Malignant pleural effusion (MPE) is an independent determinant of poor prognostic factor of non-small cell lung cancer (NSCLC). The course of anchorage independent growth within the pleural cavity likely reforms the innate molecular characteristics of malignant cells, which largely accounts for resistance to chemotherapy and poor prognosis after the surgical resection. Nevertheless, the genetic and transcriptomic features with respect to various drug responses of MPE-complicated NSCLC remain poorly understood. To obtain a clearer overview of the MPE-complicated NSCLC, we established 28 MPE-derived lung cancer cell lines which were subjected to genomic, transcriptomic and pharmacological analysis. Our results demonstrated MPE-derived NSCLC cell lines recapitulated representative driver mutations generally found in the primary NSCLC. It also exhibited the presence of distinct translational subtypes in accordance with the mutational profiles. The drug responses of several targeted chemotherapies accords with both genomic and transcriptomic characteristics of MPE-derived NSCLC cell lines. Our data also suggest that the impending drawback of mutation-based clinical diagnosis in evaluating MPE-complicated NSCLS patient responses. As a potential solution, our work showed the importance of comprehending transcriptomic characteristics in order to defy potential drug resistance caused by MPE. It is also the most prevailing cause of malignant pleural effusion (MPE), accounting for approximately 30% of such effusions3. Although recent advances in prevention and early detection have contributed to the general increase in the survival rate, the occurrence of MPE in the context of lung cancer designates terminal stage and an independent prognostic parameter of the poorer survival rate4. Lung cancer patients with MPE mostly exhibit insensitivities to certain chemotherapies mainly due to high tumor burden6. Besides, the course of acquiring anoikis resistance alters multiple tumorigenic pathways, which eventually brings about insensitivity to certain chemotherapies7. Nevertheless, the genetic and transcriptomic features with respect to various drug responses of MPE-complicated NSCLC have not been thoroughly studied yet, which makes the implication of drug insensitivities of MPE-derived tumor cell still hypothetical.Lung cancer is one of the most prevalent cause of cancer-related death worldwide, with an estimated one million deaths annually. Within Korean population, it is the most lethal disease and the third common types of cancer8. Nevertheless, the widely accepted oncogenic association of secondary or tertiary alternations by the second line chemotherapy has been focused on the primary NSCLC, and oncogenic impact of chemotherapy-derived molecular changes in MPE remains unclear.Moreover, several studies have indicated that acquired resistance to second line chemotherapy such as crizotinib is associated with aggressive disease progressionsEGFR) and anaplastic lymphoma kinase (ALK) fusion with other domains, but also demonstrated the presence of distinct translational subtypes in accordance with the mutational profiles, which was consistently reflected to the drug responses of certain targeted chemotherapies. Our data also suggest that the imminent downside of mutation-based clinical diagnosis in assessing MPE-complicated NSCLS patient responses. As a potential solution, our data showed the importance of comprehending transcriptomic characteristics in order to defy potential drug resistance caused by MPE.To obtain a clearer overview of the MPE-complicated NSCLC in accordance with heterogeneous drug responses, we established 28 lung cancer cell lines derived directly from the MPE, and two crizotinib-resistant sublines which were then subjected to genomic, transcriptomic and pharmacological analysis. Our results not only showed that MPE-derived NSCLC cell lines recapitulated representative genetic aberrations of the primary NSCLC including epidermal growth factor receptor  and targeted sequencing (CCP) on the 28 MPE-derived cell lines. The overall mutational spectrum of the MPE-derived NSCLC cell lines such as mutant-allele tumor heterogeneity (MATH) analysis and relative contribution of mutational signatures was analyzed using WES data. The targeted sequencing as well as direct Sanger sequencing re-confirmed the presence of key driver mutations minimizing the possible false-positive sequencing errors  in order to avoid misestimating the mutation