It is well known that histone modifications play an important part in various chromatin-dependent processes such as DNA replication, repair, and transcription. Using computational models to predict gene expression based on histone modifications has been intensively studied. However, the accuracy of the proposed models still has room for improvement, especially in cross-cell lines gene expression prediction. In the work, we proposed a new model TransferChrome to predict gene expression from histone modifications based on deep learning. The model uses a densely connected convolutional network to capture the features of histone modifications data and uses self-attention layers to aggregate global features of the data. For cross-cell lines gene expression prediction, TransferChrome adopts transfer learning to improve prediction accuracy. We trained and tested our model on 56 different cell lines from the REMC database. The experimental results show that our model achieved an average Area Under the Curve (AUC) score of 84.79%. Compared to three state-of-the-art models, TransferChrome improves the prediction performance on most cell lines. The experiments of cross-cell lines gene expression prediction show that TransferChrome performs best and is an efficient model for predicting cross-cell lines gene expression. Understanding the patterns of gene regulation has been one of the major focuses of biological research. A variety of biological factors are thought to be involved in the regulation of gene expression. The regulatory factors usually include transcription factors, cis-regulatory elements, and epigenetic modifications. As a type of epigenetic modifications, histone modification plays an important role in gene expression regulation . NucleosTo date, a variety of computational methods have been used to predict gene expression based on gene regulatory factors. For example, Though the above methods have achieved good performances, there are still room for improvement, and some recently emerging technologies have provided some ways. When models are trained and tested on different cell lines which is knowns as cross-cell lines prediction, the model performance is always compromised. For example, compared to training and testing on the same cell line dataset, the average prediction accuracy of DeepChrome trained on other cell lines is 2.3% lower. Because of the large variety of cell lines, it is difficult to obtain histone modification data and gene expression data for all types of cell lines. Therefore predicting gene expression using models trained on other cell lines is useful and in urgent need.Transfer learning is a machine learning technique in which a model trained on a specific task is reused as part of the training process for another similar task . TransfeIn the paper, we propose a neural network model TransferChrome with self-attention mechanism and transfer learning to predict gene expression based on histone modifications data. TransferChrome uses neural network layers with self-attention mechanism to capture global contextual information of data. In order to correct the data bias of cross-cell lines gene expression prediction, we used transfer learning. The experimental results show that TransferChrome achieved an average Area Under the Curve (AUC) score of 84.79%, which is better than other 3 state-of-the-art similar models. The cross-cell lines prediction experiments also show that TransferChrome outperforms other models.The experimental data comes from the Roadmap Epigenome Project (REMC) , which cn × m matrix x, where n is the number of histone modification marks and m is the number of bins   uses a mxl be the output of the lth densely connected convolutional layer. The input of the lth dense connected convolutional layer contains the outputs of all the previous l−1 layers as Eq. x0, x1, ……xl−1] refers to the concatenation of the feature-maps output from preceding layers. The composite function H concludes a rectified linear unit (RELU) and 1D convolutional layer.Let In TransferChrome, the dense-conv block consists of three dense-connected convolutional layers . The dense-conv block is followed by a convolutional layer  and a max-pooling layer . A dropout layer is added after each convolutional layer and the dropout rate is 0.4. The output of the max-pooling layer is input into a following self-attention layer.x.Regulatory factors at different locations may interact and act on gene expression together. Therefore effective integration of upstream and downstream information in the genome usually leads to better computational results . The TraTransferChrome contains two self-attention layers to capture the long-distance dependence. The function of each self-attention layer is as convq, convk, and convv to calculate a query matrix Q, a key matrix K and a latent variable matrix V, respectively. The number of output channels of convq and convk is half of the number of input channels, and the number of output channels of convv is equal to the number of input channels. Then the self-attention layer calculate data’s attention score matrix QKT by multiplying (matmul) Q and K. Finally, the attention score matrix will be normalized by a softmax function, and multiplied with V. At the end of the feature extraction module, there is a linear layer following the last self-attention layer. The feature extraction module outputs a low-dimensional feature vector. Then the feature vector is inputted into the label classification module and the domain classification module at the same time.Each self-attention layer uses three one-dimensional convolutional layers  λ with λ > 0 and passes it to the preceding layer.The label classification module predicts the gene expression label of the sample, which is the main task of our model. It is a binary classification task with 0 and 1 represent low expression and high expression, respectively. According to The domain classification module predicts whether the sample belongs to the target domain or the source domain. Source domains are the cell lines whose genes have gene expression labels, and the cell lines whose gene have no known gene expression information are called target domains. It is also a binary classification task with 0 and 1, where 0 indicates that the sample is from the target domain and 1 indicates that the sample is from the source domain. In cross-cell lines prediction, we try to extract those features that can not be used to discern the data domain.For model training, we chose cross entropy as the loss function:Gf and θf be the function and the parameters of the feature extraction module, respectively. Let Gd (Gy) and θd (θy) be the function and the parameters of the domain (label) classification module, respectively. For the single cell line gene expression prediction task, the optimization goal of model training is to minimize the loss Ly of the label classification module without considering the domain classification module.Let Ld is the loss function of the domain classification module.For the cross-cell lines gene expression prediction task, we train TransferChrome using the complete dataset from a source domain and a part of the dataset from a target domain to capture transfer features in different cell lines, and aim to minimize the objective function in Eq. θy and θd to minimize the label classification loss Ly and Ld. In the backward propagation, the first layer GRL of the domain classification module reverses the gradient by multiplying a negative number −λ and backward propagates it to the feature extraction module. After the model training, Gf is expected to extract transfer features in different cell lines. In the training process, the learning rate is set to 0.001, momentum is 0.85, and weight decay is 0.001. We set the max training epochs to 200 and adopted early stop strategy.In the training process, stochastic gradient descent (SGD) is used to update In the following single-cell line prediction experiments, each cell line data was partitioned into a training set, a validation set and a test set as DeepChrome . For crohttp://kipoi.org/models/AttentiveChrome/. Because ConvChrome’s code and data are not available, We implement ConvChrome_CNN1D with PyTorch. We used the AUC score as our evaluation metric. Experimental results on 56 cell lines are shown in To evaluate the effectiveness of TransferChrome, we compared it with three state-of-the-art models . DeepChrome  is a conFor the performance comparison in cross-cell lines gene expression prediction, we arbitrarily selected a cell line (E085) as the source domain and each one of other cell lines as the target domain. The results have shown that the average AUC of TransferChrome_uncross trained in a E085 cell line and tested on another dropped by 2.9% compared to those of TransferChrome_origin trained and tested on a same cell line. Similarly, the average AUCs of DeepChrome_uncross and ConvChrome_uncross dropped by 2.6% and 2.9% compared to those of DeepChrome and ConvChrome, respectively. Though TransferChrome_cross did not achieve the same effect as TransferChrome_origin, the average AUC drop is reduced to 1.5%, which showed that using domain classification module indeed improves the performance in cross-cell lines prediction.We carried experiments to see whether dense connectivity pattern and different position encoding functions have obvious impact on the performance of TransferChrome. A total of 9 cell lines out of 56 with worst , median , and best  AUC scores were selected for ablation experiments.α use sinusoidal position encoding (β is the TransferChrome without adding position information. The experimental results are shown in We compared three TransferChrome model variations to discuss the contribution of different position encoding functions. The position encoding function adopted by TransferChrome calculates the relative distance between TSS and bins. TransferChrome_encoding  as the pβ ignores sequence position information, TransferChrome_β performance worser than TransferChrome and TransferChrome_α. Meanwhile, histone modifications which are close to TSS might have more significant effect on gene expression (α.Histone modifications at different positions have different importance for gene expression prediction. Since TransferChrome_pression . Accordipression . TransfeWe also conducted comparative experiments to discuss the contribution of the dense connectivity pattern and convolutional layer kernal numbers. As shown in We proposed a new model called TransferChrome to predict gene expression levels based on histone modifications. TransferChrome uses self-attention mechanism to capture the long-distance dependence, and to learn hidden information features from the histone modifications data. Furthermore, TransferChrome adopts dense connectivity pattern to improve the feature exaction ability of convolutional neural network. Experimental results on the benchmark dataset of 56 cell lines showed that TransferChrome performed better than other 3 similar state-of-the-art models. To improve cross-cell lines gene expression prediction performance, TransferChrome uses transfer learning. Transfer learning makes the model capable of learning common features among different cell lines and reduces the data biases of different cell lines. Our experiments demonstrated that TransferChrome achieved the best accuracy in cross-cell lines gene expression prediction. We believe that it is useful to use transfer learning to improve cross-cell lines prediction accuracy. So far, gene expression prediction methods from histone modification data are mostly based on the five core histone modification marks. In future work, we will use more information from the histone modification data to predict gene expression. We also intend to increase the interpretability of the model in order to analyze the contribution of different histone modification marks on gene expression prediction.


