Multiple myeloma prognosis molecular marker, prognosis layering model and application

Abstract

The invention discloses a prognosis molecular marker for multiple myeloma (MM), a prognosis layering model and application of the prognosis molecular marker and the prognosis layering model. Specifically, six core genes significantly related to poor clinical prognosis are screened out, and an MM prognosis layering model is constructed through early clinical data based on the six core genes. Meanwhile, on the basis of the prognosis layering model, a set of Droplet digital PCR (ddPCR) detection method which is completely matched with the prognosis layering model and is efficient is constructed; and a group of brand-new clinical samples are used as a verification set to verify the prognosis layering model and the corresponding ddPCR detection method. In conclusion, the invention proves that the six-gene prognosis layering model and the ddPCR detection method thereof can effectively distinguish prognosis high-risk group patients from prognosis low-risk group patients. The method not only provides a new tool for accurate layering of multiple myeloma, but also can assist in clinical formulation of individualized treatment strategies for high-risk patients, improves the scientificity of clinical diagnosis and treatment decisions, and has important application value.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology and relates to molecular markers for prognosis stratification of multiple myeloma, and specifically to molecular markers for prognosis of multiple myeloma and a prognosis stratification model and application thereof. Background Art

[0002] Hematologic malignancies are common high-risk cancers in clinical practice and pose a serious threat to public health. Epidemiological data from my country show that lymphoma, leukemia, and multiple myeloma rank among the top three hematologic malignancies in incidence. Although breakthroughs in the development of new drugs and the application of targeted therapies and immunotherapies in recent years have significantly improved the clinical management of multiple myeloma (MM), with the median survival of patients increasing to 6-8 years, the disease remains an essentially incurable malignant proliferative disorder, and the vast majority of patients will eventually progress to relapsed / refractory (R / RMM) or even die.

[0003] Multiple myeloma (MM) is characterized by the proliferation of monoclonal plasma cells, leading to the production of monoclonal antibodies and end-organ damage, with clinical symptoms including lytic bone lesions, hypercalcemia, anemia, and renal impairment. Almost all MM patients begin with a relatively benign pre-disease asymptomatic phase called monoclonal immunoglobulinemia (MGUS), with smoldering myeloma (SMM) eventually developing into symptomatic intramedullary and extramedullary MM. MM patients are highly heterogeneous, with a high relapse rate and a median overall survival ranging from a few months to several years. Therefore, new methods are needed to more accurately risk stratify MM patients. Although there are new and improved biomarkers to determine the overall prognosis of MM patients, there are currently no routinely used predictive biomarkers for initial treatment selection in MM and for predicting patient prognosis.

[0004] Current MM clinical management has established personalized diagnosis and treatment based on risk stratification, which can significantly benefit low-risk and medium-risk patient groups. However, early and accurate identification of high-risk and ultra-high-risk patients remains a key bottleneck in clinical diagnosis and treatment. Existing standard treatment regimens, such as the proteasome inhibitor combined with the immunomodulator VRD regimen induced sequential double autologous hematopoietic stem cell transplantation, are still not ideal for 15%-25% of ultra-high-risk patients. These patients generally have characteristics such as aggressive tumor proliferation, rapid disease progression, and a median survival of less than 36 months. Therefore, exploring new biomarkers to assist in the prognostic stratification and treatment decision-making of MM patients remains a research focus in this field. At the same time, due to the lack of specific molecular markers, it is difficult to achieve early identification of high-risk / highly aggressive patients (especially ultra-high-risk groups), and then implement personalized treatment strategies based on risk stratification, which is also a major challenge in clinical practice at this stage. Summary of the Invention

[0005] Given that it is difficult to accurately identify ultra-high-risk MM patients early with existing technologies, the purpose of the present invention is to provide a multiple myeloma prognostic molecular marker and a prognostic stratification model and application.

[0006] In a first aspect of the present invention, a multiple myeloma prognosis molecular marker is provided, characterized in that the molecular marker consists of LILRB4, CRIP1, ITGB7, TUBA1B, CCND2 and CD74.

[0007] Furthermore, based on the expression readings of the molecular markers, which were detected by bulk RNA sequencing, the following prognostic stratification model was used to perform risk scoring on multiple myeloma patients:

[0008] Risk score = (LILRB4 expression level × 0.115) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × 0.105) + (CCND2 expression level × 0.001) - (TUBA1B expression level × 0.093) - (CD74 expression level × 0.081).

