Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma
Abstract Background About 10% of hematologic malignancies are multiple myeloma (MM), an untreatable cancer. Although lactate and branched-chain amino acids (BCAA) are involved in supporting various tumor growth, it is unknown whether they have any bearing on MM prognosis. Methods MM-related datasets...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-08-01
|
| Series: | Cancer Cell International |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12935-023-03007-4 |
| _version_ | 1827633065056272384 |
|---|---|
| author | Zhengyu Yu Bingquan Qiu Hui Zhou Linfeng Li Ting Niu |
| author_facet | Zhengyu Yu Bingquan Qiu Hui Zhou Linfeng Li Ting Niu |
| author_sort | Zhengyu Yu |
| collection | DOAJ |
| description | Abstract Background About 10% of hematologic malignancies are multiple myeloma (MM), an untreatable cancer. Although lactate and branched-chain amino acids (BCAA) are involved in supporting various tumor growth, it is unknown whether they have any bearing on MM prognosis. Methods MM-related datasets (GSE4581, GSE136337, and TCGA-MM) were acquired from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Lactate and BCAA metabolism-related subtypes were acquired separately via the R package “ConsensusClusterPlus” in the GSE4281 dataset. The R package “limma” and Venn diagram were both employed to identify lactate-BCAA metabolism-related genes. Subsequently, a lactate-BCAA metabolism-related prognostic risk model for MM patients was constructed by univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The gene set enrichment analysis (GSEA) and R package “clusterProfiler"were applied to explore the biological variations between two groups. Moreover, single-sample gene set enrichment analysis (ssGSEA), Microenvironment Cell Populations-counter (MCPcounte), and xCell techniques were applied to assess tumor microenvironment (TME) scores in MM. Finally, the drug’s IC50 for treating MM was calculated using the “oncoPredict” package, and further drug identification was performed by molecular docking. Results Cluster 1 demonstrated a worse prognosis than cluster 2 in both lactate metabolism-related subtypes and BCAA metabolism-related subtypes. 244 genes were determined to be involved in lactate-BCAA metabolism in MM. The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets. For the high-risk group, a total of 13 entries were enriched. 16 entries were enriched to the low-risk group. Immune scores, stromal scores, immune infiltrating cells (except Type 17 T helper cells in ssGSEA algorithm), and 168 drugs’IC50 were statistically different between two groups. Alkylating potentially serves as a new agent for MM treatment. Conclusions CKS2 and LYZ were identified as lactate-BCAA metabolism-related genes in MM, then a novel prognostic risk model was built by using them. In summary, this research may uncover novel characteristic genes signature for the treatment and prognostic of MM. |
| first_indexed | 2024-03-09T14:54:21Z |
| format | Article |
| id | doaj.art-8d4e02dd168340929d5dc848bd3a7a30 |
| institution | Directory Open Access Journal |
| issn | 1475-2867 |
| language | English |
| last_indexed | 2024-03-09T14:54:21Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj.art-8d4e02dd168340929d5dc848bd3a7a302023-11-26T14:18:18ZengBMCCancer Cell International1475-28672023-08-0123111810.1186/s12935-023-03007-4Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myelomaZhengyu Yu0Bingquan Qiu1Hui Zhou2Linfeng Li3Ting Niu4Department of Hematology, West China Hospital, Sichuan UniversityDepartment of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University Health Science CenterDepartment of Hematology, West China Hospital, Sichuan UniversityDepartment of Hematology, West China Hospital, Sichuan UniversityDepartment of Hematology, West China Hospital, Sichuan UniversityAbstract Background About 10% of hematologic malignancies are multiple myeloma (MM), an untreatable cancer. Although lactate and branched-chain amino acids (BCAA) are involved in supporting various tumor growth, it is unknown whether they have any bearing on MM prognosis. Methods MM-related datasets (GSE4581, GSE136337, and TCGA-MM) were acquired from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Lactate and BCAA metabolism-related subtypes were acquired separately via the R package “ConsensusClusterPlus” in the GSE4281 dataset. The R package “limma” and Venn diagram were both employed to identify lactate-BCAA metabolism-related genes. Subsequently, a lactate-BCAA metabolism-related prognostic risk model for MM patients was constructed by univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The gene set enrichment analysis (GSEA) and R package “clusterProfiler"were applied to explore the biological variations between two groups. Moreover, single-sample gene set enrichment analysis (ssGSEA), Microenvironment Cell Populations-counter (MCPcounte), and xCell techniques were applied to assess tumor microenvironment (TME) scores in MM. Finally, the drug’s IC50 for treating MM was calculated using the “oncoPredict” package, and further drug identification was performed by molecular docking. Results Cluster 1 demonstrated a worse prognosis than cluster 2 in both lactate metabolism-related subtypes and BCAA metabolism-related subtypes. 244 genes were determined to be involved in lactate-BCAA metabolism in MM. The prognostic risk model was constructed by CKS2 and LYZ selected from this group of genes for MM, then the prognostic risk model was also stable in external datasets. For the high-risk group, a total of 13 entries were enriched. 16 entries were enriched to the low-risk group. Immune scores, stromal scores, immune infiltrating cells (except Type 17 T helper cells in ssGSEA algorithm), and 168 drugs’IC50 were statistically different between two groups. Alkylating potentially serves as a new agent for MM treatment. Conclusions CKS2 and LYZ were identified as lactate-BCAA metabolism-related genes in MM, then a novel prognostic risk model was built by using them. In summary, this research may uncover novel characteristic genes signature for the treatment and prognostic of MM.https://doi.org/10.1186/s12935-023-03007-4Multiple myelomaPrognosisLactateBranched-chain amino acidsTumor microenvironmentDrug prediction |
| spellingShingle | Zhengyu Yu Bingquan Qiu Hui Zhou Linfeng Li Ting Niu Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma Cancer Cell International Multiple myeloma Prognosis Lactate Branched-chain amino acids Tumor microenvironment Drug prediction |
| title | Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma |
| title_full | Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma |
| title_fullStr | Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma |
| title_full_unstemmed | Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma |
| title_short | Characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma |
| title_sort | characterization and application of a lactate and branched chain amino acid metabolism related gene signature in a prognosis risk model for multiple myeloma |
| topic | Multiple myeloma Prognosis Lactate Branched-chain amino acids Tumor microenvironment Drug prediction |
| url | https://doi.org/10.1186/s12935-023-03007-4 |
| work_keys_str_mv | AT zhengyuyu characterizationandapplicationofalactateandbranchedchainaminoacidmetabolismrelatedgenesignatureinaprognosisriskmodelformultiplemyeloma AT bingquanqiu characterizationandapplicationofalactateandbranchedchainaminoacidmetabolismrelatedgenesignatureinaprognosisriskmodelformultiplemyeloma AT huizhou characterizationandapplicationofalactateandbranchedchainaminoacidmetabolismrelatedgenesignatureinaprognosisriskmodelformultiplemyeloma AT linfengli characterizationandapplicationofalactateandbranchedchainaminoacidmetabolismrelatedgenesignatureinaprognosisriskmodelformultiplemyeloma AT tingniu characterizationandapplicationofalactateandbranchedchainaminoacidmetabolismrelatedgenesignatureinaprognosisriskmodelformultiplemyeloma |