[Association between the triglyceride-glucose index and the incidence of nephrolithiasis in male individuals].

OBJECTIVE: To analyze the association between the triglyceride-glucose (TyG) index and the risk of nephrolithiasis across various demographic and clinical subgroups, aiming to enhance early diagnosis and treatment of nephrolithiasis and promote personalized care in diverse populations. METHODS: This cross-sectional study analyzed the medical records of 84 968 adults, stratified into three categories (low, middle, high) according to their TyG index scores. To evaluate the association between the TyG index and nephrolithiasis risk, multivariable Logistic regression models were employed, adjusting for potential confounders. Additionally, piecewise linear regression models were used to investigate the non-linear dynamics of the TyG index's relationship with nephrolithiasis risk. Subgroup analyses were performed to explore variations in the effects of the TyG index across different demographic and clinical populations. RESULTS: Increasing TyG index was associated with a higher risk of nephrolithiasis, rising from 4.36% in the low group to 8.96% in the high group (P < 0.001). In adjusted models, males in the middle and high TyG index categories demonstrated significantly elevated risks of nephrolithiasis, with odds ratios of 1.18 (95%CI: 1.07-1.31, P=0.002) and 1.29 (95%CI: 1.15-1.45, P < 0.001), respectively. Conversely, in females, the association was not statistically significant post-adjustment (OR=0.98, 95%CI: 0.82-1.16, P=0.778). Among males, for each unit increment in the TyG index below the critical threshold of 8.98, there was a notable 40% escalation in the risk of developing nephrolithiasis (OR=1.40, 95%CI: 1.24-1.58, P < 0.001). Surpassing this threshold, the TyG index no longer conferred a significant increase in risk (OR=0.91, 95%CI: 0.78-1.06, P=0.24). Subgroup analyses indicated that this association remained stable regardless of age, BMI, or hypertension status. CONCLUSION: The TyG index is positively associated with the risk of nephrolithiasis in males, demonstrating a nonlinear dose-response relationship that becomes especially pronounced at certain index levels. This biomarker could potentially serve as a valuable clinical tool for identifying males who are at a high risk of developing nephrolithiasis, thereby enabling targeted preventive strategies. Further research is urgently needed to explore the underlying mechanisms and to verify the applicability of these results across different populations.

Construction of a prediction model for prognosis of bladder cancer based on the expression of ion channel-related genes. / åŸºäºŽç¦»å­é€šé“ç›¸å ³åŸºå› çš„è†€èƒ±ç™Œæ‚£è€ é¢„åŽé£Žé™©è¯„ä¼°æ¨¡åž‹æž„å»º.

OBJECTIVES: To construct a prediction model for the prognosis of bladder cancer patients based on the expression of ion channel-related genes (ICRGs). METHODS: ICRGs were obtained from the existing researches. The clinical information and the expression of ICRGs mRNA in breast cancer patients were obtained from the Cancer Genome Atlas database. Cox regression analysis, minimum absolute shrinkage and selection operator regression analysis were used to screen breast cancer prognosis related genes, which were verified by immunohistochemistry and qRT-PCR. The risk scoring equation for predicting the prognosis of patients with bladder cancer was constructed, and the patients were divided into high-risk group and low-risk group according to the median risk score. Immune cell infiltration was compared between the two groups. Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the accuracy and clinical application value of the risk scoring equation. The factors related to the prognosis of bladder cancer patients were analyzed by univariate and multivariate Cox regression, and a nomogram for predicting the prognosis of bladder cancer patients was constructed. RESULTS: By comparing the expression levels of ICRGs in bladder cancer tissues and normal bladder tissues, 73 differentially expressed ICRGs were dentified, of which 11 were related to the prognosis of bladder cancer patients. Kaplan-Meier survival curve suggested that the risk score based on these 11 genes was negatively correlated with the prognosis of patients. The area under the ROC curve of the risk score for predicting the prognosis of patients at 1, 3 and 5 year was 0.634, 0.665 and 0.712, respectively. Stratified analysis showed that the ICRGs-based risk score performed well in predicting the prognosis of patients with American Joint Committee on Cancer (AJCC) stage Ⅲ-Ⅳ bladder cancer (P<0.05), while it had a poor value in predicting the prognosis of patients with AJCC stage Ⅰ-Ⅱ (P>0.05). There were significant differences in the infiltration of plasma cells, activated natural killer cells, resting mast cells and M2 macrophages between the high-risk group and the low-risk group. Cox regression analysis showed that risk score, smoking, age and AJCC stage were independently associated with the prognosis of patients with bladder cancer (P<0.05). The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1, 3 and 5 year overall survival rate of bladder cancer patients. CONCLUSIONS: The study shows the potential value of ICRGs in the prognostic risk assessment of bladder cancer patients. The constructed prognostic nomogram based on ICRGs risk score has high accuracy in predicting the prognosis of bladder cancer patients.