[0009] Furthermore, based on the expression readings of the molecular markers, which were detected by multicolor ddPCR, the following prognostic stratification model was used to perform risk scoring for multiple myeloma patients:

[0010] Risk score = (LILRB4 expression level × -0.008) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × -0.152) + (TUBA1B expression level × -3.995) + (CCND2 expression level × 0.006) + (CD74 expression level × -1.108).

[0011] Furthermore, the multicolor ddPCR detection of the expression levels of the six genes LILRB4, ITGB7, CRIP1, TUBA1B, CCND2, and CD74 was performed using 5-plex and 3-plex detection;

[0012] The 5-plex assay includes FAM-LILRB4, Atto425-CRIP1, HEX-ITGB7, TexasRed-TUBA1B, and CY5-ACTIN;

[0013] The triplex assay includes Atto425-CD74, FAM-CCND2, and CY5-ACTIN.

[0014] In a second aspect of the present invention, a multiple myeloma prognosis stratification model based on the prognostic molecular markers described in the first aspect of the present invention is provided, wherein the stratification model comprises risk scoring multiple myeloma patients using the expression levels of the prognostic molecular markers, wherein the expression levels are detected by bulk RNAseq;

[0015] Risk score = (LILRB4 expression level × 0.115) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × 0.105) + (CCND2 expression level × 0.001) - (TUBA1B expression level × 0.093) - (CD74 expression level × 0.081).

[0016] Furthermore, patients were divided into high-risk and low-risk groups according to the optimal cutoff point of the risk score.

[0017] In a third aspect of the present invention, a multiple myeloma prognosis stratification model based on the prognostic molecular markers described in the first aspect of the present invention is provided, wherein the stratification model comprises risk scoring multiple myeloma patients using the expression levels of the prognostic molecular markers, wherein the expression levels are detected by multi-color ddPCR;

[0018] Risk score = (LILRB4 expression level × -0.008) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × -0.152) + (TUBA1B expression level × -3.995) + (CCND2 expression level × 0.006) + (CD74 expression level × -1.108).

[0019] Furthermore, the multicolor ddPCR detection of the expression levels of the six genes LILRB4, CRIP1, ITGB7, TUBA1B, CCND2, and CD74 was performed using 5-plex detection and 3-plex detection;

[0020] The 5-plex assay includes FAM-LILRB4, Atto425-CRIP1, HEX-ITGB7, TexasRed-TUBA1B, and CY5-ACTIN;

[0021] The triplex assay includes Atto425-CD74, FAM-CCND2, and CY5-ACTIN.

[0022] Furthermore, patients were divided into high-risk and low-risk groups according to the optimal cutoff point of the risk score.

[0023] In a fourth aspect of the present invention, there is provided a use of the prognostic molecular marker described in the first aspect of the present invention in the preparation of a product for the prognosis evaluation of multiple myeloma.

[0024] Furthermore, based on the expression readings of the molecular markers, which were detected by bulk RNA sequencing, the following prognostic stratification model was used to perform risk scoring on multiple myeloma patients:

[0025] Risk score = (LILRB4 expression level × 0.115) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × 0.105) + (CCND2 expression level × 0.001) - (TUBA1B expression level × 0.093) - (CD74 expression level × 0.081).

[0026] Furthermore, based on the expression readings of the molecular markers, which were detected by multicolor ddPCR, the following prognostic stratification model was used to perform risk scoring for multiple myeloma patients:

[0027] Risk score = (LILRB4 expression level × -0.008) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × -0.152) + (TUBA1B expression level × -3.995) + (CCND2 expression level × 0.006) + (CD74 expression level × -1.108).

[0028] Furthermore, the multicolor ddPCR detection of the expression levels of the six genes LILRB4, CRIP1, ITGB7, TUBA1B, CCND2, and CD74 was performed using 5-plex detection and 3-plex detection;

[0029] The 5-plex assay includes FAM-LILRB4, Atto425-CRIP1, HEX-ITGB7, TexasRed-TUBA1B, and CY5-ACTIN;

[0030] The triplex assay includes Atto425-CD74, FAM-CCND2, and CY5-ACTIN.

[0031] In a fifth aspect of the present invention, a multiple myeloma prognosis evaluation kit is provided, characterized in that the kit comprises a reagent for specifically detecting the expression level of the prognostic biomarker described in the first aspect of the present invention.

