Although significant progress has been made in the treatment of myeloid malignancies, this issue remains a focus of interest for a large number of research teams. Current research is especially focused on elderly patients who are not suitable for intensive chemotherapy and bone marrow transplantation or patients who have achieved remission after such therapy but subsequently enter into relapse of the disease. Here, therapy with demethylating agents is indicated. However, we do not know of another treatment option in the case of resistance to such treatment. Therefore, knowledge of the causes and mechanisms of resistance to demethylating agents is an essential issue for improving the treatment of such patients.Three AML cell variants  were established for resistance by the same protocol using 5-azacytidine (AZA) as a selection agent. These AZA-resistant variants differ in their responses to other cytosine nucleoside analogs, including 5-aza-2′-deoxycytidine (DAC), as well as in some molecular features. Differences in global DNA methylation, protein levels of DNA methyltransferases, and phosphorylation of histone H2AX were observed in response to AZA and DAC treatment in these cell variants. This could be due to changes in the expression of uridine-cytidine kinases 1 and 2 (UCK1 and UCK2) demonstrated in our cell variants. In the M/A variant that retained sensitivity to DAC, we detected a homozygous point mutation in UCK2 resulting in an amino acid substitution (L220R) that is likely responsible for AZA resistance. Cells administered AZA treatment can switch to de novo synthesis of pyrimidine nucleotides, which could be blocked by inhibition of dihydroorotate dehydrogenase by teriflunomide (TFN). This is shown by the synergistic effect of AZA and TFN in those variants that were cross-resistant to DAC and did not have a mutation in UCK2. Myelodysplastic neoplasms  and acute myeloid leukemia (AML) share a common denominator in abnormal myeloid hematopoiesis, which leads to cytopenias and an increased presence of immature myeloid blasts in the bone marrow and peripheral blood. One of the treatment options for these diseases is the use of the hypomethylating agents (HMAs) 5-azacytidine (AZA) and 5-aza-2′-deoxycytidine (DAC) ,2.AZA, commercially available under the name Vidaza, has been approved by the European Medicines Agency (EMA) for the treatment of patients with malignancies caused by defective differentiation in the myeloid branch of hematopoiesis who are not suitable for hematopoietic stem cell transplantation. The EMA has recommended the use of Vidaza for patients with MDS, chronic myelomonocytic leukemia (CMML), AML with 20–30% blasts, and multilineage dysplasia and adult patients aged 65 years or older with AML with >30% blasts in bone marrow . MoreoveUnfortunately, approximately half of patients may not respond to AZA treatment from the start (primary resistance), and others may become unresponsive during repeated cycles of HMA therapy (secondary resistance) . For a pWe have previously described the preparation of AZA-resistant MOLM-13 and SKM-1 cell variants by sequential passaging in medium with increasing concentration of AZA. These variants presented overexpression of the multidrug efflux pump P-glycoprotein (P-gp) and exhibited resistance to P-gp relative substances in addition to resistance to AZA . SimilarStudying possible mechanisms of resistance, we mainly focused on the uptake of HMAs and their fate in sensitive and resistant cells. Both AZA and DAC are prodrugs that must be phosphorylated to the corresponding triphosphates in order to be incorporated into DNA or RNA and exert their effects . Since H2.Two cell lines were used in this study. The MOLM-13 cell line (ACC 554) and the SKM-1 cell line (ACC 547) were derived from the peripheral blood of a 20-year-old and a 76-year-old man with AML following MDS, respectively . The sensitive MOLM-13 and SKM-1 cell lines were adapted to 5-azacytidine  over a 6-month period with repeated passaging in medium containing AZA in stepwise increasing concentrations beginning at 0.1 nM up to final concentration of 1 μM. This procedure yielded AZA-resistant SKM-1/AZA and MOLM-13/AZA* cell variants. The MOLM-13/AZA cell variant was described previously . The celSensitive and resistant MOLM-13 and SKM-1 cells were incubated under standard culture conditions with various concentrations of AZA, DAC, teriflunomide , or combinations of these drugs for 72 h. The cell lines were treated with AZA/DAC/TFN every 24 h. The number and viability of cells were determined by measuring the plasma membrane integrity of individual cells through changes in electrical resistance induced by cells passing through the detector in the CASY Model TT Cell Counter  according to the manufacturer’s protocol.