Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma
BackgroundMultiple myeloma (MM) remains an essentially incurable disease. This study aimed to establish a predictive model for estimating prognosis in newly diagnosed MM based on gene expression profiles.MethodsRNA-seq data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpas...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1105196/full |
| _version_ | 1828006134168944640 |
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| author | Jing Wang Jing Wang Jing Wang Lili Guo Chenglan Lv Min Zhou Yuan Wan |
| author_facet | Jing Wang Jing Wang Jing Wang Lili Guo Chenglan Lv Min Zhou Yuan Wan |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | BackgroundMultiple myeloma (MM) remains an essentially incurable disease. This study aimed to establish a predictive model for estimating prognosis in newly diagnosed MM based on gene expression profiles.MethodsRNA-seq data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study and the Genotype-Tissue Expression (GTEx) databases. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction network analysis were performed to identify hub genes. Enrichment analysis was also conducted. Patients were randomly split into training (70%) and validation (30%) datasets to build a prognostic scoring model based on the least absolute shrinkage and selection operator (LASSO). CIBERSORT was applied to estimate the proportion of 22 immune cells in the microenvironment. Drug sensitivity was analyzed using the OncoPredict algorithm.ResultsA total of 860 newly diagnosed MM samples and 444 normal counterparts were screened as the datasets. WGCNA was applied to analyze the RNA-seq data of 1589 intersecting genes between differentially expressed genes and prognostic genes. The blue module in the PPI networks was analyzed with Cytoscape, and 10 hub genes were identified using the MCODE plug-in. A three-gene (TTK, GINS1, and NCAPG) prognostic model was constructed. This risk model showed remarkable prognostic value. CIBERSORT assessment revealed the risk model to be correlated with activated memory CD4 T cells, M0 macrophages, M1 macrophages, eosinophils, activated dendritic cells, and activated mast cells. Furthermore, based on OncoPredict, high-risk MM patients were sensitive to eight drugs.ConclusionsWe identified and constructed a three-gene-based prognostic model, which may provide new and in-depth insights into the treatment of MM patients. |
| first_indexed | 2024-04-10T07:52:38Z |
| format | Article |
| id | doaj.art-fa92315cc06448eba4c051d033fe0f70 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T07:52:38Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-fa92315cc06448eba4c051d033fe0f702023-02-23T08:54:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-02-011310.3389/fonc.2023.11051961105196Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myelomaJing Wang0Jing Wang1Jing Wang2Lili Guo3Chenglan Lv4Min Zhou5Yuan Wan6Department of Oncology and Hematology, Yizheng Hospital of Nanjing Drum Tower Hospital Group, Yizheng, ChinaDepartment of Hematology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, ChinaThe Pq Laboratory of BiomeDx/Rx, Department of Biomedical Engineering, Binghamton University, State University of New York (SUNY), Binghamton, NY, United StatesThe Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Hematology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, ChinaDepartment of Hematology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, ChinaThe Pq Laboratory of BiomeDx/Rx, Department of Biomedical Engineering, Binghamton University, State University of New York (SUNY), Binghamton, NY, United StatesBackgroundMultiple myeloma (MM) remains an essentially incurable disease. This study aimed to establish a predictive model for estimating prognosis in newly diagnosed MM based on gene expression profiles.MethodsRNA-seq data were downloaded from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study and the Genotype-Tissue Expression (GTEx) databases. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction network analysis were performed to identify hub genes. Enrichment analysis was also conducted. Patients were randomly split into training (70%) and validation (30%) datasets to build a prognostic scoring model based on the least absolute shrinkage and selection operator (LASSO). CIBERSORT was applied to estimate the proportion of 22 immune cells in the microenvironment. Drug sensitivity was analyzed using the OncoPredict algorithm.ResultsA total of 860 newly diagnosed MM samples and 444 normal counterparts were screened as the datasets. WGCNA was applied to analyze the RNA-seq data of 1589 intersecting genes between differentially expressed genes and prognostic genes. The blue module in the PPI networks was analyzed with Cytoscape, and 10 hub genes were identified using the MCODE plug-in. A three-gene (TTK, GINS1, and NCAPG) prognostic model was constructed. This risk model showed remarkable prognostic value. CIBERSORT assessment revealed the risk model to be correlated with activated memory CD4 T cells, M0 macrophages, M1 macrophages, eosinophils, activated dendritic cells, and activated mast cells. Furthermore, based on OncoPredict, high-risk MM patients were sensitive to eight drugs.ConclusionsWe identified and constructed a three-gene-based prognostic model, which may provide new and in-depth insights into the treatment of MM patients.https://www.frontiersin.org/articles/10.3389/fonc.2023.1105196/fullmyelomadata processingdrug response predictionprognostic modelRNA-seq |
| spellingShingle | Jing Wang Jing Wang Jing Wang Lili Guo Chenglan Lv Min Zhou Yuan Wan Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma Frontiers in Oncology myeloma data processing drug response prediction prognostic model RNA-seq |
| title | Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma |
| title_full | Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma |
| title_fullStr | Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma |
| title_full_unstemmed | Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma |
| title_short | Developing mRNA signatures as a novel prognostic biomarker predicting high risk multiple myeloma |
| title_sort | developing mrna signatures as a novel prognostic biomarker predicting high risk multiple myeloma |
| topic | myeloma data processing drug response prediction prognostic model RNA-seq |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1105196/full |
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