[0032] Furthermore, the reagent includes specific primers designed for the prognostic biomarkers described in the first aspect of the present invention.

[0033] In a sixth aspect of the present invention, there is provided use of a reagent for specifically detecting the gene expression level of the prognostic biomarker described in the first aspect of the present invention in preparing a multiple myeloma prognosis evaluation kit.

[0034] Furthermore, the gene expression level is the mRNA gene expression level.

[0035] Furthermore, the gene expression level is detected by bulk RNAseq or multi-color ddPCR.

[0036] Furthermore, the multicolor ddPCR detection of the expression levels of the six genes LILRB4, CRIP1, ITGB7, TUBA1B, CCND2, and CD74 was performed using 5-plex detection and 3-plex detection;

[0037] The 5-plex assay includes FAM-LILRB4, Atto425-CRIP1, HEX-ITGB7, TexasRed-TUBA1B, and CY5-ACTIN;

[0038] The triplex assay includes Atto425-CD74, FAM-CCND2, and CY5-ACTIN.

[0039] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features described in detail below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be listed here one by one.

[0040] Compared with the prior art, the present invention has the following advantages and improvements:

[0041] The present invention discloses molecular markers for the prognosis of multiple myeloma and a prognostic stratification model and application. The present invention uses a transcriptome sequencing (bulk RNAseq) analysis system in combination with a Cox regression model to select six core genes that are significantly associated with clinical prognosis based on seven genes studied in previous studies. Based on this set of specific gene expression characteristics, the present invention constructs a prognostic stratification model using previous clinical data. When analyzed using bulk RNAseq, the risk score of this gene combination = (LILRB4 expression × 0.115) + (CRIP1 expression × 0.055) + (ITGB7 expression × 0.105) + (CCND2 expression × 0.001) - (TUBA1B expression × 0.093) - (CD74 expression × 0.081). The overall model was statistically significant, indicating that the high risk score of the above 6-gene combination (LILRB4 (NM_001278426.4), CRIP1 (NM_001311.5), ITGB7 (NM_000889.3), TUBA1B (NM_006082.3), CCND2 (NM_001759.4) and CD74 (NM_001025158.3) was associated with poor prognosis of patients (p = 0.0368). At the same time, the present invention constructed a set of digital PCR detection methods that fully matched and were efficient based on the above prognostic stratification model; and used a new set of clinical samples as a validation set to verify the above prognostic stratification model and its corresponding digital PCR detection method. When using the digital PCR detection method, the risk score of the gene combination = (LILRB4 expression level × -0.008) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × -0.152) + (TUBA1B expression level × -3.995) + (CCND2 expression level × 0.006) + (CD74 expression level × -1.108). Kaplan-Meier survival analysis showed that the median survival time of patients in the high-risk group was significantly shorter than that in the low-risk group (high-risk group: 50.92 months, low-risk group: 80.0 months), and the Log-Rank test was significant (p = 0.0368). In summary, the present invention proves that the above-mentioned 6-gene prognostic stratification model and its digital PCR detection method can effectively distinguish patients with high-risk prognosis from patients in the low-risk group. The present invention not only provides a new tool for the precise typing of multiple myeloma (MM), but also guides the formulation of individualized precise treatment strategies for high-risk patients, and has important application value for improving the scientific nature of clinical diagnosis and treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0043] Figure 1 : MM transcriptome results showed that there was a significant difference in survival between patients with high and low 6-gene risk scores.

[0044] Figure 2 : 1D droplet scatter plot of 3-plex ddPCR.

[0045] Figure 3 : 1D droplet scatter plots of triplex and singleplex ddPCR FAM-CCND2.

[0046] Figure 4 : 1D droplet scatter plots of triplex and singleplex ddPCR Atto425-CD74.

[0047] Figure 5 : Scatter plots of 5-plex and single-plex ddPCR FAM-LILRB4.

[0048] Figure 6 : Scatter plots of 5-plex and single-plex ddPCR HEX-ITGB7.

[0049] Figure 7 : Scatter plot of 5-plex and single-plex Atto425-CRIP1.

[0050] Figure 8 : Scatter plots of 5-plex and single-plex ddPCR TexasRed-TUBA1B.

[0051] Figure 9 : Scatter plots of 5-plex and single-plex ddPCR CY5-ACTIN.

