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

Xin Kang, Jin Wang, Chunhe Li, Xin Kang, Jin Wang, Chunhe Li

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.

Keywords: Biological Sciences; Cancer; Cancer Systems Biology; Molecular Network.

Conflict of interest statement

The authors declare no competing interests.

Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
The Regulatory Network for the Interplay among EMT, Metabolism, and Cancer Metastasis including 16 Gene or Metabolite Nodes and 51 Regulation Links (22 Activations and 29 Inhibitions) The network from left to right corresponds to the core circuit of metabolism, EMT, and cancer metastasis, respectively. The orange nodes, yellow nodes, and pink nodes represent metabolites, microRNAs, and genes, respectively. The arrows represent activations, and the short bars represent inhibitions.
Figure 2
Figure 2
Landscape and Path for the Metabolism-EMT-Metastasis Model Shown in ZEB, HIF-1, and BACH1 Coordinates (A) Landscape is shown in a four-dimensional picture. The blue regions represent higher probability or lower potential, and the yellow regions indicate lower probability or higher potential. Solid magenta lines represent transition paths from the E to A, M, and Met states. Solid cyan lines represent transition paths from the Met to M, A, and E states. (B) Two-dimensional landscape and kinetic paths are displayed in HIF-1/ZEB coordinates. (C) Two-dimensional landscape and kinetic paths are displayed in HIF-1/BACH1 coordinates. (D) Two-dimensional landscape and kinetic paths are displayed in ZEB/BACH1 coordinates. E, epithelial state; A, abnormal metabolic state; M, mesenchymal state; Met, metastasis state. See also Figure S4 and Table S4.
Figure 3
Figure 3
Transition Paths among Different Cell States (A) Fixed points and kinetic transition paths between different cell states. Solid magenta lines represent transition paths between nearby states (from E to A, A to M, and M to Met state) in metastatic progression direction. Solid cyan lines represent transition paths between nearby states (from Met to M, M to A, and A to E state) in de-metastasis direction. The dashed lines represent the direct transition path from E to Met state and from Met to E state, respectively. (B and C) Discrete transition paths from E state to Met state (B) and from Met state to E state (C) in terms of expression levels of 16 genes and metabolites. Relative gene expression levels are discretized to 0 or 1; 1 represents that the corresponding genes are in the activated state and 0 represents that the corresponding genes are in the repressed state. X axis shows the time points along the transition path. E, epithelial state; A, abnormal metabolic state; M, mesenchymal state; Met, metastasis state. See also Table S4.
Figure 4
Figure 4
The Relation between Transition Action and Barrier Height Based on the Bistable Landscape of the Metabolism-EMT-Metastasis Model as λhh Increases (the Fold Change for the Self-Activation of HIF-1) (A) The relation between λhh and the transition action (S). (B) The relation between λhh and the barrier height (U). (C) Relative changes for the transition action S (red line) and barrier heights U (blue line) as λhh increases. (D) The barrier height changes as the transition action changes.
Figure 5
Figure 5
Global Sensitivity Analysis for Parameters Based on the Transition Action for the Metabolism-EMT-Metastasis Model Y axis represents the 25 parameters. X axis represents the percentage of the change of the transition action (S) relative to S with default parameters. Here, SE→M represents the transition action from attractor E to attractor M (magenta bars) and SM→E represents the transition action from attractor M to attractor E (cyan bars). The top 25 parameters are picked, in which the first 15 parameters represent the regulatory strength (λ) and the last 10 parameters represent the synthesis rate (g) for 10 proteins or metabolites. See Supplemental Information for the complete sensitivity analysis of 61 parameters. (A) Each parameter is increased by 10%, individually. (B) Each parameter is decreased by 10%, individually.
