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The gene and microRNA networks of stem cells and reprogramming

  • The molecular interactions and regulations are dynamically changed in stem cells and reprogramming. This review article mainly focuses on the networks of molecules and epigenetic regulations including microRNA. The stem cells have molecular networks related to the stemness and the reprogramming of differentiated cells include the signaling networks consist of the transcriptional and post-transcriptional regulation of the genes and the protein modification. The gene expression is regulated by the binding of microRNAs towards the regulating regions of the coding genes. The molecular network pathways in stem cells include Wnt/β-catenin signaling and MAPK signaling, Shh signaling and Hippo signaling pathway. The epigenetic regulation of the genes included in the signaling pathways related to stem cells is mediated by the transcription factors and microRNAs consist of 18–25 nucleotides. Molecular interactions of the signaling proteins in stem cells is at least three factors including the quantity of the molecules partly regulated by the gene transcription and protein synthesis, the modification of the proteins such as phosphorylation, and localization of the molecules. In the epigenetic regulation level, the methylation and acetylation of genomes are critical for the regulation of the transcription. The binding sites and the combination of microRNAs, and regulated genes related to the stem cells and reprogramming are discussed in this review.

    Citation: Shihori Tanabe, Ryuichi Ono. The gene and microRNA networks of stem cells and reprogramming[J]. AIMS Cell and Tissue Engineering, 2018, 2(4): 238-245. doi: 10.3934/celltissue.2018.4.238

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  • The molecular interactions and regulations are dynamically changed in stem cells and reprogramming. This review article mainly focuses on the networks of molecules and epigenetic regulations including microRNA. The stem cells have molecular networks related to the stemness and the reprogramming of differentiated cells include the signaling networks consist of the transcriptional and post-transcriptional regulation of the genes and the protein modification. The gene expression is regulated by the binding of microRNAs towards the regulating regions of the coding genes. The molecular network pathways in stem cells include Wnt/β-catenin signaling and MAPK signaling, Shh signaling and Hippo signaling pathway. The epigenetic regulation of the genes included in the signaling pathways related to stem cells is mediated by the transcription factors and microRNAs consist of 18–25 nucleotides. Molecular interactions of the signaling proteins in stem cells is at least three factors including the quantity of the molecules partly regulated by the gene transcription and protein synthesis, the modification of the proteins such as phosphorylation, and localization of the molecules. In the epigenetic regulation level, the methylation and acetylation of genomes are critical for the regulation of the transcription. The binding sites and the combination of microRNAs, and regulated genes related to the stem cells and reprogramming are discussed in this review.


    The traditional agriculture depends on the irrigation of the Fogara. The latter is a group of wells connected to each other where the man in the past decade dug them manually for the purpose of bringing water from distant areas with a number of kilometers (within 5 kilometers) of their residence. During the summer, new wells are always being added to increase the water level or to maintain the old wells, but the depth of these wells remains limited because the drilling process is manual [1].

    With the passage of time and the construction of deep wells along with the poverty of water shortage significantly, which affected the agriculture in the region, which left the farmers to their orchards and the search for jobs in another area, all of which led to the creation of unemployment among the rural population, although they were previously working In their orchards and had self-sufficiency of agricultural crops and other times sold [1,2].

    To make sure the stability of the farmers in their orchards, it is necessary to provide sufficient water for watering and the same system that depends on the Foggara because it benefits all farmers and whatever their material income or agricultural crop. The important thing in all this is that the water level remains constant regardless of the consumption of water by the peasants. This requires constant supply and controlled water for poor people, for this purpose will be supported by deep wells with large water pumps, used pumps are rotated by large electric motors. In our study, we use synchronous double-feed Machine (DFIM). To maintain the water level, we must control the level of water flow to Fogara and thus control the speed of the engine that rotates the pump.

    Therefore, in this research study, the artificial intelligence approach was applied by combining neural networks and foggy logic to develop a mysterious model of nerves in order to improve the accuracy of prediction in water flow changes and thus change in speed.

    For many reasons, the generator (DFIM) shows many advantages, like the high-power application: traction, marine propulsion, wind energy conversion [1].

    In addition, as an advantage, the DFIM machine can do power and control of stator or rotor by different structure.

    As an example, the flow orientation strategy can transform the non-linear and coupled DFIM mathematical model into a linear model leading to an attractive solution as well as to generation or motorization operations [2,3].

    In this work, a new type of controller has been built by combining the advantages of neural networks and fuzzy logic to control the speed of DFIM machine. We will present the simulations of this results in MATLAB.

    The electrical equations of DFIG in the Park frame [5,8]:

    {vds=Rsids+dφdsdtωsφqsvqs=Rsiqs+dφqsdtωsφdsvdr=Rridr+dφdrdt(ωsωr)φqrvqr=Rriqr+dφqrdt(ωsωr)φdr, (1)

    The equation of stator flux:

    {φds=Lsids+Lmidrφqs=Lsiqs+Lmiqr. (2)

    The equation of rotor flux:

    {φdr=Lridr+Lmidsφqr=Lriqr+Lmiqs (3)

    The powers of the stator (active and reactive) are:

    {Ps=vdsids+vqsiqsQs=vqsidsvdsiqs (4)

    The powers of the rotor (active and reactive) are:

    {Pr=vdridr+vqriqrQr=vqridrvdriqr (5)

    The electromagnetic torque is expressed as:

    Cem=P(φdsiqsφqsids) (6)

    With P is the number of pair poles.

    Figure 1 shows the complete blur control blocks [7,8]:

    Figure 1.  The blocs of fuzzy controller.

    The fuzzification portion converts the net values of the control inputs into fuzzy values that are defined by linguistic variables by progressively varying the membership function.

    Fuzzy sets of shapes can be triangular, trapezoidal, etc. [6,9].

    According to the application and the processing power, the defuzzification method is installed. This installation can do this by methods whose center of gravity or by methods of height are common [6].

    a. Fuzzy-PI controller

    The fuzzy controller is usually non-linear, maybe written as follows [7,12]:

    u=kee+kcece (7)

    The Fuzzy-PI output is:

    y=Kpu+Ki.u (8)

    where ke is the gain of the speed error, kce is the gain of the change of speed error kp is the proportional factor; ki is the integral factor, e is the speed error, ce is the change of speed error, u is the fuzzy output.

    To control DFIM with strategy Fuzzy and PI, Figure 2 present this method:

    Figure 2.  Block DFIM with strategy Fuzzy and PI.

    After that, the membership functions of error (e) and his change are presented in Figure 3a and Figure 3b. The Figure 3c is the proposed membership functions of output value. The inference strategy used in this system is the Mamdani algorithm.

    Figure 3.  The membership function of fuzzy controllers.

    This work, used the triangular membership function, the max-min reasoning method, and the center of gravity defuzzification method, as those methods are most frequently used in many works of literature [6,11].

    b. Neuro-fuzzy controller

    The ANFIS abbreviation is: Adaptive Neuron-Fuzzy Inference System. It works with input and output data values, the ANFIS function draws a Fuzzy inference system, whose membership function parameters are adjusted.

    This adjustment from the data they model introduce to fuzzy systems to learn. ANFIS is very complex as fuzzy inference systems and is not available for all fuzzy inference system options. Simply ANFIS only supports Sugeno type systems, which must have the following properties [8,13]:

    ● Be the zero order type Sugeno systems.

    ● A single output is required using the weighted average defuzzification.

    ● Do not share rules. Different rules can not share the same output membership function, that is, the number of output membership functions must be equal to the number of rules.

    ● Have a unit weight for each rule.

    To start ANFISGUI, type the following command at the MATLAB prompt: Anfisedit.

    The ANFIS editor's graphical window includes four separate areas to support a typical workflow. The graphical interface allows you to perform the following tasks:

    ● Loading, tracing and erasing data.

    ● Generation or loading of the initial structure of the SIF.

    ● Train the FIS.

    ● Validate the trained FIS.

    To generate the FIS structure, we used 2 membership functions, with the I/p parameters as gbellmf and the o/p type as linear.

    We use a hybrid learning algorithm to form the generated FIS.

    Figure 4 shows the generated ANFIS structure used for the design of the cruise control.

    Figure 4.  The structure of generated ANFIS.

    The DFIM used in this work is a 2.25 kW, whose nominal parameters are reported in appendix.

    Speed reversal of (157, -157 rad/s), with a load of 5 N.m applied at t = 1 s.

    The responses of current stator and rotor, electrical torque speed and stator flux and rotor flux are shown in Figure 5. The Fuzzy-PI regulator shows the good performances to achieve tracking of the desired trajectory.

