Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics.

PURPOSE: Kidney stone disease (KSD) is a common urological disease, but its pathogenesis remains unclear. In this study, we screened KSD-related hub genes using bioinformatic methods and predicted the related pathways and potential drug targets. METHODS: The GSE75542 and GSE18160 datasets in the Gene Expression Omnibus (GEO) were selected to identify common differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify enriched pathways. Finally, we constructed a hub gene-miRNA network and drug-DEG interaction network. RESULTS: In total, 44 upregulated DEGs and 1 downregulated DEG were selected from the GEO datasets. Signaling pathways, such as leukocyte migration, chemokine activity, NF-κB, TNF, and IL-17, were identified in GO and KEGG. We identified 10 hub genes using Cytohubba. In addition, 21 miRNAs were predicted to regulate 4 or more hub genes, and 10 drugs targeted 2 or more DEGs. LCN2 expression was significantly different between the GEO datasets. Quantitative real-time polymerase chain reaction (qRT-PCR) analyses showed that seven hub gene expressions in HK-2 cells with CaOx treatment were significantly higher than those in the control group. CONCLUSION: The 10 hub genes identified, especially LCN2, may be involved in kidney stone occurrence and development, and may provide new research targets for KSD diagnosis. Furthermore, KSD-related miRNAs may be targeted for the development of novel drugs for KSD treatment.

A novel prognostic model based on six methylation-driven genes predicts overall survival for patients with clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (SAA1, FUT6, SPATA18, SHROOM3, AJAP1, and NPEPL1) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.

Characteristics of Gut Microbiota in Patients With Clear Cell Renal Cell Carcinoma.

Different gut microbiota is implicated in different diseases, including cancer. However, gut microbiota differences between individuals with clear cell renal cell carcinoma (ccRCC) and healthy individuals are unclear. Here, we analyzed gut microbiota composition in 51 ccRCC patients and 40 healthy controls using 16S rRNA sequencing analysis. We observed that Blautia, Streptococcus, [Ruminococcus]_torques_group, Romboutsia, and [Eubacterium]_hallii_group were dominant and positively associated with ccRCC. We isolated and cultured Streptococcus lutetiensis to characterize specific gut microbiota that promotes ccRCC and found that it promoted in vitro ccRCC proliferation, migration, and invasion via the TGF-signaling pathway. Interactions identified between the gut microbiota and ccRCC suggest the gut microbiota could serve as a potential non-invasive tool for predicting ccRCC risk and also function as a cancer therapy target.

A Prognostic Signature for Clear Cell Renal Cell Carcinoma Based on Ferroptosis-Related lncRNAs and Immune Checkpoints.

Background: Ferroptosis is a potential target for cancer therapy, and lncRNAs can also affect ferroptosis by regulating related genes. The pathogenesis of clear cell renal cell carcinoma (ccRCC) regarding the regulation of ferroptosis by lncRNAs is still unknown. Methods: We constructed a risk model based on data in ccRCC patients obtained from the TCGA database and validated the diagnostic and prognostic value of the model. In addition, immune function and immune checkpoint variability analysis validated the association of ferroptosis with ccRCC tumor immunity. Results: The characteristics of ferroptosis-related lncRNAs (FRLs) were significantly correlated with the prognosis of ccRCC patients. The prognostic characteristics of FRLs were independent prognostic factors in ccRCC patients. Gene function in the high-risk group was associated with oxygen metabolic processes and immune pathways. Immune checkpoint variability analysis showed that HAVCR2, NRP1, and HHLA2 were upregulated in the low-risk group, while CD44, TNFRSF18, TNFSF14, TNFRSF8, CD276, and TNFRSF25 were upregulated in the high-risk group. Conclusions: The prognostic characteristics of FRLs can effectively predict the prognosis of ccRCC patients and provide a new direction for clinical treatment.

Early Changes and Predictive Value of Serum Histone H3 Concentration in Urosepsis: A Prospective Observational Study.

INTRODUCTION: To assess early changes in serum histone H3 concentration in patients with urosepsis and its predictive ability for the onset of urosepsis. METHODS: A total of 80 patients who underwent percutaneous nephrolithotripsy were enrolled in the study and divided into control and urosepsis groups based on their postoperative outcomes. Serum histone H3 concentrations were detected using an enzyme-linked immunosorbent assay, blood indexes were tested by automatic blood analyzers, and vital signs data were obtained by monitors and manual measurements. These results were correlated with the incidence of postoperative urosepsis. Repeated measurements and receiver operating characteristic curves were employed to analyze early changes and the predictive value of serum histone H3 concentration in urosepsis. RESULTS: Sixteen of the 80 patients (20%) developed urosepsis after surgery. Our data showed significant intra-group differences in terms of postoperative histone H3 concentrations (P < 0.0001) and variation trends (P < 0.0001). Among analyzed blood markers, serum histone H3 concentrations 3 h postoperation [0.825 (95% confidence interval 0.718-0.931, P < 0.0001; cut-off value 256.74 ng/ml, 93.8% sensitivity, 67.2% specificity)] and 6 h post-operation [0.834 (95% CI 0.721-0.947, P < 0.0001, cut-off value 300.875 ng/ml, 68.8% sensitivity, 87.5% specificity)] displayed a higher area under the corresponding receiver operating characteristic curves, indicating that these markers had a decent predictive value for postoperative urosepsis. CONCLUSION: Our study suggests that serum histone H3 concentration is a novel predictor of postoperative urosepsis in patients undergoing percutaneous nephrolithotripsy. The findings of this study can be validated in a larger cohort. CLINICAL TRIAL REGISTRY NUMBER: ChiCTR1800016679.

miR-101-3p Serves as a Tumor Suppressor for Renal Cell Carcinoma and Inhibits Its Invasion and Metastasis by Targeting EZH2.

