Methods: Dynamic 11C-UCB-J small-animal PET imaging was performed at clinically relevant disease stages  in the heterozygous knock-in Q175DN mouse model of HD and wild-type littermates (16–18 mice per genotype and time point). Cerebral 11C-UCB-J analyses were performed to assess genotypic differences during presymptomatic (3 mo) and symptomatic (7–16 mo) disease stages. 11C-UCB-J binding in the spinal cord was quantified at 16 mo. 3H-UCB-J autoradiography and SV2A immunofluorescence were performed postmortem in mouse and human brain tissues. Results:11C-UCB-J binding was lower in symptomatic heterozygous mice than in wild-type littermates in parallel with disease progression . Specific 11C-UCB-J binding was detectable in the spinal cord, with symptomatic heterozygous mice displaying a significant reduction (P < 0.0001). 3H-UCB-J autoradiography and SV2A immunofluorescence corroborated the in vivo measurements demonstrating lower SV2A in heterozygous mice (P < 0.05). Finally, preliminary analysis of SV2A in the human brain postmortem suggested lower SV2A in HD gene carriers than in controls without dementia. Conclusion:11C-UCB-J PET detected SV2A deficits during symptomatic disease in heterozygous mice in both the brain and the spinal cord and therefore may be suitable as a novel marker of synaptic integrity widely distributed in the central nervous system. On clinical application, 11C-UCB-J PET imaging may have promise for SV2A measurement in patients with HD during disease progression and after disease-modifying therapeutic strategies.Synaptic dysfunction is a primary mechanism underlying Huntington disease (HD) progression. This study investigated changes in synaptic vesicle glycoprotein 2A (SV2A) density by means of  The animals were housed singly in individually ventilated cages under a 12-h light/dark cycle in a temperature- and humidity-controlled environment with food and water ad libitum and at least 1 wk to acclimatize. All experiments were approved by the Ethical Committee for Animal Testing (ECD 2017–27) at the University of Antwerp  and followed European Committee Guidelines (decree 2010/63/CEE).Male wild-type (WT) mice ; longitudinal at 7, 10, and 16 mo .The Q175DN model displays moderately slow disease progression with the hallmark of mHTT accumulation increasing from 3 to 12 mo  by adapting the previously described procedure (11C-UCB-J (5.4 ± 1.3 MBq) over a 12-s interval (1 mL/min) by use of an automated pump . The activity was injected in a trace dose, keeping the cold mass within 2.0 μg/kg across time points for consistency. Data were acquired in list-mode format. After the small-animal PET scan, a 10-min CT scan  was performed for coregistration and attenuation correction. Detailed information on the scan parameters is reported in Supplemental Table 1 . Published work from our group  or 200 (n = 4) mg/kg 30 min before radioligand delivery. Representative SUV images were generated on the basis of the interval from 10 to 90 min.Small-animal PET/CT imaging was performed on 2 Siemens Inveon PET/CT scanners . Animal preparation was performed as previously described .Acquired PET data were reconstructed into 33 frames of increasing length . For quantitative analysis, all images were reconstructed using a list-mode iterative reconstruction with spatially variant resolution modeling, 8 iterations, and 16 subsets of the 3-dimensional ordered-subset expectation maximization algorithm (11C-UCB-J PET template as previously described (VT) by use of a noninvasive image-derived input function (IDIF); the VT determined by use of the IDIF [VT (IDIF)] was used as a surrogate of the VT estimate, as we recently validated  and K1 maps were generated in PMOD through voxelwise analysis   were euthanized by decapitation while anesthetized, and brains were snap-frozen in 2-metylbuthane at −35°C for 2 min and preserved at −80°C until use. Serial sagittal sections (20 μm thick) were collected starting at 1.80 mm in the lateral bregma . All material was collected from donors for or from whom written informed consent for a brain autopsy and the use of the material and clinical information for research purposes had been obtained by The Netherlands Brain Bank. Ethics permission for the study was obtained from the Committee for Medical Ethics of the University of Antwerp/Antwerp University Hospital (20/13/155).Freshly frozen postmortem superior frontal gyrus tissue was obtained from The Netherlands Brain Bank, Netherlands Institute for Neuroscience  with a postmortem interval of less than 8 h for all cases. Because SV2A has been reported to be decreased in patients with Alzheimer disease (3H-UCB-J autoradiography was performed on mice at 16 mo as well as on postmortem human tissue as previously described (3H-UCB-J plus 1 mM levetiracetam in binding buffer) to validate 3H-UCB-J specificity for SV2A . 3H-UCB-J was synthesized at Pharmaron and had a molar radioactivity of 1,295 MBq/μmol and a radiochemical purity of greater than 99%.3H-UCB-J binding was measured in triplicate on 3 manually drawn slices. Region-specific binding of 3H-UCB-J was measured by converting the mean gray matter values into radioactivity density (Bq/mg) using commercial tritium standards . Next, using 3H-UCB-J molar activity on the day of the experiment, radioactivity density was converted into binding density (fmol/mg) for each region.Regional quantification was performed without knowledge of genotype using Fiji software . SV2A immunofluorescence was determined in mice at 16 mo as well as in postmortem human tissue. Sections were air dried for 5 min and incubated with 4% paraformaldehyde for 15 min for tissue postfixation. Next, slices were rinsed using phosphate-buffered saline (PBS), and nonspecific binding sites were blocked using 20% normal donkey serum in PBS for 1 h. Then, sections were incubated with an anti-SV2A primary antibody  in antibody diluent containing 5% normal donkey serum in PBS overnight at room temperature. On the next day, sections were washed with PBS before being incubated for 1 h with a secondary donkey antirabbit antibody  in antibody diluent containing 5% normal donkey serum in PBS. After washes with PBS, sections were mounted with 4′,6-diamidino-2-phenylindole (Vector Laboratories), and coverslips were added. Images at magnifications of ×20 and ×100 were acquired for quantification with a high-throughput fluorescence microscope (Nikon) with NIS-Elements Software (Nikon).Quantification was performed without knowledge of genotype using Fiji software. Because the white matter was devoid of a specific signal, after conversion into an 8-bit gray scale, an intensity threshold was set to remove the background signal in the white matter  and convert images into binary data. Regions of interest  were manually drawn on each image, and the percentage of surface area after thresholding was measured as the positive area. Quantification was done in triplicate (3 slices) for each region, and the average was used for statistical analysis.11C-UCB-J VT (IDIF) or K1 determined by use of the IDIF as the dependent variable; genotype (WT and heterozygous), time , and the interaction between genotype and time (genotype × time) as fixed effects; and subjects as a random effect. The comparison was performed to evaluate regional temporal and genotypic differences. A 2-way ANOVA (with genotype and region as variables) was applied to investigate the 3-mo data and postmortem analyses. A 1-way ANOVA was used for blocking analysis in the spinal cord, whereas an unpaired t test was used to compare the genotypic differences in spinal cord SV2A PET. A Pearson correlation test was used to determine the relationship between variables. Normality and 2-way ANOVA were performed with GraphPad Prism (v 9.0) statistical software (GraphPad Software), analysis of the linear mixed model was performed with JMP Pro 13 (SAS Institute Inc.), and calculation of the effect size d was performed with G*Power software (http://www.gpower.hhu.de/). P values were corrected for multiple comparisons using the Tukey test. Data are represented as mean ± SD. All tests were 2-tailed, and statistical significance was set at P < 0.05.All data were normally distributed, as assessed using the Shapiro–Wilk test. Longitudinal PET data were analyzed with a linear mixed model by fitting each region separately using VT (IDIF) parametric maps of 11C-UCB-J at 7, 10, and 16 mo displayed a broad cerebral reduction of 11C-UCB-J binding in symptomatic heterozygous mice compared with WT littermates (11C-UCB-J VT (IDIF) values were significantly lower in heterozygous mice than in WT animals at all stages of disease investigated  in parallel with HD progression   , suggesting that the reduced binding did not reflect a mere decrease in cerebral perfusion.Longitudinal mean termates . Accordi11C-UCB-J VT (IDIF) values between WT and presymptomatic heterozygous mice (3 mo) was observed  (VT (IDIF) values at different ages are reported in Supplemental Table 3.No difference in triatum) . VT   .11C-UCB-J PET in the cervical spinal cord of symptomatic heterozygous mice (16 mo). 11C-UCB-J binding was significantly lower in the cervical spinal cord of heterozygous mice than in that of WT littermates (−22.5% ± 3.8% [P < 0.0001]) (r2 = 0.90 [P < 0.0001])  , and the0.0001]) .3H-UCB-J–specific binding was significantly lower in heterozygous mice than in WT littermates , in agreement with the in vivo measurement and as corroborated by SV2A immunostaining  (triatum) .3H-UCB-J binding in postmortem human cortex tissues from a control and an HD gene carrier  and the University of Antwerp . The University of Antwerp also funded the work through partial assistant professor positions for J. Verhaeghe and L. Wyffels and full professor positions for S. Stroobants and S. Staelens. L. Wyffels and S. Stroobants are also supported by Antwerp University Hospital through departmental positions. No other potential conflict of interest relevant to this article was reported.


