In this study, its effect was evaluated (MTT viability assay) against a new panel of cancer cells, namely cervical cancer (HeLa), androgen-sensitive (LNCaP) and androgen-independent (PC-3) prostate cancer, and nonsolid tumor derived cancer (Jurkat) cell lines. The effect of xanthone 1 on macrophage functions was also evaluated. The effect of xanthone 1-conditioned THP-1 human macrophage supernatants on the metabolic viability of cervical and prostate cancer cell lines was determined along with its interference with cytokine expression characteristic of M1 profile  or M2 profile  (PCR and ELISA). Nitric oxide (NO) production by murine RAW264.7 macrophages was quantified by Griess reaction. Xanthone 1 (20 μM) strongly inhibited the metabolic activity of the cell lines and was significantly more active against prostate cell lines compared to HeLa (p < 0.05). Jurkat was the cell most sensitive to the effect of xanthone 1. Compound 1-conditioned IL-4-stimulated THP-1 macrophage supernatants significantly (p < 0.05) inhibited the metabolic activity of HeLa, LNCaP, and PC-3. Xanthone 1 did not significantly affect the expression of cytokines by THP-1 macrophages. The inhibiting effect of compound 1 observed on the production of NO by RAW 264.7 macrophages was moderate. In conclusion, 1-carbaldehyde-3,4-dimethoxyxanthone (1) decreases the metabolic activity of cancer cells and seems to be able to modulate macrophage functions.Xanthone derivatives have shown promising antitumor properties, and 1-carbaldehyde-3,4-dimethoxyxanthone ( Xanthones, specifically methoxyxanthones, have already been described by our group as possessing antitumor and immunomodulatory activities ,2,3,4. M1)   was prevctivator .Nowadays, the fight against cancer entails the development of new therapeutic approaches, such as cancer immunotherapy.The tumor microenvironment (TME) is important in all stages of tumor development, and the interaction between the tumor and the cells around it defines the final outcome of the disease ,8. TumorFrom some previous studies, it can be inferred that an increase in the number of TAMs is associated with a poor outcome. However, the M1/M2 ratio is also noted as being crucial for the prognosis of disease ,12,13. T1 was tested against liquid and solid tumors using lymphocyte (Jurkat), cervical (HeLa), and androgen-sensitive (LNCaP) and androgen-independent (PC-3) prostate cancer cell lines. The effect of culture supernatants from unstimulated, LPS-stimulated, or IL-4-stimulated macrophages treated with compound 1 on the metabolic viability of solid-derived tumor cell lines was also investigated. To study the interference of xanthone 1 on macrophage function, the expression of cytokine characteristic of M1 (IL-1β and TNF-α) and M2 (TGF-β1 and IL-10) phenotypes by THP-1 human macrophages was evaluated. The effect of xanthone 1 on NO production by LPS-stimulated RAW 264.7 murine macrophage cell line was also tested. This is the first report concerning the effect of xanthone 1 on macrophage function.In this work, the antitumor activity of xanthone 1 on the metabolic viability of adherent and nonadherent cancer cell lines was evaluated . No significant differences in cytotoxic effect were seen on the different solid tumor cell lines when xanthone 1 was tested at 10 μM. For concentrations of 5 µM, it was observed that the compound was significantly more toxic to the androgen-independent PC-3 prostate cell line compared to the androgen-sensitive prostate cell line LNCaP (p < 0.05) or the cervical cancer cell line HeLa (p < 0.01).Considering the effect of the referred xanthone 1 (20 μM) against solid and liquid tumors, it was more active against the nonadherent Jurkat cell line compared to LNCaP (p < 0.05) or HeLa (p < 0.001). At 10 μM, there was always a significant difference between the effect on Jurkat compared to the other cell lines tested (p < 0.01 for PC-3 and p < 0.001 for LNCaP and HeLa). However, when the xanthone 1 was tested at 5 µM, significant differences were only observed between the effects on Jurkat versus LNCaP (p < 0.05) and Jurkat versus HeLa (p < 0.01). Analyzing the results, it seemed that the cytotoxic effect of xanthone 1 was more potent against the Jurkat cell line; however, the differences were not always statistically significant.Analyzing the activity of compound 1 on the mRNA expression of cytokines from THP-1 macrophages  was evaluated , the treatment of THP-1 macrophages with xanthone The effect of macrophage supernatants on the metabolic viability of prostate and cervical solid tumor cell lines was evaluated .1-treated macrophages.The effect of supernatants was evaluated for unstimulated, LPS-stimulated, and IL-4-stimulated macrophages. In all cases, the effect was compared between untreated and xanthone 1-treated and untreated macrophage supernatants. When macrophages were stimulated with LPS to induce an M1 phenotype, the cytotoxicity of the macrophage supernatants was not significantly affected by compound 1 treatment compared to the untreated ones. When THP-1 macrophages were stimulated with IL-4 in order to induce an M2 phenotype, the treatment with xanthone 1 (10 μM) caused a significant (p < 0.05) increase in the toxicity of macrophage supernatants against PC-3, LNCaP, and HeLa cell lines.For unstimulated macrophages, no significant differences were observed in the reduction of metabolic activity of the cancer cell lines when comparing xanthone 1 had a moderate inhibitory effect on NO production by LPS-stimulated RAW 264.7 cell line, with no relevant effect on cellular viability (1 was able to inhibit NO production by LPS-stimulated RAW 264.7 cell line at concentrations of 10 (28.1 ± 3.7) and 5 (15.3 ± 3.0) μM compared to NO production by the control cell lines.Xanthone iability . Xanthon1 against neuroblastoma cells has previously been established. Its effect is related to Tap-73 activation, leading to enhanced Tap-73 transcriptional activity, induction of cell cycle arrest, and apoptosis in p53-null and p53-mutant tumor cells [The antitumor effect of xanthone or cells . In addior cells .1 showed strong antitumor activity against the Jurkat and prostate cell lines, although it was less potent against the HeLa cell line. It is important to note that the poor aqueous solubility and bioavailability of xanthone 1 might have influenced the effects observed for this compound on the viability and metabolic function of the different cell lines [In the present work, xanthone ll lines .1 at 20 μM was more evident against the prostate cancer cell line than the cervical cancer cell line, suggesting a selectivity of its antitumor effect, but no differences were observed at 10 μM. Focusing on the prostate cancer cell lines, the effect was significantly different only at the lowest concentration tested (5 μM), and compound 1 was more active against the androgen-independent PC-3 cell line. Regarding the cervical cancer cell line, Oh and coworkers have shown that upregulation of Tap-73 expression regulates paclitaxel-induced apoptosis in HeLa cells, which impairs p53 expression due to the influence of HPV E6 and E7 oncoproteins [1 may not be as powerful as paclitaxel in inducing Tap-73 upregulation in HeLa cells. The Jurkat cell line was used in the study to allow comparison of xanthone 1 activity between cell lines obtained from solid and nonsolid tumors. At 10 μM, xanthone 1 significantly decreased the metabolic viability of Jurkat compared to the cell lines derived from solid tumor, leading to the inference that the cytotoxic effect of compound 1 is significantly higher against cell lines derived from liquid tumor than those from solid tumor. However, these differences were not consistently observed for the other concentrations tested.Prostate and cervical cancer are among the most prevalent cancers in men and women, respectively ,19. The 1 on macrophage functions was tested for unstimulated, LPS-stimulated, and IL-4-stimulated macrophages in order to achieve the M0, M1, and M2 phenotypes, respectively. Supernatants of xanthone 1-treated macrophages were tested against cancer cell lines, but they did not affect the metabolic viability of the cancer cells when macrophages were not stimulated or were stimulated with LPS. However, when macrophages were stimulated with IL-4, an increase in cytotoxicity against cancer cell lines was observed for supernatants of compound 1-treated macrophages (p < 0.05) compared to the control. Therefore, the interference of xanthone 1 on cytokine production by human macrophages characteristic of M1 profile (IL-1β and TNF-α) or M2 profile (IL-10 and TGF-β) [Immunotherapy is one of the expanding fields in cancer treatment ,9. The ed TGF-β) ,9,10 was1 treatment caused no significant difference in TGF-β1 expression by THP-1 macrophages, irrespective of the stimulation. IL-10 is an anti-inflammatory cytokine, but its effect has been described to change in accordance with the target cell [1, and the same was observed when macrophages were stimulated by LPS or IL-4. NO is produced by macrophages and several cancer cells [1 caused only a slight decrease in its production by LPS-stimulated RAW264.7 murine macrophages. Xanthone 1 had no relevant effect on RAW 264.7 macrophage viability, leading to the conclusion that the decrease in NO production was not a consequence of cell death.The effects of TGF-β are mainly dependent on the cell type and cell microenvironment , and TGFget cell . IL-10 per cells . Howeverer cells ,24. Rega1 on cytokine expression and NO production were not able to explain the dependent increase of the antitumor effect of IL-4-stimulated macrophage supernatants against the cancer cell lines. Recent studies have demonstrated that Tap-73 has a role in macrophage polarization, innate immunity, and inflammatory response [1 in macrophage antitumor activity, and additional studies are needed in order to understand the molecular mechanism behind it. Additional studies should also include more solid cancer cell lines in order to see if the synergistic effect of IL-4 plus xanthone 1 in macrophage antitumor activity is similar to what we have observed for prostate and cervical cancer cell lines.The studies on the effect of xanthone response . In factresponse . To the The reagents and media were acquired as follows: Roswell Park Memorial Institute-1640 (RPMI-1640) medium with Ultraglutamine from Lonza ; fetal bovine serum (FBS) from GE Health Care Life Sciences ; 2-mercaptoethanol from VWR International ; Dulbecco’s modified Eagle medium/F-12 nutrient mixture (Ham)  from Gibco ; phosphate-buffered saline (PBS) from Fisher Reagent ; dimethyl sulfoxide (DMSO) and phosphoric acid from Merk ; dimethylformamide (DMF) from Romil ; uncoated ELISA kits from Invitrogen by Thermo Fisher Scientific ; TripleXtractor and RNA Kit (Blood and Cultured Cells) from GRiSP ; recombinant human IL-4 from R&D Systems ; and High Capacity RNA-to-cDNA Kit and Master Mix form Applied Biosystems . When not specified, the reagents were from Sigma-Aldrich .1) was synthetized by our group at the Laboratory of Organic and Pharmaceutical Chemistry, Department of Chemical Sciences, Faculty of Pharmacy, University of Porto, as previously described [1-Carbaldehyde-3,4-dimethoxyxanthone (escribed . The pow2 incubator at 37 °C with humidified atmosphere.PC-3 cell lines were obtained from the European Collection of Cell Cultures (ECCAC) and LNCaP from the American Type Cell Culture (ATCC). The other cell lines were kindly provided by Maria José Oliveira (THP-1 and HeLa), Tumor and Microenvironment Interactions Group, Institute for Investigation and Innovation in Health, i3S, Porto, Portugal; Henrique Almeida (Jurkat), Ageing and Stress group, Institute for Investigation and Innovation in Health, i3S, Porto, Portugal; and Maria São José Nascimento (RAW 264.7), Laboratory of Microbiology, Biological Sciences Department, Faculty of Pharmacy, University of Porto, Portugal. The complete culture medium for cancer cells lines was composed of RPMI-1640 supplemented with 10% of FBS and 1 μg/mL gentamicin. For the THP-1 cell line, the complete culture media described was supplemented with 2-mercaptoethanol (0.05 mM). The RAW 264.7 cell line was cultured in DMEM/F-12 supplemented with FBS and gentamicin as already cited. Cell incubation was performed in a 5% CO4 cells/well (96-well flat-bottom culture plate). After 24 h incubation to allow cell adherence [The MTT colorimetric assay was conducted based on the original procedure proposed by Mosmann , with a dherence , supernadherence  were incdherence . Later, dherence . After 14 cells/well) were incubated for 24 h [2O (1:1) was added to the wells to dissolve formazan [Jurkat cells (1.5 × 10for 24 h . After tfor 24 h . Untreat6 cell/mL) was incubated with phorbol 12-myristate 13-acetate  for 72 h in order to promote differentiation of the cells into macrophages [THP-1 human leukemic monocyte cell line  were used in the cytotoxic assay (MTT) for adherent cancer cell lines as already described.The quantification of cytokines  was performed on conditioned macrophage supernatants using uncoated ELISA kits following the manufacturers’ instructions.