Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that  As important regulatory elements, enhancers collaborate with promoters to regulate the transcription of genes in a k-mer frequencies of a DNA fragment with a support vector machine (SVM) to classify regulatory elements k-mer and improved the prediction performance Enhancers are usually identified by high-throughput biological experiments. For example, Heintzman and Ren Over the past five years, deep learning has been incorporated into bioinformatics studies. For example, DeepBind used a convolutional neural network (CNN) to predict binding proteins and showed higher prediction power than traditional classifiers Chromatin accessibility of the genome has received more and more attention in the recent years. It is known that putative accessible regions in the genome often work with TFs, RNA polymerases, and other cellular machines to regulate gene expression With the aforementioned understanding, we propose in this study DeepCAPE, a deep CNN for the accurate prediction of enhancers, using DNA sequences and DNase-seq data. Through comprehensive experiments, we show that our model is not only superior to existing methods in the prediction of enhancers, but also able to predict enhancers across cell lines. With a visualization strategy, we show that sequence motifs discovered by our method successfully match known motifs. Through joint analysis of prediction results with genome-wide association study (GWAS) data, we show the potential ability of our method to identify genetic variants associated with liver cancer and discriminate enhancers related to lymphoma.i.e., non-enhancer fragments that do not overlap with enhancers. First, we randomly sample DNA fragments of variable length from the background genome, with the constraint that the length and GC content of negative samples should be identically distributed as those of known enhancers. The background genome is defined as the entire human reference genome (GRCh37), excluding known enhancers, promoters for coding and non-coding genes, and exonic regions for coding and non-coding genes. Second, we discard the constraint on the GC content to demonstrate the adaptability of our method to different genome contexts. The first model is more stringent and is used throughout this study. We set the ratios of positive to negative samples to 1:10 and 1:20, i.e., for each positive sample, we generate 10 and 20 negative samples, respectively.We use the promoter enhancer slider selector tool (PrESSTo) to download experimentally verified enhancers specific to 9 different cell lines from the FANTOM project, including epithelial cell of esophagus, melanocyte, cardiac fibroblast, keratinocyte, myoblast, stromal cell, mesenchymal cell, natural killer cell, and monocyte. We use two strategies to generate negative samples, S) of a DNA position as the number of reads (N) starting at this position divided by the average number of reads  model where k replicates) into a single channel. After another batch-normalization layer, a decoder component, which is also a feedforward neural network, transfers the data back to k channels. With the module well trained, the decoder is able to produce output similar to the original input, and results of the encoder component can then be used as features extracted from the original data and fed to the successive DNase module. Such an auto-encoder module benefits our model in two aspects. First, regardless of the number of replicates for different cell lines, output of the module is of the same dimension, and thus makes cross-cell line prediction possible. Second, effective dimensionality reduction significantly alleviates the computational burden of the successive prediction model.A DNase-seq experiment usually has a small number of replicates, and this number varies between cell lines, making the dimensionalities of input data variable between cell lines and preventing the use of a CNN in cross-cell line prediction. To solve this problem, we adopt auto-encoder, a neural network designed for unsupervised learning of efficient encodings The DNase module extracts multi-level features from chromatin accessibility scores and is essentially identical to the DNA module in structure, except for the number of input channels. The DNA module is fed with one-hot encoded DNA sequence and has 4 channels, while the DNase module is fed with chromatin accessibility data produced by the encoder component of the auto-encoder module and has a single channel. A statistical analysis on a total of 43,011 experimentally verified enhancers in FANTOM shows that the median and mean lengths of these enhancers are 275 and 288 bp, respectively. We therefore select 300 as the dimensionality of the auto-encoder latent space, and thus the shapes of outputs of subsequent layers in DNA and DNase modules are symmetrical.The joint module integrates multi-level features from both the DNA and DNase modules to predict the probability that the input DNA fragment is an enhancer. Drawing on the idea of skip connection in ResNet On the top of the architecture, a softmax layer predicts the probability that an input DNA fragment is an enhancer based on the integrated features, as.i.e., 1 for enhancer and 0 for non-enhancer).where We carry out 5-fold cross-validation experiments to validate the performance of our method for each cell line. Particularly, in order to avoid information leakage, we partition both positive (known enhancers) and negative (non-enhancers) samples into 5 subsets of nearly equal size before converting sequences of variable length to sequences of fixed length by the data augmentation strategy. In each fold of the experiment, we take 4 subsets to train the model and test its performance using the remaining subset.Considering that the positive and negative samples are highly imbalanced, we adopt a two-stage training strategy. First, we train an initial model using all positive samples and an equal number of negative samples sampled from the training set. After this stage, the DNA and DNase modules obtain the ability to extract features. Then, the joint module is further trained as usual using all the imbalanced samples on the training set, with learning rates of DNA and DNase modules setting to 0 With a well-trained model, we score all samples augmented from an original sequence on a test set, and then average over these scores to obtain the final probability that the sequence is an enhancer. We also used another strategy that takes the maximum of these scores as the final probability to study the effects of different statistics on the results.https://keras.io) with Tensorflow as the backend, while the Theano backend also generated very close results according to our test. The NVIDIA GeForce GTX 1080Ti GPU is used to accelerate the computation. We have released our code in Github (https://github.com/ShengquanChen/DeepCAPE).We implement DeepCAPE in Python using Keras  by counting nucleotide occurrences in the set of sequences that activate the kernels. Briefly, each kernel of the first convolutional layer is converted into a PWM by scanning along input sequences for activated positions and then calculating the PWM by pooling corresponding regions where q-value threshold 0.1 to match PWMs identified by our method to the JASPAR database We set length of kernels in the first convolutional layer to 8 and To verify the performance of DeepCAPE, we conducted a series of 5-fold cross-validation experiments using enhancers collected from FANTOM and negative data generated by the background model with the consideration of GC content (see Method). We compared the performance of our method with several baseline methods, including gkmSVM P < 2.2E − 16 for all the three baseline methods). In terms of AUROC scores, our method is on average 0.121 and 0.151 higher than gkmSVM, 0.169 and 0.151 higher than DeepSEA, and 0.150 and 0.150 higher than DeepEnhancer, when the ratios are 1:10 and 1:20, respectively. One-sided paired-sample Wilcoxon signed rank tests similar also consistently report significant results . All these results suggest the superior performance of our method over existing sequence-based approaches in predicting enhancers.The performance at different ratios of positive to negative samples (1:10 and 1:20) with augmentation stride 1 is shown in P < 2.2E − 16 for AUPRCs, P < 2.2E − 16 for AUROCs; one-sided paired-sample Wilcoxon signed rank test). Similarly, our method significantly outperforms CENTIPEDE when the ratio of positive to negative samples is 1:20 .We also compared the performance of our method to that of CENTIPEDE (with default parameters), which integrates DNA sequences and chromatin accessibility information to predict TFBSs We further validated the performance of our method on an independent test set with the following experiment P = 4.019E − 4 against gkmSVM, P = 7.908E − 4 against DeepSEA, and P = 4.571E − 3 against DeepEnhancer), suggesting that our method is not sensitive to the partition of training and test samples. Besides, our method consistently performs well in all the cell lines, while the performance of the other three methods shows significant fluctuation across cell lines, suggesting that they are sensitive to the number of training samples.Our method demonstrates much higher robustness than the baseline methods. With the variance of AUPRCs in the 5-fold experiments calculated for each cell line, one-sided Wilcoxon rank sum tests consistently show that our method achieves smaller variance than a baseline method ” model is also superior to the three baseline methods in most cases, suggesting that our model has the advantage in the case of predicting only with sequences. Secondly, taking the maximum of the scores of samples augmented from an original test sequence as the final probability generates slightly worse performance, and this may be due to the outliers with high scores in the augmented negative samples. Finally, the performance on datasets without considering GC content is slightly superior to that on datasets under the GC content constraint .In terms of model training, benefiting from the usage of dropout layers and the early stop strategy, the performance on the test set is fairly close to that on the training set, indicating that DeepCAPE is able to avoid overfitting. In addition, with regard to the efficiency of model training, DeepCAPE is superior to other deep learning models due to the zero-learning-rate strategy in the second training stage. Take the dataset with augmentation stride 1 of myoblast as an example, when the ratio of positive to negative samples is 1:20, the training time for an epoch is about 126 s for DeepCAPE, 301 s for DeepSEA, and 237 s for DeepEnhancer.A, the auto-encoder module not only makes cross-cell line