RETRACTED ARTICLE: Inhibition of ZEB1-AS1 confers cisplatin sensitivity in breast cancer by promoting microRNA-129-5p-dependent ZEB1 downregulation
Authors: Shaorong Yu
Jifeng Feng
Jin Gao
Jianwei Lu
Sainan Hu
Lili Zhang
Yuan Yuan
Journal: Cancer Cell International (2020)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: Breast cancer; Drug resistance; Long non-coding RNA ZEB1-AS1; ZEB1-AS1/miR-129-5p/ZEB1 signaling pathway.
Affiliations: Department of Medical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, No. 42, Baiziting, Nanjing, 210009, Jiangsu, People's Republic of China., Department of Medical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, No. 42, Baiziting, Nanjing, 210009, Jiangsu, People's Republic of China. .

Abstract:  Background: Breast cancer is the leading cause of cancer-related mortality in women worldwide. Long non-coding RNAs (lncRNAs) are of critical importance in tumor drug resistance. Herein, this study aims to determine the roles of lncRNA ZEB1-AS1 in drug resistance of breast cancer involving microRNA-129-5p (miR-129-5p) and ZEB1. Methods: Microarray-based gene expression profiling of breast cancer was conducted to identify the differentially expressed lncRNAs. ZEB1 expression was measured in adjacent and cancerous tissues. Next, MCF-7 and MDA-MB-231 cells were treated with a series of inhibitor, mimic or siRNA to clarify the roles of lncRNA ZEB1-AS1 and miR-129-5p in drug resistance of breast cancer. Then the target relationship of miR-129-5p with lncRNA ZEB1-AS1 and ZEB1 was verified. The expression patterns of miR-129-5p, lncRNA ZEB1-AS1, Bcl-2, MDR-1, ZEB1 and corresponding proteins were evaluated. Moreover, the apoptosis and drug resistance of MCF-7 cell were detected by CCK-8 and flow cytometry respectively. Results: LncRNA ZEB1-AS1 was observed to be an upregulated lncRNA in breast cancer, and ZEB1 overexpression was noted in breast cancerous tissues. MiR-129-5p was revealed to specifically bind to both ZEB1 and lncRNA ZEB1-AS1. Moreover, the expression levels of ZEB1-AS1, ZEB1, Bcl-2, MDR-1, and corresponding proteins were decreased, but the expression of miR-129-5p was increased with transfection of miR-129-5p mimic and lncRNA ZEB1-AS1 siRNA. Besides, drug resistance to cisplatin was inhibited, and cell apoptosis was promoted in breast cancer after transfection of miR-129-5p mimic, lncRNA ZEB1-AS1 siRNA, and ZEB1 siRNA. Conclusion: In conclusion, the study provides evidence that lncRNA ZEB1-AS1 silencing protects against drug resistance in breast cancer by promoting miR-129-5p-dependent ZEB1 downregulation. It may serve as a novel therapeutic target in breast cancer treatment. 

Retracted: Yes

PubPeer comments:
 Figure 2,  Number of feasible solutions obtained by EBA in 30 runs - for simulating the stability of **off-shore drilling rigs**. ![file] Fig 9 from [Chen et al (2021)] ,  Number of feasible solutions acquired by EBA in **40** runs - for a simulation of **vehicle suspension control**. ![file] Fig. 3 from [Chen Cheng (2020)] ,  Number of feasible solutions obtained by EBA in **30** runs - for a simulation of  a **nondimensionalized fractional model of immunogenic tumors** . ![file] Could the authors explain how different supposed optimisation simulations deliver identical results? UPDATE: Two more sightings. *[left]* Fig 4.5 from [Z.Y. Chen, Timothy Chen et al (2022)](http: www.techno-press.org content ?page=article journal=sem volume=81 num=1 ordernum=5),  Number of feasible solutions obtained in **30** runs . *[right]* Fig. 5 from [Z.Y. Chen, Timothy Chen et al (2021)] ,  Number of feasible solutions obtained by EBA in **40** runs . Both for (different) simulations of **building stabilisation during earthquakes**. ![file] Could the authors comment on these variations in their names? , 
 Table III.  Samples of the obtained feasible solutions by EBA with system eigen values - for a simulation of **stabilising off-shore drilling rigs**. ![file]  Figure 3. The angle and the velocity response of the TLP system without control . ![file] Recycled as Table 1, Fig 1 of [Chen Chen (2021)] . No details of what is being simulated. ![file] ![file] Table 1 *also* appears as Table 4 of [Chen et al (2021)] , in the context of a simulation of **vehicle suspension control**. ![] ------------------------ UPDATE: Table 2 from [Tim Chen, Kuo C. Y. J. Chen (2021)] ,  Samples of these acquired feasible solutions by EBA with nonlinear system eigenvalues . No details of what is being simulated. ![file] , 
 Fig 3 again: ![] The source appears to be Fig 7a of  A novel control algorithm for interaction between surface waves and a permeable floating structure ([Tsai et al 2016] ): ![file] Tsai et al obtained these results for a very simple simulated problem: stabilising an inverted pendulum. , 
  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 from [Tsai et al (2016)]  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 . Also 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. ,