® AQueous One Solution cell proliferation assay  was used to determine the metabolic activity of cells according to the manufacturer’s protocol. The IC50  was computed by nonlinear regression according to Equation (1) using SigmaPlot 8.02 software :50 is the half maximal inhibitory concentration; and n represents order exponents for cytotoxic effects. The data represent computed values ± standard error with 30 degrees of freedom.Sensitive and resistant MOLM-13 and SKM-1 cells were incubated under standard culture conditions with different concentrations of cytarabine or gemcitabine for 48 h and various concentrations of AZA, DAC, TFN, or their combinations for 72 h. The cell lines were treated with AZA/DAC/TFN every 24 h, and cytarabine and gemcitabine were added only once. After cultivation, the CellTiter 96ACTB.Total RNA was isolated using TRI Reagent  according to the manufacturer’s instructions. Reverse transcription (RT) was performed with 1 μg of RNA using a RevertAid™ H Minus First-Strand cDNA synthesis kit  according to the manufacturer’s protocol. PCR was performed in a total volume of 25 μL using a DreamTaq PCR kit  according to the manufacturer’s protocol. After heating the samples at 95 °C for 5 min, samples were subjected to 30 cycles of denaturation , annealing  according to the manufacturer’s protocol. PCR thermal cycling conditions were as follows: initial denaturation ; 30 cycles of denaturation ; annealing , 1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS, 150 mM NaCl, and protease inhibitor cocktail from Sigma-Aldrich . The protein concentrations were measured by a Pierce™ BCA Protein Assay kit . The protein samples were separated by sodium dodecyl sulfate-polyacrylamide electrophoresis (SDS–PAGE) in a 12% gel. The proteins were then transferred by electroblotting to a nitrocellulose membrane . The primary antibodies used were UCK1 (HPA050969) and UCK2 (SAB1411384) ; DCK (ab96599) from Abcam, Cambridge, UK; GAPDH (MAB374) ; and γ-H2AX (CST 9718) and α-tubulin (CST 3873) . Goat anti-rabbit (SC-2054) and mouse anti-rabbit antibodies (SC-2357)  and horse anti-mouse (CST 7076) and goat anti-rabbit antibodies (CST 7074) , all conjugated with horseradish peroxidase, served as secondary antibodies. Protein bands were visualized by ECL detection  and Amersham Imager 600 . Protein quantities were established by densitometry using ImageQuant software  and normalized to GAPDH or α-Tubulin.Cells were incubated with various concentrations of AZA, DAC, TFN, or a combination of these drugs for 24 or 72 h under standard culture conditions. The cells were treated with AZA/DAC/TFN every 24 h. After the incubation period, the proportions of apoptotic and necrotic cells were measured using an Annexin V /propidium iodide  assay. The cells were washed with PBS and gently resuspended in binding buffer containing 0.5 μg/mL FITC-labeled Annexin V. The mixtures were incubated for 15 min at room temperature in the dark. Finally, propidium iodide  was added to each sample, after which the samples were analyzed by flow cytometry using an Accuri C6 flow cytometer .Cells were incubated with or without 0.5 μM AZA or DAC for 72 h. The cells were treated with AZA/DAC every 24 h. Total DNA was isolated using TRI Reagent  according to the manufacturer’s instructions. Global DNA methylation status was determined by a Methylated DNA Quantification Kit  according to the manufacturer’s instructions. Numerical data are expressed as the mean ± SD of three independent measurements. Statistical significance was assessed by unpaired Student’s t test. Correlations were determined using Pearson correlation analysis. SigmaPlot 8.0 software  was used. 50) values of four (deoxy)cytidine analogs—AZA, DAC, gemcitabine (GEM), and cytarabine (AraC)—in our cell variants. M/A did not show any cross-resistance to other analogs. However, S/A showed a slightly lower sensitivity to the other three analogs, while M/A* showed considerably increased cross-resistance to the analogs, especially DAC (Three AZA-resistant cell sublines were prepared in our laboratory: one from the SKM-1 cell line (SKM-1/AZA—S/A) and two from the MOLM-13 cell line (MOLM-13/AZA—M/A and MOLM-13/AZA*—M/A*). The M/A subline was already described in our previous work . Using tally DAC . Even atally DAC .After incorporation into DNA, HMAs exert two main effects: DNA hypomethylation by DNA methyltransferase (DNMT) inhibition and DNA damage response induction . TherefoCDA) in M/A and M/A* cells compared to MOLM-13 cells. In M/A, we also observed slightly decreased expression of dihydroorotate dehydrogenase (DHODH) , but neither was expressed in any of our cell variants.Using RT–PCR, we measured the mRNA expression of genes involved in the metabolism and transport of AZA and DAC . For mos (DHODH) . We alsoFor both hypomethylating agents, phosphorylation to the corresponding monophosphate form is the rate limiting step in their activation . TherefoUCK1 and UCK2 protein coding regions were sequenced. We found a homozygous point mutation of UCK2 in the M/A cell variant , an inhibitor of dihydroorotate dehydrogenase (DHODH), exerted slightly different impacts on our three AZA-resistant cell sublines. In the case of M/A, TFN caused a decrease in viable cell counts compared to the untreated control, but it did not induce cell death even at the highest concentration used. In M/A*, the number of viable cells was also lower than in the control, but we also observed an increased number of apoptotic cells in the population. In S/A, we observed only a small decrease in the metabolic activity of the cells, with no change in their number. We also did not observe a significant increase in the apoptotic cell counts. When compared to sensitive parental cell lines, TFN had a similar or weaker effect on AZA-resistant cell variants . Except Next, we measured the impact of TFN and AZA combined treatment on our AZA-resistant cell variants . We cultWe also measured the impacts of DAC and TFN combined treatment in the two cell variants that showed cross-resistance to DAC. We used 5 μM TFN as in the previous experiments and DAC at four concentrations . However, in this case, we did not observe a significant synergistic effect .50 value cannot be calculated but is higher than 40 µM  than DAC (20 mg/m2 for 5 days). However, both AZA and DAC have demonstrated efficacy in the treatment of MDS and AML that preceded the drugs’ approval [A more pronounced effect of DAC than AZA was observed in both our parental cell lines . Consistapproval ,4,30,31.Interestingly, IC50 of GEM is much lower compared to all the other drugs tested in our cell variants, even in those showing resistance to both AZA and DAC . DrenberSLC28A1 and SLC28A3 genes encoding Na+-mediated concentrative nucleoside transporters hCNT1 and hCNT3 were not expressed in any of our cell variants (not shown). Very low or no expression of these two genes was reported in leukemia and solid tumor cell lines, as well as in blasts from AML patients [SLC29A1 or SLC29A2 encoding equilibrative nucleotide transporters hENT1 and hENT2, respectively  of four nucleoside transporters capable of transporting pyrimidine nucleosides and their analogs . The SLCpatients ,34,35. Dpatients . None ofectively A. SimilaNT5C3A) that catalyzes the dephosphorylation of pyrimidine 5’-monophosphates, as well as deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1). Oellerich et al. (2019) observed increased SAMHD1 expression at both the mRNA and protein levels in AZA-resistant cell variants developed from the HL-60 cell line, which did not express this gene. Degradation of SAMHD1 in the AZA-resistant cell subline sensitized it to DAC [HMAs, as well as GEM and AraC, are prodrugs, and to be incorporated into nucleic acids, they must first be enzymatically activated by sequential phosphorylation to mono-, di-, and triphosphate forms. We therefore measured the mRNA expression of UCK1 and UCK2, which are capable of cytidine (AZA) and uridine phosphorylation, as well as DCK, which is responsible for deoxycytidine  phosphorylation to the corresponding monophosphate forms . While Dh et al. 19 observThe only change in the gene expression of enzymes involved in the metabolism of HMAs that we observed at the mRNA level when comparing our resistant and sensitive cell variants was the expression of CDA, which was upregulated in M/A and M/A* A. SimilaDespite the lack of changes in the expression of genes at the mRNA level, we observed many changes at the protein level in the expression of UCK1, UCK2, and DCK . We deciConsistent with the absence of cross-resistance of the M/A cell variant to DAC , DCK proUnlike DAC, AZA does not induce global DNA demethylation in M/A cells; however, the slight upregulation of γ-H2AX  may indiIn M/A*, UCK1, UCK2, and DCK were all downregulated compared to the parental cell line at the protein level B,C. NeitHowever, in the case of S/A, despite a partially reduced sensitivity to DAC, DCK protein was not downregulated B,C, and In other studies, UCK2 was downregulated in AZA-resistant AML cell variants at the mRNA level  or at thConsidering the changes in AZA-resistant cell sublines related to the phosphorylation of AZA catalyzed by enzymes important in the pyrimidine salvage pathway, we were interested in the question of whether inhibition of the de novo pyrimidine synthesis pathway would have different effects on AZA-resistant cell variants compared to the parental cell lines. We expected the AZA-resistant cells to be more sensitive to the inhibition of DHODH by TFN , as It is also interesting that TFN seems to have slightly different effects on each of our AZA-resistant cell variants. In M/A*, similar to the parental cell lines, the effect of TFN on the number of viable cells correlated with the proportions of viable and apoptotic cells. However, the decrease in viable cell number in the M/A group was not reflected in the proportions of viable and apoptotic cells. It seems that in this case, TFN treatment caused cytostatic rather than cytotoxic effects on the cells. Finally, in S/A, TFN does not seem to have an effect on viable cell counts or on a significant increase in the proportion of apoptotic cells. In this cell variant, we observed an effect only on metabolic activity . We also studied the effect of AZA and TFN combination treatment on AZA-resistant cells. In S/A and M/A* cell variants, a synergistic effect of the drugs was observed. The presence of TFN potentiated AZA-induced apoptosis . This efThe differences in the response of our AZA-resistant sublines to the combination of TFN and AZA could be related to the expression and activity of the UCK2 protein. Little knowledge exists regarding UCK1 and UCK2 substrate specificity and affinity to AZA compared to naturally occurring cytidine and uridine. However, a genome-wide CRISPR/Cas9 knockout screen identified only UCK2 (and not UCK1) as a nonredundant and rate-limiting enzyme for AZA activation . Some cyTaken together, we hypothesize that UCK1 is much less effective in AZA phosphorylation than UCK2. In the cell variants with functional UCK2 (M/A* and S/A), inhibition of de novo pyrimidine synthesis , green bThe variability of the response of our AZA-resistant cell variants to HMAs, specifically highlighting differences between them, is summarized in Since there is still no treatment approved for MDS and AML patients after HMA treatment failure , the posIn our laboratory, three AZA-resistant cell variants were prepared using the same induction protocol, but with different observed outcomes. The variability among our cells is in accordance with the diverse results of other research groups. However, despite the different molecular characteristics of our models, specific mutations or changes in the expression of genes involved in the activation of HMAs were observed in all of them. The possible involvement of different pyrimidine nucleotide synthesis pathways in cells seems to play an important role in the response to AZA treatment. In AZA-resistant cells showing cross-resistance to DAC, we observed a different response to AZA and TFN combined treatment compared to the cells in which cross-resistance had not developed. After confirmation of these findings in clinical samples, the incorporation of teriflunomide or other substances inhibiting de novo pyrimidine synthesis into the MDS/AML treatment protocol should be considered for a certain group of patients with acquired AZA resistance.


Pancreatic cancer remains a disease that is very difficult to treat. S100 proteins are small calcium binding proteins with diverse intra- and extracellular functions that modulate different aspects of tumorigenesis, including tumor growth and metastasis. High mobility group box 1 (HMGB1) protein is a multifaceted protein that also actively influences the development and progression of tumors. In this study, we investigate the possible correlations, at the transcript level, between S100s and HMGB1 in pancreatic cancer. For this purpose, we calculated Pearson’s correlations between the transcript levels of 13 cancer-related S100 genes and HMGB1 in a cDNA array containing 19 pancreatic cancer tumor samples, and in 8 human pancreatic cancer cell lines. Statistically significant positive correlations were found in 5.5% (5 out of 91) and 37.4% (34 of 91) of the possible S100/S100 or S100/HMGB1 pairs in cells and tumors, respectively. Our data suggest that many S100 proteins crosstalk in pancreatic tumors either with other members of the S100 family, or with HMGB1. These newly observed interdependencies may be used to further the characterization of pancreatic tumors based on S100 and HMGB1 transcription profiles. Pancreatic cancer is a devastating disease with a current 5-year survival rate of 12% ,4. HMGB1The 8 pancreatic cancer cell lines were purchased from ATCC  and grown in their recommended media containing 10% FBS and supplemented with penicillin (100 U/mL) and streptomycin (100 μg/mL). The respective media for each cell line was as follows: AsPC-1 (RPMI), BxPC-3 (RPMI), Capan-2 (McCoy’s 5a), CFPAC-1 (IMDM), HPAF-II (EMEM), Hs 766T (EMEM), MIA PaCa-2 (DMEM), and PANC-1 (DMEM). In addition, MIA PaCa-2 cell culture media was supplemented with 2% horse serum, as recommended by ATCC. These cell lines were established from tumors or ascites isolated from pancreatic cancer patients. Four cell lines were established from primary tumors , two from ascites , and two from metastatic tissues (CFPAC-1 and Hs766T). These cell lines have been characterized at the molecular level and histologically, and showed variations in histologic differentiation, as well as mutations in at least one of the following four carcinogenesis-driving genes: K-Ras, P53, P16, and SMAD4  stably transfected with human telomerase reverse transcriptase (hTERT) were from ATCC and used as non-cancerous control cells. These cells were grown in DMEM high glucose , supplemented with 10% fetal bovine serum, 10 ng/mL human EGF  and 750 ng/mL puromycin .The pancreatic tumor cDNA PNRT101 arrays were purchased from OriGene Technologies . The arrays consisted of 23 samples including 4 normal, 2 stage IB, 2 stage IIB, 2-III, and 13 samples where the stage of the tumor was not reported. As described in the product documentation, the array cDNAs were synthesized from high quality total RNAs of pathologist-verified tissues, and normalized with b-actin in two sequential qPCR analyses. For this reason, the Ct values obtained from each of the 19 tumor samples were used, without normalization with actin, for the correlation studies.For the preparation of RNAs from the cultured cell lines, cells were grown to 70–80% confluency and lysed with a lysis solution provided with the PARIS kit . RNAs were extracted and purified using the same kit and RNA concentrations were determined by UV absorption at 260 nm. The quality of the RNAs was assessed by gel electrophoresis on 1% agarose gels. RNAs (1 μg/20 μL sample) were reverse-transcribed into cDNA using the M-MuLV reverse transcriptase .