CN114134227A - Multiple myeloma prognosis poor biomarker, screening method, prognosis hierarchical model and application - Google Patents
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Abstract
The invention provides a biomarker with poor multiple myeloma prognosis, a screening method, a prognosis hierarchical model and application, and relates to the technical field of tumor prognosis. The method analyzes the tumor cell gene expression difference of the MM patient by combining a single cell transcriptome sequencing technology with unsupervised clustering, Cox regression analysis, transcriptome sequencing and other technologies, identifies and obtains 7 genes and biomarkers related to poor prognosis of the patient, establishes a weighted average calculation formula, establishes a model through external data, and verifies internal data; the MM high-risk patients with 7 abnormal gene increases are proved to be an ultra-high-risk group, the survival time is shorter, and the prognosis is poor. The invention can promote the clinical application of the combined risk prognosis hierarchical model and assist the clinical diagnosis and treatment decision.
Description
Technical Field
The invention belongs to the technical field of tumor prognosis stratification and prognosis evaluation, and particularly relates to multiple myeloma prognosis badness biomarkers, a screening method, prognosis stratification model optimization establishment and application.
Background
Hematological tumors are one of the common malignancies that threaten human health. The domestic epidemiological data show that lymphoma, leukemia and multiple myeloma are three top-ranked hematological tumors in China. In recent years, due to the wide application of new drugs and the emergence of targeted therapy and immunotherapy, the treatment of Multiple Myeloma (MM) has been remarkably advanced, the median survival time of MM patients has been prolonged to 6-8 years, but MM is still an incurable malignant disease, and almost all patients finally develop refractory disease (RRMM) or even no treatment.
MM has now formed an overall therapeutic model and can benefit most patients, especially low and medium risk patients; however, the identification of the high-risk and high-invasion subgroups is the key point and difficulty of the current clinical work. The existing treatment mode comprises chemotherapy induction treatment (VRD scheme) and combined double autologous hematopoietic stem cell transplantation, the curative effect of the traditional Chinese medicine preparation is still poor for 15-25% of extremely high-risk patients, the tumors of the part of patients grow rapidly, the diseases progress rapidly, and the median survival time is less than 3 years. Due to the lack of effective biomarkers, how to identify the diagnosed part of high-risk and highly invasive patients early, especially ultra-high-risk patients, is still a challenge in clinical work at present to give and standardize stratified treatment as early as possible.
Disclosure of Invention
In view of the above, the present invention aims to provide multiple myeloma prognosis poor biomarker, a screening method, a prognosis hierarchical model and an application thereof, to promote clinical application of a combined risk prognosis hierarchical model, and to assist clinical diagnosis and treatment decision.
In order to achieve the above object, the present invention provides the following technical solutions:
the invention provides a method for screening biomarkers with poor prognosis of multiple myeloma, which comprises the following steps: (1) respectively carrying out single-cell transcriptome sequencing on mononuclear cells from initial-diagnosis multiple myeloma patients and healthy people, then carrying out unsupervised clustering to obtain a plurality of cell subsets, and dividing the multiple myeloma patients into a standard-risk group, a high-risk group and an ultra-high-risk group by taking the proportion of the characteristic cell subsets as a cutoff value; the proportion of patients with multiple myeloma in the characteristic cell subpopulation is significantly higher than that of healthy controls;
(2) screening the shared multiple myeloma differential expression genes in the high-risk group and the ultra-high-risk group, carrying out secondary unsupervised cluster analysis on the shared multiple myeloma differential expression genes to obtain a plurality of groups of multiple myeloma cell subclones, screening the subclones with multiple myeloma tumor initiating cell characteristics as characteristic subclones, and carrying out gene expression differential analysis on the characteristic subclones and other subclones to obtain differential expression genes;
(3) and (3) performing Wein analysis on the first 10 up-regulated expression genes of the ultra-high risk group in the step (1) and the first 10 up-regulated expression genes of the characteristic difference expression genes in the step (2) to obtain the biomarker with poor prognosis of multiple myeloma.
