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.

ObjectiveTo investigate the effect of Homebox A6 （HOXA6） on the proliferation， invasion， metastasis， and apoptosis of HepG2 hepatoma cells and its association with the PI3K/AKT signaling pathway. MethodsHepG2 hepatoma cells were cultured， and HOXA6 overexpression plasmid and siRNA were constructed and transfected into cells. The cells were randomly divided into empty plasmid group， HOXA 6 overexpression group， siRNA negative control group， and siRNA HOXA6 interference group. CCK8 assay was used to measure cell proliferation， Transwell assay was used to observe cell invasion， and wound healing assay was used to observe cell migration （related proteins TIMP3， MMP9， and MMP3）. Flow cytometry was used to measure cell apoptosis （related proteins BAX and BCL2）， the BCA method was used to measure protein concentration， and Western Blot was used to measure the expression of related proteins. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the SNK-q test was used for further comparison between two groups. ResultsCompared with the empty plasmid group， HOXA6 overexpression significantly promoted the proliferation， invasion， and migration of HepG2 hepatoma cells （all P<0.001）， and there was a significant reduction in the protein expression of TIMP3 （P<0.001）， while there were significant increases in the expression levels of MMP9 and MMP3 （both P<0.001）. Compared with the siRNA negative control group， HOXA6 interference significantly inhibited the proliferation， invasion， and migration of HepG2 hepatoma cells （all P<0.001）， and there was a significant increase in the protein expression of TIMP3 （P<0.001）， while there were significant reductions in the expression levels of MMP9 and MMP3 （both P<0.001）. Flow cytometry showed that compared with the empty plasmid group， HOXA6 overexpression inhibited the apoptosis of HepG2 hepatoma cells （P<0.001）， with a significant reduction in the expression of the apoptosis-related protein BAX and a significant increase in the expression of BCL2 （both P<0.001）. Compared with siRNA negative control group， HOXA6 interference promoted the apoptosis of HepG2 hepatoma cells （P<0.001）， with a significant increase in the expression of BAX and a significant reduction in the expression of BCL2 （both P<0.001）. Compared with the empty plasmid group， the HOXA6 overexpression group had significantly higher ratios of p-AKT/AKT and p-PI3K/PI3K （both P<0.001）， and compared with the siRNA negative control group， the siRNA HOXA6 interference group had significantly lower ratios of p-AKT/AKT and p-PI3K/PI3K （both P<0.001）. ConclusionHOXA6 can promote the proliferation， invasion， and metastasis of HepG2 hepatoma cells and inhibit their apoptosis by activating the PI3K/AKT signaling pathway through phosphorylation.

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 Using female mice to investigate the reparative effects of human umbilical cord mesenchymal stem cells on radiation-induced ovarian injury. Methods Mice were randomly divided into three groups: a blank control group, a radiation model group, and a cell therapy group. Mice in the radiation model group and the cell therapy group received a single whole-body irradiation of 5 Gy X-rays. Within 2 hours post-irradiation, mice in the cell therapy group underwent ovarian transplantation of UC-MSCs. On days 1, 7, and 14 post-irradiation, body weight was measured, ovarian index was calculated, histopathological changes in ovarian tissue were examined, serum levels of reproductive hormones (follicle-stimulating hormone, anti-Müllerian hormone, and estradiol) were determined, and the colonization of implanted UC-MSCs in the mice was observed. Results On days 1, 7, and 14 post-irradiation, both the cell therapy group and the radiation model group showed decreased body weight compared to the blank control group (P < 0.05). On day 1 post-irradiation compared to day 1 pre-irradiation within the same group, the radiation model group exhibited a greater decrease in body weight than the cell therapy group (P < 0.05). On days 1, 7, and 14 post-irradiation, the ovarian index decreased in both the radiation model group and the cell therapy group compared to the blank control group (P < 0.05). On days 7 and 14 post-irradiation, the ovarian index in the cell therapy group was significantly higher than that in the radiation model group (P < 0.05). Ovarian tissue in the radiation model group exhibited atrophy and a reduction in the number of follicles at all stages. In contrast, follicles in the cell therapy group were large and abundant. On days 1, 7, and 14 post-irradiation, serum follicle-stimulating hormone levels in the cell therapy group were lower than those in the radiation model group, while anti-Müllerian hormone and estradiol levels were higher than those in the radiation model group (P < 0.01). In vivo fluorescence imaging demonstrated that UC-MSCs successfully colonized the ovarian tissue on days 1, 7, and 14 after transplantation. Conclusion UC-MSCs exert a repair effect on radiation-induced ovarian injury in mice.

