Curcumin inhibits the growth via Wnt/β-catenin pathway in non-small-cell lung cancer cells
Authors: Y Wang
J-Y Wang
X Wang
B Liang
X-J Wang
B-Z Zheng
Journal: European review for medical and pharmacological sciences (2018)
Discipline: PHARMACOLOGY & TOXICOLOGY
Keywords: lung cancer; curcumin; NSCLC; apoptosis; proliferation
Affiliations: Department of Radiotherapy, Shanxi Province Tumor Hospital, Taiyuan, China. .

Abstract:  Objective: In recent decades, the death rate from lung cancer appears to be an increasing yearly trend, particularly for non-small-cell lung cancer (NSCLC). Curcumin is a yellow pigment found in turmeric rhizomes, reported to exhibit various anti-inflammatory, anti-angiogenic, anti-proliferative, and antioxidant properties. Many reports have suggested that curcumin could induce apoptosis in malignant cells, and therefore, has great potential in tumor treatment. However, little is known about the effect of curcumin on NSCLC or its associated mode of action. Therefore, in this study, we explored curcumin's effect on NSCLC and investigated its associated mechanism. Materials and methods: The non-small-cell lung cancer (NSCLC) cell line A549 was cultured and subjected to MTT and clonogenic survival assays to assess cell proliferation. Reactive oxygen species (ROS) levels were measured using a Fluostar Omega Spectrofluorimeter. Superoxide dismutase (SOD) and γ-glutamyl cysteine synthetase (γ-GCS) activity in A549 cells were both determined by a commercial determination kit. Expression levels of p-GSK3β (Ser9), c-Myc, cyclin D1, β-catenin α-tubulin, and proliferating cell nuclear antigen (PCNA) were analyzed by Western blot. Results: Results of the MTT and clonogenic survival assay indicated that curcumin reduced A549 proliferation. ROS levels and SOD and γ-GCS activities were detected. Curcumin decreased intracellular ROS levels and increased SOD and γ-GCS activity. Meanwhile, the ROS inhibitor N-Acetylcysteine (NAC) reversed the decrease in ROS levels and the increase in SOD and γ-GCS activity. These results indicate that oxidative stress is involved in the curcumin-induced reduction of A549 viability. Curcumin also strongly inhibited β-catenin and p-GSK3β (Ser9) protein expression, as well as the expression of downstream cyclin D1 and c-Myc. Similarly, NAC reversed the inhibition of β-catenin and p-GSK3β (Ser9) protein expression, as well as the expression of downstream cyclin D1 and c-Myc. Conclusions: We showed that curcumin inhibits NSCLC proliferation via the Wnt/β-catenin pathway. 

Retracted: No

PubPeer comments:
 The **linguistic hybrid arithmetic averaging operator** defined in Definition 5 is not an averaging operator unless the author has a different definition of u201c**averaging**u201d operators. To be specific, let $$omega_1=2 3 quad omega_2= 1 3$$ $$w_1=3 4 quad w_2= 1 4$$ If $$s_{alpha_1} =s_{alpha_2} =s_{1}$$ then $$ntimes omega_1 s_{alpha_1} = 2times (2 3) s_1 = (4 3)s_1$$ $$ntimes omega_2 s_{alpha_2} = 2times (1 3) s_1 = (2 3)s_1$$ Then, according to Definition 5, $$LHAA_{omega, w} (s_1, s_1) = (3 4)(4 3)s_1 oplus (1 4)(2 3)s_1 = (7 6)s_1$$ The ranking method based on such a notion is not sound. ![file] , 
 #1 Dear Rhipidura Albiventris, In many decision-making scenarios, we need to consider both the importance of each element itself and the importance of each position. Therefore, the LHAA operator in this paper is proposed to consider the weight of the element and the position at the same time in the integration processes of language information. This is very meaningful in actual decision-making problems. However, the integration process for the elements and the integration process for the location obviously cannot be carried out at the same time. Based on what, we need to divide this integration process into two parts. The first part corresponds to the weights of all elements themselves, and the second part corresponds to the weights of the positions. Because of this, we cannot require a two-step integration process to satisfy the nature of a one-step integration process. For the example you gave, firstly, please note that the elements participating in the integration have changed from $s_1$ and $s_1$ to $(4 3)s_1$ and $(2 3)s_1$ after completing the first step of the full integration process. And obviously, $(4 3)s_1 (7 6)s_1 (2 3)s_1$. From this perspective, the statement of  averaging is reasonable. Secondly, as we just mentioned, the integration process given in Definition 5 consists of two steps. If starting from the initial elements participating in the integration process, its properties are indeed not as perfect as the one-step integration processes. But from the perspective of practical application, this will not have a negative impact on the decision-making results obtained by this method. Of course, if you can develop an information integration method which not only has the practical significance of the method given in this article, but also has more perfect properties. It will be a very outstanding job, and we will appreciate it very much. This is also a goal we are pursuing. Thank you for your interest! , 
 #2 As pointed in #1, you and the author of this paper has a very unusual sense of u201c**averaging**.u201d See also https: pubpeer.com publications 8FDC5EEBFA6C6F5D8C356C21AC2196#3 ,