Compelling evidence suggested that lncRNAs performed vital functions in the development of breast cancer (BC). The study intended to mine the functional roles of LINC01574 in BC and further excavated its underlying regulatory mechanism.  The expression and prognosis of LINC01574 in BC were detected by integrating analysis of data mining, bioinformatics, and RT-qPCR. Then, the effect of LINC01574 knockdown on BC cell growth and metastasis was evaluated in vitro and in vivo. Interactions between miR-6745 and LINC01574 or TTYH3 were revealed by both target prediction and dual luciferase reporter assay.  Our data found that LINC01574 was markedly elevated in BC tissues and cells and was an independent prognostic risk factor for patients with BC. Further functional studies revealed that knockdown of LINC01574 remarkably inhibited the growth and metastasis of BC cells in vitro and in vivo. Mechanistically, LINC01574 competitively binds with miR-6745 to prevent the degradation of TTYH3, thereby promoting the development of BC.  Our results unmasked a novel LINC01574/miR-6745/TTYH3 regulatory axis in BC progression and suggested that LINC01574 might be a promising prognostic indicator and therapeutic target for patients with BC. Breast cancer (BC) is a known refractory tumor with a high coverage rate in women, usually occurring in the glandular epithelium of the breast, accounting for about 31% of all female cancers . The facLong noncoding RNAs (lncRNAs) are a kind of regulatory molecules, which play biological functions in the form of RNA . Increaspotential diagnostic markers and therapeutic target of BC [Mechanistically, current research shows that lncRNAs regulate gene expression through various mechanisms including chromatin modification, transcriptional control, and posttranscriptional processing, thereby affecting the development of breast cancer . Among tet of BC , 24. miRet of BC . Notablyet of BC . HoweverHence, we performed the integrated analysis of data mining, bioinformatics, and RT-qPCR and manifested that the high expression of LINC01574 in BC is an independent prognostic risk factor for patients with BC. Further, our data demonstrated that LINC01574 knockdown inhibited BC growth and lung metastasis by regulating the miR-6745/TTYH3 axis. Overall, this study illuminated the function and underlying mechanism of LINC01574 in BC and provided promising prognostic indicators as well as potential therapeutic targets for patients with BC.P < 0.05. The clinical information of BC patients from TCGA is presented in Table The differential expression of lncRNAs in BC patients was obtained from The Cancer Genome Atlas (TCGA) and screened by the criteria of ∣logFC | >2 and n = 30). Meanwhile, clinical characteristics of the patients  were also collected. Each subject was aware of and has signed written informed consent. This study was approved by Shandong Provincial Maternal and Child Health Care Hospital Ethics Committee (No. 2021-110).BC tumor tissues and paired adjacent normal tissues were collected from patients with BC  and BC cell lines  were purchased from Procell Life Science & Technology Co., Ltd. . After the cells were resuscitated, MCF-7 cells were cultured in specific medium , and other cells were cultured in L15 medium  for use in subsequent experiments.ΔΔCt- method.Total RNA was extracted from tissues and cells using RNA exaction kits . For mRNAs and lncRNAs, the total RNA was reversed into cDNA by the PrimeScript™ RT Reagent Kit with gDNA Eraser ; then, qRT-PCR procedure was performed using SYBR Premix Ex Taq™ Kit (Takara) on ABI 7500 RT-PCR system . GAPDH was used as housekeeping gene. For miRNA expression detection, miRcute miRNA Isolation Kit  was employed to separate target miRNA, and miRcute Plus miRNA First-Strand cDNA Synthesis Kit  was used to reverse miRNA into cDNA. On ABI 7500 qRT-PCR system, the expression of miRNA was determined via miRcute Plus miRNA qPCR Detection Kit . U6 was acted as housekeeping gene for quantification of miRNA. The primer sequences were listed as follows: LINC01574, forward: 5′-TGTCCGCCTTGGGTACAGAAGC-3′, reverse: 5′-GGGATAAACGCCGAATGCCTCT-3′; TTYH3, forward: 5′-AGAACGCTAATTTCCAGAACCC-3′, reverse: 5′-GTGGCGAGGTATTTGGCTCTC-3′; GAPDH, forward: 5′-AAGTATGACAACAGCCTCAAG-3′, reverse: 5′-TCCACGATACCAAAGTTGTC-3′; miR-6745 RT primer, GTCGTATCCAGTGCGTGTCGTGGAGTCGGCAATTGCACTGGATACGACAACCAG; miR-6745, forward: 5′-AAAGCGTGGGTGGAAGAAGGT-3′, reverse: 5′-GTCGTATCCAGTGCGTGTC-3′; U6 primer, AAAATATGGAACGCTTCACGAATTTG; U6, forward: 5′-CTCGCTTCGGCAGCACATATACT-3′, reverse: 5′-ACGCTTCACGAATTTGCGTGTC-3′. The calculation of gene level was realized through 2The distributions of LINC01574 in BC cells were analyzed by subcellular fractionation assay and fluorescence in situ hybridization (FISH). For subcellular fractionation, total RNA was extracted from the cytoplasm and nucleus using cytoplasmic and nuclear RNA Purification Kit , and then, the LINC01574 expression in the cytoplasm and nucleus was quantitatively determined by qRT-RCR. As a loading control, GAPDH and U6 were for the cytoplasm and nucleus, respectively. For lncRNA FISH analysis, BC cells were first fixed in 4% paraformaldehyde  and then hybridized with FAM-labeled LINC01574 probe  overnight at 37°C. After, the cells were stained with 4′, 6-diamidino-2-phenylindole (DAPI)  and the fluorescence images were then obtained under a fluorescence microscope .The short-hairpin negative control (shNC), shLINC01574 , NC mimics, miR-6745 mimics, NC inhibitor, and miR-6745 inhibitor were all constructed by RiboBio Corporation (China). The amplified products of TTYH3 were ligated into pc-DNA3.1 to construct overexpression vector . The transfection was realized through Lipofectamine 2000 . Primer sequences were listed as follows: LINC01574 shRNA#1: CCGGGGTCTGCTGTGCATCTTAACTCGAGTTAAGATGCACAGCAGACCTTTTTGAATT; LINC01574 shRNA#2: CCGGTCCCAGACCTAAGTTTGAAGATCCGAAGATCTTCAAACTTAGGTCTGGGTTTTTGAATT; LINC01574 shRNA#3: CCGGTACCATCACAGAGAGCGATAAACTCGAGAATATCGCTCTCTGTGATGGTTTTTTGAATT; sh-NC: CCGGTCCTAAGGTTAAGTCGCCCTCGCTCGAGCGAGGGCGACTTAACCTTAGGTTTTTGAATT; miR-6745 mimics, F: 5′-CCAGACCUUCUUCCACCCAUU-3′; R: 5′-GGGATAAACGCCGAATGCCTCT-3′; NC mimics, F: 5′-UUGUACUACACAAAAGUACUG-3′; R: 5′-GUACUUUUGUGUAGUACAAUU-3′; miR-6745 inhibitor, AACCAGACCUUCUUCCACCCA; NC inhibitor, CAGUACUUUUGUGUAGUACAA.Nude mouse tumorigenesis model and lung metastasis model were employed to examine the function of LINC01574 on BC cell growth and metastasis in vivo. All animal experiments were approved by the Ethics Committee of Shandong Provincial Maternal and Child Health Care Hospital (No. 2021-111).7) transfected with shNC or shLINC01574 were first added in 200 μL PBS and then subcutaneously injected into the dorsal and ventral parts of 6-week-old BALB/c nude mice. The model mice were divided into two groups: shNC group (n = 5 nude mice) and shLINC01574 group (n = 5 nude mice). The weight and tumor volume of nude mice in each group were measured, and the weight curves and tumor growth curves were then drawn. After 4 weeks, the nude mice were euthanized, and the tumor tissues were weighed and partially subjected to H&E staining and immunohistochemistry (Ki-67).For the tumorigenesis model, MDA-MB-231 cells (1 × 105) transfected with shNC and shLINC01574 were injected into the same type of mice by the tail vein. Similarly, the grouping of model mice was as described above. Nude mice were weighed every 3 days, and the growth curve of nude mice was then drawn. After 4 weeks, the nude mice were euthanized and then, their lung tissues were harvested and weighed. Meanwhile, the overall structure of lung tissues was observed and the number of tumor was calculated. In addition, H&E staining was used to determine tumor lung metastasis.For the nude mouse lung metastasis model, MDA-MB-231 cells . Briefly, the BC cells with the appropriate concentration were inoculated in a 96-well plate. Next, CCK-8 was appended to per well and cultured in the dark for 2 h. The absorbance at 450 nm was detected and represent as cell viability.BC cells at the same density were cultured for 14 consecutive days. After washing, the cells were fixed with 4% formaldehyde at 37°C for 40 min. Then, residual formaldehyde was removed and crystal violet  was added for dyeing for 25 min. The colonies were imaged via under an inverted microscope .First, BC cells were digested and transferred into 12-well plates. After cell fusion reached 80%, a cell scratch was created through a clean EP tube. Thereafter, serum-free medium was added for cell cultivation. Finally, the changes of the wound surface were observed under an inverted microscope , and pictures were recorded at 24 h.5) were prepared. Then, appropriate cell suspension was appended to the upper chamber of a transwell containing 300 serum-free medium coated with Matrigel on the bottom. Meanwhile, 500 μL serum-containing medium was appended into the lower chamber. After conventional culture and fixing, they were stained with 0.1% crystal violet  for 30 min, respectively. Finally, the number of invasive tumor cells was observed and calculated under an inverted microscope .After serum starvation for 12-24 h, the BC cell suspensions  staining and immunohistochemistry. For H&E staining, the tissues were first fixed with formaldehyde (10%) for 24 h and then were successively rinsed with water, dehydrated with alcohol, made transparent with xylene, and embedded in paraffin. After that, the slices were stained with hematoxylin  for 5 min and eosin  for 1 min. Finally, the pathological changes of BC tumor tissues and lung tissues were observed under a microscope. For immunohistochemistry, it was conducted using anti-Ki-67  according to the previous protocol . The staThe total protein was isolated by the RIPA lysis buffer (Solarbio) according to the instruction procedures, which was quantified by bicinchoninic acid (BCA) detection kit . Next, the proteins with suitable concentration were separated by SDS-PAGE gels and then transferred to PVDF membranes . After blocking with 5% blocking solution, the membranes were incubated with anti-E-cadherin , anti-N-cadherin , anti-Vimentin , and anti-TTYH3  overnight at 4°C, followed by secondary antibodies for 50 min. Finally, protein expression was detected by ECL chemiluminescence and then analyzed via Gel image processing system.Briefly, LINC01574-WT and TTYH3-WT were synthesized