Biomarkers for predicting the treatment outcome of colorectal cancer

Abstract

This invention discloses biomarkers for predicting the efficacy of colorectal cancer treatment. This application discloses a novel biomarker, HLA-DQA2, for predicting the efficacy of immunotherapy for colorectal cancer. By detecting the level of HLA-DQA2, the efficacy of immunotherapy for colorectal cancer can be assessed, which is helpful in the development of refined and individualized treatment and follow-up plans for colorectal cancer patients in clinical practice.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to biomarkers for predicting the treatment efficacy of colorectal cancer. Background Technology

[0002] Colorectal cancer is the most common malignant tumor of the digestive system. It has become one of the major malignant tumors that seriously threaten human health and life, and has brought a heavy burden to society.

[0003] With the rapid development of tumor immunology, tumor immunotherapy has become one of the main treatments for colorectal cancer. Compared with conventional chemotherapy and targeted therapy, immunotherapy has changed the treatment prospects for various solid tumors. However, the high cost and low patient response rate of immunotherapy still pose significant challenges to clinical practice. Currently, there is a lack of biomarkers that can predict the efficacy of immunotherapy for colorectal cancer. Accurately identifying effective biomarkers in the immunotherapy of colorectal cancer would undoubtedly have important guiding significance for the recovery of colorectal cancer patients. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides biomarkers for predicting the efficacy of radiotherapy combined with immunotherapy for colorectal cancer.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A first aspect of the present invention provides the use of a reagent for detecting HLA-DQA2 levels in the preparation of products for predicting the treatment efficacy of colorectal cancer.

[0006] Furthermore, the therapeutic effect refers to the effect of radiotherapy and / or immunotherapy.

[0007] Furthermore, the reagents include primers, probes, antibodies, biochips, or small molecule compounds for the specific detection of HLA-DQA2.

[0008] Furthermore, the reagent also includes a detectable tag.

[0009] Furthermore, the detectable tag includes at least one of the following: a radioactive isotope, a fluorescent group, a chemiluminescent component, an enzyme, an enzyme substrate, an enzyme cofactor, an enzyme inhibitor, a dye, a metal ion, or a ligand.

[0010] A second aspect of the invention provides a product for predicting the treatment efficacy of colorectal cancer, the product comprising a reagent for detecting HLA-DQA2 levels.

[0011] Furthermore, the product includes a reagent kit.

[0012] Furthermore, the kit also includes at least one of the following: container, packaging, adjuvant, solution, buffer, negative control, positive control, or instructions.

[0013] A third aspect of the present invention provides a computer-based method for predicting the treatment effect of colorectal cancer, the method comprising the following steps: Obtain the gene / protein expression status of the sample to be tested; Extract the level of the target gene / protein from the gene / protein, wherein the target gene / protein is HLA-DQA2; The treatment effect of colorectal cancer is predicted based on the level of HLA-DQA2. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect; if the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect.

[0014] A fourth aspect of the present invention provides a system for predicting the treatment effect of colorectal cancer, the system comprising: Acquisition Unit: Acquires gene / protein expression information of the sample to be tested; Extraction unit: Extracts the level of the target gene / protein from the gene / protein, wherein the target gene / protein is HLA-DQA2; Prediction Unit: Based on the level of HLA-DQA2, predict the treatment effect of colorectal cancer. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect; if the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect.

[0015] A fifth aspect of the present invention provides an apparatus for predicting the treatment effect of colorectal cancer, the apparatus comprising: Memory: The memory is used to store program instructions; Processor: The processor is used to invoke program instructions, which, when executed, are used to perform the method described in the third aspect of the present invention.

[0016] A sixth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the third aspect of the present invention.

[0017] A seventh aspect of the present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described in the third aspect of the present invention.