®® EvaGreen®®qPCR Mix  and 250 nM of each forward and reverse primer. For each RT-PCR, 40 cycles were run. Each cycle consisted of a 15 s dissociation step at 95 °C, followed by a 20 s annealing step at 60 °C, and a 20 s extension step at 72 °C. Dissociation plots for each sample were analyzed to ensure that specific products, with melting temperature (Tm) above 85 °C, were generated during the PCR. In addition, the presence of a PCR amplicon of the expected molecular size was assessed by agarose gel electrophoresis. The primers used to detect the transcripts of actin and of the various S100s are listed in RT-PCR experiments were performed using 10 ng cDNA in 20 mL reaction volume using the HOT FIREPolgene − Ctactin). For each gene, at least three RT-PCRs were performed, using independent RNA preparations. The Cts were then used for statistical analysis. For the RT-PCR performed with the pancreatic tumor cDNA array, the Ct values for each gene and each array sample were also obtained from the amplification plots and used for the statistical analysis. RT-PCRs were determined on average two times for each set of primers with the tumor array. As described by Origene, the cDNA arrays contained 2–3 ng cDNA per well. Ct values obtained from the tumor array were used for statistical analysis.For the RT-PCR performed with the cDNAs from cultured cells, the Ct values for each gene were obtained from the amplification plots, and the Cts were calculated using -actin as a housekeeping gene (Ct = Ctgene − Ctactin), relative to the housekeeping gene β-actin, and their standard deviations were calculated. Low Cts corresponded to high levels of transcripts, and conversely, high Cts corresponded to low levels of transcripts relative to actin  were calculated between S100s and HMGB1 transcripts using the Cts from the 8 cell lines   . The Ct ft Excel . CorrelaTranscript levels for 13 S100 proteins and HMGB1, determined by qPCR, are summarized in In cancer cell lines, S100A2 presented the lowest transcript level among the 13 selected S100 genes. Indeed, the Ct values for S100A2 were above 16 for 6 cell lines. Hs766T and BxPc3 showed the highest transcript levels, with Ct of 11.45 +/− 0.65 and 9.17 +/− 1.78, respectively. Interestingly, these two cell lines are the only cell lines in our panel that have wild-type KRAS genotypes. Overall, the lowest level of S100A2 transcripts was observed in the non-cancerous human pancreatic epithelial HPNE cell line with Ct of 19.80 +/− 0.12 .Correlation analysis identified a very strong positive correlation (R = 0.82) between S100A2 and S100A13 in cancerous cell lines , but thaIn the tumor array, S100A2 transcription levels were consistently very low. However, correlations were observed between S100A2 and S100A7 (R = 0.48), S100A11 (R = 0.54), S100A14 (R = 0.50), S10016 (R = 0.61) and S100P (R = 0.85), when comparing transcript levels from the tumor array .In cancer cell lines, the levels of S100A4 transcript were on average higher (mean Ct = 5.31 +/− 2.99) than those of other S100s, with large variations being observed between cell lines. AsPC-1 showed the highest level (Ct = 1.86 +/− 0.26), and CFPAC-1 showed the lowest transcript level (Ct = 10.13 +/− 0.73). Large variations in S100A4 levels in different pancreatic cancer cell lines have previously been reported ,19,20. ANo correlation with statistical significance was observed between S100A4 and other S100s or HMGB1 in the cell lines . HoweverThe transcript levels of S100A6 were overall very low in all cell lines (mean Ct = 12.1 +/− 0.83), with very little variation between cell lines. AsPC1 showed the highest level (Ct = 11.04 +/− 1.75) and CFPAC-1 the lowest level (Ct = 13.09 +/− 0.21). Interestingly, the immortalized HPNE cells show higher levels of S100A6 transcript (Ct = 7.93 +/− 0.31) than any of the cancer cell lines .No correlation was observed between the transcript levels of S100A6 and other S100s or HMGB1 in cells. However, in the pancreatic tumor array, correlations were observed between S100A6 and S100A11 (R = 0.49) and S100A14 (R = 0.45).Overall, all cell lines showed low levels (mean Ct = 14.19 +/− 1.53) of S100A7 transcript with little variations among the eight cell lines. CFPAC-1 presented the highest level (Ct = 12.12 +/− 0.21) and AsPC-1 the lowest (Ct = 15.9 +/− 0.53). The highest levels of S100A7 transcript were observed with the immortalized HPNE control cells (Ct = 5.93 +/− 1.64). Our results agree with those of Liu et al., who reported lower levels of S100A7 in a cohort of 45 pancreatic ductal adenocarcinomas when compared to adjacent non-tumor tissues [No statistically significant correlation was observed between S100A7 and the other S100s, or with HMGB1, in the eight cancer cell lines. However, in tumor tissues, correlations were observed between S100A7 and S100A14 (R = 0.47), and S100P (R = 0.56), suggesting crosstalk between these different S100s in tumors.All cancer cell lines showed low levels of S100A8 transcript (mean Ct = 12.79 +/− 