CN115838803A - Biomarker for prognosis and response efficiency to immune checkpoint inhibitor of colorectal cancer patient and application of biomarker - Google Patents

Abstract

The present invention relates to a biomarker for predicting the prognosis of colorectal cancer and a biomarker for the response efficiency of colorectal cancer to immune checkpoint inhibitor therapy, which is constructed from four immune-related genes: BMP5, NRG1, INHBB and HSPA1A. In addition, the present invention also relates to a kit for predicting the prognosis of colorectal cancer and the response efficiency of immune checkpoint inhibitor therapy, and the role of BMP5, NRG1, INHBB and HSPA1A proteins in preparing the prognosis of colorectal cancer patients and immune checkpoint inhibitors The application of the response efficiency biomarker in the preparation of a kit for the prediction of the prognosis of colorectal cancer or the screening of the response efficiency of immune checkpoint inhibitor therapy.

Description

Biomarkers of prognosis and response efficiency to immune checkpoint inhibitors in colorectal cancer patients Chronicles and their applications

technical field

The invention relates to the prognosis of colorectal cancer and the response efficiency of immune checkpoint inhibitors, in particular to an immune gene model 4-IRG, which is scored by combining the expression levels of immune-related genes BMP5, NRG1, INHBB and HSPA1A, and belongs to the field of biomedical technology .

Background technique

Colorectal cancer (CRC) is a common malignant tumor. According to the statistics of the American Cancer Society, the morbidity and mortality rate ranks third among cancers ^[1] ; according to the statistical model, the incidence of CRC in 2021 will be 149,500 cases (7.9% of new cancer cases) and 52,980 deaths in 2021 (8.7% of cancer-related deaths). With changes in diet, lifestyle and environment, the incidence of colorectal cancer is increasing year by year, and the population of colorectal cancer patients is getting younger and younger. Although conventional surgery combined with chemotherapy and targeted therapy has improved the overall survival of CRC patients in the past, the 5-year survival of these patients is highly dependent on tumor stage. Most patients are diagnosed at an advanced stage of the disease and do not benefit from standard surgery-based treatment; thus, most patients have a poor prognosis. Although organized, population-based screening strategies have been implemented ^[2] , successful screening remains opportunistic ^[3] . An important strategy to reduce the disease burden associated with CRC is to explore effective and simple screening methods and strategies to predict patient survival.

The rapid development of cancer immunology research has spawned several new immunotherapies that can promote comprehensive therapeutic effects on tumors including CRC ^[4-10] . Although immunotherapy with direct immune blockade has been successful in the clinical application of immune checkpoint inhibitors (Immune Checkpoint Inhibitors, ICI), such as programmed death 1 (Programmed Death 1, PD-1), cytotoxic T lymphocyte-associated protein 4 (Cytotoxic T Lymphocyte-Associated protein 4, CTLA-4) and programmed death ligand 1 (Programmed Death Ligand 1, PD-L1); however, in most patients with solid tumors such as CRC, there is no durable clinical response ^{[11- 13]} . ICI responds poorly in CRC with strong mismatch repair ability (mismatch-repair-proficient, pMMR) and low levels of microsatellite-stable (MSS) or microsatellite instability (Microsatellite Instability, MSI-L) ^{[ 14-16]} . Many studies have shown that tumor mutational burden (Tumour Mutational Burden, TMB) is related to the efficacy of ICI ^[17-19] . Although patients with high TMB generally respond better to treatment and patients who respond to immunotherapy have a higher mean TMB than patients who do not respond, the possibility of benefit from immunotherapy cannot be completely ruled out in patients with low TMB ^{[ 20]} . With the growing challenge of identifying patients who respond to immunosuppressants, how to screen cancer patients for the group that would benefit most from immunotherapy has become a research focus. The tumor immune microenvironment (Tumour Immune Microenvironment, TME) affects the occurrence and progression of tumors ^[21] , and is mainly responsible for the response to immunotherapy ^[22] . Analysis of the immune components of CRC patients facilitates the development of robust biomarkers for early screening of CRC and identification of patients who will respond to immunotherapy.

【references】

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Contents of the invention

In view of the above shortcomings of the prior art, according to the embodiments of the present invention, it is hoped to improve the prognosis of colorectal cancer patients and the response efficiency of immune checkpoint inhibition. By analyzing the expression levels of immune-related genes in colorectal cancer patients, 4-IRG The model (constructed based on BMP5, NRG1, INHBB and HSPA1A), showed that the 4-IRG low-risk group had a better prognosis in colorectal cancer, and the 4-IRG low-risk group had stronger immunogenicity and better response to ICI. The present invention shows through a large amount of data analysis and external clinical verification that the 4-IRG model can be used as a prediction model for the prognosis and survival of colorectal cancer and immune checkpoint inhibition and response efficiency, and can be applied to the survival prediction of colorectal cancer patients or the immunotherapy. screening.

