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.

AIM To study the effects of Dianxianqing Granules on Tau protein in a mouse model of Alzheimer's disease (AD).METHODS The mice expressing P301S mutant Tau variant were randomly divided into the model group,the MCC950 group (NLRP3 inhibitor,10 mg/kg),the Dianxianqing Granules group (12.48 g/kg),the MCC950+Dianxianqing Granules group,in contrast to the C57BL/6 mice of the control group.After 5 months of administration,the mice had their learning and memory ability tested by Y maze test and Morris water maze test;their cerebral morphological changes observed by HE staining;their cerebral expressions of Caspase-1 and GSDMD proteins detected by immunohistochemical method;their expression of cerebral Tau protein detected by immunofluorescence;and their cerebral expressions of Tau,p-Tau (ser202),p-Tau (thr205),NLRP3,Caspase-1,IL-1β and IL-18 detected by Western blot.RESULTS Compared with the control group,the model group displayed decreased rate of spontaneous alternate reaction and times of crossing platform (P<0.05,P<0.01);abnormal hippocampal morphology,decreased number of neurons,increased cerebral positive expressions of Caspase-1 and GSDMD (P<0.05);deposition of a large number of brown granules in cytoplasm,and increased protein expressions of Tau,p-Tau (ser202),p-Tau (thr205),NLRP3,Caspase-1,IL-1βand IL-18 in the hippocampus and the cortex (P<0.05,P<0.01).Compared with the model group,the group intervened with Dianxianqing Granules demonstrated both increased rate of spontaneous alternate reaction and times of crossing platform (P<0.05);complete and normal morphology of the brain,a diversity of fine neurons,reduced cerebral positive expressions of Caspase-1 and GSDMD (P<0.05);and decreased protein expressions of Tau,p-Tau (ser202),p-Tau (thr205),NLRP3,Caspase-1,IL-1β and IL-18 in the hippocampus and the cortex (P<0.05,P<0.01).CONCLUSION Dianxianqing Granules may inhibit Tau protein expression in the mouse model of AD via NLRP3/Caspase-1 pathway.

AIM To study the effects of Dianxianqing Granules on Tau protein in a mouse model of Alzheimer's disease (AD).METHODS The mice expressing P301S mutant Tau variant were randomly divided into the model group,the MCC950 group (NLRP3 inhibitor,10 mg/kg),the Dianxianqing Granules group (12.48 g/kg),the MCC950+Dianxianqing Granules group,in contrast to the C57BL/6 mice of the control group.After 5 months of administration,the mice had their learning and memory ability tested by Y maze test and Morris water maze test;their cerebral morphological changes observed by HE staining;their cerebral expressions of Caspase-1 and GSDMD proteins detected by immunohistochemical method;their expression of cerebral Tau protein detected by immunofluorescence;and their cerebral expressions of Tau,p-Tau (ser202),p-Tau (thr205),NLRP3,Caspase-1,IL-1β and IL-18 detected by Western blot.RESULTS Compared with the control group,the model group displayed decreased rate of spontaneous alternate reaction and times of crossing platform (P<0.05,P<0.01);abnormal hippocampal morphology,decreased number of neurons,increased cerebral positive expressions of Caspase-1 and GSDMD (P<0.05);deposition of a large number of brown granules in cytoplasm,and increased protein expressions of Tau,p-Tau (ser202),p-Tau (thr205),NLRP3,Caspase-1,IL-1βand IL-18 in the hippocampus and the cortex (P<0.05,P<0.01).Compared with the model group,the group intervened with Dianxianqing Granules demonstrated both increased rate of spontaneous alternate reaction and times of crossing platform (P<0.05);complete and normal morphology of the brain,a diversity of fine neurons,reduced cerebral positive expressions of Caspase-1 and GSDMD (P<0.05);and decreased protein expressions of Tau,p-Tau (ser202),p-Tau (thr205),NLRP3,Caspase-1,IL-1β and IL-18 in the hippocampus and the cortex (P<0.05,P<0.01).CONCLUSION Dianxianqing Granules may inhibit Tau protein expression in the mouse model of AD via NLRP3/Caspase-1 pathway.