prediction possible, but also maintains the superior performance of our method even if the dimensionality of DNase-seq data is reduced .To illustrate the contribution of auto-encoder module, we compared the performance of DeepCAPE with auto-encoder to that of DeepCAPE without auto-encoder as well as other two strategies that average the replicates or randomly select a single replicate. As shown in Fig. 3To evaluate contributions of the DNA and DNase modules, we performed a model ablation analysis. As shown in There are more than 100 million parameters in the whole neural network of DeepCAPE, and most of them are concentrated on the merge-layer of the joint module. As shown in In order to explore the effect of the number of training samples to the final performance, we repeated the cross-validation experiments on datasets of different numbers of augmentation strides for each cell line. The results show that although the performance is decreasing overall with the increasing augmentation strides C, the peAll the aforementioned observations suggest that DeepCAPE can not only achieve superior performance with limited known enhancers, but also achieve satisfactory performance with longer augmentation strides to effectively save computational time when there are massive enhancers.Experimental approaches are expensive and time-consuming for large-scale identification of enhancers across a variety of human cell lines. For a cell line whose enhancers have not been identified yet, predicting potential enhancers has great significance in guiding biological experiments for novel enhancer identification.To accurately predict enhancers across cell lines, we employed a collective scoring strategy. Given a cell line of interest and a DNA fragment, we used models trained on other cell lines to predict the probability that the fragment is an enhancer, and then averaged over these predictions to obtain a final prediction probability. The basic idea of this strategy is that a DNA sequence may be an enhancer in the new cell line if it plays a role of enhancer in some other cell lines. To better support the idea, we calculated the overlap rates between regions of enhancers of different cell lines. As shown in Table S1, the mean overlap rates of enhancers of most cell lines range from 15.4% to 21.8%, although those of natural killer cell and monocyte are 8.0% and 5.9%, respectively, due to the large number of enhancer samples in these two cell lines. In addition, we calculated the overlap rates between called DNase-seq peaks of different cell lines and found that the mean overlap rates range from 34.5% to 50.0% (Table S2). The results indicate that there are common enhancers and chromatin accessibility features in different cell lines, and thus we can utilize information from other cell lines to predict enhancers in a new cell line. To avoid introducing extra prior information, we directly averaged over the predictions from models of other cell lines to obtain a final prediction probability without other operation such as weighting different cell lines, thus making it easier to generalize the model.We used the datasets of 9 cell lines from FANTOM to demonstrate the ability of DeepCAPE to predict enhancers in a cross-cell line manner. For each cell line, we first excluded the samples that overlap with samples in other cell lines to make sure that there are not common samples with other cell lines, thus making the task more challenging. On average, 35.2% and 37.6% of samples are left in the datasets of 9 cell lines when the ratios of positive to negative samples are 1:10 and 1:20, respectively, and the corresponding ratios become 1:8.3 and 1:18.1 averagely. We next used the models of other 8 cell lines to make predictions for the filtered samples of the cell line of interest, and then averaged over the resulting 8 probabilities to obtain the final prediction probability. We also used other three baseline models to predict enhancers in this cross-cell line manner.As shown in i.e., PWMs) learned from the first convolutional layer of the DNA module. We further identified putative motifs by using the tool TomTom To interpret features extracted by DeepCAPE, we used a motif visualization strategy (see Method) to obtain sequence signatures  in ENCODE and trained a DeepCAPE model using enhancers and DNase-seq data specific to this cell line. We then calculated a probability that indicates whether a DNA fragment of 300 bp surrounding a SNP is an enhancer for each of the 334 SNPs. We finally classified the SNPs into 5 groups according to log10-transformed P values of the SNPs and drew box plots of the predicted probabilities for each group. As shown in Fig. 6A, the predicted probabilities for SNPs with smaller P values are relatively higher than those with larger P values. This observation suggests that predictions given by our method using genomic and epigenomic data are potentially correlated with P values obtained from genetic studies.To demonstrate potential applications of DeepCAPE, we collected 334 single nucleotide polymorphisms (SNPs) that were possibly associated with liver cancer from GRASP P = 7.915E − 6 for adjacent 1 kb, P = 9.032E − 7 for adjacent 3 kb; one-sided paired-sample Wilcoxon signed rank test). These results suggest that our method has the potential ability to discriminant enhancers related to lymphoma from their nearby DNA fragments.We further collected 14 enhancers that are shown to be associated with lymphoma from the literature We have introduced a deep learning framework named DeepCAPE to integrate DNA sequence information and chromatin accessibility data for predicting enhancers. Benefitting from the integration of DNase-seq data, the well-designed feature extraction modules, the skip connection strategy, and the adoption of auto-encoder, DeepCAPE is superior to existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, self-adaptable to different sizes of datasets, capable of making cross-cell line predictions, and interpretable in extracted features. We have further demonstrated the potential ability of DeepCAPE to explain functional implications of genetic variants and discriminate disease-related enhancers. Our method has two main application scenarios. Firstly, one can use our method to establish a landscape of potential enhancers specific to a cell line that still lacks systematic exploration of enhancers, thereby promoting the deciphering of regulatory mechanisms for the cell line. Secondly, one can use our method to explore functional implications of genetic variants or DNA fragments specific to a cell line, thereby bridging genomic and genetic studies toward the understanding of disease development.Certainly, our work can further be improved in several aspects. Firstly, the incorporation of the long short-term memory (LSTM) network, a kind of recurrent neural network architectures, into our framework may further improve the performance, because LSTM may be able to capture very long-range interaction in the sequence. In addition, the adaptation of an embedding representation of DNA sequences instead of the use of the one-hot encoding may also benefit the prediction accuracy https://github.com/ShengquanChen/DeepCAPE.The source code and detailed tutorial of DeepCAPE are freely available at The authors have declared no competing interests.Shengquan Chen: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. Mingxin Gan: Conceptualization, Funding acquisition, Project administration. Hairong Lv: Data curation, Validation, Methodology. Rui Jiang: Conceptualization, Funding acquisition, Project administration, Writing – review & editing.


KRAS) and tumor protein p53 (TP53), three and eight variants were identified, respectively. In conclusion, both inhibitors demonstrated in vitro efficacy on the PDAC cell lines. However, aberrations and expression of inhibitor target genes did not appear to affect the efficacy of the corresponding inhibitors. In addition, specific aberrations in TP53 and KRAS affected the efficacy of both inhibitors.Casein kinase II (CK2) and cyclin-dependent kinases (CDKs) frequently interact within multiple pathways in pancreatic ductal adenocarcinoma (PDAC). Application of CK2- and CDK-inhibitors have been considered as a therapeutic option, but are currently not part of routine chemotherapy regimens. We investigated ten PDAC cell lines exposed to increasing concentrations of silmitasertib and dinaciclib. Cell proliferation, metabolic activity, biomass, and apoptosis/necrosis were evaluated, and bioinformatic clustering was used to classify cell lines into sensitive groups based on their response to inhibitors. Furthermore, whole exome sequencing (WES) and RNA sequencing (RNA-Seq) was conducted to assess recurrent mutations and the expression profile of inhibitor targets and genes frequently mutated in PDAC, respectively. Dinaciclib and silmitasertib demonstrated pronounced and limited cell line specific effects in cell death induction, respectively. WES revealed no genomic variants causing changes in the primary structure of the corresponding inhibitor target proteins. RNA-Seq demonstrated that the expression of all inhibitor target genes was higher in the PDAC cell lines compared to non-neoplastic pancreatic tissue. The observed differences in PDAC cell line sensitivity to silmitasertib or dinaciclib did not depend on target gene expression or the identified gene variants. For the PDAC hotspot genes kirsten rat sarcoma virus ( Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignancies and ranks fourth among all cancer-related deaths in both men and women . Due to Casein kinase II (CK2) is a highly conserved serine/threonine kinase, which is constitutively active and ubiquitously expressed in mammalian cells. CK2 has a wide range of candidate physiological targets and is involved in a series of complex cellular functions . For exaCK2 is not the only protein kinase that plays a critical role in PDAC. Cyclin-dependent protein kinases (CDKs) are critical regulators of cell cycle progression. This dysregulation of the cell cycle is the fundamental process of cancer growth and spread . Within KRAS and TP53 are two of the hotspot genes frequently mutated in PDAC. It has been reported that KRAS and TP53 mutations can be found in approximately 92% and 70% of PDAC patients, respectively [KRAS mutations showed a bad response to first-line gemcitabine-based therapy and represented a poor prognosis [TP53 expression were reported to show a significant improvement in progression-free survival when compared to complete loss. Interestingly, cases showing as many as two TP53 somatic variants are reported to have a better prognosis than when compared to cases exceeding accumulation of more than three somatic variants [KRAS and TP53 on the prognosis and drug