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.

OBJECTIVES: To study the significance of serum 25-hydroxyvitamin D₃ [25-(OH)D₃] level in the clinicopathological characteristics and prognosis of children with immunoglobulin A vasculitis nephritis (IgAVN). METHODS: A retrospective analysis was conducted on the clinical data of children with IgAVN who underwent renal biopsy at Suzhou Hospital Affiliated to Anhui Medical University and Jinling Hospital of the Medical School of Nanjing University from June 2015 to June 2020. Based on serum 25-(OH)D₃ level, the patients were divided into a normal group and a lower group. The clinicopathological characteristics and follow-up data of the two groups were collected and compared. RESULTS: A total of 359 children with IgAVN were included. Compared to the normal group (62 cases), the lower group (297 cases) exhibited higher incidences of hematochezia and gross hematuria, higher levels of serum creatinine, blood urea nitrogen, urinary retinol protein, urinary N-acetyl-β-D-glucosaminidase, and quantitative urinary protein, and a longer duration from renal biopsy to urinary protein becoming negative, as well as lower estimated glomerular filtration rate and albumin level (P<0.05). Renal pathology in the lower group showed a higher occurrence of tubular interstitial injury, crescent formation, segmental sclerosis in glomeruli, and inflammatory cell infiltration in the renal interstitium compared to the normal group (P<0.05). Survival analysis indicated that the cumulative renal survival rate was lower in the lower group (P<0.05). Multivariate Cox regression analysis revealed that low serum 25-(OH)D₃ level is an independent risk factor for poor prognosis in children with IgAVN. CONCLUSIONS Children with IgAVN and low serum 25-(OH)D₃ level have relatively severe clinicopathological manifestations. Low serum 25-(OH)D₃ level is an independent risk factor for poor prognosis in children with IgAVN.
Humans ; Male ; Female ; Child ; Prognosis ; Retrospective Studies ; Calcifediol/blood* ; Child, Preschool ; Adolescent ; Glomerulonephritis, IGA/mortality* ; Vasculitis/pathology* ; IgA Vasculitis/mortality*

OBJECTIVES: To summarize the clinical and genetic characteristics of end-stage renal disease caused by PLCE1 gene mutations. METHODS: A retrospective analysis of the clinical and genetic features of three children from a family with PLCE1 gene mutations was conducted, along with a literature review of hereditary kidney disease cases caused by PLCE1 gene mutations. RESULTS: The proband was an 8-year-old male presenting with nephrotic syndrome stage 4 chronic kidney disease. Renal biopsy showed focal segmental glomerulosclerosis. Two years and five months after kidney transplantation, the patient had persistent negative proteinuria and normal renal function. Whole-exome sequencing identified two pathogenic heterozygous variants: c.961C>T and c.3255_3256delinsT, with c.3255_3256delinsT being a novel mutation. Family screening revealed no renal involvement in the parents, but among five siblings, one brother died at age of 4 years from end-stage renal disease. A 7-year-old sister presented with proteinuria and bilateral medullary sponge kidney, with proteinuria resolving after one year of follow-up. A 3-year-old brother died after kidney transplantation due to severe pneumonia. The literature review included 45 patients with hereditary kidney disease caused by PLCE1 gene mutations. The main clinical phenotype was nephrotic syndrome (87%, 39/45), and renal pathology predominantly showed focal segmental glomerulosclerosis (57%, 16/28). No mutation hotspots were identified. CONCLUSIONS Compound heterozygous mutations in the PLCE1 gene can lead to rapid progression of the disease to end-stage renal disease, with favorable outcomes following kidney transplantation. Family screening is crucial for early diagnosis, and medullary sponge kidney may be a novel phenotype associated with these gene mutations.
Humans ; Male ; Proteinuria/genetics* ; Kidney Failure, Chronic/etiology* ; Child ; Mutation ; Female ; Child, Preschool ; Retrospective Studies ; Phosphoinositide Phospholipase C