Radiofrequency electromagnetic fields are used for tumor heating as adjunct therapy, but it appears that sufficient temperatures can sometimes not be reached. We therefore aimed to study potential non-temperature-induced anticancer effects when adding amplitude modulation to the radiofrequency waves. We could demonstrate in a colorectal cancer model that radiofrequency electromagnetic fields do have anticancer effects when not being induced by increased temperature that can be further increased by amplitude modulation. Therefore, this treatment could potentially serve as a more effective tumor therapy.Non-temperature-induced effects of radiofrequency electromagnetic fields (RF) have been controversial for decades. Here, we established measurement techniques to prove their existence by investigating energy deposition in tumor cells under RF exposure and upon adding amplitude modulation (AM) (AMRF). Using a preclinical device LabEHY-200 with a novel in vitro applicator, we analyzed the power deposition and system parameters for five human colorectal cancer cell lines and measured the apoptosis rates in vitro and tumor growth inhibition in vivo in comparison to water bath heating. We showed enhanced anticancer effects of RF and AMRF in vitro and in vivo and verified the non-temperature-induced origin of the effects. Furthermore, apoptotic enhancement by AM was correlated with cell membrane stiffness. Our findings not only provide a strategy to significantly enhance non-temperature-induced anticancer cell effects in vitro and in vivo but also provide a perspective for a potentially more effective tumor therapy. Non-temperature-induced effects of radiofrequency electromagnetic fields (EMF) have been controversial for decades. Electromagnetic fields can be applied either as continuous waves (RF) or modified with additional amplitude modulation (AM), denoted as AMRF. In the literature, these effects are also, somewhat imprecisely, referred to as “nonthermal” effects. However, non-temperature-induced effects is a more suitable term, and it denotes the effects associated with the deposition of either thermal or nonthermal/isothermal energy. In the context of this work, we introduce the term of isothermal energy to denote energy absorption of a sample/tissue without resulting in a measurable temperature increase. We follow the thermodynamic term of an isothermal process, where a change in internal energy occurs without a concurrent temperature change. This is typically the case in slow processes where the rate of heat exchange is fast enough to prevent a temperature increase, for example, in cases in which the sample is in contact with a cooling reservoir. A non-temperature-induced effect is considered proven if RF or AMRF exposure leads to an increased physical or biological effect under otherwise identical conditions—in particular, identical temperature in the treated sample/region. However, the characteristics of the effects might change with the temperature.RFs have been used in cancer therapy for decades under the label hyperthermia  to improRecently, non-temperature-induced antitumoral effects have been observed clinically. AMRF either at 13.56 MHz ,8,9 or 2Furthermore, preclinical studies showed various non-temperature-induced anticancer effects in vitro and in vivo applying AMRF at 13.56 MHz . HoweverCalcium fluxes, which were observed in response to AMRF at 147 MHz, with low modulation frequencies around 16 Hz, were considered a key effect ,16. The So far, no reproducible measurement method has been established that can clearly detect differences in the power deposition in cells exposed either to RF or AMRF.There is a lack of irrefutable in vitro and in vivo experiments that compare WB heating with RF or AMRF and document additional RF and/or AMRF-induced anticancer effects. A typical knockout argument is the assertion of hidden thermal effects . Therefore, such confounders must be methodically excluded using a thorough analysis of the experimental setup.We believe that the long-standing apparent lack of clarification on the non-temperature-induced effects of harmonic RF or AMRF has two major reasons:Using a series of physical measurements and in vitro and in vivo experiments, we addressed the above limitations and substantiated the non-temperature-induced anticancer effects of RF and AMRF. We also aimed to decipher the biophysical mechanisms underlying the antitumoral effects and to open up an innovative field for clinical application, particularly for treating cancer.In this study we investigated the behavior of tumor cells in vitro and in vivo when exposed to RF and AMRF versus temperature alone (WB). We analyzed the physical parameters of the sample directly (measurement) or indirectly (system parameters), as well as the biological effectiveness of all treatments. A thorough investigation of the temperature distribution in a heterogeneous in vivo scenario was deciphered using EMF and temperature simulations.0/f . Modulation is applied with a selectable modulation index m (10–100%), where m defines the amplitude of the modulation wave with respect to the amplitude of the carrier wave (Ac). In the frequency spectrum, the (AM) wave is no longer represented by a single peak at the carrier frequency (fc) but also exhibits two side peaks at the modulation frequency (fc ± fM). Each of these peaks contributes to the total power carried by the EM wave:We used the commercially available device LabEHY-200  [From this, the proportion of the AM side band power of the total signal power can be calculated as:We used a novel in vitro applicator B that al0 × εr × A/d was calculated for each single component . A basic network analysis shows that the applicators have a total capacitance C Each applicator can be interpreted as a series connection of capacitances  1640 Medium, Thermo Fisher Scientific, Waltham, MA, USA), and tissues), the electrical parameters of which are listed in 1 and C2 (diss (W) approaches the value of the adjusted power Padj chosen for the treatment/experiment [adj and Pdiss are achieved. The complex impedance Z of the applicator unit depends on L, C, and R and is given by the equationChanges of the absorbed power in the sample represented by a loss resistance R in series with the lossless applicator E change 1 and C2 E compensperiment ,23. Typi1 and C2, power measurements  and temperatures every second and saves to an output file. We analyzed the differences of the mean C1 and C2 between different cell lines and non-modulated RF versus AMRF, assuming that they are indicative of changes in the sample’s impedance, resulting in changes in the deposited power ΔP.During measurements, Lab-EHY200 software automatically logs the internally measured values for the tuning capacitances C2T = 0  [Determining the dissipated power quantified by the specific absorption rate (SAR in W/kg) in the sample is of particular interest and carried out as follows. At this point, it is important to note that the term of dissipated energy in the physical sense directly refers to energy absorption leading to a change in temperature, whereas the total absorbed power can contain isothermal energy in addition to dissipated energy. If the power is switched on at the initial thermal equilibrium, the SAR can be derived from the temperature gradient ΔT/Δt (in °C/min) quantifying the temperature increase ΔT during a given evaluated rising time Δt. The relationship between SAR and ΔT/Δt is derived from the bioheat transfer equation that is analytically solved for ∇librium) ,25. The plicator B. Then, adj = 10 W that delivered about twice as high ΔT/Δt compared to water flow cooling ([diss in order to compensate for slight inaccuracies in tuning < Pdiss > to Padj: (ΔT/Δt)corr.If we apply our evaluation interval of Δt = 60 s and a high w = 200 mL/100 g/min, this doubles SAR with Equation (2) (compared to Equation (1)). This has previously been confirmed by temperature–time measurements without the running water flow system at the same Pcooling . If we corr, as we compared AMRF to unmodulated RF.For each experiment, we determined and recorded changes of temperature gradients (ΔT/Δt)corr from the respective datasets. We selected a reasonable fixed ramp-up time after the power is on for Pdiss to approach Padj (25 s). During the evaluation time Δt (60 s), we determined the slope of the regression line of the temperature–time curve. The length of Δt is a carefully considered tradeoff between statistics and linearity inearity F. We conFor the in vivo studies, we had to carry out a graphically supported evaluation process (determination of the tangent) due to the variable settings and adjustments.In the case of the in vitro applicator, the temperatures in the cell suspension are assumed to be sufficiently homogeneous if the water flow is running, given the small sample size and thickness and the large contact area between the sample and the flowing water. The temperature is directly monitored in the cell chamber and further analyzed B.On the contrary, we expect nonhomogeneous SAR and temperature distributions for the in vivo applicator C,D; therTo determine the electric (E-) field, SAR, and temperature distributions, and particularly to exclude undesired thermal effects in the larger and temperature-destabilized volume of the mice treated in the in vivo applicator D, a real2). The top electrode was implemented as a solid structure due to the high conductivity of all the components, neglecting its composition of multiple thin rods held in place by a conductive textile to accommodate irregular surfaces. The voltage was directly applied to the top electrode, while the bottom electrode was considered grounded (Dirichlet boundary condition). After an initial simulation with U = 100 V, the voltage was adjusted until the absorbed power matched the adjusted power Padj = 1 W chosen for the in vivo experiments. It is important to note that this power level refers to the continuous RF wave with constant amplitude, since implementing amplitude modulation is not supported in our simulation software. Given the power distribution between the carrier wave and the side bands of the modulation signal, this makes our simulation an upper bound