Exploring natural killer cell-related biomarkers in multiple myeloma: a novel nature killer cell-related model predicting prognosis and immunotherapy response using single-cell study.
Bulk transcription
Immune microenvironment
Multiple myeloma
Natural killer cells
Single-cell sequencing
Journal
Clinical and experimental medicine
ISSN: 1591-9528
Titre abrégé: Clin Exp Med
Pays: Italy
ID NLM: 100973405
Informations de publication
Date de publication:
18 Apr 2024
18 Apr 2024
Historique:
received:
12
01
2024
accepted:
03
03
2024
medline:
19
4
2024
pubmed:
18
4
2024
entrez:
18
4
2024
Statut:
epublish
Résumé
Natural killer cells (NKs) may be involved in multiple myeloma (MM) progression. The present study elucidated the correlation between NKs and the progression of MM using single-cell binding transcriptome probes to identify NK cell-related biomarkers. Single-cell analysis was performed including cell and subtype annotation, cell communication, and pseudotime analysis. Hallmark pathway enrichment analysis of NKs and NKs-related differentially expressed genes (DEGs) were conducted using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction (PPI) networks. Then, a risk model was structured based on biomarkers identified through univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis and subsequently validated. Additionally, correlation of clinical characteristics, gene set enrichment analysis, immune analysis, regulatory network, and drug forecasting were explored. A total of 13 cell clusters were obtained and annotated, including 8 cell populations that consisted of NKs. Utilizing 123 PPI network node genes, 8 NK-related DEGs were selected to construct a prognostic model. Immune cell infiltration results suggested that 11 immune cells exhibited marked differences in the high and low-risk groups. Finally, the model was used to screen potential drug targets to enhance immunotherapy efficacy. A new prognostic model for MM associated with NKs was constructed and validated. This model provides a fresh perspective for predicting patient outcomes, immunotherapeutic response, and candidate drugs.
Sections du résumé
BACKGROUND
BACKGROUND
Natural killer cells (NKs) may be involved in multiple myeloma (MM) progression. The present study elucidated the correlation between NKs and the progression of MM using single-cell binding transcriptome probes to identify NK cell-related biomarkers.
METHODS
METHODS
Single-cell analysis was performed including cell and subtype annotation, cell communication, and pseudotime analysis. Hallmark pathway enrichment analysis of NKs and NKs-related differentially expressed genes (DEGs) were conducted using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction (PPI) networks. Then, a risk model was structured based on biomarkers identified through univariate Cox regression analysis and least absolute shrinkage and selection operator regression analysis and subsequently validated. Additionally, correlation of clinical characteristics, gene set enrichment analysis, immune analysis, regulatory network, and drug forecasting were explored.
RESULTS
RESULTS
A total of 13 cell clusters were obtained and annotated, including 8 cell populations that consisted of NKs. Utilizing 123 PPI network node genes, 8 NK-related DEGs were selected to construct a prognostic model. Immune cell infiltration results suggested that 11 immune cells exhibited marked differences in the high and low-risk groups. Finally, the model was used to screen potential drug targets to enhance immunotherapy efficacy.
CONCLUSION
CONCLUSIONS
A new prognostic model for MM associated with NKs was constructed and validated. This model provides a fresh perspective for predicting patient outcomes, immunotherapeutic response, and candidate drugs.
Identifiants
pubmed: 38634972
doi: 10.1007/s10238-024-01322-2
pii: 10.1007/s10238-024-01322-2
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
79Subventions
Organisme : Key Research and Development Projects of Shaanxi Province
ID : 2023-YBSF-448
Organisme : clinical research award of the First Affiliated Hospital of Xi'an Jiao Tong University
ID : XJTU1AF-CRF-2023-037
Organisme : Institution Fundamental of the First Affiliated Hospital of Xi'an Jiao Tong University
ID : 2022MS-31
Informations de copyright
© 2024. The Author(s).
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