Preferably, in the step (1), when the proportion of the characteristic cell subpopulation is less than 10%, the characteristic cell subpopulation is a standard risk group;
when the proportion of the characteristic cell subset is more than or equal to 10 percent and less than or equal to 35 percent, the group is a high-risk group;
when the proportion of the characteristic cell subpopulation is more than 35%, the cells are in an ultra-high risk group.
Preferably, the method for screening multiple myeloma tumor initiating cells in step (2) comprises a transcription activity analysis, a cell proliferation activity analysis and a drug resistance analysis.
The invention also provides a multiple myeloma prognosis poor biomarker obtained by the screening method, wherein the prognosis poor biomarker comprises the following components: LILRB4, ITGB7, CRIP1, TUBA1B, CCND2, HIST1H4C, and CD 74.
The invention also provides a multiple myeloma risk prognosis hierarchical model based on the poor prognosis biomarker, which comprises the step of carrying out risk scoring on multiple myeloma patients by using the expression level of the poor prognosis biomarker, wherein the risk score is (LILRB4 expression level multiplied by 0.678) + (ITGB7 expression level multiplied by 0.809) + (CRIP1 expression level multiplied by 0.674) + (TUBA1B expression level multiplied by 0.800) + (CCND2 expression level multiplied by 0.120) - (HIST1H4C expression level multiplied by 1.246) + (CD74 expression level multiplied by 0.421);
patients were divided into high risk groups and low risk groups based on the best cut-off for risk scoring.
Preferably, the expression level of the biomarker with poor prognosis is an absolute expression level.
The invention also provides application of the biomarker with poor prognosis in preparation of a tool for prognosis of multiple myeloma patients.
Preferably, the means comprises a kit for detecting the expression level of mRNA gene of the poor prognosis biomarker or a kit for detecting the expression level of corresponding protein of the poor prognosis biomarker.
The invention also provides a kit for evaluating prognosis of multiple myeloma, which comprises a reagent for detecting the expression level of the biomarker with poor prognosis.
Preferably, the reagent comprises a specific primer pair designed for the poor prognosis biomarker.
Has the advantages that: the invention provides a method for screening biomarkers with poor prognosis of Multiple Myeloma (MM), which comprises the steps of firstly applying a single-cell transcriptome sequencing technology to combine unsupervised clustering, Cox regression analysis, transcriptome sequencing (bulk RNAseq) and other technologies, analyzing the expression difference of tumor cell genes of an MM patient, identifying to obtain 7 genes and biomarkers related to poor prognosis of the patient, establishing a weighted average calculation formula for the 7 genes, and verifying internal data through an external data establishment model; the MM high-risk patients with 7 abnormal gene increases are proved to be an ultra-high-risk group, the survival time is shorter, and the prognosis is poor. The invention also establishes a fine prognosis hierarchical model based on the level of 7 gene transcriptomes; a new model based on MM patient tumor cell transcriptome sequencing is developed, and 7-gene (7-gene) risk score high-risk MM prognosis stratification is adopted, so that the clinical application of the combined risk prognosis stratification model is promoted, and the clinical diagnosis and treatment decision are assisted.
Drawings
FIG. 1 shows that 17 cell subsets were obtained by unsupervised clustering;
FIG. 2 shows that cluster0 increases significantly in proportion as the risk of the patient increases;
FIG. 3 is the establishment and verification of 7 gene weighted average calculation formulas.
Detailed Description
The invention provides a method for screening biomarkers with poor prognosis of multiple myeloma, which comprises the following steps: (1) respectively carrying out single-cell transcriptome sequencing on mononuclear cells from initial-diagnosis multiple myeloma patients and healthy people, then carrying out unsupervised clustering to obtain a plurality of cell subsets, and dividing the multiple myeloma patients into a standard-risk group, a high-risk group and an ultra-high-risk group by taking the proportion of the characteristic cell subsets as a cutoff value; the proportion of patients with multiple myeloma in the characteristic cell subpopulation is significantly higher than that of healthy controls;
(2) screening the shared multiple myeloma differential expression genes in the high-risk group and the ultra-high-risk group, carrying out secondary unsupervised cluster analysis on the shared multiple myeloma differential expression genes to obtain a plurality of groups of multiple myeloma cell subclones, screening the characteristic subclones with multiple myeloma tumor initiating cells as characteristic subclones, and carrying out gene expression differential analysis on the characteristic subclones and other subclones to obtain differential expression genes;
(3) and (3) performing Wein analysis on the first 10 up-regulated expression genes of the ultra-high risk group in the step (1) and the first 10 up-regulated expression genes of the characteristic difference expression genes in the step (2) to obtain the biomarker with poor prognosis of multiple myeloma.
The method comprises the steps of respectively carrying out single-cell transcriptome sequencing on single nuclear cells from initially diagnosed multiple myeloma patients and healthy people, then carrying out unsupervised clustering to obtain a plurality of cell subsets, and dividing the multiple myeloma patients into a standard risk group, a high risk group and an ultra-high risk group by taking the proportion of the characteristic cell subsets as a cutoff value; the proportion of patients with multiple myeloma in the characterized cell subpopulation is significantly higher than that of healthy controls.
The source of the mononuclear cells is not particularly limited in the present invention, and the mononuclear cells are preferably obtained by separating a bone marrow sample through Ficoll. The invention carries out single cell transcriptome sequencing on the mononuclear cell, analyzes the components and the proportion of the bone marrow mononuclear cell of the MM initial diagnosis patient from the single cell level, and analyzes the gene expression of the tumor cell. In the embodiment of the invention, preferably, 12 clinical MM patients and 7 healthy controls are taken for bone marrow samples, and mononuclear cells are obtained by Ficoll separation; and carrying out single cell sequencing, carrying out single cell sequencing on 42 and 936 cells in total by quality inspection, and then obtaining 17 cell subsets by adopting unsupervised clustering, wherein the proportion of cluster0 in MM patients is obviously higher than that of healthy controls, and the cluster0 belongs to MM tumor cells and is a characteristic cell subset.
The unsupervised clustering is based on a gene expression matrix of filtered cells, the normalization of data is realized by using a LogNormalize algorithm through Seurat, and scale is set as a parameter, wherein the factor is 10000. Using the "vst" algorithm of "FindVariablegenes", 2000 genes with high variability were selected and Principal Component Analysis (PCA) was performed on these genes. Based on TOP50 principal component (PC number), clustering is realized by using "findsolusters" with a graph-based method, with the parameters of "user ═ 50" and "resolution ═ 0.5". And (3) further carrying out nonlinear dimensionality reduction on the data in the TOP30 principal component dimensionality by using consistent manifold approximation and projection (UMAP), realizing the visualization of the data, mapping the clustering result to a two-dimensional plane, and realizing the visualization of the clustering result. A total of 17 cell subsets were obtained and annotated using SingleR r. In the embodiment of the invention, the cluster0 proportion is used as the cutoff value, MM patients can be divided into 3 groups, and the MM patients are in a standard risk group when the MM patients are less than 10 percent; when the proportion of the characteristic cell subsets is more than or equal to 10% and less than or equal to 35%, the group is a high-risk group; if more than 35 percent, the high-risk group is an ultra-high-risk group. By using the grouping of the invention, the high-risk group patients can be further distinguished into high-risk and ultra-high-risk.
After a marker risk group, a high risk group and an ultra-high risk group are obtained, the invention screens the common multiple myeloma differential expression genes in the high risk group and the ultra-high risk group, carries out secondary unsupervised cluster analysis on the common multiple myeloma differential expression genes to obtain a plurality of groups of myeloma cell subclones, screens characteristic subclones with multiple myeloma tumor initiating cells as characteristic subclones, and carries out gene expression differential analysis on the characteristic subclones and other subclones to obtain differential expression genes.