®, Waltham, MA, USA). mRNA samples were the template for cDNA synthesis, using the High Capacity RNA-to-cDNA Kit. The reactions were performed in the StepOneTM qPCR Real-Time PCR instrument containing 1 × Master Mix, 1 × probes . The housekeeping gene B2M  served as endogenous reference, expressed at a consistent level to normalize the results. The data analysis was carried out using the StepOneTM Software v2.2  with the same baseline and threshold set for each plate to generate quantification cycle values (Cqs) for all the mRNAs in each sample.To quantify mRNA cytokine expression, THP-1 macrophages (M0) were obtained as described in 6 cell/mL, and 200 µL was added to a 96-well culture plate. Cells were incubated for 2 h to enable cellular adhesion [w/v sulfanilamide solution in phosphoric acid (5% v/v) and naphtylethylenediamide (0.1%) in deionized water) was prepared and added (100 µL) to all the wells, followed by 10 min incubation at room temperature in the dark [RAW 264.7 cell line concentration was adjusted to 1 × 101 against cancer cell lines. Normality of data distribution was validated using the Shapiro–Wilk test of normality, and homogeneity of variance assumption was confirmed using Levene’s test. One-way ANOVA was performed for the experimental cytotoxicity analysis with Bonferroni’s correction post hoc test. The Mann–Whitney test was used for analysis of statistical differences in cytokine expression. Statistical significance was considered for p < 0.05.For statistical analysis, IBM SPSS Statistics 26.0 for Windows was used. Data is presented as the mean ± SEM. Media ± 2 SD was used to get meaningful results in order to perform statistical evaluation of the cytotoxicity activity of xanthone 1 is a potent antitumor compound against prostate and cervical cancer cell lines. Xanthone 1 also has a strong antitumor effect against the Jurkat cell line.Xanthone 1 interferes with macrophage functions and increases its effectiveness against the metabolic viability of the cancer cell lines when stimulated with IL-4. However, in this study, it was not possible to elucidate the mechanism by which xanthone 1 modulates the cytotoxicity of human macrophages against cancer cells after macrophage induction of an M2 pro-tumoral phenotype.Besides the direct effect against cancer cells, xanthone 1.More studies are needed to elucidate the anticancer cytotoxic and immunomodulatory effect of xanthone


GDM) has been modified by different type neutral/acidic/basic substituents (1–7) and by quinuclidine motif (8), transformed into ammonium salts (9–13) at C(17). These compounds have been characterised by spectroscopic and x-ray methods. Derivative 8 shows better potency than GDM in MCF-7, MDA-MB-231, A549 and HeLa (IC50s = 0.09–1.06 µM). Transformation of 8 into salts 9–13 reduces toxicity (by 11-fold) at attractive potency, e.g. MCF-7 cell line (IC50∼2 µM). Our studies show that higher water solubility contributes to lower toxicity of salts than GDM in healthy CCD39Lu and HDF cells. The use of 13 mixtures with potentiators PEI and DOX enhanced anticancer effects from IC50∼2 µM to IC50∼0.5 µM in SKBR-3, SKOV-3, and PC-3 cancer cells, relative to 13. Docking studies showed that complexes between quinuclidine-bearing 8–13 and Hsp90 are stabilised by extra hydrophobic interactions between the C(17)-arms and K58 or Y61 of Hsp90.Geldanamycin ( GDM, Streptomyces hygroscopicus, has two unique structural features, namely a rigid benzoquinone ring and an aliphatic ansa-bridge that are linked together to form a characteristic basket-like structure.ansa-macrolides with benzene or benzoquinone core, it shows potent activity in different cancer cell lines.GDM impedes its future medical applications. This toxicity is believed to originate from 1,4-Michael conjugate addition/aromatisation/oxidation cascade with glutathione, yielding a C(19)-adduct.GDM was modified at the C(17), C(19) positions and at the methoxy, urethane or hydroxyl groups.,GDM are amine derivatives 17-DMAG and 17-AAG that are currently under consideration or are in various phases of clinical trials.,GDM  or those containing halogens, saccharides, phenol groups at the core, were obtained by sequences of mutasynthetic, semi-synthetic, and total synthetic approaches. These analogues showed attractive activities against the cancer cell lines, such as human breast adenocarcinoma , ovarian adenocarcinoma (SKOV-3), lung adenocarcinoma (A-549), and prostate adenocarcinoma (PC-3).,,ansa-macrolide correlated to the preparation of conjugates with biotin, foliate, or within corporated triazole bridge, that gained molecular probe features of improved biocompatibility.ansa-bridge modifications of GDM led to a less effective anticancer potency than those performed at the rigid benzene or benzoquinone cores.ansa-bridge flexibility that is necessary for binding the GDM analogues to their molecular target, i.e. heat shock proteins Hsp90. Hsp90 shows an ATPase activity, which is required to fold proteins in cells in order to achieve their full functionality.,GDM and 17-DMAG complexed with NBD of Hsp90 showed that the ligand conformation significantly differs from that of unbound molecule in solutions.,GDM at C(17) and C(19) positions sometimes resulted in antiviral potency against herpes (HSV), hepatitis B and C  or HIV-1.GDM amine analogues where the C(17) substituent was bridged with the neighbouring quinone group at C(18), despite their good water solubility (e.g. guanidine-like derivatives), it was noticed that the bulkiness of the incorporated alkyl or aryl substituent was important for anticancer potency.GDM derivatives with allylamine or N,N-dimethylethylamine in the form of salts that are considered in cancer therapy in phase I clinical trials.Geldanamycin GDM, , a naturGDM derivatives and we found that rigidity of the C(17)-arm incorporating carbonyl group and lipophilicity are essential factors influencing an efficient binding of GDM analogues to Hsp90 and their anticancer potency.GDM amine-quinuclidine analogue 8 with C(17)-arm of basic, rigid, and bulky nature. The data have been determined for nine cancerous  and two healthy  cell lines. The activities of 8 are compared with the GDM derivatives 1–7 of acidic, basic, and neutral nature -l nature 7,23,40. GDM derivatives 1–8 were obtained according to the earlier reported protocol1H, 13 C NMR, and 2D NMR, FT-IR, and ESI MS methods . Derivatives 1–3 formed single crystals that allowed to determine their structures by X-ray analysis . Comparison of the 1H and 13 C NMR spectra of 8 and its N-alkylammonium salts (9–13) with those of 1–7 showed only minor differences in the position of the proton and the carbon atom resonances assigned to the quinone and the ansa-bridge parts . The NOESY contacts for GDM analogues indicated trans-configuration of the lactam group with the lactam proton directed into the macrocyclic cavity, as shown in Figure 60S. As found for 8 and 9–13 salts, the absolute value of the H(29)-C(29)-N(17)-H(17) torsion angle is in the range of 120–130° (3JN(17)H-H(29)∼6.5–8 Hz,) and the N-alkyl substituent of nitrogen N + (30) is oriented away from the quinone core . The nitrogen atom N(17), linking the GDM and quinuclidine parts, seems to be present in a distorted sp2 hybridisation, as suggested by DFT calculations . The above data indicated similar conformations of GDM derivatives in its “free form” in solution and solid, with only small changes in orientation of the C(17)-amine part relative to the quinone core.8 and its quaternary N-alkylammonium salts (9–13) were initially tested in MDA-MB-231, MCF-7, HeLa, and HepG2 and healthy CCD39LU cell lines, and compared with biological test results of typical GDM C(17)-derivatives (1–7)  is the lowest among the studied analogues (50 = 3.51 µM). A comparison of GDM activity and its analogues in the MCF-7 indicates that derivatives 1, 3, and 8 are markedly more potent than GDM, which is reflected by their low IC50∼0.1 µM values. Furthermore, 1, 3, and 8 show higher SI indexes  than GDM for the MCF-7 cell line. Derivatives 5–7 were also more potent than GDM in the MCF-7 cell line (IC50∼1 µM), with still favourable SIs∼10. Lipophilicity of the most promising analogues  is relatively good (clogP = 0.7–3.4) and comparable with that of GDM (clogP∼2). Compound 2, bearing a terminal acidic group, showed the lowest activity in the MCF-7 cell line at a less favourable SI compared to GDM. Taking into account the binding models of GDM and its derivatives with Hsp90, one should expect that the carboxylic group might improve the binding strength as a result of the interaction with a positively charged K58,,2 takes part in competitive intramolecular interactions , preventing the expected stabilising interaction with K58 of Hsp90. The existence of the H-bonded structures of 2 in solution and its relatively low lipophilicity  = 20.8 µM, 1–8, the quinuclidine analogue 8 shows the most beneficial potency in MDA-MB-231 (IC50 = 0.14 µM) among all ansamycins studied. The activity of 8 relative to GDM was improved, not only in MDA-MB-231 but also in other studied cancer cell lines  and salts (9–13) in the HepG2 cancer cell line. The most promising quinuclidine analogue 8 exhibited relatively high toxicity in healthy cells CCD39Lu (IC50 = 0.87 µM) and had only low SI indexes  . Althougll lines . When coSIs < 1, .8 using the 2nd panel of cancer cell lines: SKBR-3, SKOV-3, PC-3, U-87, and A-549 and healthy cells HDF and to compare biological data with those of exemplary derivative 1 (8 is very active in the 2nd panel of cancer cell lines and its activity is even slightly better than that of GDM in the A-549 cell line (IC50 = 0.99 µM). The potency of 8 in SKBR-3, SKOV-3, PC-3, and U-87 cancer cell lines was lower or comparable to GDM  = 30 µM; IC50(CCD39Lu) = > 10 µM), especially in the 2nd panel of SKBR-3, SKOV-3, PC-3, U-87, and A-549 cancer cell lines, was relatively low (IC50∼7.5 µM). Taking into regard the fact that another analogue of low cytotoxicity, 2 (IC50(CCD39Lu) = 20.8 µM), shows the best water solubility among 1–8 derivatives  when compared to GDM. Very good results in MCF-7 cells were also obtained for salts 9 (IC50 = 2.34 µM) and 10 (IC50 = 2.51 µM), which, like 13, had no heteroatoms in the tail attached to the quinuclidine unit . The highest potency of 10 was registered in the A-549 cancer cell line (IC50 = 1.29 µM). Although a comparison of anticancer activities of GDM, 8 and 10  and CCD39LU (IC50 > 10 µM) at still attractive level of anticancer potency IC50∼2 µM in MCF-7, SKBR-3, SKOV-3, PC-3, U-87, and A-549 