    Figure 5.  Stator current (a), rotor current (b), electrical torque (c), speed (d), and flux and rotor flux (e), for DFIM using the Fuzzy-PI regulator.

    At these changes of loads, the Fuzzy-PI regulator rejects the load disturbance very rapidly with no overshoot and with a negligible static error as can be seen in the response of speed (see Figure 5d). The decoupling of torque-flux is maintained in permanent mode.

    The Fuzzy PI controller based drive system can handle the sudden change in load torque without overshoot, undershoot, and steady state error.

    The Figure 6 presents simulation results of direct vector control by a neuro-fuzzy speed controller. We notice an improvement in the overall performance of the system with the introduction of the neuro-fuzzy regulator compared to the fuzzy-PI. When starting and reversing the direction of rotation, the speed reaches its value with a zero overshoot. A good rejection of the disturbance due to the application of the load. This type of regulator ensures decoupling. The current is well maintained at its allowable value, and the flow has a fast dynamic to reach its reference value.

    Figure 6.  Stator current (a), rotor current (b), speed (c), and flux and rotor flux (d), for DFIM using The Neuro-fuzzy regulator.
    Figure 7.  The results of simulated Fuzzy-PI regulators and neuro-fuzzy to speed control of DFIM.

    Step change in load torque at t = 0.1 ms and speed reversal stage at t = 0.25 ms for Neuro fuzzy controller and the Fuzzy PI controller from which it can be see that the speed reached the rated value in very short period for the Neuro Fuzzy controller.

    This paper proposes a neuron-fuzzy controller for the speed control of a double feed induction machine with direct control of the orientation of the stator flow. To have a good efficiency we tested on the machine DFIM the controller blurred under operating conditions. The fuzzy neural regulator exhibits good resistance to insensitivity to load torque disturbances as well as faster dynamics with negligible stationary state error under all dynamic operating conditions.

    The simulation results exhibited correct stator flow control behavior and speed tracking performance. The Neuron-Fuzzy controller-based speed controller system for DFIM has been successfully developed in MATLAB. The performance of the system has been compared to that of the Fuzzy-PI controller.

    I declare with honor that there is no conflict of interest with respect to my manuscript.

