MicroRNA-21 promotes progression of breast cancer via inhibition of mitogen-activated protein kinase10 (MAPK10)
Authors: Lan Zhang
Shenglei Li
Qing Li
Honglei Wang
Yaqing Liu
Xiaoyue Fan
Yi Xiao
Yilin Xie
Journal: Bioscience reports (2019)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: Apoptosis; Invasion; MicroRNA-21; Migration; Mitogen-activated protein kinase10
Affiliations: The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China., The Third Affiliated Hospital of Soochow University, Changzhou City, China., The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China .

Abstract:  Objective: The present study aims to investigate microRNAs (miRNAs) and messenger RNAs (mRNAs) associated with breast cancer, and to have a better understanding of the mechanism of miRNAs in breast cancer using bioinformatics tools. Methods: Microarray analysis was performed to predict differentially expressed miRNAs related to breast cancer, followed by prediction and verification of target genes. Human breast cancer cells were transfected and divided into Blank group, NC group, miR-21-5p mimic group, miR-21-5p inhibitor group and siRNA-MAPK10 group. RT-qPCR and Western blot analysis were used to detect mRNA and protein expressions of MAPK10 in tissues and transfected cells, MTT assay, scratch test and Transwell assay for detection of the proliferation, migration and invasion, and Annexin-V-R-PE assay for apoptosis of different cell lines. Results: Highly expressed miR-21-5p and lowly expressed MAPK10 were selected for subsequent experiments, according to the microarray analysis. RT-qPCR showed that the expression of MAPK10 in breast cancer tissues was significantly lower than that in adjacent tissues, while it was reciprocal in expression of miR-21. miR-21-5p negatively regulated MAPK10. The expression of MAPK10 reduced in response to miR-21-5p mimic treatment. Compared with the Blank and NC groups, the proliferation, migration, invasion and metastasis of breast cancer cells decreased, and the apoptosis of breast cancer cells increased in the miR-21-5p inhibitor group and siRNA-MAPK10 group, while it was reciprocal in the miR-21-5p mimic group. Conclusion: MiR-21 promotes the proliferation, migration and invasion and inhibits the apoptosis of breast cancer cells by inhibiting MAPK10. 

Retracted: No

PubPeer comments:
 The author defined an additive consistent judgment matrix $C$ in Definition 3.2 as an $ntimes n$ matrix with $$c_{ij} = c_{ik} - c_{jk} + 0.5, quad forall i, j, k$$ The author then claimed that for such a matrix there are nonnegative weights such that $$w_j ge 0, quad sum_{j=1}^{n} w_j =1$$ and $$c_{ij} = 0.5(w_i - w_j +1),quad forall i, j$$ Unfortunately, this is not the case in the general, except for the scenario of $n=2$. For instance, let $$w_1 = 0.1, quad w_2 = 0.5, quad w_3 =0.9$$ The $3times 3$ matrix $C$ with $$c_{ij} = 0.5(w_i -w_j +1)$$ cannot be recovered by some nonnegative weights with the condition that $$w_1 + w_2 + w_3 = 1$$ As a result, Definition 3.4 is invalid since it is a generalization of Definition 3.2. The following results based on Definition 3.4 are meaningless as well. ![file] ![file] , 
 #1 Dear Rhipidura Albiventris Thank you very much for your interest in this paper. As you have seen in the next paragraph of Definition 3.2, we state that the weighting vector is always normalized, that is $sum olimits_{j=1}^n omega_j = 1$. Thus, if we let $omega_1 = 0.1, omega_2 = 0.5, omega_3 = 0.4$ According to $c_{ij} = 0.5 (omega_i - omega_j + 1)$, we have $C=$ $0.5 ~~~~ 0.3 ~~~~ 0.35$ $0.7 ~~~~ 0.5 ~~~~ 0.55$ $0.65~~ 0.45 ~~ 0.5$ We can see that the equation $c_{ij}=c_{ij}-c_{jk}+0.5$ is satisfied. For instance, $0.3 = c_{12}=c_{11} - c_{21} +0.5 = 0.5-0.7+0.5=0.5$ $=c_{12}-c_{22}+0.5=0.3-0.5+0.5=0.3$ $=c_{13}-c_{23}=0.5=0.35-0.55+0.5=0.3$ Generally, the equation $c_{ij}=c_{ij}-c_{jk}+0.5$ is equavalent to $c_{ij} = 0.5 (omega_i - omega_j + 1)$. In fact, if $c_{ij} = 0.5 (omega_i - omega_j + 1)$, then $2c_{ij} = omega_i - omega_j +1$ $=(omega_i - omega_k +1) - (omega_j - omega_k+1) + 1$ $=2c_{ik} - 2c_{jk} + 1$ which means $c_{ij}=c_{ij}-c_{jk}+0.5$. In fact, as to the normalization of the nonnegative weights used in the additive consistency of fuzzy preference relation, we must make some constraints to the formula of additive consistency $a_{ij}=0.5(w_i-w_j+1)$ to assure its reasonability. As weights, it is natural to consider the constraint condition of normalization which has been widely accepted and used in the existing research work over the last decades. Certainly, there are also some other reseachers to investigate some other types of normalized weights, but we found that they are not to be completly different from ours. Thus, I think that it is not suitable to say that the normalization is invalid or meaningless. As academic research, if you are really interested in this field, you can put forward a new perfect normalization method based on the existing work, I think all scholars in this field will be very glad to see such results. Moreover, I sincerely hope that you can have a correct attitude to our work. If you have any questions, you can discuss them with the authors. However, it is offensive to remark othersu2019 work with the words u201cmeaningless, invalid, not reliableu201d before you carefully study them and investigate the related work in depth and systematically. It is absurd and irresponsible to make such comments if one is unfamilar with this traditional and classical research work. Thank you! , 
 #2 Dear Shen Zhang, (1) Are you speaking for Prof. Xu (the author of this paper) or just for yourself? (2) In #1, a concret example is provided, which is additive consistent according to the definition and cannot be recovered with normalized weight. Note that a characteriazation usually means the **sufficient and necessary** conditons. (3) In academia, if something is meaningless, we say it meaningless directly; if something is invalid, we say it invalid directly. , 
 #3 Dear Rhipidura Albiventris, It should be noted that I only represent myself. And I hope my reply can reduce your doubts to some extent. I m sorry that I didn t fully understand your question when I last responded, so that my reply did not solve your problem. I have given a new reply in the link below for your reference. Thank you for your interest. https: pubpeer.com publications F305409F5CE47181FAB11AFD313F4E ,