approach to the problem. The mouse was approximated by a rectangle of 6 × 3 × 1.2 cm3, consisting of a 200 µm layer of fat enclosing homogeneous muscle tissue. The dimensions were both estimated from pictures taken during the in vivo experiments and adjusted to match the approximate weight of the mouse (here: m = 23 g) for a matching power balance. The overall resolution was set to 0.5 mm isotropic, with a high-resolution box (0.1 mm isotropic) centered underneath the electrode. For model A, a representation of the small tumor shortly after induction, a plane surface of the phantom was implemented. For model B, a sphere with a diameter of 8 mm placed at a depth of 3 mm of the rectangle (representing tumor growth) was used to bulge the surface of the phantom outwards by 1 mm. The superficial fat layer was maintained.For the E-field simulation using the quasistatic solver (13.56 MHz) of the FDTD (Finite Difference Time Domain) algorithm, we implemented the electrodes with the dimensions measured at the in vivo applicator  = 60 × ΔT/Δt (°C/min) holds if the time interval Δt is sufficiently small (Equation (1)) .If high SAR of >1000 W/kg are calculated in a small volume (approximated by a sphere with radius r (mm)), we can apply an analytical solution of the bioheat-transfer equation to quickly estimate the temperature increase :ΔTmax [°According to this equation, even an excessive SAR of 10,000 W/kg focused to a sphere of 1 mm would result in a temperature increase of only 2 °C, which is not sufficient for any relevant thermal effect. However, accurate numerical calculations of temperature distributions are performed to confirm these estimations.diss = 1 W was used as the input. For this simulation, the full geometry, as shown in 2/°C), and the perfusion w (mL/100 g/min), according to previous studies [2/°C) was doubled in comparison to hirsute mice . We generated SAR volume histograms, 2D distributions, and cross-profiles of calculated SAR and temperature datasets. Finally, a comparison of the calculated and measured SAR (via a gradient of temperature increase ΔT/Δt) and the temperatures was performed.For the steady-state temperature simulation, the electric loss density result of the EMF simulation with P studies ,29. The corr(AMRF) < (ΔT/Δt)corr(RF) after correction for Pdiss (according to If we measure different (ΔT/Δt)corr, system parameters) for in vitro and in vivo studies, if either RF or AMRF is applied.Under these conditions, an important aim of the present study was to evaluate the different physical parameters ((ΔT/Δt)2. We prepared cell suspensions with approximately one million cells per milliliter.We used five human colorectal cancer cell lines: HT29 (obtained from a primary tumor), SW480 , LoVo (left supraclavicular metastasis), SW620 (lymph node metastasis), and HCT116, which were originally purchased from the American Type Culture Collection . The frozen cells were grown in RPMI 1640 medium and maintained at 37 °C in a humidified incubator with 5% CO® THERMOMETER m3300 Biomedical Lab Kit by Luxtron, ø = 0.5 mm) was positioned in the center via a provided channel under visual control. We always selected 42 °C as the target temperature for the flow system and an adjusted power Padj of 10 W. In the case of AMRF, we selected 100/f (f ≥ 100 Hz) and a modulation index of 50%. This particular AM is also used in clinical systems, e.g., EHY-2000+  and was utilized in two clinical studies [For the in vitro experiments, two milliliters of cell suspension were injected into the cell chamber of the in vitro applicator B. After  studies ,9. The fcorr, C1, C2, tmeas = 10 min). Four cell lines  were selected for further analysis that showed the highest differences in physical parameters between the medium and cell suspensions and between RF and AMRF exposition.For each of the five cell lines and the RPMI medium, 6 short experiments, i.e., 2 triplicates, were performed per arm (RF versus AMRF) to screen the physical data ((ΔT/Δt)M (Hz) of a membrane sheet with dimensions a × b (µm2) according to an equation derived in [We hypothesized that AM with modulation frequencies of Hz to kHz (audio frequencies) can deposit energy in membranes by the excitation of mechanical vibrations . The stirived in   in comparison to our selected tumor cell lines  at different temperatures (37 °C and 41 °C) by indentation measurements using atomic force microscopy. The cells were probed using Bruker  MLCT-BIO-DC cantilevers with a spring constant of 0.01 N/m that were calibrated using a contact-based method on a JPK-NanoWizardmeas = 65 min: theat = 5 min + ttreat = 60 min). In parallel, controls were run in a water bath at 37 °C and 42 °C for the same time. Apoptosis rates were determined using FACS (fluorescence-activated cell sorting) by measuring the percentage of annexin V-positive cells. A direct comparison was performed by dividing the percentages of RF or AMRF by the mean percentage of the 42 °C water bath.The three cell lines with the lowest stiffness E were selected for further in vitro analysis . We performed 4 triplicates per cell line and arm for the biological endpoint apoptosis  GmbH Berlin-Buch . All animals were maintained in a sterile environment with a daily 12-h light/12-h dark cycle, and sterilized food and water were provided ad libitum. Animals at 6–8 weeks old with a body weight of 22–25 g were used for xenografting. All experiments were carried out in accordance with the 3R  and animal welfare (LAGESO Nummer 001/019), in accordance with the German law and Directive 2010/63/EU.7 HT29 cells in 0.1 mL of phosphate-buffered solution (PBS) was performed to form tumor xenografts. Forty mice were randomly assigned to 4 groups of n = 10 animals. We compared the control group and whole body water bath hyperthermia group (WB) at 40 °C for 35 min with the RF group and AMRF group, for both adjusting a total power of Padj = 1 W and a measurement time of 35 min (tmeas = 35 min: theat = 5 min + ttreat = 30 min). Direct temperature measurements were performed in the tumor center (as precisely as possible), at the skin in contact with the tumor, and in the rectum with the same temperature probes as used for the in vitro experiments. We determined the SAR after the onset of Padj by applying tangents to the temperature time curves using a graphical method and registered Pdiss, C1, and C2 similar to the in vitro studies.Subcutaneous injection at the femoral region with 1 × 10During the induction time of approximately 16 days (series 1), we started with the respective treatments on post-tumor inoculation days 7, 9, and 13. For this, the mice were anesthetized with isoflurane gas and received an injection of 150 mg/kg D-luciferin . We monitored the luciferase-expressing tumor xenograft activity by bioluminescence imaging using the NightOWL LB 981 imaging system . Images were analyzed by WinLight software (Berthold Technologies) and quantified using ImageJ 1.48v. In addition, tolerability and feasibility of the application were documented, i.e., potential skin irritations, treatment discontinuations, and the number of regular sessions. We measured the tumor volume (TV) by a digital caliper on study days 6 (baseline), 16, and 20.On day 20 during the tumor growth period, we started a comparison of RF and AMRF in the so-far-untreated control group. We randomized 5 mice in each group and performed the three treatments on days 20 (after baseline measurements), 23, and 27. Here, we measured the TV on days 20, 23, 30, and 34 and again conducted bioluminescence imaging on days 20, 29, and 34 as the endpoints.The anesthetized mice in the WB, RF, and AMRF groups were sacrificed on day 13 and in the control group on day 34 after the last TV measurement. The tumor samples were prepared for immunohistochemistry (IHC) by staining and evaluation for Ki67, because Ki67 protein (pKi67) expression is associated with the proliferative activity of intrinsic cell populations in tissues, including tumors. In evaluating the IHC results, the relative percentage of the immunopositive cells was assessed in relation to the total number of target cells. We defined a numerical Ki67 tumor score from 0 to 3.0 as follows: 0 (negative no stain), 0.5 (up to 12.5% of the cells show positive immunoreactivity), 1.0 (12.5–25%), 1.5 (25–37.5%), 2.0 (37.5–50%), 2.5 (50–75%), and 3.0 (75–100%).t-tests and presented either as standard boxplots or dot plots additionally showing mean and mean error bars. Significance was defined as p < 0.05 and marked with * .We used the software package GraphPad Prism v.6 for the statistics. Variance analyses between several series of measurements of two groups were performed by multiple We compared RF at 13.56 MHz with AMRF, selecting the pink noise modulation frequency spectrum with a noise density corr (°C/min) after power onset and system parameters C1 and C2.We evaluated and compared three physical parameters in five colorectal cancer cell lines prior to the in vitro and in vivo studies : temperacorr under RF exposure were significantly higher by 15–20% for all cancer cell lines  in comparison to the RPMI medium. When using AMRF, (ΔT/Δt)corr in the medium was slightly (but non-significantly) higher  higher A. If we,1 and C2  in the novel in vitro applicator to conventional water bath (WB) heating at the targeted 42 °C (measured mean of 42.3 °C) for the three selected colorectal cancer cell lines HT29, SW480, and LoVo. The measured temperature excess of 0.5 °C in the WB versus cell chamber A confirmFor HT29 cells B, we foucorr under RF/AMRF behaves similarly for all three cell lines  of hepatocytes and fibroblasts  and the selected cancer cells as a function of the temperature ).To further decipher the reason behind these varied responses of the cell lines to AM with fM of each cell according to Equation (4). A membrane resonance frequency above 100 Hz is predicted for the HT29 cells, which is thus covered by the applied AM spectrum 100/f (f ≥ 100 Hz). For the other cell lines, we obtain fundamental membrane frequencies below 100 Hz. Thus, a stimulation of fundamental membrane resonances using this AM spectrum, which might cause membrane damages, is only expected in HT29 cells (and not in LoVo and SW480 cells). This is in agreement with our in vitro data  on day 12 ; however, the experimental AMRF group showed a significant drop (same settings as the RF group). Note that the in vivo treatments were distinctly less intensive compared to the in vitro studies . NeverThe scores for proliferation marker Ki67 also showed a significant decrease for the AMRF group compared to the WB group , we conducted a second treatment series during the tumor growth period, with larger tumors (diameter 8 mm) starting on day 20 after implantation. We randomized the mice into an RF and AMRF group of n = 5 mice each treated on days 20, 23, and 27 either with RF or AMRF at the same settings as used for series 1. We again found significantly lower Ki67 scores corr for HT29 xenografts treated with the in vivo applicator and found the same significant differences between RF and AMRF as for the in vitro applicator comparing all RF and AMRF measurements  atheter) C.corr values (after power onset) tend to be higher at the surface for the RF group, but the opposite was observed for the AMRF group (skin − (ΔT/Δt)deep were significantly different when comparing RF to AMRF. Such nonhomogeneous temperature distributions  are considered unfavorable indicators both in terms of toxicity and effectiveness.For the first in vivo study , the thermally relevant (ΔT/Δt)RF group B, middlecorr in the AMRF group, which were consistent with our findings in the in vitro applicator.For the second in vivo study (Series 2: RF versus AMRF in the former control group) with larger tumors of 8 mm in diameter in the growth phase, we again found significantly lower values of (ΔT/Δt)max> in the steady state were not reduced and, in particular, were more homogeneously distributed, i.e., showed a smaller difference (ΔT/Δt)skin − (ΔT/Δt)deep , the final mean temperatures <TBoth the in vitro and in vivo data consistently show that, for the same applied power, the thermally relevant dissipated power is lower for AMRF, indicating that portions of the absorbed power are transformed into isothermal energy. The in vivo data show, in addition, that this transformation increases the tolerance and is associated with more homogeneous temperature distributions.In summary, the in vivo study confirmed the in vitro data both physically and biologically. Moreover, the AM-related effects were even more prominent in vivo.The temperatures measured in the in vitro applicator are representative for the entire cell chamber, because this small volume of 2 mL with a 3 mm thickness is entirely in close contact with a highly effective water flow system for temperature regulation.50 in the tumors are consistent with the mean values of our measurements  in the implanted tumors.We identified non-temperature-induced anticancer effects due to RF in three colorectal cancer cell lines in vitro, which were increased by AMRF in one of the cell lines in vitro, using a frequency spectrum for modulation . This could be confirmed in vivo, bearing the potential for rapid clinical translation.r up to several hundred, which can result in increased interaction of the RF with the cancer cells [2+ ions.Our measurements of the physical parameters revealed that colorectal cancer cells absorb up to 20% additional thermal power from a harmonic RF at 13.56 MHz compared to WB heating. This might be explained by the change of the tissue interactions with EM waves in the investigated frequency range. The delta dispersion on the tail of a beta dispersion  around 1er cells . Moreoveer cells ,34. WustIf we use AMRF, this excess of dissipated power from RF is decreasing and presumably converted into isothermal energy during the heating-up phase. Particularly, phase transitions—for example, the transition of the cell membrane from a solid-like to a liquid-like state, eventually leading to cell damage—consume substantial isothermal energy.A demodulation of the AMRF results in the low modulation frequency acting upon the cell membrane and the ion channels thereon . This caA mechanically induced cytotoxicity is expected if a mechanical ground resonance of the whole cell is excited, which we considered as the vibration of a spherical segment as large as possible (estimated as 7 µm diameter for a cell of 10 µm in diameter). We measured the cell membrane stiffness E of the used cell lines by atomic force microscopy and used the results to calculate the potential resonance frequencies of the cells. Our results show that, only for the cell line in which AMRF outperformed the anticancer effects of RF in vitro, the calculated resonance frequency was above 100 Hz, thus being included in the frequency spectrum used for modulation. As this explains the differences between the cell lines, it supports our hypothesis.Since the interactions at the cell membranes are considered as pure E-field effects , the proThese preclinical dosages can be compared with clinically achievable doses. An SAR of 20 W/kg (E = 200 V/m) is realistic in the pelvis and abdomen with capacitive systems . TherefoEven treatments with comparatively low levels of 1 W/kg (E = 45 V/m) for 3 h (on 3 days per week) deliver similar doses of 24,300 V∙min/m per week ,37.The existence of extraordinary modulation frequencies has been proposed by others in the past ,36,37,38Our study verified the existence of non-temperature-induced anticancer effects of RF in three tested cell lines in vitro. Moreover, we could demonstrate a further increase of the non-temperature-dependent anticancer effects due to AMRF in one tested cell line in vitro and in vivo. RF/AMRF promises a locoregional oncological therapy using both temperature effects (hyperthermia) and non-temperature-induced anticancer effects with the potential to serve as a more effective tumor therapy. More research in terms of the choice of amplitude modulation frequency and choice of carrier frequency among other variables is required.


Alternative splicing (AS) has been widely demonstrated in the occurrence and progression of many cancers. Nevertheless, the involvement of cancer-associated splicing factors in the development of esophageal carcinoma (ESCA) remains to be explored.  RNA-Seq data and the corresponding clinical information of the ESCA cohort were downloaded from The Cancer Genome Atlas database. Bioinformatics methods were used to further analyzed the differently expressed AS (DEAS) events and their splicing network. Kaplan–Meier, Cox regression, and unsupervised cluster analyses were used to assess the association between AS events and clinical characteristics of ESCA patients. The splicing factors screened out were verified in vitro at the cellular level.  A total of 50,342 AS events were identified, of which 3,988 were DEAS events and 46 of these were associated with overall survival (OS) of ESCA patients, with a 5-year OS rate of 0.941. By constructing a network of AS events with survival-related splicing factors, the AS factors related to prognosis can be further identified. In vitro experiments and database analysis confirmed that the high expression of hnRNP G in ESCA is related to the high invasion ability of ESCA cells and the poor prognosis of ESCA patients. In contrast, the low expression of fox-2 in esophageal cancer is related to a better prognosis.  ESCA-associated AS factors hnRNP G and Fox-2 are of great value in deciphering the underlying mechanisms of AS in ESCA and providing clues for therapeutic goals for further validation. It is the 6th leading cause of mortality, while ESCA mortality in developing countries rises [P = 0.0095), but in all patients with ESCA, the effective rate is only 13.1% (41/314) [P = 0.29) [According to the 2020 global cancer epidemiology report, esophageal cancer (ESCA) has the 7es rises . Althouges rises . RelatedIn recent years, the emergence of comprehensive studies on the genomics of ESCA, including whole-exome sequencing, gene mutation analysis, DNA methylation profiling, and deregulated pathways, has contributed to a deeper understanding of ESCA . DevelopMany precursor mRNAs are processed to produce only one mature mRNA, which is translated into a corresponding polypeptide, and some can be spliced into mRNAs with different structures. This phenomenon is called alternative splicing (AS). AS increases the use of a limited number of genes and is one of the mechanisms for increasing the diversity of biological proteins in multicellular eukaryotes . In the 2 cell culture incubator. Cells were passaged at intervals of 2 to 3 days, and logarithmic growth phase cells were selected for the experiment.Human esophageal epithelial cell lines Het-1A and the ESCA cell lines  were all purchased from Shanghai cell Resource Center Academy of Sciences. Cells were cultured in a complete medium containing 90% DMEM (4 mM glutamine and 1% penicillin-streptomycin) and 10% fetal bovine serum at 37°C in a COΔΔCT method was employed to calculate the relative expression levels of the molecules. Reaction conditions were as follows: 95°C predenatured for 30 s, 95°C for 5 s, 60°C for 34 s, 95°C for 15 s, and 60°C for 60 s, a total of 40 cycles.Total RNA was extracted using TRIzol Reagent (American life Technologies) and detected the absorbance of RNA at 260 nm-280 nm with a spectrophotometer to obtain concentration value. Then, the total RNA extracted was reverse-transcribed into first-strand cDNA using a reverse transcription kit from Takara Corporation of Japan. Shanghai Biological Engineering Co., Ltd. synthesized the PCR primer, and more details can be found in μg of plasmid and 4 μl jetPRIME reagent from Polyplus (850 bd Sébastien Brant-67400 Illkirch-France) to 200 μl jetPRIME buffer, spin down and incubate at room temperature for 10 minutes, and then add to 6-well plates (invasion assay) and 35 mm petri dish. The total RNA of the cells in the 35 mm petri dish was collected to verify the transfection results 48 hours after transfection, and the cells in 6-well plates were subjected to a wound healing test 24 hours after transfection.EC-109 and KYSE-150 cells plated in 6-well plates (invasion assay) and 35 mm petri dish (transfection efficiency measurement) were transfected with different sequences of siRNAs  targetinμm filter  and subsequently used to infect cells with polybrene . KYSE-150 