[0052] Figure 10 : The results of multi-color ddPCR showed that there was a significant difference in survival between patients with high and low 6-gene risk scores. DETAILED DESCRIPTION

[0053] The present invention provides a novel prognostic molecular marker for multiple myeloma and a prognostic stratification model and application. The present invention is described in detail below in conjunction with the examples to facilitate further understanding of the present invention by those skilled in the art. However, the examples described below are only part of the examples of the present invention and should not be regarded as any form of limitation to the present invention. It should be pointed out that adjustments and improvements made by those of ordinary skill in the art based on the concept of the present invention should be regarded as the scope of protection of the present invention. Specific technical operation steps and operators are not indicated in the examples, and are carried out in accordance with the general technical conditions described in the literature of this field or the relevant product instructions.

[0054] Example 1: Establishment of a 6-gene prognostic model using bulk RNAseq

[0055] Bone marrow samples were collected from 58 newly diagnosed high-risk MM patients (using the current sMART3.0 and Mayo Clinic 2018 high-risk criteria). MM cells were purified using Ficoll and CD138 immunomagnetic bead sorting methods, and bulk RNA sequencing was performed. Clinical information was also collected. To improve the operability of clinical testing and further stratify patients currently identified as high-risk, we attempted to further reduce the number of genes in the panel from the 7-gene panel. Our laboratory's preliminary data and literature reports indicate that LILRB4, CRIP1, ITGB7, TUBA1B, and CCND2 play a key role in the development and progression of MM. Therefore, we attempted to remove CD74 or HIST1H4C from the panel. We used multivariate Cox regression analysis to construct prognostic risk models for the LILRB4, CRIP1, CCND2, ITGB7, TUBA1B, CD74 and LILRB4, CRIP1, CCND2, ITGB7, TUBA1B, and HIST1H4C gene combinations, respectively, and attempted to stratify patients prognostically. The results showed that only the LILRB4, CRIP1, CCND2, ITGB7, TUBA1B, and CD74 gene combination was able to stratify patients prognostically based on gene expression levels. The risk score for this gene combination was (LILRB4 expression × 0.115) + (CRIP1 expression × 0.055) + (ITGB7 expression × 0.105) + (CCND2 expression × 0.001) - (TUBA1B expression × 0.093) - (CD74 expression × 0.081). The overall model was statistically significant (p = 0.0368). The high-risk score of the 6-gene combination (LILRB4 (NM_001278426.4), CRIP1 (NM_001311.5), ITGB7 (NM_000889.3), TUBA1B (NM_006082.3), CCND2 (NM_001759.4) and CD74 (NM_001025158.3)) was confirmed to be associated with poor prognosis of patients (p = 0.0368). Figure 1 As shown, there was a significant difference in survival between the high and low groups of patients with the 6-gene risk score.

[0056] Example 2: Multicolor ddPCR detection method for establishing a 6-gene prognostic stratification model

[0057] 1. Materials

[0058] MM patients: Hematology Hospital, Chinese Academy of Medical Sciences (Institute of Hematology, Chinese Academy of Medical Sciences)

[0059] RNA extraction kit: Yisheng Biotechnology Co., Ltd.

[0060] Reverse transcription kit: Yisheng Biotechnology Co., Ltd.

[0061] Multicolor PCR kit: Yisheng Biotechnology Co., Ltd.

[0062] CY5.5 dye: Suzhou Sinafu Technology Co., Ltd.

[0063] Multi-color fluorescence digital PCR instrument: Suzhou Sinafu Technology Co., Ltd.

[0064] Primers and probes: synthesized by Tianjin Qingke Biotechnology Co., Ltd.

[0065] 2. Methods

[0066] 1. Principle

[0067] Digital PCR refers to an analytical method that distributes the PCR reaction solution into a large number of independent micro-reaction units for PCR amplification reaction, reads the number of positive and negative micro-reaction units, and performs absolute quantitative analysis based on the volume of the micro-reaction units and the Poisson distribution principle.

[0068] 2. Purpose

[0069] Two multiplex ddPCR reaction systems were established using a 6-color fluorescent digital PCR instrument. The accuracy of the multiplex ddPCR system was tested by comparing it with a single-plex ddPCR experiment:

[0070] 5-plex assay (4 targets + 1 internal control) - FAM-LILRB4, Atto425-CRIP1, HEX-ITGB7, TexasRed (same as ROX)-TUBA1B and CY5-ACTIN;

[0071] Triplex assay (2 targets + 1 internal control) - Atto425-CD74, FAM-CCND2, and CY5-ACTIN.