The present invention evaluates the RNA sequence of CRC in the TCGA database, and analyzes the differential expression levels of immune-related genes (Immune-related Genes, IRGs) in adjacent normal colorectal tissues. Then, univariate Cox regression, LASSO regression and multivariate Cox regression analyzes were performed on the data set to establish a model for predicting colorectal cancer prognosis and survival—4-IRG, which is composed of immune-related genes BMP5, NRG1, INHBB and HSPA1A, And the combination of 4-IRG models outperformed single gene prediction.

Further analysis showed that the 4-IRG low-risk group had stronger immunogenicity and better response to ICIs. The external cohort verified that the 4-IRG model is an effective marker for predicting the survival time of patients with colorectal cancer, and the population with a lower risk score of the model has a better prognosis. In addition, among PD-1 treated patients, the response efficiency of the 4-IRG-low group was 1.42 times higher than that of the 4-IRG-high group.

Through the analysis of a large amount of transcriptome data and the verification of external tumor and immunotherapy patient cohorts, the present invention shows that the 4-IRG model can be used as a predictive biological model for the survival rate of CRC patients and the response efficiency of immunosuppressants. A promising model for the management of CRC patients.

The present invention provides a biomarker for predicting the prognosis of colorectal cancer, which is constructed from four immune-related genes: BMP5, NRG1, INHBB and HSPA1A, which can be used based on the expression levels of BMP5, NRG1, INHBB and HSPA1A in predicting the prognosis of colorectal cancer.

The present invention provides a biomarker for the response efficiency of colorectal cancer to immune checkpoint inhibitor therapy, which is constructed from four immune-related genes: BMP5, NRG1, INHBB and HSPA1A. The expression levels of BMP5, NRG1, INHBB and HSPA1A can be used to predict the response efficiency of colorectal cancer to immune checkpoint inhibitor therapy.

The present invention provides a kit for predicting the prognosis of colorectal cancer and the response efficiency of immune checkpoint inhibitor therapy, which includes BMP5, NRG1, INHBB and HSPA1A proteins.

The present invention also provides the application of BMP5, NRG1, INHBB and HSPA1A proteins in preparing predictive biomarkers for the prognosis of colorectal cancer patients and the response efficiency of immune checkpoint inhibitors.

In addition, the present invention also provides the application of BMP5, NRG1, INHBB and HSPA1A proteins in preparing a kit for predicting the prognosis of colorectal cancer or screening the response efficiency of immune checkpoint inhibitor therapy.

Compared with the prior art, the present invention shows that the 4-IRG model can predict the prognosis and survival of colorectal cancer patients and the response efficiency of immune checkpoint therapy through the analysis of tumor RNA-seq data and the in-depth analysis of immune-related genes in tumor tissues. The present invention is verified by external tumor cohorts and immunotherapy cohorts, indicating that the 4-IRG model can be used as a predictive biological model for the prognosis of colorectal cancer survival and response efficiency of immune checkpoint therapy, or the prediction of colorectal cancer prognosis and response efficiency of immunotherapy Or screening kit components, has important application value.

Description of drawings

Figures 1A-1I show the construction of the 4-IRG model.

Figures 2A-2H show the clinical characteristics of the 4-IRG model subgroups in CRC patients.

Figures 3A-3H show the molecular characteristics of 4-IRG model subgroups in CRC patients.

Figures 4A-4G show the immune signature of 4-IRG model subgroups in CRC patients.

Figures 5A-5H show the response of 4-IRG model subsets to immunosuppressants.

Figures 6A-6H show validation of the 4-IRG model in a new cohort.

Detailed ways

The present invention will be further elaborated below in conjunction with the accompanying drawings and specific embodiments. These examples should be understood as only for illustrating the present invention but not for limiting the protection scope of the present invention. After reading the contents of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

The following experiments of the present invention are based on the colorectal cancer data of the TCGA database, screening immune-related differentially expressed genes that affect its prognosis, constructing a model through regression analysis, and analyzing the characteristics of colorectal tumor patients and immunotherapy response of the 4-IRG model subgroup. Validation of the 4-IRG model for predicting prognostic survival and response efficiency to immune checkpoint inhibitors in colorectal cancer using a new cohort of external colorectal tumors.