by cloning the LINC01574 sequence and 3′-UTR of TTYH3 with miR-6745-binding site into pmirGLO reporter vectors . LINC01574-MUT and TTYH3-MUT were designed using point mutations of miR-6745-binding sites. For transfection, BC cells were cotransfected with the above vectors and miR-6745 mimics or NC mimics. Dual-Luciferase Reporter System  was used to test relative luciferase activities.P < 0.05 was the criterion of statistically significant.All the data were analyzed by Statistical Package for Social Sciences 21.0 . Kaplan-Meier survival curves were employed to analyze the survival of screened lncRNAs in BC patients. Univariate and multivariate Cox analyses were used to screen the survival-related lncRNAs. Receiver operating characteristic (ROC) analysis was performed to estimate the accuracy of LINC01574 in distinguishing BC. One-way ANOVA followed by Tukey's test was employed to evaluate the differences between the groups. P < 0.05, a total of 398 lncRNAs were evidently different between BC tumor tissues and normal breast tissues, of which 277 lncRNAs were significantly elevated and 121 lncRNAs were sharply decreased  and negative control (shNC) and transfected into the MDA-MB-231 or MDA-MB-468, respectively. In Next, the effects of LINC01574 knockdown on the tumor metastasis were assessed in vitro and in vivo. In wound healing assay, BC cells exhibited a wider relative wound healing distance in the shLINC01574 group compared with the shNC group . In tranSince LINC01574 was mainly positioned in the cytoplasm, we speculated that LINC01574 may act as a miRNA sponge. To test this hypothesis, the potential targets of LINC01574 were predicted by miRBD, and three miRNAs with scores greater than 70 were screened, including miR-6745, miR-363-5p, and miR-4291. The results of qRT-PCR revealed that overexpression of LINC01574 evidently decreased the expression of miR-6745 in BC cells, and silencing LINC01574 could significantly increase the expression of miR-6745. However, overexpression or silencing of LINC01574 did not affect miR-363-5p and miR-4291 . FurtherTo further excavate the effect of miR-6745 on the behavior of BC cells, we transfected miR-6745 mimics, miR-6745 inhibitor, and the corresponding NC mimics and NC inhibitor into BC cells, respectively. The CCK-8 evidence revealed that overexpressed miR-6745 evidently restrained the proliferation rate of BC cells, while silencing miR-6745 significantly increased the proliferation rate of BC cells . For theTo elaborate the regulatory mechanism of miR-6745 in BC, we performed bioinformatics analysis using the miRWalk/TargetScan7/miRDB algorithm and predicted 10 mRNAs with potential binding sites for miRNAs, including TTYH3 . The anaWe have further confirmed whether LINC01574 regulated TTYH3 expression by sponging miR-6745; rescue experiments were performed in vitro. BC cells were transfected with shLINC01574 alone or cotransfected with miR-6745 inhibitor. As shown in Figures To determine whether LINC01574 affected BC cell phenotype via regulating the miR-6745/TTYH3 axis, we examined the proliferation, migration, and invasion of BC cells transfected with shLINC01574 alone or cotransfected with miR-6745 inhibitor. The results of CCK-8 and colony formation assay showed that LINC01574 knockdown significantly suppressed the proliferation rate and colony formation of BC cells, while the addition of miR-6745 inhibitor partially attenuated the inhibition effect of shLINC01574 on the proliferation rate and colony formation of BC cells Figures . ConsistNext, BC cells were transfected with shLINC01574 alone or cotransfected with TTYH3 overexpression vector (TTYH3), and the expression quantification and cell function analysis were also performed. In Figures It is well known that although lncRNAs lack the ability to encode proteins, they can also regulate gene expression and participate in disease progression in various ways . RecentlThe ceRNA networks reveal the new dimensions of key molecular roles of lncRNAs. Existing data suggested that lncRNAs acted as ceRNAs and counteracted the repressive effects of miRNAs on downstream target gene expression and biological function , 33. FormiRNAs degrade mRNAs or block their translation by base pairing with target gene mRNA-directed silencing complexes, and available data showed that more than 50% of RNA molecules were controlled by miRNAs , 36. ThuIn a nutshell, LINC01574 was markedly increased in BC, which was identified as an independent prognostic risk factor of patients with BC. Further, silencing LINC01574 evidently inhibited the growth and metastasis of BC cells by in vitro and vivo experiments. Mechanistically, we demonstrated that silencing LINC01574 relieved the sponge adsorption of miR-6745, promoted the targeted binding of miR-6745 to TTYH3, and inhibited its expression, thereby restraining the proliferation, migration, and invasion ability of BC cells.


Aim: Resistance to chemotherapy is a major limiting factor that hamper the effectiveness of anticancer therapies. Doxorubicin is an antineoplastic agent used in the treatment of a wide range of cancers. However, it presents several limitations such as dose-dependent cardiotoxicity, lack of selectivity for tumor cells, and induced cell resistance. Nanotechnology represents a promising strategy to avoid these drawbacks. In this work, new albumin-based nanoparticles were formulated for the intracellular delivery of doxorubicin with the aim to overcome cancer drug resistance.Methods: Glycol chitosan-coated and uncoated albumin nanoparticles were prepared with a tuned coacervation method. The nanoformulations were in vitro characterized evaluating the physicochemical parameters, morphology, and in vitro release kinetics. Biological assays were performed on A2780res and EMT6 cells from human ovarian carcinoma and mouse mammary cell lines resistant for doxorubicin, respectively.Results: Cell viability assays showed that nanoparticles have higher cytotoxicity than the free drug. Moreover, at low concentrations, both doxorubicin-loaded nanoparticles inhibited the cell colony formation in a greater extent than drug solution. In addition, the cell uptake of the different formulations was investigated by confocal microscopy and by the HPLC determination of doxorubicin intracellular accumulation. The nanoparticles were rapidly internalized in greater extent compared to the free drug.Conclusion: Based on these results, doxorubicin-loaded albumin nanoparticles might represent a novel platform to overcome the mechanism of drug resistance in cancer cell lines and improve the drug efficacy. It is well known that repeated chemotherapy administration in patients leads to the development of drug chemoresistance. Tumor cells can exploit several mechanisms to confer resistance to one or more chemotherapeutic agents. The prevalent mechanisms comprise hampered drug uptake through transporters, altered cytotoxicity against cancer cells, and increased drug efflux via active pumps, but other factors can be involved. The resistance can involve single or multiple  drugs. In the former, the tumor can be effectively treated with alternative drugs, whereas in the latter, tumor cells become refractory to anticancer drug treatment, leading to poor clinical outcomes. In addition, the mechanisms of anticancer drug resistance can be either intrinsic due to the innate resistance to numerous anticancer drugs or acquired when mutations occur during tumor cell proliferation. Moreover, direct or indirect anticancer drug inactivation can occur.Resistance to chemotherapy is one of the main key factors that reduces the effectiveness of cancer treatment, with over 90% therapy failure rate in metastatic tumors,6.In this complex scenario, nanomedicine has been studied as a promising approach to overcome the several mechanisms of chemoresistance. Interestingly, nanodelivery systems, with their small sizes and peculiar physicochemical characteristics, have the potential to reverse MDR. They can affect pharmacokinetics and biodistribution profiles, favoring the accumulation in tumors either by passive or active targeting and can enhance the intracellular delivery of conventional chemotherapeutics-9. Among organic biomaterials, albumin has been considered as one of the most useful and versatile carrier proteins in the pharmaceutical field thanks to its biocompatible, non-immunogenic, and non-toxic characteristics. Albumin-based NPs represent an ideal nanocarrier due to low cost, high availability, easy purification, and high drug-loading capability. In addition, albumin-based NPs can be easily functionalized due to the presence of charged surface groups. A large number of albumin formulations has been proposed for anticancer drug delivery,11. Interestingly, in several recent studies doxorubicin-loaded albumin NPs have been employed as an efficient strategy to overcome the drug chemoresistance-15.A number of nanocarriers, such as polymer or lipid nanoparticles (NPs), liposomes, micelles, and inorganic NPs have been proposed as a strategy to render more efficient the delivery of conventional chemotherapeutics in order to prevent the MDR onset and overcome the chemoresistance limitations associated with conventional chemotherapeuticsin vitro characterized. The doxorubicin antiproliferative effect will be investigated in resistant cancer cell lines.The aim of this work was the development of new albumin nanoformulations in order to allow a more efficient intracellular delivery of doxorubicin within the tumor context and overcome cancer multidrug-resistance. Uncoated and glycolchitosan coated NPs were designed to promote cell internalization. The two new nanoformulations will be obtained with a purposely-tuned preparation method and The substances and laboratory reagents used in this study were from Sigma-Aldrich , unless otherwise specified. Albumin from bovine serum (PDB ID: 3v03) was used. Bovine serum albumin (BSA) is a single polypeptide chain consisting of about 583 amino acid residues (sequence: DTHKSEIAHRFKDLGEEHFKGLVLIAFSQYLQQCPFDEHVKLVNELTEFAKTCVADESHAGCEKSLHTLFGDELCKVASLRETYGDMADCCEKQEPERNECFLSHKDDSPDLPKLKPDPNTLCDEFKADEKKFWGKYLYEIARRHPYFYAPELLYYANKYNGVFQECCQAEDKGACLLPKIETMREKVLTSSARQRLRCASIQKFGERALKAWSVARLSQKFPKAEFVEVTKLVTDLTKVHKECCHGDLLECADDRADLAKYICDNQDTISSKLKECCDKPLLEKSHCIAEVEKDAIPENLPPLTADFAEDKDVCKNYQEAKDAFLGSFLYEYSRRHPEYAVSVLLRLAKEYEATLEECCAKDDPHACYSTVFDKLKHLVDEPQNLIKQNCDQFEKLGEYGFQNALIVRYTRKVPQVSTPTLVEVSRSLGKVGTRCCTKPESERMPCTEDYLSLILNRLCVLHEKTPVSEKVTKCCTESLVNRRPCFSALTPDETYVPKAFDEKLFTFHADICTLPDTEKQIKKQTALVELLKHKPKATEEQLKTVMENFVAFVDKCCAADDKEACFAVEGPKLVVSTQTALA) without