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Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma.
- Source :
-
BioMed Research International . 11/5/2020, p1-16. 16p. - Publication Year :
- 2020
-
Abstract
- Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RNA metabolism
*RNA physiology
*RNA analysis
*CELLULAR signal transduction
*GENE expression
*GENETIC techniques
*IMMUNITY
*RESEARCH methodology
*MULTIPLE myeloma
*REGRESSION analysis
*RISK assessment
*TUMOR markers
*PREDICTION models
*PHENOMENOLOGICAL biology
*PREDICTIVE validity
*PROPORTIONAL hazards models
*RECEIVER operating characteristic curves
*KAPLAN-Meier estimator
*EVALUATION
Subjects
Details
- Language :
- English
- ISSN :
- 23146133
- Database :
- Academic Search Index
- Journal :
- BioMed Research International
- Publication Type :
- Academic Journal
- Accession number :
- 146851499
- Full Text :
- https://doi.org/10.1155/2020/3813546