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Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis
- Source :
- PeerJ, Vol 9, p e11320 (2021), PeerJ
- Publication Year :
- 2021
- Publisher :
- PeerJ, 2021.
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
Multivariate statistics
Bioinformatics
Computational biology
Biology
General Biochemistry, Genetics and Molecular Biology
03 medical and health sciences
Bioinformatics analysis
0302 clinical medicine
Multiple myeloma
Gene expression
medicine
Molecular Biology
Gene
Receiver operating characteristic
WGCNA
Proportional hazards model
General Neuroscience
Univariate
Weighted correlation network analysis
Cancer
General Medicine
Prognosis
medicine.disease
Orthopedics
030104 developmental biology
Oncology
030220 oncology & carcinogenesis
Medicine
Prognostic model
General Agricultural and Biological Sciences
Medical Genetics
Subjects
Details
- ISSN :
- 21678359
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- PeerJ
- Accession number :
- edsair.doi.dedup.....e0701764466004e7029db867707524f6