Objective To investigate the changes and clinical significance of pyroptosis and inflammation in children with acute lymphoblastic leukemia after chemotherapy.Methods A retrospective analysis was con-ducted on the clinical data of 98 children with acute lymphoblastic leukemia who received chemotherapy in the pediatrics and hematology and oncology departments of the hospital from May 2023 to August 2024.Accord-ing to the results of blood culture,the selected children were divided into the Gram-positive bacteria group,the Gram-negative bacteria group,the fungal group and the non-bloodstream infection group,and drug sensitivity tests were conducted.After chemotherapy,the children were divided into the granulocytosis group and the non-granulocytosis group according to the granulocyte level.The relevant indicators were detected and com-pared by methods such as blood routine,flow microsphere array technology,enzyme-linked immunosorbent assay(ELISA),and Western blot.Results After chemotherapy,the pyroptosis related indicators caspase-1,caspase-4,caspase-5,caspase-11,interleukin(IL)-1 β,IL-18,the proportion of pyroptosis cells and the relative expression level of GSDMD protein in children of each infection type were significantly increased compared with those before chemotherapy(P＜0.05).After chemotherapy,the levels of IL-4,IL-6,IL-10 and interfer-on-γ(IFN-γ)in the granulocytosis group were significantly higher than those in the non-granulocytosis group(P＜0.05),and the granulocyte level was negatively correlated with the levels of IL-4,IL-6,IL-10 and IFN-γ(P＜0.05).There were statistically significant differences in the levels of IL-4,IL-6,IL-10 and IFN-γ in dif-ferent infection states after chemotherapy(P＜0.05).Conclusion The number of granulocytes and the levels of serum cytokines can serve as potential indicators of infection in children with leukemia.The regulation of pyroptosis may provide new strategies for the treatment of childhood leukemia.

Objective Tumor cells are able to support their malignant proliferation by changing metabolic models .Prostate cells rely much on lipid metabolism in which ATP-citrate lyase ( ACLY) plays a very important role .The aim of this research was to study the effects of downregulated ACLY on the cell proliferation , cycle distribution and apoptosis of androgen-independent prostate cancer cells DUl45. Methods DU145 cells were divided into two groups:the cells in experiment group were transfected with the small interfering RNA-mediated knockdown of ACLY , while the cells in control group were transfected with meaningless small interfering RNA.Cell counting Kit test ( CCK-8 ) was applied to detect the effects of the downregulation of ATP citrate lyase on the proliferation of DU145.Flow cytometry instrument was used to analyze the variation of cell cycle distribution and apoptosis rate between groups .Western blot was used to detect the change of intracellular Caspase-3 protein content. Results Western blot showed favorable effects of ACLY interference .Compared with control group , ACLY protein content significantly decreased in experiment group ( P<0.05) ;the acetyl coenzyme A content and the percentage of early apoptosis cells and late apoptosis cells increased significantly [(0.42±1.99) vs (0.79±2.38), (37.10±3.28) vs (6.20±2.88), P<0.05].As to the selec-tive ACLY knockdown in DU145 cells, the proliferation ability in experiment group weakened in comparison with control group , which became more and more apparent as time went on .At 48h and 72h time points, the cell absorbance between two groups at the same time point was of significant difference (P<0.05).As to the interfering ACLY expression in DU145 cells, the percentage of G1 cells in-creased without any statistical significance (P>0.05), while the percentage of G2 cells decreased and the percentage of S cells in-creased with most cell cycle blocking at G 0/G1 stage, which were of significant difference .Meanwhile the expression of apoptosis pro-tein Caspase-3 upregulated significantly . Conclusion ACLY is of vital significance to maintain the malignant proliferation of prostate cancer cells and its downregulation results in the inhibition of cell proliferation and the promotion of cell apoptosis .

OBJECTIVETo identify the biomarkers of renal cell cancer (RCC) through urine metabolic analysis.

METHODSUrine samples of 27 RCC patients, 26 patients with other urinary cancers and 26 healthy volunteers were examined with gas chromatography-mass spectrometry (GC-MS). SIMCA-P+12.0.1.0 software was used for principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) to screen for the differential metabolites.

RESULTSPCA (R2X=0.846, Q2=0.575) and OPLS-DA (R2X=0.736, R2Y=0.974, Q2Y=0.897) model were established for the RCC patients and control subjects. Fourteen metabolites were selected as the characteristic metabolites, including pentanoic acid, malonic acid, glutaric acid, adipic acid, amino quinoline, quinoline, indole acetic acid, and tryptophan, whose levels in the urine were significantly higher in the RCC patients than in the normal subjects (P<0.01); the RCC patients showed significantly higher urine contents of pentanoic acid, phenylalanine, and 6-methoxy-nitro quinoline than those with other urinary tumors (P<0.01).

CONCLUSIONThe urine metabolites identified based on GC-MS analysis can distinguish RCC patients from patients with other urinary cancers and healthy subjects, suggesting their potential as diagnostic markers for RCC.

Biomarkers ; urine ; Carcinoma, Renal Cell ; urine ; Discriminant Analysis ; Gas Chromatography-Mass Spectrometry ; Humans ; Least-Squares Analysis ; Metabolome ; Metabolomics ; Principal Component Analysis ; Software

Objective To identify the biomarkers of renal cell cancer (RCC) through urine metabolic analysis. Methods Urine samples of 27 RCC patients, 26 patients with other urinary cancers and 26 healthy volunteers were examined with gas chromatography-mass spectrometry (GC-MS). SIMCA-P+12.0.1.0 software was used for principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) to screen for the differential metabolites. Results PCA (R2X=0.846, Q2=0.575) and OPLS-DA (R2X=0.736, R2Y=0.974, Q2Y=0.897) model were established for the RCC patients and control subjects. Fourteen metabolites were selected as the characteristic metabolites, including pentanoic acid, malonic acid, glutaric acid, adipic acid, amino quinoline, quinoline, indole acetic acid, and tryptophan, whose levels in the urine were significantly higher in the RCC patients than in the normal subjects (P<0.01); the RCC patients showed significantly higher urine contents of pentanoic acid, phenylalanine, and 6-methoxy-nitro quinoline than those with other urinary tumors (P<0.01). Conclusion The urine metabolites identified based on GC-MS analysis can distinguish RCC patients from patients with other urinary cancers and healthy subjects, suggesting their potential as diagnostic markers for RCC.

Objective To identify the biomarkers of renal cell cancer (RCC) through urine metabolic analysis. Methods Urine samples of 27 RCC patients, 26 patients with other urinary cancers and 26 healthy volunteers were examined with gas chromatography-mass spectrometry (GC-MS). SIMCA-P+12.0.1.0 software was used for principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) to screen for the differential metabolites. Results PCA (R2X=0.846, Q2=0.575) and OPLS-DA (R2X=0.736, R2Y=0.974, Q2Y=0.897) model were established for the RCC patients and control subjects. Fourteen metabolites were selected as the characteristic metabolites, including pentanoic acid, malonic acid, glutaric acid, adipic acid, amino quinoline, quinoline, indole acetic acid, and tryptophan, whose levels in the urine were significantly higher in the RCC patients than in the normal subjects (P<0.01); the RCC patients showed significantly higher urine contents of pentanoic acid, phenylalanine, and 6-methoxy-nitro quinoline than those with other urinary tumors (P<0.01). Conclusion The urine metabolites identified based on GC-MS analysis can distinguish RCC patients from patients with other urinary cancers and healthy subjects, suggesting their potential as diagnostic markers for RCC.