis of the liver cancer based on the tryptophan metabolism genes comprise CDK1, TROAP, G6PD, MMP1, BAIAP2L2, PTTG1, LCAT, CYP2C9, CFHR3 and SLC22A 10.
3. The method for the prognostic evaluation of liver cancer based on tryptophan metabolism genes according to claim 1, wherein: the assessment includes prognostic signatures, clinical signatures, relative abundance and infiltration of immune cells, likelihood of immune escape, differential expression of immune checkpoint genes, differential gene metabolic pathway.
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