frequency due to inflated germinant/benign mutations. The pathogenicity of the representative driver mutations was manually inspected, and all marked mutations for calculating the frequency of mutated tumor genes in Fig. Several studies have shown that MPE-derived tumor cell lines maintain the mutational profiles of the lung adenocarcinoma10 which was composed of a total of 63 lung cancer cell lines  compared to CCLE cohort (14%). It has been reported that the EGFR mutation frequency in lung adenocarcinoma is approximately 10% as much larger cohort (TCGA)11 indicated  compared to CCLE (35%) and TCGA (30%) cohorts, which was unexpected since mutant KRAS is reported to promote malignant pleural effusion formation5. Previous study indicated that EGFR is more frequently aberrated than KRAS in Korean NSCLC population13, which suggest that the geographic factor partially shaped the mutational landscape of our cohort. Nine patients harbored driver fusion genes including EML4-ALK, KIF5B-RET and CD74-ROS1 in our cohort nes Fig. A. As a f cohort 1%. It has16. This analysis allowed to estimate the dispersion of mutant allele frequencies (MAFs) within a cell population, which reflected the intra-tumor genetic heterogeneity. The cell lines with mutant EGFR had higher MATH score than two other groups, suggesting that higher level of genetic heterogeneity is causative factor for EGFR mutation ion Fig. B. Then, ion Fig. . Except ion Fig. D.EGFR aberration, fusion genes, and others. We have applied these groups for further analysis of transcriptome and drug screening.Overall, comprehensive mutational analysis indicated that there is a distinct separation of genomic drivers in our cohort: EGFR mutation were specifically clustered with less degree of dispersion on the PCA plot compared to the fusion group, which suggests that EGFR-mutated NSCLCs in MPE display characteristic transcription patterns. Hierarchical clustering analysis also displayed that cell lines with the fusion genes were clustered distinctly from the cell lines with EGFR mutation  to identify pathways that are specifically enriched in each group. Only spermatogenesis pathway was significantly enriched in EGFR group compared to other two groups (p < 0.05). Multiple tumor driver genes that are associated with spermatogenesis pathway such as mTOR, EZH2, NF2, DCC and MLF1 had high enrichment score in the EGFR group , Hypoxia (p < 0.01), TNFα signaling (p < 0.05), Angiogenesis (p < 0.05). Genes with high enrichment scores for each signaling pathway are summarized in Supplementary Table We then verified that the pattern of mRNA expression reflects the distinctive genomic aberrations. The principle component analysis (PCA) of the mRNA expression indicated that MPE-derived cell lines were likely grouped in accordance with their genetic factors Fig. A. Cell lion Fig. B. The otoup Fig. C. We furoup Fig. D: InflamALK mutations or alternative pathway changes cause a fraction of the tumors to be resistant to ALK inhibitors18. We further validated that the acquired resistance to crizotinib, the first generation of the ALK inhibitor, alters the transcriptional patterns of MPE-derived NSCLC cell lines harboring EML4-ALK fusion gene. We established two crizotinib-resistant sublines (SNU-2550CR and SNU-2563CR) by long-term exposure to increasing concentrations of crizotinib, and performed RNA-seq. Both sublines exhibited distinct insensitivities to crizotinib, and a list of differentially expressed genes and their log fold changes together with signaling pathways topology were identified in order to detect the pathways most relevant to the crizotinib resistance. SPIA two-way analysis of RNA-seq revealed multiple signaling aberrations including p53 signaling and focal adhesion  fusion gene. However, secondary We assembled an 18-compound library for drug screening. In total, 30 cell lines including two crizotinib resistant sublines from 28 patients were successfully screened in experimental triplicate, generating > 500 measurements of cell line-drug interactions.EGFR mutations such as SNU-2556, SNU-2643, SNU-2708.1, SNU-2741, SNU2867, SNU-2887, SNU-2964 and SNU-3023 as well as EGFR inhibitors including gefitinib, erlotinib and dacomitinib were tightly clustered together. Moreover, MPE-derived NSCLC cell lines with EML4-ALK fusion and its targeted drugs including crizotinib, alectinib, and ceritinib were grouped as well , which recapitulated the role of the increased nuclear localization of β-catenin caused