Background: Ovarian cancer (OC) is highly heterogeneous and has a poor prognosis. A better understanding of OC biology could provide more effective therapeutic paradigms for different OC subtypes.
Methods: To reveal the heterogeneity of T cell-associated subclusters in OC, we performed an in-depth analysis of single-cell transcriptional profiles and clinical information of patients with OC. Then, the above analysis results were verified by qPCR and flow cytometry examine.
Results: After screening by threshold, a total of 85,699 cells in 16 ovarian cancer tissue samples were clustered into 25 major cell groups. By performing further clustering of T cell-associated clusters, we annotated a total of 14 T cell subclusters. Then, four distinct single-cell landscapes of exhausted T (Tex) cells were screened, and SPP1 + Tex significantly correlated with NKT cell strength. A large amount of RNA sequencing expression data combining the CIBERSORTx tool were labeled with cell types from our single-cell data. Calculating the relative abundance of cell types revealed that a greater proportion of SPP1 + Tex cells was associated with poor prognosis in a cohort of 371 patients with OC. In addition, we showed that the poor prognosis of patients in the high SPP1 + Tex expression group might be related to the suppression of immune checkpoints. Finally, we verified in vitro that SPP1 expression was significantly higher in ovarian cancer cells than in normal ovarian cells. By flow cytometry, knockdown of SPP1 in ovarian cancer cells could promote tumorigenic apoptosis.
Conclusion: This is the first study to provide a more comprehensive understanding of the heterogeneity and clinical significance of Tex cells in OC, which will contribute to the development of more precise and effective therapies.
Keywords: SPP1+ T cell; T cell exhaustion; immune environment; ovarian cancer; single cell RNA sequencing.
Copyright © 2023 Wang, Hou, Zhang, Ao, Luo and Li.