Objective To evaluate the effect of propofol on interleukin-1β (IL-1β)-induced increase in monolayer permeability of human umbilical vein endothelial cells (HUVECs).Methods Primary HUVECs were cultured and purified by immuno-magnetic separation.The expression of VE-cadherin in endothelial cells was determined by immunofluorescence.The HUVEC monolayer permeability was detected by the Transwell system.The cells were seeded on the upper chamber (2 × 105 cells/well) and cultured for 3 days after confluence.The cells were treated in two ways.The cells were randomly divided into 6 groups (n =36 each) and 5 of the 6 groups treated with 1,2,5,10 and 20 ng/ml IL-1β for 24 h except for control group.The cells were also randomly divided into 5 groups (n =30 each) and 4 of the 5 groups were pretreated with 0,10,50 and 100 μmol/L propofol for 30 min,and then treated with 10 ng/ml IL-1β for 24 h except for control group.The cells were radomly divided into 3 groups (n =18 each) and 2 of the 3 groups were pretreated with 50 μmol/L propofol for 30 min,and then treated with 10 ng/ml IL-1β for 24 h or 30 min.The expression of occludin protien,p38 mitogen activiated protienkinase (p38 MAPK) and phosphorylated p38 MAPK (p-p38 MAPK) was determined by Western blot.Results Compared with control group,5,10 and 20 ng/ml IL-1β significantly increased HUVEC monolayer permeability in a concentration-dependent manner (P ＜ 0.05 or 0.01).10,50 and 100 μmol/L propofol inhibited IL-1 β-induced increase in the permeability of HUVEC monolayer permeability in a concentration-dependent manner (P ＜ 0.01).IL-1β could down-regulate HUVEC occludin protein expression,and activate p38MAPK signaling pathway,and propofol inhibited IL-1β-induced down-regulation of HUVEC occludin protein expression and activation of p38 MAPK signaling pathway (P ＜ 0.01).Conclusion Propofol can alleviate IL-1β-induced increase in the permeability of HUVEC monolayer via inhibiting activation of p38 MAPK signaling pathway.

Objective To study the inducement of U251 glioblastoma cell apoptosis in vivo through up-regulating PUMA expresion and knocking down miR-221/222, and explore its mechanism.Methods Nude mouse xenograft models were established in 5-week-old BALB/c nude mice by subcutaneous vaccination of U251 glioblastomas; 1 week later, they were treated with intratumoral injection of lipofcctamine-mediated miRNA-221/222 antisense oligonucleotides (GroupA), nonsense sequences (Group B) and controls (Group C),respectively (n=8).The tumor growth was monitored until the end of observation period (28 d after the treatment) and pathological changes of the glioblastoma tissues were observed by HE staining at the end of observation.Fluorescence in situ hybridization (FISH) and real-time PCR were employed to measure the miR-221 and miR-222 expressions. Terminal deoxynucleotidyl transferase-mediated uridine 5'-triphosphate-biotin nick end labeling (TUNEL) assay was used to detect the apoptosis of glioblastomas.Immunohistochemistry and Westem blotting were used to detect the expressions of PUMA,bax,bcl-2 and p53 in removed tumor specimens. Results The volume in Group A was significantly smaller than that of those in group B and group C 6-28 dater treatment (P=0.006). The miR-221 and miR-222 mRNA expressions in Group A were significantly decreased as compared with those of those in group B and group C.HE staining indicated that decreased heteromorphism and reduced number of new vessels in Group A were noted as compared with those in group B and group C.The cell apoptotic index in Group A was significantly higher than that in group B and group C (P＜0.05).Immunohistochemistry showed that the expression levels of PUMA and bax in Group A was significantly up-regulated as compared with those in group B and group C, while the expression of bcl-2 in Group A was significantly down-regulated as compared with that in group B and group C; and no significant changes were noted in the p53 expression. Conclusion By up-regulating PUMA expresion,knocking down miR-221/222 can induce U251 glioma apoptosis in vivo.

OBJECTIVETo study further the most important and frequent genetic alterations of p53 and epidermal growth factor receptor (EGFR) in astrocytic gliomas.

METHODS(1) EGFR expression was examined in samples collected from 37 astrocytic gliomas and 6 normal brain tissue using reverse transcriptase polymerase chain reaction and immunohistochemical staining. (2) p53 gene mutation and accumulation were detected simultaneously in the same specimens using PCR-SSCP, DNA sequencing and immunohistochemical staining.

RESULTSThe frequency of p53 mutation in diffuse astrocytomas, anaplastic astrocytomas, primary glioblastomas and secondary glioblastomas was 1/10, 4/19 (21.1%), 4/6 and 2/2, respectively and the frequency of EGFR overexpression was 5/10, 10/19 (52.6%), 5/6 and 2/2, respectively. Both p53 accumulation and EGFR overexpression increased accompanied by a successive increase of degree of the glioma malignancy.

CONCLUSIONSEGFR overexpression is not infrequently seen, however, p53 mutation is rarely seen in the low grade gliomas. Both p53 gene mutation and EGFR overexpression are often associated with primary and secondary glioblastoma. Consequently, EGFR overexpression and p53 gene mutation are not mutually exclusive in astrocytic gliomagenesis but synergistically to promote the glioma progression.

Adult ; Aged ; Astrocytoma ; genetics ; metabolism ; pathology ; Base Sequence ; Brain Neoplasms ; genetics ; metabolism ; pathology ; DNA Mutational Analysis ; Female ; Gene Expression Regulation, Neoplastic ; Glioblastoma ; genetics ; metabolism ; pathology ; Humans ; Immunohistochemistry ; Male ; Middle Aged ; Molecular Sequence Data ; Mutation ; Polymerase Chain Reaction ; Polymorphism, Single-Stranded Conformational ; RNA, Messenger ; biosynthesis ; genetics ; Receptor, Epidermal Growth Factor ; biosynthesis ; genetics ; Reverse Transcriptase Polymerase Chain Reaction ; Tumor Suppressor Protein p53 ; biosynthesis ; genetics

OBJECTIVETo investigate the effect of 5-FU (5-fluorouracil) on enriching cancer stem cells of HCC cell line BEL-7402 and the biological characteristics of enriched cells.