by the elevated DKK1 on the crizotinib resistance.Among the MPE-derived NSCLC cell lines harboring fusion genes, two cell lines with the EGFR-mutated cell lines, but also revealed that cell lines harboring the EML4-ALK fusion displayed significant insensitivities to EGFR targeted therapy. As indicated earlier, cell lines with CD74-ROS fusion were specifically sensitive to MK-5108 as well as cyclopamine.We further integrated the genetic factors of the MPE-derived NSCLC cell lines with 18 drugs using Wilcox ranked sum test Fig. B. Gene-dEGFR mutations, yet still positioned adjacent to cell lines with EGFR mutations on the transcriptomic analysis. This mRNA expression pattern is reflected by the notably good responses of these cell lines to EGFR TKIs, which suggests that the shape of transcriptomic landscape can be a determinant factor for EGFR-targeting therapy  and AUCs of each drug. The AUC value of cyclopamine was in direct proportion to the DEPTH score  to find which drugs accorded with the three different transcriptional subtypes. MANOVA indicated that EGFR TKIs were significantly associated with the mRNA expression patterns  compared to those without effusions (about 12–18 months)23. In the tumor-node-metastasis (TNM) staging classification of lung cancer, patients with MPE are categorized as category M1a (stage IV) as it is a sign of metastatic dissemination24. Although few studies have focused on malignant tumor cells from pleural fluids, only genomic analysis including mutational aspects and copy number variations was conducted27. Giuseppe Roscilli et al. integrated the mutational characteristics of human lung adenocarcinoma cells derived from MPEs to the patients chemosensitivity6, yet the number of included drugs and patients were not enough to draw solid conclusion.Nearly 20% of lung cancer patients develop malignant pleural effusions (MPEs)EGFR mutations and fusion events compared to the lung adenocarcinoma-originated cell lines. Supporting the notion of distinguished EGFR and fusion aberrations, our transcriptomic analysis demonstrated that MPE-derived cell lines were subtyped in parallel with the genetic aberrations. The transcriptomic subtyping of EGFR-mutant NSCLC has been performed in other studies as well29. Our study has demonstrated that MPE-derived NSCLC cell lines also retain the transcriptional characteristics of EGFR-mutant NSCLC, which were distinctive to NSCLC cell lines with other driver mutations such as KRAS or fusion genes.In this study, we present 28 MPE-derived lung cancer cell lines and two crizotinib resistant sublines that were comprehensively characterized in accordance with multiple drug responses. These in vitro models retained the representative lung cancer mutations in parallel with mutational profiling of other external datasets consisting of lung cancer cell lines (CCLE) as well as tumor samples (TCGA). Our data revealed that MPE-derived lung cancer cell lines involves higher rates of 30. We further inspected if MET aberrations occurred in our two crizotinib sublines as well. We confirmed that MET was clearly amplified in SNU-2550CR cell line compared to its parental SNU-2550 cell line regardless of crizotinib treatment, yet no MET amplification was found in SNU-2563 and SNU-2563CR cell lines. This suggested that MET amplification indeed accounted for acquired crizotinib resistance in SNU-2550 set, and the SNU-2563CR cell lines had different resistance mechanism such as nuclear localization of β-catenin. No MET mutation was found in both resistant sublines.We suggested that the nuclear localization of β-catenin in MPE-derived NSCLC cell lines through increased DKK1 expression potentially caused crizotinib resistance. The acquire crizotinib resistance has been also associated with both MET amplification and exon 14 skipping mutation in lung cancerOur work also enabled the integration of highly prevalent genetic and transcriptional characteristics of MPE-derived lung cancer cell lines to various drug responses. We re-confirmed that targeted therapies, especially EGFR TKIs showed high sensitivities in accordance with the presence of key mutations using Wilcox ranked sum test Fig. B and MANIn conclusion, our work emphasized the significance of integrating the mutational status with transcriptomic subtypes using statistical models such as Pearson correlation coefficient and MANOVA in order for the accurate assessment of drug responses in lung cancer patients with MPE.The research protocol was reviewed and approved by the institutional review board of the Seoul National University Hospital (IRB No. 1102-098-357). The study was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients enrolled in this study.http://cellbank.snu.ac.kr) at the initial passage in order to be distributed to researchers worldwide.Cell lines were established from malignant pleural effusion (MPE) derived from pathologically proven non-small cell lung cancer (NSCLC). A total of 28 MPE specimens of human NSCLC from 28 different patients who underwent a pleural intervention for the palliation of dyspnea were obtained from Seoul National University Hospital . MPE samples were directly transferred from the operating room to the laboratory for cell culture. Tumor cells were spun down by centrifugation the MPE sample at 300 rpm for 5 min, and re-suspended with Opti-MEM I  supplemented with 5% fetal bovine serum  and 1% Penicillin/Streptomycin. Gathered cells were then seeded into T-25cm2 flasks . Confined-area trypsinization or scraping method was used to attain a pure tumor cell population when stromal cells like mesothelial cells or fibroblasts grew in the initial culture. Established cell lines were sustained in RPMI 1640 medium with 10% fetal bovine serum and 1% (v/v) penicillin and streptomycin . Cultures were maintained in humidified incubators at 37 °C in an atmosphere of 5% CO2 and 95% air. The initial passage was assigned when substantial tumor cell growth was detected, and successive passages were given at sub-confluence after trypsinization. When one culture population contains both floating and adherent cells, floating cells were gathered by centrifuging the medium and dispersed by pipetting. All cell lines introduced in this study will be deposited in the Korean Cell Line Bank  was added, the plate was incubated at 37 °C for 2 h, and the optical density of each well was calculated by Multiskan™ GO Microplate Spectrophotometer  at 450 nm wavelength. Acquired growth rate values were calibrated with GraphPad Prism 5 . The morphology of each cell lines was obtained using phase-contrast microscopy. Mycoplasma contamination was checked by the 16S-rRNA-gene-based polymerase chain reaction (PCR) amplification method using e-Myco Mycoplasma PCR Detection Kit .Genomic DNA extraction was performed using QIAamp DNA Mini kit (Qiagen). Genomic DNA extracted from each lung cancer cell line was amplified using an AmpFlSTR identifiler Polymerase Chain Reaction (PCR) Amplification Kit . A single cycle of PCR amplified 15 short tandem repeat markers  and an Amelogenin gender-determining marker containing highly polymorphic microsatellite markers. Amplified PCR products were analyzed by an ABI 3500XL Genetic analyzer (Applied Biosystems).To obtain RNA, TRIzol  was added to cell pellet acquired from cultured lung cancer cell line. After cell lysis was happened, chloroform was added. Then, the sample was vortexed and 4 °C, 12,000 g centrifugation. Each tube containing the sample was divided into aqua phase, interphase, and organic phase. In those aqua phase was transferred into new tube. Isopropanol alcohol was mixed with an equal part of the aqua phase. Mixed sample was centrifuged at 4 °C, 12,000 g after incubated at − 20 °C. RNA was acquired from pellet. The pellet was precipitated by ethanol. RNA was melted in 4th Distilled water containing RNase inhibitor (DEPC). Complementary DNA (cDNA) was synthesized from extracted RNA by reverse transcription kit . Generally measured concentration of RNA was adjusted to 1ug diluted with gDNA wipeout buffer. Extracted RNA concentration was measured nano-spectrometer . Each 1ug RNA was mixed with RT buffer, RT primer mix, and RTase. Those compounds were heated 42 °C for 30–45 min purposed on annealing, extension. Heated compound was additionally heated 95 °C for 3 min to denature synthetic cDNA. The list of primers we have used are as follows.For cDNA synthesis, QuantiTect Reverse Transcription Kit (Qiagen) was used. One microgram of total RNA, 2 μL of gDNA Wipeout Buffer, and RNase free water up to 14 μL were mixed together and incubated at 42 °C for 2 min. The mixture was mixed with Quantiscript RT Buffer, RT Primer Mix, and Quantiscript Reverse Transcriptase and incubated at 42 °C for 45 min. Then, the mixture was incubated at 95 °C for 2 min and cooled down to room temperature.PCR product was precipitated by 5% sodium acetate buffer (Sigma-Aldrich) and 95% ethanol mixed solution. Then washed product was set on ice for 10 min and centrifuged at 4 °C, 14,000 rpm. Supernatant was discarded and the product was rinsed this time by 70% ethanol and centrifuged 14,000 rpm. Supernatant was discarded then the products were dried using vacuum concentrator (Eppendorf). 