ObjectiveTo evaluate the expression level of DNA damage repair gene FANCI in hepatocellular carcinoma （HCC） and its relationship with prognosis， clinical stage and immune infiltration. MethodsIn this study， TCGA， GTEx， TIMER2.0， HPA database and qRT-PCR， western blot and immunohistochemistry were used to analyze the expression of FANCI in HCC and its correlation with different clinical stages； Kaplan-Meier Plotter database was used to explore the relationship between FANCI and the prognosis of HCC； the TISIDB database was used to analyze the relationship between FANCI and immune cell infiltration and immune checkpoints in HCC； the STRING database was used to detect the protein binding with FANCI； the TCGA and GTEx databases were used for GO and KEGG enrichment analysis； Cell experiments were used to explore the role of FANCI in HCC. ResultsCompared with normal tissues， the mRNA and protein expression levels of FANCI in tumor tissues were up-regulated （P<0.001）； and HCC patients with high expression of FANCI had poor prognosis （P<0.001）； the expression of FANCI in tumor tissues was positively correlated with the number of activated CD4⁺ T cells， the number of Th2 cells and the expression of immune checkpoints， and B-cell and macrophage infiltration was significantly lower in the FANCI high expression group （P<0.01）； GO and KEGG enrichment analysis showed that FANCI-related genes were enriched in various biological processes such as amino acid transmembrane transporter activity； Cell experiments showed that knockdown of FANCI could inhibit the proliferation， invasion and migration of HCC （P<0.05）. ConclusionsFANCI is highly expressed in hepatocellular carcinoma tissues， which may play a role in suppressing anti-tumor immunity and acting on pathways such as amino acid transmembrane transport， and is associated with poor prognosis. The proliferation， invasion and migration ability of hepatocellular carcinoma are inhibited after knocking down FANCI.

Morinda officinalis oligosaccharides (MOO) are an oral drug approved in China for the treatment of depression in China. However, MOO is hardly absorbed so that their anti-depressant mechanism has not been elucidated. Here, we show that oral MOO acted on tryptophan → 5-hydroxytryptophan (5-HTP) → serotonin (5-HT) metabolic pathway in the gut microbiota. MOO could increase tryptophan hydroxylase levels in the gut microbiota which accelerated 5-HTP production from tryptophan; meanwhile, MOO inhibited 5-hydroxytryptophan decarboxylase activity, thus reduced 5-HT generation, and accumulated 5-HTP. The raised 5-HTP from the gut microbiota was absorbed to the blood, and then passed across the blood-brain barrier to improve 5-HT levels in the brain. Additionally, pentasaccharide, as one of the main components in MOO, exerted the significant anti-depressant effect through a mechanism identical to that of MOO. This study reveals for the first time that MOO can alleviate depression via increasing 5-HTP in the gut microbiota.

ObjectiveTo investigate the effects of EZH1/2 inhibitor UNC1999 on the immune cell phenotypes in peripheral blood of healthy adults. MethodsCCK8 assay was used to measure the cell viability of peripheral blood mononuclear cells （PBMC）. Multicolor flow cytometry was performed to analyze the immunophenotypes. ResultsCompared with DMSO group， UNC1999 group showed increased classical monocytes （CD14⁺⁺CD16^-） ［（19.53±1.79）% vs. （66.60±5.02）%， t=13.31， P=0.006）， decreased intermediate monocytes （CD14⁺⁺CD16⁺） and non-classical monocytes （CD14⁺CD16⁺） ［（35.08±3.97）% vs. （15.42±2.89）%， t=6.130， P=0.026； （35.50±3.53）% vs. （8.40±3.12）%， t=25.740， P=0.002］. The proportions of CD56^dim CD16⁺， CD56^dom CD16⁺ in UNC1999 group were lower ［（3.39±0.86）% vs. （0.27±0.06）%， t=4.882， P=0.040； （80.50±0.64）% vs. （0.63±0.23）%， t=133.100， P<0.000 1］. UNC1999 group exhibited higher frequency of naive B cells ［（10.67±1.76）% vs. （37.99±3.76）%， t=17.690， P=0.003］， lower frequency of memory B cells， transitional B cells， plasmablasts B cells ［（23.39±4.20）% vs. （11.82±1.90）%， t=7.059， P=0.020； （3.58±0.47）% vs. （1.52±0.56）%， t=26.970， P=0.001； （0.18±0.03）% vs. （0.00±0.00）%， t=8.647， P=0.013］. The percentage of DC and mDC/DC was significantly elevated in UNC1999 group ［（0.20±0.05）%vs.（1.38±0.13）%， t=16.500， P=0.004； （32.41±13.14）% vs. （60.87±8.43）%， t=8.252， P=0.014］， with a significantly decreased percentage of pDC/DC ［（24.90±1.95）% vs. （12.70±2.11）%， t=7.566， P=0.017］. Higher proportions of CD8⁺ central memory T cells （TCM） and CD8⁺PD-1⁺ ［（5.62±1.24）% vs. （18.38±2.34）%， t=15.600， P=0.004； （2.50±1.02）% vs. （18.34±2.69）%， t=8.822， P=0.013］， but lower proportions of CD8⁺ effective memory T cells （TEM） and CD4⁺CD27⁺were observed in UNC1999 ［（28.27±10.15）% vs. （15.62±9.48）%， t=19.480， P=0.003； （82.77±2.66）% vs. （56.00±9.01）%， t=5.715， P=0.029］. No statistical difference was found in other cell subsets （P>0.05）. ConclusionUNC1999 can lead to changes in PBMC immunophenotypes.