ArticlePLoS computational biology2020
A novel single-cell based method for breast cancer prognosis.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integration of single-cell and bulk RNA-sequencing data reveals the prognostic potential of epithelial gene markers for prostate cancer.Molecular oncology · 2025Pooled it
- CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.NPJ digital medicine · 2026Article
- A Weibull mixture cure frailty model for high-dimensional covariates.Statistical methods in medical research · 2025Article
- Quantitative characterization of cell niches in spatially resolved omics data.Nature genetics · 2025Article
- A survey of biclustering and clustering methods in clustering different types of single-cell RNA sequencing data.Briefings in functional genomics · 2025Article
- Network-based transfer of pan-cancer immunotherapy responses to guide breast cancer prognosis.NPJ systems biology and applications · 2025Article
- Identifying phenotype-associated subpopulations through LP_SGL.Briefings in bioinformatics · 2023Article
- Dynamics of Epithelial-Mesenchymal Plasticity: What Have Single-Cell Investigations Elucidated So Far?ACS omega · 2023Review
- Breast cancer heterogeneity and its implication in personalized precision therapy.Experimental hematology & oncology · 2023Review
- Article
- Somatic Super-Enhancer Epigenetic Signature for Overall Survival Prediction in Patients with Breast Invasive Carcinoma.Bioinformatics and biology insights · 2023Article
- scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data.Nucleic acids research · 2022Article
- scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms.Bioengineering (Basel, Switzerland) · 2022Article
- Somatic Single-Nucleotide Variant Calling from Single-Cell DNA Sequencing Data Using SCAN-SNV.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models.Journal of translational medicine · 2021Article
- Predictions, Pivots, and a Pandemic: a Review of 2020's Top Translational Bioinformatics Publications.Yearbook of medical informatics · 2021Review
- An 11-Gene Signature Based on Treatment Responsiveness Predicts Radiation Therapy Survival Benefit Among Breast Cancer Patients.Frontiers in oncology · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Breast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.