[0018] Advantages and beneficial effects of the present invention: This application discloses a novel biomarker, HLA-DQA2, for predicting the efficacy of immunotherapy for colorectal cancer. By detecting the level of HLA-DQA2, the efficacy of immunotherapy for colorectal cancer can be evaluated, which helps to formulate refined and individualized treatment and follow-up plans for colorectal cancer patients in clinical practice. Attached Figure Description

[0019] Figure 1 These are HLA-DQA2 gene expression maps for cohorts one and two. 1A is the HLA-DQA2 gene expression map of CRC tissue and peripheral blood PBMCs in cohort one before immunotherapy. 1B is the HLA-DQA2 gene expression map of CRC tissue before and after immunotherapy in cohort two. 1C is a statistical graph of HLA-DQA2 gene expression levels in the CR and PR groups. 1D is an ROC curve. Figure 2 This is a diagram showing the patient information and analysis of Cohort 3. 2A shows the long-course radiotherapy combined with immunotherapy regimen and sample collection for locally advanced rectal cancer; 2B shows the myeloid cell subset analysis of rectal cancer samples; 2C shows the gene expression differences between the PR and CR groups before treatment; 2D shows the gene expression differences between the PR and CR groups after radiotherapy; 2E shows the differences in the proportion of myeloid cell subsets between the CR and PR groups; 2F shows the dendritic cell subset analysis; and 2G shows the number of dendritic cells and the HLA-DQA2 gene expression level in each patient. Figure 3 This is a graph showing the HLA-DQA2 gene expression level and diagnostic efficacy analysis of cohort 3. In this graph, 3A is the HLA-DQA2 gene expression level in rectal cancer tissue and peripheral blood of each patient in cohort 3, and 3B is the ROC curve. Figure 4 These are pseudo-temporal analysis diagrams of dendritic cells. 4A is the pseudo-temporal analysis diagram of dendritic cells; 4B is the proportion of cDC_CD1C subtypes in patients with high and low HLA-DQA2 expression before treatment; 4C is the proportion of each dendritic cell subtype in patients with high and low HLA-DQA2 expression before treatment, after radiotherapy, and after immunotherapy; 4D is the proportion of cDC_LAMP3 subtypes in patients with high and low HLA-DQA2 expression and the CR / PR group before treatment; 4E is the proportion of cDC_LAMP3 subtypes in patients with low HLA-DQA2 expression before treatment and after radiotherapy; and 4F is a differential signaling pathway analysis diagram of cDC_CD1C and cDC_LAMP3 subtypes. Figure 5 This is a schematic diagram of the computer-based method for predicting the treatment effect of colorectal cancer provided in this application; Figure 6 This is a schematic diagram of the system for predicting the treatment effect of colorectal cancer provided in this application; Figure 7 This is a schematic diagram of the device provided in this application for predicting the treatment effect of colorectal cancer. Detailed Implementation

[0020] The following provides definitions for some of the terms used in this specification. Unless otherwise stated, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0022] This invention provides the application of reagents for detecting HLA-DQA2 levels in the preparation of products for predicting the treatment efficacy of colorectal cancer.

[0023] In some embodiments, HLA-DQA2 is selected from at least one of the HLA-DQA2 gene, HLA-DQA2 mRNA, cDNA of HLA-DQA2 mRNA, or HLA-DQA2 protein.

[0024] In some implementations, the samples used to detect HLA-DQA2 are derived from the body fluids, cells, tissues, metabolites, and / or excretions of the subject being tested.

[0025] In some preferred embodiments, the sample is derived from the body fluid of the subject being tested, and the body fluid is selected from at least one of blood, plasma, extracellular fluid, tissue fluid, lymph, or cerebrospinal fluid.

[0026] The reagents include primers, probes, antibodies, biochips, or small molecule compounds that specifically detect HLA-DQA2.

[0027] In some implementations, primers refer to short nucleic acid molecules, such as DNA oligonucleotides, for example, sequences of at least 15 nucleotides, which can form a hybrid between the primer and the target nucleic acid strand through nucleic acid hybridization and annealing with a complementary target nucleic acid molecule. The primer can be extended along the target nucleic acid molecule using polymerase. Therefore, primers can be used to amplify target nucleic acid molecules, wherein the primer sequence is specific to the target nucleic acid molecule.

