Transcription factor networks and novel immune biomarkers reveal key prognostic and therapeutic insights in ovarian cancer.

ABSTRACT:

Background

Understanding the tumor microenvironment (TME) is essential for the advancement of immunotherapy for ovarian cancer (OC). Nonetheless, predicting transcription factor (TF) regulation from the TME using single-cell RNA sequencing (scRNA-seq) data is challenging.

Methods

The OC scRNA-seq data were analyzed with a specialized scRNA-seq transcriptome analysis application. The OC TME was utilized to instruct the SCENIC procedure for TF regulation. We built a risk model using Lasso regression and identified immunological subgroups using ConsensusClusterPlus. To analyze the percentage of invading immune cells, the algorithms CIBERSORT, ESTIMATE, and xCell were used. We computed the stromal score, immunological score, estimate score, and tumor purity to evaluate the risk

SUBMITTER: Zhao A 

PROVIDER: S-EPMC11904056 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature