Despite being an attractive target, CDK4/6 inhibition using palbociclib showed modest or conflicting results as monotherapy or in combination with platinum-based chemotherapy or cetuximab in HPVneg HNSCC. Thus, innovative agents to augment the efficacy of palbociclib in HPVneg HNSCC would be welcomed.Deregulation of cell-cycle pathway is ubiquitously observed in human papillomavirus negative  was demonstrated to show the most potent synergy with particularly higher efficacy in HNSCCs bearing PIK3CA alterations. Notably, when compared with cisplatin and cetuximab, alpelisib exerted stronger synergism in a broader panel of cell lines. Mechanistically, RRM2-dependent epithelial mesenchymal transition (EMT) induced by palbociclib, was attenuated by alpelisib and cetuximab rather than cisplatin. Subsequently, PDX models with distinct genetic background further validated that palbociclib plus alpelisib had significant synergistic effects in models harboring PIK3CA amplification.Palbociclib displayed limited efficacy in HPVneg HNSCC with PIK3CA alterations.This study provides insights into the systematic combinatory effect associated with CDK4/6 inhibition and supports further initiation of clinical trials using the palbociclib plus alpelisib combination in HPVThe online version contains supplementary material available at 10.1186/s12916-022-02373-6. Human breast cell line MCF-7 (RRID: CVCL_0031) was purchased from ATCC. All cell lines were routinely cultured at 37 °C with 5% CO2 according to the manufacturer recommendations. All cell lines were authenticated using short tandem repeat analysis and confirmed as mycoplasma-free (YEASEN). All cell lines were used for experiments within less than 20 passages.HPVWe purchased a chemical screening library composed of 162 compounds from Selleckchem for the combination drug screening with palbociclib. The corresponding information, including drug name, MoA (mechanism of action) and resource, is provided in Additional file In the primary drug combination screening, CAL27, HN6, FADU, and SCC9 cells were seeded into 96-well polystyrene tissue culture-treated Corning plates with 3000–6000 cells/well, depending on the specificity of each cell line. During the exponential growth phase, we added 10 μL of palbociclib and 10 μL 162 compounds (10X concentration) to individual wells for a 6×6 matrix screening  (concentration ranging from 20 to 0.032 μM). Bortezomib  was used as a positive control for cell cytotoxicity. After 72 h of treatment, 10 μL of CCK8 (Beyotime) was added to each well. The plates were transferred to a standard incubator with a stainless steel lid for 2 h. OD values were taken using Synergy H1 (Biotek). All plates passed Quality Control conditions, with a Z factor greater than 0.6. In the secondary drug combination screening, cells were seeded into 384-well polystyrene tissue culture-treated plates at a concentration of 800–2000 cells/well. A total of 10 μL of compounds were dispensed for a 10×10 matrix screening  (concentration ranging from 20 to 0.003 μM). The plates were incubated for 72 h in a standard incubator covered by a stainless steel gasketed lid to prevent evaporation. We added CCK8 and measured OD values. The efficacy of different drug combinations was estimated using the ExcessHSA score and a Bliss independence model.Cells were placed in 96-well plates to perform drug combination in three independent replicates. Cell viability was measured using Cell Counting Kit-8 assay according to the manufacturer’s instructions (Beyotime). To evaluate the effect of drug combination, cells were treated with each drug individually or in combination for 72 h. Data were processed in GraphPad Prism 5.0 . Combination index (CI) was determined using CalcuSyn software (Biosoft), which allowed us to measure drug synergy using the median effect model (T.-C. Chou) . A CI log for 5 min. The pellets were then washed and resuspended in 500 μL PI/RNase Staining Buffer (BD Pharmingen™). Cell-cycle analysis was performed on a BD Facscalibur. Data were processed using FlowJo v.10 (FlowJo LLC).After 48 h of palbociclib or combination treatment, HNSCC cells were harvested, washed, fixed in 75% ethanol overnight, and centrifuged at 300Cell proliferation ability was measured using a Click-it EdU imaging kit . Briefly, post-treatment cells were incubated in culture medium with 10 μM EdU for 2 h. After fixation with 4% PFA and permeabilization with 0.3% Triton X-100, cells were incubated in Click-iT Plus reaction cocktail at room temperature for 30 min. Cell nuclei were stained with 10 μg/mL Hoechst 33342 for 2 h. Images were captured with Axio Vert.A1 and analyzed using ZEN software.neg cell lines, PDXs, and paired patients’ tumors were subjected to whole exome sequencing. DNA concentration of the enriched sequencing libraries was measured with the Qubit 2.0 fluorometer dsDNA HS Assay (Thermo Fisher Scientific). Size distribution of the resulting sequencing libraries was analyzed using Agilent BioAnalyzer 2100 (Agilent). DNA sequencing was performed with paired-end 2 × 150 base reads on the Illumina NovaSeq6000 platform at Mingma Technologies Co., Ltd. Raw FASTQ files were initially processed by a proprietary algorithm to filter out contaminated mouse sequencing reads.All HPVFor each paired sample, somatic SNVs and InDels were detected with Sentieon TNseq. We excluded mutations in low-complexity regions, including tandem repeats and highly homologous regions. Low-confidence variants were removed in case any of the following criteria was not satisfied: total depth > 10, alternative allele depth > 3 and mutation frequency > 0.01. All high-confidence mutations were then annotated with ANNOVA (Version 2016-02-01).We used CNVkit to analyze somatic copy number variations (CNVs). CNVs were called by comparing normalized tumor and normal data. Regions with absolute log2 copy number ratios at least 0.58 (= log2 (1.5)) were viewed as losses (deletions) or gains (amplifications) .P-values were calculated using the Wald test after controlling for multiple testing using B-H procedure. A |fold change| > 2 and an adjusted P-value < 0.05 were used as cut-off criteria.RNA sequencing was performed according to our previous research . Brieflyq-value < 0.1 were selected for further analysis.Gene Set Enrichment Analysis (GSEA) was performed on normalized RNA-Seq expression data using the Desktop Application  and usin−ΔΔCT method.Total RNA was extracted using TRIzol reagent (Invitrogen) following the manufacturer’s instructions. RNA was reverse transcribed using a Prime-Script RT Reagent Kit (Takara). Quantitative real-time PCR (qRT PCR) was conducted using a Roche LightCycler system with SYBR Green Reagent (Takara). Gene expression levels were normalized based on the GAPDH level. The primers used for qRT PCR are listed in Additional file Cells were lysed in SDS Lysis Buffer supplemented with protease and phosphatase inhibitor cocktail. Protein concentration was determined using BCA assay. Gel electrophoresis was performed on a stacking gel (90 V) and a separation gel 120 V). The protein samples were then transferred to PVDF membranes with 300 mA for 1.5 h. The membranes were incubated with primary antibodies at 4 °C overnight  were constructed and transfected into FADU and CAL27 cells using Fugene HD . RRM2 ovPatient tissues and PDXs were embedded in paraffin. After this, we generated 4-μm-thick paraffin sections. Tissue morphology was determined by hematoxylin and eosin (H&E) staining. For immunohistochemistry (IHC) assays, paraffin sections were dewaxed and rehydrated through a graded ethanol series. After being treated with heat-mediated antigen retrieval using Citrate Unmasking Solution , the sections were blocked with goat serum at room temperature for 1 h. Primary antibodies  for migration, or BioCoatTM transwell champers (Corning) for invasion. 10 ~ 12 × 103, the mice were randomly divided into palbociclib and vehicle groups (6 mice in each group). Palbociclib (60 mg/kg) and vehicle  were orally administrated once a day. HN6 and FADU xenografts were treated with a continuous application of palbociclib for a total of 14 days, while CAL27 xenografts were treated with a phased schedule .To measure the antitumor efficacy of palbociclib monotherapy, we suspended CAL27, FADU, and HN6 cells in the mixture of PBS and Matrigel (1:1). Immuno-deficient athymic BALB/c-nu/nu female mice (6-week-old) were used in this experiment. A total of one million cells were injected subcutaneously into the left flank region of each mouse. When tumor size reached at least 100 mm3. The treatment scheme for each model varied from 8 to 29 days (mice were sacrificed either when  tumor volume exceeded 1500 mm3 or at the experiment endpoint). Tumor size and bodyweight were measured twice per week. The percentage of tumor growth inhibition [In the drug combination experiment on FADU xenografts, the mice were randomly assigned into four groups receiving vehicle , palbociclib , alpelisib , or both for a total of 21 days. To further evaluate the efficacy of the clinical treatment, PDX models were used for different drug combinations. Xenograft-bearing mice were randomly assigned into seven groups (7 mice per group) to receive vehicle, palbociclib, alpelisib, cetuximab , palbociclib plus cetuximab, palbociclib plus alpelisib, and palbociclib plus cisplatin . Treatment began when tumors reached at least 100 mmhibition  was defihibition .t tests, one-way ANOVA, or two-way ANOVA where appropriate. P < 0.05 was considered to be statistically significant. P-value symbols are denoted as follows: * (P < 0.05), ** (P < 0.01), *** (P < 0.001), and **** (P < 0.0001).Statistical analysis was performed using GraphPad Prism 9.0 software. Data are presented as the mean ± S.D. or S.E.M., as per indicated in the figure legends. Pairwise comparisons between experimental and control groups were performed using unpaired two-tailed Student’s neg HNSCC, we investigated the cellular response of CDK4/6 inhibition in different HPVneg cell lines. An ER-positive MCF-7 human breast cancer cell, which has been demonstrated to be highly sensitive to palbociclib [neg HNSCC cell lines showed varied degree of sensitivity to palbociclib, with a mean IC50 ranging from 0.908 to 47.88 μM, and we defined 5 cell lines with relative lower IC50 (< 5 μM) as “palbociclib sensitive” (PS) and the remaining 8 cell lines as “palbociclib resistant”  of all the 13 cell lines were annotated by whole exome sequencing , one PR cell lines (PR-HN6) and MCF7 were selected for evaluation of the cellular response to palbociclib treatment. When treated with different doses of palbociclib, PS-CAL27, PS-FADU and PR-HN6 showed significantly less reduction in DNA replication and G1 phase arrest when compared with MCF7 cells which displayed prominently decreased proliferation and G1-arrest as previously reported  . During the treatment suspension phase, tumor progressed rapidly upon the removal of palbociclib , reflecting the cytostatic rather than cytotoxic effect of CDK4/6 inhibition. When the mean tumor volumes in the treatment group reached comparable to that of the control group, palbociclib was resumed and PS-CAL27 xenografts were first responsive to palbociclib (from days 31–52) while started to regrow and progressed under drug pressure (from day 53 to day 69), indicating the emergence of acquired resistance to palbociclib in PS-CAL27  using a customized library within 6 × 6 checkerboard matrix  showed distinct patterns of synergistic effect with palbociclib and ranked all compounds based on average ExcessHSA scores , FDA-approved PI3Kα selective inhibitor alpelisib (ExcessHSA: − 227.83), dual PI3K/ mammalian target of rapamycin (mTOR) inhibitors apitolisib (ExcessHSA: − 202.16), and PI3Kα/β/γ inhibitor taselisib (GDC-0032) , and alpelisib demonstrated the most synergistic effect when combined with palbociclib  in combination with palbociclib in eight selected cell lines (4 PS and 4 PR), which represent two major genetic subtypes : HN6, PECA-PJ41, SCC9, CAL27, and UPCI-SCC-131). Drug dose-response curves showed that all four PI3K inhibitors resulted in obvious shifts of drug response curve and corresponding decreased IC50 when combined with palbociclib, especially for three PIK3CA-altered cell lines  at low concentrations . As expected, palbociclib or alpelisib monotherapy slightly delayed tumor progression, while palbociclib plus alpelisib showed significantly synergistic therapeutic efficacy  has been reported to be associated with resistance to targeted therapy through various mechanisms of action , 45. Morhenotype . In agrehenotype –48. To i50 values of palbociclib) in all three HNSCC cell lines, and PI3K inhibitor alpelisib combined with palbociclib could at least partially restore palbociclib sensitivity  and mesenchymal-related genes (n = 48) in combination treatment groups compared with that under palbociclib monotherapy to identify differences in the effects of these combinations on the EMT process in detail.To further evaluate whether EMT was involved in these synergistic or antagonistic effects, RNA-seq was conducted in PS-FADU, PS-HN30, and PS-CAL27 cells treated with DMSO, palbociclib, or the three combination treatments. GSEA analysis indicated that the EMT gene set was consistently upregulated by palbociclib rather than palbociclib plus alpelisib treatment, whereas treatment with palbociclib plus cetuximab or palbociclib plus cisplatin led to inconsistent results across the three cell lines  and significant downregulation of mesenchymal-related genes in HN30  and upregulation of mesenchymal-related genes in FADU cells , suggesting the activation of EMT process  has been reported as a therapeutic target via activating EMT in HNSCC and other cancers , 54. OurTo further understand whether RRM2 OE counteracted the synergistic effects of these two combinations via inducing EMT, we first conducted transwell assays. The results showed that RRM2 overexpression led to increased cell migration and invasion, which could be reversed by osalmid treatment  with TGI = 68.47%, 61.45%, and 63.51%, respectively  when compared with cetuximab monotherapy  and the activation of CDK2 represents one of the mechanisms of CDK4/6i resistance in breast cancer . In addiEMT is a well-studied complex biological process in embryonic development that is recapitulated during tumor progression, metastasis, and drug resistance . Previouneg HNSCCs, with particularly higher efficacy in cases with PIK3CA alterations. Additionally, by using a panel of genetically diverse cell lines and PDX models, we found that palbociclib plus alpelisib outperformed palbociclib plus either cetuximab or cisplatin, which are currently being investigated in clinical trials in HNSCC. Thus, the examination of palbociclib-based drug combinations provides insights into the systematic combinatory effects associated with CDK4/6 inhibition and supports further exploration of biomarker-guided clinical trials using palbociclib plus alpelisib combination in HPVneg HNSCC.In summary, we provided a comprehensive palbociclib-based drug-drug interaction dataset and identified alpelisib (a PI3Kα inhibitor) as the most potent synergistic agent in HPVAdditional file 1: Supplementary Figure 1. HPVneg HNSCC models showed a limited response to CDK4/6 inhibitor palbociclib monotherapy, related to Fig. Supplementary Figure 2. 10 × 10 matrix screening of palbociclib combined with four PI3K inhibitors in HPVneg HNSCC cell lines, related to Fig. Supplementary Figure 3. Assessment of palbociclib-based combinational therapy with four PI3K inhibitors in HPVneg HNSCC cell lines, related to Fig. Supplementary Figure 4. Assessment of abemaciclib-based combinational therapy with four PI3K inhibitors in HPVneg HNSCC cell lines, related to Fig. Supplementary Figure 5. GSEA and western blot analyses of palbociclib-alpelisib combined therapy in HPVneg HNSCC cell lines, related to Fig. Supplementary Figure 6. Effect and mechanism of selected drug combinations in HPVneg HNSCC cell lines, related to Fig. Supplementary Figure 7. Information of relevant in vivo experiments using HPVneg PDX models, related to Fig. Additional file 2: Table S1. Information of 162 chemicals. Table S2. Epithelial and Mesenchymal Gene lists. Table S3. Primers in this study. Table S4. Primary antibodies. Table S5. Genetic annotation of cell lines in this study. Table S6. Average ExcessHSA scores of all compounds. Table S7. Genetic annotation of PDX models.


Objective: Dihydroartemisinin (DHA) is an active metabolite of artemisinin and its derivatives, which is a potent drug extensively applied in clinical treatment of malaria. The antitumor properties of DHA have received increasing attention. However, there is no systematic summary on the pharmacological mechanisms of DHA against esophageal carcinoma (ESCA). The present study implemented network pharmacology- and molecular docking-based approaches to unveil the pharmacological mechanisms of DHA against ESCA.Methods: DHA targets were accessed through integrating the SwissTargetPrediction, HERB, as well as BATMAN-TCM platforms. In TCGA-ESCA dataset, genes with differential expression were screened between 161 ESCA and 11 normal tissue specimens. DHA targets against ESCA were obtained through intersection. Their biological significance was evaluated with functional enrichment analysis. A prognostic signature was established via uni- and multivariate cox regression analyses. DHA-target interactions were predicted via molecular docking. Molecular dynamics simulation was implemented to examine the stability of DHA binding to potential targets. Results: The study predicted 160 DHA targets as well as 821 genes with differential expression in ESCA. Afterwards, 16 DHA targets against ESCA were obtained, which remarkably correlated to cell cycle progression. The ADORA2B- and AURKA-based prognostic signature exhibited the reliability and independency in survival prediction. The stable docking of DHA-ADORA2B and DHA-AURKA was confirmed.Conclusion: Collectively, this study systematically revealed the basis and mechanism of DHA against ESCA through targeting multi-target and multi-pathway mechanisms, and thus offered theoretical and scientific basis for the clinical application of DHA. Esophageal carcinoma (ESCA) remains one of the most lethal cancers across the globe , with twvia binding to AKT1 and p70S6K  with high water solubility and antimalarial activity is the first-generation derivative of this compound that is an effective and fast-acting antimalarial agent with low toxicity . DHA disd p70S6K . Pyroptod p70S6K . Song etignaling . Adminisignaling . There iignaling . He at autophagy . PKM2 isutophagy . Additioutophagy . AltogetIn the current study, we developed a new integrated strategy to probe out the key targets and mechanisms of DHA in the treatment of ESCA on the basis of network pharmacology and molecular docking, which offered the theoretical and scientific basis for the clinical application of DHA against ESCA.https://pubchem.ncbi.nlm.nih.gov) (http://www.swisstargetprediction.ch) (http://herb.ac.cn/) (http://bionet.ncpsb.org/batman-tcm) platform in accordance with the screening conditions of score cutoff = 5 and adjusted p-value < 0.05 (http://sparql.uniprot.org/). Afterwards, DHA targets obtained from three platforms were merged, and deduplicated.The chemical structure of DHA was acquired from the PubChem chemical information resource (nih.gov) . Potentition.ch) , as well.ac.cn/)  web serve < 0.05 . DHA tarhttps://cancergenome.nih.gov/). In total, 161 ESCA and 11 normal tissue specimens were enrolled for subsequent analysis.RNA transcriptome profiles as well as clinicopathological and survival information of ESCA cases were downloaded from TCGA-ESCA dataset  between ESCA and normal tissue specimens. Adjusted https://string-db.org/)  database (db.org/) . In accodb.org/) . Degree p-value <0.05 were enrolled in a multivariate cox regression model. The prognostic signature was conducted on the basis of the linear combination of regression coefficients derived from the multivariate Cox regression model multiplied with expression values. The risk score of each case was computed. TCGA-ESCA cases were equally separated into discovery set as well as validation set. In each set, ESCA cases were further assigned to high- and low-risk subpopulations. Kaplan-Meier curves of OS between two subpopulations were plotted, and OS difference was estimated with log-rank test. Alive and dead status of cases in each set was displayed. Receiver operating characteristic (ROC) curve was drawn for evaluation of the prediction accuracy of the prognostic signature. Area under the curve (AUC) was also computed. TNMplot.com web tool was employed for comparison of survival difference between high and low expression subpopulations of DHA targets against ESCA in TCGA-ESCA dataset . Prognostic DHA targets against ESCA with  dataset . ExpressClinicopathological factors comprising gender, pathological stage, TNM staging as well as prognostic signature were enrolled for estimating their associations with TCGA-ESCA cases’ OS by use of uni- and multivariate cox regression analysis.Biological features of DHA targets against ESCA were probed out utilizing clusterProfiler package . Gene OnThe potentially altered biological processes between high and low expression subpopulations of DHA targets against ESCA were determined with GSEA tool . The “c5The 3D structures of DHA targets against ESCA were accessed from the PDB database and saved in pdb format. PyMOL software was used for protein pretreatment. In addition, the 3D structure of DHA was created using Chem3D software and saved in mol*2 format. The pre-processed targets and DHA were imported into AutoDockTools 1.5.6 software for molecular docking, and the results were saved in pdbqt format. Vina scripts were run for computing molecular binding energies as well as visualizing molecular docking results. Additionally, Discovery Studio 2019 software was implemented for searching for docking sites as well as calculating LibDockScore for flexible binding. The molecular docking results were imported into PyMOL software for visualizing the molecular docking conformation.Molecular dynamics simulation was implemented by use of Discovery Studio 2019 software. The molecular structure of DHA was added to the CHARMM force field by “simulation” module, and DHA targets against ESCA were solvated by “solvation” module. The molecular dynamics simulation parameters were set by “standard dynamics cascade module for the targets added with the solvent system. The system temperature was elevated from 50 K to 300 K under 100 ns of analog sampling; the time step was set to 1 ns; and other parameters were set to default values. The Root Mean Square Deviation (RMSD) as well as Root Mean Square Float (RMSF) values were analyzed.From the PubChem chemical information resource, we accessed the 2D chemical structure of DHA, as illustrated in p-value < 0.01 together with |log2 fold-change|>1  between 161 ESCA and 11 normal tissue specimens were screened in line with the screening condition of adjusted hange|>1 . The spehange|>1 , comprishange|>1 .The prognostic implication of DHA targets against ESCA was investigated in TCGA-ESCA dataset. Univariate cox regression analysis demonstrated that AURKA, and ADORA2B significantly correlated to ESCA cases’ OS. AURKA, and ADORA2B were utilized for establishing a multivariate cox regression model. The risk score was computed on the basis of the coefficients and expression values of AURKA, and ADORA2B. TCGA-ESCA cases were equally divided into discovery set and validation set. In discovery set, we assigned cases into high- and low-risk subpopulations. Survival difference between subpopulations was assessed. High-risk subpopulation exhibited worse OS outcome in comparison to low-risk subpopulation . SurvivaUtilizing univariate cox regression analysis, we investigated the associations of clinicopathological factors and the prognostic signature with ESCA cases’ OS. As illustrated in Biological features of DHA targets against ESCA were probed out utilizing GO and KEGG enrichment approach. For biological process, histone phosphorylation, mitotic DNA integrity checkpoint, G2/M transition of mitotic cell cycle, cell cycle G2/M phase transition, mitotic nuclear envelope disassembly, DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest, cell cycle G1/S phase transition, signal transduction involved in mitotic G1 DNA damage checkpoint, intracellular signal transduction involved in G1 DNA damage checkpoint and signaling transduction involved in mitotic cell cycle checkpoint were significantly linked to DHA targets against ESCA . CellulaAmong DHA targets against ESCA, DORA2B, and AURKA significantly correlated to ESCA prognosis. Up-regulated ADORA2B was linked to better OS, while up-regulated AURKA was correlated to worse OS in TCGA-ESCA dataset . Furthervia spliceosome, negative regulation of wound healing, sensory perception of smell, odorant binding and olfactory receptor activity displayed positive correlations to AURKA  were separately displayed as Utilizing Chem3D, we obtained the 3D structure of DHA with mol*2 format, and downloaded the 3D structure of potential targets from the PDB database with pdb format. Through AutoDockTools 1.5.6 software, the 3D structure of DHA and potential targets were converted to pdbqt format, and thus searched for the active pocket. Afterwards, we run the Vina script to compute the binding energies of DHA and potential targets. As listed in The conformations of ADORA2B-DHA and AURKADHA molecular docking were used for subsequent molecular dynamics simulation analysis. The ADORA2B-DHA complex was added by 16,738 water molecules, 44 sodium as well as 53 chloride, while the AURKA-DHA complex was added by 5,688 water molecules, 15 sodium as well as 20 chloride. We further investigated the structural stability of ADORA2B-DHA and AURKADHA complexes during molecular dynamics simulation. Meanwhile, the RMSD values of the two complexes were computed during 100 ns molecular dynamics simulation. The ADORA2B-DHA and AURKADHA complexes were stable following 100 ns molecular dynamics simulation, as illustrated in At present, the paradigms of development of single-targeted therapeutic agents are challenging, primarily because of lack of effectiveness as well as resistance. Therefore, natural compounds selectively acting on two or more targets exhibit higher efficacy in comparison to single-target drugs. The current study employed network pharmacology as well as molecular docking approaches to predict multi-targets and multi-pathways of DHA against ESCA.Previous studies have proposed that DHA exhibits the anti-ESCA property through targeting distinct molecules as well as pathways . Thus, wvia improving the activity of distinct pathways such as EGFR-PI3K-Akt  as well as multiple pathways  on the basis of network pharmacology and molecular docking approaches, and thus provided the theoretical basis for the pharmacological research of DHA against ESCA.


Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug–cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug–cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases. In cancer therapy, it is difficult to provide effective treatment using a single drug . HoweverWith the progress of computer technology, the machine learning model has been developed to explore drug combinations for cancer diseases . More anIn this work, we propose a novel framework, extreme gradient boosting for network embedding (NEXGB), that utilizes PPI network information to identify drug combinations with synergistic effects in specific cancer cell lines. NEXGB combines the struc2vec  componenIn this part, we list the key parameters of the model, illustrating the effect of the feature dimension on model performance. Furthermore, we demonstrate the superiority of the NEXGB model and its predictive performance in specific tissues and specific cell lines.In this study, struc2vec is used to generate feature vectors for proteins. We set the length of random walk sequences to 80, and each node generates 20 random walk sequences. Finally, a 64-dimensional vector is generated for each gene through the Skip-Gram model. We evaluate performance on two recently developed large drug combination datasets, Oncology-Screen  and DrugWe perform five-fold cross-validation on each of the two datasets. Cross-validation randomly splits the dataset and reports the average performance after repeating the experiment five times . Our modThe output of our choice is a 64-dimensional vector of nodes. Therefore, the feature vectors for drugs and cancer cell lines are 64-dimensional, and the input to training is 192-dimensional drug combination–cancer cell line data. The struc2vec component can choose the dimension of the output node feature, and we output the 32-dimensional, 64-dimensional, and 128-dimensional node feature vectors through the PPI network. To illustrate the effect of feature dimension on the model, we train three different dimensions of features on the Oncology-Screen dataset. Through the ROC curve and AUC value, we find that the dimension of features has no significant effect on the performance of NEXGB. This proves that struc2vec has learned the features of the nodes, but the increase in dimension does not mean that the learned feature vector is correct and effective. Thus, in order to facilitate subsequent operations, we use all 64-dimensional feature vectors for experiments and explanations. The area under receiver operating characteristic curve (AUC-ROC) under five-fold cross-validation is shown in We apply the XGBoost method, which has been widely used in recent years, for classification and compare it with some classic machine learning methods: random forests (RF) , k-neareThe predictive performance of the model is closely related to the associated proteins of the drug and cell line. We further investigate the performance of the NEXGB model on the Oncology-Screen dataset for specific cell lines and specific tissues. The performance is represented by the ROC-AUC value under cross-validation. NEXGB has strong predictive power (ROC-AUC greater than 0.7) for over 75% of cell lines a. The ROWe performed t-SNE analysis on high-dimensional vector representations of cell lines, reflecting the relationships between cell lines in 2D space. Ovarian cancer is mainly classified into five histological types due to the characteristics of ovarian tissue: high-grade serous, low-grade serous, clear cell, endometrioid, and mucinous . OvarianNEXGB is inspired by the latest advances in network medicine ,22. To bWith the improvement of more biological network data, we will be able to acquire more network information to train NEXGB. However, the prescription of clinical combinations requires knowledge of pharmacokinetic interactions between drugs. Personalized treatment needs to take into account the individual conditions of different patients, such as tolerance to the drug and unique genomic information. These factors are mostly ignored by NEXGB and other existing methods. The increase in available information brings corresponding problems. Different measurement standards in the same drug combination leads to different results. How to balance these biological experimental data is also a critical problem. Nonetheless, we believe that further studies in pharmacology, pharmacokinetics, toxicology, and genetic heterogeneity, accompanied by new computational methods, can rapidly overcome these limitations.Drug–Drug–Cell Line synergy. We evaluate NEXGB on two anti-cancer drug combination datasets: (a) Oncology-Screen , an oncoProtein–Protein Interaction (PPI) Network. The PPI network (accessed on 17 September 2021) contains 15,970 nodes (unique proteins) and 217,160 edges (interactions). The network is composed of 15 commonly used databases and experimental evidence ,25. ProtDrug–Protein Associations. A total of 15,051 drug–protein associations are established based on FDA-approved or clinically investigated drugs . The datCell–Protein Associations. The cell–protein association dataset was obtained from the Cancer Cell Line Encyclopedia (CCLE) . This dahttps://github.com/lysmfj/NEXGB (accessed on 30 September 2021).Related information is available at The related process of NEXGB is illustrated in We further formulate the synergistic drug combination prediction problem as a binary classification problem. According to the definition of the synergy score, a synergy value greater than 0 indicates a synergistic effect between two drugs . We regaIn the PPI network, a node represents the gene encoding of a protein, and the existence of an edge between two nodes indicates that the two proteins interact. PPI networks are an undirected graph. We utilize the struc2vec  model inThe calculation of the structural similarity In general, ng (DTW) :(3)gsRkuEach layer A biased random walk is applied to generate a sequence of nodes in a multi-layer graph. When performing a random walk, staying at the current layer with the probability kip-Gram  model toXGBoost is an improvement of the gradient boosting algorithm, and the internal decision tree uses a regression tree . We applIn this study, we propose a novel network embedding model NEXGB for predicting the relationships between drug combinations and cancer cell lines in cancer. The results show that NEXGB can effectively identify anticancer drug combinations through the topological features of the PPI network, the biological mechanisms of drug-related proteins, and proteins associated with cancer cell lines. In addition, the results on specific cell lines suggest that ES2 cell lines may have unique biological properties among ovarian cancer cell lines.


Each of the three mammalian Ajuba family proteins, AJUBA, LIMD1 and WTIP, exhibit tension-dependent localization to adherens junctions, and can associate with Lats kinases. However, only LIMD1 has been directly demonstrated to directly regulate Lats activity in vivo. To assess the relationship of LIMD1 to AJUBA and WTIP, and the potential contributions of AJUBA and WTIP to Lats regulation, we examined the consequences of over-expressing AJUBA and WTIP in MCF10A cells. Over-expression of either AJUBA or WTIP reduced junctional localization of LIMD1, implying that these proteins can compete for binding to adherens junctions. This over-expression also reduced junctional localization of LATS1, implying that AJUBA or WTIP are unable to efficiently recruit Lats kinases to adherens junctions. This over-expression was also associated with increased YAP1 phosphorylation and decreased YAP1 nuclear localization, consistent with increased Lats kinase activity. These observations indicate that AJUBA and WTIP compete with LIMD1 for association with adherens junctions but have activities distinct from LIMD1 in Hippo pathway regulation. They further suggest that the ability of Ajuba family proteins to associate with Lats kinases in solution is not sufficient to enable regulation in vivo, and that tumor suppressor activities of AJUBA and WTIP could stem in part from competition with LIMD1 for regulation of Lats kinases at cell junctions. IntroductionDrosophila) are kinases that play key roles in Hippo signaling . Once activated by upstream cues, Lats kinases phosphorylate Yap family transcriptional co-activator proteins . Phosphorylation of Yap proteins promotes their cytoplasmic localization and degradation, whereas unphosphorylated Yap proteins can accumulate in the nucleus, where they promote transcription of downstream target genes.The Ajuba family of LIM domain proteins influence a variety of cellular processes . One notable function is physical interaction with and inhibition of Lats family kinases . Lats proteins . Hippo signaling is regulated by a variety of upstream inputs, including cytoskeletal tension . Ajuba family proteins play a key role in one mechanism by which cytoskeletal tension modulates Hippo signaling. This was discovered inDrosophila wing imaginal discs, where theDrosophilaAjuba LIM protein (Jub) associates with an open form of α-catenin that is induced by tension at adherens junctions (AJ) . Once bound to AJ, Jub then recruits and inhibits Warts. Recruitment of Warts to AJ by Jub inDrosophilasequesters Wts from upstream activators of Hippo signaling .Hippo signaling controls growth and cell fate in animals from et al., 2018). The recruitment and inhibition of Lats kinases at AJ cells when there is tension in the actin cytoskeleton is conserved in mammals . All Ajuba family proteins are characterized by three conserved C-terminal LIM domains . Structure-functions studies have revealed that the LIM domains play a key role in association of Ajuba family proteins with AJ, presumably due to association with an open form of α-catenin . Each of the mammalian Ajuba family proteins has also been reported to be able to associate with Lats kinases . Association of Ajuba family proteins with Lats kinases is also mediated by LIM domains . Since the LIM domains are conserved between AJUBA family proteins, this raises the question of whether they compete with each other for association with either AJ or Lats kinases, and if so, what are the functional consequences of this competition?There are three mammalian Ajuba family proteins, AJUBA, WTIP, and LIMD1. Each of them can be recruited to AJ under tension , knockdown experiments in confluent MCF10A cells cultured at low density revealed that LIMD1 is specifically required for tension-dependent recruitment of Lats kinases to AJ, and that this recruitment inhibits Lats activity . Conversely, AJUBA and WTIP were not required for localization or regulation of Lats kinases. Whether this reflects differences in expression levels amongst Ajuba family proteins, or differences in activity, was unclear. As one way to address this, we describe here investigations of the consequences of replacing LIMD1 at AJ with AJUBA or WTIP through over-expression of AJUBA or WTIP in MCF10A cells.Although in some contexts it has been suggested that mammalian Ajuba family proteins act redundantly to regulate Lats kinases . For those prior experiments, cell lines transduced with Doxycycline (Dox)-inducible transgenes were created so that they could be expressed at or near endogenous levels. Western blotting with antibodies against AJUBA led to estimates that induction with 0.025 µg/mL Dox led to levels of EGFP:AJUBA comparable to endogenous AJUBA in an EGFP:AJUBA MCF10A cell line . Antibodies specifically recognizing WTIP were not available, so instead the lowest dose enabling readily detection of EGFP:WTIP in an EGFP:WTIP MCF10A cell line was used (0.05 µg/mL Dox). Induction of AJUBA or WTIP at these low levels did not have noticeable effects on Hippo signaling. For the present experiments, we wanted to use high, saturating levels of Dox that would induce maximal expression of these proteins, so we induced expression of EGFP:AJUBA or EGFP:WTIP using 5 µg/mL Dox, which based on earlier studies of AJUBA  is expected to generate expression levels well above endogenous levels.To investigate the potential for competition between AJUBA or WTIP and LIMD1, we took advantage of cell lines expressing EGFP-tagged proteins that were created previously for analysis of the influence of cytoskeletal tension on the localization of Ajuba family proteins . Induction of high level EGFP:AJUBA expression, using Dox, led to robust detection of AJUBA:GFP at AJ . At the same time, detection of LIMD1 at AJ was significantly reduced . This suggests that EGFP:AJUBA expressed at high levels has out-competed endogenous LIMD1 for association with α-catenin at AJ.MCF10A cells cultured at low cell density normally have a punctate distribution of LIMD1 along cell junctions, reflecting the tension-dependent recruitment of LIMD1 to AJ under tension , but evidence that Ajuba family proteins can undergo phase separation has been reported previously , and the N-terminal halves of Ajuba family proteins are predicted to be intrinsically disordered , which is a hallmark of proteins that form biomolecular condensates . Despite over-expressed EGFP:WTIP protein being distributed between AJ and cytoplasmic puncta, it also effectively removed endogenous LIMD1 from AJ .Induction of high level EGFP:WTIP expression, using Dox, led to detection of EGFP:WTIP both at AJ and in puncta dispersed throughout the cell . Cytoplasmic puncta of WTIP were not observed when EGFP:WTIP was induced at low levels . However, all three Ajuba family proteins have been observed to associate with Lats kinases in co-immunoprecipitation experiments , suggesting that AJUBA or WTIP might be able to substitute for LIMD1. To investigate this possibility, MCF10A cells expressing high levels of EGFP:AJUBA or EGFP:WTIP were stained for expression of LATS1. In control MCF10A cells cultured at low density, LATS1 is predominantly localized to AJ . High level expression of EGFP:AJUBA or EGFP:WTIP led to loss of LATS1 from AJ . In contrast, low level expression of these proteins does not displace Lats kinases . As a control, we also examined LATS1 localization when LIMD1 was over-expressed using 5 µg/mL Dox (prior experiments used 0.05 µg/mL Dox to match endogenous expression) . Over-expression of LIMD1 did not remove LATS1 from AJ . These observations indicate that despite localizing to AJ, EGFP:AJUBA or EGFP:WTIP are unable to recruit LATS1 there.LIMD1 is required for recruitment of Lats kinases to AJ in MCF10A cells  on western blots of lysates from cells over-expressing EGFP:AJUBA or EGFP:WTIP. Induction of either AJUBA or WTIP was sufficient to increase YAP1 phosphorylation . Phosphorylation of YAP1 results in a shift in localization from nuclear towards cytoplasmic, decreasing its activity as a transcriptional co-activator . Consistent with this, over-expression of EGFP:AJUBA or EGFP:WTIP were also associated with a relative decrease in YAP1 nuclear localization . Conversely, over-expression of LIMD1 did not decrease YAP1 nuclear localization .Conclusions et al., 2018), even when over-expressed they are unable to substitute for LIMD1 in Hippo pathway regulation.The existence of three Ajuba family proteins in mammals raises the question of whether they act redundantly, or instead have distinct activities. Our investigations of over-expressed AJUBA and WTIP in MCF10A cells imply that these proteins are distinct from LIMD1 in lacking the ability to regulate Hippo signaling through tension at AJ. Not only are AJUBA and WTIP not normally required for regulation of Lats and Yap proteins in MCF10A cells , it is nonetheless surprising in light of observations that AJUBA or WTIP can associate with Lats kinases. However, evidence for association of AJUBA or WTIP with Lats kinases has been based on co-immunoprecipitation experiments using over-expressed proteins, only a fraction of which is at AJ. To explain the LIMD1-specific recruitment of Lats kinases to AJ, we thus propose that association with AJ alters the conformation of Ajuba family proteins in a way that enhances LIMD1-Lats association, but inhibits AJUBA and WTIP-Lats association, such that Lats association with AJ requires LIMD1.Based on the structural similarity amongst Ajuba family proteins, and their shared ability to localize to AJ under tension, they would be expected to be able to compete with each other for binding to AJ. This competition would enable over-expressed AJUBA or WTIP to displace LIMD1, providing a simple explanation for the loss of LIMD1 from AJ. However, since AJUBA and WTIP can also associate with Lats kinases, they would have also been expected to substitute for LIMD1 at AJ, maintaining tension-dependent regulation of Lats kinases. Instead, we found that they fail to recruit LATS1 to AJ, resulting in increased Lats activity. While this is consistent with observations that knockdown of AJUBA or WTIP did not affect Lats or Yap activity . Several mechanisms have been proposed for AJUBA’s links to oncogenesis, including effects on Hippo signaling. Notably, in some cases oncogenic activity of AJUBA has been correlated with increased YAP activity , whereas tumor suppressor activity of AJUBA has been correlated with decreased YAP activity . Additional studies will be required to decipher the complex links between AJUBA and Hippo signaling that impact oncogenesis, but our results suggest that competition with LIMD1 for Lats binding provides a potential mechanism by which AJUBA could increase Lats activity, and thereby suppress YAP-dependent oncogenesis.Cell CultureMCF10A-EGFP-LIMD1 cells were cultured in DMEM/F-12 (Life Technologies) supplemented with 5% horse serum, 1% antibiotic-antimycotic, epidermal growth factor (20 μg/mL), insulin (10 μg/mL), hydrocortisone (0.5 μg/mL), cholera toxin (0.1 μg/mL) and puromycin (2 μg/mL) at 37⁰C and 5% CO₂ . For immunostaining cells were grown on coverslips (Fisherbrand Microscope Cover Glass) coated with 0.6 mg/mL of collagen for 15 minutes at room temperature and washed with PBS. For the EGFP-LIMD1 overexpression experiment 60,000 cells per well were seeded in 24 wells plate, and 24 hours post-seeding cells were induced with 5 μg/mL of doxycycline for 24 hours.Immunostainings and ImagingCells were fixed with 4% paraformaldehyde in phosphate-buffered saline (PBS) supplemented with 0.5 mM CaCl₂ and 1 mM MgCl₂ (PBS++) for 10 minutes at room temperature. Then cells were washed with PBS++ three times for 10 minutes each, followed by permeabilization with 0.5% Triton X-100 in PBS for 20 min. After blocking with 5% bovine serum albumin (BSA) in PBS for 1 hour, cells were incubated in primary antibodies diluted in 5% BSA in PBS solution overnight at 4⁰C. Primary antibodies used for immunostaining were: mouse anti-LIMD1 , rabbit anti-LIMD1 , rabbit anti-LATS1 , rat anti-E-cadherin . Following the incubation with primary antibodies cells were washed three times with PBS for 5 minutes each, and incubated with secondary antibodies at room temperature for 2 hours. The secondary antibodies used for immunostaining were: Cy3 and Alexa Flour 647-conjugated (Jackson ImmunoResearch) in the dilution of 1:100. Upon incubation with secondary antibodies the cells were washed four times with PBS. Cell nuclei was stained with Hoechst 333342  and mounted with fluorescent mounting medium (Dako). Images were acquired on a Leica TCS SP8 confocal microscope system using a HC PL APO 63x/1.40 objective.Western BlottingCells were lysed in 2x Laemmli (Bio-Rad) buffer supplemented with protease inhibitor (Roche) and phosphatase inhibitor cocktail , sonicated for 5 seconds. Protein samples were denatured at 95⁰C for 5 minutes, centrifuged for 1 minute at max speed and loaded in 4% to 15% gradient gels (Bio-Rad). Primary antibodies used for the immunoblotting were: rabbit anti-phospho-YAP (S127) , rabbit inti-YAP  and mouse anti-GFP . Mouse anti-α-tubulin  was used as a loading control. Blots were stained using fluorescent-conjugated secondary antibodies from LI-COR and visualized on an Odyssey Imaging System (LI-COR Biosciences).Image Quantitation and Statistical AnalysisConfocal images were quantified using Volocity (Perkin Elmer) and Fiji  software. E-cad staining was used to define a junctional region for quantitation of LIMD1 and LATS1, while Hoechst staining was used to define a nuclear region for quantitation of YAP1 localization. Comparisons between control and experimental conditions were performed by unpaired t test on the log of the ratios, using Prism software (Graphpad). et al., 2018) and deposited in Addgene.EGFP:AJUBA, EGFP:WTIP and EGFP:LIMD1 expression vectors have been previously described (Ibar


N = 224; 56.28%) and female  with ages ranging from 11 to 13 years . The instruments used were the Maslach Burnout Inventory—Student Survey (MBI-SS) and the Academic Motivation Scale (AMS), as well as academic marks as a measure of performance. Results: The results revealed significant correlations between the three constructs under study, the variables that can be used to predict academic performance, and both adaptive and non-adaptive behavior patterns. Conclusions: The importance of motivation, burnout, and academic performance in primary education is manifested, whose interrelation can give rise to adaptive behaviors based on high school motivation away from academic burnout that leads to a higher academic performance in students.Background: The final years of primary school (early adolescence) are regarded as key for the academic and personal development of students. This study aims to analyze the relationship between academic motivation, academic burnout, and academic performance, differentiating between adaptive and non-adaptive patterns according to the results of the three constructs studied. Methods: The sample comprised 398 students, both male ( Currently, in schools, teachers face multiple obstacles to reach their students owing to the large number of personal and academic variables that affect their everyday performance. Teaching requires great degree of commitment, and many teaching professionals feel compelled to adopt a perfectionist and self-demanding attitude in order to respond to the demands posed by their students and society more broadly .In parallel to this, students not only need to adapt to the idiosyncrasies of their teachers, but to a wide array of everyday variables that enhance or undermine their personal and academic skills. These factors, along with their interaction with their peers and family, play a central role in their academic experience . StudentOne of the most important variables is academic burnout, defined as the loss of interest in academic topics and the lack of commitment to school tasks, along with self-doubt concerning the student’s ability to meet their academic targets, resulting in stress and poor motivation . The so-The factors that lead to academic burnout are particularly prevalent in the transition from primary to secondary education, resulting in low levels of academic wellbeing and performance, potentially leading to school dropout . PreviouAnother important variable for learning is motivation. Today, motivation is a recurrent factor in explanations concerning varying levels of school performance. In order to learn something new, it is necessary to have the required skills, knowledge, and strategies, but also the predisposition and will to learn . It is fIn this way, academic motivation is a key factor in terms of academic commitment, as it represents the will and conscious decision of students to actively engage in their tasks . PreviouFinally, academic performance is the level of academic achievement, understood both quantitatively and qualitatively . It is nTherefore, academic performance is a multidimensional construct constituted by multiple variables. Fierro et al.  argue thThere are hardly any studies that directly relate the constructs of our study and there are fewer studies that relate them in primary education; hence one of the novelties of this study. Trigueros et al.  in a samIn this context, the main aim of this study is to examine the relationship between academic motivation, burnout, and academic performance in students in the two final years of primary education. We define adaptive behavior as the set of conceptual, social, and practical skills that the individual has learned and that allow him to respond to the circumstances of daily life. By non-adaptive behavior, we understand socially annoying or disturbing behaviors that prevent or reduce the possibilities of adaptation to the environment, as well as the development or new academic and social behaviors.The four starting hypotheses are as follows:Hypothesis 1.Academic motivation, burnout, and academic performance are closely related.Hypothesis 2.Adaptive behavior patterns are characterized by high intrinsic motivation and academic performance and low academic burnout and amotivation.Hypothesis 3.Non-adaptive behavior patterns are characterized by high extrinsic motivation, burnout, amotivation, and poor academic performance.Hypothesis 4.Intrinsic and extrinsic motivation, amotivation and burnout can be used to predict academic performance.Sample: The sample comprised 398 students, both male  and female  in the two final years of primary education in Zaragoza, Aragón, Spain. The schools selected to carry out the study were chosen by random sample. The data were collected in January and February 2022. All questionnaires were anonymous, and all participants were volunteers.Instruments: Burnout was measured using the Spanish version of the Maslach Burnout Inventory—Student Survey (MBI-SS) [(MBI-SS) . This isAcademic Motivation Scale (AMS), adapted for the academic context [Academic motivation was measured using the  context . This isAcademic performance was measured as the average marks of students after the first school term, ranging from 0 to 10. This is one of the most widely used and reliable methods available to measure academic performance ,25. ThisProcedure: The questionnaires were handed out in the classroom with the authorization of the school management and the parents/tutors, who signed an informed consent form. The study followed all the ethical guidelines set out in the Declaration of Helsinki [Helsinki . The stuData analysis: Statistics were collected to establish the socio-demographic variables of the sample, including gender, age, kind of school, and the averages of these variables. Afterwards, SPSS v25 software was used to carry out Pearson correlation analyses on the scales, since they are numerical, scalar, and continuous, in order to establish the relationship between academic motivation, burnout, and academic performance (Hypothesis 1). Secondly, cluster analysis K-medias was used to divide the sample into three significant groups. Clustering is the process of dividing all the data into three large groups, as the number most used in the scientific literature that allows us to verify the behavioral profiles under investigation, also known as clusters, based on the patterns of all the data of the investigation (Hypothesis 2 and 3). Finally, this was followed by regression analysis (‘introduction’ method) using academic performance as the criterion variable over the other two dimensions (Hypothesis 4). For all the operations, a p ≤ 0.05 level of significance was adopted, with a 95% confidence level.N = 224; 56.28%) and female  with ages ranging from 11 to 13 years   .d = −0.220), whereas amotivation was found to be higher among boys (Cohen’s d = −0.280).Concerning the descriptive variables of the study , self-efr = 0.535) and cynicism (r = 0.395), as well as with some extrinsic motivations, and negatively correlated with predominantly intrinsic motivations. Intrinsic motivations were also shown to correlate positively with one another, and with the most self-determined dimension of burnout: self-efficacy. Finally, academic performance was shown to be positively correlated with intrinsic knowledge motivation (r = 0.160) and achievement (r = 0.277), and with self-efficacy (r = 0.310).Correlation analyses  demonstrN = 42; 10.55%); group 2 ; and group 3 . As presented in K-means cluster analysis was undertaken to divide the sample into three significant groups: group 1 , a result supported by most previous studies ,37,38, oFinally, intrinsic motivation and amotivation, the two opposite ends of the SDT spectrum, were found to be negatively correlated , as alsoOur third hypothesis held that non-adaptive behavior patterns are characterized by high extrinsic motivation, burnout, amotivation, and poor academic performance. Our results partially support this hypothesis.First, no significant relationship was found between extrinsic motivation and academic performance. Some previous studies argue for a direct relationship between extrinsic motivations and poor school results. Baena et al. , for insSecond, our results do not suggest a direct relationship between extrinsic motivation and academic burnout. Barbosa et al.  also argThird, the relationship between extrinsic motivation and amotivation was not confirmed. This means that extrinsic motivation does not necessarily crystallize in lack of academic motivation and poor school results. Tsouloupas et al.  argue thFinally, our results point to a negative correlation between academic burnout and academic performance. This hypothesis is divided into two sections. First, taking the subvariables of our study, previous studies emphasize this relationship, and some even claim that burnout is also negatively correlated with emotional intelligence. Caballero et al.  and SalaSecondly, referring to Bresó et al.  other stFinally, our fourth hypothesis held that intrinsic and extrinsic motivation and burnout can be used to predict academic performance. The hypothesis was partially confirmed as the number of predictive variables found was lower than expected. Few previous studies have directly addressed this issue. Kumar and Tankha  establisBased on our result, the importance of variables, such as motivation, academic burnout, and academic performance, for the adoption of adaptive and non-adaptive behaviors in primary school students cannot be overemphasized.The limitations of the study include the fact that the data were collected at a given point in time, so the evolution of variables depending on the personal circumstances of students cannot be tracked over time. Moreover, the schools were chosen by random sampling, instead of reflecting a stratified and representative sample, so results may be affected by sampling bias. In practical terms, the study should encourage teachers to implement practices to prevent academic burnout and improve performance and, with this, support a more satisfactory academic and personal life for their students. Similarly, educational programs should cultivate the psychological variables that lead to the harmonious development of the students’ skills and their personal and academic development.For the future, it would be interesting to pay specific attention to other factors that have a direct impact on the academic performance of students, and even examine variables which contribute to increase academic motivation and prevent burnout in more depth. Furthermore, longitudinal studies could help to understand the evolution of these psychological variables over time, including other educational stages.


TRAF3 is an adapter protein that regulates signals through many receptors important for B cell differentiation and function. Loss-of-function mutations or deletions of TRAF3 are common in B cell malignancies. Glycogen Synthase Kinase 3 (GSK3) regulates signaling in growth, survival, and metabolism pathways. GSK3 inhibitors have been effective against many solid tumors; the inhibitor used in this work is currently being tested for efficacy against BCLs. We found that TRAF3 and GSK3 associate in multiple BCL cell lines, and that BCLs with low TRAF3 have a higher susceptibility to GSK3 inhibition. In contrast to BCL cell lines, GSK3 inhibition has little effect on TRAF3-sufficient and deficient resting primary B cells. These results suggest TRAF3 level as a predictor of BCL responsiveness to GSK3 inhibitor therapy.TNF receptor-associated factor 3 (TRAF3) is an adapter protein that inhibits many signals that promote B cell survival and activation. Mice with a B cell-specific TRAF3 deficiency and humans with a rare haploinsufficiency in TRAF3 have enhanced development of BCLs as they age. Loss-of-function mutations in TRAF3 are common in B cell malignancies. Recent studies show that pharmacological inhibition of the enzyme glycogen synthase kinase 3 (GSK3), which regulates cellular growth, survival, and metabolism, inhibits growth and survival of BCL-derived B cells. In this study, we found that TRAF3 and GSK3 associated in B cells. The relative levels of TRAF3 in BCL cell lines correlated positively with the ratio of inactive to total GSK3β, and negatively correlated with susceptibility to GSK3 inhibition by the GSK3 inhibitory drug 9-ING-41, currently in clinical trials. Uniquely in BCLs with low TRAF3, GSK3 inhibition caused increased loss of the TRAF3-regulated, anti-apoptotic protein Mcl-1. GSK3 inhibition also blocked hyperresponsiveness to IL-6 receptor signaling in TRAF3-deficient BCL cells. Together, these results support the utility of 9-ING-41 as a treatment for BCL, and suggest that a decrease or loss of TRAF3 in BCLs could act as a biomarker for increased susceptibility to GSK3 inhibitor treatment. Many of of BCLs ,9. This gnancies ,11,12,13gnancies ,15. ThisGlycogen Synthase Kinase 3 (GSK3) is a ubiquitously expressed serine/threonine kinase with both alpha and beta isoforms. GSK3 kinases have at least 40 substrates involved in essential cellular processes including growth, proliferation, apoptosis and metabolism ,17. GSK3GSK3 participates in several of the pathways modulated by TRAF3 in B cells, thus we investigated the interplay between TRAF3 and GSK3 in BCL-derived human cell lines and primary B cells. GSK3β, but not GSK3α, has a predicted TRAF3 binding site and indeed, we found that GSK3β associated with TRAF3 in multiple BCL lines. The relative amount of TRAF3 in BCL lines was positively correlated with the ratio of inactive to total GSK3β, and negatively correlated with susceptibility to GSK3 inhibition by CHIR-99021 and the drug 9-ING-41, currently in clinical cancer trials (NCT03678883). Susceptibility of TRAF3 low or negative BCL to 9-ING-41 was accompanied by increased loss of the anti-apoptotic molecule Mcl1, which is elevated in TRAF3-deficient primary B cells and contributes to their enhanced homeostatic survival . InteresCd19-Cre+/−flox/Traf3flox) was performed as previously described [Cd19-Cre+/−+/+Traf3 or Cd19-Cre−/−flox/floxTraf3 are referred to throughout as “WT”. Mice were maintained under specific pathogen free conditions and used in accordance with the National Institute of Health guidelines under animal protocols 1990801 and 1062397, approved by the Animal Care and Use Committee at the University of Iowa. Both male and female mice aged 2–6 mos were used in similar numbers in these studies. Generation of mice with a B cell-specific TRAF3 deficiency (escribed . B cells−/−Traf3 A20.2J [TRAF3 gene were prepared using the CRISPR design tool (crispr.mit.edu) maintained by Dr. Feng Zhang. The double-stranded synthetic oligonucleotides 5′ CACCGCCATCATATCCTCTCATGCA 3′, and 5′ AAACTGCATGAGAGGATATGATGGC 3′ were used to target intron one. Exon five was targeted with the following pairs: 5′ CACCGGTTCCGATGATCGCGCTGC 3′ and 5′ AAACGCAGCGCGATCATCGGAACC 3′. BJAB cells suspended in Optimem (Invitrogen) were transfected by electroporation at 225 V for 30 ms in the presence of: 2.5 μg of each of the two guide RNA/Cas9 vectors, 0.5 μg pEGFP-C1 , and 5 μg double-stranded filler DNA oligonucleotides (random sequence). Twenty-four hours later, GFP-expressing cells were sorted using a Becton Dickinson FACS Fusion and plated at 1 cell/well into 96-well plates. Clones were screened by PCR of genomic DNA using the following primers: TRAF3 targeting primers include 5′ CTGAAAGACAGCAGGTCTCAGGCAC 3′, and 5′ GAATGTATCATATAGGAATTGAGTGG 3′ (Int-5R3), and by Western blotting. GSK3 inhibitors CHIR-99021 (Sigma) and 9-ING-41  were reconstituted in