Figure 6
Figure 6
The Tristable Landscape for EMT-metabolism Model and Comparisons with Experimental Data The EMT-metabolism model corresponds to the subnetwork of the whole network without consideration of metastasis circuit in Figure 1. (A) Three dimensional landscape and transition paths. Solid magenta lines represent transition paths from the E to A, and M state. Solid white lines represent transition paths from the M to A and to E state. The dashed lines represent the direct transition path from E to M state and from M to E state, respectively. (B) Two dimensional landscape and transition paths. (C and D) Landscapes are compared with the gene expression data of single-cell RNA-seq data for a genetic mouse model of skin squamous cell carcinoma (SCC) undergoing EMT including 383 single cells (C) and clinically annotated adult cases of de novo AML from TCGA including 173 samples (D). The gene expression data have been rescaled to fit the landscape. Each point represents a gene expression pattern in ZEB and HIF-1 coordinates for one sample from experiments. (E-G) PCA plots for the SCC data with respect to HIF-1 (E), ZEB (F), and SNAI1 (G). (H-J) PCA plots for the AML data with respect to HIF-1 (H), ZEB (I), and SNAI1 (J). (K-M) PCA plots for the PTC data with respect to HIF-1 (K), ZEB (L), and SNAI1 (M). Three clusters have been marked in the PCA coordinates, which correspond to E state (purple ovals, with low HIF-1/low ZEB expression), A state (green ovals, with high HIF-1/low ZEB expression), and M state (orange ovals, with high HIF-1/high ZEB expression), respectively. The colors in the PCA plots represent the expression levels of key marker genes (e.g., HIF-1, ZEB, and SNAIL) for E, A, and M phenotypes. E, epithelial state; M, mesenchymal state; A, abnormal metabolic state.
Figure 7
Figure 7
Landscape in Terms of HIF-1 and BACH1 in Response to Different Drugs in Different Levels (A–E) (A) The metabolism-EMT-metastasis network with new added green nodes representing the hypothetical drugs. The simulated drugs include 3BP (B), metformin (C), combined 3BP and metformin therapy (D), and combined 3BP and BACH1-inhibitor therapy (E). In B–E, the hypothetical drug levels increase from left to right, which represent the drug level of 0, 100, 130, and 250, respectively. E, epithelial state; M, mesenchymal state; A, abnormal metabolic state; Met, metastasis state; Met2, metastasis-like state; BI, BACH1 inhibitor.

References

    1. Agrawal N., Akbani R., Aksoy B.A., Ally A., Arachchi H., Asa S.L., Auman J.T., Balasundaram M., Balu S., Baylin S.B. Integrated genomic characterization of papillary thyroid carcinoma. Cell. 2014;159:676–690.
    1. Bocci F., Jolly M.K., George J.T., Levine H., Onuchic J.N. A mechanism-based computational model to capture the interconnections among epithelial-mesenchymal transition, cancer stem cells and notch-jagged signaling. Oncotarget. 2018;9:29906.
    1. Boroughs L.K., Deberardinis R.J. Metabolic pathways promoting cancer cell survival and growth. Nat. Cell Biol. 2015;17:351.
    1. Brabletz T., Kalluri R., Nieto M.A., Weinberg R.A. Emt in cancer. Nat. Rev. Cancer. 2018;18:128.
    1. Cancer Genome Atlas Research Network. Ley T.J., Miller C., Ding L., Raphael B.J., Mungall A.J., Robertson A., Hoadley K., Triche T.J., Jr., Laird P.W., Baty J.D. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 2013;368:2059–2074.
    1. Carmichael C.L., Goossens S., Wang J., Nguyen T., Haigh K., Berx G., Kile B., Haigh J.J. The EMT modulator snai1 drives AML development via its interaction with the chromatin modulator lsd1. Blood. 2016;128:2688.
    1. Chen C., Baumann W., Xing J., Xu L., Clarke R., Tyson J. Mathematical models of the transitions between endocrine therapy responsive and resistant states in breast cancer. J. R. Soc. Interface. 2014;96:20140206.
    1. Chen K., Qian W., Li J., Jiang Z., Cheng L., Yan B., Cao J., Sun L., Zhou C., Lei M. Loss of AMPK activation promotes the invasion and metastasis of pancreatic cancer through an hsf1-dependent pathway. Mol. Oncol. 2017;11:1475–1492.
    1. Cheong J.H., Park E.S., Liang J., Dennison J.B., Tsavachidou D., Nguyen-Charles C., Cheng K.W., Hall H., Zhang D., Lu Y. Dual inhibition of tumor energy pathway by 2-deoxy glucose and metformin is effective against a broad spectrum of preclinical cancer models. Mol. Cancer Ther. 2011;10:2350–2362.