and EC109 cells were infected by incubation with lentivirus-containing supernatant for 48 h. Transduced cells were purified by puromycin  selection. The transfection effect was observed under a fluorescence microscope. qPCR was performed to analyze the efficiency of IAH1/hnRNP G/FOX2 overexpression.For LV IAH1/hnRNP G/FOX2-GFP-KYSE-150/EC109 cells, the target sequences were cloned into vectors PCDH-CMV-MCS-EF1-copGFP-T3A-Puro  and pLV-C-GFPSpark®  and transfected into 293 T cells. The medium was changed the following day, and the viral containing supernatant was collected 48 h after transfection, filtered through a 0.45 5 per well) were seeded onto six-well plates and incubated at 37°C in a 5% CO2 humidified incubator for 24 hours. Use a sterile tip pipette tip to make a smooth scratch on the cells perpendicular to the well plate, wash the exfoliated cell debris and take a picture under an inverted microscope, and record it as the result of 0 hours. Place the cells in a serum-free DMEM medium. The same marked field of view was photographed under the microscope at 24 and 48 hours. According to the cell scratch healing area, calculate the scratch healing rate of each group of cells.The transfected cells (5 × 10RNA sequence expression data are available at The Cancer Genome Atlas (TCGA) database. We obtained data on the AS events related to ESCA. We analyzed it using SpliceSeq, a java program that provides a comprehensive view of AS patterns and highlights their biological consequences . AS event-test yielded a P value of <0.05 and fold change (FC) > 1.5 or FC < 2/3.On analysis of the AS spectrum from ESCA and adjacent tissue samples, the AS events were required to fulfill the condition that a Download the data of patients with complete clinical data and OS greater than 30 days from the TCGA database. To explore the effect of AS on the OS in ESCA patients, we divided the patients into two groups based on the median PSI. In order to remove any genes that may not be independent predictors of prognosis, LASSO Cox regression was used for further analysis of variable shear events associated with survival in seven types. ClusterProfiler analysis was used to conduct Gene Ontology (GO) enrichment analysis to identify the effects of survival-related AS events on biological processes, cellular components, and molecular functions. Multivariable Cox regression was conducted to analyze the difference between the seven patterns of AS events and OS. It then eliminated the genes that had no relevance with survival and confirmed the prognostic predictor. In addition, we plotted the accuracy of the receiver-operating characteristic (ROC) curves to compare the predictive models for each type of AS.http://ualcan.path.uab.edu/index.html), as a newly developed interactive web server, enables gene expression data of 184 ESCA from the TCGA database to be analyzed through standard processing [The online database UALCAN .Through database filtering, a list of 67 human splicing factors was created . The splThe unsupervised consensus method analyzed the association between ESCA subtypes and OS based on Consensus Cluster Plus.Overall, 50342 AS events of 10765 genes were identified in patients with ESCA: 4144 AA events in 2870 genes, 2589 AD events in 2463 genes, 10033 AP events in 4046 genes, 8848 AT events in 3690 genes, 20842 ES events in 7173 genes, 245 MT events in 237 genes, and 3038 RI events in 2001 genes . These rThere were 3988 differently expressed AS (DEAS) events in 2818 genes of patients with ESCA  among all AS events that occurred in ESCA. A forest map of the differential gene expression in the six types of AS events was plotted based on the top 15 survival-related AS events , as shown in Figures Considering the interaction between AS events and the prognosis of patients with ESCA, we attempted to verify if the six types of AS events could serve as the predictive element of ESCA by choosing the highly correlated AS events as the candidates to analyze the predictive factors in ESCA. AA and RI each have one prognostic factor, AD has three prognostic factors, AP has 22 prognostic factors, the independent prognostic factors were associated with the AT 6, and ES has 13 prognostic factors and did not identify the independent prognostic factors related to ME. According to our data, each splicing type performed well in predicting the prognosis of patients with ESCA  showed a significant correlation with OS in patients with ESCA. The representative splicing factors and survival-associated AS events are shown in P = 0.0312; KYSE0-150, P = 0.0135). The migration and invasion ability of si-FOX2 cells was higher than that of the control, and the difference was statistically significant  , while the splicing factor Fox-2 was positively correlated with the OS of ESCA patients, and the difference was not statistically significant (P = 0.16) (P = 0.032) (P = 0.23)  . Through= 0.032) . There w = 0.23) .n = 36, 20.7%), C2 , C3 , and C4   are processed to multiple mRNAs through AS events . GrowingSimilarly, changes in AS events in lung cancer will also affect the transcripts of VEGFA, MACF1, APP, and NUMB genes, thereby promoting the process of tumorigenesis . MutatioIn this study, we identified AS events and profiles of regulatory splicing factors and established the interrelation between them in ESCA through an analysis of the TCGA program. A total of 50342 AS events of 10765 genes were detected in samples of tumors with seven splicing types , of which ES was the most common splicing type. It seems that AS event is an ordinary process in ESCA, and that most splicing patterns are active. There are 3988 DEAS events between ESCA and nontumor tissues: 2758 upregulated AS events and 1230 downregulated AS events involving 2818 genes. Then, we found that 217 AS events among those DEAS events were significantly associated with the survival rate of patients with ESCA. Most of these AS events showed a critical influence on tumor biology. The top 15 splicing events of RI in DEAS events lead to the poor survival of patients with ESCA, which is consistent with some findings regarding AS events and tumor prognosis . In addiMoreover, we established a prognostic model for each splicing type using multivariate Cox regression analysis. Each type of AS event performed reasonably well in showing a positive or negative prognosis. The six survival-associated types  of AS events showed different AUCs of ROC curves, and ES showed the maximum efficiency in predicting the survival of ESCA patients with the best AUC value: 0.793. The integrated predictive model with six types together showed a high correlation with survival. Further specific functional experiments to determine how alternative splicing modulates tumor prognosis are needed. Given that certain genes and splicing factors may exhibit extensive” spliceosome mutations,” leading to cancer-specific mis-splicing, we therefore focused on the correlation network of splicing factors and survival-associated AS events . Five spβ, and differences in the radio of hnRNP G/Tra2β mRNA have been found in different tissues of the human body, which may indicate that hnRNP G possesses the cellular splicing preferences [The immunohistochemistry staining of heterogeneous nuclear ribonucleoprotein G (hnRNP G) is more prominent in the normal oral cavity than in premalignant and malignant human oral tissue. hnRNP G exhibited tumor suppressor activity, including inhibition of cell proliferation, cell capacity, and enhancement of DNA repair capabilities in human oral squamous cell carcinoma (HOSCC) . Anotherferences .2O2-induced death, which is common in tumor cell lines [Castle et al. found that the AS events are tightly controlled in mammalian, while are exceptionally variable to genetic and environmental variability (such as the tumor), using the quantitative reverse transcription-PCR amplification . Venablell lines . There ill lines . AccordiTo this end, we verified the expression of splicing factors related to prognosis in esophageal cells. The results showed that splicing factors hnRNP G negatively correlated with prognosis were highly expressed in ESCA cell lines. In contrast, the expression of splicing factors Fox-2 positively associated with prognosis in ESCA cell lines was significantly lower than that of normal esophageal epithelial cells. By transfecting siRNAs sequences to construct knockdown cell lines, Fox-2 deletion was shown to increase the invasiveness of ESCA cells. Correspondingly, this study also proved that the absence of hnRNP G can significantly reduce the invasion ability of ESCA cells; this trend can be seen from the images of the cell wounding healing test. In addition, TCGA database analysis showed that hnRNP G and FOX-2 were highly expressed in ESCA patients. In addition, all ESCA patients with high hnRNP G expression had a shorter median survival time, while patients with high FOX-2 expression had a longer median survival time. In the analysis of hnRNP G and tumor grade on ESCA patient survival, the higher expression of hnRNP G in the same grade has the worse survival. On the contrary, both the high and low Fox-2 expression groups were concentrated in grade 2 of ESCA, and the higher Fox-2 expression was, the better the prognosis was. In the future, the sample size can be expanded to confirm its impact on the overall survival and progression-free survival of ESCA patients, and the mechanism of action can be explored through basic experiments.In summary, this was a comprehensive and up-to-date profile of AS events between ESCA and its corresponding nontumor tissues, uncovering the interevent correlations in splicing factors and prognostic signatures in ESCA. The interaction network of splicing factors and prophetic AS events highlights AS events' value and ESCA's tumorigenesis at the genome level. This analysis of survival-associated splicing factors and tumor-specific AS events also points out that new underlying clinical biomarkers still need to be validated in future mechanistic research and clinical trials. These splicing factors and genes can serve as potential prognostic biomarkers to guide the clinical treatment for ESCA patients and expound a novel etiology of ESCA in the future.


