Up-regulation of long noncoding RNA FALEC predicts poor prognosis and promotes melanoma cell proliferation through epigenetically silencing p21
Authors: Jianfang Sun
Yiqun Jiang
Nana Ni
Hao Song
Xiaopo Wang
Xiulian Xu
Journal: Biomedicine & Pharmacotherapy (2017)
Discipline: PHARMACOLOGY & TOXICOLOGY
Keywords: EZH2; FALEC; H3K27me3; Melanoma; p21.
Affiliations: Department of Pathology, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China; Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Nanjing, 210042, China., Department of Pathology, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China., Department of Pathology, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, 210042, China. Electronic address: .

Abstract:  Accumulating evidences have suggested that focally amplified lncRNA on chromosome 1 (FALEC) serves as an oncogenic long non-coding RNA (lncRNA) and has been identified to be dysregulated in various tumors. However, the expression, clinical values, and biological function of FALEC in melanoma are still unknown. In this study we detected the expression level of FALEC in tumor tissues and cell lines and measured the prognostic value of FALEC for melanoma patients and the biological effects of FALEC on melanoma cell proliferation, cell cycle, and apoptosis. Our results indicated that FALEC was more highly expressed in melanoma tissues and cell lines than in non-neoplastic nevi tissues and normal cell lines. Moreover, functional assays showed that silenced FALEC suppressed the proliferation of melanoma cells, resulted in cell cycle arrest, and induced apoptosis. Mechanically, we discovered that FALEC boosted melanoma progression via epigenetically repressing p21 through recruiting EZH2 to the promoter of p21. Generally, our results suggested that FALEC acted as an oncogene in melanoma and had the potential to be a prognostic biomarker and therapeutic target for melanoma. 

Retracted: No

PubPeer comments:
 Papale and colleagues apply variance partitioning to a searchlight representational similarity analysis in order to localize unique and shared contributions of five visual models to cortical object representation. Three models capture shape according to silhouette, curvature, and medial-axis u201cskeletalu201d structure, and models of inked area and category identity are included as well. I was invited to review this paper after an initial round of review, and have (private) access to the initial reviews and the authorsu2019 response. The authors seem to have substantially improved the manuscript in response to the first round of review, and have also added several helpful supplementary figures. The manuscript is already at a relatively high level. My central comments address a couple issues of statistical interpretation that should be made more explicit in the manuscript. I also offer a handful of wording suggestions to improve clarity. *Major comments:* The authors should be careful about their use of the term u201corthogonal.u201d As far as I understand, u201cuniqueu201d contributions recovered by commonality analysis are not strictly orthogonal in the statistical sense (i.e. uncorrelated; see e.g. Creager, 1971). I think using the term u201corthogonalu201d more loosely when thinking conceptually about the u201cuniqueu201d and u201csharedu201d variance accounted for by each model is probably fineu2014but I think it would be useful to include a sentence with the caveat that these unique portions of variance are not strictly orthogonal in a statistical sense. (The authors can correct me if Iu2019m misunderstanding the statistics here.) In general, I think this variance-partitioning (or commonality analysis) approach to RSA (as in e.g. Groen et al., 2012, 2018; Ramakrishnan et al., 2014; Greene et al., 2016; Hebart et al., 2018) is very fruitful, and I expect weu2019ll see more of it in the future. The central analyses apply variance partitioning to the group-average RDMs (and there is some precedent for this in the literature, e.g. Groen et al., 2018). However, it should be made clear that this treats subject (and stimulus) as a fixed effect and does not license statistical inference to the population. Although fixed-effects analyses are somewhat atypical, I think itu2019s fine as long as authors make sure that their interpretations match the analysis and state explicitly in the manuscript that this is a fixed-effects analysis so that readers correctly interpret the results. (Alternatively, the authors could perform subject-level commonality analysis, yielding R-squared values per subject which could be submitted to a statistical test for population inferenceu2014I suspect this would yield qualitatively similar results.) The supplementary figures are present in my (private) copy of the response to reviews and on [Zenodo] , but I donu2019t see any reference to them in the actual manuscript text (on my private copy from the journal or on bioRxiv). Maybe this is just a versioning issue? In any case, I think these figures are very useful, and the authors should make sure they are adequately described (the current captions are very brief) and provide pointers to them in the main text. Iu2019m trying to understand the reasoning behind the