RORŒ± Suppresses Epithelial-to-Mesenchymal Transition and Invasion in Human Gastric Cancer Cells via the Wnt/Œ≤-Catenin Pathway
Authors: Fang Liu
Qi Su
Hui Ling
YouHua Wu
Bo Su
Ying Zeng
Hong Xia
Jian Su
Juan Li
XiaoHong Zhao
Xi Zeng
JiZhen Zhang
Ying Shi
Journal: Frontiers in oncology (2019)
Discipline: CLINICAL MEDICINE
Keywords: RORŒ±; Wnt/Œ≤-catenin pathway; epithelial-to-mesenchymal transition; human gastric cancer; invasion
Affiliations: Hunan Province Key Laboratory of Cancer Cellular and Molecular Pathology, Cancer Research Institute, University of South China, Hengyang, China., Department of Pathology, Second Affiliated Hospital, University of South China, Hengyang, China., Key Laboratory for Pharmacoproteomics of Hunan Provincial University, Institute of Pharmacy and Pharmacology, University of South China, Hengyang, China., Center for Gastric Cancer Research of Hunan Province, First Affiliated Hospital, University of South China, Hengyang, China., Department of Gynaecology, Hainan Maternal and Child Health Hospital, Haikou, China., Department of Gastroenterology, Loudi Center Hospital, Loudi, China., Department of Pathology, Affiliated Hospital, Jinggangshan University, Ji'an, China., Department of Pathology and Pathophysiology, Xiamen Medical College, Xiamen, China.

Abstract:  Retinoid-related orphan receptor alpha (RORŒ±) is involved in tumor development. However, the mechanisms underlying RORŒ± inhibiting epithelial-to-mesenchymal transition (EMT) and invasion are poorly understood in gastric cancer (GC). This study revealed that the decreased expression of RORŒ± is associated with GC development, progression, and prognosis. RORŒ± suppressed cell proliferation, EMT, and invasion in GC cells through inhibition of the Wnt/Œ≤-catenin pathway. RORŒ± overexpression resulted in the decreased Wnt1 expression and the increased RORŒ± interaction with Œ≤-catenin, which could lead to the decreased intranuclear Œ≤-catenin and p-Œ≤-catenin levels, concomitant with downregulated T-cell factor-4 (TCF-4) expression and the promoter activity of c-Myc. The inhibition of Wnt/Œ≤-catenin pathway was coupled with the reduced expression of Axin, c-Myc, and c-Jun. RORŒ± downregulated vimentin and Snail and upregulated E-cadherin protein levels in vitro and in vivo. Inversely, knockdown of RORŒ± attenuated its inhibitory effects on Wnt/Œ≤-catenin pathway and its downstream gene expression, facilitating cell proliferation, EMT, migration, and invasion in GC cells. Therefore, RORŒ± could play a crucial role in repressing GC cell proliferation, EMT, and invasion via downregulating Wnt/Œ≤-catenin pathway. 