Litopenaeus vannamei and its Drosophila orthologue Ectoderm-expressed (Ect4) are also involved in immune defense against pathogen infection. However, the functional mechanism of the protective effect remains unclear. In this study, we show that Ect4 is essential for the viral load in flies after a Drosophila C virus (DCV) infection. Viral load is increased in Ect4 mutants resulting in higher mortality rates than wild-type. Overexpression of Ect4 leads to a suppression of virus replication and thus improves the survival rate of the animals. Ect4 is required for the viral induction of STAT-responsive genes, TotA and TotM. Furthermore, Ect4 interacts with Stat92E, affecting the tyrosine phosphorylation and nuclear translocation of Stat92E in S2 cells. Altogether, our study identifies the adaptor protein Ect4 of the Toll pathway contributes to resistance to viral infection and regulates JAK/STAT signaling pathway.Sterile alpha and HEAT/Armadillo motif-containing protein (SARM) is conserved in evolution and negatively regulates TRIF-dependent Toll signaling in mammals. The SARM protein from  Anotignaling . Deficieignaling .Drosophila, JAK/STAT signaling is initiated by three cytokines of the Unpaired (Upd) family . The binding of Upd induces Domeless (Dome) dimerization and activation of the receptor-associated JAK molecules (termed Hopscotch). Activated Hopscotch then phosphorylates Dome, creating a docking site for the single Drosophila STAT family transcription factor, Stat92E. Phosphorylated Stat92E migrates into the nucleus in dimers, promoting target genes transcription (virus-induced RNA 1 (vir-1) and stress response genes Turandot A and M (TotA and TotM)  . Althougallenges , 19.Drosophila and Pacific white shrimp (Litopenaeus vannamei), the production of antimicrobial peptides (AMPs) was downregulated by Ect4 and LvSarm  protein is evolutionarily conserved from arthropods to mammals . The mamd LvSarm , 24, sugfections . The pos22.11118w flies were used as wild-type control. The IRw; L811fsXdcr-2 mutant flies have been previously described . For the generation of Ect4 mutant lines, transgenic U6:3-gRNA-Ect4 flies were crossed with the nos-Cas9 flies to get male F0  that were crossed with 1118w; TM3, Sb/TM6B, Tb to obtain F1 progenies. Singular F1 flies were crossed with 1118w; TM3, Sb/TM6B, Tb. PCR products amplified from F1 flies before being cloned into the pMD19-T vector according to the manufacturer’s instructions (TAKARA) for mutation identification.escribed . ubi-Galescribed . Ect4-IR2.2pAC5.1-Ect4-Flag was made by cloning Ect4 cDNA into pAC5.1-Flag vectors. For pAC5.1-Ect4-GFP constructs, the EGFP fragment was amplified from pEGFP-C1 and assembled with the Ect4 fragment into pAC5.1-V5 vectors using ClonExpress MultiS One Step Cloning Kit (Vazyme). Stat92E cDNA was inserted in pAC5.1-HA to generate pAC5.1-Stat92E-HA. The hop (or Dome) cDNA was inserted in pAC5.1-V5 to generate pAC5.1-hop-V5 (or pAC5.1-Dome-V5). For the truncated Ect4 constructs, ARM domain (aa 318-701), SAM domain (aa 680-826), and TIR domain (aa 829-1360) were amplified from pAC5.1-Ect4-Flag before assembled into pAC5.1-Flag empty vector, respectively.2.3S2 cells were cultured at 25°C in Sf-900™ III SFM (Gibco). All S2 cell transfection experiments were carried out with the Effectene Transfection Reagent (QIAGEN). For a co-immunoprecipitation assay, S2 cells were transfected with different plasmids. After 48h, cells were collected and lysed in lysis buffer . Lysates were incubated overnight at 4°C with Anti-Flag M2 affinity gel (Sigma) or EZview Red Anti-HA Affinity Gel (Sigma). After centrifugation, pellets were washed with 1ml lysis buffer three times before resuspension in 2X Laemmli SDS-PAGE buffer and detection by Western blot. Western blot was performed according to standard procedures.Primary antibodies: Mouse anti-V5 ; mouse anti-HA ; mouse anti-α-Tubulin ; goat anti-Stat ; mouse anti-FLAG ; rabbit anti-DCV ; mouse anti-PY20 . Secondary antibodies: HRP-linked anti-mouse IgG ; HRP-linked anti-rabbit IgG ; HRP-linked anti-goat IgG (1:5000 Millipore AP106P); Alexa Fluor 555 goat anti-mouse IgG .2.4RpL32 , vir-1 , DCV , TotA , TotM , Ect4 , Vago .According to the manufacturer’s instructions, total RNA was extracted from infected flies using RNAiso Plus (TAKARA), and cDNA was synthesized with the HiScript II Q RT SuperMix (Vazyme). The ChamQ SYBR qPCR Master Mix (Vazyme) was used for quantitative. Expression of the gene of interest was normalized to the Rpl32 RNA level. The following primers were used for qPCR: 2.52 and injected with PBS (Gibco) or virus suspension intra-thoracically using the Nanoject II injector (Drummond). Infected flies were monitored daily for survival rate or frozen for RNA analysis at the indicated time points.Virus stocks were prepared as described previously . All fly2.6pAC5.1-Ect4-GFP and pAC5.1-Stat92E-HA plasmids, and approximately 1×106 cells were transferred to 24 well plates containing coverslips 48 h after transfection. Twelve h later, cells were washed in 0.5ml PBS and fixed with 4% formaldehyde in PBS for 15 min, then washed twice in 0.5 PBT (PBS containing 0.1% Tween-20) before blocking with 5% BSA in TBST for 1 h. Cells were then incubated with primary antibody (anti-HA 1:1000) overnight at 4°C before 2×5 min TBST washes. The secondary antibody was incubated for 4 h at room temperature. Nuclei were stained with PBS with 10 μg/ml DAPI for 5 min. Immunostaining samples were photographed with a Zeiss confocal microscope.S2 cells were transfected with d old, raised at 25°C) of each indicated genotype were homogenized in methanol . After centrifugation, the supernatants were measured for absorbance at 480 nm.For eye pigment assay, the heads of 50 female flies  pre-incubated for 15 min was added to S2 cells to induce tyrosine phosphorylated Stat92E for 30 min. Cell lysates were prepared with the lysis buffer before immune precipitation with an anti-Stat92E antibody at 4°C and incubated with Pierce Protein A/G Plus Agarose (Thermo Scientific) beads. Co-immuno-precipitated proteins were detected with an anti-Stat92E antibody or anti-PY20 antibody.dsRNA targeting 2.8t-test. Grey value analysis was performed by the ZEN 2012 (blue edition) system. P-values below 0.05 were considered significantly different.Survival data were analyzed by the Kaplan-Meier method using GraphPad Prism. Quantification of immunoblots was performed with ImageJ 1.51p. Altered protein levels were presented as normalized fold change compared to the control value. Statistical analysis was performed using the Student’s 33.1Ect4 in Drosophila antiviral defense, an Ect4 mutant line was generated with the CRISPR/Cas9 system. The mutation, 17Ect4, covers a genomic deletion of 17 bp in the coding region of Ect4  position 598, located in the last exon, has the strongest effect on DCV susceptibility , thus limiting the effect of discordance in the SNP profile between different fly lines to the difference in DCV resistance.Variation in the pastrel gene is associated with natural resistance to DCV infection in tibility . SequencEct4 transcription in 17/+Ect4 heterozygotes was reduced by 45% compared with the wild-type flies . Therefore eye pigmentation of a Dcr-2 null mutant (L811fsX/dcr-2L811fsXdcr-2) in GMR>UAS-wIR background is red, whereas Dcr-2 heterozygous mutants (L811fsX/+dcr-2) display pale orange  acts as the first line of defense against viruses in osophila . There wosophila , 34. Sine orange . Our res Dicer-2 , did not3.4Drosophila (via the JAK/STAT pathway including virus-induced RNA-1 (vir-1), the stress-induced genes Turandot A and M (TotA and TotM) . As previously reported, DCV infection induced a strong up-regulation of vir-1, TotA, and TotM in wild-type flies , which encodes a hyperactive JAK kinase due to a G341E substitution  . To exampe flies . Howevertitution . Reducin hopTum-l, which e hopTum-l, which e hopTum-l, which e3.5Ect4 and JAK/STAT pathway, we examined whether Ect4 interacted with any known components of the JAK/STAT pathway. Differentially tagged forms of JAK/STAT pathway components and Ect4 were expressed in S2 cells, and co-immunoprecipitation studies were performed. As shown in To unravel the molecular mechanism underlying the relationship between Ect4 protein harbor three different domains: ARM (Armadillo motif) domains followed by two SAM (Sterile Alpha motif) domains and TIR (Toll -Interleukin-1 receptor) domain  was not detected by immunostaining, likely due to the transient activity of p-Stat92E dimers. Therefore, is Ect4 required for the nuclear translocation of Stat92E? As expected, reduced nuclear translocation of Stat92E in response to the pervanadate stimulus was detected in cells treated with Ect4 RNAi  and L. vannamei (LvSarm) were required to express antimicrobial peptides. Depletion of both led to decreased survival of the animals upon bacterial infections , and t stress , 46. TheEct4 reduced phosphorylated Stat92E upon pervanadate treatment in S2 cells, which suggests a role of Ect4 in regulating cell death through the modulation of JAK/STAT via the interaction with Stat92E.Ect4 interacts with Stat92E in S2 cells through the highly conserved TIR domain of SARM family origin, which have roles in cell death and neuronal destruction in mammals . As an ain vivo study revealed that Ect4 mutants showed enhanced mortality and increased viral load upon DCV challenge. We seek to further elucidate the unknown mechanisms of antiviral response in Drosophila by assessing whether Ect4 affects host resistance to viral infection by regulating cell death.In addition to antiviral immune defense, apoptosis is a conserved mechanism of programmed cell death that can prevent the infection before viral replication is completed , 51. OurDrosophila Ect4 in regulation of JAK/STAT signaling pathway and protection against DCV infection. It is still unclear if Ect4 also participated in the control of other virus infection. The contribution of JAK/STAT signaling to Drosophila antiviral protection is virus-specific. Although JAK/STAT pathway can be activated by RNA viruses, including DCV, CrPV, FHV, and DXV, it is only required for resistance against two Dicistroviridae family members, DCV and CrPV  polymerase PARP9 was recently reported as a noncanonical sensor for RNA viruses that depends on the PI3K/AKT3 pathway to produce antiviral type I interferon . PARP9 iThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.ZH and PX carried out experiments. SG contributed to data collection. YH analyzed confocal images. YL, SY, and FS conducted statistical analysis. KA and W-MD revised the manuscript. JL and RJ contributed to the conception and design of the experiments. WW wrote the first draft of the manuscript. JC directed the studies. All authors contributed to the article and approved the submitted version.