carbohydrates. At pH 5-7 it contains 17 intrachain disulfide bridges and 1 sulfhydryl group. Ultrapure water was obtained using a 1-800 Millipore system . The tubular semi-permeable cellulose membrane was from Carl Roth . Cell culture reagents were purchased from Gibco/Invitrogen  except where otherwise indicated. All reagents were of analytical grade.In order to obtain stable and reproducible albumin NPs, a preformulation study was carried out. Different BSA concentrations were tested with different amounts of the nonionic surfactant Polysorbate 80 to reach the optimal conditions to load doxorubicin hydrochloride (DOX).Once optimized the process, blank BSA NPs were prepared based on a tuned coacervation method. Briefly, 25 mg of BSA were dissolved in 1 mL of 50 mmol/L TRIS buffer at pH 7.4 containing Polysorbate 80 (2% w/v) as surfactant. Then, ethanol was added dropwise under sonication in a 220-230 V Bransonic® 3510 Ultrasonic Cleaner  at 60 °C for 1 h. When the nanosuspension was obtained, the ethanol was evaporated under nitrogen flux. Moreover, glycol chitosan-coated nanoparticles (blank GC-NPs) were obtained by adding dropwise under constant stirring an aqueous solution of glycol chitosan (2.7% w/v) to the preformed blank BSA-NPs.Albumin NPs loaded with doxorubicin were obtained by a purposely tuned coacervation method. Briefly, 25 mg of BSA were dissolved in 1 mL of 50 mmol/L TRIS buffer at pH 7.4 containing Polysorbate 80 (2% w/v). DOX was dissolved in the BSA solution with a concentration of 3 mg/mL Then, ethanol was added dropwise under sonication in a 220-230 V Bransonic® 3510 Ultrasonic Cleaner  at 60 °C for 1 h. When the nanosuspension was obtained, the ethanol was evaporated under nitrogen flux. In addition, glycol chitosan-coated nanoparticles (GC DOX-NPs) were obtained by adding dropwise under constant stirring an aqueous of glycol chitosan (2.7% w/v) to the preformed DOX-loaded nanoparticles (DOX-NPs).The average diameter, dispersity and zeta potential were measured by photocorrelation spectroscopy using a particle size analyser at a scattering angle of 90° and a temperature of 25 °C. NP suspensions were diluted in deionized filtered water before measurement. For zeta potential determination, already diluted NP formulations were placed in the electrophoretic cell, where an electric field of approximately 15 V/cm was applied. The morphology of formulations was evaluated by Transmission Electron Microscopy (TEM), using a Philips CM10  instrument. Plain NPs and DOX-NP nanosuspensions were sprayed on Formvar-coated copper grid and air-dried before observation.-1-650-1.Thermal analysis was carried out using a DSC/7 differential scanning calorimeter  equipped with a TAC 7/DX instrument controller and the Pyris program. A heating rate of 10 °C per min was used in the 30-230 °C temperature range. Standard aluminium sample pans for solids (Perkin-Elmer) were used and about 20 mg of the freeze-dried NPs weighed. Doxorubicin and BSA-NPs were analyzed for comparison purposes. Attenuated Total Reflection (ATR)-Fourier transformed infrared (FTIR) spectra of the DOX-NPs, GC DOX-NPs, free DOX, and blank BSA-NPs were obtained using a Perkin Elmer Spectrum 100 FT-IR in the region 4000The stability of the nanoformulations stored at 4 °C was evaluated overtime, checking the physicochemical parameters and the doxorubicin concentration of the NPs.exc = 480 nm and λem = 560 nm. To calculate the drug concentration, a linear calibration curve was set up with a concentration range of 0.25-2.5 μg/mL with a regression coefficient of 0.999.HPLC system consisted of a pump  equipped with a fluorescence detector  and analyses were performed using an Agilent TC C18 column . A degassed solution containing 65% v/v phosphate buffer (pH = 1.4), 25% v/v acetonitrile, and 10% distilled water was used as mobile phase. The flow rate was 1 mL/min and the fluorimetric detector (Shimadzu) was set at λThe encapsulation efficiency of DOX-NPs and GC DOX-NPs was determined with a centrifugal filter system. A part of the two nanoformulations was placed in a centrifugal filter device (Amicon® Ultra-0.5) and centrifuged for 15 min at 15,000 rpm with a Beckman Coulter 64R Centrifuge. Then, the filtered solution containing free DOX was analyzed at the HPLC as previously described. The encapsulation efficiency of DOX was quantified by Encapsulation Efficiency (EE):The loading capacity was determined on freeze-dried NP samples. Briefly, a weighted amount of freeze-dried DOX-NPs and GC DOX-NPs was suspended in 10 mL of filtered water. After sonication and centrifugation, the supernatant was diluted with mobile phase and analyzed by HPLC. The loading capacity was calculated by Loading Capacity (LC):in vitro release of DOX from the NPs was carried out with a multi-compartment rotating cell. DOX-NPs, GC DOX-NPs, and DOX solution as control were used as donors against a receiving phase consisting of phosphate buffered saline pH 7.4. The chambers were separated with a semi-permeable cellulose membrane (cut-off 14kDa). Samples were drawn at fixed time intervals and the same volume was replaced with fresh medium. Then, DOX concentration was determined using HPLC, as previously described.The In vitro release profiles of doxorubicin from DOX-loaded albumin-nanoformulations were analyzed to assess the mechanism of drug release using four kinetic models: zero-order kinetic model, first-order kinetic model, simplified Higuchi model, and Korsmeyer-Peppas model. Briefly, for each model, a graph was constructed using Microsoft Excel from which the rate constant and correlation values were obtained by applying a linear regression fit. The zero-order kinetic model was obtained by plotting cumulative percent drug release versus time. The first-order kinetic model was analyzed by plotting log cumulative percent of drug remaining versus time. The Higuchi model was evaluated by plotting cumulative percent drug release versus square root of time, while the Korsmeyer-Peppas model was analyzed by plotting log cumulative percent drug release versus log time.2 atmosphere.To evaluate the contribution of doxorubicin-loaded albumin-based NPs, two different cell lines resistant to doxorubicin were employed. A2780res human ovarian carcinoma cells, and EMT6/AR10 murine cancer mammary cells were incubated at 37 °C with 5% COEMT6/AR10 cells were cultured in MEM (EBSS) medium, whereas A2780res cells were cultured in RPMI. All the media were supplemented with 10% v/v Fetal Bovine Serum (FBS), 2 mmol/L Glutamine, and antibiotics. To maintain the resistance, A2780 ADR and MCF7 ADR were cultured in media containing 100 nmol/L doxorubicin HCl twice a week.3 cells/well) and incubated with the albumin-based nanoformulations at different DOX concentration. After 24 h and 48 h of incubation, viable cells were evaluated by 3--2,5-diphenyl-2H-tetrazolium bromide (MTT) inner salt reagent at 570 nm. The readouts from treated cells were expressed as percentage versus control measured on untreated cells.The cytotoxic effect of DOX-NPs and GC DOX-NPs, and of their blank counterparts was evaluated. The EMT6/AR10 and A2780res cells were seeded in a 96-well plate  and incubated overnight at 37 °C in a 5% CO2 atmosphere. Then, the cells were incubated with DOX-NPs, GC DOX-NPs, and DOX solution at a final concentration of 5 µg/mL for 1 h. After that, the cells were washed with PBS, fixed with 4% Paraformaldehyde (PAF) for 15 min at room temperature, and stained with 4’,6-diamidine-2-phenylindole (DAPI). Finally, coverslips were mounted. The samples were analyzed with a TCS SP2 AOBS confocal microscope  equipped with 63X/1.40 HCX Plan-Apochromat oil-immersion objective.A2780res cells were seeded onto Corning® cover glasses (Sigma-Aldrich) in a 24-well plate (4 × 103 cells/well) and incubated with the albumin nanoformulations at different DOX concentration  for 2 h and 4 h at 37 °C. Following the incubation, the cells were washed three times with phosphate buffered saline (PBS 1X) and lysed with a saturated solution of ammonium sulphate. After centrifugation  cell lysates were frozen and stored at -80 °C. Immediately prior to the analysis, cell lysates were thawed and centrifuged . The supernatants were analyzed by HPLC after suitable dilution with the mobile phase, as described above, to determine the amount of doxorubicin inside the cells. The experiment was performed in triplicate.The cellular uptake of DOX-loaded albumin NPs was evaluated measuring the intracellular doxorubicin concentration by HPLC analysis. The A2780res cells were seeded in a 96-well plate .t-tests were performed to analyze the statistical significance between two groups.Data are presented as mean ± standard deviation (SD). Statistical analyses were performed with GraphPad Prism program . Two-tailed Student’s Albumin NPs were purposely designed and developed for the cellular delivery of doxorubicin. A preparation process was tuned to obtain stable and reproducible formulations. The schematic representation of the preparation method is reported in The physicochemical parameters of BSA-NPs are reported in All the formulations presented an average diameter between 360 and 385 nm. The uncoated formulations presented a negative surface charge due to the contribution of the carboxyl groups of albumin. On the other hand, the presence of the coating of glycol chitosan did not modify the sizes of the NPs but affected their surface charges. Indeed, when coated with glycol chitosan, the zeta potential of the nanoformulations turned to positive because of the amine groups of the polymer. The increased zeta potential confirmed the interaction between the albumin and the polymer. All the formulations presented a physiological pH in the range 6.8-7.4.The spherical morphology of albumin nanoformulations and their nanometric sizes were confirmed by TEM analysis. All the NPs presented a reproducible spherical shape. As an example, the morphological appearance of GC DOX-NPs is presented in The interaction of the drug with albumin was investigated by Differential Scanning Calorimetry (DSC) and ATR-FTIR analyses. DSC thermograms and ATR-FTIR spectra of free DOX, DOX-NPs, GC