The secondary unsupervised clustering analysis is based on the cell principal component analysis result, and utilizes a graph-based method of FindClusters to realize clustering, wherein the parameter is 20 in use and 0.5 in resolution. Non-linear dimensionality reduction of the data in the TOP20 principal component dimension was performed using UMAP, yielding 13 MM cell subclones. Specifically, the method comprises the following steps: the MM cell gene expression in the high-risk group and the MM cell gene expression in the ultra-high-risk group are preferably compared and analyzed, 691 differentially expressed genes are found in the embodiment, wherein the up-regulation expression is 606, and the down-regulation expression is 85. The method for screening multiple myeloma tumor initiating cells preferably comprises a transcription activity analysis, a cell proliferation activity analysis and a drug resistance analysis, so as to screen cell subclones with the characteristics of MM tumor initiating cells. In the embodiment of the invention, 13 MM cell subclones are obtained in total, and through analysis of transcription activity, cell proliferation activity, drug resistance and the like, GSVA is preferably used for identifying the differential expression pathways of the 13 MM cell subclones, the differential standard is FDR <0.05, and GSEA enrichment analysis is carried out; and analyzing the average expression conditions of UAMS 70 high-risk genes and 56 drug-resistant genes of each MM subclone. The subcloned MM cell No. 4 is found to have the characteristics of high expression of MM high-risk genes and drug resistance genes, and is obviously enriched on related channels of tumor initiating cells. The subclone MM cell No. 4 is suggested to have the characteristics of high proliferation, high invasion, drug resistance and the like, and gene expression analysis is carried out on the group of cells, so that the subclone No. 4 has 1320 gene expression differences (logFC >0.25, p <0.01) compared with the subclones of other MM cells, wherein 1263 genes are up-regulated and 57 genes are down-regulated.
After the data of the ultrahigh-risk group data and the data of the characteristic differential expression genes are obtained, the method carries out Wien analysis on the first 10 bits of the up-regulated expression genes of the ultrahigh-risk group in the step (1) and the first 10 bits of the up-regulated expression genes of the characteristic differential expression genes in the step (2) to obtain the biomarker with poor prognosis of the multiple myeloma. The method of wien analysis is not particularly limited in the present invention, and is preferably performed by using online software (http:// bioinformatics. psb. element. be/webtools/Venn /). By utilizing the method, 7 commonly up-regulated genes are obtained by searching, and can be used as a biomarker for poor prognosis of multiple myeloma.
The invention also provides a multiple myeloma prognosis poor biomarker obtained by the screening method, wherein the prognosis poor biomarker comprises the following components: LILRB4(NM _001278426.4), ITGB7(NM _000889.3), CRIP1(NM _001311.5), TUBA1B (NM _006082.3), CCND2(NM _001759.4), HIST1H4C (NM _003542.4), and CD74(NM _ 001025158.3).
After the poor prognosis biomarkers are obtained, the invention preferably further comprises carrying out single-factor and multi-factor Cox regression analysis to verify the relevance of the poor prognosis biomarkers to the prognosis of the patient, wherein the results of the example show that 7 genes in the single-factor analysis are all relevant to the poor prognosis of the patient (p <0.01), and the multi-factor analysis further proves that the 7 genes in the high-expression patient are relevant to the poor prognosis (p < 0.01).
The invention also provides a myeloma risk prognosis hierarchical model based on the poor prognosis biomarker, which comprises the step of carrying out risk score on multiple myeloma patients by using the expression level of the poor prognosis biomarker, wherein the risk score is (LILRB4 expression level multiplied by 0.678) + (ITGB7 expression level multiplied by 0.809) + (CRIP1 expression level multiplied by 0.674) + (TUBA1B expression level multiplied by 0.800) + (CCND2 expression level multiplied by 0.120) - (HIST1H4C expression level multiplied by 1.246) + (CD74 expression level multiplied by 0.421);
patients were divided into high risk groups and low risk groups based on the best cut-off for risk scoring.
The myeloma risk prognosis hierarchical model has significant statistical significance as a whole model (p is 9.5825 e-6). In the embodiment of the invention, after the myeloma risk prognosis hierarchical model is obtained, external data modeling and internal data verification are preferably included, and the MM high-risk patients with 7 abnormally-increased genes are proved to be an ultra-high-risk group, so that the survival time is shorter, and the prognosis is poor. The external data of the invention preferably comprises domestic and foreign databases, such as GSE2658 data set and GSE136324 data set. In the present invention, the expression level of the poor prognosis biomarker is preferably an absolute expression level.
The invention also provides application of the biomarker with poor prognosis in preparing a tool for prognosis of myeloma patients.
The tool comprises a kit for detecting the mRNA gene expression level of the poor prognosis biomarker or a kit for detecting the corresponding protein expression level of the poor prognosis biomarker.