    1. Cho K.H., Lee S., Kim D., Shin D., Joo J.I., Park S.M. Cancer reversion, a renewed challenge in systems biology. Curr. Opin. Syst. Biol. 2017;2:49–58.
    1. Creighton C.J., Chang J.C., Rosen J.M. Epithelial-mesenchymal transition (EMT) in tumor-initiating cells and its clinical implications in breast cancer. J. Mammary Gland Biol. Neoplasia. 2010;15:253–260.
    1. Davudian S., Mansoori B., Shajari N., Mohammadi A., Baradaran B. Bach1, the master regulator gene: a novel candidate target for cancer therapy. Gene. 2016;588:30–37.
    1. Dowling R.J., Goodwin P.J., Stambolic V. Understanding the benefit of metformin use in cancer treatment. BMC Med. 2011;9:33.
    1. Ganapathy-Kanniappan S., Vali M., Kunjithapatham R., Buijs M., Syed L., Rao P., Ota S., Kwak B., Loffroy R., Geschwind J. 3-bromopyruvate: a new targeted antiglycolytic agent and a promise for cancer therapy. Curr. Pharm. Biotechnol. 2010;11:510–517.
    1. Gatenby R.A., Gillies R.J. Why do cancers have high aerobic glycolysis? Nat. Rev. Cancer. 2004;4:891.
    1. Ge H., Qian H. Landscapes of non-gradient dynamics without detailed balance: stable limit cycles and multiple attractors. Chaos. 2012;22:023140.
    1. Ge H., Qian H. Mesoscopic kinetic basis of macroscopic chemical thermodynamics: a mathematical theory. Phys. Rev. E. 2016;94:052150.
    1. Gibbons D.L., Lin W., Creighton C.J., Rizvi Z.H., Gregory P.A., Goodall G.J., Thilaganathan N., Du L., Zhang Y., Pertsemlidis A. Contextual extracellular cues promote tumor cell EMT and metastasis by regulating mir-200 family expression. Genes Dev. 2009;23:2140–2151.
    1. Gill J.G., Piskounova E., Morrison S.J. Cancer, oxidative stress, and metastasis. Cold Spring Harb. Symp. Quant. Biol. 2016;81:163–175.
    1. Goldman A., Majumder B., Dhawan A., Ravi S., Goldman D., Kohandel M., Majumder P.K., Sengupta S. Temporally sequenced anticancer drugs overcome adaptive resistance by targeting a vulnerable chemotherapy-induced phenotypic transition. Nat. Commun. 2015;6:6139.
    1. Goldman A., Khiste S., Freinkman E., Dhawan A., Majumder B., Mondal J., Pinkerton A.B., Eton E., Medhi R., Chandrasekar V. Targeting tumor phenotypic plasticity and metabolic remodeling in adaptive cross-drug tolerance. Sci. Signal. 2019;12:eaas8779.
    1. Han T., Kang D., Ji D., Wang X., Zhan W., Fu M., Xin H.B., Wang J.B. How does cancer cell metabolism affect tumor migration and invasion? Cell Adh. Migr. 2013;7:395–403.
    1. Hanahan D., Weinberg R.A. The hallmarks of cancer. Cell. 2000;100:57–70.
    1. Hanahan D., Weinberg R.A. Hallmarks of cancer: the next generation. Cell. 2011;144:646–674.
    1. Hong T., Watanabe K., Ta C.H., Villarreal-Ponce A., Nie Q., Dai X. An ovol2-zeb1 mutual inhibitory circuit governs bidirectional and multi-step transition between epithelial and mesenchymal states. PLoS Comput. Biol. 2015;11:e1004569.
    1. Huang S. Genetic and non-genetic instability in tumor progression: link between the fitness landscape and the epigenetic landscape of cancer cells. Cancer Metastasis Rev. 2013;32:423–448.
    1. Huang R., Zong X. Aberrant cancer metabolism in epithelial–mesenchymal transition and cancer metastasis: mechanisms in cancer progression. Crit. Rev. Oncol. Hematol. 2017;115:13–22.