DOX-NPs, and blank BSA-NPs were shown in The disappearance of the endothermic peak related to doxorubicin melting point (at about 220 °C) in the thermogram of DOX-NPs confirmed the interaction between doxorubicin and albumin. Moreover, FTIR spectra confirmed the drug incorporation in the albumin NPs.Both albumin NPs were stable up to 1 year, stored at 4 °C. Indeed, no changes in the physicochemical characteristics (size and surface charge) and no aggregation phenomena were observed. The doxorubicin content in the albumin NPs did not decreased overtime.in vitro release kinetics profile of doxorubicin from DOX-NP and GC DOX-NP formulations is showed in The For both formulations, the release of the drug was slow and prolonged over time, compared to free solution diffusion. After 24 h, only 30% of the encapsulated drug was released from the DOX-NPs and GC DOX-NPs. On the other hand, the solution of DOX hydrochloride passed completely into the receiving phase. No initial burst effect was observed indicating that the drug is not absorbed on the nanoparticle surface but incorporated. The coating with glycol chitosan did not affect the doxorubicin release profile of GC DOX-NPs.in vitro release experimental results best fitted with the simplified zero-order kinetic for both DOX-NPs and GC DOX-NPs, indicating that the doxorubicin is released from the NPs by passive diffusion through the albumin matrix. Indeed, zero-order kinetics fits with nanostructures, the release of which is not due to the erosion of the matrix. In fact, albumin NPs provide constant and slow release kinetics of the encapsulated drugs.The sustained release may be primarily due to the slow diffusion of doxorubicin across the albumin matrix of NPs. This could represent a favorable condition to minimize the systemic adverse side effects of doxorubicin and to enhance the efficiency of NPs at the target site. In addition, the mechanism of drug release was also analyzed using four mathematical kinetics models corresponding to: zero-order kinetics, first-order kinetics, simplified Higuchi, and Korsmeyer-Peppas models. For each kinetics model, a graph was constructed in Microsoft Excel from which the rate constant and correlation values were obtained by applying a linear regression fit in vitro on A2780res and EMT6/AR10 cell lines, with a Doxorubicin resistant profile. After 24 h and 48 h of incubation, DOX-NPs and GC DOX-NPs inhibited A2780res and EMT6/AR10 viability to a greater extent than free DOX in vitro than free DOX solution because they acted as “trojan horses,” reducing the contribution of the P-gp in pumping out the drug from the cells. The NPs remain inside the cells, where they release the drug for a prolonged time.The cytotoxicity of DOX solution, DOX-NPs, GC DOX-NPs, and their blank counterparts was evaluated 50 values of free DOX, DOX-NP, and GC DOX-NP in A2780res were calculated Moreover, ICin vitro. The cells were incubated for 3 h and 20 h with the different formulations, and then cultured for additionally 10 days. After that time, the colony was evaluated. As shown in The anti-proliferative effect of the albumin doxorubicin-loaded NPs was tested In order to study the ability of our formulations to facilitate the doxorubicin entry within the cells, we performed a confocal analysis on A2780res cells treated with DOX-NPs, GC DOX-NPs, and DOX solution. Confocal analysis was performed by exploiting DOX fluorescence, whereas DAPI was used to visualize the nuclei. The images reported in Moreover, the cellular uptake of DOX-loaded NPs was investigated to determine the intracellular DOX concentration in A2780res cells by HPLC analysis A higher intracellular accumulation of the drug in cells treated with both DOX-NPs was observed in comparison with free drug-treated cells. In particular, the highest intracellular DOX concentrations were found after incubation with GC DOX-NPs. These results are in agreement with the confocal analyses.Multidrug resistance (MDR) represents a big barrier for the effectiveness of anticancer drugs, and it can be attributed to the over-expression of ATP-binding cassette (ABC) transporter, which causes drug efflux.. Indeed, nanomedicine can contribute to escape the mechanism of efflux pumps , which are overexpressed in MDR cancer cells. The NPs avoid being pumped out from the cells, thus increasing the amount of the carried chemotherapeutic and its efficacy.The nanoparticle technology can play an important role in counteracting the development of drug resistance in different cancer diseases showed that the physicochemical characteristics of NPs can affect the membrane transporter function.Interestingly, Salomon and Ehrhardt. DOX can confer drug resistance in a mechanism that involves an altered or increased expression of ABC transporters. Through this mechanism, cancer cells can easily efflux DOX through up-regulating efflux transporters, with a consequent decrease in intracellular drug concentration and effectiveness. Thus, to enhance the chemotherapeutic outcome, high drug doses would be necessary causing severe systemic toxicity. The nanotechnological approach may be a promising solution. In fact, a liposomal doxorubicin nanoformulation (Doxil®) is on the market but much research is still focused on the design of nanocarriers for the delivery of doxorubicin to tumor cells-27.It is worth noting that doxorubicin-based chemotherapy can fail either for inherent or acquired resistance,29. In this study, BSA-based NPs were formulated for doxorubicin intracellular delivery aimed at overcoming the drug resistance.Interestingly, the co-delivery of doxorubicin combined with modulators of drug resistance within one nanocarrier has shown to be effective in overcoming MDR mediated effluxAlbumin was selected for the NP manufacturing because it is a component of the human blood, a notable carrier protein with multiple cellular receptors and binding sites, and a protein capable of transporting different endogenous and exogenous molecules. Albumin from bovine serum, cost effective compared to human albumin, was used in this study which consisted of a formulation design of new albumin NPs. However, preliminary experiments confirmed that the purposely tuned preparation method described in this paper can also be adopted for the preparation of human albumin NPs.,31.Worthy of note is the FDA approval of Abraxane® in 2005, which is a paclitaxel albumin-bound nanoparticle formulation for the treatment of metastatic breast cancer, showing a significantly longer time prior to tumor progression with respect to the free drug. However, in the literature a few studies demonstrated that nanoparticle albumin-bound (nab)-paclitaxel can induce drug resistance by up-regulating P-gp-15. In the literature different techniques for the albumin NP fabrication were explored such as desolvation, chemical crosslinking, emulsification, thermal gelation, spray drying, and self-assembly. Here, a coacervation method was purposely tuned to obtain stable and reproducible albumin NPs. The procedure is cost-effective and up-scaled manufacturing can be achieved.In this contest, albumin nanosystems have been previously proposed for the delivery of doxorubicin to overcome multidrug resistanceTwo types of albumin-based formulations were developed, such as uncoated and glycolchitosan coated albumin-NPs. The design criteria took into account that size, shape, and surface charge are the key parameters to affect NP biodistribution and cell internalization. The glycolchitosan coating was added to reduce the negative surface charge of albumin NPs in order to favor their interaction with the cellular membrane. In fact, positively charged NPs are taken up more easily than negatively charged ones. Nanocarriers with positive zeta potential bind more strongly to the cell membrane, facilitating the NP internalization. Glycolchitosan coating provides a positive surface charge to albumin nanoformulations. The strong zeta potential variation of NPs confirmed the interaction between the polymer chains and albumin..Interestingly, the modification of NP surface with hydrophilic polysaccharides is an alternative to PEGilation to modify pharmacokinetics parameters of NPsin vivo efficacy in animal models. Indeed, a peculiar characteristic of nanoparticulate systems is to enable a dose reduction, due the different pharmacokinetics profile compared to the free drug, as demonstrated also for Doxil®.The two types of albumin NPs were able to load doxorubicin in a good extent, reaching a loading capacity of 4.3% and 6.4 % for GC DOX-NPs and DOX-NPs, respectively. The encapsulation efficiency was above the 85% for both nanoformulations. These percentages might be sufficient for in vitro release study showed that the drug is released in a constant and sustained manner over time.The In addition, the coating did not affect the doxorubicin release profile from NPs. This result suggests that the polymer covers the albumin NP as a thin layer on the surface.et al. evaluated the anticancer activity of doxorubicin incorporated in PLA- and PLGA-based NPs on P-gp expressing cancer cells showing that the investigated NPs did not bypass transporter-mediated drug efflux. Confocal microscopy analysis underlined that our albumin NPs are readily internalized in tumor cells with nuclear localization. On the contrary, doxorubicin in solution is mainly localized at cell surfaces. Through confocal analysis, we clearly demonstrated that both the formulations were more efficient for drug internalization compared with free doxorubicin. The internalization capability of DOX-NPs and GC DOX-NPs was confirmed by the cell accumulation study. The intracellular doxorubicin concentration after the incubation with albumin NPs was greater than with doxorubicin solution. For example, in cells treated with 10 µmol/L dose an increase in DOX intracellular concentration of 5.5 folds compared to free DOX was reached. The highest DOX accumulation was observed for CG DOX-NPs, confirming the role of glycol chitosan coating in promoting cell internalization. Together our findings indicate that these albumin NPs might be effective to overcome MDR.Cell viability assay showed that albumin NPs were able to enhance doxorubicin antiproliferative effect in resistant cancer cells in comparison to doxorubicin solution. Notably, a significant decrease of IC50 values was observed for coated and uncoated albumin NPs. These results might suggest that albumin NPs are able to be internalized and might play a role in decreasing P-gp efflux pump activity. This hypothesized behavior of albumin NPs can be a promising advantage as not all nanoformulations are able to overcome efflux-mediated resistance. Recently, Pieper in vitro viability and proliferation of cell lines resistant to doxorubicin, thus demonstrating the importance of the nanoformulation to counteract drug resistance. Taken together, these in vitro results showed that GC DOX-NPs and DOX-NPs may represent a novel platform to affect the mechanism of drug resistance in cancer cell lines, improving the efficacy of the chemotherapy.In conclusion, it is worth noting that resistance to chemotherapy limits the effectiveness of anticancer drug treatment. Overcoming this limitation can the change the survival of cancer. In this work, a reproducible coacervation method to obtain albumin-NPs with narrow size distribution was fine-tuned. Notably, glycol chitosan-coated and uncoated albumin-based doxorubicin-loaded NPs were obtained. These albumin NPs significantly inhibited the