Integrated analysis to identify the prognostic and immunotherapeutic roles of coagulation-associated gene signature in clear cell renal cell carcinoma.

The coagulation system is closely related to the physiological status and immune response of the body. Recent years, studies focusing on the association between coagulation system abnormalities and tumor progression have been widely reported. In clear cell renal cell carcinoma (ccRCC), poor prognosis often occurs in patients with venous tumor thrombosis and coagulation system abnormalities, and there is a lack of research in related fields. Significant differences in coagulation function were also demonstrated in our clinical sample of patients with high ccRCC stage or grade. Therefore, in this study, we analyzed the biological functions of coagulation-related genes (CRGs) in ccRCC patients using single-cell sequencing and TCGA data to establish the 5-CRGs based diagnostic signature and predictive signature for ccRCC. Univariate and multivariate Cox analyses suggested that prognostic signature could be an independent risk factor. Meanwhile, we applied CRGs for consistent clustering of ccRCC patients, and the two classes showed significant survival and genotype differences. The differences in individualized treatment between the two different subtypes were revealed by pathway enrichment analysis and immune cell infiltration analysis. In summary, we present the first systematic analysis of the significance of CRGs in the diagnosis, prognosis, and individualized treatment of ccRCC patients.

Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis. / åŸºäºŽå ç–«ç»†èƒžæµ¸æ¶¦è¯„åˆ†å®žçŽ°è†€èƒ±ç™Œåˆ†åž‹åŠé¢„åŽé£Žé™©è¯„ä¼°.

OBJECTIVES: To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer. METHODS: The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas (TCGA) database. Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was achieved by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were identified. A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified. RESULTS: B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. The patients were clustered into two groups (Cluster 1 ´ and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1 ´, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). 35 genes related to key immune cells were screened out by WGCNA and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients. CONCLUSIONS: According to the immune cell infiltration score, bladder cancer patients can be classified. Furthermore, bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Establishment and Validation of a Tumor Microenvironment Prognostic Model for Predicting Bladder Cancer Survival Status Based on Integrated Bioinformatics Analyses.

This study was designed to analyze the characteristics of bladder cancer-related genes and establish a prognostic model of bladder cancer. The model passed an independent external validation set test. Differentially expressed genes (DEGs) related to bladder cancer were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. WGCNA was used to fit the GSE188715, TCGA, and GTEx RNA-Seq data. Fusing the module genes with the high significance in tumor development extracted from WGCNA and DEGs screened from multiple databases. 709 common prognostic-related genes were obtained. The 709 genes were enriched in the Gene Ontology database. Univariate Cox and LASSO regression analyses were used to screen out 21 prognostic-related genes and further multivariate Cox regression established a bladder cancer prognostic model consisting of 8 genes. After the eight-gene prognostic model was established, the Human Protein Atlas (HPA) database, GEPIA 2, and quantitative real-time PCR (qRT-PCR) verified the differential expression of these genes. Gene Set Enrichment Analysis and immune infiltration analysis found biologically enrichment pathways and cellular immune infiltration related to this bladder cancer prognostic model. Then, we selected bladder cancer patients in the TCGA database to evaluate the predictive ability of the model on the training set and validation set. The overall survival status of the two TCGA patient groups in the training and the test sets was obtained by Kaplan-Meier survival analysis. Three-year survival rates in the training and test sets were 37.163% and 25.009% for the low-risk groups and 70.000% and 62.235% for the high-risk groups, respectively. Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUCs) for the training and test sets were above 0.7. In an external independent validation database GSE13507, Kaplan-Meier survival analysis showed that the three-year survival rates of the high-risk and the low-risk groups in this database were 56.719% and 76.734%, respectively. The AUCs of the ROC drawn in the external validation set were both above 0.65. Here, we constructed a prognostic model of bladder cancer based on data from the GEO, TCGA, and GTEx databases. This model has potential prognostic and clinical auxiliary diagnostic value.