    [1] Salim A, Amjesh R, Chandra SS (2017) An approach to forecast human cancer by profiling microRNA expression from NGS data. BMC Cancer 17: 77. doi: 10.1186/s12885-016-3042-2
    [2] Bartel DP (2009) microRNAs: target recognition and regulatory functions. Cell 136: 215–233. doi: 10.1016/j.cell.2009.01.002
    [3] Bartel DP (2004) microRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281–297. doi: 10.1016/S0092-8674(04)00045-5
    [4] Agarwal V, Bell GW, Nam JW, et al. (2015) Predicting effective microRNA target sites in mammalian mRNAs. eLife 4: e05005. doi: 10.7554/eLife.05005
    [5] Siciliano V, Garzilli I, Fracassi C, et al. (2013) miRNAs confer phenotypic robustness to gene networks by suppressing biological noise. Nat Commun 4: 2364. doi: 10.1038/ncomms3364
    [6] Cuccato G, Polynikis A, Siciliano V, et al. (2011) Modeling RNA interference in mammalian cells. BMC Syst Biol 5: 19. doi: 10.1186/1752-0509-5-19
    [7] Santpere G, Lopez-Valenzuela M, Petit-Marty N, et al. (2016) Differences in molecular evolutionary rates among microRNAs in the human and chimpanzee genomes. BMC Genomics 17: 528. doi: 10.1186/s12864-016-2863-3
    [8] Beg F, Wang R, Saeed Z, et al. (2017) Inflammation-associated microRNA changes in circulating exosomes of heart failure patients. BMC Res Notes 10: 751. doi: 10.1186/s13104-017-3090-y
    [9] Hannafon BN, Trigoso YD, Calloway CL, et al. (2016) Plasma exosome microRNAs are indicative of breast cancer. Breast Cancer Res 18: 90. doi: 10.1186/s13058-016-0753-x
    [10] Bengoa-Vergniory N, Gorroño-Etxebarria I, González-Salazar I, et al. (2014) A switch from canonical to noncanonical Wnt signaling mediates early differentiation of human neural stem cells. Stem Cells 32: 3196–3208.
    [11] Zhou R, Yuan Z, Liu J, et al. (2016) Calcitonin gene-related peptide promotes the expression of osteoblastic genes and activates the WNT signal transduction pathway in bone marrow stromal stem cells. Mol Med Rep 13: 4689–4696. doi: 10.3892/mmr.2016.5117
    [12] Dokanehiifard S, Yasari A, Najafi H, et al. (2017) A novel microRNA located in the TrkC gene regulates the Wnt signaling pathway and is differentially expressed in colorectal cancer specimens. J Biol Chem 292: 7566–7577. doi: 10.1074/jbc.M116.760710
    [13] Cheleschi S, De Palma A, Pecorelli A, et al. (2017) Hydrostatic pressure regulates microRNA expression levels in osteoarthritic chondrocyte cultures via the Wnt/b-catenin pathway. Int J Mol Sci 18: 133. doi: 10.3390/ijms18010133
    [14] Liang J, Huang W, Cai W, et al. (2017) Inhibition of microRNA-495 enhances therapeutic angiogenesis of human induced pluripotent stem cells. Stem Cells 35: 337–350. doi: 10.1002/stem.2477
    [15] Yata K, Beder LB, Tamagawa S, et al. (2015) microRNA expression profiles of cancer stem cells in head and neck squamous cell carcinoma. Int J Oncol 47:1249–1256. doi: 10.3892/ijo.2015.3145
    [16] Hodges WM, O'Brien F, Fulzele S, et al. (2017) Function of microRNAs in the osteogenic differentiation and therapeutic application of adipose-derived stem cells (ASCs). Int J Mol Sci 18: 2597. doi: 10.3390/ijms18122597
    [17] Yang CL, Zheng XL, Ye K, et al. (2018) microRNA-183 acts as a tumor suppressor in human non-small cell lung cancer by down-regulating MTA1. Cell Physiol Biochem 46: 93–106. doi: 10.1159/000488412
    [18] Zheng J, Wang W, Yu F, et al. (2018) microRNA-30a suppresses the activation of hepatic stellate cells by inhibiting epithelial-to-mesenchymal transition. Cell Physiol Biochem 46: 82–92. doi: 10.1159/000488411
    [19] Liu W, Li M, Chen X, et al. (2018) microRNA-1 suppresses proliferation, migration and invasion by targeting Notch2 in esophageal squamous cell carcinoma. Sci Rep 8: 5183. doi: 10.1038/s41598-018-23421-3
    [20] Yu T, Wang LN, Li W, et al. (2018) Downregulation of miR-491-5p promotes gastric cancer metastasis by regulating SNAIL and FGFR4. Cancer Sci 109: 1393–1403. doi: 10.1111/cas.13583
    [21] Li Y, Huo J, Pan X, et al. (2018) microRNA 302b-3p/302c-3p/302d-3p inhibits epithelial-mesenchymal transition and promotes apoptosis in human endometrial carcinoma cells. Onco Targets Ther 11: 1275–1284. doi: 10.2147/OTT.S154517