Ultimately, the cellular base for all of these in vitro model systems is derived from human cell lines or patient samples, to investigate mechanisms of cancer and screen novel therapeutics, all of which aim to maximize successful outcomes in clinical trials. This in itself offers an opportunity to potentiate effective treatments for many groups of people, when diverse variables like genetic ancestry are consciously included into study design. This Perspective highlights the need for conscious inclusion of genetic ancestry in preclinical cancer tissue engineering, especially when it pertains to determining therapeutic outcomes.Diverse factors contribute to significant and dire disparities in cancer risk and treatment outcomes. To address this, there was a call for inclusion of sex as a biological variable, which resulted in more instances of careful inclusion of sex in preclinical studies of cancer. Another variable in cancer treatment is genetic ancestry. Although this is considered explicitly in clinical research, it is considerably neglected in preclinical studies. Preclinical research can use several 3D  Summary: Genetic determinants, like ancestry, impact cancer risk and therapeutic outcomes. Hence, this is an important variable to consider at the very initial stages of biomedical research at the bench. Several types of in vitro models are used widely in cancer research, including spheroids/organoids therapy and mortality. Even in cancers that are not related to sex organs, there are significant disparities in chemotherapy outcomes. For example, response to chemotherapy is more favorable in female compared to male melanoma patients . Furthern cancer . ImportaBRAF V600E mutation benefit from improved survival with vemurafenib treatment . Whether in monolayer cultures or 3D in vitro settings, these cell lines allow testing of targeted anti-cancer therapies (Cancer Cell Line Encyclopedia (CCLE) and the Catalogue of Somatic Mutations in Cancer (COSMIC) cell lines project, expanded the number of cell line models available for preclinical cancer research. However, the opportunity to include self-reported and verified genetic ancestry data was missed. Of the 1018 cancer cell lines in the COSMIC, 701 lines had unreported ancestry, of which 453 were inferred to be European , should compulsorily use diverse cell lines? The answer is likely no, because mandating this will exclude many scientists who simply lack the resources to do so, despite the awareness of conscious inclusion.”The Cancer Genome Atlas (TCGA), a multi ‘omics database, initially propelled the identification of oncogenes and driver mutations in cancer , but onlpatients . A comprTable S1 and in vitro models will enable the identification of the root cause of disparity in treatment, pharmacological variables and even tumor biology. If we continue to ignore this variable as an inconvenience, we are willfully contributing to a system of perpetuating inequity.To assess how much of this inclusion problem permeated into cancer spheroid models, I reviewed the literature published from 2017 to present (the past 5 years) using colorectal cancer spheroids. From a random cohort of 30 reviewed papers, 73.4% used established cell lines like HCT-116 and HT-29 in their studies, both of which cluster under white/European ancestry profiles, while only 26.7% used patient-derived cells , and there is occasional mis-reporting of this information in commonly used cell lines . SignifiOne obvious strategy to incentivize the inclusion of diverse samples in studies is recognizing and acknowledging the gap and adding it to the ‘Rigor’ criteria of research proposal evaluations. This lies in the hands of large funding agencies, and perhaps even journal publishers. This approach, while essentially forcing researchers' hands, has been widely successful in incorporating sex as a biological variable, and has thus set a precedent for including ancestry information as an equally relevant variable.A cautionary aspect of forcing inclusion, however, is the lack of formalized definitions for ancestral breakdown. The United States Census-based definition lacks inclusivity  and cons“If universities can extend their educational reach through global partnerships, they certainly can extend cooperative hands to stock biorepositories that capture ethnic diversity globally.”Does this mean that every study, at every point , should compulsorily use diverse cell lines? The answer is likely no, because mandating this will exclude many scientists who simply lack the resources to do so, despite the awareness of conscious inclusion. However, researchers who enjoy the privilege of using patient-derived cells/lines should report the known ethnicity of their samples when disseminating scientific data through peer-reviewed publications. The charge and onus to change this lies with bigger institutions, universities and funding agencies like the NCI. Universities that house precision medicine centers and biorepositories could curate ethnically diverse cell lines for broader use by their researchers. If universities can extend their educational reach through global partnerships, they certainly can extend cooperative hands to stock biorepositories that capture ethnic diversity globally. Federal agencies like the National Institutes of Health (NIH) and its institutes leading this charge will have implications not just on cancer research, but on all disease models that benefit from preclinical research.Any time a change in our fundamental way of doing things is asked of academics, there is resistance. However, combating health disparity is a collective responsibility borne by everyone at every training level in academic research, and not just medical and health professionals. Being conscious of inclusion during our development of disease models and using the weight of bioengineering to power preclinical studies are sure to make a difference. The aspirational goal of cancer research should be to make a difference for all people. Talking about disparity and inequity is simply not enough anymore, if it does not permeate into action at the academic research level. The time for that action is now.


Oral squamous cell carcinoma (OSCC) is the sixth most common cancer worldwide with 840,000 new cases and 420,000 deaths in 2020. Anticoagulants are widely prescribed medications routinely administered to help prevent blood clots. Despite the great relevance of these two topics, there is complete lack of knowledge regarding the potential effects that these drugs could exert on oral cancer patients. In this in vitro study, we comprehensively investigated the effect of anticoagulants on OSCC activity. This includes the effect of these drugs on cancer cell ability to survive, migrate to colonise distant sites, and resist treatment with conventional chemotherapy. We have demonstrated for the first time that various anticoagulants have anticancer effects on OSCC. Moreover, some of the anticoagulants tested were able to reduce the migratory ability of cancer cells. Finally, the great majority of anticoagulants studied reduced the effectiveness of the tested chemotherapeutic agent, allowing an increase in cancer cell proliferation. Our results highlight the need for urgent further research in the field, to improve the anticoagulant strategies in patients with oral cancer, and in turn their prognosis.Oral squamous cell carcinoma (OSCC) is the most common head and neck cancer. With anticoagulant usage on the rise, it is important to elucidate their potential effects on tumour biology and interactions with chemotherapeutics. The aim of the present study was to investigate the effects of anticoagulants on OSCC cell lines and their interactions with the drug 5-fluorouracil (5-FU). Cell proliferation was assessed using an MTS in vitro assay in two human OSCC cell lines (H357/H400) and in normal oral keratinocytes (OKF6) treated with the 5-FU (0.2/1/5/10 μg/mL), conventional anticoagulants warfarin (1/5/10/20 μM) and heparin (5/20/80 U), as well as four new oral anticoagulants, dabigatran (5/10/20 μM), rivaroxaban (5/10/20 μM), apixaban (0.1/1/5 μg/mL), and edoxaban (5/10/20 μM). Cell migration was assessed at 3 h intervals up to18 h using a wound healing assay. Our results clearly demonstrate, for the first time, that commonly prescribed anticoagulants exert in vitro antiproliferative effects on OSCC cells. Furthermore, treatment with some anticoagulants reduced the migration of OSCC cell lines. Nevertheless, most of the anticoagulants tested reduced the effectiveness of the chemotherapeutic agent tested, 5-FU, highlighting potential flaws in the current pharmacological management of these patients. Our findings showed the need for the immediate translation of this research to preclinical animal models. Oral squamous cell carcinoma (OSCC) is the most common head and neck cancer, accounting for 90–96% incidence of all head and neck cancers . CurrentInterest has grown in the potential of anticoagulants for treating/preventing cancer, due to links between coagulation and cancer biology and prognosis. Certain angiogenic processes are favoured in carcinogenesis, due to cell-to-cell interactions, localised hypoxia, and the expression of particular cytokines and growth factors ,5. Anticoagulants are widely prescribed medications, routinely administered to help prevent blood clots. They are administered to people at high risk of blood clots, to reduce their chances of developing serious conditions such as stroke and heart attack. This may include people with atrial fibrillation or an irregular heartbeat. Anticoagulants may also be prescribed to people who have had major surgery, such as aortic valve replacement, or those with certain blood disorders. The most