    1. Huang S., Eichler G., Bar-Yam Y., Ingber D.E. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 2005;94:128701.
    1. Ishay-Ronen D., Diepenbruck M., Kalathur R.K.R., Sugiyama N., Tiede S., Ivanek R., Bantug G., Morini M.F., Wang J., Hess C. Gain fat lose metastasis: converting invasive breast cancer cells into adipocytes inhibits cancer metastasis. Cancer Cell. 2019;35:17–32.
    1. Ishikawa K., Takenaga K., Akimoto M., Koshikawa N., Yamaguchi A., Imanishi H., Nakada K., Honma Y., Hayashi J.I. Ros-generating mitochondrial DNA mutations can regulate tumor cell metastasis. Science. 2008;320:661–664.
    1. Jia D., Jolly M.K., Tripathi S.C., Den Hollander P., Huang B., Lu M., Celiktas M., Ramirez-Peña E., Ben-Jacob E., Onuchic J.N. Distinguishing mechanisms underlying EMT tristability. Cancer Converg. 2017;1:2.
    1. Jia D., Lu M., Jung K.H., Park J.H., Yu L., Onuchic J.N., Kaipparettu B.A., Levine H. Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways. Proc. Natl. Acad. Sci. U S A. 2019;116:3909–3918.
    1. Jiang L., Xiao L., Sugiura H., Huang X., Ali A., Kuro-o M., Deberardinis R.J., Boothman D.A. Metabolic reprogramming during tgfβ 1-induced epithelial-to-mesenchymal transition. Oncogene. 2015;34:3908–3916.
    1. Jolly M.K., Boareto M., Huang B., Jia D., Lu M., Ben-Jacob E., Onuchic J.N., Levine H. Implications of the hybrid epithelial/mesenchymal phenotype in metastasis. Front. Oncol. 2015;5:155.
    1. Jolly M.K., Jia D., Boareto M., Mani S.A., Pienta K.J., Ben-Jacob E., Levine H. Coupling the modules of EMT and stemness: a tunable ’stemness window’ model. Oncotarget. 2015;6:25161–25174.
    1. Jolly M.K., Preca B.T., Tripathi S.C., Jia D., George J.T., Hanash S.M., Brabletz T., Stemmler M.P., Maurer J., Levine H. Interconnected feedback loops among esrp1, has2, and cd44 regulate epithelial-mesenchymal plasticity in cancer. APL Bioeng. 2018;2:031908.
    1. Kaern M., Elston T.C., Blake W.J., Collins J.J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 2005;6:451–464.
    1. Kalluri R. EMT: when epithelial cells decide to become mesenchymal-like cells. J. Clin. Invest. 2009;119:1417–1419.
    1. Kamarajugadda S., Stemboroski L., Cai Q., Simpson N.E., Nayak S., Tan M., Lu J. Glucose oxidation modulates anoikis and tumor metastasis. Mol. Cell. Biol. 2012;32:1893–1907.
    1. Korpal M., Lee E.S., Hu G., Kang Y. The mir-200 family inhibits epithelial-mesenchymal transition and cancer cell migration by direct targeting of e-cadherin transcriptional repressors zeb1 and zeb2. J. Biol. Chem. 2008;283:14910–14914.
    1. Lambert A.W., Pattabiraman D.R., Weinberg R.A. Emerging biological principles of metastasis. Cell. 2017;168:670–691.
    1. Lee J., Lee J., Farquhar K.S., Yun J., Frankenberger C.A., Bevilacqua E., Yeung K., Kim E.J., Balazsi G., Rosner M.R. Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions. Proc. Natl. Acad. Sci. U S A. 2014;111:E364–E373.
    1. Lee J., Yesilkanal A.E., Wynne J.P., Frankenberger C., Liu J., Yan J., Elbaz M., Rabe D.C., Rustandy F.D., Tiwari P. Effective breast cancer combination therapy targeting bach1 and mitochondrial metabolism. Nature. 2019;568:254.
    1. Li C. Identifying the optimal anticancer targets from the landscape of a cancer–immunity interaction network. Phys. Chem. Chem. Phys. 2017;19:7642–7651.
