Biomarker for poor prognosis of multiple myeloma and screening

Abstract

The invention relates to the technical field of prognosis diagnosis, and discloses a multiple myeloma poor prognosis biomarker and screening. A group of miRNA marker combinations capable of being used for prognosis analysis is screened from multiple myeloma patients, and the prognosis model constructed based on the miRNA can accurately analyze the prognosis effect of the patients, has relatively high diagnostic value and provides a good application prospect for prognosis analysis of the multiple myeloma patients.

Description

Technical Field

[0001] The present invention relates to the technical field of prognosis diagnosis, and in particular to a biomarker for poor prognosis of multiple myeloma and its screening. Background Art

[0002] Multiple myeloma (MM) is a malignant plasma cell disease. Its tumor cells originate from plasma cells in the bone marrow, and plasma cells are cells that develop from B lymphocytes to the final functional stage. Therefore, multiple myeloma can be classified as a type of B lymphocyte lymphoma. WHO classifies it as a type of B cell lymphoma, called plasma cell myeloma / plasmacytoma. It is characterized by abnormal proliferation of bone marrow plasma cells accompanied by excessive production of monoclonal immunoglobulins or light chains (M proteins). Very few patients may have non-secretory MM that does not produce M proteins. Multiple myeloma is often accompanied by multiple osteolytic lesions, hypercalcemia, anemia, and kidney damage. Because the production of normal immunoglobulins is suppressed, various bacterial infections are prone to occur.

[0003] Due to individual differences among cancer patients, the survival status of different multiple myeloma patients varies greatly. In order to provide targeted and precise treatment for cancer patients, it is particularly important to find prognostic markers for patients. At present, in the field of multiple myeloma research, the screening and development of relevant prognostic markers is still in the early scientific research stage. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a multiple myeloma poor prognosis biomarker and screening. The present invention screened a group of miRNA marker combinations that can be used for prognostic analysis from multiple myeloma patients. The prognostic model constructed based on the miRNA can accurately analyze the prognostic effect of patients and has a high diagnostic value, providing a good application prospect for the prognostic analysis of multiple myeloma patients.

[0005] One of the objects of the present invention is to provide biomarkers for poor prognosis of multiple myeloma, wherein the biomarkers include one or more of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and / or miR-4664-3p.

[0006] Preferably, the multiple myeloma poor prognosis biomarkers include a combination of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and miR-4664-3p.

[0007] Furthermore, the present invention also provides a method for screening biomarkers for poor prognosis of multiple myeloma, the screening method comprising the following steps: 1) Analysis of multiple myeloma data in online databases; 2) Use R language to analyze and process the raw data in step 1); 3) Use the limma package to screen differentially expressed miRNAs; 4) Construct a prognostic risk scoring model; 5) Validation and analysis of the prognostic risk scoring model.

[0008] Preferably, the online database in step 1) is selected from TCGA database and GEO database.

[0009] Preferably, the screening condition in step 3) is |log2fold-change|>1, P<0.05.

[0010] Preferably, the prognostic risk scoring model in step 4) is LASSO Cox regression analysis.

[0011] Preferably, the prognostic risk score in step 5) = 0.125 × Exp (miR-27a-5p) + 0.102 × Exp (miR-30a) + 0.148 × Exp (miR-96) + 0.165 × Exp (miR-99b) + 0.215 × Exp (miR-155) - 0.089 × Exp (miR-188) - 0.078 × Exp (miR-505) - 0.121 × Exp (miR-887) + 0.169 × Exp (miR-3154) - 0.213 × Exp (miR-4664).

[0012] Preferably, the biomarkers obtained in step 5) include one or more of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and / or miR-4664-3p.

[0013] Preferably, the multiple myeloma poor prognosis biomarkers include a combination of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and miR-4664-3p.

[0014] Another aspect of the present invention is to provide a multiple myeloma poor prognosis assessment kit, which includes one or more of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and / or miR-4664-3p.