cell lines.Considering the above results, we decided to further test compound vative 1 . Our stue to GDM . In cont for GDM . The potivatives , we decill lines  revealed CCD39Lu . Among qine unit . Other bLu cells . Anticanll lines  revealed8 and 10  showed t,,,8, 10, and 13 into the target, i.e. ATP-binding pocket of Hsp90, requires trans/cis isomerisation of the lactam group and drastic conformational changes within the ansa-bridge owing to atropisomerisation process, i.e. flipping of the ansa-bridge from one side of the benzoquinone ring to the other . DFT calculated energy barrier is 36.97 kcal/mol for the total atropisomerisation process of 10 requiring trans-cis lactam isomerisation crucial for binding with NBD of Hsp90 . This value is higher than that estimated from earlier studies of GDM .,GDM and 8, showed that the mutual arrangements of the ansa-bridges and quinone cores relative to the Hsp90 key amino acids are similar in both cases.,GDM analogues from the trans-lactam form (dominating in solution) into the cis-lactam one (bound with Hsp90) is evoked by the mutual induced fitting of human Hsp90 and the GDM derivative. The 8-Hsp90 complex is stabilised by the formation of intermolecular H-bonds between the ansamycin and F138, D54, D93, K112, and T184 of Hsp90, whereas, e.g. M98 and K58 are involved in hydrophobic interactions of the ligand in the ATP-binding pocket = − 39 kcal/mol) or 8 (ΔH°f(8) = − 47 kcal/mol) with Hsp90 indicates the privileged formation of 8-Hsp90 complex. This result is in line with higher anticancer potency of higher lipophilic 8 , has two alternative binding modes to Hsp90 . Overall, the higher anticancer activity of 8 compared to 10 and 13 can be explained by a higher energetic profit of binding 8 to Hsp90  than for 10  and 13 . Changes in lipophilicity (clogP) do not explain the differences in the anticancer effect of the tested salts (9–13). In turn, the better water solubility of salts 9, 10, and 13 ∼0.6 µM vs. IC50(13)∼2 µM). Interestingly 13 + PEI mixture showed even slightly higher anticancer potency (IC50 = 0.62 µM) than PEI itself in SKOV-3 cells. Unfortunately, with the increased potency of mixture 13 + PEI, its toxicity also showed a 3-fold increase in HDF cells in comparison with 13. In turn, the combined use of 8 with PEI resulted in decreased anticancer activity and cytotoxicity, both relative to 8 and PEI. The use of 8+DOX and 13+DOX mixtures improved potency in SKBR-3 cells relative to 8 and 13, respectively. For the 13+DOX mixture, the analogous result was also obtained in SKOV-3 and PC-3, in contrast to 8+DOX. The markedly enhanced anticancer activities of 13+DOX in studied cancer cell lines relative to that of 13, is accompanied by nearly preserved toxicity in HDF cells (IC50 level∼3 µM). Thus, our studies showed that the use of better water-soluble salt 13 than compound 8, in an equimolar mixture with PEI and DOX, contribute to markedly improved anticancer activity in SKBR-3, SKOV-3, and PC-3 (IC50∼3 µM), however, at the expense of increased toxicity in normal cells, when referred to 13. Furthermore, tests with 13+DOX mixture showed some available compromise between relatively preserved cytotoxicity (at the level IC50∼3 µM) and enhanced anticancer potency to IC50∼1 µM, especially in the PC-3 cell line (SI∼3).As shown in e of GDM . Compoun8 of GDM and its N-alkylammonium salts 9–13 have been synthesised and tested in nine cancer  and two normal (CCD39Lu and HDF) cell lines. The biological activities of these compounds were compared with those of GDM and C(17)-analogues 1–7. Structural studies of 1–13 using 1D and 2D NMR and x-ray crystallography methods revealed nearly identical conformation of the ansa-bridge, the trans configuration of the lactam group and similar arrangement of the C(17) substituents relative to the quinone core in solution and in solid. The MTT assay revealed that 8 is the most active derivative among studied C(17)-analogues of GDM in the 1st panel of MDA-MB-231, MCF-7, HeLa and HepG2 cancer cell lines at low IC50s∼0.1–1 µM. The anticancer activities of 8 in these cancer cell lines were markedly higher than GDM, except for the HepG2 cell line. A higher potency of 8 than GDM was noted also in the A-549 cell line of the 2nd panel. Unfortunately, alongside the attractive potency of 8, higher and lower toxicities than GDM were noted in CCD39Lu and HDF normal cell lines, respectively. Studies in MCF-7 cancer cell line revealed improved activities of N-alkylammonium salts 9–13 when compared to GDM (e.g. IC50(13) = 2.31 µM vs. IC50(GDM) = 3.51 µM), at together reduced toxic effects in HDF and CCD39Lu normal cell lines (IC50 even > 10 µM). Our studies showed that the limited toxicity of 9–13 salts can be linked with their low clogP and improved water solubility relative to GDM . In studied cancer cell lines, among 9–13 salts the most potent were those without heteroatoms in the attached tail at the nitrogen of C(17)-quinuclidine basket. Molecular docking of the most potent salts 8, 10, and 13 indicated the intermolecular hydrophobic stabilisation of the quaternary C(17)-quinuclidine arm with K58 or with Y61 of Hsp90. A more beneficial binding energy of the 8-Hsp90 complex than that for 10 or 13 complexes explains a higher anticancer activity of 8 than 10 and 13. Our studies also showed that quaternisation of the nitrogen within C(17)-quinuclidine containing arm can be a useful strategy in decreasing the toxicity of GDM analogues in normal cells, at a simultaneously improved or preserved anticancer activity in MCF-7 and A-549 cancer cell lines, respectively (e.g. compound 10). The use of 13 with potentiator PEI leads to improved or comparable anticancer activities relative to those of the salt and PEI, respectively. However, this beneficial anticancer effect was observed at the expense of increased toxicity in normal cells, when referred to 13.The quinuclidine analogue Click here for additional data file.


Currently it is unclear how in situ breast cancer progresses to invasive disease; therefore, a better understanding of the events that occur during the transition to invasive carcinoma is warranted. Here we have conducted a detailed molecular and cellular characterization of two, patient-derived, ductal carcinoma in situ (DCIS) cell lines, ETCC-006 and ETCC-010.Human DCIS cell lines, ETCC-006 and ETCC-010, were compared against a panel of cell lines including the immortalized, breast epithelial cell line, MCF10A, breast cancer cell lines, MCF7 and MDA-MB-231, and another DCIS line, MCF10DCIS.com. Cell morphology, hormone and HER2/ERBB2 receptor status, cell proliferation, survival, migration, anchorage-independent growth, indicators of EMT, cell signalling pathways and cell cycle proteins were examined using immunostaining, immunoblots, and quantitative, reverse transcriptase PCR (qRT-PCR), along with clonogenic, wound-closure and soft agar assays. RNA sequencing (RNAseq) was used to provide a transcriptomic profile.ETCC-006 and ETCC-010 cells displayed notable differences to another DCIS cell line, MCF10DCIS.com, in terms of morphology, steroid-receptor/HER status and markers of EMT. The ETCC cell lines lack ER/PR and HER, form colonies in clonogenic assays, have migratory capacity and are capable of anchorage-independent growth. Despite being isogenic, less than 30% of differentially expressed transcripts overlapped between the two lines, with enrichment in pathways involving receptor tyrosine kinases and DNA replication/cell cycle programs and in gene sets responsible for extracellular matrix organisation and ion transport.For the first time, we provide a molecular and cellular characterization of two, patient-derived DCIS cell lines, ETCC-006 and ETCC-010, facilitating future investigations into the molecular basis of DCIS to invasive ductal carcinoma transition.The online version contains supplementary material available at 10.1186/s12885-021-08511-2. Biomedical research relies heavily on the use of cellular in vitro models. In cancer research, a wide variety of cell lines are available to study the molecular details of cancer initiation and progression and are enormously beneficial to develop and test therapeutic drugs. For breast cancer research, MCF-7, T47D and MDA-MB-231 are utilized in more than two thirds of the studies using cell lines . DCIS reSome cellular models of early stage breast cancer have been developed. MCF10DCIS.com is the DCIS cell line most used by researchers according to the literature. MCF10DCIS.com was derived from a xenograft lesion following two trocar passages of the premalignant cell line MCF-10AT , 9. In xIn 2014, Yong et al. .Yong et al. stated that the ETCC lines that they derived were ER positive, based on their observations using immunoblotting and immunofluorescence . LeadingIn detail, we determined the cellular phenotypes of the two ETCC DCIS cell lines by examining general cell morphology, along with proliferation, survival, migration and anchorage-independent growth. Next we examined molecular features including, hormone-receptor status, markers for epithelial-mesenchymal transition (EMT) and protein expression levels of several signalling proteins in the AKT and ERK pathways and in the cell cycle. In parallel, RNA expression of several DCIS markers was measured by qRT-PCR and the transcriptomic profile of ETCC-006 and ETCC-010 was determined by RNA sequencing (RNAseq) and compared to an existing RNAseq data set from MCF-10A cells  and to R2 and were mycoplasma-free.ETCC-006 and ETCC-010 were purchased from Leibniz-Institute DSMZ . MCF10DCIS.com cells were obtained from Wayne State University . MCF-10A, MCF-7, MDA-MB-231, originally from ATCC, were a kind gift from Prof Rosemary O’Connor . Cell lines were authenticated using short, tandem repeat (STR) profiling (Eurofins Genomics). Cells were cultured according to the following media requirements. MCF-10A cells were maintained in DMEM/F12 supplemented with 5% horse serum, 10 μg/ml insulin, 20 ng/ml EGF, 100 ng/ml cholera toxin and 0.5 μg/ml hydrocortisone. MCF-7 and MDA-MB-231 cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin/streptomycin. MCF10DCIS.com were cultured in DMEM/F12 supplemented with 5% horse serum, 1.05 mM calcium chloride and 10 mM HEPES. ETCC-006 and ETCC-010 were cultured in RPMI supplemented with 10% FBS and 1% P/S. Cells were maintained at 37 °C with 5% COWestern blotting and indirect immunofluorescence were performed using the following primary antibodies: β actin ; β1 integrin ; cyclin B1 (#4135), estrogen receptor α (#8644), HER2 (#4290), EGFR (#4267), IGF-1R (#3027), pan AKT (#2920), pan ERK (#4696), pan p-AKT (#4060), pan p-ERK (#4377), p-EGFR (#3777), progesterone receptor A/B (#8757) ; cyclin D1 (ab16663) and N-cadherin (ab19348) ; E-cadherin ; GAPDH (sc47724) and vimentin (sc32322) . Secondary antibodies for western blots were IRDye 800CW or 680RD ; and the secondary antibody used for β actin indirect immunofluorescence was DyLight 488 donkey anti-mouse .4, 0.06% (w/v) NaHCO3) for 1 min. The cells were then counterstained with eosin Y 1% Alcohol  for 5 min and imaged using EVOS FL Auto Imaging System .Cells were seeded onto sterile coverslips in 6-well plates and, after 24 h, were fixed with 4% paraformaldehyde (PFA) for 15 min. Cells were permeabilized with 0.2% triton X-100 in phosphate buffered saline (PBS) for 10 min and then placed in 10% methanol for 10 min. All samples were stained with Harris’ hematoxylin  for 10 min, differentiated in 0.5% acid alcohol for 30 s, and blued in Scott’s Tap Water (1% (w/v) MgSO2, pH 6.9), quenched with 50 mM ammonium chloride and permeabilized with 0.1% Triton X in PHEM buffer for 5 min. Non-specific antibody binding was blocked by incubating the coverslips with 5% goat serum/PHEM for 30 min. Cells were stained with anti-β actin (1:400) overnight at 4 °C in a humidified chamber and