[摘 要] 目的：探究组蛋白去乙酰化酶（HDAC）抑制剂帕比司他对黑色素瘤生长和免疫性的影响及其机制。方法：常规培养黑色素瘤细胞B16F0，用不同浓度的帕比司他处理细胞，WB法检测帕比司他对B16F0细胞中HDAC表达的影响，CCK-8法、划痕愈合实验、Transwell实验和流式细胞术分别检测帕比司他对B16F0细胞增殖、迁移和侵袭能力，以及细胞凋亡和周期的影响。转录组学检测帕比司他对B16F0细胞基因表达的影响，用qPCR法加以验证。流式细胞术检测帕比司他对B16F0细胞表面MHC Ⅰ/Ⅱ类分子表达的影响，B16F0与骨髓来源树突状细胞（BMDC）共培养检测帕比司他对BMDC细胞表达CD11c、CD80和CD86的影响，B16F0细胞移植瘤实验检测帕比司他对移植瘤生长和裸鼠免疫功能的影响。结果：帕比司他促进B16F0细胞中组蛋白3（H3）和α-微管蛋白（α-TUB）蛋白乙酰化（P < 0.01或P < 0.001或P < 0.000 1），抑制B16F0细胞增殖、迁移和侵袭能力，促进其凋亡，并使细胞周期阻滞于G1期（P < 0.05或P < 0.001或P < 0.000 1），促进B16F0细胞表面表达MHC Ⅰ/Ⅱ类分子表达并促进共培养BMDC成熟（均P < 0.01）。转录组学检测结果显示，帕比司他促进B16F0细胞中E-cadherin和抗原提呈相关基因的表达，抑制N-cadherin、vimentin、c-Myc和CDK1的表达，qPCR法验证了这些结果。帕比司他抑制裸鼠移植瘤的生长并增强荷瘤裸鼠的免疫功能（P < 0.05, P < 0.000 1）。结论：帕比司他可抑制B16F0细胞的恶性生物学行为，促进其凋亡，调控其免疫性，增强荷瘤裸鼠的免疫功能。

Epigenetic pathways play a critical role in the initiation, progression, and metastasis of cancer. Over the past few decades, significant progress has been made in the development of targeted epigenetic modulators (e.g., inhibitors). However, epigenetic inhibitors have faced multiple challenges, including limited clinical efficacy, toxicities, lack of subtype selectivity, and drug resistance. As a result, the design of new epigenetic modulators (e.g., degraders) such as PROTACs, molecular glue, and hydrophobic tagging (HyT) degraders has garnered significant attention from both academia and pharmaceutical industry, and numerous epigenetic degraders have been discovered in the past decade. In this review, we aim to provide an in-depth illustration of new degrading strategies (2017-2023) targeting epigenetic proteins for cancer therapy, focusing on the rational design, pharmacodynamics, pharmacokinetics, clinical status, and crystal structure information of these degraders. Importantly, we also provide deep insights into the potential challenges and corresponding remedies of this approach to drug design and development. Overall, we hope this review will offer a better mechanistic understanding and serve as a useful guide for the development of emerging epigenetic-targeting degraders.

Objective To prepare flumazenil sublingual tablets and study its bioavailability. Methods Flumazenil sublingual tablets were prepared by compressing flumazenil inclusion compound with hydroxypropyl-β-cyclodextrin as the inclusion material. In a double-cycle crossover trial, twelve beagle dogs were randomly divided into two groups, one group receiving flumazenil sublingual tablets and the other receiving flumazenil injections. LC-MS method was developed and validated to determine flumazenil plasma concentration. The pharmacokinetic parameters and bioavailability were calculated using WinNonlin pharmacokinetic software. Results In the pharmacokinetic study, AUC_last of flumazenil injection and sublingual tablet was （8.41±2.15） and （8.86±2.83） h·ng·ml⁻¹, respectively; C_max was （10.96±2.62） and （6.36±2.14） ng/ml, respectively; t_max was （0.18±0.05） and （0.58±0.24） h, respectively. The bioavailability of flumazenil sublingual tablet was 52.68%. Conclusion Clathrates were used to prepare flumazenil sublingual tablets to achieve safe and efficient delivery. LC-MS method was established for the determination of flumazenil plasma concentration, and the advantages were simple, accurate and sensitive.