DMSO. All experiments including inhibitor treatments also included control groups with equivalent concentrations (v/v) of DMSO only. Survival of primary B cells and BCL cell lines during GSK3 inhibitor treatment was quantified by propidium iodide exclusion. Where indicated, 20 μg/mL LPS  was added to primary mouse B cell cultures 2 h prior to addition of 9-ING-41 for survival analysis. For IL-6 receptor (IL-6R) signaling studies, cells were pre-treated with 5 μM 9-ING-41 for two hours followed by stimulation with 20 ng/mL IL-6  in the presence of 5 μM 9-ING-41. Splenic mouse B cells were enriched using a negative selection kit . Peripheral blood mononuclear cells were obtained from human whole blood leukocyte reduction cones obtained from DeGowin Blood Center at the University of Iowa. Healthy donors 17 to 70 years of age provided written consent for their blood to be used in research projects in compliance with the University of Iowa’s Institutional Review Board. Mononuclear cells were isolated by centrifugation in Histopaque 1077 , and B cells were enriched with a negative selection kit (Stemcell Technologies). Primary mouse B cells, mouse B cell lymphoma cell lines, and human B cell lymphoma cell lines were cultured in complete medium: RPMI 1640 (Life Technologies) containing 10% heat-inactivated fetal calf serum, 10 μM β-mercaptoethanol , 2 mM L-Glutamine  and 100 U/mL penicillin-streptomycin (Gibco). Provenance of cell lines is as follows: A20.2J  and Traf− A20.2J  were gen− A20.2J  was purc− A20.2J  and Karp− A20.2J  were pro− A20.2J  was prov− A20.2J , Ramos [− A20.2J , Ramos 2− A20.2J , and SKW− A20.2J  were pur− A20.2J  was purc− A20.2J . Briefly2, 1 mM CaCl2, 2 mM Na3VO4, and EDTA-free cOmplete mini protease inhibitor cocktail ) on ice for 30 min. Lysates were pre-cleared for 10 min with protein G Dynabeads (Invitrogen). Antibody (Ab) recognizing one of the following was added: TRAF3  #sc-1828, 1:1000, or Cell Signaling Technologies  Ab #4729, 1:100) or GSK3β . Lysates were incubated with IP Ab for 2 h at 4 °C, then Ab-bound targets were captured with protein G Dynabeads for 20 min at 4 °C. Dynabeads were washed 3X with IP washing buffer  then resuspended in IP lysis buffer for Western blotting analysis.Cells were lysed in Immunoprecipitation (IP) lysis buffer , Mcl1 (#94296), phospho-STAT3Y705 (#9145) STAT3 (#4904), TRAF3 (#4729); Santa Cruz Biotechnology: β-Actin (#sc-47778), GAPDH (#sc-47724). Following 1° Ab incubation, membranes were washed and incubated with HRP-tagged goat anti-mouse IgG  or HRP-tagged goat anti-rabbit IgG  for 2 h at room temperature. Blots were imaged using SuperSignal West Pico or Femto substrate  and a low-light imaging system . Densitometry of Western blots was performed in ImageStudio Lite software  or MultiGauge Imaging software . Density of bands for proteins of interest was divided by density for loading control (Actin or GAPDH) from the same sample to normalize protein levels.Cell lysates were prepared in 1X Radio Immunoprecipitation Assay  Buffer containing EDTA-free cOmplete mini protease inhibitor cocktail (Roche). Proteins were separated on a NuPAGE gel (Invitrogen) and transferred to a polyvinylidene difluoride membrane using the XCell II blotting system (Invitrogen), following manufacturer recommendations. Membranes were blocked with 5% nonfat milk and incubated with 1° Ab overnight at 4 °C. The following 1° Abs were used for Western blotting: Cell Signaling Technologies: GSK3β (#12456), phospho-GSK3βData were graphed and the indicated statistical tests performed using GraphPad Prism software.Traf3 deletion develop spontaneous BCLs [TRAF3 regulates many aspects of B cell biology. These include several pathways restraining homeostatic survival, as well as signaling by the B cell antigen receptor (BCR), various tumor necrosis factor receptor superfamily members, Toll-like receptors (TLRs) and cytokine receptors [ous BCLs . The pleous BCLs . We therous BCLs , we founSpontaneous genetic loss of TRAF3 in BCLs has been reported in both humans and dogs , but varRecent work showed efficacy of the GSK3 inhibitor 9-ING-41 in halting BCL cell cycle progression in vitro and in limiting growth of a lymphoma xenograft in vivo . Given tPrimary BCLs and BCL-derived cell lines exhibit a high degree of heterogeneity, so it was possible that factors besides TRAF3 status were affecting the responsiveness of these cell lines to 9-ING-41 treatment. We thus generated a TRAF3-deficient version of the human BCL line BJAB, which normally has a high level of TRAF3, to more specifically test the effect of TRAF3 level on susceptibility to GSK3 inhibition. Two distinct clones of TRAF3-deficient BJAB cells showed significantly lower tolerance for GSK3 inhibition by 9-ING-41, arguing that loss of TRAF3 alone is sufficient to increase BCL susceptibility to killing by GSK3 inhibition C.One of the mechanisms by which TRAF3 restrains homeostatic B cell survival is by targeting the transcription factor cycling AMP-responsive element binding protein (CREB) for degradation in the nucleus . In B ceY705 compared to TRAF3-sufficient cells , TLRs and the IL-6R . In suppThough long appreciated for its roles in cell growth and metabolism, the multi-functional kinase GSK3 has only recently been appreciated for its importance as a metabolic regulator in B cells , and sti50 of 9-ING-41 of two different subclones of TRAF3-deficient BJAB cells compared to the parent line. Among BCL cell lines with naturally varied levels of TRAF3, we observed a greater decline in the anti-apoptotic molecule Mcl1 in TRAF3 low or negative BCL cell lines compared to those with an intermediate or high level of TRAF3. Finally, we explored the possibility that GSK3 inhibition could differentially affect other TRAF3-regulated pathways based on TRAF3 status. TRAF3 regulates IL-6R signaling through its recruitment of the inhibitory phosphatase PTPN22, which dampens IL-6-induced JAK1 and STAT3 activation [In this study, we report an interesting negative correlation between the ratio of inactive to total GSK3β and the amount of TRAF3 cellular protein across several different types of BCL cell lines, but not in primary mouse B cells. Consistent with the reduced amount of inactive GSK3 in BCL cell lines with low or no TRAF3, these cell lines were more susceptible to killing with both the promising new GSK3 inhibitor 9-ING-41, and CHIR-99021, an older GSK3 inhibitor. Previous studies showed no effect of GSK3 inhibition on primary B cell survival, and indeed we saw little effect of 9-ING-41 treatment on survival of resting B cells. There was also only a minimal differential effect of TRAF3 status on survival following 9-ING-41 treatment in primary mouse B cells. We confirmed that the differential susceptibility of BCL cell lines was likely attributable to TRAF3 by using CRISPR/Cas9 to delete TRAF3 from the BCL cell line BJAB. TRAF3 deficiency significantly decreased the ICA high priority for future studies is assessment of how TRAF3 status can predict other therapies likely to synergize with GSK3 inhibition. The study that initially described the efficacy with which GSK3 inhibition kills BCL cells tested GSK3 inhibition in combination with other inhibitors but found no universally successful combination . This isThe enhanced loss of Mcl1 in 9-ING-41-treated BCL cell lines with low or no TRAF3 is of particular interest. GSK3 activity can increase or decrease cellular Mcl1 levels. GSK3 promotes Mcl1 translation via pathways that involve the kinases Akt and Erk, but it can also directly phosphorylate Mcl1 at key residues that target it for ubiquitination and degradation . This maWe here show a relationship between GSK3 activation status and TRAF3 protein level in BCL-derived cell lines that has the potential to allow more accurate prediction of responders to GSK3 inhibitor therapy. TRAF3 low or negative BCL cell lines showed much higher susceptibility to GSK3 inhibition, but GSK3 inhibition showed no effect on viability of non-transformed, resting B cells. These findings provide an important foundation for further studies of how TRAF3-regulated pathways exploited by malignant B cells for survival can be targeted indirectly for therapeutic benefit.


In Huang et alThe corrected Figure The corrected Figure 7 legend is given below:Figure 7 RGS16 played an oncogene role in glioma cell lines. (A) The expression of RGS16 protein in the human astrocytes (HA) cell line and glioma cell lines. (B) The real‐time quantitative PCR (qPCR) assay of RGS16 mRNA expression in LN229, U87, and U251 cell lines after infection with RGS16 siRNA or negative control. (C) The Western blot analysis of RGS16, TCF8, N‐cadherin, and β‐catenin protein expression in LN229, U87, and U251 cell lines after applying RGS16 siRNA or negative control.  Transwell assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control.  Cell scratch assay of LN229 and U87 cell lines treated with RGS16 siRNA or negative control.  Clonogenic assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control.It earlier reads as:RGS16 played an oncogene role in glioma cell lines. (A) The expression of RGS16 protein in the human astrocytes (HA) cell line and glioma cell lines. (B) The real‐time quantitative PCR (qPCR) assay of RGS16 mRNA expression in LN229, U87, and U251 cell lines after infection with RGS16 siRNA or negative control. (C) The Western blot analysis of RGS16, TCF8, N‐cadherin, and β‐catenin protein expression in LN229, U87, and U251 cell lines after applying RGS16 siRNA or negative control.  Transwell assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control. (D) Cell scratch assay of LN229 and U87 cell lines treated with RGS16 siRNA or negative control. (E) Clonogenic assay of LN229 and U251 cell lines treated with RGS16 siRNA or negative control.The authors apologize for these errors.


Weighted Correlation Network Analysis (WGCNA) of serial whole-transcriptome data of cell lines treated with nimesulide revealed that the sets of genes upregulated after treatment with nimesulide were enriched for genes associated with autophagy and apoptosis, and on the other hand, the sets of downregulated genes were associated with cell cycle and RNA splicing. Serial transcriptome of bone marrow patient sample confirmed the upregulation of genes associated with autophagy after the response to nimesulide. Lastly, we demonstrated that nimesulide potentiates the cytotoxic in vitro effect of several Food and Drug Administration (FDA)-approved chemotherapy drugs used in AML, including cytarabine.Acute myeloid leukemia (AML) is a hematologic malignancy associated with high morbidity and mortality. Here we describe a case of a patient with AML who presented a partial response after utilization of the non-steroidal anti-inflammatory drug nimesulide. The response was characterized by complete clearance of peripheral blood blasts and an 82% decrease of bone marrow blasts associated with myeloblast differentiation. We have then shown that nimesulide induces  Despite therapeutic advances in recent years, acute myeloid leukemia (AML) is still associated with high morbidity and mortality, with most patients succumbing to the disease , 2. AML Drug repurposing, that is, the identification of new uses for existing drugs, is a strategy with the potential to improve treatment outcomes with significantly cheaper, faster, and safer preclinical and clinical development protocols . Severalin vitro leukemic cell death, also induces myeloid differentiation  have anti-proliferative and pro-apoptotic effects in AML cell lines and xenograft models –13. In antiation . There intiation .In this article, we report the first-in-human clinical response to nimesulide in AML and provide insights into the mechanisms of action involved in this effect through whole exome, transcriptome, and cell culture experiments.Peripheral blood and bone marrow samples were obtained from an AML patient after the informed consent form was signed. Blood count was performed on an XP-300 hematology analyzer , and the bone marrow smear was processed according to the standards of the diagnostic medical laboratory. Bone marrow mononuclear cells (BMMC) were obtained using Ficoll-Paque  and used for immunophenotyping, DNA, and total RNA extraction for whole-exome and transcriptome sequencing  at 37°C in a 5% CO2-humidified atmosphere.The leukemic cell lines HL-60, THP-1, OCI-AML2, and OCI-AML3 were obtained from American Type Culture Collection  or Leibniz Institute DSMZ (German Collection of Microorganisms and Cell Cultures GmbH) and cultured in RPMI-1640 medium containing 10% fetal bovine serum, 2 mM of The drugs nimesulide , prednisolone , and cytarabine  were used in the cellular experiments. Nimesulide was diluted in dimethyl sulfoxide (DMSO) in a 10-mM stock solution, and prednisolone and cytarabine, presented in liquid form, were diluted in RPMI medium immediately before being applied to the cells. Food and Drug Administration (FDA)-approved drug library  was used for drug screening in combination with nimesulide.The eBioscience™ Annexin V Apoptosis Detection Kit FITC  was used for the detection of early and late apoptosis in treated cell lines, following the manufacturer’s instructions. For the cell cycle analysis, treated cells were washed with cold phosphate-buffered saline (PBS) twice and fixed with 70% cold ethanol at 4°C overnight. After cells were washed again with PBS twice, they were incubated with PBS containing 10 mg/ml of RNase at 37°C for 30 min, followed by DNA staining with 1 mg/ml of propidium iodide . After being left in a dark place at 4°C for 10 min, the cell DNA content was evaluated using a flow cytometer to analyze the distribution of DNA content in cell cycle phases. All experiments were performed using flow cytometer FACScan Fortessa  and recorded at least 10,000 events/sample. The analysis of the experiments was performed by FlowJo software.® Luminescent Cell Viability Assay  according to the manufacturer’s instructions. After 24 h of incubation of cell line with drugs alone or in combination with nimesulide, the cells were incubated for 10 min with Cell Titer-Glo reagent and then assayed by a luminescence plate reader. The ATP content of cells was calculated using the ATP standard curve in a preconfigured CellTiter-Glo protocol on GloMax® Discover Microplate Reader (Promega).To identify potential effects of small molecules in combination with nimesulide, an FDA-approved drug library was purchased from Selleck Chemicals for a high-throughput drug screening with selected drugs to assess the roles of drugs in cells lines. The following drugs approved for the treatment of AML were selected: azacitidine (1 μM), gilteritinib fumarate (0.1 μM), cladribine (10 μM), doxorubicin hydrochloride (6.7 μM), mitoxantrone hydrochloride (0.33 μM), fludarabine (0.09 μM), thioguanine (1.78 μM), and decitabine (0.5 μM). All drugs were diluted in RPMI 1640 culture medium (Gibco) to obtain all the above concentrations, based on the lowest concentration found in the literature. Cell viability was analyzed using the CellTiter-GloRNA was extracted with RNeasy Mini Kit , and the library preparation and sequencing were performed at the Oklahoma Medical Research Foundation Genomics Core (USA)  package from R  was emplThe strategy used to compare the molecular findings from cell line transcriptomes with the patient’s transcriptomes was the CAMERA  package.For flow cytometry assays, ANOVA statistical test was used, followed by Tukey’s test for multiple comparisons. The statistical computations were conducted using GraphPad Prism 7.0, and the significance level adopted was 5% (p < 0.05). For the bioinformatics analysis, statistical methods specific to each computer program were used.3  and a platelet count of 139,000/mm3. Bone marrow evaluation was compatible with the diagnosis of AML with 56% blasts on bone marrow smear. Blast immunophenotyping was positive for CD117, CD64, CD36, CD33, HLA-DR, and negative for CD34. Cytogenetics analysis revealed a diploid karyotype , and whole-exome sequencing revealed the following driver mutations NPM1:NM_002520:c.861_862insTGCT:p.L287fs, DNMT3A:NM_175629:c.1658delA:p.N553fs, IDH1:NM_005896:c.G395A:p.R132H, NRAS : NM_002524:c.G38T:p.G13V, and PTPN11:NM_080601:c.G794A:p.R265Q with wild-type FLT3 and CEBPA. Her final diagnosis was AML with mutated NPM1, and the European LeukemiaNet risk stratification was a favorable risk  count of 4,740/mmThe analysis of bone marrow whole exome at diagnosis suggests an ancestral clone harboring the DNMT3A mutation with an allele frequency of 34.5%; a second subclone with IDH1 and NRAS with allele frequencies of 27% and 26%, respectively; and the third subclone with NPM1 and PTPN11 with allele frequencies approximately 5% , G0/G1, S, and G2/M phases Figure 2To characterize the mechanism of cell death, the proportions of early and late apoptotic cells were evaluated by flow cytometry after treatments for 24 h Figure 3In order to find a common mechanism behind the induction of cell death in AML cell lines after treatment with nimesulide alone and in combination with prednisolone, we decided to perform whole-transcriptome sequencing for each condition. All samples had at least 30 million reads and a Phred Score above Q30. We then performed principal component analysis (PCA) on raw gene expression data, normalized by FPKM  with death levels: downregulated Lightyellow module (p = 0.00052), whose expression levels are inversely correlated with death levels, and upregulated modules Tan (p = 0.000038) and Lightcyan (p = 0.00025), which showed a positive correlation , we created two gene expression ratios based on the FPKM ratio for each gene. Ratio 1 was FPKM from D+21 after nimesulide treatment (early) divided by FPKM at diagnosis, and ratio 2 was FPKM from D+63 after nimesulide treatment (late) divided by FPKM at diagnosis. Then, both ratios were ranked from the highest to lowest in order to show at the top of the rank the genes with the highest fold-change and at the bottom of the rank the genes with the lowest fold-change (including downregulated genes). Afterward, we performed an enrichment test to see if the genes from the modules of experiment 1  generated by the WGCNA had a non-random distribution along with this ranked list, that is, if upregulated genes tended to occur more on the top or the bottom of the ranks and vice versa.We verified that when comparing the patient’s early rank with the genes from modules from experiment 1, related to autophagy (Lightcyan) and RNA splicing and cell cycle (Lightyellow), those genes presented a non-random distribution, being concentrated among the genes with the highest fold-change, with p-values of 0.003 and 0.004, respectively Table 2.We have also evaluated changes in gene expression induced by nimesulide in mutated driver genes in the cell lines. The change in expression of DNMT3A was heterogeneous. While it was slightly reduced in THP1, it was marginally increased in HL60 and OCI-AML3 and significantly increased in OCI-AML3  patients , 20. Whiin vitro  and a xein vitro , 22, makin vitro induction of cell death and cell cycle arrest in AML cell lines treated with COX-2 inhibitors, including nimesulide .The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI's Sequence Read Archive (SRA) with BioProject ID: PRJNA817613  and The Applebaum Foundation.Author RP was employed by company Grupo Pardini.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.