Scholarlink

<p>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&#xa0;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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Investigating prognostic features in high-grade serous ovarian cancer through gene regulatory network inference with single-cell transcriptomic profiles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月10日

查看全文
  • Toshiyuki Itai,
  • Yulin Dai,
  • Wendao Liu,
  • Dung-Fang Lee,
  • Zhongming Zhao

摘要

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.