1.16). The lowest level was observed in MiaPaCa-2 cells (Ct = 13.87 +/− 0.36) and the highest level in HPAF-2 cells (Ct = 10.85 +/− 1.63). No transcript was detected in the immortalized control HPNE cells.In cells, correlations were observed between S100A8 and S100A9 (R = 0.82), and S100A8 and S100P (R = 0.91). In the tumor array, correlations were observed between S100A8 and S100A4 (R = 0.66), S100A9 (R = 0.62) and S100A10 (R = 0.46).All cell lines showed overall low levels of S100A9 transcript (mean Ct = 11.11 +/− 1.99), with the lowest level in MiaPaCa-2 (Ct = 13.20 +/− 0.36) and the highest in BxPC3 (Ct = 7.17 +/− 1.01). As observed with S100A8, no S100A9 transcript was detected in immortalized HPNE control cells.In cells, correlations were only observed between S100A9 and S100A8 (R = 0.82). In the tumor array, correlations were observed between S100A9 and S100A8 (R = 0.62), S100A4 (R = 0.78), S100A10 (R = 0.51), S100A12 (R = 0.76), and HMGB1 (R = 0.80).In the cancer cell lines, the transcript levels of S100A10 were among the highest of all S100s, with a mean Ct = 4.15 +/− 0.88. The highest level was observed in AsPC-1 (Ct = 3.34 +/− 0.90) and the lowest in MiaPaCa-2 (Ct = 5.97 +/− 0.33). Overall, the lowest level was observed with the immortalized HPNE control cell line (Ct = 6.93 +/− 1.05) . Our resIn cells, no statistically significant correlation was found between S100A10 and the other S100s or HMGB1. However, in tumors, correlations were observed between S100A10 and S100A4 (R = 0.78), S100A8 (R = 0.46), S100A9 (R = 0.51), S100A12 (R = 0.51), S100A16 (R = 0.48), and HMGB1 (R = 0.70).The transcript levels of S100A11 were very similar across all eight cell lines (mean Ct = 6.71 +/− 0.65), the lowest level was observed in MiaPaCa2 (Ct = 7.49 +/− 0.13) and the highest in BxPC3 (Ct = 5.38 +/− 1.29). The lowest level was noted in the control HPNE pancreatic cell line (Ct = 7.70 +/− 0.87).A statistically significant correlation was found between S100A11 and S100P (R = 0.73), both in cell lines  and in tOverall, the levels of S100A12 transcript were lower than those of S100A11 in all cell lines (mean Ct = 9.01 +/− 1.22). The lowest level was found in Panc-1 cells (Ct = 10.37 +/− 0.05) and the highest in CFPAC-1 (Ct = 6.54 +/− 0.2). S100A12 was detected at a similar level in the control HPNE cells (Ct = 8.12 +/− 0.67) and in the cancer cell lines , in agreNo statistically significant correlation was found between S100A12 and the other S100s or HMGB1 in cells; however, correlations were observed between S100A12 and S100A4 (R = 0.64), S100A9 (R = 0.76), S100A10 (R = 0.51) and HMGB1 (R = 0.62) in the tumor array.The transcript levels of S100A13 were relatively high in all cell lines with the highest level observed in Hs766T cells (Ct = 5.27 +/− 0.01) and the lowest in AsPC1 (Ct = 8.33 +/− 0.11). The levels of S100A13 transcripts were higher in the control HPNE cell line (Ct = 4.78 +/− 1.33) than in the cancer cell lines .A statistically significant correlation was found between S100A13 and S100A2 (R = 0.82), in cell lines, but no correlation between S100A13 and S100A2 transcript levels was observed in tumors. However, a correlation was found between the transcript levels of S100A13 and S100A11 (R = 0.67), in the tumor array.Very low levels of S100A14 transcript were found in all cell lines examined, with the lowest being observed in MIAPaCa-2 (Ct = 21.26 +/− 0.53) and the highest in BxPC3 (Ct = 8.96 +/− 1.14). No S100A14 transcript could be detected in the HPNE cell line .In the cell lines, no statistically significant correlation was found between S100A14 transcripts and the other S100s, or HMGB1. However, in the tumor array, positive correlations were observed between S100A14 and S100A2 (R = 0.50), S100A4 (R = 0.55), S100A6 (R = 0.45), S100A7 (R = 0.47), S100A11 (R = 0.65), S100A16 (R = 0.57) and S100P (R = 0.66).S100A16 transcript levels were relatively low in all cell lines, with the lowest level observed in Panc-1 cells (Ct = 9.27 +/− 0.23), and the highest ion AsPC1 cells (Ct = 6.25 +/− 1.02). The lowest levels were observed in the control HPNE cell line (Ct = 10.12 +/− 0.33) .As observed with S100A14, no statistically significant correlation was found between S100A16 and other S100s or HMGB1 in cells. However, in tumors, positive correlations were found between S100A16 and S100A2 (R = 0.61), S100A4 (R = 0.60), S100A10 (R = 0.48), S100A11 (R = 0.78), S100A14 (R = 0.57) and S100P (R = 0.54).Very large variations in S100P transcript levels were observed between cell lines, with the lowest level observed in MIAPaCa-2 (Ct = 12.22 +/− 1.48) and the highest in BxPC3 (Ct = 4.18 +/− 0.18). S100P transcripts were not detected in HPNE control cells .In the cancer cell lines, statistically significant correlations were found between S100P and S100A8 (R = 0.91), and S100A11 (R = 0.73). A correlation between S100P and S100A11 was also observed in tumors (R = 0.62). In addition, positive correlations were found in tumors between S100P and S100A2 (R = 0.85), S100A7 (R = 0.56), S100A14 (R = 0.66) and S100A16 (R = 0.54).HMGB1 levels were very similar in all cell lines, with the lowest level in CFPAC-1 (Ct = 8.67 +/− 