Retracted : Effects of FOSL1 silencing on osteosarcoma cell proliferation, invasion and migration through the ERK/AP‐1 signaling pathway
Authors: Xingyu Zhao
Yu Han
Yutong Sui
Yifu Sun
Jianguo Liu
Journal: Journal of Cellular Physiology (2019)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: Fos-like antigen-1 (FOSL1); apoptosis; extracellular-signal-regulated kinase (ERK)/activator protein-1 (AP-1) signaling pathway; gene silencing; invasion
Affiliations: Joint Surgery Department, No.1 Hospital of Jilin University, Changchun, China., Jilin Provincial Key Laboratory for Molecular Biology of Special Economic Animals, Institute of Special Animal and Plant Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.

Abstract:  Osteosarcoma (OS), as the most frequent primary malignancy of bone, is characterized by the presence of malignant mesenchymal cells. In the current study, our aim was to explore the possible effects Fos-like antigen-1 (FOSL1) had on the silencing regarding OS cell proliferation, invasion, and migration through the activation of the extracellular-signal-regulated kinase (ERK)/activator protein-1 (AP-1) signaling pathway. After the collection of OS on top of already having the adjacent normal tissue samples, the protein positive expression rate of FOSL1 was then measured by implementing the use of immunohistochemistry and discovered that FOSL1 was robustly expressed in OS. Later, to better grasp the impact FOSL1 projects on OS and its underlying mechanism, we determined the OS related genes as well as the ERK/AP-1 signaling pathway related genes expression by using a reverse-transcription quantitative polymerase chain reaction and western blot assay techniques. The results of the aforementioned two experiments revealed that the FOSL1 depletion had downregulated the expression of OS related genes by simultaneously downregulating the ERK/AP-1 signaling pathway. Moreover, cell proliferation, cycle, apoptosis, invasion, and migration of FOS1 were all tested by using a cell counting kit-8 assay, flow cytometry, Transwell assay, and scratch test, and these results presented that silencing of the FOSL1 gene inhibited OS cell proliferation, invasion, and migration. Our findings revealed a novel mechanism by which FOSL1 depletion played a significantly negative role in the OS progression through the regulation of the ERK/AP-1 signaling pathway. Functional suppression of FOSL1 might be a future therapeutic strategy regarding OS. 