The QSP model was sufficient to describe the variability inherent in the anti-tumor responses post anti-PD-(L)1 treatments. Local sensitivity analysis identified tumor cell proliferation rate, PD-1 expression on CD8+ T cells, PD-L1 expression on tumor cells, and the binding affinity of PD-1:PD-L1 as strong influencers of tumor growth. It also suggested that treatment-mediated tumor growth inhibition is sensitive to T cell properties including the CD8+ T cell proliferation half-life, CD8+ T cell half-life, cytotoxic T-lymphocyte (CTL)-mediated tumor cell killing rate, and maximum rate of CD8+ T cell influx into the tumor microenvironment. Each of these parameters alone could not predict anti-PD-(L)1 treatment response but they could shift an individual mouse’s treatment response when perturbed. The presented preclinical QSP modeling framework provides a path to incorporate potential sources of response variability in human translation modeling of anti-PD-(L)1.While anti-PD-1 and anti-PD-L1 [anti-PD-(L)1] monotherapies are effective treatments for many types of cancer, high variability in patient responses is observed in clinical trials. Understanding the sources of response variability can help prospectively identify potential responsive patient populations. Preclinical data may offer insights to this point and, in combination with modeling, may be predictive of sources of variability and their impact on efficacy. Herein, a quantitative systems pharmacology (QSP) model of anti-PD-(L)1 was developed to account for the known pharmacokinetic properties of anti-PD-(L)1 antibodies, their impact on CD8 Immune checkpoint inhibitors have revolutionized cancer therapy . The firHigh clinical variability in responses can make data interpretation challenging, especially in Phase 1 trials, where multiple dose levels are investigated using a relatively small number of patients. This hinders the ability to make informed decisions for subsequent trial designs. Rational design, such as patient inclusion and exclusion criteria and dosing regimen/dose selection, is necessary to effectively evaluate PK, safety, and efficacy of therapeutics under investigation. Preclinical data can help predict clinical values, such as PK and efficacious dose levels . The datin vivo tumor growth 1 therapies as an example. Previous models of anti-PD-(L)1 focused on using clinical data to identify potential biomarkers for pembrolizumab and ipilimumab in melanoma  and for All mouse studies were approved by the Pfizer Institutional Animal Care and Use Committee according to established guidelines.+ inactive T cells [CD8+CD4-TCF7+ , 0.5% methylcellulose (control), water (control), anti-mouse-PD-1 antibody , or anti-mouse PD-L1 antibody . Tumor sizes were measured using calipers. Tumor volumes were calculated as (width x width x length)/2. Every study had a control group and 1-2 anti-PD-(L)1 treatment groups. Details of the five studies are shown in A QSP model was originally designed to investigate sources of variability in response to anti-CTLA4  and the + T cells, and other PD-1+ T cells were modeled. Tumor cells are damaged through interactions with CTLs or through anti-PD-L1 ADCC. Damaged tumor cells undergo apoptosis at a constant rate. Other PD-1+ T cells serve as an alternate binding site for PD-L1 on tumor cells. PD-1+CD8+ T cells include CD8+ T cells in the inactive, active, proliferating, CTL, and exhausted CTL stages depicted in + T cells are synthesized as inactive cells  . A total1...10) . Each CT (ED0) . All CTL+ T cells  causing inactive PD-1+CD8+ T cells to enter the tumor compartment at the rate of + T cells reach a steady state concentration during control simulations. The maximum influx and activation rates are denoted The binding of PD-L1 expressed on tumor cells to PD-1 expressed on inactive PD-1+CD8partment –3. In thks,IVt . The infEquations describing the subsequent T cell stages follow the set up in  and can Parameters describing receptor expression, antibody half-lives, binding constants, and plasma concentrations were sourced from relevant literature . The unk+ T cell activation rate, T cell proliferation half-life, CD8+ T cell half-life, CTL half-life, CTL-mediated tumor cell killing rate, and baseline influx rate of inactive CD8+ T cells were constrained using average concentrations of CD8+ T cells from untreated CT26 tumors in Balb/c mice that were derived from flow cytometry data. Full details can be found in Second, T cell kinetic parameters including maximum CD8in vivo mouse studies. Non-linear mixed effects (NLME) model fitting and local sensitivity analysis were performed iteratively to identify the key parameters to capture inter-individual variability. Local sensitivity analysis was centered on the population parameter estimates. In each iteration, sensitive parameters were included as random effects in the NLME model. Based on the results of fitting the inter-individual variability, study site ID of the five in vivo mouse studies was identified as a significant covariate. An exponential covariate model was included to capture study site level effects. Goodness of fit was evaluated using Akaike information criteria, Bayesian information criteria and visual inspection of individual fits. The control and treatment groups of all the studies were fit simultaneously.Third, the full QSP model was fit to tumor volume profiles from five Model fitting was performed in Monolix 2020R1 . Local sensitivity analysis was performed in MATLAB version R2020b (MathWorks software). Plots were generated in R version 4.0.3.Local sensitivity analysis was performed using either the population fit or a chosen fitted animal as the starting parameter set. Parameters were perturbed ± 30% one at a time. The percent change in the area under the tumor volume curve (AUC) was calculated for control, anti-PD-1, and anti-PD-L1 treatment simulations. Three doses of 10 mg/kg given every 3 days (Q3D) were simulated with the first dose administered on day 7. The AUC was chosen to summarize the changes to tumor volume over the entire 30 days.Local sensitivity analysis was performed in MATLAB version R2020b (MathWorks software). Plots were generated in R version 4.0.3.+ T cell influx rate, CD8+ T cell half-life, CTL half-life, maximum CD8+ T cell activation rate, CD8+ T cell proliferation half-life, and CTL-mediated tumor cell killing rate. These parameters were constrained using the average concentrations of CD8+ T cells from untreated CT26 tumors in Balb/c mice (+ T cell populations were defined by cell surface markers (The QSP model describes the pharmacokinetics of anti-PD-(L)1, interactions between the antibodies and their molecular targets, tumor cell dynamics, intra-tumoral T cell dynamics, and CTL-mediated tumor cell killing . Five stb/c mice  that wer markers .+ T cell activation rate limits the maximum amount of change in the CTL concentration post antibody treatments. Specifically, simulations indicate that anti-PD-1 treatments administered Q3Dx3 induced a maximum increase of 2.8% in CTL concentration and 0.017% reduction in tumor volume 10 days after treatment initiation compared to control simulations, whereas anti-PD-L1 treatment administered Q3Dx3 induced a maximum increase of 56% in CTL concentration and 52% reduction in tumor volume 1 treatments.The maximum CD8r volume . Differe+ T cell influx was incorporated, the increase in CTL concentration due to anti-PD-(L)1 treatments significantly increased , as did the reduction in tumor volume  (When a PD-1:PD-L1 interaction dependent CD8i-PD-L1) .+ T cells in CT26 + tumor microenvironment. Model simulations indicate that dependence of the influx of CD8+ T cells to the tumor microenvironment on the amount of PD-1:PD-L1 interactions between T cells and tumor cells is necessary for anti-PD-(L)1-mediated anti-tumor responses.In summary, intratumor T cell profiling data were used to characterize kinetic parameters of CD8in vivo studies with the CT26 syngeneic mouse tumor model. The dataset consisted of 136 mice with CT26 tumors treated with PBS, 0.5% MC or water (control group), anti-PD-1 antibody (RMP1-14) or anti-PD-L1 antibody (10F.9G2) (treatment details in Significant variability in tumor volume profiles and responses to anti-PD-(L)1 treatments was observed in the p = 0.0054).To quantify the differences in individual tumor volume profiles, the observed rates of tumor volume change between day 7 and day 13 after treatment start were calculated for each mouse. The observed rates of tumor volume change varied significantly between studies, as identified through a Kruskal-Wallis rank sum test  despite the same dosing regimen being used in the studies and similar tumor sizes at treatment start in studies 3 and 5 1 treatments. PK parameters for RMP1-14 and 10F.9G2 were estimated based on literature data  Supplem. The tumcapacity . Model fcapacity . The modcapacity . Removin+ T cell proliferation half-life is clustered around the average value, indicating difficulty estimating the distribution given the available data 1 treatments were still categorized based on the observed rates of tumor volume change between day 7 and day 13 after treatment initiation. Mice with an observed rate of tumor volume change below 25 mm+ T cell proliferation half-life, tumor cell proliferation rate, and tumor carrying capacity were compared between responders and non-responders , potentially due to the high shrinkage values . For the local sensitivity analysis, each parameter was perturbed by 30% in both directions from the nominal parameter value one at a time. The new parameter set was used to simulate tumor volume-time profiles under the same conditions. The simulated AUCs were compared to the simulated AUCs with the nominal parameter values .