washed with PHEM. The coverslips were then incubated with donkey, anti-mouse, 488-fluorophore (1:1000) conjugated secondary antibody and DAPI  at room temperature for 1 h then washed with PHEM. The coverslips were mounted onto Fluoroshield histology mounting medium . Images were acquired using a Leica DM6000 upright fluorescence microscope.Cells were seeded in a 6-well plate onto sterile coverslips and allowed to grow for 48 h. Cells were then fixed with 4% paraformaldehyde (PFA) in PHEM buffer  for 30 min on ice. Cleared lysates were prepared by centrifugation for 30 min at 12,000 g at 4 °C. Protein levels were quantified using Pierce Bicinchoninic Acid (BCA) Protein Assay Kit  and equal amounts of protein samples (40–50 μg) were resuspended in SDS loading buffer and electrophoresed on 10% SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis) at 100 V for 2 h. Proteins were transferred onto polyvinylidene difluoride (PVDF) membrane  for 1 h at 100 V then blocked with 5% milk or bovine serum albumin (BSA) for 1 h at room temperature and incubated with the primary antibody overnight at 4 °C. The next day, the membrane was washed with Tris-buffered saline + 0.1% Tween-20 (TBST) for 5 min, 3 times, incubated with secondary antibody for 1 h at room temperature, washed with TBST for 5 min, 3 times, and visualized using an Odyssey Imaging System (LI-COR Biosciences).Total RNA was extracted from cells using TRIzol (Thermo Fisher Scientific). Briefly, 0.2 mL of chloroform was added per 1 ml of TRIzol reagent, samples were homogenized and then left at room temperature for 3 min. The aqueous phase was separated by centrifugation, and RNA was precipitated using isopropanol. After two washes using 75% ethanol, the RNA pellet was airdried, resuspended in water and incubated at 58 °C for 10 min.95 °C 10 min94 °C 30 s, 57 °C 45 s, 72 °C 1 min repeated 39 times94 °C 30 s, 57 °C 45 s, 72 °C 15 minMelting curve stage1 μg of RNA was treated with DNase to eliminate contaminating DNA using TURBO DNase  then used in a cDNA synthesis reaction using Superscript II  as per manufacturer instructions. Reactions lacking reverse transcriptase enzyme were also run in the same condition as controls. The synthesized cDNA was diluted 1:5 and used in qRT-PCR reactions. Briefly, 25 ng cDNA was combined with SYBR Green JumpStart Taq ReadyMix  in 20 ul reactions and run using the following conditions:UBC ; hoSince we found that ETCC-006 and ETCC-010 display different characteristics to what was published by Yong et al. 2014) [ [14], weMKI67 (Ki67), STK15 (AURKA), BIRC5 (survivin), CCNB1 (cyclin B1), MYBL2 (MYB proto-oncogene like 2); PGR (progesterone receptor), GSTM1 (glutathione S-transferase M1) and five reference genes. While it is not relevant to calculate a DCIS score for cell lines, we decided to assess the expression levels of those markers in the DCIS cell lines, ETCC-006 and ETCC-010, compared to the other cell lines in our panel.The Oncotype DX DCIS score is a 12-gene panel that generates predicted 10-year risk of local DCIS and invasive recurrence following treatment by breast conserving surgery . It inclMKI67, STK15, BIRC5, CCNB1, MYBL2, GSTM1 and PGR. MCF-7 cells show high expression of the proliferation markers (Panel A) along with high expression of GSTM1 and PGR (Panel B). MDA-MB-231 cells show high expression of four proliferation markers, very low expression of PGR and reduced GSTM1 expression compared to MCF-7 cells. Regarding MCF10DCIS.com, we observed lower expression of the proliferation markers, when compared to the breast cancer cell lines, and low GSTM1 and PGR expression. It is important to note that while PGR RNA levels appeared low as determined by qRT-PCR; PR protein was detectable by immunoblotting  of ETCC-006 and ETCC-010 cell lines. RNAseq allows the identification of transcripts present in the ETCC-006 and ETCC-010 cells at a genome-wide level. By comparing the level of expression of transcripts in ETCC-006 and ETCC-010 to those in MCF-10A cells, we aimed to identify if genes in some cancer-related pathways showed altered expression. Before performing RNAseq, we analyzed the quality of the total RNA extracted from our cell lines using bioanalyzer traces (Agilent) to calculate an RNA integrity (RIN) value that is indicative of the level of degradation of the RNA. RIN values range from 0 (degraded) to 10 (intact). We obtained RIN values of 10 for RNA extracted from ETCC-006 and ETCC-010, each with two replicates. This indicated that the RNA was of good quality and could be used for library preparation.p-value over 0.05. Figure After RNA sequencing, reads were trimmed and filtered for quality with Trimmomatic  and aligCCND1, MCM family, MYBL2, etc.). CCND1 is an important regulator of the G1/S transition [Next, reactome pathway-based analyses were performed using an R/Bioconductor package, ReactomePA , using tansition , the MCMansition , and MYBansition . Interesansition . OverallTo expand our comparison of ETCC DCIS RNAseq data, we decided to analyze the ETCC-006 and ETCC-010 datasets to RNAseq data from MCF10A, MCF10DCIS.com, MCF7 and MDA-MB-231 cell lines from Klijn et al. (2015) . This daCOL4A1/A2 (collagen type IV) genes within the receptor tyrosine kinase STRING analysis  using trypan blue staining. Figure To extend our characterization of the ETCC cell lines, we used the clonogenic assay to monitor cell survival by testing the cells’ ability to go through cell division with no limit in time , 48. TheSince the integrin cell surface interactions pathway was enriched in ETCC-006 and ETCC-010 Fig. B and D aAnchorage-independent growth, a property of most malignant cells to grow independently of a solid surface, was evaluated by a soft agar colony formation assay . As expeTaken together, our data has shown that the DCIS cell lines ETCC-006 and ETCC-010 show enrichment in migration pathways Fig. , extraceTo examine the level of expression of key EMT markers, whole cell lysates from each cell line were prepared. Vimentin, E-cadherin, N-cadherin, β-catenin and β1-integrin expression was analyzed by immunoblotting. High expression of vimentin was observed in ETCC-006 and ETCC-010 at similar levels to those observed in MDA-MB-231; while low expression of vimentin was detected in MCF10DCIS.com at level comparable to MCF-10A Fig. B and C.Most strikingly, while MCF10DCIS.com cells expressed E-cadherin, we could not detect expression of E-cadherin in ETCC-006 and ETCC-010. Instead, expression of N-cadherin was observed in ETCC-006 and ETCC-010 lysates. β-catenin is a transcription factor induced by the WNT pathway that stimulates transcription of genes involved in the EMT , and itsOverall, the co-expression of β-catenin, vimentin, β1 integrin and N-cadherin suggests that ETCC-006 and ETCC-010 have undergone an EMT transition. Although MCF10DCIS.com cells express some markers of EMT, overall it is more representative of cells with an epithelial phenotype.Since signalling by receptor tyrosine kinases was most enhanced in ETCC-006 cells according to our RNAseq data and ReactomePA analysis Fig. , and ETCCommon downstream targets of EGFR and IGF-1R include the Ras/Raf/MEK/ERK and PI3K/ AKT pathways, known to stimulate proliferation, migration and EMT when activated after being phosphorylated . While tNext, we aimed to investigate whether AKT is constitutively activated in MCF10DCIS.com cells. To do this, we compared the phosphorylation levels of AKT under normal and serum starvation conditions Fig. E and couSince high levels of cyclin D1 are associated with DCIS , we examChoosing the correct model, animal or cellular, is critical in research. To study DCIS in an in vitro cell model, we choose to use three DCIS cell lines: MCF10DCIS.com, a well characterized DCIS cell line model, and ETCC-006 and ETCC-010, two patient-derived cell lines. To date, other research groups have not used these recently established patient-derived lines; therefore, we conducted a baseline phenotypic and molecular characterization of DCIS cell lines, ETCC-006 and ETCC-010.Despite some differences in in vitro and in vivo assays as highlighted in the original publication, ETCC-006 and ETCC-010 share the same genetic background. For many of the properties examined in this work, we mostly found that ETCC lines behave quite similar to each other in vitro, with similar rates of proliferation, colony formation and anchorage-independent growth. Still the two lines have very different transcriptional outputs, with less than 30% of differentially expressed transcripts being common between the two cell lines. Similarly, ETCC-006 shows more enrichment of proliferative markers than ETCC-010, with cyclin D1 more highly expressed in ETCC-006 compared to ETCC-010 in our RNAseq data  Fig. , suggestThroughout this work, we have compared ETCC-006 and ETCC-010 to MCF10DCIS.com cells, a well-characterized DCIS cell line that produces comedo DCIS lesions in xenografts , 12.Compared to the ETCC lines, MCF10DCIS.com appears to have conserved an epithelial phenotype with cells showing tight contact, no expression of EMT markers and limited migratory abilities. We did show constitutive activation of AKT, likely explained by a mutation in the PI3K gene , 63. IndWhere do DCIS cell lines fit in terms of the clinical representation of DCIS? As previously described, DCIS is an early stage of breast cancer, with a large spectrum of cases classified from low-grade disease to high-grade DCIS. High-grade DCIS is associated with a higher risk of developing IDC ; therefoIn the clinical DCIS spectrum, MCF10DCIS.com cells have been characterized as a model of human comedo DCIS in xenografts , 12. In Patient-derived DCIS cell lines, ETCC-006 and ETCC-010, exhibit molecular markers and cellular behaviour consistent with a more aggressive form of DCIS. Although both lines were derived from the same DCIS lesion, they display markedly different transcriptomic profiles, supporting intra-tumoral heterogeneity within DCIS affected areas of the breast. Our results provide a baseline phenotypic and molecular characterization of these newly established DCIS cell lines for further exploration by the research community.Additional file 1. Differentially expressed genes in ETCC-006 and ETCC-010 cell lines compared to MCF10A cells as determined by RNAseq. Genes that are over- and under-expressed and are unique to ETCC-006 and ETCC-010 cell lines are listed in separate sheets , as well as genes common between the two lines (Common_up and Common_down). Included in the files are the Ensembl gene identifiers, gene name and differential expression criteria, most notably log2(FC) and p-value. Only genes with a p-value < 0.05 are included.Additional file 2: Supplemental Figure 1. Expression level of Oncotype DX DCIS score markers in the DCIS cell lines analyzed by qRT-PCR. (A) Relative expression of the proliferation markers is associated with an increased risk of DCIS recurrence whereas (B) GSTM1 and PGR have a protective role against the DCIS risk of recurrence. qRT-PCR was performed using cDNA synthesized from total RNA isolated from normal-like and breast cancer cell lines, MCF-10A, MCF-7, MDA-MB-231, and DCIS cell lines, MCF10DCIS.com, ETCC-006 and ETCC-010. All data was normalized to UBC (polyubiquitin-C) and statistical analysis was done using one-way ANOVA, with **** p-value < 0.0001; *** p-value < 0.001; ** p-value < 0.01; * p-value < 0.05. Horizontal bars show the mean +/− SEM (GraphPad Prism v.8.3.0).Additional file 3: Supplemental Figure 2. GO analysis from the differential expression of RNA transcripts in ETCC-006 and ETCC-010 DCIS cell lines compared to MCF10A, MCF10DCIS.com, MCF7 and MDA-MB-231 cell lines. RNAseq datasets of ETCC-006 and ETCC-010 were compared to datasets available in Klijn et al. (2015) [. (2015) . Using G. (2015) , shown aAdditional file 4.Additional file 5.Additional file 6.


