Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma
Xinge Sheng,1,2,* Shuo Wang,2,* Meijiao Huang,1 Kaiwen Fan,1,2 Jiaqi Wang,1,2 Quanyi Lu1,2 1Department of Hematology, Zhongshan Hospital Xiamen University, Xiamen, People’s Republic of China; 2Clinical Medicine Department, School of Medicine, Xiamen University, Xiamen, People’s Repub...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2022-09-01
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| Series: | International Journal of General Medicine |
| Subjects: | |
| Online Access: | https://www.dovepress.com/bioinformatics-analysis-of-the-key-genes-and-pathways-in-multiple-myel-peer-reviewed-fulltext-article-IJGM |
| _version_ | 1818054798782496768 |
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| author | Sheng X Wang S Huang M Fan K Wang J Lu Q |
| author_facet | Sheng X Wang S Huang M Fan K Wang J Lu Q |
| author_sort | Sheng X |
| collection | DOAJ |
| description | Xinge Sheng,1,2,* Shuo Wang,2,* Meijiao Huang,1 Kaiwen Fan,1,2 Jiaqi Wang,1,2 Quanyi Lu1,2 1Department of Hematology, Zhongshan Hospital Xiamen University, Xiamen, People’s Republic of China; 2Clinical Medicine Department, School of Medicine, Xiamen University, Xiamen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Quanyi Lu, Tel +86 13600959425, Email luqy111@163.comObjective: To study the differentially expressed genes between multiple myeloma and healthy whole blood samples by bioinformatics analysis, find out the key genes involved in the occurrence, development and prognosis of multiple myeloma, and analyze and predict their functions.Methods: The gene chip data GSE146649 was downloaded from the GEO expression database. The gene chip data GSE146649 was analyzed by R language to obtain the genes with different expression in multiple myeloma and healthy samples, and the cluster analysis heat map was constructed. At the same time, the protein-protein interaction (PPI) networks of these DEGs were established by STRING and Cytoscape software. The gene co-expression module was constructed by weighted correlation network analysis (WGCNA). The hub genes were identified from key gene and central gene. TCGA database was used to analyze the expression of differentially expressed genes in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.Results: We identified four genes (TNFSF11, FGF2, SGMS2, IGFBP7) as hub genes of multiple myeloma. Then, TCGA database was used to analyze the survival of TNFSF11, FGF2, SGMS2 and IGFBP7 in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.Conclusion: The study suggests that TNFSF11, FGF2, SGMS2 and IGFBP7 are important research targets to explore the pathogenesis, diagnosis and treatment of multiple myeloma.Graphical Abstract: Keywords: multiple myeloma, bioinformatics, RT qPCR, PPI |
| first_indexed | 2024-12-10T12:02:47Z |
| format | Article |
| id | doaj.art-80c826e2297a4590b1338ddfd3a1faf1 |
| institution | Directory Open Access Journal |
| issn | 1178-7074 |
| language | English |
| last_indexed | 2024-12-10T12:02:47Z |
| publishDate | 2022-09-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | International Journal of General Medicine |
| spelling | doaj.art-80c826e2297a4590b1338ddfd3a1faf12022-12-22T01:49:33ZengDove Medical PressInternational Journal of General Medicine1178-70742022-09-01Volume 156999701677941Bioinformatics Analysis of the Key Genes and Pathways in Multiple MyelomaSheng XWang SHuang MFan KWang JLu QXinge Sheng,1,2,* Shuo Wang,2,* Meijiao Huang,1 Kaiwen Fan,1,2 Jiaqi Wang,1,2 Quanyi Lu1,2 1Department of Hematology, Zhongshan Hospital Xiamen University, Xiamen, People’s Republic of China; 2Clinical Medicine Department, School of Medicine, Xiamen University, Xiamen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Quanyi Lu, Tel +86 13600959425, Email luqy111@163.comObjective: To study the differentially expressed genes between multiple myeloma and healthy whole blood samples by bioinformatics analysis, find out the key genes involved in the occurrence, development and prognosis of multiple myeloma, and analyze and predict their functions.Methods: The gene chip data GSE146649 was downloaded from the GEO expression database. The gene chip data GSE146649 was analyzed by R language to obtain the genes with different expression in multiple myeloma and healthy samples, and the cluster analysis heat map was constructed. At the same time, the protein-protein interaction (PPI) networks of these DEGs were established by STRING and Cytoscape software. The gene co-expression module was constructed by weighted correlation network analysis (WGCNA). The hub genes were identified from key gene and central gene. TCGA database was used to analyze the expression of differentially expressed genes in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.Results: We identified four genes (TNFSF11, FGF2, SGMS2, IGFBP7) as hub genes of multiple myeloma. Then, TCGA database was used to analyze the survival of TNFSF11, FGF2, SGMS2 and IGFBP7 in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.Conclusion: The study suggests that TNFSF11, FGF2, SGMS2 and IGFBP7 are important research targets to explore the pathogenesis, diagnosis and treatment of multiple myeloma.Graphical Abstract: Keywords: multiple myeloma, bioinformatics, RT qPCR, PPIhttps://www.dovepress.com/bioinformatics-analysis-of-the-key-genes-and-pathways-in-multiple-myel-peer-reviewed-fulltext-article-IJGMmultiple myelomabioinformaticsrt qpcrppi |
| spellingShingle | Sheng X Wang S Huang M Fan K Wang J Lu Q Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma International Journal of General Medicine multiple myeloma bioinformatics rt qpcr ppi |
| title | Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma |
| title_full | Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma |
| title_fullStr | Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma |
| title_full_unstemmed | Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma |
| title_short | Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma |
| title_sort | bioinformatics analysis of the key genes and pathways in multiple myeloma |
| topic | multiple myeloma bioinformatics rt qpcr ppi |
| url | https://www.dovepress.com/bioinformatics-analysis-of-the-key-genes-and-pathways-in-multiple-myel-peer-reviewed-fulltext-article-IJGM |
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