BACKGROUND: The role of miRNAs in renal cell carcinoma (RCC) is not certain. We wanted to study the biological functions and potential mechanisms of miR-101-3p in RCC. METHODS: miR-101-3p was inhibited in A498 and OSRC-2 (two RCC cell lines). We studied its effect on cell invasion and proliferation. Target EZH2 of miR-101-3p was designated by different methods, including luciferase functional analysis and Western blotting. The expression level of the target gene in treated cells was quantitatively analyzed by quantitative real-time polymerase chain reaction. In addition, induction of miR-101-3p to prevent tumor formation of A498 cells in mice was further studied. RESULTS: The overexpression of miR-101-3p significantly inhibited the proliferation, migration, and invasion in two RCC cells. Western blotting and luciferase functional analysis indicated that miR-101-3p regulated the expression of EZH2 in two cell lines. Mice inoculated with A498 and OSRC-2 cells transfected with miR-101-3p mimics showed significantly smaller xenografts and weaker EZH2 expression levels than the control group. CONCLUSIONS: miR-101-3p inhibited RCC cell proliferation, migration, and invasion by targeting EZH2.

Erratum: Silencing miR-454 suppresses cell proliferation, migration and invasion via directly targeting MECP2 in renal cell carcinoma.

[This corrects the article on p. 4277 in vol. 12, PMID: 32913504.].

Silencing miR-454 suppresses cell proliferation, migration and invasion via directly targeting MECP2 in renal cell carcinoma.

Renal cell cancer (RCC) is one of the most common malignant tumors of the urinary system. MicroRNA-454 (miR-454) has been reported to play an important role in various cancer progressions, such as hepatocellular carcinoma, breast cancer and glioblastoma. Nevertheless, its effect on RCC still remains unknown. We aimed to investigate the biological function and underlying mechanisms of miR-454 in RCC. The expressions of miR-454 and MECP2 in RCC tissues were assessed using data from TCGA database and our own clinical samples. Functional experiments Cell Counting Kit-8 (CCK-8), colony formation, wound healing and Transwell assays were applied to detect the effects of miR-454 and MECP2 in RCC. The interaction between miR-454 and MECP2 was assessed by western blot and luciferase reporter assays. MiR-454 was upregulated in RCC tissues and cell lines compared with matched adjacent normal tissues and the normal kidney tubular epithelial cell line HK-2. MiR-454 inhibition and methyl-CpG binding protein 2 (MECP2) overexpression could both decrease the proliferative, migrative and invasive abilities of RCC cells. Higher expression of miR-454 predicted a poor overall survival (OS) (HR: 1.8; P < 0.05), while MECP2 level was positively related with RCC OS (HR: 0.55; P < 0.05) and disease-free survival (HR: 0.56; P < 0.05). Mechanistically, we showed that miR-454 could directly target the downstream gene MECP2. Our findings indicated that miR-454 accelerates RCC progression via suppressing MECP2 expression, which may provide a novel potential target of RCC treatment in the future.

Bioinformatic gene analysis for potential biomarkers and therapeutic targets of diabetic nephropathy associated renal cell carcinoma.

BACKGROUND: Numerous epidemiological studies have confirmed that diabetes can promote the development of malignant tumors. However, the relationship between renal cell carcinoma (RCC) and diabetic nephropathy (DN) is still controversial. This study aimed to investigate the genes that are co-expressed in DN and RCC in order to gain a better understanding of the relationship between these diseases, and to identify potential biomarkers and targets for the treatment of DN-related RCC. METHODS: We evaluated the differentially expressed genes (DEGs) that are co-expressed in DN and RCC using a wide range of target prediction and analysis methods. Twenty-four genes were identified by intersecting the differential genes of 3 DN datasets and 2 RCC datasets. We predicted the micro-ribonucleic acids (miRNAs) of these genes that may be controlled using the miRNA Data Integration Portal (mirDIP) database, and rated them according to each data forecast based on the Comparative Toxicogenomics Database (CTD) and the StarBase database. RESULTS: Four genes were associated with DN and RCC patients: the predicted miRNAs hsa-miR-200b-3p and hsa-miR-429 of fibronectin 1 (FN1); the predicted miRNA hsa-miR-29c-3p of collagen type 1 alpha 2 (COL1A2); the predicted miRNA hsa-miR-29c-3p of collagen type 3 alpha 1 (COL3A1); and the predicted miRNA hsa-miR-29a-3p and hsa-miR-200c-3p of glucose-6-phosphatase catalytic subunit (G6PC). These genes may serve as potential biomarkers or specific targets in the treatment of DN-related RCC. CONCLUSIONS: A significant correlation was identified between DN and RCC. The FN1, COL1A2, COL3A1, and G6PC genes could be novel biomarkers of DN-related RCC.