[0028] In some embodiments, a probe is a molecule capable of binding to a specific sequence, subsequence, or other portion of another molecule. Unless otherwise specified, a probe typically refers to a polynucleotide probe capable of binding to another polynucleotide (often called a target polynucleotide) through complementary base pairing. Depending on the stringency of the hybridization conditions, the probe can bind to a target polynucleotide that lacks complete sequence complementarity with the probe. The probe can be labeled directly or indirectly. Hybridization methods include, but are not limited to, solution-phase, solid-phase, mixed-phase, or in situ hybridization assays.

[0029] In some embodiments, primers or probes may be chemically synthesized using phosphorimide solid-phase support or other well-known methods. Modifications may also be performed using many techniques known in the art. Non-limiting examples of such modifications include methylation, capping, substitution with one or more analogs of natural nucleotides, and modifications between nucleotides. For example, modifications may be made to uncharged linkers (e.g., methyl phosphate, triphosphate, phosphorimide, carbamate, etc.) or charged linkers (e.g., thiophosphate, dithiophosphate, etc.).

[0030] The reagent also includes a detectable label.

[0031] In some embodiments, a detectable tag refers to a composition capable of generating a detectable signal indicating the presence of a target polynucleotide in a sample being tested. Suitable detectable tags include any composition detectable by fluorescence, spectroscopy, photochemistry, biochemistry, immunology, electrical, optical, or chemical means. Specifically, they include at least one of radioactive isotopes, fluorescent groups, chemiluminescent components, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, dyes, metal ions, or ligands.

[0032] The product includes a reagent kit.

[0033] In some embodiments, the kit includes at least one of the following: Western blot kit, enzyme-linked immunosorbent assay kit, radioimmunoassay kit, radioimmunodiffusion kit, two-dimensional biphasic immunodiffusion kit, rocket immunoelectrophoresis kit, immunohistochemical staining kit, immunoprecipitation assay kit, complement fixation assay kit, fluorescence-activated cell sorting kit, aptamer chip kit, microarray kit, protein chip kit, qPCR kit, or flow cytometry kit.

[0034] In some embodiments, the kit components may be packaged in an aqueous medium or in a lyophilized form. Suitable containers in the kit typically include at least one vial, test tube, long-necked flask, PET bottle, syringe, or other container for holding one component, and preferably, for appropriate aliquoting. When more than one component is present in the kit, the kit will also typically include a second, third, or other additional container for separately holding the additional components. However, different combinations of components may be contained in a single vial. The kit of this application will also typically include a container for containing the reactants, sealed for commercial sale. Such a container may include injection-molded or blow-molded plastic containers for holding the desired vials.

[0035] The invention is further illustrated below with reference to specific embodiments. It should be understood that the specific embodiments described herein are by way of example and are not intended to limit the invention. The main features of the invention can be used in various embodiments without departing from the scope of the invention.

[0036] Example 1. Patient enrollment and sample collection Data from two cohorts of Chinese dMMR / MSI-H CRC patients receiving anti-PD-1 monotherapy (GSE236581 and GSE205506).

[0037] Between January 2023 and August 2024, 20 untreated patients aged 18-80 years with microsatellite stable (pMMR / MSS) tumors were recruited. MRI confirmed clinical staging as T2-T4N+, with the distal margin of the tumor within 10 cm of the anal verge. All patients received long-course radiotherapy combined with PD-1 monoclonal antibody immunotherapy. The specific regimen included long-course radiotherapy (5 times per week for 5 weeks, totaling 45-50.4 Gy), followed by weekly intravenous sintilimab (200 mg for 3 weeks). Before treatment, tumor tissue samples were collected via colonoscopy, and 5 mL of peripheral blood was drawn. One week after the completion of radiotherapy, a second tumor biopsy was performed via colonoscopy. Surgery was scheduled 8 weeks later, and post-treatment tumor tissue and 5 mL of peripheral blood were collected. Treatment efficacy was assessed through postoperative histopathological evaluation and RECIST 1.1 criteria. Tumor regression grading (TRG) is determined according to the American Joint Committee on Cancer (AJCC) classification: TRG0 (complete response, no surviving tumor cells), TRG1-3 (partial response, varying degrees of residual tumor cells), and TRG4 (no response).