    1. Li C., Balazsi G. A landscape view on the interplay between EMT and cancer metastasis. NPJ Syst. Biol. Appl. 2018;4:34.
    1. Li C., Wang J. Quantifying cell fate decisions for differentiation and reprogramming of a human stem cell network: landscape and biological paths. PLoS Comput. Biol. 2013;9:e1003165.
    1. 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;111:14130–14135.
    1. Li C., Wang J. Quantifying the underlying landscape and paths of cancer. J. R. Soc. Interface. 2014;10:20140774.
    1. Li C., Wang J. Quantifying the landscape for development and cancer from a core cancer stem cell circuit. Cancer Res. 2015;75:2607–2618.
    1. Li C., Hong T., Nie Q. Quantifying the landscape and kinetic paths for epithelial–mesenchymal transition from a core circuit. Phys. Chem. Chem. Phys. 2016;18:17949–17956.
    1. Liao C., Lu T. A minimal transcriptional controlling network of regulatory t cell development. J. Phys. Chem. B. 2013;117:12995–13004.
    1. Liao D., Corle C., Seagroves T.N., Johnson R.S. Hypoxia-inducible factor-1α is a key regulator of metastasis in a transgenic model of cancer initiation and progression. Cancer Res. 2007;67:563–572.
    1. Lu M., Jolly H., Levine H., Onuchic J., Ben-Jacob E. MicroRNA-based regulation of epithelial-hybrid-mesenchymal fate determination. Proc. Natl. Acad. Sci. U S A. 2013;110:18144–18149.
    1. Lu M., Jolly M.K., Onuchic J., Ben-Jacob E. Toward decoding the principles of cancer metastasis circuits. Cancer Res. 2014;74:4574–4587.
    1. Lu M., Onuchic J., Ben-Jacob E. Construction of an effective landscape for multistate genetic switches. Phys. Rev. Lett. 2014;113:078102.
    1. Lv C., Li X., Li F., Li T. Energy landscape reveals that the budding yeast cell cycle is a robust and adaptive multi-stage process. PLoS Comput. Biol. 2015;11:e1004156.
    1. Meyer S.E. From EMT to HSC to AML: Zeb2 is a cell fate switch. Blood. 2017;129:400–401.
    1. Moody S.E., Perez D., Pan T.C., Sarkisian C.J., Portocarrero C.P., Sterner C.J., Notorfrancesco K.L., Cardiff R.D., Chodosh L.A. The transcriptional repressor snail promotes mammary tumor recurrence. Cancer Cell. 2005;8:197–209.
    1. Munozpinedo C., Mjiyad N.E., Ricci J. Cancer metabolism: current perspectives and future directions. Cell Death Dis. 2012;3:e248.
    1. Ohashi S., Natsuizaka M., Wong G.S., Michaylira C.Z., Grugan K.D., Stairs D.B., Kalabis J., Vega M.E., Kalman R.A., Nakagawa M. Egfr and mutant p53 expand esophageal cellular subpopulation capable of epithelial-to-mesenchymal transition through zeb transcription factors. Cancer Res. 2010;70:4174.
    1. Pastushenko I., Brisebarre A., Sifrim A., Fioramonti M., Revenco T., Boumahdi S., Van Keymeulen A., Brown D., Moers V., Lemaire S. Identification of the tumour transition states occurring during EMT. Nature. 2018;556:463.
    1. Piskounova E., Agathocleous M., Murphy M.M., Hu Z., Huddlestun S.E., Zhao Z., Leitch A.M., Johnson T.M., DeBerardinis R.J., Morrison S.J. Oxidative stress inhibits distant metastasis by human melanoma cells. Nature. 2015;527:186.
    1. Porporato P.E., Payen V.L., Pérez-Escuredo J., De Saedeleer C.J., Danhier P., Copetti T., Dhup S., Tardy M., Vazeille T., Bouzin C. A mitochondrial switch promotes tumor metastasis. Cell Rep. 2014;8:754–766.