METHODSThe enriching concentration of 5-FU was determined by CCK-8 (cell counting kit-8). Flow Cytometry was used to determine the changes in cell cycle and positive expression ratio of surface marker CD56, CD54, EpCAM and CD133. The self-renewal and differentiation of positive cells were tested by colony formation assay, and were compared with the control group.

RESULTSEnriching concentration of 5-FU was determined as 10 μg/ml with 48 h incubation. After enrichment, G0/G1 phase cells increased from 57.50 %+/-0.98% to 68.70%+/-3.41% (P<0.05). Whereas S phase cells decreased from 40.26%+/-4.12% to 31.80%+/-4.15% (P<0.01); G2/M phase cells disappeared in experimental group, and was 5.80%+/-1.87% in control group (P<0.01). The proportion of the cell cycle changed with significant statistical differences. Meanwhile, positive rate of cell surface makers CD56, CD54, EpCAM and CD133 increased from 0.57%+/-0.12%, 8.10%+/-6.79%, 0.3%+/-0.01% and 3.20%+/-0.99% to 4.13%+/-0.06%, 50.08%+/-1.69%, 0.55%+/-0.07% and 10.51%+/-1.13%, respectively. The difference was significant (P<0.05). The colony forming ratio of CD56, CD54, EpCAM and CD133 negative cells and positive cells were 2.11%+/-0.21%, 3.32%+/-0.31%; 0.86%+/-0.101%, 2.40%+/-0.52 %; 7.19%+/-0.56%, 7.73%+/-0.71%; 2.70%+/-0.26%, 5.75%+/-0.81%, respectively, and significant differences were found between (P<0.05).

CONCLUSION5-fluorouracil enriched the cancer stem cell population in HCC cell line BEL-7402. CD56 and CD54 can be used as important surface markers in research of liver cancer stem cells.

Cell Cycle ; drug effects ; Cell Line, Tumor ; Cell Proliferation ; drug effects ; Fluorouracil ; pharmacology ; Humans ; Neoplastic Stem Cells ; cytology ; drug effects ; metabolism

Objective:To investigate the effect of Qideng Mingmu capsule on the formation and remodeling of retinal neovascularization in mice with oxygen-induced retinopathy (OIR).Methods:Thirty-six postnatal day 7 (P7)SPF grade C57BL/6J pups were divided into normal group, OIR group, Qideng Mingmu capsule group and apatinib group by random number table method, with 9 mice in each group.The mice in the normal group were raised in normal environment.The mice in the other three groups were fed in hyperoxic environment of (75±2)% oxygen concentration for 5 days from P7 to P12 and then were fed in normal environment for 5 days from P12 to P17 to establish the OIR model.From P12, mice in Qideng Mingmu capsule group and apatinib group were given intragastric administration of Qideng Mingmu capsule (900 mg/kg) and vascular endothelial growth factor receptor 2 inhibitor apatinib (70 mg/kg) respectively, once a day for 5 consecutive days.On P17, paraffin sections of mouse eyeballs were made and stained with hematoxylin-eosin to count the number of vascular endothelial cells that broke through the internal limiting membrane.The retinal slices were prepared and stained with FITC-dextran to quantify the retinal non-perfusion area, neovascularization density and total vascular density.The distribution and fluorescence intensity of retinal vascular endothelial cell marker CD31 and pericyte marker α-smooth muscle actin (α-SMA) were observed by double immunofluorescence staining.Immunohistochemical staining was used to detect the expression and distribution of retinal hypoxia inducible factor-1α (HIF-1α) and vascular endothelial cadherin (VE-cadherin).The use and care of animals were in accordance with the Regulations on the Management of Laboratory Animals issued by the Ministry of Science and Technology.This study was approved by the Animal Ethics Committee of Chengdu University of Traditional Chinese Medicine (No.2019-30).Results:The number of vascular endothelial cells breaking through the internal limiting membrane in normal group, OIR group, Qideng Mingmu capsule group and apatinib group were (2.83±4.40), (37.33±5.43), (23.83±6.79) and (14.00±9.34), respectively, with a statistically significant overall difference ( F=28.313, P<0.001).There were more vascular endothelial cells breaking through internal limiting membrane in OIR group than in normal group, Qideng Mingmu capsule group and apatinib group, showing statistically significant differences (all at P<0.05).In the observation of mouse retinal slices, there were large non-perfusion areas, neovascularization buds and disordered distribution of blood vessels in OIR group.The distribution of blood vessels was more uniform and the areas of non-perfusion and neovascularization were smaller in Qideng Mingmu capsule group and apatinib group than in OIR group.The relative area of central retinal non-perfusion area and neovascularization density were significantly lower in normal group, Qideng Mingmu capsule group and apatinib group than in OIR group (all at P<0.05).The immunofluorescence intensity of CD31 and the absorbance value of HIF-1α were significantly lower, and the immunofluorescence intensity of α-SMA and the absorbance value of VE-cadherin were significantly higher in normal group, Qideng Mingmu capsule group and apatinib group than in OIR group (all at P<0.05). Conclusions:Qideng Mingmu capsule can inhibit retinal neovascularization formation, increase vascular pericyte coverage, relieve retinal hypoxia and increase vascular integrity in OIR mice.It can protect the retinal vessels of OIR mice.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.