    [22] Xu R, Zhu X, Chen F, et al. (2018) LncRNA XIST/miR-200c regulates the stemness properties and tumourigenicity of human bladder cancer stem cell-like cells. Cancer Cell Int 18: 41. doi: 10.1186/s12935-018-0540-0
    [23] Liu X, Wang S, Xu J, et al. (2018) Extract of Stellerachamaejasme L(ESC) inhibits growth and metastasis of human hepatocellular carcinoma via regulating microRNA expression. BMC Complement Altern Med 18: 99. doi: 10.1186/s12906-018-2123-y
    [24] Zhang J, Chen D, Liang S, et al. (2018) miR-106b promotes cell invasion and metastasis via PTEN mediated EMT in ESCC. Oncol Lett 15: 4619–4626.
    [25] Ma J, Zhang L, Hao J, et al. (2018) Up-regulation of microRNA-93 inhibits TGF-β1-induced EMT and renal fibrogenesis by down-regulation of Orai1. J Pharmacol Sci 136: 218–227. doi: 10.1016/j.jphs.2017.12.010
    [26] Yin C, Mou Q, Pan X, et al. (2018) miR-577 suppresses epithelial-mesenchymal transition and metastasis of breast cancer by targeting Rab25. Thorac Cancer 9: 472–479. doi: 10.1111/1759-7714.12612
    [27] Miyazaki H, Takahashi RU, Prieto-Vila M, et al. (2018) CD44 exerts a functional role during EMT induction in cisplatin-resistant head and neck cancer cells. Oncotarget 9: 10029–10041.
    [28] Li D, Zhang Y, Zhang H, et al. (2018) CADM2, as a new target of miR-10b, promotes tumor metastasis through FAK/AKT pathway in hepatocellular carcinoma. J Exp Clin Cancer Res 37: 46. doi: 10.1186/s13046-018-0699-1
    [29] Li J, Zou K, Yu L, et al. (2018) microRNA-140 inhibits the epithelial-mesenchymal transition and metastasis in colorectal cancer. Mol Ther Nucleic Acids 10: 426–437. doi: 10.1016/j.omtn.2017.12.022
    [30] Clark RJ, Craig MP, Agrawal S, et al. (2018) microRNA involvement in the onset and progression of Barrett's esophagus: a systematic review. Oncotarget 9: 8179–8196.
    [31] Gao Y, Ma H, Gao C, et al. (2018) Tumor-promoting properties of miR-8084 in breast cancer through enhancing proliferation, suppressing apoptosis and inducing epithelial-mesenchymal transition. J Transl Med 16: 38. doi: 10.1186/s12967-018-1419-5
    [32] Chen J, Gao F, Liu N (2018) L1CAM promotes epithelial to mesenchymal transition and formation of cancer initiating cells in human endometrial cancer. Exp Ther Med 15: 2792–2797.
    [33] Xu M, Li J, Wang X, et al. (2018) miR-22 suppresses epithelial-mesenchymal transition in bladder cancer by inhibiting Snail and MAPK1/Slug/vimentin feedback loop. Cell Death Dis 9: 209. doi: 10.1038/s41419-017-0206-1
    [34] Guo H, Zhang X, Chen Q, et al. (2018) miR-132 suppresses the migration and invasion of lung cancer cells by blocking USP9X-induced epithelial-mesenchymal transition. Am J Transl Res 10: 224–234.
    [35] Li J, Huang Y, Deng X, et al. (2018) Long noncoding RNA H19 promotes transforming growth factor-β-induced epithelial-mesenchymal transition by acting as a competing endogenous RNA of miR-370-3p in ovarian cancer cells. Onco Targets Ther 11: 427–440. doi: 10.2147/OTT.S149908
    [36] Yáñez-Mó M, Siljander PR, Andreu Z,et al. (2015) Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles 4: 27066. doi: 10.3402/jev.v4.27066
    [37] de Jong OG, Verhaar MC, Chen Y, et al. (2012) Cellular stress conditions are reflected in the protein and RNA content of endothelial cell-derived exosomes. J Extracell Vesicles 1.
    [38] Raposo G, Nijman HW, Stoorvogel W, et al. (1996) B lymphocytes secrete antigen-presenting vesicles. J Exp Med 183: 1161–1172. doi: 10.1084/jem.183.3.1161
    [39] Raposo G, Stoorvogel W (2013) Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol 200: 373–383. doi: 10.1083/jcb.201211138
    [40] Colombo M, Raposo G, Théry C (2014) Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol 30: 255–289. doi: 10.1146/annurev-cellbio-101512-122326
    [41] Yoshioka Y, Konishi Y, Kosaka N, et al. (2013) Comparative marker analysis of extracellular vesicles in different human cancer types. J Extracell Vesicles 2.
    [42] Valadi H, Ekstrom K, Bossios A, et al. (2007) Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol 9: 654–659. doi: 10.1038/ncb1596
