CN117385034A - Application of marker combination in preparation of product for predicting curative effect of prognosis treatment of liver cancer patient - Google Patents
Application of marker combination in preparation of product for predicting curative effect of prognosis treatment of liver cancer patient Download PDFInfo
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Abstract
The invention belongs to the field of biomedicine, and particularly relates to application of a marker combination in preparing a product for predicting curative effect of prognosis treatment of a liver cancer patient, wherein the marker combination is a combination of ASF1A and HJURP. The prognosis of a liver cancer patient can be accurately predicted by detecting the expression of the two molecules.
Description
Technical Field
The invention relates to the biomedical field, in particular to application of a marker combination in preparation of a product for predicting curative effect of prognosis treatment of a liver cancer patient.
Background
Hepatocellular carcinoma (Hepatocellular Carcinoma, HCC) is one of the most common malignant tumors in liver cancer, and is the third leading cause of cancer-related death worldwide. Although the means for treating liver cancer clinically, including traditional treatment and immunotherapy, have been significantly advanced, the prognosis of liver cancer patients is poor and the survival rate of 5years is about 18% because most liver cancer patients are already advanced at the time of diagnosis, some patients are susceptible to primary or acquired drug resistance to chemotherapy, or insensitive to immunotherapy, and the prognosis and treatment effect of patients are evaluated by lack of effective prognostic markers and prognostic models.
Tumor immunotherapy mainly activates the immune system of the organism by inhibiting immune negative regulatory factors, enhancing the recognition capability of immune cells on tumor cell surface antigens and the like, and realizes killing and clearing of tumor cells. High heterogeneity is an important feature of tumors and is also an important cause of differences in susceptibility to immunotherapy among different patients, and effective discrimination and differentiation of patients who are susceptible to immunotherapy will greatly improve prognosis of this part of patients.
Histone chaperones affect all chromosomal processes, including chromosome segregation, gene expression, and DNA damage repair, by affecting the folding, stability, assembly, etc. of histones in nucleosomes. For most H3-H4 chaperones, they are usually identified as tumor promoting factors by upregulation or mutation. More and more studies have further demonstrated the role of this function in different types of cancer. Several studies have elucidated the clinical relevance of H3-H4 chaperone expression, for example as a novel biomarker for guiding therapeutic selection. However, the value of all H3-H4 chaperones in liver cancer, especially in prognosis of liver cancer, is still unclear.
Disclosure of Invention
The TCGA-LIHC data are used as a training set to construct a liver cancer prognosis risk model based on H3-H4 histone chaperones, the model can also be used for predicting sensitivity of liver cancer patients to immune checkpoint therapies, ICGC-LIRI and GSE154520 data are used as a testing set to evaluate the efficacy of the model, and liver cancer tissue chips containing prognosis information are further subjected to immunohistochemical staining to serve as a verification set to prove the effect of the model in predicting prognosis of the liver cancer patients.
In a first aspect, the invention relates to a prognosis marker combination of liver cells, a prognosis model comprising the prognosis marker combination, and a construction method of the model.
The marker combination is as follows: ASF1A and HJURP.
Preferably, the prognostic risk model calculates the risk value according to the following formula:
risk value (HC Score) HC-score=0.343×asf1a+0.247×hjurp
The "prognostic risk model" is also referred to herein as a "prognostic model", "risk model".
The "risk value" is also referred to herein as "HC Score", "HC-Score", "risk Score".
ASF1A and HJURP in the risk model represent the expression values of two molecules at the gene or protein level.
Preferably, the prognosis is overall survival of 1-5 years.
Preferably, the prognosis is overall survival of 1 year, overall survival of 3 years, overall survival of 5 years.
Preferably, the prognostic therapeutic efficacy of a liver cancer patient is predicted by detecting the expression level of the marker composition;
the expression level of the marker is the expression level of mRNA and the expression level of protein.
In another aspect, the invention also provides a method for detecting at least one of ASF1A and HJURP, a kit, and the use of the prognostic model in:
1) Predicting prognosis of a liver cancer patient;
2) Predicting the sensitivity of a liver cancer patient to an immunotherapy comprising anti-PD 1, CTLA4, LAG3 immune checkpoint inhibitor therapy.
