CN110580956B - Liver cancer prognosis markers and application thereof - Google Patents
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Abstract
The invention discloses a gene set for predicting prognosis of a patient with liver cancer and targeting therapy of liver cancer, because the prior art lacks enough biomarkers to predict the prognosis of the patient with liver cancer, especially for the patient with late liver cancer, the traditional treatment methods such as liver transplantation, surgical hepatectomy, early radio frequency treatment and the like are limited, therefore, the exploration of the novel biomarker is important for the treatment target of the liver cancer, the invention further screens the genes related to the liver cancer prognosis in the G2M checkpoint gene set through COX survival analysis, and a model influencing the prognosis of the liver cancer patient is constructed through multi-factor COX survival analysis, the accuracy and the specificity of the model are verified by a K-M plot curve, an ROC curve and the survival time and the survival state of the patient, therefore, the prediction model has important significance for prognosis and targeted therapy of the liver cancer patients.
Description
Technical Field
The invention belongs to the field of biological medicines, relates to a liver cancer prognosis marker and application thereof, and particularly relates to a group of novel cell cycle G2M checkpoint-related mRNAs, wherein the gene set can be used as a liver cancer prognosis marker.
Background
Liver cancer is one of the most fatal cancers in the world. According to statistics from 2003 to 2018, the mortality rate of liver cancer in all cancers rises sharply. The therapeutic efficacy of liver cancer depends to a large extent on the time interval from diagnosis to treatment, especially for patients with early stage liver cancer. Despite improvements in liver transplantation, surgical hepatectomy, early radio frequency therapy, etc., approximately 70% or more of liver cancer patients are diagnosed at an advanced stage, which limits the application of traditional therapies.
Genomic instability is an important marker of cancer, contributing to the development of cancer. Recent epidemiological studies report that two-thirds of cancers are caused by DNA replication errors. The G2/M checkpoint is the last checkpoint that prevents cells with DNA damage from entering the mitotic phase and therefore plays a crucial role in genomic stability and cancer development. However, there is currently no systematic study on the G2/M checkpoint-related genes of liver cancer.
In view of the lack of sufficient biomarkers in the prior art to predict the prognosis of patients with liver cancer, especially for patients with advanced liver cancer, traditional treatment methods such as liver transplantation, surgical hepatectomy, early rf treatment, etc. have been limited. The invention provides 4 mRNA as liver cancer prognosis markers and establishes a model for predicting the prognosis of a liver cancer patient. Finally, the accuracy and specificity of the model are verified through a K-M plot, an ROC curve and the survival time and survival state of the patient. In order to prolong the survival time of the liver cancer patient, the prognosis of the liver cancer patient can be interfered by targeting the gene in the model and regulating the expression level of the gene.
Disclosure of Invention
In order to prolong the life cycle of a liver cancer patient, the invention intervenes the prognosis of the liver cancer patient by the gene in the target model and regulating the expression level of the gene, and provides a basis for the prognosis analysis of the liver cancer patient and the target treatment of the liver cancer.
In order to achieve the above objects, the present invention provides a gene set for predicting prognosis and target therapy of liver cancer in a liver cancer patient, the gene set is composed of AMD1, KPNA2, SFPQ and UCK2, and prognosis of the liver cancer patient is characterized by a Risk score composed of the sum of products of gene expression levels and corresponding coefficients in the gene set:
Risk score＝0.325*AMD1+0.2129*KPNA2+0.6572*SFPQ+0.3166*UCK2；
the AMD1, KPNA2, SFPQ and UCK2 respectively express the expression level of each gene in a liver cancer patient.