[0038] Fresh tissue and peripheral blood samples were prepared into single-cell suspensions through steps such as dissociation, filtration, erythrocyte lysis, and removal of dead cells. Cell viability was assessed using trypan blue staining, and cells with a viability greater than 85% were retained. The cell count was determined using an automated cell counter, and the final concentration was adjusted to 700-1200 cells / μL. Finally, single-cell transcriptome sequencing was performed.

[0039] 2. Raw data processing and quality control of single-cell RNA sequencing data Sequencing results generated by the Illumina platform were converted to FASTQ format using bcl2fastq. CellRanger was used to process the FASTQ files, performing alignment with the reference genome, transcript counting, and cell barcoding based on the 5' ends of captured transcripts. The resulting matrix was imported into the Seurat package in R for further analysis. A rigorous quality control procedure was performed on low-quality cells for each sample: cells with fewer than 500 characteristic molecular identifiers (UMIs) or fewer than 300 genes were excluded; cells with high mitochondrial gene content were also excluded. Double cells were eliminated using DoubletFinder and manual screening.

[0040] The gene expression matrix was log-normalized using the NormalizeData algorithm, and highly variable genes were identified using the FindVariableFeatures algorithm (nfeatures=3000). Dimensionality reduction was performed using ScaleData and RunPCA. Neighborhood graphs were constructed and cluster analysis was conducted using the FindNeighbors and FindClusters algorithms. Finally, two-dimensional visualization was performed using the RunUMAP algorithm.

[0041] 3. Cell type annotation Cell types were annotated based on the expression of standard marker genes. Lymphocytes included B cells (CD79A, MS4A1), plasma cells (MZB1, IGHG1), CD4+ T cells (CD4), CD8+ T cells (CD8A), γδ T cells (TRDC), and NK cells (NCAM1, GNLY). Myeloid cells included mast cells (CPA3, GATA2), plasmacytoid dendritic cells (IRF7, LILRA4), conventional dendritic cells (CD1C, LAMP3), monocytes (FCN1, VCAN), macrophages (CD68, C1QC), and neutrophils (G0S2, CSF3R). Stromal cells included fibroblasts (COL1A1, COL3A1), pericytes (RGS5), smooth muscle cells (MYH11), and endothelial cells (PECAM1, VWF). Epithelial cells were identified based on the expression of EPCAM and KRT19. Other populations include proliferating cells (MKI67, STMN1), hematopoietic stem cell-like cells (CD34, SOX4), and platelets (PPBP).

[0042] 4. Differential gene expression analysis Dendritic cells were extracted based on HLA-DQA2 expression levels, and differential gene expression analysis was performed before and after treatment using the DESeq2 algorithm (|log2FC|>0.5, P.adj<0.05).

[0043] 5. Pseudo-time series analysis Diffusion mapping was performed using the R package destiny (v3.4.0) to infer the maturation trajectory of dendritic cells (DCs) from scRNA-seq data. Distances between DCs were calculated based on their expression profiles to construct an affinity matrix, which was then used to generate diffusion components via eigenvalue decomposition. This dimensionality-reduced representation captures the continuous progression of DCs from naive to activated states.

[0044] 6. Constructing ROC curves Receiver operating characteristic (ROC) curves were used to assess the differential efficacy of HLA-DQA2 expression levels in distinguishing between complete remission (CR) and partial remission (PR) with LARC radiotherapy combined with immunotherapy. By systematically altering the diagnostic threshold, the relationship between the true positive rate (Sensitivity) and the false positive rate (1-Specificity) was calculated and plotted, and the area under the ROC curve (AUC) was calculated using a nonparametric method.