efficacy of PDAC, we explored the influence of the somatic variants of these two genes on the response of PDAC cell lines to CK2 and CDK inhibitors.Different genetic aberrations affecting direct drug target genes, downstream pathways, or key oncogenic regulators also have an impact on drug efficacy. ectively ,34. Morerognosis . PDAC pavariants ,37. TherKRAS oncogene and TP53 tumor suppressor genes.Akin to the above-mentioned studies, several other publications have demonstrated the influence of CK2 and CDKs on the pathophysiology of PDAC. However, for these drug target genes, it is still poorly understood if and which somatic variants affect sensitivity to the respective inhibitors ,23. In g50 values for proliferation and biomass showed a range from 2.131 μM to 16.20 μM for proliferation and a matching range from 1.691 μM to 14.32 μM for biomass  initiated significant reductions at 5 μM . The IC5 biomass .50 values of cell proliferation and biomass were applied in the following bioinformatic clustering  for sensitivity classification of cell lines. Ten PDAC cell lines were separated into three groups with low , moderate , and high sensitivity (AsPc-1 and BxPc-3) (IC BxPc-3) .50 values ranged from 0.001253 μM to 0.01111 μM (proliferation) and 0.002146 μM to 0.01390 μM (biomass) , but responses varied between cell lines . At the biomass) .50 values of cell proliferation and biomass were applied in bioinformatics clustering  for sensitivity classification of the cell lines. Ten PDAC cell lines were separated into three groups with low (Panc-1 and SU.86.86), moderate , and high  sensitivity . Significant increases in apoptotic/necrotic cells were observed starting at a concentration of 0.0075 μM . InteresCSNK2A1, CSNK2A2, and CSNK2B; for dinaciclib: CDK1, CDK2, CDK5, and CDK9) was evaluated in all cell lines by RNA-Seq. The expression level was estimated as Log2 (transcripts per kilobase million (TPM) + 1) and compared to the expression data of non-neoplastic pancreatic tissue, which was chosen as a control. All target genes were expressed higher in the PDAC cell lines than in normal pancreatic tissue. The inhibitor target gene expression in PDAC compared with the control are as follows (PDAC Minimum–Maximum vs. control): CSNK2A1 (5.83–7.69 vs. 3.63), CSNK2A2 (5.41–6.47 vs. 4.43), CSNK2B (6.53–7.52 vs. 6.00), CDK1 (5.73–8.51 vs. 0.41), CDK2 (4.37–6.95 vs. 2.83), CDK5 (3.51–5.32 vs. 1.98), and CDK9 (4.57–6.63 vs. 4.50) (The expression of target genes for each inhibitor (for silmitasertib: s. 4.50) .CSNK2A1, CSNK2A2, CSNK2B) and dinaciclib  were selected to analyze transcript variants by WES.The target genes for silmitasertib , KRAS c.35G>T , and KRAS c.183A>C (p.Gln61His), all of them were missense variants. KRAS c.35G>A were observed in AsPc-1 : 100), Colo357 (VAF: 23.8), Panc-1 (VAF: 62.1), and SU.86.86 (VAF: 83.7). KRAS c.35G>T were identified in Capan-1 (VAF: 97.1), PaTu8902 (VAF: 100), PaTu8988S (VAF: 96.9), and PaTu8988T (VAF: 98). KRAS c.183A>C was identified in T3M4 (VAF: 32.6).WES identified ll lines . Three dKRAS in all PDAC cell lines were higher than those compared to non-neoplastic pancreas tissue (4.16–7.09 vs. 2.14)  and SU.86.86 (7.09), respectively. The expressions of all KRAS c.35G>T variants, which were identified in Capan-1, PaTu8988S, PaTu8988T, and PaTu8902 were similar to wild type BxPc-3 , the expression of KRAS c.183A>C in T3M4 and KRAS c.35G>A in AsPc-1, and Panc-1 and SU.86.86 were higher than wild type BxPc-3 (4.79–7.09 vs. 4.53).The expressions of s. 2.14) . Both thKRAS status revealed that PDAC cell lines carrying the KRAS variant appeared to be less sensitive to silmitasertib and the high sensitive group contained only the wild-type and one KRAS mutant cell line, while the rest of the KRAS mutant carrying cell lines were all classified into the moderate or low sensitivity groups  were classified into each of the three sensitivity groups, while wild-type (BxPc-3) was in the moderate sensitivity group. Interestingly, the sensitivity of the KRAS c.183A>C mutant cell line (T3M4) was higher than BxPc-3  and TP53 c.267delC (p.Ser90fs), were identified in AsPc-1 : 96.4) and Colo357 (VAF: 100), respectively. Missense variants, TP53 c.476C>T  and TP53 c.818G>A (p.Arg273His), were identified in Capan-1 (VAF: 100) and Panc-1 (VAF: 98.8), respectively. Double missense mutation including TP53 c.733G>A (p.Gly245Ser) and TP53 c.1079G>T  were identified in SU.86.86 . TP53 c.659A>G (p.Tyr220Cys) was identified in BxPC-3 (VAF: 99) and T3M4 (VAF: 100). TP53 c.844C>T (p.Arg282Trp) was identified in PaTu8902 (VAF: 100), PaTu8988S (VAF: 100), and PaTu8988T (VAF: 100). The expressions of TP53 with frameshift variants were lower than that of the missense variants (1.24–2.13 vs. 4.39–5.42) and control (2.83) (Two different types of variants including frameshift (fs) variant and missense variant of ll lines . Fs varil (2.83) .TP53 status demonstrated that the two cell lines carrying fs variants (Colo357 and AsPc-1) were in the dinaciclib high sensitive group, while cell lines carrying point mutations were distributed in the three sensitivity groups  in all of the tested PDAC cell lines were higher than in non-neoplastic pancreatic tissue. This result suggests that these cell lines could be sensitive to silmitasertib. Indeed, the inhibition of CK2 by silmitasertib significantly affected cell proliferation of all cell lines except Panc-1, and significantly reduced the cell biomass in all PDAC cell lines. However, silmitasertib did not perform well in reducing the metabolic activity of the PDAC cell lines, and the effects of silmitasertib in inducing apoptosis were also very weak, with significant effects only observed in AsPc-1 and T3M4, which indicates that silmitasertib may inhibit the proliferation of PDAC cells by inducing cell-cycle arrest or cell autophagy rather than apoptosis [This study demonstrated that the expression levels of silmitasertib target genes  of PDAC patients, and only 0.2% (3/1228) involved protein structural changes, while the majority involved gene amplification [CDK1, 0.3%, CDK2, 1.1%, CDK5, 2%, CDK9, 1%) of PDAC patients, and only 0.5% (6/1228) involved protein structural changes, while the majority involved gene amplification [CK2 gene aberrations were detected in 2.9% (34/1228: fication . Similarfication . TherefoKRAS in nine of ten PDAC cell lines including KRAS p.Gly12Asp (c.35G>A), KRAS p.Gly12Val (c.35G>T), and KRAS p.Gln61His (c.183A>C). It was reported that patients with KRAS mutations showed a weak response to first-line gemcitabine-based therapy and had a poor prognosis [KRAS c.35G point mutation and wild-type cell lines, suggesting the inhibitory effect of dinaciclib is not affected by the KRAS c.35G point mutation. Interestingly, T3M4 cells, which carry a KRAS c.183A>C variant are more sensitive to dinaciclib than wild-type BxPc-3 cells, suggesting that dinaciclib may improve the efficacy of patients with specific KRAS c.183A>C mutation, but due to the limited number of cell lines, further experiments are still needed to verify the relationship between this KRAS mutation and the efficacy of dinaciclib. However, a comprehensive analysis of silmitasertib efficacy and KRAS mutations suggests that carrying the KRAS variants reduced the PDAC sensitivity to silmitasertib. Since AKT is an important effector kinase of CK2, inhibition of CK2 causes a reduced activation of AKT, whereas mutant KRAS directly activates the PI3K/AKT pathway [KRAS-mutated cell lines to ssilmitasertib. Overall, blocking CDKs with dinaciclib in monotherapy may be beneficial to patients with the specific KRAS c.183A>C mutation, whereas silmitasertib monotherapy in patients with KRAS point mutations may not be a good option.We identified three different amino acid substitution variants of y12Asp c.G>A, KRAS61His c.13A>C. It  pathway . This anTP53 mutation that causes amino acids to change. Our sequencing data revealed that the expression of TP53 with fs mutations were lower than those of TP53 with point mutations. Fs variants resulted in a strong disruption of TP53 function, and low TP53 mRNA expression was associated with a poor prognosis in PDAC patients [TP53 in PDAC cell lines was higher than in the control, and it has been reported that some specific point mutations inactivate TP53  and confer an advantage in tumor growth [TP53 p.Ala159Val, p.Tyr220Cys, and p.Gly360Val variants, which demonstrated similar expression properties. In addition, combined with the results of the PDAC inhibitory assays, cell lines carrying specific TP53 variants  were less sensitive to silmitasertib. These cell lines were all in the low  sensitivity group. Knockdown of CK2 causes the enhanced transactivation of p53, thereby increasing apoptosis [TP53, inhibition of CK2 did not transactivate these proteins. This may be the reason for the reduced efficacy of silmitasertib in cell lines with specific mutations in TP53. AsPc-1 and Colo357, which carry the fs variant, are both in the dinaciclib high sensitivity group, suggesting that dinaciclib may be able to improve the poor prognosis of the TP53 fs mutation. However, the expression level of TP53 cannot fully explain the observed responses of all cell lines to silmitasertib or dinaciclib. These results indicate that TP53 variants are an indicator of an inhibitory response, while the expression level of TP53 is not. Furthermore, the results of the PDAC inhibitory assays indicate that patients with TP53 mutations may benefit from a potential application of dinaciclib and silmitasertib.We identified that all tested PDAC cell lines contained at least one patients ,46. On tr growth ,48. The Our study focused on univariate genetic variants and did not evaluate the potential effect of complex variant landscapes. Thus, our conclusions are limited to direct genetic variants observed in the respective target genes of the evaluated inhibitors. Interactions between different gene aberrations, influence on downstream signaling as well as expression deregulations can also have significant influence. Accordingly, a bioinformatical complex analysis allowing drug target, target downstream signaling as well as bioinformatical modeling is needed. Furthermore, the complex validation of predicted mechanistic targets on cell biological level should be performed in the future to further evaluate factors influencing drug response.Kinase inhibitors, silmitasertib (CK2 inhibitor) and dinaciclib (CDK1/2/5/9 inhibitor) were purchased from Selleck Chemicals . According to the manufacturer’s instructions, silmitasertib and dinaciclib were separately dissolved in dimethyl sulfoxide (DMSO)  as a stock solution at a final concentration of 10 mM. The stock solutions were stored at −80 °C and diluted into corresponding working concentrations before each experiment.2 