Objective:This study aimed to evaluate the prognostic factors of clinical and histopathological parameters,including age,gender,tumor stage,tumor grade,tumor differentiation,lymph node metastasis(LNM),tumor frequency,and tumor count,in patients undergoing radical cystectomy(RC)combined with Mainz Pouch Ⅱ bladder reconstruction.Methods:A total of 237 bladder cancer patients(198 male and 39 female)who underwent RC combined with Mainz Pouch Ⅱ bladder reconstruction without chemotherapy or radiotherapy,from January 2004 to January 2023,were included in this study.Kaplan-Meier and Cox regression analyses were performed to assess the impact of age,tumor grade,tumor stage,tumor differentiation,LNM,tumor frequency,and tumor count on 5-year overall survival(OS)and 5-year cancer-specific survival(CSS).Results:The mean age at diagnosis was 59.8 years,with 198 male and 39 female patients.The mean follow-up duration was 47.8 months.In univariate analysis,patients younger than 65 years had significantly higher 5-year OS and 5-year CSS compared to those aged≥65 years.Patients with urothelial carcinoma showed better 5-year OS than those with non-urothelial carcinoma.Additionally,tumor stage,tumor grade,and LNM were negatively associated with 5-year OS and 5-year CSS.On multivariate analysis,only tumor grade and LNM remained statistically significant(P<0.05).Conclusions:Tumor grade and LNM were identified as independent prognostic risk factors for 5-year OS and 5-year CSS following RC combined with Mainz PouchⅡ bladder reconstruction.Moreover,the application of RC combined with Mainz Pouch Ⅱ bladder reconstruction should consider the patient's preferences and physical condition.

Objective:This study aimed to evaluate the prognostic factors of clinical and histopathological parameters,including age,gender,tumor stage,tumor grade,tumor differentiation,lymph node metastasis(LNM),tumor frequency,and tumor count,in patients undergoing radical cystectomy(RC)combined with Mainz Pouch Ⅱ bladder reconstruction.Methods:A total of 237 bladder cancer patients(198 male and 39 female)who underwent RC combined with Mainz Pouch Ⅱ bladder reconstruction without chemotherapy or radiotherapy,from January 2004 to January 2023,were included in this study.Kaplan-Meier and Cox regression analyses were performed to assess the impact of age,tumor grade,tumor stage,tumor differentiation,LNM,tumor frequency,and tumor count on 5-year overall survival(OS)and 5-year cancer-specific survival(CSS).Results:The mean age at diagnosis was 59.8 years,with 198 male and 39 female patients.The mean follow-up duration was 47.8 months.In univariate analysis,patients younger than 65 years had significantly higher 5-year OS and 5-year CSS compared to those aged≥65 years.Patients with urothelial carcinoma showed better 5-year OS than those with non-urothelial carcinoma.Additionally,tumor stage,tumor grade,and LNM were negatively associated with 5-year OS and 5-year CSS.On multivariate analysis,only tumor grade and LNM remained statistically significant(P<0.05).Conclusions:Tumor grade and LNM were identified as independent prognostic risk factors for 5-year OS and 5-year CSS following RC combined with Mainz PouchⅡ bladder reconstruction.Moreover,the application of RC combined with Mainz Pouch Ⅱ bladder reconstruction should consider the patient's preferences and physical condition.