10 μL of distilled water was added to dilute precipitated sample. When the product is all diluted in distilled water, cyclic PCR was carried out. Two separate mixtures for forward and reverse sequences were made where they each include 5X sequencing buffer (Applied Biosystems), Big Dye (Applied Biosystems), forward or reverse primer, distilled water, and product from the previous step. Cyclic PCR was carried out with denaturation step at 96 °C, annealing temperature at 55 °C, and elongation at 60 °C for 25 cycles. The cyclic PCR product was then precipitated with 5% sodium acetate buffer and 95% ethanol mixed solution and set on ice for 10 min then it was centrifuged at 4 °C and supernatants were carefully discarded and the final product was dried using the vacuum concentrator. 10 μL Hi-Di formamide (Applied Biosystems) was added to dilute the dried product. This final product was transferred to 96 well PCR plate and denatured at 95 °C for 2 min before taken to 3500xL Genetic Analyzer (Applied Biosystems) for sequencing.CCP (Comprehensive Cancer Panel) is technology using Ion Ampliseq (Thermofisher Scientific). CCP panel could detect 409 genes mutations on exomes. There are various mutation types about driver-genes. According to the manufacturer’s instructions as described, gDNA was extracted from cell pellet specimen using QIAamp DNA Mini kit. Quantity and quality of obtained DNA samples was evaluated using the Qubit fluorometer and the Qubit dsDNA HS assay kit . AmpliSeq library was assembled with NEBNext® Ultra DNA Library Prep Kit for Illumina (NEB #E7370S/L) and the Ion AmpliSeqTM Library Kit 2.0 . And then, the amplicons were purified with AMPure XP reagent . The products were processed in end repair by NEBNext® End Prep kit (NEB #E7442). NEBNext® Multiplex Oligos for illumina (NEB #E7335 or NEB #E7500) were ligated with DNA ligase. Adapter-ligated products were then purified by AMPure XP reagent , and PCR-amplified for a total of 7 cycles. The complete library was purified by AMPure XP reagent (Beckman Coulter) and GeneRead size selection kit(Qiagen). Size and concentration of the library were analyzed by Agilent 2100 BioAnalyzer and Agilent BioAnalyzer DNA High-Sensitivity LabChip (Agilent Technologies). The amplified libraries’s quality was proved by capillary electrophoresis . Qualified libraries were mixed in index tagged in equimolar amounts of the pool, after QPCR that was taken by using SYBR Green PCR Master Mix (Applied Biosystems). Sequencing was completed using an Illumina NextSeq 500 system following referred protocols for 2 × 150 bp sequencing.2 × 10^5 to 4 × 10^5viable cells from each cell line were seeded into well of 96 well plate in triplicate to measure drug sensitivity of 18 compounds. After 72 h-incubation at 37 °C, 10ul EZ-Cytox solution was added to well of each seeded lung cancer cells. After 2 h-incubation at 37 °C, optical density of EZ-Cytox-treated cells was calculated by Multiskan™ GO Microplate Spectrophotometer (Thermo Fisher Scientific). These steps were repeated in triplicate. The half maximal effective concentration (EC50) were measured by GraphPad Prism 5 (GraphPad Software). The maximum concentration and solvent are as follows.Cultivated cells that had full confluency were harvested with cell scrapper. Cell pellet was treated by EzRIPA buffer  after washed by cool PBS. Whole protein was extracted by this step. Protein concentration of each cell line was determined by SMART micro BCA protein assay kit (Intron biotechnology). Proteins that fixed into equal concentration were loaded on a 4–12% Bis–Tris gel (Invitrogen) at 70 V for 3 h and then proteins on loaded gel were transferred to a PVDF membrane (Invitrogen) by electro-blotting in condition under a constant current of 80 mA at 4 °C overnight. Proteins of transferred membrane was blocked by incubating in 1.5% to 2.0% skim milk and 0.05% Tween 20-TBS buffer including 1 mM MgCl2 for an hour at room temperature. Primary antibodies were used against DKK1, β-catenin, E-cadherin, and Vimentin and β-actin. Mouse or rabbit IgG 2nd antibody  (1:5000) conjugated with peroxidase that matched with used 1st antibody was added to membrane. After chemiluminescent working solution, WESTZOLTM (Intron biotechnology) was treated to the membrane, the membrane was exposed to Fuji RX film  for 1–5 min. The raw and unprocessed blotting images are provided with supplementary data file. The blotting image for DKK1 was closely cropped near the membrane edges, and we provide replicates performed as well. The MET protein level was confirmed with ChemiDoc Touch Imaging System  using consecutive exposing mode with 1-min interval for 5 min.Cells were seeded on chambered coverglass  with a desirable cell confluency. The chambered coverglass was designed to be hydrophilic and no ECM component was treated before seeding. 