» Articles » PMID: 31739095

Exposing the Underlying Relationship of Cancer Metastasis to Metabolism and Epithelial-Mesenchymal Transitions

Overview
Journal iScience
Publisher Cell Press
Date 2019 Nov 19
PMID 31739095
Citations 19
Authors
Affiliations
Soon will be listed here.
Abstract

Cancer is a disease governed by the underlying gene regulatory networks. The hallmarks of cancer have been proposed to characterize the cancerization, e.g., abnormal metabolism, epithelial to mesenchymal transition (EMT), and cancer metastasis. We constructed a metabolism-EMT-metastasis regulatory network and quantified its underlying landscape. We identified four attractors, characterizing epithelial, abnormal metabolic, mesenchymal, and metastatic cell states, respectively. Importantly, we identified an abnormal metabolic state. Based on the transition path theory, we quantified the kinetic transition paths among these different cell states. Our results for landscape and paths indicate that metastasis is a sequential process: cells tend to first change their metabolism, then activate the EMT and eventually reach the metastatic state. This demonstrates the importance of the temporal order for different gene circuits switching on or off during metastatic progression of cancer cells and underlines the cascading regulation of metastasis through an abnormal metabolic intermediate state.

Citing Articles

Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models.

Chen Z, Lu J, Zhao X, Yu H, Li C Adv Sci (Weinh). 2024; 11(41):e2404854.

PMID: 39258786 PMC: 11538663. DOI: 10.1002/advs.202404854.


Landscape quantifies the intermediate state and transition dynamics in ecological networks.

Lv J, Wang J, Li C PLoS Comput Biol. 2024; 20(1):e1011766.

PMID: 38181053 PMC: 10796024. DOI: 10.1371/journal.pcbi.1011766.


Unbalanced response to growth variations reshapes the cell fate decision landscape.

Zhu J, Chu P, Fu X Nat Chem Biol. 2023; 19(9):1097-1104.

PMID: 36959461 DOI: 10.1038/s41589-023-01302-9.


Decoding the coupled decision-making of the epithelial-mesenchymal transition and metabolic reprogramming in cancer.

Galbraith M, Levine H, Onuchic J, Jia D iScience. 2022; 26(1):105719.

PMID: 36582834 PMC: 9792913. DOI: 10.1016/j.isci.2022.105719.


Inferring structural and dynamical properties of gene networks from data with deep learning.

Chen F, Li C NAR Genom Bioinform. 2022; 4(3):lqac068.

PMID: 36110897 PMC: 9469930. DOI: 10.1093/nargab/lqac068.


References
1.
Huang S, Eichler G, Bar-Yam Y, Ingber D . Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys Rev Lett. 2005; 94(12):128701. DOI: 10.1103/PhysRevLett.94.128701. View

2.
Zhao H, Kang X, Xia X, Wo L, Gu X, Hu Y . miR-145 suppresses breast cancer cell migration by targeting FSCN-1 and inhibiting epithelial-mesenchymal transition. Am J Transl Res. 2016; 8(7):3106-14. PMC: 4969447. View

3.
Gibbons D, Lin W, Creighton C, Rizvi Z, Gregory P, Goodall G . Contextual extracellular cues promote tumor cell EMT and metastasis by regulating miR-200 family expression. Genes Dev. 2009; 23(18):2140-51. PMC: 2751985. DOI: 10.1101/gad.1820209. View

4.
Li C, Wang J . Landscape and flux reveal a new global view and physical quantification of mammalian cell cycle. Proc Natl Acad Sci U S A. 2014; 111(39):14130-5. PMC: 4191801. DOI: 10.1073/pnas.1408628111. View

5.
Powell E, Piwnica-Worms D, Piwnica-Worms H . Contribution of p53 to metastasis. Cancer Discov. 2014; 4(4):405-14. PMC: 4063123. DOI: 10.1158/2159-8290.CD-13-0136. View