incubator at 37 °C with a humidified atmosphere.PDAC cell lines AsPc-1, BxPc-3, Capan-1, Colo357, Panc-1, PaTu8902, PaTu8988T, PaTu8988S, SU.86.86, and T3M4 were kindly provided by the University of Greifswald. AsPc-1, BxPc-3, Colo357, Panc-1, SU.86.86, and T3M4 were cultured in RPMI1640 medium  supplemented with 10% heat-inactivated fetal calf serum (FCS) (PAN-Biotech) and 1% penicillin-streptomycin solution  (PAN-Biotech). PaTu8902, PaTu8988T, PaTu8988S were cultured in DMEM/F12 medium (PAN-Biotech) supplemented with 10% heated-inactivated FCS and 1% penicillin-streptomycin solution. Capan-1 was cultured in RPMI1640 medium supplemented with 15% heat-inactivated FCS and 1% penicillin-streptomycin solution. After verifying that all cell lines were not contaminated by mycoplasma, these PDAC cell lines were maintained in a 5% CO4 cells per milliliter in a 6-well plate , 24-well plate , or 96-well plate . For viability assays, after 24 h, the supernatant was discarded and media containing increasing concentrations (range from 1–10 μM for silmitasertib and 0.001–1 μM for dinaciclib) of inhibitors or vehicle  were added to the corresponding PDAC cell lines. For the apoptosis/necrosis analysis, the inhibitor concentrations were selected according to the results of the cell viability assays. The inhibitor concentrations were adjusted according to the response observed in the viability assays for further analysis of the induced apoptotic and necrotic events. The treated cells were incubated for 72 h at 37 °C with 5% CO2. At the indicated time points, cell proliferation, metabolic activity, cell biomass, or apoptosis/necrosis was evaluated in at least three biologically independent replicates.For all assays, the PDAC cell lines were seeded at the density of 3.3 × 10Cell proliferation was evaluated by absolute counting and Trypan blue  staining. After drug exposure in 24-well plates, the cells were harvested and washed by 1× PBS (PAN-Biotech). Following the cells being stained with Trypan blue, the number of viable cells was determined by counting with a hemocytometer. Proliferation was expressed as a percentage of viable cells treated with the inhibitor to the vehicle-treated control (control = 100%).®-Multi Microplate Multimode Reader  and the metabolic activity was calculated as recommended by the manufacturer. Metabolic activity was expressed as a percentage of the inhibitor-treated group to the vehicle-treated controls (control = 100%).Metabolic activity was tested by using the Water Soluble Tetrazolium—1 (WST-1) assay . After exposure to the corresponding inhibitor, the cells were incubated with 15 μL WST-1 for 2 h in 96-well plates. Absorbances at 450 nm and the reference wavelength of 620 nm were measured by Promega GloMax®-Multi Microplate Multimode Reader for background normalization. CV cell biomass estimation result was expressed as a percentage of the inhibitor-treated group to vehicle-treated controls (control = 100%).Biomass quantification was carried out by Crystal Violet (CV) (Sigma-Aldrich Chemie GmbH) staining. After exposure to the corresponding inhibitors in 96-well plates, the cells were washed once with PBS and stained with 50 μL 0.2% CV solution on a shaker at room temperature for 10 min. Following this, the plates were washed twice with PBS. To elute bound CV, 100 μL 1% sodium dodecyl sulfate (SDS)  was added to each well and incubated on a shaker at room temperature for 10 min. Finally, absorbances at 570 nm and reference wavelength at 620 nm were measured by a Promega GloMax50 values were calculated based on cell proliferation, metabolic activity, and biomass after 72 h of inhibitor exposure. GraphPad Prism Version 8.0.2  was used to evaluate IC50. Briefly, after transforming concentrations and normalizing the results of the three vitality assays, nonlinear regression model  was used to evaluate the IC50 values. Calculate the IC50 corresponding to the three vitality assays, and apply these results to the response-based clustering analysis in order to evaluate the sensitivity of cell lines to inhibitors.IC−/PI− cells are considered to be viable cells, YO-PRO-1+/PI− cells are considered to be apoptotic cells, and PI+ cells are considered to be necrotic cells. Flow cytometry measurement was performed on FACSverse , Heidelberg, Germany) and all data were analyzed by BD FlowJo software (BD).Apoptosis and necrosis were evaluated by YO-PRO-1  and propidium iodide (PI) (Sigma-Aldrich Chemie GmbH) double staining by flow cytometry. After exposure to the corresponding inhibitor, supernatants were collected and cells were harvested and washed twice with cold PBS. Following this, cells were resuspended in 200 μL YO-PRO-1  solution. After incubating at room temperature for 20 min in the dark, cells were washed twice in cold PBS and resuspended in 400 μL cold PBS. Then, cells were stained with PI  straightway before measurement. Unstained and single-stained cells were used as controls and measured in every single experiment. YO-PRO-1® Tissue Kit  according to the manufacturers’ instructions. In brief, 5 × 106 cells were harvested from each continuous cultural cell line and washed twice with cold sterile PBS. Cell pellets were lysed, then the lysis that contained genomic DNA were extracted and purified by a silica membrane of the NucleoSpin column. Finally, genomic DNA was eluted by 30 μL of nuclease-free water.Genomic DNA was extracted by the NucleoSpin6 cells were harvested from each continuous cultural cell line and washed twice with cold sterile PBS. Cell pellets were resuspended in 700 μL QIAzol Lysis Reagent (QIAGEN GmbH), then the aqueous phase that contained the total RNA of the lysed cells were extracted and purified by a silica membrane of RNeasy Mini spin columns. Finally, total RNA was eluted by 30 μL of nuclease-free water.Total RNAs were extracted by miRNeasy Mini Kit  according to the manufacturers’ instructions. In brief, 5 × 10After extraction, nucleic acid concentrations as well as OD 260/280 and OD 260/230 ratios were measured with a NanoDrop 1000 Spectrophotometer .Barcoded sequencing libraries were generated after enrichment with the SureSelect Human All Exon Kit , pooled and sequenced on a HiSeq4000  instrument using 150 paired-end protocol to yield at least 20× coverage for >98% of the target region and an overall average depth of coverage above 100×. An in-house bioinformatics pipeline including read alignment to human genome reference hg 19, variant calling , and variant annotation with publicly available data based was used.After WES, the sequencing data from ten PDAC cell lines were obtained and filtered in order to select variants with the expected highest impact on gene function. Briefly, variants were filtered based on quality , VAF, depth of coverage (DP), and variant type. In order to exclude false positive variants, only variants with qual > 100, VAF > 20, and DP > 9 were included in our analysis. Germline mutations were excluded through a comparison with the COSMIC and dbSNP databases. Then, variant types were excluded that were not able to cause amino acid substitution, RNA structure change, or base insertions/deletions (indels). These variant types include synonymous variants, intronic variants, upstream or downstream variants, 3 prime or 5 prime UTR variants. After this filtering procedure, missense variants, splice region variants, inframe indels, frameshift variants, gene fusion, start/stop gain, or lost were kept for further analysis .Barcoded sequencing libraries were prepared with the TruSeq Stranded mRNA Kit (Illumina), pooled, and sequenced on a NextSeq 500 System (Illumina) using the 75 bp paired-end protocol. At least 30 million reads were obtained for each sample. The reads were aligned to reference genome GRCh37/Release 38 with STAR V.2.7.6a using the two-pass mode .The expression data of non-neoplastic pancreatic tissue from The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas Program (TCGA) were chosen as the control. Non-inhibitor target genes were analyzed to exclude the tumor-induced upregulation of all genes.50 values of cell proliferation and biomass. Then, these IC50 results were applied to the Sci-kit learn package using Python programming language to predict optimal clusters. The Silhouette score was used to detect the clustering density and the separation between clusters [The cell sensitivity grouping was performed by the k-means++ clustering method based on an unsupervised machine learning algorithm. Briefly, after performing viability assays on all ten PDAC cell lines, we obtained the ICclusters . Ten celp < 0.033, ** p < 0.002, *** p < 0.001 versus the control group.Data were replicated with at least three biologically independent experiments. Results of proliferation, metabolic activity, biomass quantification, and apoptosis/necrosis analysis were expressed as mean ±  standard deviation (SD). Statistical significance was determined by one-way ANOVA (after proving the data within each group conformed to the Gaussian distribution) or Kruskal–Wallis test (the data within each group conformed to non-Gaussian distribution) and displayed as * KRAS variants may reduce the sensitivity of PDAC cell lines to silmitasertib. Specific TP53 variants including c.267delC, c.818G>A, and c.844C>T, reduced the sensitivity of silmitasertib to the PDAC cell lines. Interestingly, cell lines carrying TP53 frameshift variants are highly sensitive to dinaciclib compared to cell lines carrying TP53 point mutations. Thus, both inhibitors displayed excellent in vitro efficacy on PDAC cell lines, and further experiments are still needed to verify the in vivo efficacy and the effects of the target genes and hotspot genes on the efficacy of the inhibitors.Our present study revealed distinct sensitivities of the PDAC cell lines when treated with dinaciclib or silmitasertib. Neither the expression level of the inhibitor target genes nor gene variants could affect the differences in the observed sensitivity to these drugs. For PDAC hotspot genes, the


