Knockdown of long non-coding RNA ANRIL inhibits proliferation, migration, and invasion but promotes apoptosis of human glioma cells by upregulation of miR-34a
Authors: Gang Zhao
Xuechao Dong
Yunqian Li
Haiyang Xu
Yong Chen
Xinyu Hong
Chengyuan Ma
Zheng Jin
Journal: Journal of Cellular Biochemistry (2018)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: ANRIL; PI3K/AKT; Sirt1; glioma; mTOR
Affiliations: Department of Neurosurgery, The First Hospital of Jilin University, Changchun, P.R. China.

Abstract:  Gliomas are the most common types of primary central nervous system malignancy found in adults. Long non-coding RNA antisense non-coding RNA in the INK4 locus (ANRIL) variants are associated with glioma and miR-34a is markedly downregulated in U251 glioma cells. The 3'-untranslated region (3'UTR) of silent information regulator 1 (Sirt1) contains a conserved site that is targeted directly by miR-34a. Therefore, in this study, we investigated the roles of ANRIL, miR-34a, and Sirt1 in glioma and their potential interactions. Firstly, expression of ANRIL in normal glia cells and five glioma cell lines was measured. Then, effects of ANRIL suppression on cell proliferation, apoptosis, migration and invasion of U251 cells as well as expression of miR-34a were assessed. Meanwhile, effects of miR-34a on U251 cells silencing ANRIL were tested. Whether Sirt1 is a target of miR-34a was verified, followed by estimating the role of Sirt1 overexpression in U251 cells overexpressing miR-34a. Finally, the involved signaling pathways were assessed. ANRIL was upregulated in glioma cells and its suppression inhibited cell proliferation, migration and invasion but promoted cell apoptosis. ANRIL acted as a sponge of miR-34a, and Sirt1 is a target of miR-34a. Then, Sirt1 was proved to function through activation of the PI3K/AKT and mTOR signaling pathways. In conclusion, ANRIL was upregulated in glioma, and its inhibition could repress cell proliferation, migration and invasion but inhibit cell apoptosis through miR-34a-mediated downregulation of Sirt1, involving the inactivation of the PI3K/AKT and mTOR pathways. 

Retracted: No

PubPeer comments:
 This paper belongs to the series of papers on hospital management authored by Prof. Huchang Liao and his co-authors. The comments on some papers in this series may be found on https: pubpeer.com publications 30D1E825B301040C32107F65C9BA65 https: pubpeer.com publications 4B5683F29DC5135F86013819F4F3E4 https: pubpeer.com publications F4789E04A9A5F30C33EFBCC7A92CE9 https: pubpeer.com publications 3F7D47FFB4F1758C27D81E047605E6 https: pubpeer.com publications 95CB34C28C43C1DBBB6F10FBE9A446 The authors of this paper have a very unusual sense of data summary. In Figure 1, it is clear that for the number of beds illustrated, $$57.20% + 20.48% + 18.73% = 96.41%  eq 100%$$ ![file] , 
 It should be noted that, in China, some small private clinics also have a small number of beds, accounting for nearly 3.59%, which are not shown in Fig. 1. In addition, the hospitalization is derived by the number of admissions divided by number of visits for a certain hospital, such as public hospital, private hospital and so on. , 
 #2 Thank you for your explanation, which raises two additional questions. (a) Are the other types of hospitals referred to in the paper include some small private clinics because $$frac{57.20%}{20.48% + 18.73% } = 1.4588$$ ![file] (b) Is it a coincidence for the numbers of hospitalization that $$77.17% + 12.20% + 10.63% =100%$$ , 
 Just as previous statements, in China, some small private clinics also have a small number of beds, accounting for nearly 3.59%, which are not shown in Fig. 1. In addition, the hospitalization is derived by the number of admissions divided by number of visits for a certain hospital, such as public hospital, private hospital and so on. So, your computation is incorrect, and it should be revised as: 57.2% (20.48%+17.73%+3.59%)=1.33. , 
 #4 Dear Dr. Zhang, Thank you for your explanation. As pointed out in #1, the authors of this paper have a very unusual sense of data summary. Besides, according to your comment in #2 and #4  In addition, the hospitalization is derived by the number of admissions divided by number of visits for a certain hospital, such as public hospital, private hospital and so on. **is it a coincidence for the numbers of hospitalization that** $$77.17% + 12.20% + 10.63% =100%$$ ,