Objective:To explore the association of central obesity, pain, their joint effect, and interaction with frailty in middle-aged and old people in China.Methods:A total of 14 359 participants aged ≥45 years in 2011, 2013 and 2015 were selected from the China Health and Retirement Longitudinal Study to construct a cohort database. Cox proportional hazards regression models were used to estimate the association of waist-to-height ratio (WHtR) and pain with the risk for frailty. Joint effect and interaction analyses were performed.Results:In the follow-up of 77 783 person-years, frailty developed in 3 198 participants, with an incidence density of 41.11 per 1 000 person-years. Compared with the Q1 level of WHtR, its Q2, Q3 and Q4 level increased risk for frailty by 17% ( HR=1.17, 95% CI: 1.05-1.31), 24% ( HR=1.24, 95% CI: 1.11-1.40), and 43% ( HR=1.43, 95% CI: 1.25-1.63), respectively. Compared with painlessness, suffering from pain increased the risk for frailty by 97% ( HR=1.97, 95% CI: 1.83-2.11), and having 1, 2, and ≥3 pain sites increased the risk by 42% ( HR=1.42, 95% CI: 1.25-1.61), 86% ( HR=1.86, 95% CI: 1.64-2.11), and 138% ( HR=2.38, 95% CI: 2.18-2.60), respectively. The results of restricted cubic spline showed that WHtR level was associated with the risk for frailty in a J-type dose-response relationship (total P<0.001, nonlinear P<0.001), and pain quantity was positively associated with the risk in a nonlinear dose-response relationship (total P<0.001, nonlinear P<0.001). Threshold effect analysis revealed that the inflection points of WHtR and pain site number were 0.46 and 2.00, respectively ( P<0.001). Joint effect analysis showed that the Q2, Q3 and Q4 levels of WHtR combined with pain increased the risk for frailty by 146% ( HR=2.46, 95% CI: 2.11-2.87), 169% ( HR=2.69, 95% CI: 2.30-3.16), and 157% ( HR=2.57, 95% CI: 2.18-3.03). Conclusions:The risk for frailty increased with the level of WHtR and the number of pain sites in middle-aged and old people, and there was joint effect between WHtR and pain. Comprehensive management and intervention of obesity and pain are significant for the early prevention of frailty.

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.

Objective:To explore the association of central obesity, pain, their joint effect, and interaction with frailty in middle-aged and old people in China.Methods:A total of 14 359 participants aged ≥45 years in 2011, 2013 and 2015 were selected from the China Health and Retirement Longitudinal Study to construct a cohort database. Cox proportional hazards regression models were used to estimate the association of waist-to-height ratio (WHtR) and pain with the risk for frailty. Joint effect and interaction analyses were performed.Results:In the follow-up of 77 783 person-years, frailty developed in 3 198 participants, with an incidence density of 41.11 per 1 000 person-years. Compared with the Q1 level of WHtR, its Q2, Q3 and Q4 level increased risk for frailty by 17% ( HR=1.17, 95% CI: 1.05-1.31), 24% ( HR=1.24, 95% CI: 1.11-1.40), and 43% ( HR=1.43, 95% CI: 1.25-1.63), respectively. Compared with painlessness, suffering from pain increased the risk for frailty by 97% ( HR=1.97, 95% CI: 1.83-2.11), and having 1, 2, and ≥3 pain sites increased the risk by 42% ( HR=1.42, 95% CI: 1.25-1.61), 86% ( HR=1.86, 95% CI: 1.64-2.11), and 138% ( HR=2.38, 95% CI: 2.18-2.60), respectively. The results of restricted cubic spline showed that WHtR level was associated with the risk for frailty in a J-type dose-response relationship (total P<0.001, nonlinear P<0.001), and pain quantity was positively associated with the risk in a nonlinear dose-response relationship (total P<0.001, nonlinear P<0.001). Threshold effect analysis revealed that the inflection points of WHtR and pain site number were 0.46 and 2.00, respectively ( P<0.001). Joint effect analysis showed that the Q2, Q3 and Q4 levels of WHtR combined with pain increased the risk for frailty by 146% ( HR=2.46, 95% CI: 2.11-2.87), 169% ( HR=2.69, 95% CI: 2.30-3.16), and 157% ( HR=2.57, 95% CI: 2.18-3.03). Conclusions:The risk for frailty increased with the level of WHtR and the number of pain sites in middle-aged and old people, and there was joint effect between WHtR and pain. Comprehensive management and intervention of obesity and pain are significant for the early prevention of frailty.

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.

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis