Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis
Background Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles. Methods Gene expression datas of MM (GSE...
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PeerJ Inc.
2021-06-01
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| Online Access: | https://peerj.com/articles/11320.pdf |
| _version_ | 1797424392599240704 |
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| author | Ying Pan Ye Meng Zhimin Zhai Shudao Xiong |
| author_facet | Ying Pan Ye Meng Zhimin Zhai Shudao Xiong |
| author_sort | Ying Pan |
| collection | DOAJ |
| description | Background Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles. Methods Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters. Results GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients. Conclusion In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients. |
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| language | English |
| last_indexed | 2024-03-09T08:01:40Z |
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| spelling | doaj.art-6792a982447e43daacfb874d85fceb592023-12-03T00:40:49ZengPeerJ Inc.PeerJ2167-83592021-06-019e1132010.7717/peerj.11320Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysisYing PanYe MengZhimin ZhaiShudao XiongBackground Multiple myeloma (MM), the second most hematological malignancy, has high incidence and remains incurable till now. The pathogenesis of MM is poorly understood. This study aimed to identify novel prognostic model for MM on gene expression profiles. Methods Gene expression datas of MM (GSE6477, GSE136337) were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in GSE6477 between case samples and normal control samples were screened by the limma package. Meanwhile, enrichment analysis was conducted, and a protein-protein interaction (PPI) network of these DEGs was established by STRING and cytoscape software. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA). Key genes were identified both from hub genes and the DEGs. Univariate and multivariate Cox congression were performed to screen independent prognostic genes to construct a predictive model. The predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Finally, univariate and multivariate Cox regression analyse were used to investigate whether the prognostic model could be independent of other clinical parameters. Results GSE6477, including 101 case and 15 normal control, were screened as the datasets. A total of 178 DEGs were identified, including 59 up-regulated and 119 down-regulated genes. In WGCNA analysis, module black and module purple were the most relevant modules with cancer traits, and 92 hub genes in these two modules were selected for further analysis. Next, 47 genes were chosen both from the DEGs and hub genes as key genes. Three genes (LYVE1, RNASE1, and RNASE2) were finally screened by univariate and multivariate Cox regression analyses and used to construct a risk model. In addition, the three-gene prognostic model revealed independent and accurate prognostic capacity in relation to other clinical parameters for MM patients. Conclusion In summary, we identified and constructed a three-gene-based prognostic model that could be used to predict overall survival of MM patients.https://peerj.com/articles/11320.pdfMultiple myelomaPrognosisWGCNABioinformatics analysisPrognostic model |
| spellingShingle | Ying Pan Ye Meng Zhimin Zhai Shudao Xiong Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis PeerJ Multiple myeloma Prognosis WGCNA Bioinformatics analysis Prognostic model |
| title | Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis |
| title_full | Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis |
| title_fullStr | Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis |
| title_full_unstemmed | Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis |
| title_short | Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis |
| title_sort | identification of a three gene based prognostic model in multiple myeloma using bioinformatics analysis |
| topic | Multiple myeloma Prognosis WGCNA Bioinformatics analysis Prognostic model |
| url | https://peerj.com/articles/11320.pdf |
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