0.32) and the highest in Capan-2 (Ct = 7.10 +/− 0.77). The lowest level of transcript was observed in the control HPNE cell line (Ct = 9.63 +/− 0.40) .A statistically significant positive correlation was observed between HMGB1 and S100P in cells (R = 0.71), and this correlation was also observed in tumors (R = 0.52). Additionally in tumors, correlations were found between HMGB1 and S100A4 (R = 0.50), S100A9 (R = 0.80), S100A10 (R = 0.70) and S100A12 (R = 0.62).For S100A2, S100A8, S100A9, S100A10, S100A11, S100A14, S100A16, S100P, and HMGB1, we observed higher transcript levels in the cancer cell lines than in the HPNE control cell lines. The higher levels of S100A2, S100A11, and S100A14 transcript in the eight cancer cell lines than in the HPNE control cell line suggest that these three S100s might modulate the behavior of the cancer cells. S100A2, S100A11, and S100A14 are three S100s with complex roles in cancer, as they have been described as either tumor suppressors or tumor promoters. For instance, S100A2 functions either as a tumor promoter or tumor suppressor, depending on the type of cancer ,9,29. EvLike S100A2, S100A11 is described to function as either a tumor suppressor, such as in bladder cancer, or a tumor promoter, such as in pancreatic cancer ,29,33,34Similarly to S100A2 and S100A11, S100A14 appears to have a complex role in cancer, showing overexpression in breast, liver and cervical cancer, but downregulation in other cancers such as rectal, kidney, colon and esophageal cancers . Chen etThe absence of detectable transcripts for S100A8 and S100A9 in HPNE cells was anticipated because these two S100s are mainly expressed in immune cells, such as monocytes and neutrophils . For thiFor S100A10, our observations agree with the current literature describing the role of this S100 in promoting the development of pancreatic tumors. S100A10 is upregulated in pancreatic cancer and contributes to cell proliferation, migration, invasion, and tumor growth ,26,27,46S100A16 is one of the latest S100 family members identified and characterized . AlthougOur observations with S100P, where transcripts could only be detected in the cancer cell lines and not in the HPNE control cell line, are also in agreement with many studies that demonstrated the involvement of this S100 in the progression of pancreatic cancer ,54. BecaHMGB1 was also found at lower levels in the HPNE control cells than in the cancer cell lines. The role of HMGB1 is complex with different cellular functions depending on the cellular localization . In the Although for most S100s, and for HMGB1, the transcript levels were lower in the control HPNE cells than in the cancer cell lines, this was not the case for S100A4, S100A6, S100A7, S100A12, and S100A13. For S100A7, the lower transcript levels observed in the cancer cell lines than in the control cell line are in agreement with the study of Liu et al., who reported lower levels of S100A7 in a cohort of 45 pancreatic ductal adenocarcinomas when compared to adjacent non-tumor tissues . These oFor S100A13, based on the current literature, we anticipated observing lower levels of S100A13 in HPNE cells than in cancer cells. Indeed, a recent metadata analysis using Oncomine and GEPIA found significantly higher levels of S100A13 transcripts in pancreatic tumors than in control tissues . Higher In our study, the transcripts of both S100A4 and S100A6 were found at a high level in the HPNE cells when compared to the pancreatic cancer cells . As descThe higher transcript levels of S100A12 observed in HPNE cells than in the cancer cells could also be indirectly caused by changes in the senescence abilities of these cells. Indeed, S100A12 was recently identified as 1 of the 20 upregulated genes of a senescence associated secretory phenotype (SASP) cluster . TherefoThe difference in transcript levels among the eight cancer cell lines was the smallest for HMGB1, with a Ct of 1.59, corresponding to a 3-fold difference among cell lines. S100A6, S100A10 and S100A11 showed between 4-fold and 8-fold differences in transcript levels among the cell lines. S100A7, S100A8, S100A12 and S100A16 showed larger differences with 8-to-16-fold differences among cell lines. Finally, S100s that showed the largest difference among cell lines were S100A9 (65-fold), S100A4 (308-fold), S100A2 (867-fold), S100P (324-fold) and S100A14 (5000-fold). It is reasonable to hypothesize that the observed variations in the transcript levels among the cell lines would also be observed at the protein levels; however, additional experiments that are beyond the scope of this study would be necessary to support this hypothesis. It is also important to note that our study only gives a snapshot of the levels of S100 and HMGB1 transcripts in the cancer cells in standard cell culture growth conditions and that cancer cells’ growth conditions are very different in the tumor environment. In our standard cell culture growth conditions, we only observed the following few correlations in transcript levels between S100s and HMGB1: correlations were observed between S100A2 and S100A13, S100A8 and S100A9, S100A8 and S100P, S100A11 and S100P and HMGB1 