1. Experimental materials and methods

1.1 Patients and Datasets

RNA-sequencing (RNA-seq) expression profiles and corresponding The clinical information was downloaded from the TCGA dataset (https://portal.gdc.com). Human CRC tissue microarrays were purchased from Shanghai OutdoBiotech Company (Shanghai, China; HColA180Su19). Group analyzes of tissue microarrays were based on medians. Microarray data for CRC samples (62 samples from GSE12945, 177 samples from GSE17536, 55 samples from GSE17537) were downloaded from the GEO database (HTTPS://www.ncbi.nlm.nih.gov/geo/) and survival information. IRGs are downloaded from the InnateDB (HTTPS://www.innateDBdb.com/) database and ImmPort (HTTPS://www.immport.org/shared/home). In the cohort group analysis, patients were divided into 4-IRG high group and 4-IRG low group according to 4-IRG score, and the Youden index was used as the cut-off point. The genomic and transcriptomic characteristics and clinical data of 12 patients who did not respond to PD1 treatment and 14 patients who responded to PD1 treatment were obtained from Hugo ^[23] . The reliability of the 4-IRG model for immunosuppressant response prediction was assessed by a bootstrap method using 1000 bootstrap replicates, resulting in the proportion of bootstrap for each internal branch in the model subgroups based on the median ^[30] .

1.2 Construction of immune-related models

In order to identify important differentially expressed IRGs involved in CRC development, the present invention divided all CRC patients into a training set (TRA) and a test set (TES). TRA was used to construct the immune-related prognosis model, and TES was used to verify the role of the model. The present invention uses the limma package to screen differentially expressed genes (differentially expressed genes, DEGs) between CRC and paracancerous tissue samples in the TCGA database, and the adjusted P value is <0.05 and |log2 (fold change)|>1. The present invention selected 811 differential gene groups and 1241 overlapping genes among immune genes as the DEG-IRG group (164). Then, regression analysis was performed on the dataset, including univariate Cox regression, LASSO regression, and multivariate Cox regression to build a prognostic model. ROC curves were used to assess the relationship between predictive model scores and survival. Kaplan-Meier survival curves for the high-risk and low-risk groups were drawn using the survival package ^[24] , which demonstrated the overall survival of the patients. The area under the curve (AUC) of the high-risk and low-risk groups was calculated using the survival ROC package to verify the prognostic ability of the immune-related risk signature ^[25] . The present invention then compares the model scores for age, gender, race, and different clinicopathological stages and presents the results.

1.3 Analysis of mutation characteristics

Mutations were analyzed based on the mutation annotation format obtained from TCGA. Tumor mutational burden in CRC subgroups was defined as (total mutations/total covered bases)*10 ⁶ . MIS scores were calculated for subgroups, and the correlation between model scores and MSI scores was assessed by Pearson's correlation coefficient (r).

1.4 GO and KEGG analysis and GSEA

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzes were performed to detect 164 DER-IRGs between CRC and paracancerous tissue samples. Significantly altered pathways were selected using a p-value and false discovery rate (FDR) (corrected p-value) threshold of <0.05. Annotated and graphical results are presented by using the DAVID database to explore the molecular features of the DEG-IRG set. Gene Set Enrichment Analysis (GSEA) and GO Enrichment Analysis were used to identify molecular enrichment signatures of 4-IRG subgroups and were analyzed using GSEA software (V3.0, ^[26] and MSigDB (V7.0, ^[27]) Genomes of marker pathways and KEGG pathways.

1.5 Evaluation of the immune signature of the model

Cancer Immunomic Atlas (TCIA, https://tcia.at/home) is based on the TCGA database, integrates other tumor research projects, and analyzes the immune data of 20 solid tumors ^[28] . In order to further explore the immune characteristics of the 4-IRG model, the present invention uses this database to analyze the proportions of 22 immune cells in different model subgroups.

1.6 Immunophenotype scoring

Immunophenotypic score (IPS) is used to calculate the scores of four different immunophenotypes: MHC-related molecules, effector cells (activated CD4 ⁺ T cells, activated CD8 ⁺ T cells, effector memory CD4 ⁺ T cells and effector memory CD8 ⁺ T cells), immunosuppressive and myeloid-derived suppressor cells, and immune checkpoints or immunomodulators. The IPS z-score is a combination of the four immunophenotypes, and the higher the IPS z-score of a sample, the higher the immunogenicity.