Secondly, the invention provides a method for determining a gene set AMD1, KPNA2, SFPQ and UCK2 as liver cancer prognosis markers, which comprises the following steps:
(1) data were obtained from the TCGA database: downloading clinical data of liver cancer patients and RNA-Seq transcriptome data of liver cancer tissues and paracancer normal tissues, wherein 373 cases of liver cancer tissues and 50 cases of paracancer normal tissues in a TCGA database are subjected to RNA-Seq, wherein clinical data and RNA-Seq data which are simultaneously possessed by 369 cases of liver cancer patients are obtained;
(2) enrichment analysis by GSEA function: screening out a gene set with differential expression in RNA-Seq transcription group data of a liver cancer tissue and a paracancer normal tissue by taking ' NES ' >1 and NOM p-val <0.05 as standards, wherein NES represents an enrichment analysis score after normalization, NOM p-val represents a corrected p value, and the reliability of an enrichment result is characterized, wherein G2M checkpoint gene set of which the ' NES ' | ' is 2.02NOM p-val is 0.005 is a gene set with larger difference between the liver cancer tissue and the paracancer normal tissue, and further analyzing the gene set;
(3) survival assay using one-way COX: screening genes in a G2M checkpoint gene set which influence the prognosis of a liver cancer patient by taking P value <0.05 as a standard;
(4) survival assay using multifactorial COX: in order to more accurately construct a model for predicting the prognosis of a liver cancer patient, genes with HR >1.5 and P value <0.05 in a single-factor analysis result are screened, a survival model for affecting the prognosis of the liver cancer patient is constructed by utilizing multi-factor COX survival analysis, wherein HR represents a Risk coefficient of a specific gene influencing the prognosis of the liver cancer, AMD1, KPNA2, SFPQ and UCK2 in a G2M check point gene set are finally determined by the multi-factor COX survival analysis to serve as factors influencing the prognosis in the model, and the prognosis of the liver cancer patient is characterized by Risk score formed by the sum of products of gene expression levels and corresponding coefficients in the gene set:
AMD1, KPNA2, SFPQ and UCK2 of Risk score 0.325 AMD1+0.2129 KPNA2+0.6572 SFPQ +0.3166 UCK2 respectively express the expression level of each gene in liver cancer patients;
(5) the accuracy of the constructed prognosis model is verified by using the K-M plot survival curve of the total liver cancer patients: calculating the Risk score value of each patient according to the Risk score formula in the step (4), then sorting the patients from low to high according to the Risk score values of the patients, dividing the patients into a high Risk score group and a low Risk score group by taking the median of the Risk score values of all the patients as a division point, drawing a K-M plLiving curve of the patients in the high-Risk group and the low-Risk group, judging whether the survival time of the patients in the high-Risk group is different by using a Log-rank (Mantel-Cox) test method, and judging whether the difference exists between the two groups by using the Log-rank P ue <0.05 as a standard so as to verify the accuracy of the constructed prognosis model;
(6) the accuracy and specificity of the constructed prognosis model are verified by using an ROC curve: calculating the Risk score value of each patient according to the Risk score formula in the step (4), sequencing the patients from low to high according to the Risk score values of the patients, dividing the patients into a high Risk score group and a low Risk score group by taking the median of the Risk score values of all the patients as a dividing point, drawing an ROC curve, solving the area under the ROC curve, and verifying the accuracy and the specificity of the constructed prognosis model by utilizing the ROC curve;
(7) the accuracy of the constructed prognosis model is verified by comparing the survival time and the survival state of liver cancer patients of different groups of prognosis models: calculating the Risk score value of each patient according to the Risk score formula in the step (4), and then sorting the patients from low to high according to the Risk score values of the patients. Dividing the patients into a high Risk score group and a low Risk score group by taking the median of Risk score values of all the patients as a dividing point, drawing a scatter diagram of the survival time and the survival state of the patients by taking the Risk score value of the patients as an abscissa and the survival time of the patients as an ordinate, and comparing the survival time and the survival state of the patients between the high Risk score group and the low Risk score group to verify the accuracy of the constructed prognosis model;
(8) statistical analysis: data are shown as mean ± sd, with P value <0.05 as the criterion to determine whether statistical significance is present;
in the step (1), a TCGA database is adopted, and the type of the downloaded transcriptome data is HT Seq-FPKM value;
in the step (2), only screening a gene set of which the RNA-Seq data of the liver cancer tissue and the RNA-Seq data of the paracancer normal tissue have statistical difference and the absolute value of the enrichment score is greater than 1, and finally selecting a G2M gene set;
in the step (3), COX survival analysis is used for screening out genes related to liver cancer prognosis in the G2M gene set.