category identity RDM with relation to the stimulus set. The object identity RDM doesnu2019t really take into account semantic relationships between categories (e.g. animacy, real-world size), but does group different exemplars (sometimes with different orientations) into the same category. Importantly, this RDM demarcates the shared category membership between visually similar yet different stimuli, e.g. fish, teapot, and u201cfish-shaped teapot.u201d The motivation for this relies heavily on the experimental logic behind using a stimulus set like thisu2014I had a hard time understanding it without looking at the actual stimuli in Figure S7. I think readers could benefit from a bit more explanation of the structure (e.g. exemplars per category, crucial for understanding the rows columns of the RDMs) and reasoning behind the stimulus set and what it entails for the identity model. In certain parts of the manuscript (e.g. Figure 4), the authors make fairly specific anatomical claims about topographic cortical organization. However, all of the analyses are performed using searchlights where the result for any given searchlight is mapped to the center voxel of that searchlight, which introduces some unavoidable imprecision in localization. These are 6 mm radius searchlights with 2 mm isotropic voxels yields (probably ~120 voxels per searchlight depending on the sphere implementation). So, for example, values for searchlight centers near the collateral sulcus will aggregate information from voxels elsewhere, potentially including voxels nearer to the occipitotemporal sulcus. This caveat should be noted in the manuscript. I would encourage the authors to make the fMRI data and stimuli publicly available in the future (e.g. on OpenNeuro) so that other groups can test different models and different analysis techniques against this dataset. For example, it would be straightforward and interesting to apply the RSA-based partitioning approach within predefined ROIs (e.g. Wang et al., 2015) rather than searchlights; and surely there are many other object recognition and shape models that may provide additional insights. Ideally, the code used for this project should also be made publicly available (e.g. on GitHub). *Minor comments:* News Noteworthy: I would be careful with language like u201cWhich shape description is encoded in our brain and thus really mirrors the way we perceive shapes?u201d Seems like there may be many intermediate representations of shape in the brain that are not very directly related to how we psychologically perceive shape (except perhaps under peculiar task demands). Page 3, line 45: u201cmodulating its radialu201d u201cmodulating radialu201d Page 3, line 45: see u201cNews Noteworthyu201d comment; the u201ctrulyu201d is a bit grating Page 3, line 62: u201cattendedu201d u201cviewedu201d (since attention was presumably directed toward the fixation task) Figure 1 caption, E: u201ccontrolu201d u201ccontrolsu201d Functional MRI data acquisition: Make sure youu2019re not missing any u201cmandatoryu201d acquisition parameters as outlined in the COBIDAS report: http: www.humanbrainmapping.org files 2016 COBIDASreport.pdf Page 9, line 172: u201c3dtshiftu201d u201c3dTshiftu201d Page 9, line 177: I donu2019t understand what you mean by u201clinear (FLIRT) and nonlinear registrationt(FNIRT) to MNI152 standard space.u201d Do you analyze the results of both linearly and nonlinearly normalized data? Or is there a linear alignment step followed by a nonlinear alignment step? Or do you mean the anatomical is linearly aligned to the functional images, and also nonlinearly aligned to the MNI template? (This might just be my ignorance of FSL pipelines) Page 10, line 193: u201cPearsonu2019s rhou201d should be u201cPearsonu2019s ru201d because youu2019re using the sample statistic r (rho is typically reserved for the population statistic) Page 11, line 223: I donu2019t understand u201cpartial correlation coefficientu201d here in the context of commonality analysis. Are you actually computing a partial correlation? Or are you converting from the unique and shared R-squared values from the commonality analysis? Page 11, line 227: What is randomized in the TFCE analysis? Is it flipping the sign on the regression coefficients? TFCE is providing nonparametric cluster-level inference, but what does u201cminimum cluster size = 10 voxelsu201d mean? Is this an arbitrary cluster-extent threshold imposed after TFCE to clean up isolated u201csignificantu201d voxels here and there? Make sure these things are explicitly stated. Page 12, line 239: I might begin this sentence with something like u201cTo evaluate whether orthogonality varies from posterior to anterior visual areas, u2026u201d just so the reader immediately understands why youu2019re talking about XY slices here. Page 13, line 261: The u201ccollinearityu201d observed here is a function of both the models and this particular stimulus set (which may or may not be u201crepresentativeu201d of objects in the world) Page 13, line 269: u201cmildlyu201d seems like an odd word choice Page 21, line 390: reword u201cassessedu201d Page 21, line 392: u201cindependentlyu201d u201cuniquelyu201d Page 23, line 438: reword u201cWhile this may apparently result in contrastu201d *References:* Creager, J. A. (1971). Orthogonal and nonorthogonal methods for partitioning regression variance. *American Educational Research Journal*, *8*(4), 671u2013676. Greene, M. R., Baldassano, C., Esteva, A., Beck, D. M., Fei-Fei, L. (2016). Visual scenes are categorized by function. *Journal of Experimental Psychology: General*, *145*(1), 82u201394. Groen, I. I., Ghebreab, S., Lamme, V. A., Scholte, H. S. (2012). Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories. *PLoS Computational Biology*, *8*(10), e1002726. Groen, I. I., Greene, M. R., Baldassano, C., Fei-Fei, L., Beck, D. M., Baker, C. I. (2018). Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior. *eLife*, *7*, e32962. Hebart, M. N., Bankson, B. B., Harel, A., Baker, C. I., Cichy, R. M. (2018). The representational dynamics of task and object processing in humans. *eLife*, *7*, e32816. Ramakrishnan, K., Scholte, H. S., Groen, I. I., Smeulders, A. W., Ghebreab, S. (2015). Visual dictionaries as intermediate features in the human brain. *Frontiers in Computational Neuroscience*, *8*, 168. Wang, L., Mruczek, R. E., Arcaro, M. J., Kastner, S. (2015). Probabilistic maps of visual topography in human cortex. *Cerebral Cortex*, *25*(10), 3911u20133931. , 
 #1 Dear Dr. Nastase, We are thankful for the time you spent reviewing our work. Our manuscript has greatly benefited from your feedback as well as from the discussion generated by your tweet referring to this open review. I m pasting below our response to your comments, copied from the rebuttal letter that we just submitted and (hopefully) you will receive soon. On behalf of all authors, Paolo Papale --- Papale and colleagues apply variance partitioning to a searchlight representational similarity analysis in order to localize unique and shared contributions of five visual models to cortical object representation. Three models capture shape according to silhouette, curvature, and medial-axis  skeletal structure, and models of inked area and category identity are included as well. I was invited to review this paper after an initial round of review, and have (private) access to the initial reviews and the authors response. The authors seem to have substantially improved the manuscript in response to the first round of review, and have also added several helpful supplementary figures. The manuscript is already at a relatively high level. My central comments address a couple issues of statistical interpretation that should be made more explicit in the manuscript. I also offer a handful of wording suggestions to improve clarity. We thank Dr. Nastase for appreciating the improvements and the overall quality of our work. We provide below a detailed response to his comments. Major comments: The authors should be careful about their use of the term  orthogonal. As far as I understand,  unique contributions recovered by commonality analysis are not strictly orthogonal in the statistical sense (i.e. uncorrelated; see e.g. Creager, 1971). I think using the term  orthogonal more loosely when thinking conceptually about the  unique and  shared variance accounted for by each model is probably fine-but I think it would be useful to include a sentence with the caveat that these unique portions of variance are not strictly orthogonal in a statistical sense. (The authors can correct me if I m misunderstanding the statistics here.) In general, I think this variance-partitioning (or commonality analysis) approach to RSA (as in e.g. Groen et al., 2012, 2018; Ramakrishnan et al., 2014; Greene et al., 2016; Hebart et al., 2018) is very fruitful, and I expect we ll see more of it in the future. We thank the Reviewer for pointing out this issue, his interpretation is indeed correct. We agree that a sentence is required and we added it in the revised Methods: *u201cIt should be noted that the so obtained orthogonal unique partitions are not strictly uncorrelated from a statistical viewpoint (Creager, 1971). However, referring to this procedure as orthogonalization, even though not fully accurate, yet makes its core philosophy clearer and easier to understand than the term residualisation that would be more statistically correct.u201d* The central analyses apply variance partitioning to the group-average RDMs (and there is some precedent for this in the literature, e.g. Groen et al., 2018). However, it should be made clear that this treats subject (and stimulus) as a fixed effect and does not license statistical inference to the population. Although fixed-effects analyses are somewhat atypical, I think it s fine as long as authors make sure that their interpretations match the analysis and state explicitly in the manuscript that this is a fixed-effects analysis so that readers correctly interpret the results. (Alternatively, the authors could perform subject-level commonality analysis, yielding R2 values per subject which could be submitted to a statistical test for population inference-I suspect this would yield qualitatively similar results.) While we agree with the Revieweru2019s premise, this is not the case for the actual study. The suggested procedure is exactly what our results are based upon: we performed the searchlight procedure (searchlight encoding + variance partitioning) at the subject level (see Supplementary figures 1-4), and afterwards we performed a group-level one-sample test using FSLu2019s randomise. However, we acknowledge that this misunderstanding is probably due to lack of clarity on our side. Following also suggestions from Reviewer #1, we now further clarify this whole procedure in the revised Methods, by stressing when analyses were performed u201cin each subjectu201d and dividing the text into more sub-paragraphs, as u201csingle-subject encodingu201d and u201cgroup-level testu201d. The supplementary figures are present in my (private) copy of the response to reviews and on Zenodo, but I don t see any reference to them in the actual manuscript text (on my private copy from the journal or on bioRxiv). Maybe this is just a versioning issue? In any case, I think these figures are very useful, and the authors should make sure they are adequately described (the current captions are very brief) and provide pointers to them in the main text. This is a mistake on our side. We apologize for that. The link to the supplementary information is now correctly reported in the revised Methods. Also, we now refer to the supplementary figures in the revised Results: *u201cboth the effect sizes and spatial patterns are consistent across subjects (Figures S1-5)u201d u201cneither ventral nor dorsal streams alone are responsible for the overall effect (Figure S6).u201d* And in the revised Methods: *u201cResults from four representative subjects are shown in Figures S1-4, while the similarity between the fittings of the full-model across subjects is shown in Figure S5.u201d* I m trying to understand the reasoning behind the category identity RDM with relation to the stimulus set. The object identity RDM doesn t really take into account semantic relationships between categories (e.g. animacy, real-world size), but does group different exemplars (sometimes with different orientations) into the same category. Importantly, this RDM demarcates the shared category membership between visually similar yet different stimuli, e.g. fish, teapot, and  fish-shaped teapot. The motivation for this relies heavily on the experimental logic behind using a stimulus set like this-I had a hard time understanding it without looking at the actual stimuli in Figure S7. I think readers could benefit from a bit more explanation of the structure (e.g. exemplars per category, crucial for understanding the rows columns of the RDMs) and reasoning behind the stimulus set and what it entails for the identity model. Following similar comments from Reviewer #1, we acknowledge that this is indeed a critical aspect. We now present the stimuli in the revised Figure 1 and we devote more attention to their description. It should be noted, however, that we refer to u201cobject identityu201d, and not to u201ccategoryu201d as this was intended as a high-level visual model, not a semantic one. We agree that merging two semantically different objects into a single exemplar would be problematic to model using a semantic perspective and would likely need an independent behavioral characterization (see for instance: Handjaras et al., Neuroimage, 2016). However, from a purely visual perspective, perceiving the overlapping identity of two different objects merged into a hybrid is immediate and unambiguous. In certain parts of the manuscript (e.g. Figure 4), the authors make fairly specific anatomical claims about topographic cortical organization. However, all of the analyses are performed using searchlights where the result for any given searchlight is mapped to the center voxel of that searchlight, which introduces some unavoidable imprecision in localization. These are 6 mm radius searchlights with 2 mm isotropic voxels yields (probably ~120 voxels per searchlight depending on the sphere implementation). So, for example, values for searchlight centers near the collateral sulcus will aggregate information from voxels elsewhere, potentially including voxels nearer to the occipitotemporal sulcus. This caveat should be noted in the manuscript. We thank the Reviewer for this observation. This is true and worth a note in the u201climitationsu201d section of the revised Discussion: *u201cIn addition, the searchlight procedure introduces some unavoidable imprecision in localization (but see: Lettieri et al. 2019 for an analytical exploration of this issue), thus further studies using the same shape descriptions might found slightly different locations as those found in this study.u201d* Also, concerning the anatomical localization of our results, it is worth mentioning a work that just got published, showing a causal role of right OTC in visual agnosia (Freud Behrmann, 2020). We now mention this work in the revised Discussion: *u201cOur result favors the hypothesis of a key role of right OTC in coding object shape. In line with our view, recent neuropsychological evidence found visual agnosia in a subject with a cortical lesion circumscribed to right OTC. Moreover, this focal right OTC lesion affected shape selectivity across the whole visual cortex, leading to large-scale alterations that were stable over