Spontaneous renal rupture due to renal calculi: A case report and literature review.

Spontaneous renal parenchymal rupture is a rare clinical emergency. The formation of benign and malignant tumors is the most common underlying cause of spontaneous rupture of renal parenchyma. To the best of our knowledge, 15 cases of renal parenchymal rupture have been reported to date. This report describes a rare case of renal parenchyma rupture in the lower left kidney caused by kidney calculi. Furthermore, previously published cases and articles were reviewed. The patient underwent four extracorporeal shockwave lithotripsy procedures within 2 years. The renal parenchyma rupture caused by the stones was successfully treated by removing the stones and repairing the kidney. However, a large hematoma was discovered around the lower pole of the left kidney, suggesting the possibility of a renal tumor. Therefore, radical nephrectomy was performed. Postoperative pathology revealed the lesion to be consistent with an intrarenal stone, where no malignancy, infection or vascular disease was observed. The present case highlights the requirement to also take into account the patient's clinical history in cases where imaging cannot completely identify the underlying cause of renal parenchymal rupture. Accurate identification of the underlying etiology of spontaneous renal rupture may determine the best treatment for the patient. The purpose of the present report is to facilitate the identification of the disease and reduce the rate of clinical misdiagnosis.

The Ion Channel-Related Gene Signatures Correlated With Diagnosis, Prognosis, and Individualized Treatment in Patients With Clear Cell Renal Cell Carcinoma.

Background: Early detection and precise prognostic evaluation of clear cell renal cell carcinoma (ccRCC) are crucial for patient life expectancy. Ion channel-related genes (ICRGs) are of great diagnostic and prognostic value as components that maintain the normal structure of the kidney. Therefore, we systematically explored the diagnostic, prognostic, and therapeutic value of ICRGs in ccRCC using the multi-database. Methods: RNA transcriptome profiles and clinical data of ccRCC patients were extracted and integrated from public databases including The Cancer Genome Atlas, ICGC, GEO, and E-MTAB databases. Ion channel-related genes were obtained from the literature collection. The diagnostic signature was performed using the LASSO and SVM-REF analyses. Meanwhile, the prognostic signature was conducted using the LASSO analyses. Molecular subtyping was performed using the ConsensusClusterPlus and the corresponding therapeutic targets were evaluated using the pRRophetic package. In addition, a prognostic nomogram was constructed based on the results of cox regression analyses. Results: We successfully constructed diagnostic signatures for five ICRGs and prognostic signatures for 10 ICRGs with AUC values greater than 0.7, showing good predictive performance. Based on the median risk score, we found that high-risk patients had a significantly worse prognosis. We also divided ccRCC patients into two clusters according to prognostic ICRGs, and there was a significant survival outcome between the two clusters and different sensitivity to diverse clinical therapeutic strategies. Meanwhile, we constructed a nomogram based on clinical molecules and signatures, and its predictive efficacy was better than the signature or the present tumor-node-metastasis staging system. Conclusion: In this study, we established useful signatures for early detection, prognosis evaluation, and individualized treatment for ccRCC. Moreover, KCNJ16 deserves to be explored comprehensively in the future.