72 h after cell seeding, cells were washed with cold DPBS for 15 min three times. Then, cells were fixed and permeabilized with BD Cytofix/Cytoperm™ . After cells were washed with washing solution (BD science), DPBS containing 2% FBS  was applied for an hour for blocking. After cells were washed with cold DPBS β-catenin antibody  diluted in 0.05% of PBS.T was applied for a 1.5 h in room temperature. Thereafter, cells were washed with 0.05% of PBS.T, and Alexa 488 and Alexa 568 secondary antibodies  diluted in 0.05% of PBS.T were applied for an hour in room temperature. 1 × DAPI and Rhodamine-conjugated Phalloidin  were diluted in distilled water and applied for 30 min in room temperature. The cells were washed with DPBS three times, and pictured under confocal microscope. LSM800 Confocal Laser Scanning Microscope and ZEN software  was used to examine cells. Diverse magnifications were used for different growth patterns and sizes of cells. The intensity of each channel was fixed for the comparison of target protein expression between samples. Digital resolution, scan speed and the number of pictures averaged were set to 1024 × 1024, 40 s per one channel, and 8 pictures respectively. The pictures were focused on the very bottom of the fixed cells for investigating protruding region of cell colonies and the location of β-catenin.Total RNA was isolated from cell lysate using Trizol (Qiagen) and Qiagen RNeasy Kit (Qiagen). Sequencing libraries were prepared using the Illumina TruSeq Stranded Total RNA Library Prep Kit. Fifty-one million reads were obtained from the cell lysates. Following base-calling and alignment with the Tuxedo Suite, rejected reads were analyzed using FusionMap, ChimeraScan, and Defuse with default parameters for RNA and alignment to GRCh37.72. The output was filtered to include in-frame fusions, with at least one rescued read and two unique seed reads, and exclude known, recurrent artifacts.SureSelect sequencing libraries were prepared according to manufacturer’s instructions  using the Bravo automated liquid handler. Three micrograms of genomic DNA were fragmented to a median size of 150 bp using the Covaris-S2 instrument . The adapter ligated DNA was amplified by PCR, and the PCR product quality was assessed by capillary electrophoresis . The hybridization buffer and DNA blocker mix were incubated for 5 min at 95 °C and then for10 minutes at 65 °C in a thermal cycler. The hybridization mixture was added to the bead suspension and incubated for 30 min at RT while mixing. The beads were washed, and DNA was eluted with 50 ml SureSelect elution buffer (Agilent). The flow cell loaded on HISEQ 2500 sequencing system (Illumina).p < 0.05 was considered statistically significant. For hierarchical cluster analysis on a set of dissimilarities, each object was assigned to its own cluster, which an algorithm proceeds through iteratively. Two of the most similar clusters are joined at each stage until there is a single cluster. Distances between clusters are recomputed at each stage by the Lance–Williams dissimilarity update formula according to the particular clustering method being used. Clustering methods include: Ward's minimum variance method, complete linkage method, k-means method, and single linkage method.Statistical analysis was performed using R program version 3.3.1  with various packages including maftools, PerformanceAnalytics, survminer, survival, iplot, gplot, and lattice. Fisher’s exact test was used to analyze GO analysis of various genes. A multivariate analysis of variance (MANOVA) model was applied to the drug response data matrix with various factors such as the mutational status and the three different transcriptional subtypes. Approximate F value, p-value and Pillai’s trace score were obtained for each of the factors/drug pairs. A value of Supplementary Information 1.Supplementary Table S3.Supplementary Table S5.