[0045] 7. Experimental Results Data from a cohort of Chinese dMMR / MSI-H CRC patients receiving anti-PD-1 monotherapy indicated that patients with low HLA-DQA2 expression were more likely to achieve good treatment outcomes (P=0.0015). Figure 1AC). Furthermore, the ROC curve results also indicate that HLA-DQA2 expression levels have some potential in predicting treatment efficacy. Figure 1 These results (D, AUC=0.703) further illustrate that HLA-DQA2 holds promise as an important biomarker for predicting the efficacy of immunotherapy.

[0046] Twenty patients with pMMR / MSS locally advanced rectal cancer (LARC) who received long-course preoperative radiotherapy combined with anti-PD-1 immunotherapy at Peking University People's Hospital were included in this study. Tumor tissue and peripheral blood samples were collected before and after treatment for single-cell transcriptome sequencing. Patients were divided into two groups based on the AJCC tumor regression grade (TRG): TRG 0 patients were assigned to the complete response (CR) group (n=6), and the rest were assigned to the partial response (PR) group (n=14). Figure 2 A). Myeloid cells were extracted from all LARC single-cell transcriptome data and further subdivided into 16 subtypes to investigate their functions ( Figure 2 B). However, prior to treatment, the proportions of these subtypes were largely consistent in the CR and PR groups (P>0.05). Figure 2 E), indicating that no single subtype primarily affects the efficacy of radiotherapy combined with immunotherapy. Therefore, we compared the differences in gene expression of these subtypes in the CR and PR groups at each treatment stage, and the results showed that HLA-DQA2 expression was higher in the PR group than in the CR group before treatment and after radiotherapy. Figure 2 CD).

[0047] Then, we extracted all the dendritic cells ( Figure 2 F), and examined the HLA-DQA2 expression in these cells in LARC tissues after radiotherapy in all patients to rule out the possibility that the difference in HLA-DQA2 expression between the CR and PR groups was caused by a single patient. The results showed that patients P4, P5, P10, and P14 (TRG0), as well as patient P9 (TRG1), all showed low levels of HLA-DQA2 expression. Figure 2 G).

[0048] We then evaluated the expression of HLA-DQA2 and other MHC-II molecules in dendritic cells (DCs) from LARC tissue and PBMCs in each patient across all treatment phases. Patients with low HLA-DQA2 expression consistently exhibited this pattern in LARC tissue before treatment, after radiotherapy, after immunotherapy, and in PBMCs before and after treatment. Figure 3 A). Furthermore, the ROC curve results also indicate that HLA-DQA2 expression levels have high potential in predicting treatment efficacy. Figure 3 B, AUC=0.833).

[0049] We then performed pseudo-time series analysis on all dendritic cells and found that cDC_LAMP3, a mature subtype of dendritic cells located at the end of its developmental trajectory, was more abundant in pre-treatment samples from patients with high HLA-DQA2 expression (P=0.066), including patients in the CR group (P1 and P15). In contrast, patients with low HLA-DQA2 expression had a higher proportion of immature cDC_CD1C subtypes before treatment (P=0.05). However, these cells rapidly matured and were activated after radiotherapy, subsequently increasing further after immune checkpoint inhibitor therapy, and exhibiting functional characteristics such as positive regulation of enhanced T cell activation, positive regulation of leukocyte intercellular adhesion, and differentiation. In contrast, dendritic cells from patients with high HLA-DQA2 expression formed a cDC_CD1C-dominated tumor microenvironment after combined radiotherapy and immunotherapy, playing a greater role in neutrophil activation. This dynamic change in dendritic cell behavior may lead to chronic or dysregulated immune activation, making it difficult for patients to benefit from treatment. Figure 4 ).

[0050] Figure 5 This application provides a computer-based method for predicting the treatment outcome of colorectal cancer, specifically including: 101: Obtain the gene / protein expression status of the sample to be tested.

[0051] 102: Extract the level of the target gene / protein from the gene / protein, wherein the target gene / protein is HLA-DQA2.