1.7 Multicolor immunofluorescence

OpalTM multicolor immunohistochemical (IHC) staining was performed using BMP5 (Proteintech, 12353-1-AP), HSPA1A (Abcam, ab181606), NRG1 (Santa Cruz, SC-393006) and INHBB (Abcam, ab69286) to verify expression levels based on 4-IRG in tumor tissue samples. Specimens were collected and formalin-fixed paraffin-embedded tissue sections were prepared as previously described ^[29] . Antigens were recovered by AR9 buffer (pH 6.0, PerkinElmer) and boiled in an oven for 15 minutes. After 10 min pre-incubation with blocking buffer at room temperature, sections were incubated with the above antibodies for 1 hour at room temperature. Horseradish peroxidase-conjugated secondary antibody (PerkinElmer) was added and incubated at room temperature for 10 minutes. Signal amplification was performed using TSA working solution diluted 1:100 in 1× Amplification Diluent (PerkinElmer) and incubated for 10 min at room temperature. Multispectral images were collected at 20× magnification using the Mantra Quantitative Pathology Workstation (PerkinElmer, CLS140089) and analyzed using InForm Advanced Image Analysis Software (PerkinElmer) version 2.3. For each patient, a total of 8-15 high-power field images were obtained depending on tumor size.

1.8 Statistical Analysis

Independent t-tests were performed to compare continuous variables between subgroups. Differences between groups were tested by one-way ANOVA or two-way ANOVA followed by Tukey's post hoc test. Pairwise comparisons were performed using the Wilcoxon rank sum test. Univariate survival analyzes were performed using Kaplan–Meier survival analysis with the log-rank test. Multivariate survival analyzes were performed using Cox regression models. LASSO is used to minimize overfitting. Statistical significance of P-values is reported.

2. Experimental results

2.1 4-IRG model establishment

In the analysis of tumor tissue and adjacent normal tissue, the present invention performed cross analysis on 975 DEGs and 1793 IRGs, and obtained 164 overlapping DEG-IRGs (as shown in FIG. 1A ). To identify potential prognostic DEG-IRGs, the present inventors constructed TRA (n=412) and TES (n=177) for validation, which were randomly assigned from the entire patient set. The clinicopathological features of TRA and TES are shown in Table 1. Then, four DEG-IRGs were identified as significant genes by LASSO analysis. The relative contribution of the four IRGs was assessed using a multivariate Cox regression analysis model weighted by the multivariate Cox regression coefficients (β): BMP5*-0.0666, NRG1*-0.0876, INHBB*0.1583, and HSPA1A*0.1853. Among the four DEG-IRGs, BMP5 and NRG1 were negatively associated with the risk phenotype, and INHBB and HSPA1A were positively associated with the risk phenotype (as shown in Figure 1B). Based on these results, a 4-IRG model was preliminarily constructed.

2.2 Survival analysis of different 4-IRG subgroups

According to Youden's coefficient, TRA was divided into high-risk group (n=175) and low-risk group (n=237), and there were significant differences among different subgroups. The AUC values for 1-, 3-, 5-, and 7-year survival rates were 0.58, 0.57, 0.64, and 0.75, respectively (as shown in Figure 1C). The present invention analyzes the risk score in TRA, and the results are shown in Figures 1D-1F. Validation in TES (4-IRG high = 76, 4-IRG low = 101) also showed similar results (as shown in Figures 1G-1I). These results show that the 4-IRG model constructed in the present invention has a good predictive ability for CRC.

2.3 Distribution of clinicopathological features of 4-IRG

Next, the inventors performed a subgroup analysis of 4-IRG using various clinicopathological parameters. There were no significant differences in 4-IRG scores by age (≥60, <60) or sex (as shown in Figure 2A, Figure 2B). Ethnographic analysis revealed differences in 4-IRG scores between Asian and Caucasian individuals (p=0.03) (as shown in Figure 2C). CRC patients tended to have higher 4-IRG scores and higher T and N stages (as shown in Figure 2D, Figure 2E). M stage and clinical stage were significantly correlated with 4-IRG score (as shown in Figure 2F, Figure 2G). The 4-IRG score associated with CRC recurrence was higher than in the cohort without recurrence (as shown in Figure 2H). In conclusion, the present study shows that the 4-IRG model can be used to predict the clinical outcome of patients, especially the depth of tumor invasion, distant metastasis, and lymph node metastasis of local recurrence; thus, 4-IRG may be an appropriate model to predict the rapid progression of CRC.