Preferably, the product for detecting the expression amount of mRNA is any one of the following list: PCR-based detection platforms, southern hybridization platforms, northern hybridization platforms, dot hybridization platforms, fluorescent in situ hybridization platforms, DNA microarray platforms, ASO platforms, high throughput sequencing platforms.
Preferably, the product for detecting the protein expression level is any one of the following list: western blotting-based platforms, enzyme-linked immunosorbent assay platforms, radioimmunoassay platforms, sandwich assay platforms, immunohistochemical staining platforms, mass spectrometry detection platforms, immunoprecipitation assay platforms, complement fixation assay platforms, flow cytometry assay platforms, and protein chip platforms.
Preferably, the kit comprises reagents for detecting the combined expression levels of ASF1A and HJURP, wherein the expression levels are the expression level of mRNA and the expression level of protein.
Compared with the prior art, the invention has the beneficial effects that:
1. the prognosis of a liver cancer patient can be accurately predicted by detecting the expression of two molecules, and the method has the advantages of few molecules to be detected and good detection effect.
2. Meanwhile, the risk and the post model are verified at the gene and protein levels, and compared with the high requirements of instruments, operators and environment required for detecting the gene expression levels, the method for detecting the protein expression levels by immunohistochemistry is easier to realize in primary hospitals with poorer conditions in all aspects, so that the model has the advantage of wider applicability.
3. The model can also reflect the sensitivity of the patient to the existing immune checkpoint inhibitor therapy while predicting the prognosis of the patient.
Drawings
FIG. 1 is a comparison of the expression of a histone chaperone in liver cancer normal/tumor tissue; wherein (a) is the difference in expression of 19H 3-H4 chaperones in normal (n=50) in TCGA-LIHC and in tumor tissue (n=367), and (B) is the difference in expression of 19H 3-H4 chaperones in normal (n=40) in GEO (GSE 121248 +gse33006) and in tumor cell tissue (n=73);
TCGA-LIHC, cancer genome-hepatocellular carcinoma data; GEO, gene expression comprehensive database; ns is insignificant, p <0.05, p <0.01, p <0.001, p <0.0001;
note that: in fig. 1 (a) and (B), the left side is normal tissue, and the right side is tumor tissue;
FIG. 2 is a graph showing the relationship between the expression of histone chaperone and prognosis of liver cancer patients; FIGS. 2A-S show the prognostic relationship of KM survival curve analysis APLF, ASF1A, ASF1B, ATRX, DAXX, CHAF1A, CHAF1B, RBBP, DEK, DNAJC9, SUPT16H, SSRP1, HIRA, CABACN 1, UBN1, HJURP, MCM2, NASP, NPM1 and liver cancer patients;
FIG. 3 is a construction of an HCC risk model based on H3-H4 chaperones, where (A) is a profile of LASSO coefficients for each independent variable, (B) is bias likelihood bias for LASSO Cox regression analysis, (C) is a multivariate Cox analysis of H3-H4 chaperones and clinical pathology variables, 95% confidence interval, P value <0.05 is considered statistically significant;
FIG. 4 shows the evaluation of the predicted efficacy of HCC prognosis models, wherein (A) is the risk model tested in ICGC-LIRI using Kaplan-Meier survival curve analysis, and (B) is the ROC curve of 1, 3, 5year survival of patients with liver cancer predicted by the risk model in ICGC-LIRI;
FIG. 5 is an evaluation of predicted efficacy of a prognosis model for HCC, wherein (A) is a modified risk model using Kaplan-Meier survival curve analysis test in GSE14520, and (B) is a ROC curve of 1 year, 3 years, 5years survival of a patient with liver cancer predicted by the risk model in GSE 14520;