    [43] Kosaka N, Iguchi H, Yoshioka Y, et al. (2010) Secretory mechanisms and intercellular transfer of microRNAs in living cells. J Biol Chem 285: 17442–17452. doi: 10.1074/jbc.M110.107821
    [44] Pegtel DM, Cosmopoulos K, Thorley-Lawson DA, et al. (2010) Functional delivery of viral miRNAs via exosomes. Proc Natl Acad Sci U S A 107: 6328–6333. doi: 10.1073/pnas.0914843107
    [45] Zhang Y, Liu D, Chen X, et al. (2010) Secreted monocytic miR-150 enhances targeted endothelial cell migration. Mol Cell 39: 133–144. doi: 10.1016/j.molcel.2010.06.010
    [46] Kalluri R, Zeisberg M (2006) Fibroblasts in cancer. Nat Rev Cancer 6: 392–401. doi: 10.1038/nrc1877
    [47] Fang T, Lv H, Lv G, et al. (2018) Tumor-derived exosomal miR-1247-3p induces cancer-associated fibroblast activation to foster lung metastasis of liver cancer. Nat Commun 9: 191. doi: 10.1038/s41467-017-02583-0
    [48] Tominaga N, Kosaka N, Ono M, et al. (2015) Brain metastatic cancer cells release microRNA-181c-containing extracellular vesicles capable of destructing blood-brain barrier. Nat Commun 6: 6716. doi: 10.1038/ncomms7716
    [49] Sieuwerts AM, Mostert B, Bolt-de Vries J, et al. (2011) mRNA and microRNA expression profiles in circulating tumor cells and primary tumors of metastatic breast cancer patients. Clin Cancer Res 17: 3600–3618. doi: 10.1158/1078-0432.CCR-11-0255
    [50] Ohshima K, Inoue K, Fujiwara A, et al. (2010) Let-7 microRNA family is selectively secreted into the extracellular environment via exosomes in a metastatic gastric cancer cell line. PLoS One 5: e13247. doi: 10.1371/journal.pone.0013247
    [51] Taylor DD, Gercel-Taylor C (2008) microRNA signatures of tumor-derived exosomes as diagnostic biomarkers of ovarian cancer. Gynecol Oncol 110: 13–21. doi: 10.1016/j.ygyno.2008.04.033
    [52] Jones CI, Zabolotskaya MV, King AJ, et al. (2012) Identification of circulating microRNAs as diagnostic biomarkers for use in multiple myeloma. Br J Cancer 107: 1987–1996. doi: 10.1038/bjc.2012.525
    [53] Tanaka M, Oikawa K, Takanashi M, et al. (2009) Down-regulation of miR-92 in human plasma is a novel marker for acute leukemia patients. PLoS One 4: e5532. doi: 10.1371/journal.pone.0005532
    [54] Hu Z, Chen X, Zhao Y, et al. (2010) Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol 28: 1721–1726. doi: 10.1200/JCO.2009.24.9342
    [55] Aushev VN, Zborovskaya IB, Laktionov KK, et al. (2013) Comparisons of microRNA patterns in plasma before and after tumor removal reveal new biomarkers of lung squamous cell carcinoma. PLoS One 8: e78649. doi: 10.1371/journal.pone.0078649
    [56] Ho AS, Huang X, Cao H, et al. (2010) Circulating miR-210 as a novel hypoxia marker in pancreatic cancer. Transl Oncol 3: 109–113. doi: 10.1593/tlo.09256
    [57] Wang J, Chen J, Chang P, et al. (2009) microRNAs in plasma of pancreatic ductal adenocarcinoma patients as novel blood-based biomarkers of disease. Cancer Prev Res (Phila) 2: 807–813. doi: 10.1158/1940-6207.CAPR-09-0094
    [58] Camacho L, Guerrero P, Marchetti D (2013) microRNA and protein profiling of brain metastasis competent cell-derived exosomes. PLoS One 8: e73790. doi: 10.1371/journal.pone.0073790
    [59] Cheng HH, Mitchell PS, Kroh EM, et al. (2013) Circulating microRNA profiling identifies a subset of metastatic prostate cancer patients with evidence of cancer-associated hypoxia. PLoS One 8: e69239. doi: 10.1371/journal.pone.0069239
    [60] Jones K, Nourse JP, Keane C, et al. (2014) Plasma microRNA are disease response biomarkers in classical Hodgkin lymphoma. Clin Cancer Res 20: 253–264. doi: 10.1158/1078-0432.CCR-13-1024
    [61] Shimomura A, Shiino S, Kawauchi J, et al. (2016) Novel combination of serum microRNA for detecting breast cancer in the early stage. Cancer Sci 107: 326–334. doi: 10.1111/cas.12880
    [62] Carter JV, Roberts HL, Pan J, et al. (2016) A highly predictive model for diagnosis of colorectal neoplasms using plasma microRNA: improving specificity and sensitivity. Ann Surg 264: 575–584. doi: 10.1097/SLA.0000000000001873
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