Despite their documented antitumor effects, thienopyrazole-based compounds remain an underexplored class of molecules. In this study, a screening of 2000 novel molecules revealed a unique thienopyrazole derivative, Tpz-1, that elicited potent and selective programmed cell death in human blood, breast, colon, and cervical cancer cell lines when compared to non-cancerous human fibroblast (Hs27) cells. Furthermore, in HL-60 leukemia cells, Tpz-1 interfered with components of signaling pathways and the cytoskeleton that are important to cell shape, internal organization, growth, and division. These findings encourage the continued investigation and development of Tpz-1 and other thienopyrazole derivatives to target and treat cancers.In recent years, the thienopyrazole moiety has emerged as a pharmacologically active scaffold with antitumoral and kinase inhibitory activity. In this study, high-throughput screening of 2000 small molecules obtained from the ChemBridge DIVERset library revealed a unique thienopyrazole derivative (Tpz-1) with potent and selective cytotoxic effects on cancer cells. Compound Tpz-1 consistently induced cell death at low micromolar concentrations (0.19 μM to 2.99 μM) against a panel of 17 human cancer cell lines after 24 h, 48 h, or 72 h of exposure. Furthermore, an in vitro investigation of Tpz-1’s mechanism of action revealed that Tpz-1 interfered with cell cycle progression, reduced phosphorylation of p38, CREB, Akt, and STAT3 kinases, induced hyperphosphorylation of Fgr, Hck, and ERK 1/2 kinases, and disrupted microtubules and mitotic spindle formation. These findings support the continued exploration of Tpz-1 and other thienopyrazole-based compounds as potential small-molecule anticancer agents. Small-molecule agents have become a mainstay of targeted cancer treatment in the past 20 years. Unlike macromolecule drugs, small molecules can penetrate cells and bind a wide array of intra- or extracellular targets; additionally, they can be selective or broad-spectrum agents ,2,3.Pyrazoles and thiophenes are well-studied pharmacophores known for their diverse and remarkable biological activity, particularly in cancer, and are common scaffolds in small-molecule drug design ,5. PyrazThienopyrazoles are a bicyclic combination of the pyrazole and thiophene moieties and have been endorsed as antiproliferative, antiviral, antimicrobial, and anti-inflammatory agents ,11,12. TThienopyrazoles are less-explored than the other isomers, despite growing evidence detailing their significant phosphodiesterase 7A (PDE7A) ,14, puriFormally named N’-(2-methoxybenzylidene)-3-methyl-1-phenyl-1H-thienopyrazole-5-carbohydrazide, we found that this compound was also identified as cytotoxic and a mitotic inhibitor in two previous studies under the aliases 5248881  and MTPCLeukemias are a leading cancer type in children and adults ,22. In aIn this study, the cytotoxicity of Tpz-1 was evaluated against a panel of sixteen cancer cell lines and one non-tumorigenic human cell line. All cell lines were obtained from the American Type Culture Collection .2 humidified 37 °C environment.Ten of the cell lines were of blood cancer origin: CCRF-CEM , HL-60 , Ramos , Jurkat , MOLT-3 , NALM6 , MM.1S , MM.1R , U266 , RPMI 8226 ; and were cultured in RPMI-1640 medium complete with 10% heat-inactivated fetal bovine serum (FBS), 100 U/mL penicillin, and 100 µg/mL streptomycin. An exception was made for HL-60, which required supplementation with an additional 10% FBS. In addition, two colorectal adenocarcinoma cell lines, COLO 205 (ATCC CCL-222) and HT-29 (ATCC HTB-38), were identically maintained in RPMI-1640 complete medium. Two triple-negative breast adenocarcinoma cell lines, MDA-MB-231 (ATCC CRM-HTB-26) and MDA-MB-468 (ATCC HTB-132), and estrogen-receptor-positive (ER+) cell line MCF7 (ATCC HTB-22) were cultured in DMEM medium complete with 10% FBS, 100 U/mL penicillin, and 100 µg/mL streptomycin; cervical adenocarcinoma cell line HeLa (ATCC CRM-CCL-2) and non-tumorigenic foreskin fibroblast Hs27 (ATCC CRL-1634) cells were also maintained in this medium. Lastly, dopaminergic neuroblastoma cell line SH-SY5Y (ATCC CRL-2266) was cultured in DMEM/F12 medium with 10% FBS and antibiotics added as described above. All cells were cultivated in a 5% COBefore use in experiments, the viability of each cell culture was quantified by flow cytometry following propidium iodide (PI) staining or visualized by trypan blue exclusion . Only ce2O2) was alternatively diluted in 1× phosphate-buffered saline (PBS).All compounds described herein were commercially purchased. Experimental compounds from the ChemBridge DIVERset library were obtained pre-diluted to 10 mM in dimethyl sulfoxide (DMSO). This ChemBridge stock of N’-(2-methoxybenzylidene)-3-methyl-1-phenyl-1H-thienopyrazole-5-carbohydrazide , also known as Tpz-1, was used for the experiments detailed below. Lyophilized reference compounds Paclitaxel and Cytochalasin-D were similarly prepared in DMSO; and hydrogen peroxide  is a live cell imaging assay that uses two nuclear fluorescent dyes to distinguish between live and dead populations based on cell permeability . To achiv/v DMSO (vehicle control), 1 mM H2O2 (positive control), or left untreated (UNT) for 24–72 h. Four independent measurements were used for the samples, except for the vehicle control in which eight DMSO-treated independent measurements were used to ensure the average total cell count was representative for each experiment series. One to two hours before the end of treatment, a 1 μg/mL final concentration of Hoechst 33342 and PI was quickly added to each well and incubated until the remaining time elapsed. Four contiguous images, taken in 2 × 2 montages, were captured via GE InCell Analyzer 2000 with a 10x objective from the two fluorescent channels corresponding to Hoechst and PI emission. After analysis, the captured high-content images were segmented using the GE InCell Analyzer Workstation 3.2 software to obtain live and dead cell counts and percentages within each well. Due to technical problems, for the MCF7 cell line, the BD Pathway 855 bioimaging system was used to capture a similar 2 × 2 montages per well, and images were segmented with its associated AttoVision v1.6.2 software . To account for the lower total cell count in Tpz-1 or H2O2-treated (Treatment) samples caused by cell destruction (disappearance from count) over the course of incubation, cell death percentages were normalized to the vehicle control (DMSO) average total cell count by the following equation:For DNS analysis, cells were first seeded in 96-well plates at a density of 10,000 cells/well in 100 μL of complete medium and incubated overnight to facilitate cell attachment and acclimation. The next day, cells were exposed to Tpz-1 concentrations ranging from 0.01 to 10 μM, 1% 50 values.Normalized percentages of dead cells per treatment replicate were subsequently averaged and used to estimate CC50) for each cell line indicates the average concentration (μM ± S.D.) needed to kill 50% of the cell population at a given time point. From the percentage of cell death for each technical replicate at a concentration above and below 50% cell death, CC50 for each cell line was calculated via linear interpolation and averaged to obtain the reported values pyrazole derivative with potent and specific anticancer activity in vitro. Despite compelling evidence of the diverse biological activity of thienopyrazoles, these findings have brought to light a scarcity of publications focused on this unique scaffold. While the contribution of the thienopyrazole moiety to Tpz-1’s biological activity remains unknown, our data parallels previously studied thienopyrazoles. Research to further elucidate Tpz-1’s mechanism of action is ongoing. Until then, we hope that this work inspires our colleagues to consider thienopyrazole-based compounds in small-molecule anticancer drug formulations.


Academic procrastination is a specific sub-type of procrastination, assessing the tendency to delay academic tasks in connection with the preparation of courses or exams.To determine the impact of academic procrastination on studies and academic performances and identify associated factors.We conducted a cross-sectional descriptive study in students from three different universities: a medical school, a law school and an engineering school. Socio-demographic, clinical and academic data were collected. Procrastination was assessed using the Academic Procrastination Scale. We further administered the Short Version of the impulsive behaviour scale, the Satisfaction with Life Scale, the Perfectionism scale, and the one item Self-esteem Scale.Our sample consisted of 1019 students. The mean age was 22 ± 2.25 years, and 62% were females. About one third of study participants used tobacco or alcohol, and 10% used drugs (cannabis or others). We found a significant positive correlation between procrastination and academic failure (r=0.22 p= 0.00) and a negative correlation with academic success (r= -0.27 p=0.00). Multivariate regression analysis showed the following risk factors for academic procrastination: alcohol consumption , study field , and impulsivity .Academic procrastination has a negative impact on academic achievement and performance. This impact appears to differ depending on the field of study. It also seems closely related to impulsiveness and alcohol use. Our findings might contribute to find new ways of helping students to improve academic performance.No significant relationships.


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.