[0072] 3. Methods

[0073] 3.1 Six-gene dd single-color PCR primers and probes were purchased from Tianjin Qingke Biotechnology Co., Ltd.

[0074] The primer probe sequences are as follows:

[0075]

[0076] 3.2 Six-gene multicolor ddPCR primers and probes were purchased from Tianjin Qingke Biotechnology Co., Ltd.

[0077] The primer probe sequences are as follows:

[0078]

[0079] Select the five fluorescence channels of FAM, HEX, ROX, Atto425, and CY5 as detection channels (TexasRed is detected using ROX). When setting the experimental information "distribution", you need to change the default reference channel ROX to CY5.5.

[0080] Single-plex ddPCR reaction system (22ul in total):

[0081]

[0082] Triplex ddPCR reaction system:

[0083]

[0084]

[0085] 5-plex ddPCR reaction system:

[0086]

[0087] Amplification conditions:

[0088]

[0089] 4. Results and Evaluation

[0090] The single-plex ddPCR system and the multiplex ddPCR system were compared to evaluate the droplet clustering effect of the scatter plot and the consistency and accuracy of the copy number results.

[0091] 4.1 1D droplet scatter plot of triplex ddPCR ( Figure 2 ): FAM-CCND2, Atto425-CD74, and CY5-ACTIN. As shown, droplet clustering for both targets and the internal control was qualified.

[0092] 4.2 1D droplet scatter plots of triplex and singleplex ddPCR FAM-CCND2 ( Figure 3 ): Droplet clustering is normal, and the results of triplex and singlex are consistent.

[0093] 4.3 1D droplet scatter plots of triplex and singleplex ddPCR Atto425-CD74 ( Figure 4 ): Droplet clustering is normal, and the results of triplex and singlex are consistent.

[0094] 4.4 Pentaplex and Singleplex ddPCR FAM-LILRB4 ( Figure 5 ): Droplet clustering is normal, and the results of 5-plex and single-plex are consistent.

[0095] 4.5 5-plex and single-plex ddPCR HEX-ITGB7 ( Figure 6): Droplet clustering is normal, and the results of 5-plex and single-plex are consistent.

[0096] 4.6 5-plex and single-plex Atto425-CRIP1( Figure 7 ): Droplet clustering is normal, and the results of 5-plex and single-plex are consistent.

[0097] 4.7 Pentaplex and Singleplex ddPCR TexasRed (same as ROX)-TUBA1B ( Figure 8 ): Droplet clustering is normal, and the results of 5-plex and single-plex are consistent.

[0098] 4.8 5-plex and single-plex ddPCR CY5-ACTIN ( Figure 9 ): Droplet clustering is normal, and the results of 5-plex and single-plex are consistent.

[0099] 4.9 Summary table of copy number concentration (cp / ul) detected by ddPCR system:

[0100]

[0101]

[0102] The results showed that droplet clustering in the multiplex ddPCR reaction system was normal, and the results of multiplex and singleplex ddPCR were consistent. The multiplex ddPCR reaction system amplified genes without interference and could be used for further sample testing.

[0103] Example 3: Detection of 6 gene expressions in CD138-positive tumor cells from bone marrow of 102 MM patients using a multicolor fluorescence ddPCR instrument

[0104] 1. Purpose:

[0105] Based on the 6-gene prognostic stratification model derived from transcriptome sequencing data, ddPCR was used to detect the expression of 6 genes in 102 MM patients. The 6-gene prognostic stratification model was validated by combining the patients' survival data as a validation set.

[0106] 2. Materials are the same as in Example 2

[0107] 3. Methods

[0108] 3.1 Total RNA was extracted from CD138-positive tumor cells of 102 MM patients and reverse transcribed. The cDNA samples were diluted 1:1000 and then subjected to triplex and quintuplex ddPCR in two tubes, respectively. The ddPCR system and amplification conditions were the same as in Example 2.

[0109] 3.2 Calculation of expression levels of 6 genes in MM patients

[0110] The calculation formula is as follows: Patient expression level of each gene = number of copies of each pathway gene / number of copies of the ACTIN gene 3.3. Cox regression analysis was performed on the six-gene expression data and patient survival data from MM patients to obtain the COX regression coefficient for each gene. Each patient's risk score was calculated based on the COX regression coefficient. Risk score = gene1 coefficient * gene1 expression + gene2 coefficient * gene2 expression + ... + gene6 coefficient * gene6 expression. Patients were divided into high and low risk groups based on their risk scores (using quartiles as cutoffs) and their overall survival was analyzed.