2.4 Molecular characterization of 4-IRG subgroups

Based on the important findings of previous 4-IRG models, the present invention further analyzes the molecular characteristics of 4-IRG subgroups, including gene enrichment function analysis and gene mutation characteristics. GSEA revealed significant functional differences in gene enrichment between groups (as shown in Figure 3A, Figure 3B). In the GO map, several significantly upregulated genes were highlighted in red, and downregulated genes were highlighted in blue (as shown in Fig. 3C, Fig. 3D). Functional enrichment analysis revealed that many cancer-related signaling pathways, including neuroactive ligand-receptor interaction, pancreatic secretion, cAMP signaling pathway, and calcium signaling pathway, were in the 4-IRG-high and 4-IRG-low subclasses. enriched in the group. Notably, 4-IRG-high was enriched for regulation of protein digestion and absorption, retrograde endocannabinoid signaling, and salivary secretion, whereas the 4-IRG-low subgroup was enriched for dopaminergic synapses. The overexpression of CST1, COL10A1, COL11A1, CST2, GRIN2D, HTR1D, CEL and TNN13 was significantly enriched in the 4-IRG-high subgroup, while the overexpression of GRIN2D, CEL and HTR1D was in the 4-IRG-low subgroup significantly enriched.

Gene mutation analysis can identify characteristic mutations and gene expression patterns at the genome-wide level, which may contribute to a deeper understanding of tumor-related molecular mechanisms. Therefore, the present invention studies gene mutations in different 4-IRG subgroups. The type of mutation is depicted in colour: missense mutations, nonsense mutations, frameshift deletions and frameshift insertions are the highest mutations reported. The cohort analysis of the present invention reconfirmed that APC, TP53 and KRAS are the main drivers of CRC progression (as shown in Figures 3E-3G). Furthermore, group analysis of gene mutations showed that KRAS mutations were statistically significant in subgroups (p=0.01, 48% vs. 37%) (shown in Figure 3H). Compared with the low-risk group, the high-risk group also had RYR1, RYR3, ADGRV1, and CCDC169 gene mutations. These analyzes confirmed that 4-IRG-based subgroups had distinct gene signatures.

2.5 Analysis of immune infiltration characteristics in 4-IRG subgroups

Multiple factors, including the immune microenvironment, can affect the prognosis and treatment response of tumor patients, so the present invention next analyzes the immune heterogeneity of immune cell types in CRC subgroups in relation to the 4-IRG model. Activated dendritic cells (DC), activated memory CD4 T cells, eosinophils, M0 macrophages, M1 macrophages, M2 macrophages, monocytes, naive B cells, neutrophils, The abundance of plasma cells, regulatory T cells, resting DCs, resting mast cells, and resting memory CD4 T cells defined two distinct subpopulations of 4-IRG (as shown in Figure 4A). Further analysis revealed that the abundance of plasma cells, resting mast cells, and resting memory CD4+ T cells was negatively correlated with the 4-IRG score, while the abundance of M0 macrophages, M1 macrophages, and regulatory T cells was positively correlated Correlates with risk score (as shown in Figures 4B-4G). Therefore, the present inventors speculate that better modulation of the immune response targeting tumor cells contributes to the prognostic potential of the 4-IRG model.

2.6 Benefits of ICI treatment in 4-IRG model

Immune checkpoint modulators are emerging as powerful therapeutic tools to overcome tumor immune escape mechanisms. Immune infiltration in tumor tissue is an important marker to characterize the response to ICI. A previous study revealed heterogeneity of the immune microenvironment in 4-IRG subgroups. The present invention uses IPS, IPS-PD1/PDL1/PDL2, IPS-CTLA4 and IPS-PD1/PDL1/PDL2/CTLA4 to evaluate the potential application value of ICI based on 4-IRG model in CRC patients. As shown in Figure 5A, the immunogenicity of the 4-IRG low-risk group may be stronger than that of the 4-IRG high-risk group. The results showed that the IPS, IPS/PD1/PDL1/PDL2, IPS/CTLA4 and IPS/PD1/PDL1/PDL2/CTLA4 scores of the 4-IRG low-risk group were higher than those of the high-risk group, and the difference was also observed in the expression of PDL1 groups, although there were no statistically significant differences (as shown in Figures 5B-5D). Furthermore, ROC curve analysis indicated that the use of expression levels of immune checkpoint molecules to predict the survival of CRC patients was not optimistic (as shown in Figures 5E-5G). Based on these results, the present inventors deduce that the 4-IRG model is an important potential indicator of whether CRC patients respond to ICI, and this model is an effective model independent of MSI (as shown in FIG. 5H ).

2.7 Visualization and validation of the predictive validity of the 4-IRG model based on tumor microarrays

The present invention analyzes the expression of BMP5, NRG1, INHBB and HSPA1A on 180 human colorectal tissue samples (including 94 CRC tissue samples and 86 paracancerous tissue samples) by multicolor immunofluorescence. The detection results showed that BMP5, NRG1, INHBB and HSPA1A were observed in the cytoplasm. Further analysis revealed that high INHBB and HSPA1A expression levels were positively correlated with cancer tissue samples, while high BMP5 and NRG1 expression levels were positively correlated with paracancerous tissue samples (as shown in Figure 6A). After quantitative analysis of the staining of the different genes, the inventors grouped the microarray data according to the median cut-off value and reconfirmed that the high-risk group had a wider area and stronger INHBB signal compared to the low-risk group The expression levels of HSPA1A and BMP5 and NRG1 were opposite (as shown in Fig. 6B). The ROC survival curve of the microarray data also verified the predictability of the 4-IRG model (as shown in Fig. 6C).

2.8 Validation of the prognostic role of 4-IRG in the CRC cohort in the GEO database

Based on the above observations and analysis, the present inventors conclude that 4-IRG may play an important prognostic role in the evaluation of CRC patients. The present invention conducted a new cohort study to verify the predictive validity of the 4-IRG model of the present invention for CRC. Data used for predictive survival validation came from the GEO database. A total of 55 CRC patients from GSE17537 had AUC values of 0.619, 0.5822, 0.506, and 0.8002 for 1-, 3-, 5-, and 7-year survival, respectively (as shown in Figure 6D). For CRC patients from GSE17536 (n=177), the ROC AUCs for 1-, 3-, 5-, and 7-year survival were 0.5698, 0.6183, 0.5793, and 0.5255, respectively (as shown in Figure 6E). The ROC curve of GSE12945 (n = 62) also verified the predictive performance of the 4-IRG model (shown in Fig. 6F). These data further demonstrate that the 4-IRG model is associated with CRC progression and disease outcome and demonstrate potential clinical utility for CRC patient prognosis.

2.9 Validation of the 4-IRG model for predicting immune responses

The above analysis of the data demonstrates that the 4-IRG model has significant predictive potential for response to immunosuppressive therapy. Therefore, the present inventors next validated the effectiveness of 4-IRG in a cohort of immunosuppressed responding and non-responding populations. Utilize the genomic and transcriptomic characteristics of the response to anti-PD-1 therapy in metastatic melanoma patients that have been reported ^[23] . All patients receiving anti-PD1 therapy were evaluated according to 4-IRG score and divided into 4-IRG high group and 4-IRG low group according to the median. Next, the model was internally validated by the bootstrap method (confidence interval = 95%; number of simulations = 1000) ^[29] . The results showed that the response rate of the 4-IRG low group was 1.42 times that of the 4-IRG high group (RR=1.422692, 95% CI: 1.045429-1.964286). In fact, despite the limited sample size, PD1-inhibitor non-responders had a higher model risk score than PD1-inhibitor responders (as shown in Figure 6G). ROC curve analysis based on the response rate of 4-IRG PD1 (as shown in FIG. 6H ).

【references】

23. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G et al: Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell 2016,165(1):35-44.

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26. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles .Proceedings of the National Academy of Sciences of the United States of America 2005,102(43):15545-15550.

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Claims (5)

1. A biomarker for predicting the prognosis of colorectal cancer, which is constructed from 4 immune-related genes: BMP5, NRG1, INHBB and HSPA1A.

2. A biomarker for the efficiency of colorectal cancer response to treatment with an immune checkpoint inhibitor, characterized in that it is constructed from 4 immune-related genes: BMP5, NRG1, INHBB and HSPA1A.

3. A kit for prediction of colorectal cancer prognosis and efficiency of response to treatment with an immune checkpoint inhibitor, comprising BMP5, NRG1, INHBB and HSPA1A proteins.

Use of bmp5, NRG1, INHBB and HSPA1A proteins for the preparation of biomarkers for colorectal cancer patient prognosis and response efficiency of immune checkpoint inhibitors.

Use of bmp5, NRG1, INHBB and HSPA1A proteins for the preparation of a kit for the prediction of colorectal cancer prognosis or screening of the response efficiency of immune checkpoint inhibitor treatment.

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