OBJECTIVE: To detect and analyze the expression and clinical significance of long non-coding RNA tyrosine kinase non-catalytic region adaptor protein 1-antisense RNA1 (NCK1-AS1) in patients with acute myeloid leukemia (AML). METHODS: 89 AML patients and 23 healthy controls were included from the People's Hospital Affiliated to Jiangsu University. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of NCK1-AS1 and NCK1 in bone marrow samples. The relationship between the expression of NCK1-AS1 and the clinical characteristics of patients were analyzed, as well as the correlation between NCK1-AS1 and NCK1. RESULTS: The expression level of NCK1-AS1 in all AML, non-M3 AML and cytogenetically normal AML (CN-AML) patients was significantly higher than that in the control group (P < 0.01, P < 0.05, P < 0.01, respectively). In non-M3 AML, patients with high NCK1-AS1 expression had a significantly lower hemoglobin level than those with low NCK1-AS1 expression (P =0.036), furthermore, NCK1-AS1 ^high patients had shorter overall survival than NCK1-AS1^low patients (P =0.0378). Multivariate analysis showed that NCK1-AS1 expression was an independent adverse factor in patients with non-M3 AML ( HR =2.392, 95% CI :1.089-5.255, P =0.030). In addition, NCK1 expression was also significantly upregulated in all AML, non-M3 AML and CN-AML patients compared with controls (P < 0.01, P < 0.01, P < 0.001, respectively). There was a certain correlation between NCK1-AS1 and NCK1 expression (r =0.37, P =0.0058). CONCLUSION High expression of NCK1-AS1 in AML indicates poor prognosis of AML patients.
Humans ; Leukemia, Myeloid, Acute/genetics* ; RNA, Long Noncoding/genetics* ; Oncogene Proteins/genetics* ; Adaptor Proteins, Signal Transducing/genetics* ; Prognosis ; Male ; Female ; Middle Aged ; Adult ; Case-Control Studies ; Clinical Relevance

OBJECTIVE: To evaluate the anti-hepatocellular carcinoma (HCC) activity of total alkaloids from Gelsemium elegans Benth. (TAG) in vivo and in vitro and to elucidate their potential mechanisms of action through transcriptomic analysis. METHODS: TAG extraction was conducted, and the primary components were quantified using high-performance liquid chromatography (HPLC). The effects of TAG (100, 150, and 200 µg/mL) on various tumor cells, including SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116, were assessed. Effects of TAG on HCC proliferation and apoptosis were detected by colony formation assays and cell stainings. Caspase-3, Bcl-2, and Bax protein levels were detected by Western blotting. In vivo, a tumor xenograft model was developed using H22 cells. Totally 40 Kunming mice were randomly assigned to model, cyclophosphamide (20 mg/kg), TAG low-dose (TAG-L, 0.5 mg/kg), and TAG high-dose (TAG-H, 1 mg/kg) groups, with 10 mice in each group. Tumor volume, body weight, and tumor weight were recorded and compared during 14-day treatment. Immune organ index were calculated. Tissue changes were oberseved by hematoxylin and eosin staining and immunohistochemistry. Additionally, transcriptomic and metabolomic analyses, as well as quatitative real-time polymerase chain reaction (RT-qPCR), were performed to detect mRNA and metabolite expressions. RESULTS: HPLC successfully identified the components of TAG extraction. Live cell imaging and analysis, along with cell viability assays, demonstrated that TAG inhibited the proliferation of SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116 cells. Colony formation assays, Hoechst 33258 staining, Rhodamine 123 staining, and Western blotting revealed that TAG not only inhibited HCC proliferation but also promoted apoptosis (P<0.05). In vivo experiments showed that TAG inhibited the growth of solid tumors in HCC in mice (P<0.05). Transcriptomic analysis and RT-qPCR indicated that the inhibition of HCC by TAG was associated with the regulation of the key gene CXCL13. CONCLUSION TAG inhibits HCC both in vivo and in vitro, with its inhibitory effect linked to the regulation of the key gene CXCL13.
Animals ; Apoptosis/drug effects* ; Liver Neoplasms/genetics* ; Carcinoma, Hepatocellular/genetics* ; Humans ; Alkaloids/therapeutic use* ; Gelsemium/chemistry* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Mice ; Xenograft Model Antitumor Assays

ObjectiveTriple-negative breast cancer (TNBC) is the breast cancer subtype with the worst prognosis, and lacks effective therapeutic targets. Colony stimulating factors (CSFs) are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells, playing an important role in the malignant progression of TNBC. This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes (CRGs), and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy. MethodsWe downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database. Through LASSO Cox regression analysis, we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score (CRRS). We further analyzed the correlation between CRRS and patient prognosis, clinical features, tumor microenvironment (TME) in both high-risk and low-risk groups, and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy. ResultsWe identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model. Kaplan-Meier survival curves, time-dependent receiver operating characteristic curves, and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival, and the predictive ability of CRRS prognostic model was further validated using the GEO dataset. Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients. Moreover, patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil, ipatasertib, and paclitaxel. ConclusionWe have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs, which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment. Moreover, the key genes within this model may represent potential molecular targets for future therapies of TNBC.