[0111] 4. Results and Evaluation

[0112] The specific formula for the risk score of each MM patient is: (LILRB4 expression × -0.008) + (CRIP1 expression × 0.055) + (ITGB7 expression × -0.152) + (TUBA1B expression × -3.995) + (CCND2 expression × 0.006) + (CD74 expression × -1.108). Taking the quartile of the risk score of all patients -0.46 as the cutoff, the patients were divided into a high-risk group of 16 cases (6-gene expression greater than -0.46) and a low-risk group of 86 cases (6-gene expression less than -0.46). Kaplan-Meier survival analysis showed that the median survival time of patients in the high-risk group was significantly shorter than that of patients in the low-risk group (high-risk group: 50.92 months, low-risk group: 80.0 months), and the Log-Rank test was significant (p = 0.0368). The results of multi-color ddPCR are as follows Figure 10 As shown, there was a significant difference in survival between the high and low groups of patients with the 6-gene risk score.

[0113] Conclusion: This paper established a clinically simple and feasible multi-color fluorescence digital PCR detection scheme based on 6 gene biomarkers to guide the prognostic stratification and treatment selection of MM patients.

[0114] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. For those skilled in the art, any modifications and changes made to the above embodiment based on the technical essence of the present invention should fall within the scope of protection of the technical solution of the present invention.

Claims

1. A molecular marker for the prognosis of multiple myeloma, characterized in that: The molecular markers consist of LILRB4, CRIP1, ITGB7, TUBA1B, CCND2 and CD74.

2. A multiple myeloma prognosis stratification model based on the prognostic molecular marker of claim 1, wherein the stratification model comprises risk scoring multiple myeloma patients using the expression level of the prognostic molecular marker, wherein the expression level is detected by transcriptome sequencing; Risk score = (LILRB4 expression level × 0.115) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × 0.105) + (CCND2 expression level × 0.001) - (TUBA1B expression level × 0.093) - (CD74 expression level × 0.081).

3. A multiple myeloma prognostic stratification model based on the prognostic molecular marker of claim 1, wherein the stratification model comprises risk scoring multiple myeloma patients using the expression level of the prognostic molecular marker, wherein the expression level is detected by multi-color ddPCR; Risk score = (LILRB4 expression level × -0.008) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × -0.152) + (TUBA1B expression level × -3.995) + (CCND2 expression level × 0.006) + (CD74 expression level × -1.108).

4. Use of the prognostic molecular marker according to claim 1 in the preparation of a product for the prognosis evaluation of multiple myeloma.

5. The use according to claim 4, characterized in that Based on the expression readings of the molecular markers, which were detected by transcriptome sequencing, the following prognostic stratification model was used to perform risk scoring for multiple myeloma patients: Risk score = (LILRB4 expression level × 0.115) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × 0.105) + (CCND2 expression level × 0.001) - (TUBA1B expression level × 0.093) - (CD74 expression level × 0.081).

6. The use according to claim 4, characterized in that Based on the expression readings of the molecular markers, which were detected by multicolor ddPCR, the following prognostic stratification model was used to perform risk scoring for multiple myeloma patients: Risk score = (LILRB4 expression level × -0.008) + (CRIP1 expression level × 0.055) + (ITGB7 expression level × -0.152) + (TUBA1B expression level × -3.995) + (CCND2 expression level × 0.006) + (CD74 expression level × -1.108).

7. A multiple myeloma prognosis evaluation kit, characterized in that: The kit comprises a reagent for specifically detecting the expression amount of the prognostic biomarker according to claim 1.

8. Use of a reagent for specifically detecting the gene expression level of the prognostic biomarker according to claim 1 in the preparation of a multiple myeloma prognosis evaluation kit.

9. The use according to claim 8, characterized in that The gene expression levels were detected by transcriptome sequencing or multi-color ddPCR.

10. The use according to claim 9, characterized in that The multicolor ddPCR detection of the expression levels of the six genes LILRB4, CRIP1, ITGB7, TUBA1B, CCND2, and CD74 was performed using 5-plex and 3-plex detection; The 5-plex assay includes FAM-LILRB4, Atto425-CRIP1, HEX-ITGB7, TexasRed-TUBA1B, and CY5-ACTIN; The triplex assay includes Atto425-CD74, FAM-CCND2, and CY5-ACTIN.

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