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Recent evidence suggests that numerous long non‑coding RNAs (lncRNAs) are dysregulated in cancer, and have critical roles in tumour development and progression. The present study investigated the ghrelin receptor antisense lncRNA growth hormone secretagogue receptor opposite strand (GHSROS) in breast cancer. Reverse transcription‑quantitative polymerase chain reaction revealed that GHSROS expression was significantly upregulated in breast tumour tissues compared with normal breast tissue. Induced overexpression of GHSROS in the MDA‑MB‑231 breast cancer cell line significantly increased cell migration in vitro, without affecting cell proliferation, a finding similar to our previous study on lung cancer cell lines. Microarray analysis revealed a significant repression of a small cluster of major histocompatibility class II genes and enrichment of immune response pathways; this phenomenon may allow tumour cells to better evade the immune system. Ectopic overexpression of GHSROS in the MDA‑MB‑231 cell line significantly increased orthotopic xenograft growth in mice, suggesting that in vitro culture does not fully capture the function of this lncRNA. This study demonstrated that GHSROS may serve a relevant role in breast cancer. Further studies are warranted to explore the function and therapeutic potential of this lncRNA in breast cancer progression.

Scratch assays are difficult to reproduce. Here we identify a previously overlooked source of variability which could partially explain this difficulty. We analyse a suite of scratch assays in which we vary the initial degree of confluence (initial cell density). Our results indicate that the rate of re-colonisation is very sensitive to the initial density. To quantify the relative roles of cell migration and proliferation, we calibrate the solution of the Fisher-Kolmogorov model to cell density profiles to provide estimates of the cell diffusivity, D, and the cell proliferation rate, λ. This procedure indicates that the estimates of D and λ are very sensitive to the initial density. This dependence suggests that the Fisher-Kolmogorov model does not accurately represent the details of the collective cell spreading process, since this model assumes that D and λ are constants that ought to be independent of the initial density. Since higher initial cell density leads to enhanced spreading, we also calibrate the solution of the Porous-Fisher model to the data as this model assumes that the cell flux is an increasing function of the cell density. Estimates of D and λ associated with the Porous-Fisher model are less sensitive to the initial density, suggesting that the Porous-Fisher model provides a better description of the experiments., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Background: Standard methods for quantifying IncuCyte ZOOM(™) assays involve measurements that quantify how rapidly the initially-vacant area becomes re-colonised with cells as a function of time. Unfortunately, these measurements give no insight into the details of the cellular-level mechanisms acting to close the initially-vacant area. We provide an alternative method enabling us to quantify the role of cell motility and cell proliferation separately. To achieve this we calibrate standard data available from IncuCyte ZOOM(™) images to the solution of the Fisher-Kolmogorov model., Results: The Fisher-Kolmogorov model is a reaction-diffusion equation that has been used to describe collective cell spreading driven by cell migration, characterised by a cell diffusivity, D, and carrying capacity limited proliferation with proliferation rate, λ, and carrying capacity density, K. By analysing temporal changes in cell density in several subregions located well-behind the initial position of the leading edge we estimate λ and K. Given these estimates, we then apply automatic leading edge detection algorithms to the images produced by the IncuCyte ZOOM(™) assay and match this data with a numerical solution of the Fisher-Kolmogorov equation to provide an estimate of D. We demonstrate this method by applying it to interpret a suite of IncuCyte ZOOM(™) assays using PC-3 prostate cancer cells and obtain estimates of D, λ and K. Comparing estimates of D, λ and K for a control assay with estimates of D, λ and K for assays where epidermal growth factor (EGF) is applied in varying concentrations confirms that EGF enhances the rate of scratch closure and that this stimulation is driven by an increase in D and λ, whereas K is relatively unaffected by EGF., Conclusions: Our approach for estimating D, λ and K from an IncuCyte ZOOM(™) assay provides more detail about cellular-level behaviour than standard methods for analysing these assays. In particular, our approach can be used to quantify the balance of cell migration and cell proliferation and, as we demonstrate, allow us to quantify how the addition of growth factors affects these processes individually.