Single-cell RNA sequencing and spatial transcriptomics reveal heterogeneity and key subgroups in ovarian cancer:...

Ovarian cancer (OC), as one of the gynecological malignancies, seriously threatens people's life, health and safety with its high recurrence rate and high mortality rate.

The GEO database served as the source of Single-cell RNA sequencing (scRNA-seq) data used in this study. We used scRNA-seq and spatial transcriptomics (ST), including CytoTRACE, Slingshot pseudotime analysis, CellChat, MultiNicheNet, pySCENIC, etc. From the perspective of time and space, the heterogeneity in OC was clarified, and key cell subgroups were obtained, as well as key transcription factors (TFs) and cell interaction pathways that control disease progression. Besides, this study further integrated clinical factors extracted from extensive RNA sequencing data in the TCGA database, and on this basis, constructed a prognostic model.

Through the combined analysis of scRNA-seq and ST techniques in this study, the key tumor subgroup, C4 SAA1⁺ tumor cells, was discovered. This subgroup mainly exists in untreated OC patients, and the SAA1 was closely related to the progression of OC. Furthermore, the key TF HOXA1 in this cell subpopulation was a risk factor in various cancers, including OC. We also revealed the correlations between different groups and the prognosis, immune infiltration, and drug sensitivity of patients by constructing risk-prognosis models, including high and low SAA1⁺ Tumor Cells Risk Score (STRS) groups.

This study systematically mapped the single cell map of ovarian cancer. The identified key genes, cell subgroups, and the newly developed STRS prognostic model all showed significant clinical application value. These findings not only deepen the understanding of the pathogenesis of OC, but also provide a theoretical basis for optimizing diagnosis and treatment strategies, thus promoting the transformation from research to clinical practice to better meet the needs of patients.