FIG. 6 shows the expression levels of ASF1A and HJURP in successive sections of liver cancer and normal liver tissue, wherein (A) is Immunohistochemical (IHC) staining of ASF1A and HJURP in successive sections of HCC and normal liver tissue. The black arrows indicate positive cells of ASF1A or HJURP, scale top,30um, bottom,10um; (B) For comparison of average densities of ASF1A in HCC and normal liver tissue; (C) For comparison of the average concentration of ASF1A in successive sections of HCC and healthy liver tissue in patients of different stages; (D) For comparison of ASF1A expression in HCC and normal tissue of patients with different stages in TCGA-LIHC; (E) For comparison of average densities of HJURP in HCC and normal liver tissue; (F) Comparison of mean densities of HJURP in normal liver tissue and HCC sequence sections for different stage patients; (G) For comparison of the expression of HJURP in HCC and normal tissues of stage patients in TCGA-LIHC,
wherein Mean of the integrated optical densities of the positive pixel regions analyzed by AIPATHWELL software, which represents the relative levels of the expressed protein in the tissue; * P <0.01, p <0.001, p <0.0001;
FIG. 7 shows the results of immunohistochemical staining and survival analysis of liver cancer tissue chip data, wherein (A) ASF1A and HJURP have the results of immunohistochemical staining in liver cancer tissue chip with a scale bar of 100um; comparison of ASF1A (B) and HJURP (C) average densities in liver cancer and normal tissue chips; average density comparison of ASF1A (D) and HJURP (E) in different stages of liver cancer and normal tissue chips; (F) In order to verify the risk model in liver cancer tissue chip data through Kaplan-Meier curve analysis; analyzing the relation between ASF1A (G) and HJURP (H) expression and prognosis of a liver cancer patient by using a Kaplan-Meier curve in liver cancer tissue chip data;
mean intensity, the average of the integrated optical densities of positive pixel area analysis by AIPATHWELL software, represents the relative level of protein expressed in tissue. ns, not significant; * P <0.05, < p <0.0001;
FIG. 8 is an analysis of the difference in immunoinfiltration between the high-risk and low-risk groups, (A) the difference in the proportion of 22 immune cells infiltrated in the low-risk and high-risk groups; wherein, the left side is low risk, the right side is high risk; (B) A thermal profile of the distribution of 28 immune cells in high/low risk liver cancer patients; spearman correlation analysis of risk score with infiltrated CD 4T cells (C), natural killer T cells (D), type II helper T cells (E); spearman correlation analysis between risk score and PD-1 (F), CTLA4 (G) and LAG3 (H) expression;
the R value represents the Spearman correlation coefficient, ns, not significant; * P is less than 0.05; * P <0.001; * P <0.0001.
Detailed Description
The following detailed description of specific embodiments of the invention is, but it should be understood that the invention is not limited to specific embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. The experimental methods described in the examples of the present invention are conventional methods unless otherwise specified.
The invention will be described in further detail below with the understanding that the terminology is intended to be in the nature of words of description rather than of limitation.
The term "biomarker" refers to an indicator of a patient's phenotype (e.g., pathological state or possible responsiveness to a therapeutic agent) that can be detected in a biological sample of the patient.
The "biological sample" of the present invention includes: biopsy tissue, peripheral blood, tissue, blood, serum, plasma, urine, saliva, semen, milk, cerebral spinal fluid, tears, sputum, mucus, lymph, and cytosol.
Specifically, the biomarker comprises ASF1A and HJURP, and a prediction model formed by the biomarker can predict prognosis of a liver cancer patient and the curative effect of anti-PD-1, CTLA4 and LAG3 immune checkpoint treatment based on the expression level of the biomarker. The term "expression level" or "expression level" as used herein generally refers to the amount of expression of a biomarker in a biological sample.
The detection of the level of gene expression described herein may employ assays known in the art, including but not limited to methods of detecting the amount of RNA transcript of the biomarker or the amount of polypeptide encoded by the biomarker.