Retracted: Yes

PubPeer comments:
  Tinny intelligence ? From the present paper: * Furthermore, swarm intelligence is also a popular field, which catches many researchersu2019 attention. Many algorithms, which are inspired by the **tinny intelligence** from creatures in the Mother Nature, are included in this field. In general, the swarm intelligence method requires evolutionary computing and is imitating the particular behaviors or the survival skills of creatures. For instance, Cat Swarm Optimization (CSO) is proposed by modeling the catu2019s behaviors in the mathematic forms (Chu et al., 2006, 2007); Interactive Artificial Bee Colony (IABC) is proposed by simulating the nectar collection behavior of bees (Tsai et al., 2009); and Evolved Bat Algorithm (EBA) is proposed based on the prey finding process of bats (Tsai et al., 2012). These algorithms have been applied to solve many problems in engineering and finance fields .* Recycled in [Chen, Khurram Chen (2019)] : * Furthermore, intelligence is also a popular field, which catches many researchersu2019 attention. Many algorithms, which are inspired by the **tinny intelligence** from creatures in the Mother Nature, are included in this field. In general, the swarm intelligence method requires evolutionary computing and is imitating the particular behaviors or the survival skills of creatures. For instance, cat swarm optimization (CSO) is proposed by modeling the catu2019s behaviors in the mathematic forms (Chu et al., 2006, Chu and Tsai, 2007); interactive artificial bee colony (IABC) is proposed by simulating the nectar collection behavior of bees (Tsai et al., 2009); and evolved bat algorithm (EBA) is proposed based on the prey finding process of bats (Tsai et al., 2012). These algorithms have been applied to solve many problems in engineering and finance fields. * Recycled in [Chen et al (2020)] : * Furthermore, intelligence is also a popular field, which catches many researchersu2019 attention. Many algorithms, which are inspired by the **tinny intelligence** from creatures in the Mother Nature, are included in this field. In general, the swarm intelligence method requires evolutionary computing and is imitating the particular behaviors or the survival skills of creatures. For instance, cat swarm optimization (CSO) is proposed by modeling the catu2019s behaviors in the mathematical forms (Chu et al. 2006, 2007); interactive artificial bee colony (IABC) is proposed by simulating the nectar collection behavior of bees (Chu and Tsai 2007); and evolved bat algorithm (EBA) is proposed based on the prey-finding process of bats (Tsai et al. 2012). These algorithms have been applied to solve many problems in engineering and finance fields. * ... * The field of swarm intelligence includes various algorithms inspired by **tinny intelligent** from creatures in the Mother Nature. Several algorithms in swarm intelligence are proposed one after another. Most of the algorithms are developed by imitating behaviors or the particular survival skills of creatures. For example, Tsai et al. (2012) proposes a newly developed swarm intelligence algorithm called Evolved Bat Algorithm (EBA) in the early 2012. EBA is an algorithm inspired by the theory of the bat echolocation in the natural world. * ---------------------------- Other manifestations in [Tsai et al (2015)] :  [Tsai et al (2016)](http: www.icicelb.org ellb contents 2016 7 elb-07-07-09.pdf): * Inspired by the **tinny intelligent** behaviors in biome, many swarm intelligence algorithms have been developed such as flower pollination algorithm [10], Artificial Bee Colony (ABC) algorithm [6], Particle Swarm Optimization (PSO) algorithm [7], firefly algorithm [19], and Bat Algorithm (BA) [18]. Optimization problem can be solved efficiently by these swarm intelligence algorithms. Algorithms in swarm intelligence are also employed to solve engineering problems in many different fields. * [Tsai et al (2015)](http: bit.kuas.edu.tw ~jihmsp 2015 vol6 JIH-MSP-2015-06-012.pdf): * Swarm Intelligence (SI) is a solid branch in evolutionary computing. Most algorithms in SI take **tinny intelligence** observed from creatures in Mother Nature as the guidance for dragging the artificial agents to find new solutions in optimization. Many algorithms in SI are proposed one after another in recent years. For instant, Cat Swarm Optimization (CSO) [1, 2], utilizes the seeking mode to simulate the cat is observing the environment and the tracing mode to imitate the coursing behavior. * [Tsai et al (2016)] : * Swarm Intelligence (SI) is a solid branch in evolutionary computing. Most algorithms in SI take **tinny intelligence** observed from creatures in Mother Nature as the guidance for dragging the artificial agents to find new solutions in optimization. Many algorithms in SI are proposed one after another in recent years. For instant, Cat Swarm Optimization (CSO) [1], [2] utilizes the seeking mode to simulate the cat is observing the environment and the tracing mode to imitate the coursing behavior. * , 
 Fig 6,  Feasible solutions found by EBA in 25 runs.  These seem to repeat the results shown in Fig 2 of  A novel criterion for nonlinear time-delay systems using LMI fuzzy Lyapunov method [Tsai Chen 2014] ,  Search results of EBA in 30 runs .  SImulations were for the very simple control situation of a nonlinear inverted pyramid. , 
 Fig 7a b:  The same results appear as Figs 3, 5 of  A novel criterion for nonlinear time-delay systems using LMI fuzzy Lyapunov method ([Chai Chen 2014] :  - In  LMI Robust Fuzzy C-Means Control for Nonlinear Systems ([Chen Chen 2021] )  - in numerous other papers. , 
 *[left]* Fig 4; *[right]* Fig 2 of  Modified Evolved Bat Algorithm of Fuzzy Optimal Control for Complex Nonlinear Systems ([Chen et al 2020](http: www.romjist.ro full-texts paper672.pdf)):  The first sighting of this innovation appears to be Fig 3.1 in [Hsiao et al (2005)] :  Later recycled as *[left]* Fig 2 from [Chen, Meng Chen (2022)] ,  Nonlinear approximations based on fuzzy models and PDC controllers . *[right]* Fig 1 from Chen, Kuo Chen (2021),  Tu2013S fuzzy controller design of PDC concept .  ,