Translational cancer research · 2026
Identification of exhausted CD8+ T-cell signatures for prognosis in multiple myeloma by integrated analysis of single-cell and bulk RNA-sequencing.
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Zhan Huien, Lai Jing, Chen Jie, Xu Ying, Zhong Liye
Аннотация
BACKGROUND: Multiple myeloma (MM) exhibits an exhausted CD8+ T cell state. Therefore, this study aimed to investigate exhausted CD8+ T cell-related signatures and the underlying mechanisms in MM.
METHODS: Single-cell RNA sequencing (scRNA-seq) and RNA-seq data were downloaded, and the MM immune landscape was observed. Through differential expression analysis and weighted gene co-expression network analysis, differential exhausted CD8+ T cell-related genes (DETG) were selected. Univariate Cox, Kaplan-Meier analysis, machine learning algorithms, and receiver operator characteristic curves were used to select optimal prognostic DETGs and construct and evaluate a risk score model.
RESULTS: Thirteen DETGs that were mainly involved in tumor-related pathways were selected. Among these DETGs, JunD proto-oncogene (JUND) and nuclear factor kappa B inhibitor alpha (NFKBIA) correlated strongly with MM survival and immune response. Hence, these genes were considered as effective prognostic genes in MM and used to construct a prognostic risk score model. The risk score model exhibited promising predictive ability on MM survival. JUND and NFKBIA expression levels in the cell communication pseudo-temporal analysis were synchronous with CD8+ T cell differentiation.
CONCLUSIONS: A novel exhausted CD8+ T cell-related prognostic model was successfully established, which exhibited promising predictive ability for clinical prognosis and potential immunotherapeutic efficacy in patients with MM.
METHODS: Single-cell RNA sequencing (scRNA-seq) and RNA-seq data were downloaded, and the MM immune landscape was observed. Through differential expression analysis and weighted gene co-expression network analysis, differential exhausted CD8+ T cell-related genes (DETG) were selected. Univariate Cox, Kaplan-Meier analysis, machine learning algorithms, and receiver operator characteristic curves were used to select optimal prognostic DETGs and construct and evaluate a risk score model.
RESULTS: Thirteen DETGs that were mainly involved in tumor-related pathways were selected. Among these DETGs, JunD proto-oncogene (JUND) and nuclear factor kappa B inhibitor alpha (NFKBIA) correlated strongly with MM survival and immune response. Hence, these genes were considered as effective prognostic genes in MM and used to construct a prognostic risk score model. The risk score model exhibited promising predictive ability on MM survival. JUND and NFKBIA expression levels in the cell communication pseudo-temporal analysis were synchronous with CD8+ T cell differentiation.
CONCLUSIONS: A novel exhausted CD8+ T cell-related prognostic model was successfully established, which exhibited promising predictive ability for clinical prognosis and potential immunotherapeutic efficacy in patients with MM.