time (Freud Behrmann, 2020).u201d* I would encourage the authors to make the fMRI data and stimuli publicly available in the future (e.g., on OpenNeuro) so that other groups can test different models and different analysis techniques against this dataset. For example, it would be straightforward and interesting to apply the RSA-based partitioning approach within predefined ROIs (e.g. Wang et al., 2015) rather than searchlights; and surely there are many other object recognition and shape models that may provide additional insights. Ideally, the code used for this project should also be made publicly available (e.g. on GitHub). The custom Matlab code is now shared on GitHub. However, data sharing is particularly challenging due to legal and ethical issues. The data were acquired well before the enforcement of the most recent EU privacy laws (GDPR) and we did not explicitly mention data sharing in the consent form. As a result, we cannot share them publicly on a platform such as OpenNeuro. However, according to GDPR guidelines we can publicly store the preprocessed data for each subject under restricted access (on Zenodo), and we will make them accessible on request, after signing and submitting a data user agreement. Both code and data availability are now mentioned in the revised Methods: *u201cCustom Matlab code is available on GitHub: https: github.com PPthe2nd ShapeVar. Preprocessed data for all subjects are publicly available on Zenodo: [link will be available upon acceptance]. Access to the data is possible upon submission of a Data User Agreement, available along with supplementary information on Zenodo: https: doi.org 10.5281 zenodo.4003861.u201d* Minor comments: News Noteworthy: I would be careful with language like  Which shape description is encoded in our brain and thus really mirrors the way we perceive shapes? Seems like there may be many intermediate representations of shape in the brain that are not very directly related to how we psychologically perceive shape (except perhaps under peculiar task demands). We agree that the actual statement might sound as not appropriate and thus we modified it as follows: *u201cWhich shape description better describes our brain responses while we passively perceive objects? * Functional MRI data acquisition: Make sure you re not missing any  mandatory acquisition parameters as outlined in the COBIDAS report: http: www.humanbrainmapping.org files 2016 COBIDASreport.pdf We thank the Reviewer for this suggestion. The COBIDAS report was used as a guide for reporting acquisition and analysis parameters when we drafted the manuscript, and we checked again throughout the Methods section, to ensure that no important information is missing. Page 9, line 177: I don t understand what you mean by  linear (FLIRT) and nonlinear registration (FNIRT) to MNI152 standard space. Do you analyze the results of both linearly and nonlinearly normalized data? Or is there a linear alignment step followed by a nonlinear alignment step? Or do you mean the anatomical is linearly aligned to the functional images, and also nonlinearly aligned to the MNI template? (This might just be my ignorance of FSL pipelines) In this pipeline, linear registration with FLIRT was performed as a first step, followed by a nonlinear optimization with FNIRT. We applied this procedure to the anatomical images, and then used the same matrices and nonlinear parameters (applied sequentially) to warp to the MNI space the functional coefficients as well. Page 11, line 223: I don t understand  partial correlation coefficient here in the context of commonality analysis. Are you actually computing a partial correlation? Or are you converting from the unique and shared R-squared values from the commonality analysis? We are indeed converting the R-squared values following the procedure of de Heer et al., JNeurosci 2017. We clarified this in the Methods: *u201cAfter applying variance partitioning, the obtained R2 for each component of unique and shared variance in each subject were z-scored, turned in the partial correlation coefficient (de Heer et al. 2017)u201d* Page 11, line 227: What is randomized in the TFCE analysis? Is it flipping the sign on the regression coefficients? TFCE is providing nonparametric cluster-level inference, but what does  minimum cluster size = 10 voxels mean? Is this an arbitrary cluster-extent threshold imposed after TFCE to clean up isolated  significant voxels here and there? Make sure these things are explicitly stated. This is all correct: sign is randomized and cluster size is arbitrary. We apologize for not making it more explicit. We clarified this in the Methods: *u201cGroup-level test For each model, threshold free cluster enhancement (TFCE: Smith and Nichols 2009) was used to detect group-level clusters significantly explained by the corresponding unique variance component (5000 randomizations of the sign with 6mm variance smoothing, as implemented in FSLu2019s randomise: www.fmrib.ox.ac.uk fsl randomise). Statistical maps were then thresholded at one-tailed p 0.05, corrected for multiple comparisons across gray matter voxels and finally a further arbitrary cluster size threshold of 10 voxels was applied (Figure 2).u201d* ,