Anopheles cell lines are used in a variety of ways to better understand the major vectors of malaria in sub-Saharan Africa. Despite this, commonly used cell lines are not well characterized, and no tools are available for cell line identification and authentication.Utilizing whole genome sequencing, genomes of 4a-3A and 4a-3B ‘hemocyte-like’ cell lines were characterized for insertions and deletions (indels) and SNP variation. Genomic locations of distinguishing sequence variation and species origin of the cell lines were also examined. Unique indels were targeted to develop a PCR-based cell line authentication assay. Mitotic chromosomes were examined to survey the cytogenetic landscape for chromosome structure and copy number in the cell lines.Anopheles coluzzii ancestry. Cytogenetic analysis indicates that the two cell lines are essentially diploid, with some relatively minor chromosome structural rearrangements. Whole-genome sequence was generated, and analysis indicated that SNPs and indels which differentiate the cell lines are clustered on the 2R chromosome in the regions of the 2Rb, 2Rc and 2Ru chromosomal inversions. A PCR-based authentication assay was developed to fingerprint three indels unique to each cell line. The assay distinguishes between 4a-3A and 4a-3B cells and also uniquely identifies two additional An. coluzzii cell lines tested, Ag55 and Sua4.0. The assay has the specificity to distinguish four cell lines and also has the sensitivity to detect cellular contamination within a sample of cultured cells.The 4a-3A and 4a-3B cell lines are female in origin and primarily of Anopheles cell lines was used to develop a simple diagnostic assay that can distinguish these cell lines within and across research laboratories. A cytogenetic survey indicated that the 4a-3A and Sua4.0 cell lines carry essentially normal diploid chromosomes, which makes them amenable to CRISPR/Cas9 genome editing. The presented simple authentication assay, coupled with screening for mycoplasma, will allow validation of the integrity of experimental resources and will promote greater experimental reproducibility of results.Genomic characterization of the 4a-3A and 4a-3B The online version contains supplementary material available at 10.1186/s13071-022-05590-3. Anopheles mosquitoes are major vectors of malaria parasites across sub-Saharan Africa. Vector control tools such as insecticide-treated bed nets and indoor residual spraying have long been the cornerstone of vector control strategies aimed at curbing malaria, arboviral and other vector-borne disease transmission. However, given increased insecticide resistance within mosquito populations, other control strategies are needed  and the variants fixed similarly in both cell lines, but having a genotype distinct relative to the AgamP4 genome assembly (Same GT), were significantly different across the genome from the expectation if SNPs and indels occurred randomly across the chromosome arms  and 4a-3B as 2Rb , consistent with both the molecular karyotyping results and the whole genome sequence analysis  predicted 4a-3A as 2R + c  and 4a-3B as 2Rc , again consistent with the whole genome analysis , with 3 An. gambiae SNPs and 17 An. coluzzii SNPs  were diploid. Visual examination of the cytological preparations indicated that the homologous chromosomes in the majority of 4a-3A cells have similar lengths, suggesting that there is little autosomal rearrangement  were diploid. In Sua4.0 cells, all homologous chromosomes appear to have different lengths consistent with the presence of some rearrangements. For both cell lines, 5% of observed cells were tetraploid, which can be an artifactual result of cell treatment with colcemid [Cells from the 4a-3A and Sua4.0 cell lines were cytologically examined for ploidy levels. 4a-3A is a female cell line, as shown by the presence of a pair of X chromosomes, and of 331 4a-3A cells examined, 95%  and Sua4.0. Thus, with three PCR reactions using identical cycling conditions, an investigator can distinguish among four Anopheles cell lines. This indel-based PCR assay allows for clear differentiation on an agarose gel, is easy to execute and requires no specialty laboratory equipment or reagents. This assay serves not only to identify cell lines but also as a tool to detect cell line contamination.Whole genome sequencing of two An. gambiae. The current genomic assessment clearly demonstrates an origin more closely rooted in An. coluzzii, with fewer than 20% of species diagnostic SNPs consistent with An. gambiae origin.The initial report of the 4a-3A and 4a-3B cell lines (5), as well as many subsequent publications utilizing them, refers to them as originating from Anopheles colonies [Similar identification and authentication tools should be developed and utilized for commonly used anopheline mosquito colonies, as contamination and mislabeling of colonies can lead to spurious results. The Malaria Research and Reference Reagent Resource Center used an authentication matrix to distinguish among genetically similar colonies . Frequencolonies . Experimcolonies .One notable difference between the 4a-3A and 4a-3B cell lines is their expression of six prophenoloxidase genes, PPO1-6, which 4a-3A cells do not express, while 4a-3B cells express all six PPO genes constitutively . CurrentCytogenetic analysis demonstrates that both the 4a-3A and Sua4.0 cell lines are diploid. Knowledge of ploidy is a relevant and important consideration for experiments involving genetic modification via CRISPR or similar technologies. While greater ploidy does not preclude genetic modification, using diploid cell lines simplifies the experimental problem.Anopheles cell lines 4a-3A and 4a-3B reveals that both are diploid cell lines of female origin with the bulk of the genetic variation between them resulting from a fixation of the 2Rb, c and u inversions within the 4a-3B cell line. Contrary to previous reports of an An. gambiae species origin, these cell lines are primarily of An. coluzzii ancestry. An authentication assay based on PCR amplification of three variable indels is capable of distinguishing not only 4a-3A and 4a-3B, but also Ag55 and Sua4.0 cell lines.Genome characterization of Additional file 1: Table S1. Primer sequences for indels selected for cell line diagnostic. Fasta file of indel region sequences. Sequence of the three diagnostic indel regions, 2R_25670547, 3R_11788474 and 3R_11809836, in each of the four tested cell lines. 2La inversion. Figure S1. Molecular karyotyping of the 2La inversion in four cell lines. Sua4.0 cells are fixed for the 2La inversion, and 4a-3A, 4a-3B and Ag55 cells are fixed for 2L + a. Lane 1: 100-bp ladder, lane 2: 4a-3A, lane 3: 4a-3B, lane 4: Ag55, lane 5: Sua.40 and lane 6: GeneRuler 1-kb Plus DNA Ladder. Expected band sizes are 492 bp for 2La and 207 bp for 2L + a (46). Figure S2. Passage number does not affect the molecular assay to differentiate cell lines. Molecular fingerprints for three PCRs amplifying indel regions in gDNA isolated from 4a-3A cells at passage 4  and passage 29  and gDNA isolated from Sua4.0 cells at passage 5  and passage 57 . Indel 2R.25670547 (lanes 2–5), indel 3R.11788474 (7–10), indel 3R.11809836 (lanes 12–15) and 1-kb Plus DNA Ladder . Band sizes are as expected; for indel 2R.25670547 282 bp in 4a-3A and 361 in Sua4.0, for indel 3R.11788474 735 bp for 4a-3A cells and 736 bp for Sua4.0 cells with the single nucleotide difference not resolvable on an agarose gel or important for cell line authentication and for indel 3R.11809836 263 bp in 4a-3A cells and 262 bp in Sua4.0 cells. As 4a-3A cells originate from the 4a r/r strain of mosquitoes and Sua4.0 cells from the Suakoko 2La strain of mosquitoes, minor differences in sequence are expected [


The data were analyzed using a latent variable approach. The results of the study show that academic buoyancy is a significant predictor of math achievement and that this relation is explained through academic self-efficacy, even when controlling for gender. Implications for practice and further research are also discussed.Academic buoyancy describes the ability to successfully overcome and recover from setbacks in an academic context . This day-to-day form of academic resilience has recently been defined in the context of positive psychology. The present study aimed to gain insights into the mechanisms of academic buoyancy by predicting math achievement. Since there is already evidence that this relationship is rather indirect than direct, we were particularly interested in investigating a potential actor of an indirect effect, namely academic self-efficacy. For this purpose,  According to the PISA  survey, in 2018 around 21% of 15-year-old students in Germany achieved the two lowest levels of competence in mathematics and reading skills . Yun et al. .The few studies devoted to the direct relationship between academic buoyancy and achievement suggest that academic buoyancy is a significant predictor of achievement, although the strength of the relationships varied. Martin  found a n et al.  reportedMoreover, Colmar et al.  examinedThus, we consider constructs that have conceptual overlap with academic buoyancy  to be relevant to the relationship between academic buoyancy and achievement and will therefore take a closer look at empirical predictors of academic buoyancy in the following section.confidence, which is mostly assessed via self-efficacy; coordination, which is represented by planning; commitment, which is assessed via persistence; composure, which is determined by low anxiety; and lastly control, which is represented by one’s response to uncertainty. The predictive power of the 5C’s has been repeatedly demonstrated in numerous studies  and that the expression of the construct at the earlier time predicted the expression at the later time (β = .21) ; coordination ; commitment ; composure ; control  . Furthermore, more than 93% of studies report positive correlations between self-efficacy and performance at the interindividual level  of N = 11 schools (Level 2) in southwestern Germany . Of the participating students, 50.2% were female. The students were in Grades 5 to 10 and were between 10 and 19 years old . According to Fritz and MacKinnon . As this indicated our missing data to be MCAR, we decided to apply the recommended full information maximum likelihood (FIML) estimations . We had 3.59% missing values in our variables but no systematic patterns of missing data. To analyze if the missing values were missing completely at random (MCAR), we conducted Little’s  MCAR tesvery good”) and 0 points representing the worst grade (“insufficient”). The reported grades in mathematics, which were used as a performance measure, had an average of M = 8.86 points (SD = 2.94). Finally, students completed the items on self-efficacy and academic buoyancy.Data were collected from October to December 2019 in schools whose principals had agreed to participate in the study. Prior to data acquisition, declarations of consent were distributed in class. Participation was voluntary and only those students who could provide a signed consent form from a parent or guardian were allowed to participate in the study. Teachers received the questionnaires in advance, with detailed instructions to ensure objectivity of implementation. Questionnaires were then completed in class. First, students provided information about their gender, age, grade level, and their most recent report card grade in mathematics. School grades were given according to a 15-point system, with 15 points representing the best possible grade  and the data protection committee of the Ministry of Education in Saarland. All data were handled anonymously.