We previously described involvement of the MYC/miR-150/MYB/ZDHHC11 network in the growth of Burkitt lymphoma (BL) cells. Here we studied the relevance of this network in the two other B-cell lymphomas: Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL). Expression levels of the network components were assessed at the RNA and protein level. The effect of modulating levels of the network components on cell growth was determined through GFP competition assay. AGO2-RNA immunoprecipitation was performed to validate targeting by miR-150. Expression levels of MYC, MYB and ZDHHC11 were increased, while miR-150 levels were decreased similar to the pattern observed in BL. The knockdown of MYC, MYB and ZDHHC11 decreased the growth of HL and DLBCL cells. In contrast, overexpression of miR-150 did not induce clear phenotypes in HL, and limited the effects in DLBCL. This could not be explained by the differences in overexpression levels. Furthermore, we showed that in HL, ZDHHC11 and MYB are efficiently targeted by miR-150. To conclude, MYC, MYB and ZDHHC11 are critical for the growth of HL and DLBCL cells consistent with the role observed in BL cells, while low endogenous miR-150 levels appeared to be less critical for the growth of HL and DLBCL cells despite the effective targeting of ZDHHC11 and MYB. Burkitt lymphoma (BL) is a highly aggressive germinal center (GC) B-cell derived lymphoma subtype occurring most commonly in children. The hallmark of BL is the translocation of the MYC gene locus to one of the immunoglobulin gene loci, which results in high expression of the oncogenic transcription factor MYC . MYC reg2.Cell lines used throughout the manuscript are listed in GCB-cells (defined as CD20+IgD−CD38+) were sorted from routinely removed tonsil specimens as described previously . WrittenLentiviral shRNA constructs targeting all ZDHHC11 transcripts, and MYC and MYB were described and tested previously . LentiviThe percentages of GFP positive cells were measured on the BD Accuri™ C6 Plus Cell Analyzer  at day 4 post-transfection and monitored tri-weekly for three weeks. Data were analyzed using FlowJo software . To determine the effect on cell growth, the percentage of GFP positive cells at day 4, 6 or 8  was set to 100% and the fold difference relative to this starting point was calculated for each time point. To determine significant differences in the GFP assays, we used mixed model analysis as described previously . Total RNA was isolated using the miRNeasy mini or micro kit  in combination with Phase Lock Gel Heavy tubes . The primers used for the quantification of the transcript levels are shown in Immunoprecipitation of AGO2-containing RISC complexes was performed as described previously  with somTotal RNA of the total fractions (TF) and immunoprecipitated (IP) fractions were isolated as described above and used for analysis by microarray using an Agilent SurePrint G3 Human GE 8x60K microarray (Agilent ID 72363) as described previously . The micWhole cell protein lysates were prepared using cell lysis buffer  supplemented with 1 mM PMSF. The total protein concentration was measured using the Pierce BCA Protein Assay kit . After denaturing by boiling for 5 min at 100 °C in loading buffer, proteins were separated by SDS-PAGE using 10% acrylamide gels. Next, the proteins were transferred to nitrocellulose membranes using the Bio-Rad Wet electroblotting system (Life Science Technologies). Antibodies were diluted in 5% milk in Tris-buffered saline + Tween-20. The primary, secondary and tertiary antibody solutions used are listed in http://www.ncbi.nlm.nih.gov/geo/ accessed on 23 April 2021; GSE39132 (5 CD77+ GC B-cell samples) and GSE39133 (29 HRS cell samples) for the Steidl cohort; GSE12453 (10 CD77-/+ GC B-cell samples and 12 HRS cell samples) for the Tiacci cohort; GSE12453 (12 HRS cell samples) and GSE83441 (5 CD30+ GC B-cell samples) for the Weniger cohort) . The knockdown of all ZDHHC11 transcripts also inhibited growth of HL and DLBCL cell lines, albeit at variable levels ranging from mild to strong effects. In HL, both shRNAs showed a strong phenotype in SUPHD1 and L540, with a reduction of 78% and 40% for shRNA-1 and 82% and 70% for shRNA-2. The effects on growth of KMH2 and L428 cells were milder with 40 and 20% for shRNA-1 and 35 and 40% for shRNA-2. In DLBCL cell lines U2932 and WSU-DLCL2, the reduction in GFP was most prominent for shRNA-2 with 40 and 60%, and moderate for shRNA-1 with 10 and 40%. In the two other DLBCL cell lines, SUDHL4 and SUDHL5, only mild effects were observed for shRNA-1 and no effects for shRNA-2. Thus, similar to BL, the inhibition of all ZDHCC11 transcripts resulted in a decrease in growth in all HL and two of the four DLBCL cell lines, while the knockdown of MYC and MYB decreased cell growth in all cell lines.To establish an effect of miR-150 overexpression on cell growth, we first tested the overexpression efficiency in all individual cell lines . The folTo explore whether differences in the genes targeted by miR-150 could explain the lack of a phenotype for miR-150 overexpression, we performed AGO2-RNA immunoprecipitation (AGO2-RIP) in two HL cell lines, i.e., L428 and SUPHD1. A good efficiency of the AGO2-RIP was confirmed by showing enrichment of the AGO2 protein by Western blot as well as by enrichment of miR-150, miR-16 and miR-17 by RT-qPCR in the IP fractions . The numThe MYC/miR-150/MYB/ZDHHC11 network was previously established in BL and was shown to have a cell growth regulatory effect [Next, we explored why miR-150 overexpression did not affect the growth of HL and DLBCL cell lines. A first potential explanation might be that the achieved miR-150 levels are insufficient to induce a phenotype. A comparison of miR-150 overexpression efficiencies of HL and DLBCL with BL cell lines showed that the combined endogenous and ectopic levels were in the same range in all three lymphoma subtypes and thus an unlikely explanation for the lack of phenotype upon overexpression of miR-150 in HL and DLBCL.As a second possible explanation we determined whether miR-150 targets MYB and ZDHHC11 in HL because these cell lines showed the weakest effect on cell growth upon miR-150 overexpression. Probes for both MYB and ZDHHC11 were among the most enriched targets, with IP enrichment levels similar to those observed in BL [A third possible explanation is that in BL the effect of miR-150 on cell growth is caused by a combined regulation of multiple target genes including MYB and ZDHHC11, but also other genes. This could imply that in HL the efficiency of miR-150 for targeting MYB and ZDHHC11 is insufficient to cause a clear effect on cell growth. It has indeed been shown that the miR-150 target gene repertoire can highly differ between different hematopoietic malignancies, e.g., MYB, FLT3, CBL and EGR2 in MLL-rearranged AML cells ,26, and We were unable to define the miR-150 target genes in DLBCL cell lines, as the lentiviral infection efficiencies were insufficient to allow isolation of sufficient infected cells for the AGO2-IP procedure. We expect that the efficiencies for the targeting of miR-150 to MYB and ZDHHC11 in DLBCL will also be good but this needs to be established. Thus, we cannot rule out that in DLBCL the limited phenotype upon miR-150 overexpression is related to poor efficiency to target MYB and ZDHHC11. A second limitation of our study is that we used a subset of the cell lines for studying the effect of miR-150 overexpression on growth. Therefore, we cannot exclude the possibility that miR-150 may inhibit growth in other HL or DLBCL cell lines. Lastly, as the efficiencies of the shRNAs against MYC, MYB and ZDHHC11 were not retested in all cell lines, we cannot rule out that differences in the strength of the phenotype are caused by differences in shRNA efficiencies between cell lines.In conclusion, although MYC, MYB and ZDHHC11 all have important roles in the growth of HL and DLBCL, the network, as defined in BL with a critical role of miR-150, is not broadly applicable to other GC B-cell derived lymphoma subtypes. Based on our data, the induction of miR-150 might present an attractive therapeutic approach for BL, but not for other GC B-cell derived lymphomas. The lymphoma specific role of ZDHHC11 remains to be elucidated and studying the individual ZDHHC11 products might provide further mechanistic insights in different B-cell lymphoma subtypes.


