ArticlePLoS computational biology2013
Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 2 of them syntheses that pooled it.
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Who cites it
84 citing papers in PubMed, 2 syntheses or guidelines pooled it, 188 citations in OpenAlex.
- Prognostic gene expression signature for high-grade serous ovarian cancer.Annals of oncology : official journal of the European Society for Medical Oncology · 2020Pooled it
- Advancing Alzheimer's research: A review of big data promises.International journal of medical informatics · 2017Pooled it
- Leveraging single-cell foundation models for accurate survival outcome prediction.Bioinformatics advances · 2026Article
- Subtype Characterization of Ovarian Cancer Cell Lines Using Machine Learning and Network Analysis: A Pilot Study.Cancers · 2025Article
- A Network-Driven Framework for Drug Response Precision Prediction of Acute Myeloid Leukemia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Sample-specific network analysis identifies gene co-expression patterns of immunotherapy response in clear cell renal cell carcinoma.iScience · 2025Article
- Interactions between cancer-associated fibroblasts and the extracellular matrix in oesophageal cancer.Matrix biology : journal of the International Society for Matrix Biology · 2025Review
- Development and validation of hybrid machine learning approach for predicting survival in patients with cervical cancer: a SEER-based population study.Frontiers in oncology · 2025Article
- GPS-Net: Discovering prognostic pathway modules based on network regularized kernel learning.American journal of human genetics · 2024Article
- Deep contrastive learning for predicting cancer prognosis using gene expression values.Briefings in bioinformatics · 2024Article
- GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.bioRxiv : the preprint server for biology · 2024Article
- Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.Briefings in bioinformatics · 2024Article
- Prioritizing Disease Diagnosis in Neonatal Cohorts through Multivariate Survival Analysis: A Nonparametric Bayesian Approach.Healthcare (Basel, Switzerland) · 2024Article
- Integrating multiomics and prior knowledge: a study of the Graphnet penalty impact.Bioinformatics (Oxford, England) · 2023Article
- Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2023Article
- omicsGAT: Graph Attention Network for Cancer Subtype Analyses.International journal of molecular sciences · 2022Article
- Article
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- The Fibrillin-1/VEGFR2/STAT2 signaling axis promotes chemoresistance via modulating glycolysis and angiogenesis in ovarian cancer organoids and cells.Cancer communications (London, England) · 2022Article
- PRER: A patient representation with pairwise relative expression of proteins on biological networks.PLoS computational biology · 2021Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2) or L(1). This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.
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Registered trials
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.