[0015] Preferably, the kit also includes a RT-PCR primer pair for detecting the relative expression level of the above miRNA.

[0016] Preferably, the RT-PCR upstream primer sequence is selected from one or more of SEQ ID NO.1-10.

[0017] Preferably, the RT-PCR downstream primer sequence is SEQ ID NO.11.

[0018] Preferably, the kit also includes an internal reference U6.

[0019] Preferably, the kit also includes an RT-PCR primer pair for amplifying the internal reference U6.

[0020] Preferably, the sequence of the internal reference U6 is shown as SEQ ID NO.12-13.

[0021] The advantages of the present invention are as follows: the present invention screens a group of miRNA marker combinations that can be used for prognostic analysis from multiple myeloma patients. The prognostic model constructed based on the miRNA can accurately analyze the prognostic effect of the patient and has a high diagnostic value, providing a good application prospect for the prognostic analysis of multiple myeloma patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 To analyze the diagnostic value of the prognostic model; Figure 2 Prognostic analysis for patients with multiple myeloma. DETAILED DESCRIPTION

[0023] The present invention is further described in detail below in conjunction with specific embodiments so that those skilled in the art can understand the present invention more clearly.

[0024] The following embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. Based on the specific embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work belong to the protection scope of the present invention.

[0025] In the examples of the present invention, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art; in the examples of the present invention, unless otherwise specified, the technical means used are conventional means well known to those skilled in the art.

[0026] Example 1 Screening of biomarkers for poor prognosis in multiple myeloma 1. Data download The multiple myeloma prognostic miRNA expression profile data analysis from the TCGA database was downloaded as a training set, and the multiple myeloma prognostic miRNA expression profile data was downloaded from the GEO database as a validation set.

[0027] 2. Screening of differentially expressed miRNAs The TCGA data were merged and standardized using R language. The differentially expressed miRNAs were screened using the limma package. The differential expression analysis of the miRNA data of normal tissues and tumor tissues was performed. Genes with |log2fold-change|>1 and P<0.05 were considered to be significantly differentially expressed.

[0028] Using univariate Cox analysis, miRNAs significantly correlated with the prognosis of multiple myeloma were screened from differentially expressed genes. There were 10 miRNAs significantly correlated with prognosis, namely miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154, and miR-4664-3p.

[0029] Table 1. miRNAs significantly associated with prognosis and their differential expressions

[0030] 3. LASSO Cox regression analysis Based on the different combinations of the above 10 miRNAs significantly associated with the prognosis of multiple myeloma, LASSOCox regression analysis was performed to construct a LASSO regression model; the correlation coefficient of the prediction model was output and the risk score was calculated. The model constructed by the above 10 miRNAs is as follows: Risk score = 0.125 × Exp (miR-27a-5p) + 0.102 × Exp (miR-30a) + 0.148 × Exp (miR-96) + 0.165 × Exp (miR-99b) + 0.215 × Exp (miR-155) - 0.089 × Exp (miR-188) - 0.078 × Exp (miR-505) - 0.121 × Exp (miR-887) + 0.169 × Exp (miR-3154) - 0.213 × Exp (miR-4664). Multiple myeloma patients with risk scores equal to or higher than the median were classified as high-risk group, and multiple myeloma patients with risk scores below the median were classified as low-risk group.

[0031] 4. Validation and analysis of the prognostic risk scoring model The above-mentioned prognostic risk scoring model was used to verify and analyze the multiple myeloma data downloaded from the GEO database. The results are as follows Figure 1 As shown, the prognostic risk scoring model of the present invention has a high diagnostic sensitivity for predicting the 1-, 3-, and 5-year overall survival of multiple myeloma patients, confirming that the present invention successfully constructs a prognostic model for multiple myeloma.