(“strongly agree) to 6 (“strongly disagree”). For better understanding, items were recoded so that high scores indicated high expressions of the relevant factor. Scale means were calculated for the questionnaires. To determine internal consistency, Cronbach’s alpha was calculated for all scales.The following section presents the instruments used to collect the data on the relevant constructs. To answer the questionnaire items, students were presented a six-point Likert scale to indicate how strongly they agreed with the given statements. The possible answers ranged from 1 χ2/df-ratio = 3.76.To assess students’ academic self-efficacy, we slightly adapted an instrument by Schwarzer and Jerusalem  comprisiχ2/df-ratio = 3.05.For academic buoyancy, we used a translated version of a common questionnaire by Martin and Marsh , b. The “insufficient”) to 15 points (“very good”).In addition to questionnaire items regarding academic self-efficacy and academic buoyancy, students’ most recent report card grades in mathematics were collected, ranging from 0 points . RMSEA and SRMR usually vary between 0 and 1 and excellent model fit is indicated with values of .05 or less. The χ2/df-ratio should be below 3 for perfect model fit. CFI, taking values from 0 to 1, should be .95 or greater for excellent fit of the data to the hypothesized model.Model fit was determined according to common recommendations , as recommended in the common literature , indicating that academic buoyancy significantly predicts academic self-efficacy in students. The higher the students’ academic buoyancy, the higher their self-efficacy.To test Hypothesis 1 (path a), we used a simple latent regression model with academic buoyancy predicting academic self-efficacy. The model showed acceptable fit with CFI = .96, RMSEA = .06, SRMR = .04, and β = .57, p < .001). The model for this hypothesis showed good fit to the data . The greater the students’ self-efficacy, the higher their math grade.For Hypothesis 2 (path b), we tested the path from academic self-efficacy to math achievement, which was significant (χ2/df-ratio = 5.20) and resulted in a significant regression . The more buoyant the students, the better their math grade.For Hypothesis 3 (path c), we tested the effect of academic buoyancy on math achievement. This model had acceptable fit . Academic self-efficacy was a significant predictor in the multiple regression with academic buoyancy  which justified testing the mediation model in the next step.In order to estimate the model for Hypothesis 4 (path c’), we conducted a multiple latent regression with the predictors—academic buoyancy and academic self-efficacy—regressed on the dependent variable, math achievement. This model showed good model fit  via self-efficacy was significant ; the 99% CI with bias-corrected bootstrapping did not include zero [0.57–1.33]. In the multiple regression with academic self-efficacy and academic buoyancy predicting achievement (path c’), academic buoyancy was not a significant predictor, which suggests that academic self-efficacy completely mediates the relationship between academic buoyancy and achievement .For the final mediation model, we estimated a model with academic buoyancy predicting math achievement χ2/df-ratio = 4.22. The indirect effect of academic buoyancy on math achievement via self-efficacy was again significant  and the 99% confidence interval with bias-corrected bootstrapping did not include zero [0.52–1.37]. In this case, academic buoyancy was also not a significant predictor in the multiple regression, which further indicates that academic self-efficacy completely mediates the relationship . Moreover, correlations with the covariate gender indicated that male students  were, on average, significantly more buoyant  but did not experience more self-efficacy  nor were they higher achieving in math  . This is in line with prior research .The significant predictive power of academic buoyancy on achievement, which we investigated in our second hypothesis is also in line with previous findings and shows that academic buoyancy is a performance-relevant construct. These findings are consistent with those of Martin  and Yun To answer our third hypothesis, we further examined whether academic self-efficacy is a significant predictor of math achievement in a multiple regression model with academic buoyancy—if academic self-efficacy predicts variance in math achievement beyond academic buoyancy—and found a significant effect. This justified testing a mediation model. However, we only had one time point at which we measured the predictors as well as the criterium.r = .48) and that is why we have to assume a multicollinearity between both variables. This also could cause the decreased relationship of academic buoyancy and achievement in the mediation model. As academic buoyancy and self-efficacy are theoretically linked and interwoven (Smith, The concluding mediation analyses showed that the relationship between academic buoyancy and achievement is fully associated through academic self-efficacy. Thus, when self-efficacy is included in the model, the direct relationship between academic buoyancy and math achievement is not significant. This complete indirect association of the relationship reveals that the performance-relevant effects of academic buoyancy do not directly impact performance. However, this by no means limits the relevance of academic buoyancy to performance. Rather, the results show, especially considering the current research background, that little is known about the mechanisms of academic buoyancy. Previous work, such as that by Colmar et al.  and Colln Smith, , it has Moreover, as recommended by Colmar et al. , we accoTaking these findings together, we have significantly contributed to the research on academic buoyancy and its relationship with achievement. We have provided another study suggesting that academic buoyancy is crucial to academic achievement, but especially influences other variables that, in turn, impact achievement. Moreover, we replicated previous findings with a large sample allowing structural equation modeling in a German-speaking country where academic buoyancy has rarely been considered thus far.However, the results of the study should, of course, not be interpreted without considering its limitations.The fundamental difficulties that can arise from data collection by questionnaire should be pointed out. The questionnaires used to assess academic self-efficacy and academic buoyancy were self-report methods. This means that it cannot be guaranteed that the questionnaire scores given by the students correspond to their actual characteristics in the corresponding constructs. This may be due to the effect of social pressure. It is also conceivable that students did not provide honest information for fear of the information being passed on to the teachers. Nevertheless, there is no appropriate alternative to self-report questionnaires for academic buoyancy or academic self-efficacy, and other studies have implemented them as well.The cross-sectional design of the study is limiting the implications as well. The project in which the present study took place was initially planned as a long-term study, with two data collection points separated by six months. However, due to the global COVID-19 pandemic, the second measurement point in spring 2020 had to be canceled. Datu and Yang  criticizAnother limitation is the use of the most recent grade in mathematics as a measure of academic performance. First of all, looking at a single grade on a school report is not sufficiently informative regarding students’ overall academic performance (Südkamp et al., Further, it could be problematic that the achievement measure was domain-specific and self-efficacy and academic buoyancy were measured domain-general. It is generally assumed that self-efficacy and performance measures correlate especially high when there is the greatest possible correspondence in terms of content (Feng et al., The results of the present study show that academic buoyancy is a relevant construct that should be further explored in future educational research.The predictors of academic buoyancy which are referred to as the 5C’s (Martin & Marsh, Moreover, research on academic buoyancy has so far been conducted almost exclusively in industrialized countries, mainly Australia and the UK, and individualistic societies (for a review, see Datu & Yang, Most studies on academic buoyancy have focused on students in higher grades. This focus is justified by the assumption that academic buoyancy plays a more significant role among older students. However, future studies should also look at younger students. As with many educational-psychological constructs with a performance-relevant effect, it can be assumed that academic buoyancy is acquired and trainable in childhood (e.g., self-regulated learning; Dignath et al., which students become buoyant and what exactly makes them buoyant—for example, by applying person-centered approaches. This could contribute to the development of interventions to make students more resilient to future academic adversity. If an intervention claims to make students more resilient and thus supports them in their everyday school life, academic buoyancy must play an essential role as students do not only suffer from “major negative outcomes,” but also the sum of “low-level negative outcomes” that burden them in the long run (Martin, More research should be dedicated to the questions of  Martin, , p. 496. Martin,  showed tThe significant association between academic buoyancy and achievement has been discussed in detail in this paper. However, performance is not the only variable that is positively influenced by academic buoyancy. Miller et al.  addresseBy examining the relationships between academic buoyancy, academic self-efficacy, and achievement, the present study provided insights into the mechanisms of academic buoyancy. The results are consistent with the previous state of research and extend it in a significant way. However, as academic buoyancy has been little researched so far, further studies dedicated to the construct are urgently needed. With better knowledge, interventions or classroom strategies to support students can be derived. Interventions can be targeted at all students, as the ability to cope with small setbacks is important for all learners. If students are able to recover from the adversities of everyday academic life, this can also be important for later confrontations with greater difficulties. The construct of academic buoyancy should therefore be the subject of greater research interest in the future.


