Long noncoding RNA TTN-AS1 promotes the proliferation and migration of prostate cancer by inhibiting miR-1271 level
Authors: Z Yang
Y Zhu
P Xu
X-H Luo
Journal: European review for medical and pharmacological sciences (2019)
Discipline: PHARMACOLOGY & TOXICOLOGY
Keywords: Prostate cancer; long noncoding RNAs; lncRNA TTN-AS1; RT-qPCR; tumor progression
Affiliations: Department of Radiation Oncology, Baoji Central Hospital, Baoji, China. .

Abstract:  Objective: Prostate cancer is one of the most common malignant tumors. Recently, the role of long noncoding RNAs (lncRNAs) in tumor progression has been well concerned by numerous researchers. In this research, lncRNA TTN-AS1 was studied to identify its biological function in the progression of prostate cancer. Patients and methods: Firstly, Real Time-quantitative Polymerase Chain Reaction (RT-qPCR) was conducted to measure TTN-AS1 expression in prostate cancer tissues. Furthermore, in vitro role of TTN-AS1 in regulating prostate cancer cells was assessed. Tumor formation assay was conducted in NOD/SCID mice to explore the in vitro function of TTN-AS1. In addition, the luciferase reporter gene assay was performed to analyze the relationship between TTN-AS1 and miR-1271. Results: TTN-AS1 expression was remarkably higher in prostate cancer samples compared with that of corresponding ones. Moreover, proliferation and migration of prostate cancer cells were inhibited after TTN-AS1 was silenced. MiR-1271 was upregulated after the silence of TTN-AS1. Further mechanism assays showed that miR-1271 was a direct target of TTN-AS1 in prostate cancer. In addition, tumor formation and metastasis abilities were inhibited after in vivo knockdown of TTN-AS1. Conclusions: Our study discovers a potential oncogene in prostate cancer and demonstrates that TTN-AS1 enhances cell proliferation and migration via sponging miR-1271. 

Retracted: Yes

PubPeer comments:
 Figure 9 in this paper appears to be a replication (without attribution) of a figure from a previous publication by the same authors, namely Figure 4 in: - Esmaeli, H., Roshandel, R. (2017). Thermal Model Development for Solar Greenhouse Considering Climate Condition. In: Proceedings of SWC2017 SHC2017 (pp. 1u201312). Freiburg, Germany: International Solar Energy Society. https: doi.org 10.18086 swc.2017.26.04 (hereinafter  the previous study .) The figure describes a validation of the model presented in this study, which seems identical to the validation presented in the previous study: besides the identical figure, both validations reported identical values for  highest temperature difference and  average difference . However, the models described here and in the previous study seem different. For example, the model here does not seem to include the influence of sky temperature while the previous model does; the model here has different U values for day and night while these are not described in the previous one; the model here uses a differential equation for the indoor air temperature while the previous one seems to assume that the air temperature is in steady state (see Eq 1 here vs Eq 19 in the previous study). The only place in this paper where the authors mention their previous work is in the introduction, stating:  It is possible to do scenario analysis as it was presented on the [42,44] studies, but they do not provide the generalize tool to compare different scenarios for regionalization purposes. (44 is a reference to the previous study). Hopefully the authors could clarify the following issues: 1. Are the models presented here and in the previous study identical? 2. If they are identical, why is this not mentioned here, how can the seeming discrepancies between the models be explained, and what are the tools developed here that were not presented in the previous study? 3. If the two models are different, why do they seem to give identical outputs? ,