The invention also provides a kit for evaluating prognosis of multiple myeloma, which comprises a reagent for detecting the expression level of the biomarker with poor prognosis.
The reagent comprises a specific primer pair designed aiming at the poor prognosis biomarker. The method for designing the specific primer pair is not particularly limited, and the specific primer pair can be designed by utilizing the conventional specific primer design method in the field and synthesized by a biological company.
The multiple myeloma prognosis poor biomarker and screening method, prognosis stratification model and application provided by the present invention are described in detail below with reference to examples, but they should not be construed as limiting the scope of the present invention.
Example 1
Collecting bone marrow samples of 12 clinical MM patients and 7 healthy controls, and obtaining mononuclear cells from the bone marrow samples through Ficoll separation; and entrusts Beijing Nuo and the SourceSculture Co., Ltd to carry out single cell sequencing, analyzes the components and the proportion of the bone marrow mononuclear cells of the MM initial diagnosis patient from the single cell level, and analyzes the gene expression of the tumor cells.
Meanwhile, bone marrow samples of 58 primary-diagnosis high-risk MM patients (the existing sMART3.0, Mayo Clinic2018 standard high-risk) are collected, MM cells in the bone marrow samples are purified by a Ficoll and CD138 immunomagnetic bead sorting method, and transcriptome sequencing (bulk RNAseq) is carried out. Clinical information is collected from the patient.
A total of 42, 936 cells were single cell sequenced by quality inspection. Then 17 cell subsets were obtained by unsupervised clustering (FIG. 1), and the proportion of each cell subset in each sample was analyzed, and it was found that cluster0 was significantly higher in MM patients than in healthy controls, and belonged to MM tumor cells. Furthermore, the proportion of cluster0 in MM patients increases significantly with increasing risk to the patient (FIG. 2). The proportion of cluster0 is used as the cutoff value, MM patients can be divided into 3 groups, less than 10% is used as a standard risk group, 10% -35% is used as a high risk group, and more than 35% is used as an ultra-high risk group. The risk groups have better consistency with the current groups, and the standard risk of other samples except the #16 sample is completely consistent with the standard risk groups. Comparing and analyzing the gene expression of MM cells of the ultra-high risk patients with the gene expression of MM cells of the high risk patients to find 691 differentially expressed genes, wherein 606 up-regulated genes are expressed, and 85 down-regulated genes are expressed.
Further carrying out secondary unsupervised cluster analysis on the MM cell clone identified in the single cell sequencing to obtain 13 MM cell subclones, and discovering that the No. 4 subclone MM cell has the characteristics of high proliferation, high invasion, drug resistance and the like and has the characteristics of MM tumor initiating cells through analysis of transcription activity, cell proliferation activity, drug resistance and the like. Gene expression analysis found that subclone No. 4 had 1320 gene expression differences (logFC >0.25, p <0.01) compared to other MM cell subclones, 1263 of which were up-regulated and 57 of which were down-regulated.
The first 10 sites of the up-regulated expression genes of the ultra-high risk group found by the previous analysis and the first 10 sites of the up-regulated expression gene of the subclone No. 4 are subjected to Wien analysis, wherein 7 common up-regulated genes are respectively as follows: LILRB4(NM _001278426.4), ITGB7(NM _000889.3), CRIP1(NM _001311.5), TUBA1B (NM _006082.3), CCND2(NM _001759.4), HIST1H4C (NM _003542.4), and CD74(NM _ 001025158.3).
In order to verify whether the 7 genes can further stratify the clinically currently identified high-risk patients, single-factor and multi-factor Cox regression analysis is respectively carried out, the result shows that the 7 genes are all related to poor prognosis of the patients (p <0.01) by the single-factor analysis, and the multi-factor analysis further proves that the 7 genes are related to poor prognosis of the patients with high expression (p < 0.01).