prescribed anticoagulant is warfarin, and for short-term reversible use, heparin. Overall, Vitamin K antagonists have been successfully used over the last five decades; however, more recently, newer types of anticoagulants have become available and are becoming increasingly common. These new oral anticoagulants (NOACs) include rivaroxaban, dabigatran, apixaban, and edoxaban. Warfarin and NOACs are taken as tablets or capsules, while heparin can be given by injection. The new types of anticoagulants have some advantages compared to vitamin K antagonist, e.g., they target a specific single factor of the coagulation cascade rather than inhibiting the whole vitamin K synthesis. Nevertheless, even if NOACs are becoming more popular, they have not fully replaced the use of warfarin and heparin presently.Recently, a study by Khan et al. systematically reviewed the use of new oral anticoagulants and direct oral anticoagulants in malignant patients . A totalWith anticoagulant usage on the rise, it is important to elucidate their potential effects on tumour biology and interactions with chemotherapeutics. However, little is known about these drugs’ effect on OSCC behaviour. Recently, we systematically reviewed the literature about this crucial topic, and have shown that there is a significant dearth of information surrounding interactions between anticoagulant therapy and OSCC . The stuThe aim of the present study was to investigate the in vitro effects of anticoagulants on OSCC cell lines and their interactions with the drug 5-FU. Our results have clearly demonstrated, for the first time, that commonly prescribed anticoagulants exert in vitro antiproliferative effects on OSCC cells. Furthermore, treatment with some anticoagulants reduced the migration of OSCC cell lines. Nevertheless, most of the anticoagulants tested reduced the effectiveness of the chemotherapeutic agent tested, 5-FU, unveiling a whole new dimension of chemotherapy resistance with dramatic potential clinical implications. Our findings showed the need for the immediate translation of these experimental conditions from bench to preclinical animal models.Two human OSCC cell lines derived from different intra-oral sites, H357/H400, and one normal human oral keratinocyte cell line, OKF6, were selected for this study. The OSCC adherent cell lines were established at Bristol Dental School, University of Bristol, UK, from primary explants of tongue (H357) and alveolar process (H400) squamous cell carcinoma . All OSC2, 37 °C). The normal human oral mucosal epithelial cell line, OKF6, was instead cultured in 100 mm Petri plastic dishes  and grown to 60–80% confluence before being further subcultured. OKF6 cells were cultured using keratinocyte serum-free medium (K-SFM)  containing 25 μg/mL bovine pituitary extract and 0.2 ng/mL human recombinant epidermal growth factor (as per manufacturer’s instructions), 0.4 mM CaCl2, 1% penicillin streptomycin mixture , and supplemented with 1% Newborn Calf Serum (NCS) . OKF6 cells were incubated at 37 °C, 5% CO2 for 5 to 7 days to reach 80% confluency. Epithelial cells grown to 80% confluency were subsequently detached via a pretreatment of 10 mM EDTA for 10 min, followed subsequently with incubation with a 0.25% trypsin in 1 mM EDTA solution  for 5 min. The viability of the keratinocytes was confirmed by trypan blue exclusion while passaging cells and during the seeding phase of each cell culture experiment .The OSCC cell lines were cultured as previously described . Cell liWarfarin , Enoxaparin sodium , Edoxaban , Apixaban , Dabigatran , Rivaroxaban R-enantiomer , and 5-FU , were reconstituted in acetone, filtered water, methanol, Dimethyl Sulfoxide (DMSO), HCl, DMSO, and DMSO, respectively. ® 3596, Corning, NY, USA) under standard conditions and treated with a combination of anticoagulant and/or chemotherapeutic agents at therapeutic concentrations at T = 0 [All the cell lines were tested with the chemotherapeutic agent 5-FU (0.2/1/5/10 μg/mL), as well as the two conventional anticoagulants warfarin (1/5/10/20 μM) and heparin 5/20/80 U), and four new oral anticoagulants, dabigatran (5/10/20 μM), rivaroxaban (5/10/20 μM), apixaban (0.1/1/5 μg/mL), and edoxaban (5/10/20 μM). H357, H400, and OKF6 cells were seeded in 96-well plates  and analysed with ImageJ Software . Due to the overall number of drugs to be tested within these experiments, pairs of anticoagulants were tested simultaneously with a shared control sample. All the wound healing assay experiments were performed in quadruplicate.Statistical analyses were performed using GraphPad Prism 8.0.1 . Differences between groups were analysed using the two-way ANOVA test.The conventional anticoagulants tested showed antiproliferative effects in the two cancer cell lines tested. Warfarin, at concentrations of 1 μM and 5 μM, significantly reduced cell growth at 72 h in H357 cell line . TreatmeTo assess if the anticoagulant effects observed were specific to oral malignant cells, the normal human keratinocyte cell line OKF6 was used as normal control in this study. Similarly, all the anticoagulants except heparin exerted antiproliferative effects in OKF6 cell line. This was seen with warfarin at 5 μM (time 48 and 72 h); 5 μM and 20 μM dabigatran at time points 72 and 48 h, respectively; 10 μM rivaroxaban at time 72 h; 0.1 μg/mL apixaban at time 72 h. Interestingly, edoxaban appeared to have no effects on OKF6 cell growth, even though it has to be noted that the drug solvent for edoxaban, 0.04% methanol, showed cytotoxic effects on OKF6 as early as 24 h.p ≤ 0.0001), but with the addition of 5 μg/mL and 20 μg/mL in H357; and with 20 μg/mL in H400, the growth was comparable with controls again, masking the antiproliferative effect of 5-FU. On the other hand, warfarin, edoxaban, and apixaban appear to not interact with 5-FU in H400 cell lines, and heparin, dabigatran, and rivaroxaban did not interfere with 5-FU effectiveness in H357 cell lines. Comparing the cancer cell lines with OKF6 control, all concentrations of warfarin and 10 and 20 μM rivaroxaban negated the chemocytotoxicity of 0.2 μg/mL 5-FU at time 72. All concentrations of heparin, 10 and 20 μM rivaroxaban, 0.1 μg/mL apixaban, and 10 μg/mL edoxaban reduced the effectiveness of 1 μM 5-FU at 72 h. Conversely, 10 μM dabigatran was able to act synergistically with 0.2 μg/mL 5-FU at 72 h to reduce OKF6 cell proliferation.The effect of anticoagulants on chemotherapy-induced cytotoxicity was assessed overtime in OSCC cells. The majority of anticoagulants either combated the chemocytotoxicity of 5-FU or appeared to not interact with 5-FU at all. In total, 5 and 10 μM concentrations of warfarin tested reduced the chemocytotoxicity of 5-FU in H357 cells at time 24 h. All concentrations of apixaban in H357 cells reduced the cytotoxicity of 0.2 μg/mL 5-FU at 72 h. With H400 cells, 20 U, 80 U heparin, and 5 μM dabigatran were able to decrease the effectiveness of 0.2 μg/mL 5-FU at 72 h. Edoxaban also showed potential to decrease cytotoxicity of 5-FU at 72 h. Across both cell lines, 0.2 μg/mL 5-FU alone caused significant inhibition of proliferation compared to its control (Conventional anticoagulants significantly impaired cancer cell migration. Addition of all concentrations of warfarin attenuated the ability of H400 cells to migrate . HoweverA comprehensive summary of the main findings from our in vitro experiments can be found in The present study has demonstrated for the first time that various anticoagulants exert antiproliferative effects on OSCC cell lines. In our experimental conditions, warfarin reduced proliferation of the H357 cancer cell line. Similarly, a study by Kirane et al. observed the antiproliferative effects of warfarin in pancreatic cancer cell lines . Bai et In vitro studies have shown that heparin and its derivatives are able to reduce tumour proliferation, migration, and invasion in epithelial cancers ,24,25,26The present study did not observe interaction between 5-FU and heparin in H357 cell lines. Interestingly, some concentrations of heparin attenuated the effects of low-concentration 5-FU on H400 proliferation; this effect has not been previously observed and warrants further investigation. Conversely, heparin and 5-FU worked synergistically to inhibit wound closure and thus migratory capacity of both cancer cell lines. While this drug combination has not been studied in human OSCC cell lines, the combination of cisplatin and heparin has been observed to result in reduced cell viability and cell migration capacity, as well as increased apoptosis of human OSCC cells ,33.Dabigatran, a direct thrombin inhibitor, has very limited research in the field of OSCC. Our study is the first to identify antiproliferative effect in OSCC cell lines. However, dabigatran failed to interfere with OSCC cells’ migratory phenotype. Decreasing cell viability is a finding supported by various studies testing dabigatran in numerous breast cancer cell lines ,37. De FLiterature regarding the pharmacodynamic interactions between anticoagulants, with the exception of heparin, and chemotherapeutic agents is sparse. The majority of anticoagulants studied reduced the chemocytotoxicity of 5-FU, allowing an increase in both normal and cancer cell proliferation. This novel finding warrants further urgent research. Additional in vitro research involving the use of further chemotherapeutic agents, such as cisplatin, and translation of this research line to preclinical animal models would add breadth to the present study and consolidate its findings. Our study demonstrated that there is real space for imminent and invaluable discoveries regarding the effect of anticoagulants in OSCC, with great impact on the future pharmacological management of oral cancer patients.