In this context, the RNA transcript of the biomarker may be converted to its complementary cDNA by methods known in the art, and the amount of RNA transcript may be obtained by determining the amount of complementary cDNA. In this context, RNA transcripts may be detected and quantified by methods such as hybridization, amplification, sequencing, including but not limited to methods of hybridizing RNA transcripts to probes or primers, methods of detecting the amount of RNA transcripts or their corresponding cDNA products by various quantitative PCR techniques or sequencing techniques based on Polymerase Chain Reaction (PCR). Such quantitative PCR techniques include, but are not limited to, fluorescent quantitative PCR, real-time PCR, or semi-quantitative PCR techniques. Such sequencing techniques include, but are not limited to, sanger sequencing, second generation sequencing, third generation sequencing, single cell sequencing, and the like.
The liver cancer refers to liver cell liver cancer.
The "kit" of the invention is an article of manufacture, e.g., a package or container, comprising a kit for specifically detecting a biomarker gene or protein of the invention. In certain embodiments, the article of manufacture is promoted, distributed, or sold as a unit for carrying out the methods of the present invention.
The indexes of prognosis include the following indexes: overall survival (1-5yearsOverall survival rate,OS), immunotherapy sensitivity (Sensitivity to immunotherapy) for 1-5 years.
The platform comprises a corresponding detection instrument and a detection reagent when the corresponding method is used, such as a PCR-based detection platform, wherein the detection instrument comprises a PCR amplification instrument, and the detection reagent comprises amplification primers, enzymes and ddH 2 O 2 。
Example 1
Constructing a liver cancer prognosis risk model based on H3-H4 histone chaperone, and testing the prognosis effect
1. Building a model
(1) Using TCGA-LIHC comprising survival information and liver cancer data of gene expression (non-tumor=50, tumor=375) as training set to build a model;
ICGC-LIRI data (tumor=230) and GSE14520 (tumor 209) data as test sets;
liver cancer tissue chip data (80 cases) containing information is used as a verification set.
(2) 19 known H3-H4 chaperones were obtained by literature, 19H 3-H4 chaperones being APLF, ASF1A, ASF1B, ATRX, DAXX, CHAF A, CHAF1B, RBBP4, DEK, DNAJC9, SHPT16H, SSRP1, HIRA, CABACin 1, UBN1, HJurp, MCM2, NASP, NPM1, respectively.
(3) In TGCA-LIHC data (training set), the expression of H3-H4 chaperones in normal and liver cancer (LIHC) tissues was compared, and all 19H 3-H4 chaperones were found to be significantly highly expressed in liver cancer tumor tissues (fig. 1A). While validating chaperone expression by analysis of the dataset of GSE121248 and GSE33006 in GEO database, similar results were found with almost all H3-H4 chaperones significantly over-expressed in tumor tissue except APLF, HIRA and NASP (fig. 1B).
Analysis of the expression of each H3-H4 chaperone versus liver cancer prognosis using the Kaplan-Meier curve showed that high expression of 16 chaperones was associated with poor liver cancer prognosis (FIG. 2). Further, single factor Cox was used to regress to 15H 3-H4 chaperones that were significantly correlated with liver cancer prognosis (table 1).
TABLE 1 univariate Cox regression analysis of H3-H4 chaperones in TCGA-LIHC
Taken together, these results indicate that H3-H4 chaperone protein is significantly more expressed in HCC tissue than in normal tissue in the liver, and that H3-H4 chaperone protein is a good candidate for predicting prognosis.
(4) LASSO-Cox regression analysis was used to reduce the number of candidate H3-H4 chaperones in the prognostic model. The change trace of each candidate H3-H4 histone is shown in FIGS. 3A and 3B. After LASSOCox analysis, 5 histone partners are screened together for multivariate Cox regression, wherein the 5 histone partners are APLF, ASF1A, HJURP, NASP and NPML respectively, the result of the multivariate Cox regression analysis is shown in fig. 3C, and it can be seen from the figure that ASF1A, HJURP is still obviously related to prognosis of a liver cancer patient in multifactor Cox analysis, and is an independent risk factor affecting prognosis of the liver cancer patient.
Based on the 2 molecules screened by the analysis result, constructing a liver cancer prognosis risk model based on H3-H4 histone chaperones. The specific formula of the model is:
HC-Score＝0.343*ASF1A+0.247*HJURP
median HC scores for all patients were used as cutoff values and patients in both test sets were divided into two high and low risk groups. Kaplan-Meier survival analysis was performed and the ability of the prognosis model to predict survival in1, 3, 5years for liver cancer patients was assessed by ROC curve.
