ArticlePLoS computational biology2013
Biomolecular events in cancer revealed by attractor metagenes.
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 73 papers, 1 of them a synthesis that pooled it.
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Who cites it
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Spatial dynamics of cancer-associated fibroblasts links fibroblastic differentiation to immune exclusion and tumor progression.Frontiers in immunology · 2026Article
- Identifying Molecular Modulators of the Vascular Invasion in Rectal Carcinoma: Role ofInternational journal of molecular sciences · 2025Article
- Development and validation of disulfidptosis-related genes signature for patients with glioma.Discover oncology · 2024Article
- A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies.BioData mining · 2024Article
- The fibro-adipogenic progenitor APOD+DCN+LUM+ cell population in aggressive carcinomas.Cancer metastasis reviews · 2024Review
- Systematic Characterization of p53-Regulated Long Noncoding RNAs across Human Cancers Reveals Remarkable Heterogeneity among Different Tumor Types.Molecular cancer research : MCR · 2024Article
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- CASCC: a co-expression-assisted single-cell RNA-seq data clustering method.Bioinformatics (Oxford, England) · 2024Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Landscape of the intratumroal microenvironment in bladder cancer:Computational and structural biotechnology journal · 2023Article
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- An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation.Current genomics · 2021Review
- Immune cell infiltration signatures identified molecular subtypes and underlying mechanisms in gastric cancer.NPJ genomic medicine · 2021Article
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- Single-cell analysis reveals the pan-cancer invasiveness-associated transition of adipose-derived stromal cells into COL11A1-expressing cancer-associated fibroblasts.PLoS computational biology · 2021Article
13 more citing papers are in PubMed but not listed here.
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mining gene expression profiles has proven valuable for identifying signatures serving as surrogates of cancer phenotypes. However, the similarities of such signatures across different cancer types have not been strong enough to conclude that they represent a universal biological mechanism shared among multiple cancer types. Here we present a computational method for generating signatures using an iterative process that converges to one of several precise attractors defining signatures representing biomolecular events, such as cell transdifferentiation or the presence of an amplicon. By analyzing rich gene expression datasets from different cancer types, we identified several such biomolecular events, some of which are universally present in all tested cancer types in nearly identical form. Although the method is unsupervised, we show that it often leads to attractors with strong phenotypic associations. We present several such multi-cancer attractors, focusing on three that are prominent and sharply defined in all cases: a mesenchymal transition attractor strongly associated with tumor stage, a mitotic chromosomal instability attractor strongly associated with tumor grade, and a lymphocyte-specific attractor.
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