and S100P. On the other hand, in the context of the tumor, we observed 33 distinct positive correlations.S100s have the following complex functions in cells: they can act intracellularly by activating different target proteins, mostly in a calcium-dependent manner, resulting in multiple cellular activities . For insOur observation that a larger number of correlations was observed between the transcript levels of S100s and HMGB1 in the tumor array than in cells suggests that the tumor microenvironment provides complex transcriptional regulation mechanisms for S100s and HMGB1. It also suggests that many S100s and HMGB1 act in concert in pancreatic tumors. The upregulation of multiple S100s and HMGB1 in tumors could be a means for tumors to upregulate multiple S100-dependent pathways simultaneously.As many of the S100s and HMGB1 are ligands of RAGE, it is reasonable to consider RAGE as one of the receptors responsible for the concerted upregulation of S100s and HMGB1 in tumors ,77,78. IOverall, we observed only the following five statistically significant correlations between S100s and S100/HMGB1 in cells: correlations were observed between S100A2 and S100A13, S100A8 and S100A9, S100P and S100A8 and S100A11, and S100P and HMGB1. However, when looking at the data from the tumor array, we observed 33 statistically significant correlations between S100s and S100/HMGB1. These positive correlations suggest similar mechanisms of transcriptional regulation between these genes and that many S100 family members act in concert in pancreatic tumors. This difference in data between cells and tumors probably reflects the complexity of tumors. Indeed, tumors consist not only of cancer cells but also non-cancerous cells, such as fibroblasts, immune cells, and endothelial cells. All these cells communicate with each other, and many of them express several S100 proteins. The transcription of S100s and HMGB1 in tumors is a very well-orchestrated process that integrates multiple signals from all cells present in the tumor.


Additionally, the observation of downregulation of phosphomevalonate kinase is significant as the brain tumor initiating cells (BTIC) are maintained by the metabolism occurring via the mevalonate pathway. Results from the current study, if translated in vivo, could provide additional efficacious treatment options for glioblastoma with minimal side effects.Glioblastoma multiforme (GBM) is a major aggressive primary brain tumor with dismal survival outcome and few therapeutic options. Although Temozolomide (TMZ) is a part of the standard therapy, over time, it can cause DNA damage leading to deleterious effects, necessitating the discovery of drugs with minimal side effects. To this end, we investigated the effect of cinnamaldehyde (CA), a highly purified, single ingredient from cinnamon, on the GBM cell lines U87 and U251 and the neuroglioma cell line H4. On observing similar impact on the viability in all the three cell lines, detailed studies were conducted with CA and its isomer/analog, trans-CA (TCA), and methoxy-CA (MCA) on U87 cells. The compounds exhibited equal potency when assessed at the cellular level in inhibiting U87 cells as well as at the molecular level, resulting in an increase in reactive oxygen species (ROS) and an increase in the apoptotic and multicaspase cell populations. To further characterize the key entities, protein profiling was performed with CA. The studies revealed differential regulation of entities that could be key to glioblastoma cell circuits such as downregulation of pyruvate kinase-PKM2, the key enzyme of the glycolytic pathway that is central to the Warburg effect. This allows for monitoring the levels of PKM2 after therapy using recently developed noninvasive technology employing PET [ The majority of malignant tumors of the brain are gliomas. Glioblastoma is an aggressive, major primary brain cancer with poor survival outcome . GlioblaThe chemotherapeutic drug currently in the clinic for treating GBM is temozolomide (TMZ), which has improved patient survival and is a DNA methylating agent with slow disease progression when compared to other drugs ,7. Howev2. U87 cells expressing enhanced green fluorescent protein (U87eGFP) was derived as reported previously DASA-23, which comprises 1-((2-fluoro-6-[18F] fluorophenyl)sulfonyl)-4((methoxyphenyl)sulfonyl)piperazine, visualization of aberrant expression of PKM2 in cell culture, mouse models of glioblastoma multiforme (GBM), healthy human volunteers and patients with GBM have been reported [The drug TMZ, which is part of the current standard of care for glioblastomas, causes changes in cell metabolism and, in particular, decreased expression and activity of pyruvate kinase-PKM2. The significant decrease in the conversion of pyruvate to lactate observed in TMZ-treated glioblastoma cells is monitored by administering hyperpolarized (1–13 C) pyruvate and tracked by employing magnetic resonance spectroscopic imaging (MRSI). This method has been shown to be more reliable, more rapid and forms a biosensor for assessing the therapeutic effect of TMZ over measuring the tumor size, which takes days . In the reported . Therefo