Abstract
Background: Ovarian cancer, with high mortality and often late diagnosis, shows high recurrence despite treatment. The variable effectiveness of immunotherapy highlights the urgent need for personalized, advanced therapeutic strategies.
Methods: To investigate T-cell marker genes, single-cell RNA-sequencing (scRNA-seq) data were sourced from the Gene Expression Omnibus (GEO) database. Additionally, bulk RNA-sequencing data along with clinical information from ovarian cancer patients were retrieved from the Cancer Genome Atlas (TCGA) database to establish a prognostic signature. This study involved survival analysis to evaluate associations between different risk groups, and explored cellular communication and relevant pathway analyses, including metabolic pathways.
Results: We identified 41 genes showing varied expression between two T-cell subclusters, marking subcluster 0 with CCL5 and GZMA, and attributing the rest to subcluster 1. These markers delineate four prognostic groups within the TCGA OV dataset, with T-cluster 2 exhibiting the poorest survival, in contrast to T-cluster 3, which shows the best. Analysis suggests subcluster 1 T-cells might be dysfunctional, potentially exacerbating ovarian cancer progression. We also developed a T-cell scoring model using eight significant genes, showing improved survival in the low-score group. Moreover, cellular and metabolic pathway analyses underscored the importance of CCL, IL2 and MGMT pathways in these subclusters.
Conclusions: The study identifies CCL-5 as a biomarker for T-cell subtypes in ovarian cancer using scRNA-seq and bulk RNA-seq data. A T-cell scoring model based on eight genes predicts survival and progression rates, independent of clinical features. This model could be a prognostic indicator and CCL-5 a potential immunotherapy target in ovarian cancer.