Investigating Prognostic Features in High-Grade Serous Ovarian Cancer Through Gene Regulatory Network Inference With Single-Cell Transcriptomic Profiles

Authors

Toshiyuki Itai
Yulin Dai
Wendao Liu
Dung-Fang Lee
Zhongming Zhao

Language

English

Publication Date

11-10-2025

Journal

Scientific Reports

DOI

10.1038/s41598-025-22937-9

PMID

41214061

PMCID

PMC12603141

PubMedCentral® Posted Date

11-10-2025

PubMedCentral® Full Text Version

Post-print

Abstract

This study aimed to identify prognostic features in high-grade serous ovarian cancer (HGSOC) through the application of gene regulatory network (GRN) inference with single-cell RNA-sequencing (scRNA-seq) profiles. To achieve this goal, we developed a workflow comprising scRNA-seq analysis, metacell construction, GRN inference, and a binary classification task for prognosis prediction. We curated 118,173 cells from HGSOC patients in three conditions (Before-chemotherapy, After-chemotherapy, and control samples) from previous studies, and then constructed 1,211 metacells. GRN inference analysis revealed 312 regulons, each consisting of one transcription factor and its targeted features. For prognosis evaluation, we used bulk RNA-seq data covering 342 HGSOC patients from The Cancer Genome Atlas (TCGA) and defined a binary outcome of overall survival ≥ 2 years from initial diagnosis, with censored cases at last follow-up assigned to the appropriate class by observed time. We prioritized the features of the TCGA data based on regulon information and differentially expressed features extracted from the metacell data. Our results demonstrated that regulon-based prognostic features were more effective than differential expression-based features in both Before-chemotherapy and After-chemotherapy groups. Our framework can be generalized to other types of cancer when single-cell data for GRN inference and bulk RNA-seq data with clinical outcomes are available.

Keywords

Humans, Female, Ovarian Neoplasms, Gene Regulatory Networks, Single-Cell Analysis, Prognosis, Transcriptome, Gene Expression Regulation, Neoplastic, Cystadenocarcinoma, Serous, Gene Expression Profiling, Neoplasm Grading, Biomarkers, Tumor, Cancer genetics, Cancer genomics, Gynaecological cancer

Published Open-Access

yes

Recommended Citation

Itai, Toshiyuki; Dai, Yulin; Liu, Wendao; et al., "Investigating Prognostic Features in High-Grade Serous Ovarian Cancer Through Gene Regulatory Network Inference With Single-Cell Transcriptomic Profiles" (2025). Faculty, Staff and Student Publications. 703.
https://digitalcommons.library.tmc.edu/uthshis_docs/703