[0052] 103: Predict the treatment effect of colorectal cancer based on the level of HLA-DQA2. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect. If the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect.

[0053] Figure 6 The system provided in this application for predicting the treatment outcome of colorectal cancer specifically includes: 201 Acquisition Unit: Acquire the gene / protein expression status of the sample to be tested.

[0054] 202 Extraction Unit: Extracts the level of the target gene / protein in the gene / protein, wherein the target gene / protein is HLA-DQA2.

[0055] 203 Prediction Unit: Predicts the treatment effect of colorectal cancer based on the level of HLA-DQA2. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect; if the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect.

[0056] Figure 7 The device provided in this application for predicting the treatment effect of colorectal cancer specifically includes: Memory: The memory is used to store program instructions.

[0057] Processor: The processor is used to call program instructions, and when the program instructions are executed, they are used to perform the above methods.

[0058] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0059] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0060] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0061] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. Application of reagents for detecting HLA-DQA2 levels in the preparation of products for predicting the treatment efficacy of colorectal cancer; The therapeutic effect refers to the therapeutic effect of radiotherapy combined with PD-1 antibody or PD-1 antibody alone.

2. The application according to claim 1, characterized in that, The reagents include primers, probes, or antibodies that specifically detect HLA-DQA2.

3. The application according to claim 2, characterized in that, The reagent also includes a detectable label.

4. The application according to claim 3, characterized in that, The detectable tag includes at least one of the following: a radioactive isotope, a fluorescent group, a chemiluminescent component, an enzyme, a dye, a metal ion, or a ligand.

5. The application according to claim 1, characterized in that, The product includes a reagent kit.

6. The application according to claim 5, characterized in that, The kit also includes at least one of the following: container, packaging, buffer solution, negative control, positive control, or instructions.

7. A system for predicting the treatment effect of colorectal cancer, characterized in that, The system includes: Acquisition Unit: Acquires gene / protein expression information of the sample to be tested; Extraction unit: Extracts the level of the target gene / protein from the gene / protein, wherein the target gene / protein is HLA-DQA2; Prediction Unit: Based on the level of HLA-DQA2, predict the treatment effect of colorectal cancer. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect; if the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect. The therapeutic effect refers to the therapeutic effect of radiotherapy combined with PD-1 antibody or PD-1 antibody alone.

8. A device for predicting the treatment effect of colorectal cancer, characterized in that, The device includes: Memory: The memory is used to store program instructions; Processor: The processor is used to invoke program instructions, which, when executed, are used in the following methods: Obtain the gene / protein expression status of the sample to be tested; Extract the level of the target gene / protein from the gene / protein, wherein the target gene / protein is HLA-DQA2; The treatment effect of colorectal cancer is predicted based on the level of HLA-DQA2. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect; if the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect. The therapeutic effect refers to the therapeutic effect of radiotherapy combined with PD-1 antibody or PD-1 antibody alone.

9. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps of the following method: Obtain the gene / protein expression status of the sample to be tested; Extract the level of the target gene / protein from the gene / protein, wherein the target gene / protein is HLA-DQA2; The treatment effect of colorectal cancer is predicted based on the level of HLA-DQA2. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect; if the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect. The therapeutic effect refers to the therapeutic effect of radiotherapy combined with PD-1 antibody or PD-1 antibody alone.

10. A computer program product, comprising a computer program, characterized in that, When this computer program is executed by a processor, it performs the following steps: Obtain the gene / protein expression status of the sample to be tested; Extract the level of the target gene / protein from the gene / protein, wherein the target gene / protein is HLA-DQA2; The treatment effect of colorectal cancer is predicted based on the level of HLA-DQA2. If the HLA-DQA2 level is low, the test sample is classified as having a good treatment effect; if the HLA-DQA2 level is high, the test sample is classified as having a poor treatment effect. The therapeutic effect refers to the therapeutic effect of radiotherapy combined with PD-1 antibody or PD-1 antibody alone.

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