[0032] Example 2 Prediction of poor prognosis of multiple myeloma Peripheral blood samples were collected from 35 patients clinically diagnosed with multiple myeloma between January 2018 and May 2018. All patients were treated with clinical treatment plans and followed up every 3 months to collect corresponding survival information until October 2023. Overall survival was defined as the survival period from the date of the first surgery to the last follow-up or death.

[0033] Total RNA was extracted using an RNA extraction kit and reverse transcribed into cDNA using the HRbio™ miRNA reverse transcription kit. RT-PCR was then used to quantitatively analyze the expression of the above miRNAs, with GAPDH as the internal reference gene. The primer sequences of the 10 target genes are shown in Table 2.

[0034] Table 2 RT-PCR amplification primer sequence information

[0035] 20μL RT-PCR reaction system includes: 10μL 10×PCR Buffer, 2μL SYBR Green I fluorescent dye, 2μL dNTP, 1μL each of upstream and downstream primers, 2μL Taq DNA polymerase and 2μL cDNA; PCR reaction program: pre-denaturation at 94°C for 10 min; 94°C for 45 s, 56°C for 30 s, extension at 72°C for 45 s, for a total of 35 cycles; extension at 72°C for 10 min; storage at 4°C.

[0036] The relative expression of each miRNA was expressed as 2 -△△ Ct indicates that the relative expression levels of the 10 miRNAs were used to replace Expgene in the risk score model to validate the model at the transcriptional analysis level.

[0037] The results are as follows Figure 2 As shown, the model was used to evaluate multiple myeloma patients into low-risk group and high-risk group. The results showed that the 5-year survival rate of the low-risk group was significantly higher than that of the high-risk group, and the difference between the two was significant (P<0.05), which further confirmed the feasibility of the prognostic model of the present invention and provided strong evidence support for the prognostic diagnosis of multiple myeloma patients.

[0038] The above embodiments are intended to illustrate the essential content of the present invention, but are not intended to limit the protection scope of the present invention. Those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and protection scope of the technical solution of the present invention.

Claims

1. A biomarker for poor prognosis in multiple myeloma, characterized in that: The biomarkers include one or more of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and / or miR-4664-3p.

2. The biomarker according to claim 1, characterized in that The multiple myeloma poor prognosis biomarkers include a combination of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and miR-4664-3p.

3. A method for screening biomarkers for poor prognosis in multiple myeloma, characterized in that: The screening method comprises the following steps: 1) Analysis of multiple myeloma data in online databases; 2) Use R language to analyze and process the raw data in step 1); 3) Use the limma package to screen differentially expressed miRNAs; 4) Construct a prognostic risk scoring model; 5) Validation and analysis of the prognostic risk score model.

4. The screening method according to claim 3, characterized in that In step 1), select the TCGA database and the GEO database as the online database.

5. The screening method according to claim 3, characterized in that The screening conditions in step 3) were |log2fold-change|>1, P<0.

05.

6. The screening method according to claim 3, characterized in that The prognostic risk score model in step 4) is LASSOCox regression analysis.

7. The screening method according to claim 3, characterized in that The prognostic risk score in step 5) = 0.125 × Exp (miR-27a-5p) + 0.102 × Exp (miR-30a) + 0.148 × Exp (miR-96) + 0.165 × Exp (miR-99b) + 0.215 × Exp (miR-155) - 0.089 × Exp (miR-188) - 0.078 × Exp (miR-505) - 0.121 × Exp (miR-887) + 0.169 × Exp (miR-3154) - 0.213 × Exp (miR-4664).

8. The screening method according to claim 3, characterized in that The biomarkers obtained in step 5) include one or more of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and / or miR-4664-3p.

9. The screening method according to claim 8, characterized in that The multiple myeloma poor prognosis biomarkers include a combination of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and miR-4664-3p.

10. A multiple myeloma poor prognosis assessment kit, characterized in that: The kit includes one or more of miR-27a-5p, miR-30a, miR-96, miR-99b, miR-155, miR-188, miR-505, miR-887, miR-3154 and / or miR-4664-3p.

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