Construction of a Prognostic Model of Multiple Myeloma Based on Metabolism-Related Genes
OBJECTIVE: To screen the prognostic biomarkers of metabolic genes in patients with multiple myeloma (MM), and construct a prognostic model of metabolic genes.
METHODS: The histological database related to MM patients was searched. Data from MM patients and healthy controls with complete clinical information were selected for analysis.The second generation sequencing data and clinical information of bone marrow tissue of MM patients and healthy controls were collected from human protein atlas (HPA) and multiple myeloma research foundation (MMRF) databases. The gene set of metabolism-related pathways was extracted from Molecular Signatures Database (MSigDB) by Perl language. The biomarkers related to MM metabolism were screened by difference analysis, univariate Cox risk regression analysis and LASSO regression analysis, and the risk prognostic model and Nomogram were constructed. Risk curve and survival curve were used to verify the grouping effect of the model. Gene set enrichment analysis (GSEA) was used to study the difference of biological pathway enrichment between high risk group and low risk group. Multivariate Cox risk regression analysis was used to verify the independent prognostic ability of risk score.
RESULTS: A total of 8 mRNAs which were significantly related to the survival and prognosis of MM patients were obtained (P<0.01). As molecular markers, MM patients could be divided into high-risk group and low-risk group. Survival curve and risk curve showed that the overall survival time of patients in the low-risk group was significantly better than that in the high risk group (P<0.001). GSEA results showed that signal pathways related to basic metabolism, cell differentiation and cell cycle were significantly enriched in the high-risk group, while ribosome and N polysaccharide biosynthesis signaling pathway were more enriched in the low-risk group. Multivariate Cox regression analysis showed that the risk score composed of the eight metabolism-related genes could be used as an independent risk factor for the prognosis of MM patients, and receiver operating characteristic curve (ROC) showed that the molecular signatures of metabolism-related genes had the best predictive effect.
CONCLUSION: Metabolism-related pathways play an important role in the pathogenesis and prognosis of patients with MM. The clinical significance of the risk assessment model for patients with MM constructed based on eight metabolism-related core genes needs to be confirmed by further clinical studies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
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| Enthalten in: |
Zhongguo shi yan xue ye xue za zhi - 31(2023), 1 vom: 10. Feb., Seite 162-169 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Liu, Ge-Liang [VerfasserIn] |
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| Links: |
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| Themen: |
English Abstract |
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| Anmerkungen: |
Date Completed 09.03.2023 Date Revised 09.03.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.19746/j.cnki.issn.1009-2137.2023.01.026 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM352752149 |
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| 245 | 1 | 0 | |a Construction of a Prognostic Model of Multiple Myeloma Based on Metabolism-Related Genes |
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| 500 | |a Date Revised 09.03.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: To screen the prognostic biomarkers of metabolic genes in patients with multiple myeloma (MM), and construct a prognostic model of metabolic genes | ||
| 520 | |a METHODS: The histological database related to MM patients was searched. Data from MM patients and healthy controls with complete clinical information were selected for analysis.The second generation sequencing data and clinical information of bone marrow tissue of MM patients and healthy controls were collected from human protein atlas (HPA) and multiple myeloma research foundation (MMRF) databases. The gene set of metabolism-related pathways was extracted from Molecular Signatures Database (MSigDB) by Perl language. The biomarkers related to MM metabolism were screened by difference analysis, univariate Cox risk regression analysis and LASSO regression analysis, and the risk prognostic model and Nomogram were constructed. Risk curve and survival curve were used to verify the grouping effect of the model. Gene set enrichment analysis (GSEA) was used to study the difference of biological pathway enrichment between high risk group and low risk group. Multivariate Cox risk regression analysis was used to verify the independent prognostic ability of risk score | ||
| 520 | |a RESULTS: A total of 8 mRNAs which were significantly related to the survival and prognosis of MM patients were obtained (P<0.01). As molecular markers, MM patients could be divided into high-risk group and low-risk group. Survival curve and risk curve showed that the overall survival time of patients in the low-risk group was significantly better than that in the high risk group (P<0.001). GSEA results showed that signal pathways related to basic metabolism, cell differentiation and cell cycle were significantly enriched in the high-risk group, while ribosome and N polysaccharide biosynthesis signaling pathway were more enriched in the low-risk group. Multivariate Cox regression analysis showed that the risk score composed of the eight metabolism-related genes could be used as an independent risk factor for the prognosis of MM patients, and receiver operating characteristic curve (ROC) showed that the molecular signatures of metabolism-related genes had the best predictive effect | ||
| 520 | |a CONCLUSION: Metabolism-related pathways play an important role in the pathogenesis and prognosis of patients with MM. The clinical significance of the risk assessment model for patients with MM constructed based on eight metabolism-related core genes needs to be confirmed by further clinical studies | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a gene set enrichment analysis | |
| 650 | 4 | |a metabolism | |
| 650 | 4 | |a multiple myeloma | |
| 650 | 4 | |a risk prognostic model | |
| 700 | 1 | |a Chen, Xi-Meng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Jun-Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Hao-Ran |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zi-Ning |e verfasserin |4 aut | |
| 700 | 1 | |a Zhi, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhuo-Yang |e verfasserin |4 aut | |
| 700 | 1 | |a He, Pei-Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Xue-Chun |e verfasserin |4 aut | |
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