ArticleCancer informatics2015
Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis.
Article in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Cross-Species Transcriptomic and Network Analysis of Human and Canine Diffuse Large B-Cell Lymphoma Identifies a Conserved Replication-DNA Damage Module for Drug Repurposing.Veterinary and comparative oncology · 2026Article
- Inter-individual gene expression variability implies stable regulation of brain-biased genes across organs in three ray-finned fishes.bioRxiv : the preprint server for biology · 2026Article
- Improving data interpretability with new differential sample variance gene set tests.BMC bioinformatics · 2025Article
- A differential diagnosis between uterine leiomyoma and leiomyosarcoma using transcriptome analysis.BMC cancer · 2023Article
- Article
- NoVaTeST: identifying genes with location-dependent noise variance in spatial transcriptomics data.Bioinformatics (Oxford, England) · 2023Article
- clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution.PeerJ · 2023Article
- Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.NAR genomics and bioinformatics · 2022Article
- An R package for divergence analysis of omics data.PloS one · 2021Article
- Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data.Cancer research · 2019Article
- Digitizing omics profiles by divergence from a baseline.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Single-cell systems biology: probing the basic unit of information flow.Current opinion in systems biology · 2018Article
- GSAR: Bioconductor package for Gene Set analysis in R.BMC bioinformatics · 2017Article
- Extracting the Strongest Signals from Omics Data: Differentially Expressed Pathways and Beyond.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Epigenetic modulators, modifiers and mediators in cancer aetiology and progression.Nature reviews. Genetics · 2016Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings.
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Registered trials
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