Single and multifactorial Cox regression analysis: the gene expression matrix of the naive MM patient was modeled by one-way COX regression using survivval package "coxph" function, and the PH test was performed using "COX. zph" function. The 7 genes all fit the precondition PH hypothesis of the cox equal proportional risk model (cox. zph test p > 0.05). The p-value and risk ratio HR of single-factor COX are all statistically significant, and are LILRB 4: p is 0.027, HR is 3.7; ITGB 7: p is 3.40E-06, HR is 3.1; CRIP 1: p is 0.0004, HR is 2.2; TUBA 1B: p is 2.71E-06, HR is 3.9; CCND 2: p is 0.004, HR is 1.9; HIST1H 4C: p is 0.012 and HR is 0.55; CD 74: p is 0.0008 and HR is 0.47.
To further verify whether the 7 genes can further stratify the clinically currently identified high-risk patients, the 7 MM genes are subjected to multi-factor Cox regression analysis modeling by using the gene chip data of the R-ISS stage III patient and a "coxph" function, and Variance expansion factors (VIF) of each gene are calculated, wherein the square roots of all the VIFs are less than 2, and the 7 genes are considered to have no co-linear relationship. A prognostic risk model is constructed by using a multifactorial Cox regression coefficient, wherein the risk score is (LILRB4 expression amount multiplied by 0.678) + (ITGB7 expression amount multiplied by 0.809) + (CRIP1 expression amount multiplied by 0.674) + (TUBA1B expression amount multiplied by 0.800) + (CCND2 expression amount multiplied by 0.120) - (HIST1H4C expression amount multiplied by 1.246) + (CD74 expression amount multiplied by 0.421). The model identity index (C-index) was 0.7, and the overall model had significant statistical significance (p-9.5825 e-6).
The external data modeling process (FIG. 3) is as follows: downloading 118 high-risk MM patient transcriptome sequencing data (high-risk definition is R-ISS III phase) in a public database as training cohort, and defining a high-risk group or a low-risk group according to the expression quantity of 7-gene signature, wherein the specific standard is as follows: the high risk group is R-ISS III stage and the expression quantity of the 7-gene signature is more than 16.44; the low risk group is R-ISS stage III and the expression level of the 7-gene signature is less than 16.44.
The GSE2658 dataset was published in 2006 by the MM research center of small Stone City, incorporated into 414 cases of naive MM patients, and analyzed for bone marrow CD138 using the GPL570 chip platform+MM cells, and obtaining gene expression data. Single factor Cox regression analysis was performed on 7 high-risk MM genes in this dataset, and the results showed that all were significantly correlated with MM prognosis.
The GSE136324 dataset was published by the american seille gene in 2019 and was included in 436 first-visit MM patients, including 118R-ISS stage iii patients. All patients subsequently received multiple autologous hematopoietic stem cell transplants. Analysis of Whole bone marrow and CD138 Using the GPL27143 chip platform+MM cell to obtainTo gene expression chip data. In the data set GSE136324, a risk score is generated for each R-ISS phase III patient based on the risk score function described above. The turning point of the ROC function curve is selected as the best cut-off point (cut-off value: 16.44) for grouping of the risk scores, and 15 cases of high risk groups and 108 cases of low risk groups are obtained. Kaplan-Meier survival analysis shows that the median survival time of the patients in the high risk group is obviously shorter than that of the patients in the low risk group (the high risk group is 44.0 months, the low risk group is 147.0 months), and the Log-Rank test has significant significance (p)<0.0001，HR＝3.015)。
Kaplan-Meier survival analysis showed that the survival time of patients in the high risk group was significantly shortened in the lower risk group (high risk group: month 44.0, low risk group: month 147.0, p <0.0001, HR ═ 3.015). The results indicate that high expression of 7 genes can further stratify the high-risk patients in the stage III of R-ISS.
The internal data validation (fig. 3) process is as follows: in order to verify the reliability of the prognosis hierarchical model, verification was performed by using the data of patients admitted by the lymphoma center in the hematological disease hospital of the Chinese academy of medicine (the institute of hematology of the Chinese academy of medicine). The specific process is as follows:
firstly, collecting bone marrow samples of MM high-risk patients (n is 58) collected by a lymphoma center of a Chinese medical academy of sciences hospital (institute of hematology of Chinese medical academy of sciences) according to the definition of sMART3.0(Mayo clinical 2018 revision), separating mononuclear cells by Ficoll, and enriching CD138 in the mononuclear cells by immunomagnetic beads+Tumor cells, and performing transcriptome sequencing. 7 gene expression quantities of 58 samples are calculated and evaluated, 7 gene expression quantities are obtained according to a weighted average calculation formula of the 7 gene expression quantities, and according to the best cutoff point (cutoff is 29.40) of the risk score, the patients are divided into 10 cases (7-gene expression quantity is larger than 29.40) of high risk groups and 48 cases (7-gene expression quantity is smaller than 29.40) of low risk groups. Kaplan-Meier survival analysis showed that median survival time was significantly shorter in the high risk group than in the low risk group (high risk group: 19.0 months, low risk group: 50.0 months), and the Log-Rank test was significant (p-0.0198, HR-2.739).
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for screening biomarkers for poor prognosis of multiple myeloma comprising the steps of: (1) respectively carrying out single-cell transcriptome sequencing on mononuclear cells from initial-diagnosis multiple myeloma patients and healthy people, then carrying out unsupervised clustering to obtain a plurality of cell subsets, and dividing the multiple myeloma patients into a standard-risk group, a high-risk group and an ultra-high-risk group by taking the proportion of the characteristic cell subsets as a cutoff value; the proportion of patients with multiple myeloma in the characteristic cell subpopulation is significantly higher than that of healthy controls;
(2) screening the shared multiple myeloma differential expression genes in the high-risk group and the ultra-high-risk group, carrying out secondary unsupervised cluster analysis on the shared multiple myeloma differential expression genes to obtain a plurality of groups of multiple myeloma cell subclones, screening the characteristic subclones with multiple myeloma tumor initiating cells as characteristic subclones, and carrying out gene expression differential analysis on the characteristic subclones and other subclones to obtain differential expression genes;
(3) and (3) performing Wein analysis on the first 10 up-regulated expression genes of the ultra-high risk group in the step (1) and the first 10 up-regulated expression genes of the characteristic difference expression genes in the step (2) to obtain the biomarker with poor prognosis of multiple myeloma.
2. The screening method according to claim 1, wherein, in the step (1),
when the proportion of the characteristic cell subset is less than 10%, the cell subset is a standard risk group;
when the proportion of the characteristic cell subset is more than or equal to 10 percent and less than or equal to 35 percent, the group is a high-risk group;
when the proportion of the characteristic cell subpopulation is more than 35%, the cells are in an ultra-high risk group.
3. The screening method according to claim 1, wherein the method for screening multiple myeloma tumor initiating cells according to step (2) comprises a transcription activity assay, a cell proliferation activity assay and a drug resistance assay.
4. A biomarker for poor prognosis of multiple myeloma obtained by the screening method according to any one of claims 1 to 3, wherein the biomarker for poor prognosis comprises: LILRB4, ITGB7, CRIP1, TUBA1B, CCND2, HIST1H4C, and CD 74.
5. A multiple myeloma risk prognosis stratification model based on the poor prognosis biomarker of claim 4, wherein the stratification model comprises the expression level of the poor prognosis biomarker is used for carrying out risk scoring on multiple myeloma patients, and the risk score is (LILRB4 expression level x 0.678) + (ITGB7 expression level x 0.809) + (CRIP1 expression level x 0.674) + (TUBA1B expression level x 0.800) + (CCND2 expression level x 0.120) - (HIST1H4C expression level x 1.246) + (CD74 expression level x 0.421);
patients were divided into high risk groups and low risk groups based on the best cut-off for risk scoring.
6. The layered model of claim 5, wherein the expression level of the biomarker with poor prognosis is an absolute expression level.
7. Use of the poor prognosis biomarker of claim 4 in the preparation of a tool for prognosis of multiple myeloma patients.
8. The use of claim 7, wherein the means comprises a kit for detecting the expression level of mRNA gene of the poor prognosis biomarker or a kit for detecting the expression level of the corresponding protein of the poor prognosis biomarker.
9. A kit for assessing prognosis of multiple myeloma, comprising a reagent for detecting the expression level of the biomarker with poor prognosis according to claim 4.
10. The kit of claim 9, wherein the reagents comprise a specific primer pair designed for the poor prognosis biomarker of claim 4.
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