V600E mutation; however, acquired resistance is unavoidable. The present study aimed to identify a potential target to reverse resistance.Vemurafenib (VEM) is a commonly used inhibitor of papillary thyroid cancer (PTC) and melanoma with the BRAFV600E melanoma cell line (A375/VR) was also established. RNA sequencing and bioinformatics analyses were conducted to identify dysregulated genes and construct a transcription factor (TF) network. The role of a potential TF, forkhead box P2 (FOXP2), verified by qRT-PCR, was selected for further confirmation.A VEM-resistant PTC cell line (B-CPAP/VR) was established by gradually increasing the drug concentration, and a VEM-resistant BRAFp < 0.05). RNA sequencing identified 9177 dysregulated genes in the resistant cell lines, and a TF network consisting of 13 TFs and 44 target genes was constructed. Alterations in FOXP2 expression were determined to be consistent between the two VEM-resistant cell lines. Finally, silencing FOXP2 resulted in an increase in drug sensitivity and significant suppression of the migration and colony formation abilities of the two resistant cell lines (p < 0.05).The two resistant cell lines were tolerant of VEM and displayed higher migration and colony formation abilities (The present study successfully established two VEM-resistant cell lines and identified a potential target for VEM-resistant PTC or melanoma. V600E mutant functions as the major driver of the MAPK pathway and is involved in the secondary genetic alteration of members of the PI3K-AKT pathway, thus leading to the aggressive development of PTC -2,5-diphenyltetrazolium bromide  for another 3 h. Next, the supernatant was removed and dimethyl sulfoxide  was added to each well and incubated for 15 min. The optical density (OD) of each well was measured at 490 nm using a microplate reader . The survival rate (%) was calculated according to the following formula [B-CPAP, A375, B-CPAP/VR, and A375/VR cell lines were collected and seeded at 5 × 10 formula :Survival rate (%) = mean OD of experimental group/mean OD of control group × 10050 was calculated according to the survival rate using SPSS version 17.0. The drug resistance index was calculated according to the formula [The IC formula :50 of drug-resistant cell line/ IC50 of corresponding parental cell lineResistance index (RI) = IC3 cells/well in 96-well plates. The OD of each well was measured at 490 nm using the MTT assay every day for 5 days. Four replicate wells were used for each time point. The proliferation rate was calculated using the following formula [The B-CPAP, A375, B-CPAP/VR, and A375/VR cell lines were collected and seeded at 3 × 10 formula :n = 1–5)Proliferation rate = mean OD of day measured (n)/mean OD of first day  inserted into a 24-well plate was used to measure cell motility. The B-CPAP, A375, B-CPAP/VR, and A375/VR cell lines were collected and resuspended in FBS-free culture medium, seeded in a chamber at 2 × 10B-CPAP, A375, B-CPAP/VR, and A375/VR single cells resuspended in the required medium were counted, and 800 cells/well were seeded in 6 well plates. Three days later, VEM (2 μM) was added to the medium and cells were cultured for an additional 11 days. After cloning, the cells were fixed with 4% paraformaldehyde for 30 min and stained with crystal violet for 10 min. Finally, cells were washed twice with PBS for two times and photographed using a digital camera.2 fold change (absolute) |Log2 FC | > 1 , and the resulting genes were used for further bioinformatics analyses.Total RNA of B-CPAP, A375, B-CPAP/VR, and A375/VR was extracted using TRIzol reagent  according to the manufacturer’s instructions. RNA quality and concentration were determined from OD260/280 readings using a NanoDrop ND-1000 spectrophotometer  and assessed on a 1% gel electrophoresis. RNA samples were subjected to BHBIO  for RNA sequencing. Differentially expressed genes (DEGs) were screened under a threshold of logKyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted to analyze the enriched signaling pathways of the DEGs. A gene number > 2 was used as a threshold when screening for relevant KEGG pathways. TF-binding networks were constructed using the top 2000 dysfunctional genes utilizing weighted correlation network analysis (WGCNA).5 cells/well) were seeded into six-well plates. After the cells grew to 80% confluence, si-FOXP2 and control siRNA (si-NC) were transfected into VEM-resistant cell lines using Lipofectamine® 3000 (Invitrogen), according to the standard protocol. Forty-eight hours after transfection, transfection efficiency was determined by quantitative real-time PCR (qRT-PCR). The siRNA sequences for si-FOXP2 were as follows: sense (5′-3′): GGCUAGACCUCACUACUAATT, anti-sense (5′-3′): UUAGUAGUGAGGUCUAGCCTT.B-CPAP/VR and A375/VR cells  and then reverse-transcribed using a PrimeScript RT Reagent Kit with gDNA Eraser (TaKaRa) according to the manufacturer’s instructions. The complementary DNA template was amplified by qRT-PCR using SYBR Premix Ex Taq (TaKaRa Bio). qRT-PCR was conducted using the StepOne Plus Real-Time PCR System (Thermo Fisher Scientific). Briefly, the reaction system for quantification included 0.5 µL cDNA, 1 µL primers, 5 µL SYBR Green, and 3.5 µL deionized water. Amplification was performed as follows:−∆∆Ct method and were normalized to GAPDH expression. The primer sequences synthesized by Sangon Biotech  are listed in Table 94 °C for 5 min and 40 cycles of 94 °C for 5 s and 60 °C for 1 min. Gene expression data were analyzed using the 2p < 0.05.SPSS statistical software was used for statistical analysis. The data were tested using the Students’ t-test to analyze the differences between the different groups. Statistical significance was set at 50 value of B-CPAP/VR increased to 304.3 μM, around 44 times of the value of parental B-CPAP (6.92 μM). IC50 values of A375/VR and A375 were 76.76 μM and 1.23 μM respectively, and the resistance index was 62.3 . A transwell assay was performed to evaluate cell motility. A higher number of B-CPAP/VR and A375/VR cells traversed the membrane than B-CPAP and A375 cells, respectively, indicating that the migration ability of resistant cells was significantly enhanced . The colony formation assay results indicated that the colony number of both B-CPAP/VR and A375/VR was more than twice that of their parental cell lines after treatment with VEM for 11 d . All phenotypic changes suggested that the VEM-resistant cell lines were successfully established.The MTT assay results indicated that the proliferation ability of B-CPAP/VR and A375/VR was reduced when compared to the original cell lines . MTT analyses showed that after knockdown FOXP2, the IC50 values of B-CPAP/VR and A375/VR were reduced to 90.77 and 13.05 µM, respectively, much lower than the si-NC group cells  , and the FOXP2 knockdown group showed weaker clone formation and migration abilities . All the phenotype assays indicated that after downregulating FOXP2 expression, the VEM-resistant cell lines B-CPAP/VR and A375/VR were more sensitive to VEM.The expression level of FOXP2 in B-CPAP/VR and A375/VR were knocked down using siRNA. qRT-PCR results indicated a significant decrease in FOXP2 expression in the si-FOXP2 group compared to that in the si-NC group  Fig. . In addiTo reveal the possible mechanism and role played by FOXP2 in VEM resistance, we searched for FOXP2 in Table S1, but no relevant pathway was found. Next, we focused on the target gene, lysophosphatidic acid receptor 3 (LPAR3), of FOXP2, which might establish some connections with the pathways. LPAR3 was involved in five pathways Table ; interesV600E is the most common mutation in some types of cancer. It is highly related to high mortality in patients with PTC or melanoma, which also stimulates research on BRAF inhibitors. To date, the development of new targets is still an important strategy to reverse resistance to VEM and prolong the effective treatment of drug-resistant patients.Acquired drug resistance often occurs when it comes to clinical treatment for patients with cancer, especially for those carrying a mutation in a specific gene. BRAFIn clinical and pre-clinical studies on BRAF inhibitor resistance, the time for VEM resistance development ranged from 3 weeks to 1 year, and very few patients survived without developing resistance . It was V600E; however, to prevent drug resistance, more targets belonging to related pathways need to be identified.According to RNA sequencing data, many of the DEGs involved in the pathways were related to human diseases, but most of them were classified in signal transduction pathways. Many reports have also revealed that signaling pathways are indeed related to drug resistance in TC and melanoma based on in vitro and clinical studies . ClinicaTFs are proteins that control DNA transcripts to mRNA, which can not only upregulate downstream gene expression but also silence specific genes. Therefore, dysregulated TFs induce specific diseases, including cancer, which makes TFs interesting targets for future medications . Many dyKEGG analysis showed that FOXP2 does not directly participate in some signaling pathways, as FOXP2 functions as a TF. Nevertheless, it is possible for FOXP2 to manipulate some downstream genes to turn on/off signal transduction, and the TF network suggests that there is a connection between LPAR3 and FOXP2. LPAR3 encodes a member of the G protein-coupled receptor family and the EDG family of proteins . It is nV600E mutant PTC and melanoma cell lines, which can be used as in vitro models for other VEM-resistance studies. FOXP2 is a potential target and should be further explored as an innovative medication to reverse VEM resistance. The present study also identified a large number of DEGs, thereby providing a databank for target development.In this study, we established robust VEM-resistant BRAFTable S1