N6-methyladenosine (m6A) is the most abundant RNA modification in eukaryotes. Accumulating evidence suggests that dysregulation of m6A modification significantly correlates with tumorigenesis and progression. In this study, we observed an increased expression and positive correlations of all 25 m6A regulators in esophageal cancer (ESCA) data obtained from the TCGA database. Through expression profiling of these regulators, a prognostic score model containing HNRNPA2B1, ALKBH5, and HNRNPG was established, and the high-risk subgroup exhibited strong positive correlations with ESCA progression and outcome. The risk score obtained from this model may represent an independent predictor of ESCA prognosis. Notably, the gene most frequently associated with increased risk was HNRNPA2B1; in ESCA, the increased expression of this gene alone predicted poor prognosis by affecting tumor-promoting signaling pathways through miR-17-92 cluster. An experimental study demonstrated that elevated HNRNPA2B1 expression was positively associated with distant metastasis and lymph node stage, and predicted the poor outcomes of ESCA patients. Knockdown of HNRNPA2B1 significantly decreased the expression of miR-17, miR-18a, miR-20a, miR-93, and miR-106b and inhibited the proliferation of ESCA cells. Therefore, our study indicated that the dynamic changes in 25 m6A regulators were associated with the clinical features and prognosis of patients with ESCA. Importantly, HNRNPA2B1 alone may affect the prognosis of patients with ESCA by regulating the miR-17-92 cluster. Esophageal cancer (ESCA) is a common disease of the digestive system that is associated with poor prognosis and high mortality rates. As of 2015, the 5-year relative survival rate for ESCA was only 20% in the United States, and it is estimated that ESCA will be the sixth leading cause of cancer-related deaths in males by 2020 . There aN6-methyladenosine (m6A) is the most prevalent and abundant RNA modification in eukaryotes . The seqIn the present study, we systematically analyzed the mRNA expression and interaction of 25 m6A regulators in ESCA data obtained from The Cancer Genome Atlas (TCGA) datasets. Next, we performed a molecular classification and established a prognostic score model with three m6A regulators. We further evaluated the correlation between m6A regulators and the clinical outcome and pathological characteristics. Next, we focused on the binding protein HNRNPA2B1 and investigated its expression and role in the prognosis of patients with ESCA. Moreover, we examined the mechanism by which HNRNPA2B1 may affect the prognosis of ESCA patients by regulating the miR-17-92 cluster.R2 = 0.64, P < 0.001)  . To expl< 0.001) . The resDue to the current lack of clinical tumor molecular classification for ESCA, we investigated the role played by m6A regulators in molecular classification. Based on the expression data of m6A regulators, we classified 123 ESCA patients through a consensus clustering approach.k = 2 was considered the best number of groups using the expression signature of 25 m6A regulators. Therefore, ESCA patients were divided into two clusters: 81 samples from the ESCA cluster 1 (EC1) and 42 samples from ESCA cluster 2 (EC2) subgroups  of ESCA . A KaplaTo further understand the prognostic value of m6A regulators in ESCA, a univariate Cox proportional hazards regression model was first performed to screen the potential genes initially. We observed that higher expression of ALKBH5  was associated with better survival in ESCA. Conversely, higher expression of three m6A splicing-related readers, HNRNPA2B1, HNRNPG, and TRA2A, was associated with worse survival in ESCA patients . Next, tRisk scores may provide prognostic value for clinical applications. We investigated the relationship between the risk scores and clinicopathological features of patients with ESCA. We found that the two groups had significant differences in tumor (T) volume. The high-risk group showed a larger tumor size . MoreoveIn the previous study, we found HNRNPA2B1 showed the highest expression among 25 m6A regulators in ESCA. The relative expression of 25 m6A regulators were compared in 13 types of common cancer cell lines . Intereshigh group presented significantly higher expression of almost all miRNAs in patients with ESCA in TCGA datasets  and lymph node stage  in ESCA patients. However, no relationship was found with age, sex, tumor stage, or clinical stage  ESCA tissues . HNRNPA2al stage . On KaplWe also collected the 14 paired esophagus cancer tissues and tumor-adjacent tissues to validate the bioinformatic analysis in TCGA datasets. We found that the significantly positive correlation between HNRNPA2B1 and miR-17/miR-18a/miR-106b in 14 paired esophagus cancer tissues and tumor-adjacent tissues . MoreoveR2 = 0.69, P = 0.021), miR-20a , miR-93 , and miR-106b   . To text= 0.049) . Next, w= 0.049) . Knockdo= 0.049) . Moreove= 0.049) . These dIn the present study, we demonstrated the generally high expression and positive correlation of 25 m6A regulators in ESCA. Next, we used 25 m6A regulators to classify the ESCA patients into two subgroups (EC1 and EC2) and established a prognostic score model (high-risk and low-risk) according to HNRNPA2B1, ALKBH5, and HNRNPG expression levels. These patterns exhibited a strong correlation with the clinical features and outcome of patients with ESCA. The risk score obtained from this model may represent an independent prognostic predictor for ESCA. Importantly, high expression of HNRNPA2B1 suggested a poor outcome in patients with ESCA and was positively associated with distant metastasis and lymph node stage. Knockdown of HNRNPA2B1 decreased the expression levels of miR-17, miR-18a, miR-20a, miR-93, and miR-106b and inhibited the proliferation of ESCA cells.Previous studies have shown that dysregulated m6A modification is associated with a number of cancers, such as hepatocellular carcinoma, lung cancer, and pancreatic cancer . In our It was recently demonstrated that HNRNPA2B1 mediated m6A-dependent primary microRNA processing events . TherefoThe results of this study demonstrate that dysregulation of 25 m6A regulators is associated with the clinical features and outcome of patients with ESCA. HNRNPA2B1 may affect the prognosis of ESCA by regulating the miR-17-92 cluster.1. The RNA-seq data across all tumors and their control samples were expressed as FPKM and normalized by GDC (Genomic Data Processing) mRNA quantification analysis pipeline. The RNA expression profiles of 27 ESCA cell lines were obtained from the Cancer Cell Line Encyclopedia (CCLE) datasets2. We extracted the clinical data, including sex , pathological state status , TNM status, survival time and vital status   . The HNRThe datasets of ESCA patients were obtained from the GEO database (see text footnote 3). Two mRNA datasets of ESCA  were selected in the present study.A total of 106 tumor tissues were collected from ESCC patients undergoing standard resection without chemotherapy or radiotherapy at the Cancer Hospital of Shantou University Medical College from May 2010 to August 2020 with a 10-year follow-up period. And we also collected 14 paired esophagus cancer tissues and tumor-adjacent tissues from the Cancer Hospital of Shantou University Medical College. Clinicopathological features  were collected to analyze the correlation with the expression of HNRNPA2B1 . This reSeven human ESCA cell lines  were obtained from the Oncological Research Lab in Cancer Hospital of Shantou University Medical College and were cultured in RPMI 1640 medium  supplemented with 10% fetal bovine serum (FBS) (Gibco) with 5% CO2 at 37°C.We organized the 25 m6A regulators that were divided into three groups according to the function of m6A RNA regulation: “writers,” “erasers,” and “readers.” “Writers” consisted of METTL4, METTL14, METTL3, RBM15, KIAA1429, WTAP, and ZC3H13. FTO and ALKBH5 were the “erasers.” IGF2BP1/2/3, YTHDC1/2, YTHDF1/2/3, EIF3A, SRSF3/9/10, TRA2A, and HNRNPC/G/A2B1 were the “readers” and were assigned to the “translation,” “splicing,” and “decay” subgroups .P-values < 0.01 and | log2 fold change| > 1 as the threshold. The correlation analysis was performed, and the results were visualized using the “corrplot” package4.To identify differentially expressed genes in ESCA, we used the Wilcox test to identify differentially expressed genes by the “vioplot” package, with adjusted k” increased 2–9, k = 3 had the smaller CDF value. However, k = 2 was eventually selected due to the selection criteria that the sample size between each group should not vary excessively. The OS difference between different clusters was calculated by the Kaplan-Meier method and log-rank test. Chi-square test was used to compare the distribution of age, gender, grade and TNM stage between two clusters.According to the mRNA expression of 25 m6A regulators, we used a consensus clustering approach to divide 123 ESCA patients into an optimal number of groups by the k-means clustering algorithm in the “ConsensusClusterPlus” R package . When thP < 0.05 were reserved for subsequent analysis. LASSO regression is frequently used to build a fit generalized linear model that can achieve variable selection and regularization and be called as m6A prognostic score model. LASSO regression was performed to identify the most efficient components contributing to survival prediction by the “glmnet” package. After cross-validation, the optimal number of feature variables was determined, and the coefficient of each m6A regulator included in our m6A prognostic score model was extracted by the “coef” function. By multiplying the expression of each gene and its coefficient, the sum scores of each gene can be calculated as the risk score of each patient in the prognostic score model. The sensitivity and specificity of the model were evaluated by ROC curve and its AUC.Univariate analysis was performed to determine the prognostic value of each m6A regulator. The m6A regulators with TM miRNA qRT-RCR TB Green Kit . The cDNA was used as a template for quantitative real-time PCR analysis using TB Green@Premix EX TaqTM II . The relative HNRNPA2B1 expression levels were calculated using the 2Ct–ΔΔ method with the levels normalized to β-actin mRNA. Since the endogenous U6 gene is generally used to normalize the expression of miRNAs, we calculated the expression of five miRNAs relative to U6 expression in ESCA cells. The specific primers used in this study are listed in Total RNA was isolated from human ESCA cells using an RNA simple Total RNA Kit , and RNA was subsequently reverse-transcribed with a Mir-XHuman ESCA tumor tissue paraffin-embedded sections were deparaffinized and treated with 3% H2O2 to block the endogenous peroxidase activity for 1 h. Prior to immunohistochemistry staining, the sections were immersed in an epitope-retrieving buffer at 95–100°C for 5 min. Then the proteins in the sections were blocked by normal goat serum blocking solution at room temperature for 20 min. Subsequently, they were incubated with anti-HNRNPA2B1 antibody  at 4°C overnight. Next, secondary antibodies were added and incubated at 37°C for 30 min. Finally, the sections were visualized by using 3,3′-diaminobenzidine. According to the extent and intensity of staining, each specimen was assigned into four grades .The total RNA was isolated by TRIZOL method. Polyadenylated RNA isolated from the cells indicated were fragmented into ∼100 nt sections using the NEBNext Magnesium RNA Fragmentation Module . RNA was incubated with the m6A, HNRNPA2B1 or IgG antibody (5 μg) for immunoprecipitation overnight at 4°C, according to the standard protocol of the EpiMark N6-Methyladenosine Enrichment Kit (NEB). The enrichment of certain fragments was determined using real-time PCR. The enrichment of the RNAs was normalized to input.Small interfering RNAs targeting HNRNPA2B1 were designed and synthesized. SiRNAs were added to cells following the protocol of Lipofectamine 3000 . Cell transfection was performed in six-well plates. Then, RNA and protein of treated cells were collected after 2–4 days. The siRNA sequences were as follows: siRNA-HNRNPA2B1: CCAUACCAUCAAUGGUCAU; siRNA-Control: GGAACAUCACCUUAGAGAU.Total protein was extracted with RIPA buffer  and quantified by using the BCA Protein Assay Kit . Twenty micrograms of protein was loaded and separated by 12% SDS-PAGE. After transfer to a polyvinylidene fluoride (PVDF) membrane, the membrane was incubated overnight at 4°C with an antibody against HNRNPA2B1  or a mouse monoclonal antibody against beta ACTIN . After incubation with peroxidase-conjugated anti-mouse IgG (Santa Cruz Biotechnology) at room temperature for 1–2 h, bound proteins were visualized with ECL  and detected by using BioImaging Systems . The relative protein levels were visualized by normalizing to beta-actin protein as a reference by using ImageJ.3/well) were plated into 96-well plates for 24 h and transfected with siRNA. After incubation for 24, 48, 72, and 96 h, cells were stained with 20 μL CCK8 reagent. Absorbance was measured at 450 nm wavelength by using a microplate reader to calculate the relative cell proliferation rate.Cell Counting Kit-8  was used to evaluate cell proliferation. Human ESCA cells The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by The Cancer Hospital of Shantou University Medical College. The patients/participants provided their written informed consent