Acta BiotheoreticaSystems BiologyPeer-reviewed journal article
The geometry of normal tissue and cancer gene expression manifolds
Abstract
A recent paper shows that in gene expression space the manifold spanned by normal tissues and the manifold spanned by the corresponding tumors are disjoint. The statement is based on a two-dimensional projection of gene expression data. In the present paper, we show that, for the multi-dimensional vectors defining the centers of cloud samples: 1. The closest tumor to a given normal tissue is the tumor developed in that tissue, 2. Two normal tissues define quasi-orthogonal directions, 3. A tumor may have a projection onto its corresponding normal tissue, but it is quasi-orthogonal to all other normal tissues, and 4. The cancer manifold is roughly obtained by translating the normal tissue manifold along an orthogonal direction defined by a global cancer progression axis. These geometrical properties add a new characterization of normal tissues and tumors and may have biological significance. Indeed, normal tissues at the vertices of a high-dimensional simplex could indicate genotype optimization for given tissue functions, and a way of avoiding errors in embryonary development. On the other hand, the cancer progression axis could define relevant pan-cancer genes and seems to be consistent with the atavistic theory of tumors.
- Citations
- 5
- FWCI
- 0.68
- Field percentile
- top 37%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Analyzing cancer gene expression data through the lens of normal tissue-specificity
H. R. Frost·bioRxiv·Jan 25, 2021
- Parallel, independent reversions to an embryonic expression phenotype in multiple types of cancer.
Corey M. Hudson, Gavin C. Conant·bioRxiv·Jun 1, 2016
- Tracking cancer dynamics from normal tissue to malignancy using perfect N- and T-gene expression markers
Gabriel Gil, Rolando Perez, Augusto Gonzalez·bioRxiv·Nov 4, 2024
- The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data
Laurence de Torrenté, Samuel E. Zimmerman, … Jessica C. Mar·BMC BioinformaticsPeer-reviewed·Dec 1, 2020
- Systematic Normalization with Multiple Housekeeping Genes for the Discovery of Genetic Dependencies in Cancer
Oliver Bonham-Carter, Yee Mon Thu·bioRxiv·Jan 29, 2020
- Fingerprints of cancer by persistent homology
A. Carpio, L. L. Bonilla, … A. R. Tannenbaum·bioRxiv·Sep 23, 2019