+ T cell also caused the tumor to regress. The results suggest these intrinsic tumor properties determine the growth of the tumor in the absence of treatments  or the fraction of tumor volume not occupied by cells (tumor void fraction) caused the tumor to completely regress. Decreasing the tumor cell proliferation rate, PD-L1 per tumor cell, or PD-1 per CD8eatments .The intrinsic tumor cell proliferation rate is also an important determinant of treatment response. A 30% decrease in the tumor cell proliferation rate caused the non-responder to experience complete tumor regression in both anti-PD-1 and anti-PD-L1 treatment simulations. Similarly, a 30% increase in the tumor cell proliferation rate caused the responder to experience a large increase in tumor volume . This isIncreasing PD-L1 expression has a minimal impact on treatment response due to high receptor engagement during either treatment. At the nominal PD-L1 expression level, during anti-PD-1 simulations, receptor engagement can drop from approximately 85% to as low as 53% between doses. Consequently, increasing PD-L1 expression induced a 0.9% and 6.1% increase in tumor volume AUC for the non-responder and responder, respectively . In anti+ T cell influx rate, and the CTL-mediated tumor cell killing rate influenced response to anti-PD-(L)1 treatments 1 treatments, baseline tumor growth and treatment response are sensitive to both intrinsic tumor properties and immune system properties. Variability beyond the three parameters fit to individuals in this analysis should be considered to improve the prediction intervals in future analyses.In summary, while random effects on tumor carrying capacity, tumor cell proliferation rate, and CD8PD-1 and PD-L1 expression levels are potential sources of variability in clinical populations. In analysis of clinical trial results, patients are often stratified by PD-L1 expression . The preThe model simulations indicate that low PD-(L)1 expression was associated with spontaneous tumor regression in mice with low estimated tumor cell proliferation rates . Tumor vIn summary, PD-1 and PD-L1 expression levels determine treatment response, but not independent of the tumor cell proliferation rate. Mice with higher receptor expression levels or faster growing tumors may benefit from higher dose levels to saturate the receptors.Quantitative systems pharmacology models can be used to inform preclinical to clinical translation and dose selection for first-in-patient studies. Often the models are characterized based on pooled preclinical data and translated to generate predictions for an average human patient. Immune checkpoint inhibitors are promising treatments for many forms of cancers; however, patients’ responses are highly variable. When either preclinical or clinical treatment responses are heterogenous, models built on aggregated data fail to represent the full spectrum of responses observed in clinical studies. Herein, we developed a QSP model of anti-PD-(L)1 treatments in Balb/c mice bearing CT26 syngeneic tumors to identify sources of variability in preclinical anti-tumor response, and generated hypotheses for potential sources of variability for pre-clinical to clinical translation.+ T cells in five stages, namely inactive, active, proliferating, cytotoxic, and exhausted. Intra-tumoral T cell concentration data were used to characterize the rate of progression through these stages at baseline, which determines the magnitude of increase in the CTL concentration upon anti-PD-(L)1 treatments. The inhibitory effect of PD-1:PD-L1 binding between CD8+ T cells and tumor cells on the influx of inactive CD8+ T cells into the tumor microenvironment is a novel mechanism in the presented model. The association between PD1:PD-L1 binding and infiltration of inactive CD8+ T cells is necessary for predicting anti-PD-(L)1 treatment responses while maintaining the observed baseline CD8+ T cell distribution across the five stages. The influx mechanism represents activation of naïve T cells within the lymph nodes which then travel to the tumor microenvironment and is supported by recent publications 1 antibodies, the biology of their molecular and cellular targets, and their effect on T cell activation. The model summarizes the progression of PD-1+CD8ications . BlockinThe use of intra-tumoral T cell concentration data was necessary to understand the baseline tumor microenvironment, increase confidence in baseline model behavior, reduce parameter uncertainty, and justify the new model mechanism of PD-1:PD-L1 dependent T cell influx. Ideally, longitudinal data showing intra-tumoral T cell concentrations before and after treatments would support fitting this model mechanism; however, generating longitudinal data of this nature is expensive and time expensive and time consuming. Nevertheless, this type of data is necessary for building mechanistic building mechanistic understanding of the biology and pharmacology and deriving meaningful results and well-supported hypotheses from QSP modeling. Further, QSP modeling allows incorporation and translation of preclinical data to inform and augment clinical modeling efforts.in vivo studies, mice were given a defined number of doses and tumor volumes were collected after treatment cessation until study termination. As a result, disease progression due to treatment cessation or acquired resistance is indistinguishable in the preclinical in vivo data. Grouping treatment response by the rate of tumor volume change between day 7 and day 13 after treatment start allows for identification of mice whose tumor progression is slowed as well as reversed.In the clinic, treatments and tumor measurements are continued until disease progression or censoring. Treatment response is categorized using Response Evaluation Criteria in Solid Tumor (RECIST) criteria  and the + T cell proliferation half-life, tumor cell proliferation rate, and tumor carrying capacity. Parameter estimates were similar to the previous anti-CTLA4 model 1 treatments, such as the CD8+ T cell proliferation half-life or maximum CD8+ T cell influx rate. Variability in T cell influx after treatments may be caused by variability in macrophage subtypes (The density of tumor infiltrating lymphocytes have been explored as a biomarker for anti-PD-(L)1 treatment response . Howeversubtypes .Influx of T cells into the tumor comes from the peripheral blood or the lymph nodes. Other published models of anti-PD-(L)1 have included the lymph nodes . While r+ T cell proliferation half-life together was enough to capture the inter-study and inter-individual variability in the preclinical in vivo data. The tumor cell proliferation rate strongly correlates with the observed rate of tumor volume change after treatments, but the tumor cell proliferation rate alone is unable to determine whether a tumor will respond to anti-PD-(L)1 treatments. Other parameters, such as the CD8+ T cell half-life and maximum CD8+ T cell influx rate may be additional sources of variability in response to anti-PD-(L)1 treatments as they impact the increase in CD8+ T cells after treatments. Here, these parameters were quantified using baseline intra-tumoral T cell concentration data stratified by cell surface markers. New methods supporting high throughput quantification of T cells result in an accumulation of data describing T cells within the tumor microenvironment 1 mediated anti-tumor response and identified PD-(L)1 receptor expression, CD8+ T cell half-life, CTL-mediated tumor cell killing rate, and maximum rate of CD8+ T cell influx into the tumor microenvironment as having substantial impact on anti-tumor response. This model can be further translated to simulate clinical populations through reparameterization using published human PK and tumor properties. Inter-individual variability can be incorporated into first-in-human dose predictions through virtual population methods 1 QSP mouse model captures the variability inherent in the longitudinal tumor volume profiles through individual variability in the tumor cell proliferation rate, tumor carrying capacity, and CD8 methods .


To clarify the role of long intergenic nonprotein-coding RNA 1232 (LINC01232) in the progression of gastric cancer and the potential mechanism, we analyzed the expression of LINC01232 in TCGA database using the GEPIA online tool, and the LINC01232 level in gastric cancer cell lines was detected by quantitative real time-polymerase chain reaction (qRT-PCR) as well. Cell proliferation assay, colony formation assay, transwell assay and tumor formation experiment in nude mice were conducted to observe the biological behavior changes of gastric cancer cells through the influence of LINC01232 knockdown. LncATLAS database and subcellular isolation assay were used for subcellular distribution of LINC01232 in gastric cancer cells. The interaction among LINC01232, zeste homolog 2 (EZH2) and kruppel-like factor 2 (KLF2) was clarified by RNA-protein interaction prediction (RPISeq), RNA immunoprecipitation (RIP), qRT-PCR and chromatin immunoprecipitation (ChIP) assay. Rescue experiments were further conducted to elucidate the biological function of LINC01232/KLF2 axis in the progression of gastric cancer. LINC01232 was upregulated in stomach adenocarcinoma (STAD) tissues and gastric cancer lines. LINC01232 knockdown inhibited the proliferative capacities of gastric cancer cells in vitro, and impaired in vivo tumorigenicity. LINC01232 was mainly distributed in the cell nucleus where it epigenetically repressed KLF2 expression via binding to the enhancer of EZH2, which was capable of binding to promoter regions of KLF2 to induce histone H3 lysine 27 trimethylation (H3K27me3). LINC01232 exerts oncogenic activities in gastric cancer via inhibition of KLF2, and therefore, the knockdown of KLF2 could reverse the regulatory effect of LINC01232 in the proliferative ability of gastric cancer cells. Gastric cancers form malignant tumors originating from the gastric mucosal epithelium, and they rank fourth and second in the incidence and mortality of malignant tumors respectively . Each yeet al, [Long non-coding RNAs (lncRNAs) are a type of RNA with a length of over 200 nucleotides that are incapable of coding proteins. A number of studies have confirmed that lncRNAs are involved in the regulation of tumor growth signals -6. LncRNet al, , 9. In tet al. found that reduced KLF2 signaling is a common feature of human pulmonary arterial hypertension and highlights the potential therapeutic role of KLF2-regulated exosomal miRNAs in pulmonary arterial hypertension and other diseases associated with vascular remodeling [et al. showed that lncRNAs can interact with RNA-binding proteins (RBPs) to induce gene expression regulation at the transcriptional level [Kruppel-like factors (KLFs) are a subclass of the zinc-finger family of DNA-binding transcription factors, which play a diverse role in various biological processes including cellular growth and differentiation . Sindi Hmodeling . Furtheral level . Through2, and saturated humidity.Human gastric mucosal cell GES-1 and human gastric cancer cells AGS, SGC-7901, MKN-45, MKN-28, and HGC-27 were purchased from Punosai Life Science and Technology Co., Ltd. (China). AGS cells were cultured in Ham's F-12 medium  containing 10% fetal bovine serum , 100 μg/ml streptomycin and 100 U/ml penicillin. GES-1, SGC-7901, MKN-45, MKN-28 and HGC-27 cells were cultured in RPMI 1640 medium  containing 10% FBS, 100 μg/ml streptomycin and 100 U/ml penicillin. All cells were cultured at 37°C, 5% COCell transfection was performed by using Lipofectamine 3000 transfection reagent  based on the manufacturer's instructions. ShRNA sequences used in this study were as follows: sh-NC: CCGCGGACTTGCCTCCTACACTACTCHAGTAGTGTAGGAGGCAAGTCCTTTTTG; sh-LINC01232#1: CCG CGACACGTCATCTAGAATAACTCHAGTTATTCTAGATGACGTGTCTTTTTG; sh-LINC01232#2: CCGCCA AGAGTGCTGGATCTAAACTCHAGTTTAGATCCAGCACTCTTGTTTTTG; sh-LINC01232#3: CCGCGA TTGGTTGCTTTCTGCAACTCHAGTTG CAGAAAGCAACCAATCTTTTTG; sh-KLF2: CACCGCAAG ACCTACACCAAGAGTTCGAAAACTCTTGGTGTAGGTCTTG. si-EZH2: 5'-GACUCUGAAUGCAGUUGC UTT-3'; si-NC: 5'-UUCUCCGAACGUGUCACGUTT-3'.http://gepia2.cancer-pku.cn/#index. The RBP was predicted by RPISe, http://pridb.gdcb.iastate.edu/RPISeq/#. The lncRNA subcellular localization was predicted by lcnatlas, lncrnahttps://lncatlas.crg.eu/.The expression of lncRNA was analyzed by GEPIA, −ΔΔCT method and using GAPDH or U6 expression as normalizing control. The primer sequences were as follows: LINC01232: 5′-AGGATGCGCCTAAGAAAGGG-3′ (F) and 5′-CCGGGGGATTGAGGAAACAT-3′ (R); p21: 5′-TGTCCGTCAGAACCCATGC-3′ (F) and 5′-AAAGTCGAA GTTCCATCGCTC-3′ (R); p53: 5′-CAGCACATGACGGAGGTTGT-3′ (F) and 5′-TCATCCAAATACTCC ACACGC-3 (R); KLF2: 5′-CTGCACATGAAACGGCACAT-3′ (F) and 5′-CAGTCACAGTTTGGGAGGGG-3′(R); LATS2: 5′-ACCCCAAAGTTCGGACCTTAT-3′ (F) and 5′-CATTTGCCGGTTCACTTCTGC-3′ (R); EZH2: 5′-AATCAGAGTACATGCGACTGAGA-3′ (F) and 5′-GCTGTATCCTTCGCTGTTTCC-3′ (R); GAPDH: 5′-GGACACAATGGATTGCAAGG-3′ (F) and 5′-TAACCACTGCTCCACTCTGG-3′ (R); U6: 5′-CTCGCTTCGGCAGCACA-3′ (F) and 5′-AACGCTCACGAATTTGCGT-3′ (R).Total RNA was extracted by TRIzol Reagent  and RNA was then transcribed into cDNA using SuperScript III Reverse Transcriptase . QRT-PCR was performed using the SYBR Green PCR Master Mix . The experiment was repeated for three times and the relative expression level was calculated by 2Logarithmic growth cells were cultured in 96-well plates at 37°C with a density of 2,000 cells/well. After 0, 24, 48, 72, and 96 h, 5 mg/ml MTT (Japan) was added to the cells and incubation continued for another 2 h. Then, the medium was removed and 200 μl DMSO was added to dissolve the formazan crystal. The optical density (OD) was measured by microplate reader  at 490 nm.Next, 1,000 cells/well were planted in 12-well plates and cultured for 14 days, with the medium being changed every 3 days. The colonies were then fixed with paraformaldehyde and stained with crystal violet, and photographs were taken to calculate the number of colonies formed.6 cells/ml in serum-free medium. Next, 100 μl cell suspension was added into the upper compartment of the chamber and incubated in a cell incubator for 10 min. For cell invasion, 50 μl matrix gel was precoated on the membrane in the Transwell chamber and cured in an incubator at 37°C for 30 min. Then, 100 μl cell solution was added to the surface of the matrix gel. Following this, 600 ul of complete medium containing 10% FBS was added to the lower chamber. After 24 h incubation, the migrating or invaded cells were fixed with methanol, stained with 1% crystal violet  for 10 min and observed under a light microscope  at ×200 magnification.After the precipitated cells were resuspended, the cell density was adjusted to 1×10RNA was isolated from the cytoplasm or nucleus using cytoplasmic and nuclear RNA purification kits . The collected cells were lysed on ice and centrifuged at 12,000 g for 3 min. The RNA levels in the cytoplasm and nucleus of the supernatant were analyzed using U6 as nuclear control and GAPDH as cytoplasmic control.RIP assay was performed using the Magna RIP RNA-Binding Protein Immunoprecipitation Kit . Cell extracts were harvested with RIP lysis buffer and mixed with magnetic beads and antibodies against EZH2, LSD1 and SUZ12 . With anti-IgG antibody as control, the co-precipitated RNA was purified and analyzed by qRT-PCR.Transferred cells were collected and fixed with 4% PFA for 15 min. After removing the fixation solution, the cells were incubated with 0.65M glycine solution under mild agitation for 5 min. The cells were fixed with PBS and lysed in ChIP lysis buffer on ice for 15 min. Then, the lysates were ultrasonized and the chromatin DNA was cut into 200-800 bp fragments. After centrifugation, the supernatant was collected and the chromatin fragments were incubated overnight at 4°C with anti-EZH2 , anti-H3K27Me3  or recombinant rabbit IgG . Protein agarose/agarose was used to precipitate endogenous DNA-protein complexes. Finally, the precipitate was decross-linked overnight at 65°C, and the DNA fragments were purified and extracted with phenol/chloroform. Input  was used as the internal control. The sequence of ChIP primers was as follows: KLF2: 5'-CCTCAGTTTCCCTGCACTTGAC-3' (F) and 5'-GAGATACAATCACACCACTAC-3' (R).Cells were lysed in RIPA buffer  and the protein concentration was measured with bicinchoninic acid . After electrophoresis on 8% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, protein extract was transferred to a polyvinylidene difluoride membrane and blocked with 3% (w/v) skim milk. Mouse anti-KLF2 antibody  or mouse anti-GAPDH antibody  were used as primary antibodies and horseradish peroxidase-conjugated goat anti-mouse IgG  was used as the secondary antibody. The signals were detected by enhanced chemiluminescence method with GAPDH as an internal control.SPF Balb/c female nude mice  were purchased from the National Laboratory Animal Center (China). All animal experiments were conducted in accordance with the National Institutes of Health guidelines  and all the procedures were reviewed and approved by Yantai Yuhuangding Hospital.6 HGC-27 cells were inoculated into the soft skin of the right rear forelimb of nude mice and the tumor volume was calculated every 7 days. Tumor volume = (long diameter × short diameter2)/2. Four weeks later, the nude mice were anesthetized by intraperitoneal injection of 3% sodium pentobarbital (40 mg/kg body weight) and sacrificed by cervical dislocation, and the cancerous tissue was removed and weighed.A total of 2×10t-test and one-way analysis of variance was used to analysis the differences between multiple groups. p < 0.05 was regarded as statistically significant.Data were presented as means ± SD and analyzed using SPSS19.0 statistical software . The differences between the two groups were analyzed using Student's p < 0.05, p < 0.05, respectively, The expression of LINC01232 in TCGA database was analyzed using the GEPIA online tool, and the results showed that the expression of LINC01232 in STAD tissues was higher than that in normal tissues . Moreover, a significant decrease of tumor volume and weight was noted in the sh-LINC01232 group compared with that of the sh-NC group . These findings demonstrated that LINC01232 knockdown inhibited proliferative capacities of gastric cancer cells in vitro, and impaired in vivo tumorigenicity.Three shRNAs and 1 sh-NC targeting LINC01232 were designed and transfected into SGC-7901 and HGC-27 cells, respectively. QRT-PCR showed that the mRNA expression of LINC01232 was significantly reduced by those 3 shRNAs, and sh-LINC01232#3 had the highest knockdown efficiency . TherefoLncATLAS database  and subcTo further clarify the functions of LINC01232 and KLF2 in the progression of gastric cancer, the expression of KLF2 in SGC-7901 and HGC-27 cells was knocked down on the basis of LINC01232 silencing. QRT-PCR  and westp < 0.05, respectively). Meanwhile, the knockdown of KLF2 could reverse the regulatory effect of LINC01232 in the proliferative ability of gastric cancer cells. These results demonstrated that the inhibition of KLF2 is essential to the tumor-promoting effect of LINC01232.Compared with Sh-NC group, the cell viability , colony Helicobacter pylori infection, age, high salt intake, and diets low in fruit and vegetables [Gastric cancer is one of the most common cancers in the world, with risk factors that include getables . Early ggetables . Due to getables . The expgetables . Non-codgetables . Previougetables , 9. In tEmerging evidence suggests that LncRNAs influence transcription of downstream factors through regulating histone acetylation or methylation as well as DNA methylation or hydroxy methylation -22. EZH2In conclusion, our study showed that LINC01232 exerts oncogenic activities in gastric cancer and LINC01232 knockdown could inhibit the proliferation of gastric cancer cells in vivo and in vitro. The possible mechanism is related to inhibition of the expression of KLF2 through H3K27me3 histone methylation. The regulation of LINC01232 may be an effective potential therapeutic and preventive approach for gastric cancer. However, in future studies, an in-depth study of relationship among LINC0123, LSD1 and LATS2 still needs to be performed.