to participate in this study.WC, KL, JC, and DZ conceived and designed the project, acquired research data, and wrote the manuscript. KL, XL, YL, BQ, DX, SM, and YW acquired research data. JC provided clinical tissue sections and cell lines. WC and DZ contributed to helpful discussion and reviewed the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Liver cancer is the third leading cause of cancer death worldwide. Representing such a dramatic impact on our lives, liver cancer is a significant public health concern. Sustainable and reliable methods for preventing and treating liver cancer require fundamental research on its molecular mechanisms. Cell lines are treated as in vitro equivalents of tumor tissues, making them a must-have for basic research on the nature of cancer. According to recent discoveries, certified cell lines retain most genetic properties of the original tumor and mimic its microenvironment. On the other hand, modern technologies allowing the deepest level of detail in omics landscapes have shown significant differences even between samples of the same cell line due to cross- and mycoplasma infection. This and other observations suggest that, in some cases, cell cultures are not suitable as cancer models, with limited predictive value for the effectiveness of new treatments. HepG2 is a popular hepatic cell line. It is used in a wide range of studies, from the oncogenesis to the cytotoxicity of substances on the liver. In this regard, we set out to collect up-to-date information on the HepG2 cell line to assess whether the level of heterogeneity of the cell line allows in vitro biomedical studies as a model with guaranteed production and quality. Cell lines have revolutionized scientific research because of their similarity to primary tissues, low cost, and ease of use and culture. In addition, such cells provide an unlimited supply of biomaterials, and their use in research avoids ethical problems associated with the utilization of animal and human tissues . The widThe liver is an organ with high regenerative capacity and complex functions . It mainIn many studies, immortalized hepatic cell lines are used instead of liver biopsies due to their high cost, high invasiveness, and reduced activity of several key enzymes. Moreover, there is no technique enabling the maintenance of liver biopsies in culture for time-consuming studies . At the It should also be noted that, in addition to epithelial cells, the liver includes Kupffer cells , stellate cells (~8–10%), and liver sinusoidal endothelial cells (~3%) see . This diThe use of immortalized hepatic tumor cell lines has become a widespread practice not only for the cancer research, but also for the study of hepatitis B (HBV) and hepatitis D (HDV) viral infections. Thus, the complete cell cycle of HDV replication and expression/replication of HBV were found in cell lines HepG2 and Huh7 ,25. Addihttps://pubmed.ncbi.nlm.nih.gov/, accessed on 23 November 2021) contained a total of 34,021 articles available after a search for “HepG2”; 6455 for the Huh7 cell line; 2994 articles for the Hep3B cell line; 876 about HepaRG; and 625 articles on SK-Hep-1  . On the A patent for the HepG2 cell line, “a human hepatoma-derived cell line”, was filed in 1980 by researchers at the Wistar Institute. Since then, HepG2 cells have been entered into the ATCC  repository as a human cell line (HB 8065), “derived from the liver tissue of a 15-year-old white male with a well-differentiated hepatocellular carcinoma” .As in several cases, a classification error crept in, and the HepG2 cell line was mistakenly labeled as hepatocellular carcinoma (HCC) instead of hepatoblastoma (HB), which caused considerable confusion for 30 years. This was discovered after an investigation by Lopez-Terrada D. et al.  on the nRegarding the differences between the HepG2 cell line and normal hepatocytes, the crucial point is the weak or absent expression of the cytochrome P450 (CYP) superfamily, CYP3A4, CYP2C9, CYP2C19, CYP2A6, CYP2D6, etc. .One of the possible reasons for the popularity of the use of the HepG2 cell line is related to the fact that most cases of primary liver cancer are hepatocellular carcinoma . Liver cEach type of liver cancer and its subtype differs in cytological and molecular characteristics . As mentThe key features of HCC ,58 and sHepatic AS and ICCA also have a TP53 gene mutation associated with reduced survival ,66,67. IOne of the biomarkers of tumor processes in the liver is a change in the level of alpha-fetoprotein (AFP)—plasma protein produced by the yolk sac and the fetal liver during fetal development ,73. An iThus, existing differences and similarities in the molecular characteristics of liver cancer types raise a logical question about the applicability of the HepG2 cell line as a model of normal and pathological cellular processes in each case.According to their cytological characteristics, HepG2 cells are certainly the most similar to tumor cells in hepatoblastoma, but at the same time, they retain features characteristic of normal hepatocytes see . Thus, tAbout 20% of human hepatocytes are binucleated or polyploid ,77; ofteHepatocytes are rich in the smooth endoplasmic reticulum (SER) and mitochondria, reflecting their intense protein synthesis and energy metabolism, respectively . HepG2 SThus, according to the cytological characteristics, it can be noted that HepG2 cells occupy an intermediate state between normal hepatocytes and tumor cells, for which significant changes in the epigenetic regulation of nuclear and mitochondrial genes are observed ,82.Numerous studies have shown that this cell line contains fairly stable chromosomal abnormalities ,84,85,86In addition to the marker mutations discussed above see  30], in, in30], In an article by Tianyou et al. that studied polymorphisms in the NRAS and KRAS genes in Chinese children, no significant association was found between the risk of developing hepatoblastoma and these polymorphisms . There aThe HepG2 cell line carries a mutation in the TERT promoter that is common in HCC—C228T . In HB, The TP53 gene can be found in liver cancer in two types: mutant and wild. Wild-type TP53  is obserThus, it is worth noting that parts of the driver mutations in the genes of the HepG2 cell line coincide with mutations in HCC and HB. Furthermore, a marker deletion in the reading frame of the CTNNB1 gene for HB also occurs in HCC in 19% of patients .A transcript is the next stage after the genome in the transfer of biological information, at which new characteristics appear, such as changes in the number of transcripts encoded by one gene and their expression. In total, about 14,000 genes are expressed in HepG2 cells . At the An analysis comparing results of the transcriptome profiling of hepatic cell lines and tumor cells in HB and HCC showed significant differences in the level of DLK1 gene expression in HepG2 cells ,103,104,In HepG2, there is increased expression of the Wnt signaling pathway antagonist DKK1 , increasAnother example of a gene with altered expression is the GPC3 gene encoding glypican-3, which is overexpressed in HepG2 cells and tumor cells in HCC ,119. GPCIn a study by Tyakht et al. , increasCompared with normal hepatocytes, HepG2 cells exhibit a decreased expression level of CYP genes ,127,128,Based on the analysis of transcriptome profiling data, common gene expression patterns are observed in HB, HCC, and HepG2 cells. The HepG2 has also been compared with other commonly used hepatic cell lines at the transcriptomic level. Thus, a study comparing HepG2 and HepaRG showed that the HB cell line has a greater affinity with normal hepatocytes . HoweverThe HepG2 cell contains, on average, 170 pg of total protein, which is almost three times less than in hepatocytes (600 pg) . The livAccording to the results of transcriptome profiling in HepG2 cells, the cytochrome P450 superfamily genes are weakly expressed, as expected—at the proteomic level, the corresponding proteins are either absent or found at very low concentrations . Thus, iA significant difference in the proteomic profiles of hepatocyte and HepG2 cells is also observed in the expression of phase II drug-metabolizing enzymes. Thus, the expression of enzymes of the UGT family—UGT1A1, UGT1A4, UGT1A6, UGT2B7, UGT2B15, and GSTM1, which play a key role in the metabolism of a wide range of anticancer agents—was observed either at very low levels in the HepG2 cell line or was not detected at all in comparison with hepatocytes ,139. It For other phase II drug-metabolizing enzymes, SULT1A1 and SULT2A1, responsible for transferring a sulfate group from 3’-phosphoadenylyl sulfate to the hydroxyl group of an acceptor, no differences in titers were found between hepatocyte cells and HepG2. At the same time, it should be noted that in 50% of HCC cases, there is a decrease in the level of SULT2A1 ,142. ConThus, at the proteomic level, HepG2 cells retain and multiply changes in systems associated with the metabolism of endogenous and exogenous substances compared with normal hepatocytes. At the same time, the observed changes are similar to processes in hepatoblastoma and hepatocellular carcinoma cells, which justifies the use of the HepG2 cell line to study the metabolism of anticancer drugs and tumor processes.Most of the metabolomic studies performed on the HepG2 cell line aim to investigate the effect of drugs and chemicals on cells. In total, a small part of the work was carried out on the comparison of HepG2 and other cell lines. In particular, Chen et al. (2018)  comparedWhen comparing HepG2 cells with other tumor cell lines—MCF7 (angiosarcoma of the breast), PC3 (prostatic adenocarcinoma), 143B (osteosarcoma), and HEK293 (embryonic kidney)—it was shown that HepG2 cells had a decreased content of amino acids such as glutamine, proline, asparagine, aspartate, arginine, methionine, alanine, lysine, threonine, and leucine . MoreoveThe meta-analysis of the literature revealed almost no metabolic studies, including those comparing HCC and HB with hepatocytes and hepatic cell lines. Thus, due to the lack of relevant studies, we cannot conclude the similarity of HepG2 with other cells for use as models; however, the opportunity for further research is open.In the study of tumor processes, special attention is paid to signaling pathways that are responsible for the aberrant cell growth, survival, genome maintenance, etc. . SignaloThe case of liver cancer focuses on the following pathways: the transforming growth factor β (TGF-β) , proto-oAs noted above, one of the key features of HB due to the exon 3 mutation CTNNB1 gene is a violation of the Wnt/β-catenin signaling pathway . This paIn the case of HCC, the major pathways involved in the oncogenic process, in addition, to Wnt/b-catenin , are HedBased on the meta-analysis of articles, we have summarized the recommendations for the use of the HepG2 cell line . The tabDespite the illustrated differences between the HepG2 cell line and normal hepatocytes, the toxic effects of heavy metals, nanoparticles, and drugs are frequently studied in vitro ,186,187.The validity of using HepG2 cells as a model of hepatocytes is controversial because crucial proteins involved in the metabolism of substances—the liver’s primary function—are poorly expressed. Furthermore, the shortage of uptake transporters and phase I enzymes is observed in HepG2 cells, which indicates the need for careful use of this cell line to predict the metabolism and elimination of xenobiotics in hepatocytes. At the same time, the use of HepG2 cells to study the metabolism of anticancer drugs is acceptable because there is a similarity in the expression of phase I, II, and III drug metabolism/transport proteins in cells with HCC and HB. In addition, due to the low basal activity of CYP proteins , the cell line can be used in studies of CYP inducers .Work is under way to modify the HepG2 cell line to increase the expression of cytochromes for the correct use of HepG2 cells as a model of hepatocytes in the study of drug metabolism . AnotherAccording to molecular profiling data, the HepG2 cell line can serve as a model for hepatoblastoma. However, the use of HepG2 cells as an HCC model is incorrect, and as shown in the study by Choi et al. (2015), there is no expression of the marker of HCC—hGSTP1 .For 40 years, the HepG2 cell line has been widely used as a model for normal hepatocytes, hepatocellular carcinoma, and hepatoblastoma cells in various studies. Researchers are studying its molecular composition at the genomic, transcriptomic, and proteomic levels, each time confirming that the HepG2 is only partially similar to primary hepatocytes and cancer cells. However, even after the publication of such articles, researchers continue to call the genesis of the cell line differently. The “surprising case” of the HepG2 cell line lies in its erroneous annotation. This is not critical for some work, such as studying the amount of protein in a cell. However, if the metabolism of drugs or the processes that occur in a particular type of cancer are being studied, this is important and may call into question the relevance of the study.In this manuscript, we wanted to combine the accumulated knowledge about the cell line and compare it at different molecular levels with normal hepatocytes, HCC and HB. Hopefully, our review will simplify the scientists’ question: “Should I use HepG2 in my research?”