Lenvatinib is a multi-kinase inhibitor approved as a first-line treatment for patients with unresectable advanced hepatocellular carcinoma (HCC). However, its response rate is unsatisfactory, primarily due to the acquisition of resistance, which limits its clinical significance for treating patients with HCC. Recent evidence suggests that epidermal growth factor receptor (EGFR) activation can trigger Lenvatinib-resistance; and is considered an important therapeutic target in HCC. Curcumin, one of the most studied naturally occurring botanicals with robust anti-cancer activity, is also reported to be a potent tyrosine kinase inhibitor. In this study, we hypothesized that the anti-EGFR potential of Curcumin might help overcome Lenvatinib resistance in HCC. We established two Lenvatinib-resistant cells and discovered that a combination of Curcumin and Lenvatinib exhibited a synergistic anti-tumor efficacy in the resistant HCC cell lines. In line with previous reports, Lenvatinib-resistant cell lines revealed significant activation of the EGFR, and genomewide transcriptomic profiling analysis identified that the PI3K-AKT pathway was associated with Lenvatinib resistance. The combination treatment with Curcumin and Lenvatinib dramatically suppressed gene and protein expression of the EGFR-PI3K-AKT pathway, suggesting Curcumin overcomes Lenvatinib resistance via inhibition of EGFR. We further validated these findings in tumor spheroids derived from resistant cell lines. In conclusion, we, for the first time, report that Curcumin reverses Lenvatinib resistance in HCC, and that their combination has clinical application potential for adjunctive treatment in HCC. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the third leading cause of cancer-related deaths . PatientHowever, the overall response rate of Lenvatinib is only ~24% , and eveSeveral naturally occurring botanical compounds have recently gained more attention from researchers and clinicians because of their inherent multi-targeted and safer therapeutic efficacy compared to conventional chemotherapy and targeted drugs ,26,27,282 atmosphere. Curcumin was generously provided by EuroPharma USA, Green Bay, WI, USA, and Lenvatinib was purchased from Sigma-Aldrich, St. Louis, MO, USA.Five human HCC cell lines  were examined in this study. SNU449 was obtained from the American Type Culture Collection . Huh-7 and PLC-PRF-5 (PLC) were obtained from the RIKEN BioResource Center Cell Bank . Dr. Ke, Department of Diabetes Complications and Metabolism, Beckman Research Institute of City of Hope, kindly provided us with SNU398 and Sk-Hep-1 cell lines. All cell lines were cultured in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum  and 1% penicillin-streptomycin. All cells were maintained in a 37 °C incubator with a 5% humidified COFor these assays, five thousand cells were seeded into 96-well plates. After overnight incubation for cell adherence to the plates, all cell lines were treated with Lenvatinib, Curcumin, or their combination using different concentrations for 48 h. Following completion of the treatments, MTT assays were performed using the Cell Counting Kit-8  according to the manufacturer’s instructions. The absorbance of the final colored product was analyzed at 450 nm using a plate analyzer . To evaluate the effects of the drug combination, isobolograms were generated using ComboSyn software , and the combination index (CI) < 1.0 was considered a synergistic effect.Lenvatinib-resistant cells were developed from Huh-7 and PLC cell lines. Parental cells were continuously exposed to a Lenvatinib-supplemented medium. Surviving cell clones were transferred to a medium with a higher concentration of Lenvatinib, and the concentration of the drug was increased by 0.5 μM weekly for 10 months. Finally, resistant cells could stably grow in a 20 μM Lenvatinib-supplemented medium and were selected for final experimentation.4 cells were seeded into the Matrigel invasion chambers  with 8.0 µm pore-sized PET membranes. After another two days of the invasion, invaded cells were fixed and stained by Diff-Quick and counted.All HCC cell lines were seeded in 6-well plates and treated with drugs for 48 h. After treatment, 2.5 × 10One thousand cells were seeded in 6-well plates. After 3–5 days of incubation, cells were treated with individual drugs or their combination for 48 h. Following the completion of the treatment period, cells were cultured in a standard culture medium for another week and harvested. The cells were fixed in the culture plates with 100% methanol and stained with 1% crystal violet. Colony formation rates were quantified as the percentages of the stained area by ImageJ 1.53 software , as previously reported .Apoptosis and oxidative stress assays were performed following the treatment of cancer cell lines with each compound for 16 h. Cell apoptosis and oxidase stress levels were examined by the Muse Cell Analyzer  using the Annexin V and Dead Cell Kit and the Oxidative Stress Kit, per the manufacturer’s instructions.The method for these assays was previously described ,39. Briep < 0.05 and a Log2FC > 1.0. Subsequently, enrichment analysis of the KEGG pathways was carried out using the DAVID bioinformatic database. The top 50 enrichment pathways were ranked by the number of included genes. In addition, we also examined the prognostic impact of the EGFR pathway in HCC using the GSE10141 dataset.To discover significantly and differentially expressed genes in Lenvatinib-resistant vs. parental cell lines, we analyzed GSE211850, a publicly available dataset comparing gene expression profiling between the parental and Lenvatinib-resistant Huh-7 cell lines. Significantly upregulated genes were defined if they had a Total RNA was extracted from cancer cells and spheroids treated with Lenvatinib, Curcumin, or their combination for 16 h. cDNA synthesis was performed using a Reverse Transcription Kit . The qRT-PCR assays were performed using a SensiFAST SYBR Lo-ROX Kit  and the QuantStudio 6/7 Flex RT-PCR System . GAPDH was used as the housekeeping gene, and gene expression changes were calculated by the delta Ct method. The primer sequences used in this study are described in Western blotting was performed as previously reported ,39. Cellp < 0.05 was considered to be statistically significant. The overall survival analysis curves were created using the Kaplan-Meier method, and the differences in patient survival were analyzed by the log-rank test. All analyses were conducted using JMP 8.0.1 .All assays were performed in triplicate, and data were described as the mean ± standard deviation. Statistical comparisons were made using the Wilcoxon test (for 2 groups) or the Tukey-Kramer method (for 3 or more groups), and At first, we examined the sensitivity of each HCC cell to Lenvatinib. Cell viability assays following treatment with Lenvatinib (0–100 μM) revealed that Huh-7 cells ; <10 μM) were sensitive to Lenvatinib, and SNU398 cells , Sk-Hep-1 , SNU449 , and PLC  were relatively insensitive to Lenvatinib A. To detWe next evaluated whether Curcumin could exert anti-proliferative effects in these three cell lines B. CurcumThe primary aim of our study was to determine whether Curcumin could help overcome Lenvatinib resistance in HCC. To achieve this goal, we established two Lenvatinib-resistant cell lines, one from Huh-7, an endogenously Lenvatinib-sensitive cell line, and the other from PLC, the most Lenvatinib-insensitive cell line. Following the selection of resistant clones over several months, we noticed that resistant Huh-7 cells (rHuh-7) were more than 20 times more resistant than parental Huh-7 . Similarly, resistant PLC cell lines (rPLC) were more than two times more resistant than the parental PLC cells  and rPLC  compared to control and Lenvatinib treatment  and rPLC cells   B. In lin < 0.12) .Next, we examined the effect of Curcumin and Lenvatinib on the intracellular accumulation of reactive oxygen species (ROS) and apoptosis in HCC cells. The baseline percentages for the ROS-positive cell population were 3.9% (rHuh-7) and 9.9% rPLC; A. In botp < 0.01; Lenvatinib vs. combination, p < 0.01; Curcumin vs. combination, p < 0.01) and rPLC cells  and PLC (p < 0.01) cells, suggesting that EGFR expression might not be associated with the endogenous sensitivity to Lenvatinib  and 1.23 times higher in rPLC (p < 0.01) compared to the parental cell lines  in rHuh-7 compared to parental cells. The KEGG pathway analysis for these upregulated genes is depicted in To identify specific EGFR downstream pathways associated with Lenvatinib resistance, we performed genomewide expression profiling analysis between parental and resistant Huh-7 cells (GSE211850). There were 2522 significantly upregulated genes  and rPLC cells  and rPLC cell lines , compared to Curcumin treatment alone  and 2.3  in rHuh-7 cells, and from 15.0 (control) to 5.7  and 2.3  in rPLC cell line  and rPLC cells , we developed cancer spheroids from two Lenvatinib-resistant cells. Following the completion of treatments, the number and size of spheroids were evaluated. The Curcumin treatment and Lenvatinib significantly reduced spheroids’ number and size A. The nuWe also evaluated the expression of CD44 and CD133, which are the cancer stemness markers in many cancers, including HCC . InteresHepatocellular carcinoma (HCC) is the most common primary liver malignancy and is known to be a highly malignant tumor type due to its poor prognosis . UnresecCurcumin is a naturally-occurring botanical derived from turmeric and is one of the most widely studied, safe and inexpensive adjunctive treatment approaches used in various human cancers ,29,30,31According to our hypothesis, Curcumin enhanced the anti-tumor effects of Lenvatinib in both endogenously sensitive and relatively insensitive HCC cell lines by inhibiting cell proliferation, invasion, and colony formation ability. Moreover, we noted that Curcumin could reverse acquired resistance to Lenvatinib by suppressing the EGFR-PI3K-AKT signaling cascade D. In thiOur findings can potentially provide a new adjunctive therapeutic strategy in HCC. However, there were a few potential limitations to our study that we would like to acknowledge. First, we revealed that EGFR inhibition by Curcumin could overcome the acquired resistance of Lenvatinib using genomewide analysis and following experiments. However, we have not investigated how Curcumin could enhance the anti-tumor effect of Lenvatinib on endogenously insensitive cells. Secondly, we examined the effects of combination treatment in cancer cells and cancer spheroids but not in pre-clinical animal models of HCC. While this might provide additional insight, unfortunately, no robust animal models capture human HCC. In addition, although Curcumin is a safe compound, we did not directly evaluate any potential toxicity when combined with Lenvatinib. In the future, validation studies for the effect and toxicity of this combination in such animal experimental models or clinical studies might shed additional insight, given the safety profile of Curcumin with other chemotherapies and targeted drugs reported to date, the likelihood of any adverse combinatorial effects are highly unlikely.In conclusion, we provide the first evidence that Curcumin could reverse Lenvatinib resistance in HCC cells via suppression of EGFR and its downstream targets, which can offer a novel adjunctive, safe and efficacious therapeutic strategy in HCC.