    1. Powell E., Piwnica-Worms D., Piwnica-Worms H. Contribution of p53 to metastasis. Cancer Discov. 2014;4:405–414.
    1. Qiu G., Lin Y., Zhang H., Wu D. mir-139-5p inhibits epithelial–mesenchymal transition, migration and invasion of hepatocellular carcinoma cells by targeting zeb1 and zeb2. Biochem. Biophys. Res. Commun. 2015;463:315–321.
    1. Sánchez-Tilló E., Siles L., De Barrios O., Cuatrecasas M., Vaquero E.C., Castells A., Postigo A. Expanding roles of zeb factors in tumorigenesis and tumor progression. Am. J. Cancer Res. 2011;1:897–912.
    1. Sciacovelli M., Frezza C. Metabolic reprogramming and epithelial-to-mesenchymal transition in cancer. FEBS J. 2017;284:3132–3144.
    1. Singh A., Settleman J. Emt, cancer stem cells and drug resistance: an emerging axis of evil in the war on cancer. Oncogene. 2010;29:4741–4751.
    1. Stavropoulou V., Kaspar S., Brault L., Sanders M.A., Juge S., Morettini S., Tzankov A., Iacovino M., Lau I.J., Milne T.A. Mll-af9 expression in hematopoietic stem cells drives a highly invasive AML expressing EMT-related genes linked to poor outcome. Cancer Cell. 2016;30:43–58.
    1. Swain P.S., Elowitz M.B., Siggia E.D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. U S A. 2002;99:12795–12800.
    1. Tam W.L., Weinberg R.A. The epigenetics of epithelial-mesenchymal plasticity in cancer. Nat. Med. 2013;19:1438.
    1. Thattai M., Van O.A. Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. U S A. 2001;98:8614–8619.
    1. Thiery J.P., Acloque H., Huang R.Y., Nieto M.A. Epithelial-mesenchymal transitions in development and disease. Cell. 2009;139:871–890.
    1. Trendowski M. The inherent metastasis of leukaemia and its exploitation by sonodynamic therapy. Crit. Rev. Oncol. Hematol. 2015;94:149–163.
    1. Waddington C.H. Allen and Unwin; 1957. The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology.
    1. Wang J. Landscape and flux theory of non-equilibrium dynamical systems with application to biology. Adv. Phys. 2015;64:1–137.
    1. Wang J., Xu L., Wang E.K. Potential landscape and flux framework of non-equilibrium networks: robustness, dissipation and coherence of biochemical oscillations. Proc. Natl. Acad. Sci. U S A. 2008;105:12271–12276.
    1. Wang J., Zhang K., Xu L., Wang E.K. Quantifying the Waddington landscape and biological paths for development and differentiation. Proc. Natl. Acad. Sci. U S A. 2011;108:8257–8262.
    1. Wang L., Wu X., Wang B., Wang Q., Han L. Mechanisms of mir-145 regulating invasion and metastasis of ovarian carcinoma. Am. J. Transl. Res. 2017;9:3443.
    1. Ye X., Weinberg R.A. Epithelial–mesenchymal plasticity: a central regulator of cancer progression. Trends Cell Biol. 2015;25:675–686.
    1. Yu L., Lu M., Jia D., Ma J., Ben-Jacob E., Levine H., Kaipparettu B.A., Onuchic J.N. Modeling the genetic regulation of cancer metabolism: interplay between glycolysis and oxidative phosphorylation. Cancer Res. 2017;77:1564–1574.
    1. Zhang B., Wolynes P.G. Stem cell differentiation as a many-body problem. Proc. Natl. Acad. Sci. U S A. 2014;111:10185–10190.
    1. Zhang J., Tian X., Zhang H., Teng Y., Li R., Bai F., Elankumaran S., Xing J. TGF-β-induced epithelial-to-mesenchymal transition proceeds through stepwise activation of multiple feedback loops. Sci. Signal. 2014;7:ra91.
    1. Zhao H., Kang X., Xia X., Wo L., Gu X., Hu Y., Xie X., Chang H., Lou L., Shen X. mir-145 suppresses breast cancer cell migration by targeting FSCN-1 and inhibiting epithelial-mesenchymal transition. Am. J. Transl. Res. 2016;8:3106.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa