Integrating Single-Cell RNA-Seq and Bulk RNA-Seq to Construct a Novel γδT Cell-Related Prognostic Signature for Human Papillomavirus-Infected Cervical Cancer

BackgroundGamma delta (gamma delta) T cells play dual roles in human tumors, with both antitumor and tumor-promoting functions. However, the role of gamma delta T cells in HPV-infected cervical cancer is still undetermined. Therefore, we aimed to identify gamma delta T cell-related prognostic signatures in the cervical tumor microenvironment.MethodsSingle-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data, and corresponding clinical information of cervical cancer patients were obtained from the TCGA and GEO databases. The Seurat R package was used for single-cell analysis, and machine learning algorithms were used to screen and construct a gamma delta T cell-related prognostic signature. Real-time quantitative PCR (RT-qPCR) was performed to detect the expression of prognostic signature genes.ResultsSingle-cell analysis indicated distinct populations of gamma delta T cells between HPV-positive (HPV+) and HPV-negative (HPV-) cervical cancers. A trajectory analysis indicated gamma delta T cells clustered into differential clusters with the pseudotime. High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) identified the key gamma delta T cell-related gene modules. Bulk RNA-seq analysis also demonstrated the heterogeneity of immune cells, and the gamma delta T-score was positively associated with inflammatory response and negatively associated with MYC stemness. Eight gamma delta T cell-related hub genes (GTRGs), including ITGAE, IKZF3, LSP1, NEDD9, CLEC2D, RBPJ, TRBC2, and OXNAD1, were selected and validated as a prognostic signature for cervical cancer.ConclusionWe identified gamma delta T cell-related prognostic signatures that can be considered independent factors for survival prediction in cervical cancer.