Inhibition of MEK has been proposed as a means to address mutant RAS colorectal cancer (CRC). However, MEK inhibitor adaptive resistance has led to reduced clinical utility of this new drug. Our studies have suggested the potential for the addition of an SRC inhibitor to prevent the development of resistance to MEK inhibitors. Moreover, we have identified that gene expression signature scores for RAS pathway activation, and MEK inhibitor resistance may be useful biomarkers in determining CRC drug sensitivity to the novel combination of Trametinib and Dasatinib.Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. The RAS pathway is activated in more than 55% of CRC and has been targeted for therapeutic intervention with MEK inhibitors. Unfortunately, many patients have de novo resistance, or can develop resistance to this new class of drugs. We have hypothesized that much of this resistance may pass through SRC as a common signal transduction node, and that inhibition of SRC may suppress MEK inhibition resistance mechanisms. CRC tumors of the Consensus Molecular Subtype (CMS) 4, enriched in stem cells, are difficult to successfully treat and have been suggested to evade traditional chemotherapy agents through resistance mechanisms. Here, we evaluate targeting two pathways simultaneously to produce an effective treatment by overcoming resistance. We show that combining Trametinib (MEKi) with Dasatinib (SRCi) provides enhanced cell death in 8 of the 16 tested CRC cell lines compared to treatment with either agent alone. To be able to select sensitive cells, we simultaneously evaluated a validated 18-gene RAS pathway activation signature score along with a 13-gene MEKi resistance signature score, which we hypothesize predict tumor sensitivity to this dual targeted therapy. We found the cell lines that were sensitive to the dual treatment were predominantly CMS4 and had both a high 18-gene and a high 13-gene score, suggesting these cell lines had potential for de novo MEKi sensitivity but were subject to the rapid development of MEKi resistance. The 13-gene score is highly correlated to a score for SRC activation, suggesting resistance is dependent on SRC. Our data show that gene expression signature scores for RAS pathway activation and for MEKi resistance may be useful in determining which CRC tumors will respond to the novel drug combination of MEKi and SRCi. Although colorectal cancer (CRC) is one of the leading causes of cancer deaths in the United States, it can be surgically cured with early detection. Nevertheless, many CRC tumors are not diagnosed until later stages when cure rates are low  . Mutation = 4151) of human tumors to best define CRC [SRC signaling may be largely responsible for drug resistance to targeted inhibition of the RAS/MEK/ERK. It is known that SRC activity and expression is increased in CRC tumors as compared to the SRC activity found in normal colon tissue ,15,16 anfine CRC ,25. SRC fine CRC . CMS4 CRfine CRC . Even thHere, we studied an 18-gene RAS pathway activation score and a 13-gene MEKi resistance score. Notably the 13-gene score predicts drug resistance caused by “bypass” proliferation/survival signaling pathways in the presence of active MEK but does not depend on PI3K activity . TherefoHCT116, SW48, WiDr, SW1417, SNU-C2B, SNU-2CA, and SW480 were sourced from the American Type Culture Collection (ATCC). LIM2099 was purchased from MilliporeSigma . SW837 was sourced from Amgen , DiFi was donated from Robert Coffey , RW7213 was donated from Sanjay Goel’s lab  and SNU-1411 was donated from Sandra Misale’s lab . All cell lines had biweekly tests for mycoplasma contamination with MycoAlert Mycoplasma Detection Kit from Lonza Walkersville, Inc. . Cell lines were cultured using RPMI 1640 supplemented with 10% FBS and 1% penicillin and streptomycin from Gibco .® CLx Imaging System was used for imaging all immunoblots . Antibodies were typically duplexed using Li-Cor secondary antibodies, Goat anti-Rabbit IRDye 680RD and Goat anti-Mouse IRDye 800CW. Rabbit primary antibodies were used unless specified and were obtained from Cell Signaling: phospho-Erk1/2 T202/Y204 (Cat. No. 4370); Cleaved PARP (Asp214) (D64E10); phosphor-SRC Y416 (2101) and SRC (mouse 2105).Cells were lysed using 1×RIPA buffer  containing 10 µM PMSF, Phosphatase Inhibitor Cocktail 2 (P5726 MilliporeSigma), Protease Inhibitor Cocktail , and Phosphatase Inhibitor Cocktail 3 (P0044 MilliporeSigma). Protein samples were run on 4–15% Mini-PROTEAN TGX Precast Protein Gels from Bio-Rad . The LI-COR Odysseyp-values for normalized band density were determined using the Student’s t-test.Band density was determined using LI-COR’s Image Studio™. Density readings for Phospho-ERK and Phospho-SRC were normalized using the density readings of the total protein for each respectively. Cleaved PARP density readings were normalized using readings for ß-Actin. All westerns were performed in triplicate. Normalized band density was then averaged, and standard deviation was calculated. The ® 2.0 Cell Viability Assay (G9241) was performed to determine cell viability. Plates were read using the Thermo Fisher Scientific  Varioskan LUX Multimode Microplate Reader.Cells were plated in white opaque Corning™ 96-Well, Cell Culture-Treated, Flat-Bottom Microplate (cat. 07200628). Following 24 h of growth, cells were then treated with the inhibitors at various dilutions ranging from 0.01–1 µM and all the permutation combinations of Trametinib and Dasatinib. After appropriate hours of treatment, the Promega Cell Titer-GloAll CRC cell lines were treated with Trametinib (HY-10999) or Dasatinib (HY-10181) sourced from Medchem Express. Treatments ranged from 48–96 h as noted in each assay. For all assays, cells were plated and allowed to grow for 24 h, then treated with inhibitors. Following treatments, Annexin V apoptosis assays were performed as described previously ,30. The The 18-gene MEK pathway activation, 13-gene MEKi resistance, and SRC activation signatures were adopted from previous analyses reported from other groups ,28. Of nFor CMS classification, CRC cell line or tumors were classified by CMScaller  as described by Eide et al. .For the Medico et al. CRC cell line dataset , we usedhttps://www.cbioportal.org/datasets, accessed on 5 October 2021). The downloaded expression values were normalized by the version 2 cBioportal pipeline (RSEM upper quartile Log2(n + 1)). A total of 101 of the samples were significant classified into a CMS subtype and used in this analysis.For human colorectal tumor validation, we used the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset . RNA expThe signature scores were calculated as previously described ,36. For Cell culture experiments were done in triplicates, and mean, median and standard deviation were calculated as indicated. A one-tailed, paired t test was used to determine the statistical significance of comparison as needed. Correlation analysis, t test and Chi square trend test as well as CMS classification were performed using GraphPad Prism version 8.00  and R version 3.6.2.The CRC cell lines listed in We determined the change in cell viability caused by the inhibition of MEK activity using Trametinib (MEKi) in cultured CRC cell lines . Cells wWe suggest that cell viability might not be the best measure for determining drug sensitivity when the effect of a strong growth suppressor, such as a MEK inhibitor, is measured. Thus, we sought to determine the effectiveness of the combination of the MEK inhibitor with SRC inhibition directly on apoptosis. The cell lines  were groWe hypothesized a subgroup of CRC tumors may be sensitive to treatment with the combination of MEK and SRC inhibitors and can be selected utilizing gene expression signatures translated into quantitative biomarker scores. We have shown that scores measuring pathway activation and drug sensitivity can be useful to identify subpopulations of patients whose tumors are sensitive to a drug therapy . RecentlCMS4 tumors are most resistant to chemotherapy, and our hypothesis predicts they are sensitive to the combination of MEK and SRC inhibition. The CMS 1 group is rich in MSI tumors. We hypothesized that cell lines with a high RAS pathway score and a high 13 gene MEKi resistant score wound be sensitive to the combination of Trametinib and Dasatinib. In order to test our hypothesis, we used the gene expression scores of the cell lines in p < 0.0001) for these cell lines shows a distinctive trend for CMS4 cell lines to have higher 18-gene scores and higher 13-gene scores, which fall in the right upper quadrant (RUQ)  a. Notablnt (RUQ) b, nearlyNext, we extended our in vitro and in silico cell line experiments to human tumors. We have demonstrated that the combination of a MEKi and SRCi can effectively kill many CRC cell lines , whereasWe have pioneered the use of large CRC databases with DNA sequencing and global RNA expression to determine tumors that are sensitive to specific drugs and to discover genes that are associated with tumors and tumor progression. For example, we used a validated RNA gene expression signature that reflects cetuximab sensitivity ,21 and dUsing a validated 18-gene RAS signature score that measures response to KRAS pathway activation, we explored a CRC database to search for genes that altered the KRAS pathway signature. We documented that mutations in PTPRS brought about increased RAS pathway activation . Our pubHere we used signature scores to explore drug response to determine predictive biomarkers for drug sensitivity. The signature score represents the numerical value of all the genes altered in the signature. For example, the score of the 18-gene RAS pathway signature is the expression values of all eighteen upregulated genes. This KRAS signature score is indicative of the tumor’s expression of the RAS/MEK/ERK pathway. Dry et al.  developeThe apoptosis data and cell viability data do suggest that a single gene score for 18-gene or 13-gene is not inherently an accurate predictor of sensitivity for these cell lines in regard to cell death. Cell lines such as LIM2099, LS123, SNU-2CB and SNU-2CA fit well with their higher 13-gene scores, representing MEKi resistance. However other cell lines with higher 13-gene scores such as SW480, HCA-7, HCT116, and WiDr all show some amount of sensitivity to Trametinib alone in terms of apoptosis. However, with the 13-gene score we did find as a strong predictive score for MEKi and SCRi combination sensitivity.We believe this to be highly clinically relevant as the MEKi and SRCi combination sensitive cell lines were all shown to be CMS4 cells. It should also be noted that our plotting of tumor and cell line expression data revealed that CMS4 class tumors and cell lines are more inclined to have higher 18-gene and 13-gene scores than the other CMS classes. This finding is clinically relevant as CMS4 tumors have been shown to be chemotherapy-resistant and have a worse prognosis. Moreover, this class is enriched with cancer associated fibroblasts and cancer stem cells. Our data are consistent with a recent report suggesting that MEKi activate WNT signaling by downregulating AXIN and elevating LGR5 levels, resulting in the induction of stem cell plasticity in CRC. Our cell line data being consistent with the tumor data is a positive sign that this analysis of the 18-gene and 13-gene scores might be a useful prognostic tool for clinicians down the line. Looking forward, MEKi and SRCi treatments on xenographs with various CMS classes and different 18-gene and 13-gene scores would reveal if our findings are consistent in living organisms. The most definitive finding, though, is a positive 13-gene score with a positive 18-gene score that enhanced apoptotic sensitivity to the combination of Trametinib and Dasatinib, and this drug combination may be effective in the treatment for CRC. Importantly, the biomarkers presented here can predict the subpopulation of tumors that are sensitive to MEKi combined with SRCi. MEK inhibitors are a new class of drugs designed to treat RAS mutant CRC. Unfortunately, the effectiveness of these drugs has been limited due to de novo resistance or to the development of adaptive resistance. We identified SRC as a potential Achilles heel in treating MEKi-resistant CRC. We postulated that two gene expression signature scores representing MEKi sensitivity and MEKi adaptive resistance, the latter correlating strongly with SRC pathway activation, might be useful in predicting which CRC subpopulations might respond to the combination of a MEKi and an SRCi. We demonstrated that these signature scores might serve as useful biomarkers to predict in vivo drug sensitivity. Moreover, we determined the same using in silico approaches with both hundreds of cell lines and human tumors. These results lay the groundwork to develop clinically useful biomarker scores to select patients who may be sensitive to this new drug combination in future clinical trials. Moreover, this drug combination might be most effective in the difficult to treat CMS4 class of CRC enriched in stem cells.