ArticlePloS one2023
On the gene expression landscape of cancer.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Mapping cancer dynamics from normal tissue to malignancy using N- and T-gene expression markers.Frontiers in systems biology · 2026Article
- N-Gene and T-Gene deregulation networks: a data-driven causal framework for the analysis of gene interventions in cancer.Frontiers in systems biology · 2026Article
- Bidirectional network hubs: NT-genes as candidate targets for partial cancer reversal.Frontiers in systems biology · 2026Article
- Exposure-inducible genes may contribute to missingness in RNAseq-based gene expression analyses.Scientific reports · 2025Article
- Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.International journal of molecular sciences · 2025Article
- Article
- Common and novel haplotype structures between different types of cancer.Cancer reports (Hoboken, N.J.) · 2024Article
- Estimating the number of available states for normal and tumor tissues in gene expression space.Biophysical reports · 2022Article
- A one-dimensional parameter-free model for carcinogenesis in gene expression space.Scientific reports · 2022Article
- Gene expression rearrangements denoting changes in the biological state.Scientific reports · 2021Article
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4 authors.
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Abstract
Kauffman picture of normal and tumor states as attractors in an abstract state space is used in order to interpret gene expression data for 15 cancer localizations obtained from The Cancer Genome Atlas. A principal component analysis of this data unveils the following qualitative aspects about tumors: 1) The state of a tissue in gene expression space can be described by a few variables. In particular, there is a single variable describing the progression from a normal tissue to a tumor. 2) Each cancer localization is characterized by a gene expression profile, in which genes have specific weights in the definition of the cancer state. There are no less than 2500 differentially-expressed genes, which lead to power-like tails in the expression distribution functions. 3) Tumors in different localizations share hundreds or even thousands of differentially expressed genes. There are 6 genes common to the 15 studied tumor localizations. 4) The tumor region is a kind of attractor. Tumors in advanced stages converge to this region independently of patient age or genetic characteristics. 5) There is a landscape of cancer in gene expression space with an approximate border separating normal tissues from tumors.
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