Retracted: Yes

PubPeer comments:
 **THE FAILURE OF BLOBOLOGY: FMRI Misinterpretation, maleficence and muddleu2014it s the data structure not the signal!** Botvinik-Nezer et al, (2020) just published a remarkable paper on something neuroimaging researchers already knew: Interpretations of fMRI results depend on decisions in the course of data workflow that can vary widely among researchers. In this study using the identical data set (Botvinik-Nezer et al, 2019), and common preprocessing software, 70 independent groups of researchers produced surprisingly little agreement on 9 binary hypotheses about regions of the brain activated during a decision task. As the paper unfolds, one of main culprits at the root of the divergence is something apparently unremarkable and common: the smoothness of the maps (which as we know can result from a number of factors including smoothing, head motion, stat-packages etc..). Although the number of active voxels varied from 0 to 10 of thousands over hypotheses, the overlap between group maps was very low. This primed the researchersu2019 biases and top down visual clustering to identify map outcomes to be most consistent with the ROI hypothesis they may have favored. To be fair to the 70 groups, actually, the measure of agreement is also a source of the divergence across the 9 hypotheses. If we assume, quite reasonably, that the assessment concerns the consistency of the whole hypothesis set, then the research groups had 2^9 possible configurations (512) to choose from. Thus, the probability of any given configuration by chance is (u00bd)^9 or 0.0002, which might also have fairly high variation in the distribution across all hypotheses (some well below and some above u00bd). Surprisingly, the actual hypothesis configuration they did observe (.371*.224*;229*.329*.843*.329*.057*.057*.057= 0.0000003) is well below chance with only one case (#5, .843) above chance. In fact, to be frank, this vector of outcomes is shockingly unlikely, and although apparently further supporting the conclusion of the authors, it s anomalous nature urges one to take a closer scrutiny at the entire exercise. But before we do that, it s worth clearing up at least one grievous misinterpretation of this paper, that in some larger way, fMRI research is somehow fundamentally flawed or just so subjective as to be uninformative about brain structure and its mapping to cognitive and social function. Of course this is not true. Nor, in fact, do the authors imply that the lack of replication across these 70 groups relates to the fMRI signal itself or to MR in general. Their clever demonstration highlights other problems with human brain mapping: in particular, we will argue, the presumed data structure, the lack of model based analysis and the focus on u201creal estate inferenceu201d. Simpsonu2019s Paradox: a well known kind of maleficence can occur due to individual differences in which averaging (smoothing) produces aggregates that misrepresents sub-group or individual differences. It is very likely there is a lack of homogeneity of activity throughout the brain, in that one group of subjects may have relatively sparse activity, and in another activity may be densely distributed. Worse, given the spatial distribution of activity in the brain the topological properties may vary in terms of both the convexity, and the amount of diffusion of activity (as in a Gaussian density or as an otherwise non-central set of spindles). In any case, individual differences in these patterns can lead to u201cmodesu201d which maximum likelihood averaging (or smoothing) can result, therefore in non-representative group-wise clusters. Given different levels of smoothing, at the hands of different research groups, very different densities of activity and locations can emerge. In some cases there may be an easy way to mitigate this diversity (use better and more appropriate clustering methods) and in other cases the lack of a common topology of brain activity would make this challenging. In that case, individual differences are critical to understandu2014not just smooth into an aggregate brain pattern. A second complication is that the authors used a meta-gambling task asking subjects to accept or reject a prospective choice of loss against gain on each trial. Consequently, those subjects with high risk aversion are going to look very different from those with low risk aversion. This could put the u201cgas pedalu201d on increasing individual differences since subjects may accommodate the decision pressures in very different ways across relevant brain networks. Clearly a simple perceptual decision task, showing stimuli such as numbers or colors (with ordering or identification tasks) would not have these problems. Hence, the authorsu2019 observations need to be tempered considerably from the implied generality of this analysis problem, as this variation in interpretation is most likely based on factors increasing individual differences and making the aggregation highly susceptible to Simpsonu2019s paradox. Whatu2019s the alternative? It is well known that the functional nature of an ROI depends on the cohort and context of other ROIs, in effect a network (Reid, et al, 2020). In fact, the problems revealed hear are less likely or not likely at all to arise if the fMRI time series are modeled as a network (although depending on the specific model, similar problems may arise). Afterall, the various hypotheses clearly involve multiple areas, and as any GLM will reveal are a larger set of cohort areas underlying various decision supporting functions (e.g., working memory, risk assessment, reward evaluation and so on). It is clear that the reliability of the fMRI result would increase if modeled as a network or a graph, not just because the data structure requires more weight of evidence, but because, with the right type of graph search, individual differences can be more naturally accommodated. Our fMRI collaboration group (Ramsey et al, 2010; 2011) developed some 15 years ago, a graph search method for combining fMRI time series together across subjects while avoiding Simpsonu2019s paradox-called IMaGES. This method takes as input the raw time series from all subjects ROIs and finds the most likely graph (nodes and edges) that best represents all subjects as estimation is dynamically updated. Instead of averaging it produces a graph that is a high probability compromise from constraint satisfaction over all sets of subject ROI time series taking into account the heterogeneity of the individual differences in subjects and ROI time series. Tangled up with this analysis mess, is the tenacious grip that specific identification of u201creal estateu201d has had on this community from the beginning. In effect, point estimation was considered to be the essential statistical problem and one that would eventually be informed by known ROIs that could be based on either theoretical or anatomical considerations or both. In the 1990s, some 30 years ago, this seemed plausible, now, given the advent of data science and the conventional workflow involving full brain analysis and high-end computing, is a disastrous recipe for malfeasance and muddle. One of the main recommendations of the paper: u201cFinally, publicly sharing data and analysis code should become common practice, to enable others to run their own analysis with the same data or validate the code used..u201d, is not going to necessarily increase reliability of the result, since the individual differences are left as a random variable in the work flow and is an invitation to further confounding good science with bad science, good methods with bad methods. Rather, we propose, instead, it s time to move on, abandon the obsession with point estimates and embrace the ubiquitous networks in almost every neuroimaging study which will organically increase not only the reliability of the results, but their validity. --Stephen Josu00e9 Hanson PPS. On a second read, the PIs agreement table is predicated on Neyman-Pearson Null Hypothesis testing logic. If we simply allow the PIs to reject the Alternative (whatever they think that might be) the agreement is now 80%, suggesting that there isn t really that much variability after all. Bibliography Botvinik-Nezer, R., Holzmeister, F., Camerer, C.F. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84u201388 (2020). Botvinik-Nezer, R. et al. fMRI data of mixed gambles from the Neuroimaging Analysis Replication and Prediction Study. Sci. Data 6, 106 (2019). Reid, A.T., Headley, D.B., Mill, R.D. et al. Advancing functional connectivity research from association to causation. Nat Neurosci 22, 1751u20131760 (2019). Ramsey, J. D. et al. Six problems for causal inference from fMRI. Neuroimage 49, 1545u20131558 (2010). , 
 Dear Dr. Hanson, Thank you for taking the time to read and comment on our paper. We are happy for the scientific discussions our paper initiated and would like to briefly respond to your comments: 1. The aim of our study was to evaluate the variability of the commonly used univariate approach and its effect on fMRI results. It is not at all clear to us that modeling the data as a network or graph, as opposed to voxelwise analysis, would necessarily reduce the effects of analytic variability, given that there are many different methods for graphical analysis just as there are many different methods for voxelwise analysis. We invite you to perform a similar study to empirically assess the variability and effect on outcomes with your proposed methods. 2. We want to emphasize again that we never claimed that fMRI is fundamentally flawed. See the last paragraph in the discussion section of our paper: u201cOur findings emphasize the urgent need to develop new practices and tools to overcome the challenge of variability across analysis pipelines and its effect on analytical results. Nonetheless, we maintain that fMRI can provide reliable answers to scientific questions, as strongly demonstrated in the meta-analytical results across teams along with numerous large-scale studies in the literature and replication of many findings using fMRI. Moreover, although the present investigation was limited to the analysis of a single fMRI dataset, it seems highly likely that similar variability will be present for other fields of research in which the data are high dimensional and the analysis workflows are complex and varied.u201d 3. Interpreting the likelihood of the observed outcomes only makes sense if we know the ground truth of the hypotheses, which we donu2019t. It may well be the case that 8 of the 9 hypotheses are in fact false, in which case the observed data would be reasonably likely. In addition, you did not take into account the lack of independence of our outcomes. 4. Regarding your claims about heterogeneity in the data, these are empirical claims that may or may not be true. However, it is not clear to us how they relate to the fundamental point of the paper, which regards the effects of analytic variability holding the data constant. 5. Regarding the complexity of the task and hypotheses, we agree that the outcome might have been different had a different task or hypotheses been used, as we state in the Supplementary Discussion. We stand by the use of a relatively complex task, as the majority of task fMRI studies in the literature today use similarly complex tasks. Sincerely, Rotem Botvinik-Nezer Thomas E. Nichols Russell A. Poldrack Tom Schonberg ,