2. Model testing
(1) To evaluate the accuracy of a prognosis model consisting of 2 molecules in predicting liver cancer prognosis, kaplan-Meier survival analysis was performed on the test set ICGC-LIRI, and the ability of the prognosis model to predict survival in1, 3, 5years for liver cancer patients was analyzed using ROC curve, and the respective AUC values were compared, and the results are shown in fig. 4 and 5.
The Kaplan-Meier survival analysis results showed that the prognosis of high risk scoring liver cancer patients in the ICGC-LIRI cohort was worse, as shown in fig. 4A, ROC curve analysis found that the area under the curve (AUC) corresponding to survival rates of 1 year, 3 years and 5years of the model predicted liver cancer patients was 0.767,0.731,0.809, respectively, as shown in fig. 4B. The result shows that the prognosis model constructed by ASF1A and HJURP can accurately predict survival rate of liver cancer patients for 1 year, 3 years and 5 years.
Next, using the same formula in test set GSE14520, a risk score was calculated for each sample, as shown in fig. 5, and survival analysis and subject work feature (ROC) curve analysis were performed, which showed that the area under the curve (AUC) corresponding to survival rates of 1 year, 3 years and 5years was 0.782,0.781,0.732, respectively, and the results showed the same trend as that of training set ICGC-LIRI.
These results indicate that a risk score calculated based on 2 risk features can better predict prognosis of liver cancer patients.
(2) To further verify the protein levels of ASF1A and HJURP in liver cancer and normal tissues, 62 clinical samples were collected from the beijing hospital for immunohistochemical staining, and the protein expression levels of ASF1A and HJURP were counted for each sample, and as a result, it was found that both molecules were significantly highly expressed in liver cancer tissues compared to normal tissues, and the higher the stage in which liver cancer patients were located, the higher the expression of both molecules was, as shown in fig. 6.
Subsequently, using a liver cancer tissue chip containing prognosis information as an external verification data set, verifying the constructed risk model on the protein level, and performing immunohistochemical staining on the liver cancer tissue chip to obtain the protein expression levels of ASF1A and HJURP of each sample, and further verifying the previous results by comparing the expression conditions of the two molecules in normal liver cancer and tumor tissues, namely that in liver cancer tumor tissues, the expression levels of the two molecules of ASF1A and HJURP are higher, as shown in figures 7A-E.
Furthermore, the protein expression levels corresponding to the two molecules are substituted into the constructed risk model, and the risk score of each patient is calculated to carry out Kaplan-Meier survival analysis. As a result, it was found that when ASF1A or HJURP alone was used for the prognostic analysis, although the prognosis of the patient highly expressing ASF1A or HJURP was relatively poor, there was no statistical difference as shown in FIGS. 7G, H. While prognosis analysis using the risk models constructed with ASF1A and HJURP may find that the high risk scoring patient prognosis is worse, as shown in fig. 7F. The result shows that the risk model can be used for predicting prognosis of liver cancer patients, and the prediction effect is superior to that of ASF1A or HJURP molecules singly.
In summary, the model was tested not only at the gene level using transcriptome sequencing data and probe chip data, but also at the protein level using tissue chip data. Therefore, the model has wider applicability.
Example 2
Risk models are used to distinguish patients who are susceptible to immunotherapy
Based on the constructed risk model, HC scores of all patients in the training set are calculated, median values of the HC scores are used as cutoff values, and the patients in the training set are divided into two groups of high risk and low risk. The proportion of immune cells in tumor tissue 22 was evaluated in each patient using cibelort and ssGSEA, comparing the differences in immune cell infiltration in the higher and lower risk groups of patients. Meanwhile, the correlation between HC score and immune cell infiltration proportion and the expression of immune checkpoints, namely PD-1, CTLA4 and LAG3, are analyzed.
The CIBERSORT algorithm is used to quantify cellular composition from gene expression profiles. In this study, the proportion of 22 immune cells between the high risk group and the low risk group was analyzed using cibert. To explore the correlation of risk scores with immune cell infiltration, samples were selected from the first 50 and the last 50 of the risk scores and ssGSEA analysis was performed using R-package "GSVA". Spearman correlation analysis was used to assess the correlation between immune cell abundance and risk score.
The difference in infiltration of 22 immune cells between the high/low risk groups was evaluated using cibelort and found that immune cell populations including cd8+ T cells, cd4+ memory resting T cells, cd4+ memory activated T cells, follicular helper T cells, activated NK cells, monocytes, macrophages, M2 macrophages and neutrophils were significantly different between the two groups as shown in fig. 8A.
Based on the RNA-seq data of the TCGA-LIHC cohort, abundance of 28 immune cells was estimated using ssGSEA. The results show that there is more immune cell infiltration in patients with high risk scores, as shown in figure 8B. The correlation coefficient between immune cell abundance and risk score was then calculated by Spearman correlation analysis and the risk score was found to correlate positively with the abundance of activated CD 4T cells (r=0.47, p < 2.2E-16), natural killer T cells (r=0.22, p= 0.000039) and type II T helper cells (r=0.4, p=6.2E-15), as shown in fig. 8C-E.
In addition, the correlation of risk scores with a number of predictors of immune therapy response was analyzed, and as a result, it was found that the risk scores correlated positively with known immune checkpoint genes, including PD-1 (r=0.21, p=6.3 e-05), CTLA4 (r=0.28, p=9.8 e-08), and LAG (r=0.26, p=1.3 e-06), as shown in fig. 8F-H.
The above results indicate that high risk scoring liver cancer patients may be more sensitive to immune checkpoint therapies.
Taken together, the above results, the immune cell infiltration was more in the high risk group patients and the risk score correlated significantly positively with the expression of the patient's immune checkpoint, suggesting that high risk patients are more likely to benefit from immunotherapy despite their poorer prognosis.
It should be noted that, when the claims refer to numerical ranges, it should be understood that two endpoints of each numerical range and any numerical value between the two endpoints are optional, and the present invention describes the preferred embodiments for preventing redundancy.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The application of the marker combination in preparing a product for predicting the curative effect of prognosis treatment of a liver cancer patient is characterized in that the marker combination is a combination of ASF1A and HJURP.
2. The use according to claim 1, wherein the prognosis is overall survival of 1-5 years.
3. The use according to claim 2, wherein the prognosis is overall survival of 1 year, overall survival of 3 years, overall survival of 5 years.
4. The use according to claim 1, wherein the product is used to detect the expression level of the marker composition;
the expression level of the marker is the expression level of mRNA and the expression level of protein.
5. The use according to claim 4, wherein the product for detecting the expression level of mRNA level is any one of the following list: PCR-based detection platforms, southern hybridization platforms, northern hybridization platforms, dot hybridization platforms, fluorescent in situ hybridization platforms, DNA microarray platforms, ASO platforms, high throughput sequencing platforms.
6. The use according to claim 1, wherein the product for detecting the expression level of the protein is any one of the following list: western blotting-based platforms, enzyme-linked immunosorbent assay platforms, radioimmunoassay platforms, sandwich assay platforms, immunohistochemical staining platforms, mass spectrometry detection platforms, immunoprecipitation assay platforms, complement fixation assay platforms, flow cytometry assay platforms, and protein chip platforms.
7. A model for predicting the efficacy of a prognostic treatment for a liver cancer patient, said model being constructed using the marker combination of claim 1, said model being as follows:
risk value = 0.343 asf1a+0.247 hjurp.
8. Use of the model of claim 7 for the preparation of a product for predicting the efficacy of a prognostic treatment in a patient suffering from liver cancer.
9. Use of the model of claim 7 for the preparation of a product for predicting the efficacy of anti-PD-1, CTLA4, LAG3 immune checkpoint therapy in a liver cancer patient.
10. The use of claim 9, wherein the immune checkpoint therapy efficacy is sensitivity to an anti-immune checkpoint therapy.
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