The invention further provides application of the marker for liver cancer prognosis prediction in preparation of a kit for assisting in liver cancer prognosis judgment.
Finally, the invention also provides a kit for assisting in judging liver cancer prognosis, and the kit contains the liver cancer prognosis marker disclosed by the invention.
Advantageous effects
Because the prior art lacks sufficient biomarkers to predict the prognosis of liver cancer patients, especially for patients with advanced liver cancer, traditional treatment methods such as liver transplantation, surgical hepatectomy, early stage radio frequency treatment, etc. are limited. Therefore, the exploration of novel biomarkers is crucial for the therapeutic targets of liver cancer. According to the invention, genes in the G2M checkpoint gene set related to liver cancer prognosis are further screened out through COX survival analysis, a model influencing the prognosis of a liver cancer patient is constructed through multifactor COX survival analysis, and the accuracy and specificity of the model are verified by a K-M plot curve, a ROC curve and the survival time and survival state of the patient, so that the prediction model has important significance for the prognosis and targeted therapy of the liver cancer patient.
Drawings
Fig. 1 is a technical flow chart of the invention.
FIG. 2 shows the GSEA analysis of the G2M checkpoint of example 2, wherein 195 genes are contained in the G2M gene set.
FIG. 3 is a graph of Risk score of 379 liver cancer patients of example 5, ranked from low to high, and divided into two groups, high Risk group and low Risk group, according to the median of Risk score of 379 liver cancer patients.
FIG. 4 is a K-M plot survival curves of 379 liver cancer patients divided into two groups of high Risk group and low Risk group according to their median Risk score in example 5.
FIG. 5 is a ROC curve for the total constructed model in example 6, with an area under the curve of 0.77.
FIG. 6 is a scattergram showing the survival time and survival status of the liver cancer patient in example 7.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
TCGA database: https:// portal.gdc.cancer.gov/, clinical data of liver cancer patients and RNA-Seq transcriptome data of liver cancer tissues and paracancer normal tissues were downloaded. In the TCGA database, 373 cases of liver cancer tissues and 50 cases of paracancerous normal tissues were subjected to RNA-Seq. Among them, 369 liver cancer patients had clinical data and RNA-Seq data at the same time. Since the mRNA expression profile data has been normalized by TCGA, no further normalization was performed on these data, and the pathological parameters of liver cancer patients are shown in table 1:
table 1.
Example 2
GSEA functional enrichment analysis is utilized, a gene set with differential expression in RNA-Seq transcription group data of liver cancer tissues and normal tissues beside cancer is screened out by taking the conditions that the absolute value of NES is larger than 1 and the NOM p-val is smaller than 0.05 as standards, NES represents an enrichment analysis score after normalization, and NOM p-val represents a corrected p value, so that the credibility of an enrichment result is represented. As shown in fig. 2, the G2M checkpoint gene set | NES | ═ 2.02NOM p-val ═ 0.005 is a gene set with large differences between liver cancer tissues and paracancerous normal tissues, and 195 genes are contained in the gene set and are to be further analyzed.
Example 3
Utilizing single-factor COX survival analysis, screening genes in the G2M checkpoint gene set that affect the prognosis of a liver cancer patient with P value <0.05 as a standard, in order to more accurately construct a model for predicting the prognosis of a liver cancer patient, in this example, genes with HR >1.5 and P value <0.05 are screened out from single-factor analysis results, utilizing multi-factor COX survival analysis, constructing a survival model that affects the prognosis of a liver cancer patient, wherein HR represents a risk coefficient of a specific gene affecting the liver cancer prognosis, and from the single-factor COX survival analysis results, 12 genes with HR >1.5 and P value <0.05 are screened out in total, and the results are shown in table 2:
table 2.
Example 4
Utilizing multifactor COX survival analysis to construct a survival model influencing the prognosis of the liver cancer patient, finally determining AMD1, KPNA2, SFPQ and UCK2 in a G2M checkpoint gene set as factors influencing the prognosis by the multifactor COX survival analysis, wherein the prognosis of the liver cancer patient is characterized by Risk score formed by the sum of products of the gene expression level and corresponding coefficients in the gene set:
Risk score＝0.325*AMD1+0.2129*KPNA2+0.6572*SFPQ+0.3166*UCK2
in the above formula, AMD1, KPNA2, SFPQ and UCK2 express the expression level of each gene in liver cancer patients respectively;
the detailed information results of the four genes in the multifactor COX survival assay model are shown in table 3:
TABLE 3
Example 5
The accuracy of the constructed prognosis model was verified using the K-M plot survival curves of the total liver cancer patients, the Risk score value of each patient was calculated according to the Risk score formula in example 4, as shown in fig. 3, and then the patients were ranked from low to high according to their Risk score values. Dividing the patients into a high Risk score group and a low Risk score group by taking the median of Risk score values of all the patients as a division point, drawing a K-M plot survival curve of the patients in the high Risk group and the low Risk group as shown in figure 4, judging whether the survival time of the patients in the high Risk group and the low Risk group is different by using a Log-rank (Mantel-Cox) test method, and judging whether the difference exists between the two groups by using the Log-rank P value <0.05 as a standard so as to verify the accuracy of the constructed prognosis model.
Example 6
The accuracy and the specificity of the constructed prognosis model are verified by using an ROC curve, the Risk score value of each patient is calculated according to the Risk score formula in the embodiment 4, then the patients are ranked from low to high according to the Risk score values of the patients, the median of the Risk score values of all the patients is taken as a division point, the patients are divided into a high Risk score group and a low Risk score group, as shown in FIG. 5, the ROC curve is drawn, the accuracy and the specificity of the constructed prognosis model are verified by using the ROC curve, the area under the ROC curve is 0.77, and the model is proved to have better accuracy and specificity in the process of predicting the prognosis of the liver cancer patient.
Example 7
Verifying the accuracy of the constructed prognosis model by comparing the survival times and survival states of the liver cancer patients of different groups of prognosis models, calculating the Risk score of each patient according to the Risk score formula of example 4, then ranking the patients from low to high according to the Risk score values of the patients, dividing the patients into a high Risk score group and a low Risk score group by taking the Risk score values of the patients as median of all the Risk score values of the patients as a dividing point, plotting the survival times and the survival states of the patients by taking the Risk score values of the patients as an abscissa, comparing the survival times and the survival states of the patients between the high Risk score group and the low Risk score group to verify the accuracy of the constructed prognosis model, as shown in FIG. 6, the survival times of the patients in the high Risk score group are significantly lower than those in the low Risk score group, and the number of the patients in the high Risk score group is significantly greater than that in the low Risk score group, these results further validate the accuracy of the model's effect in predicting patient prognosis.
Statistical analysis: data are shown as mean ± sd, with P value <0.05 as a criterion to determine whether statistical significance is present.
Claims (7)
1. A group of gene sets for predicting prognosis and target treatment of liver cancer patients is characterized in that the gene sets are composed of AMD1, KPNA2, SFPQ and UCK2, the prognosis condition of the liver cancer patients is characterized by Risk score formed by the sum of products of gene expression quantity and corresponding coefficients in the gene sets, wherein the Risk score is calculated in the following mode: risk score 0.325 AMD1+0.2129 KPNA2+0.6572 SFPQ +0.3166 UCK 2.
2. The set of genes for predicting prognosis and targeted therapy of liver cancer in a patient with liver cancer according to claim 1, comprising the following steps:
(1) data were obtained from the TCGA database: downloading clinical data of a liver cancer patient and RNA-Seq transcriptome data of a liver cancer tissue and a paracancer normal tissue;
(2) enrichment analysis by GSEA function: screening out a gene set with differential expression in RNA-Seq transcription group data of a liver cancer tissue and a paracancer normal tissue by taking ' NES ' >1 and NOM p-val <0.05 as standards, wherein NES represents an enrichment analysis score after normalization, NOM p-val represents a corrected p value, and the reliability of an enrichment result is characterized, wherein G2M checkpoint gene set of which the ' NES ' | ' is 2.02NOM p-val is 0.005 is a gene set with larger difference between the liver cancer tissue and the paracancer normal tissue, and further analyzing the gene set;
(3) survival assay using one-way COX: screening genes in a G2M checkpoint gene set which influence the prognosis of a liver cancer patient by taking P value <0.05 as a standard;
(4) survival assay using multifactorial COX: constructing a survival model influencing the prognosis of a liver cancer patient, screening genes with HR being more than 1.5 and P value being less than 0.05 in a single-factor analysis result, constructing the survival model influencing the prognosis of the liver cancer patient by utilizing multi-factor COX survival analysis, wherein HR represents a risk coefficient of the influence of a specific gene on the prognosis of the liver cancer, finally determining AMD1, KPNA2, SFPQ and UCK2 in a G2M checkpoint gene set as factors influencing the prognosis in the model by the multi-factor COX survival analysis, and the prognosis condition of the liver cancer patient is characterized by Riskscore formed by the sum of products of gene expression levels in the gene set and corresponding coefficients:
AMD1, KPNA2, SFPQ and UCK2 of Risk score 0.325 AMD1+0.2129 KPNA2+0.6572 SFPQ +0.3166 UCK2 respectively express the expression level of each gene in liver cancer patients;
(5) the accuracy of the constructed prognosis model is verified by using the K-M plot survival curve of the total liver cancer patients: calculating the Risk score value of each patient according to the Risk score formula in the step (4), then sorting the patients from low to high according to the Risk score values of the patients, dividing the patients into a high Risk score group and a low Risk score group by taking the median of the Risk score values of all the patients as a dividing point, drawing a K-M plot survival curve of the patients in the high-Risk group and the low-Risk group, judging whether the survival time of the patients in the high-Risk group is different by using a Log-rank test method, and judging whether the difference exists between the two groups by using a Log-rank P value <0.05 as a standard so as to verify the accuracy of the constructed prognosis model;
(6) the accuracy and specificity of the constructed prognosis model are verified by using an ROC curve: calculating the Risk score value of each patient according to the Risk score formula in the step (4), sequencing the patients from low to high according to the Risk score values of the patients, dividing the patients into a high Risk score group and a low Risk score group by taking the median of the Risk score values of all the patients as a dividing point, drawing an ROC curve, solving the area under the ROC curve, and verifying the accuracy and the specificity of the constructed prognosis model by utilizing the ROC curve;
(7) the accuracy of the constructed prognosis model is verified by comparing the survival time and the survival state of liver cancer patients of different groups of prognosis models: calculating the Risk score value of each patient according to the Risk score formula in the step (4), then sorting the patients from low to high according to the Risk score values of the patients, dividing the patients into a high Risk score group and a low Risk score group by taking the median of the Risk score values of all the patients as a division point, drawing a scatter diagram of the survival time and the survival state of the patients by taking the Risk score value of the patients as an abscissa and the survival time of the patients as an ordinate, and comparing the survival time and the survival state of the patients between the high Risk score group and the low Risk score group to verify the accuracy of the constructed prognosis model;
(8) and (5) carrying out statistical analysis.
3. The set of genes for predicting prognosis and targeting therapy of liver cancer in a patient with liver cancer according to claim 2, wherein the TCGA database is used in step (1), and the transcriptome type selected for downloading is HT Seq-FPKM value.
4. The set of gene sets according to claim 2, wherein in step (2), only the gene sets with the RNA-Seq data of the liver cancer tissue and the paracancer normal tissue having statistical difference and the absolute value of the enrichment score greater than 1 are selected, and the G2M checkpoint gene set is finally selected.
5. The set of genes for predicting the prognosis of a patient with liver cancer and targeting the liver cancer therapy as set forth in claim 2, wherein in the step (3), the genes related to the prognosis of liver cancer in the G2M checkpoint gene set are screened by COX survival assay.
6. A set of markers for prognosis prediction of liver cancer, which contains a set of gene sets for predicting the prognosis of a patient with liver cancer and targeting therapy of liver cancer according to claim 1, and the application of the markers in preparing a kit for assisting in judging the prognosis of liver cancer.
7. A kit for assisting in determining prognosis of liver cancer, the kit comprising the set of genes for predicting prognosis and targeting therapy of liver cancer in a patient with liver cancer according to claim 1.
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