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Background: Carbonic anhydrase 4 (CA4) maintains homeostasis of carbon dioxide and bicarbonate. It is suggested to be a potential prognostic biomarker, while the correlations between CA4 and different cancers are indistinct. Methods: Differential mRNA expression of CA4 among different cancers and corresponding normal tissues was compared based on datasets on the Cancer Genome Atlas (TCGA) platforms. Then, survival analysis was performed using Tumor-immune system interactionsplatform and TCGA cohort on the basis of distinct comparison expression of CA4 in five kinds of tumors. In addition, molecular penal analysis and functional annotations of CA4-related genes was elaborated. The correlation between CA4 mRNA expression and tumor immune microenvironment were analyzed in detail. Results: Compared with adjacent normal tissues, CA4 mRNA expressions were found significantly lower in various tumors. Moreover, decreased expression of CA4 was significantly related to worse overall survival (OS) and progression-free survival (PFS) in kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), lung adenocarcinoma (LUAD) and uveal melanoma (UVM), and worse OS of prostate adenocarcinoma (PRAD) (p

Clear cell renal cell carcinoma (ccRCC) remains one of the most common cancer types globally, and while it has been extensively studied, the molecular basis for its pathology remains incompletely understood. Herein, we profiled three previously published datasets (GSE66272, GSE100666, and GSE105261) in a single integrated analysis aimed at identifying disease-associated patterns of gene expression that may offer mechanistic insight into the drivers of this disease. We pooled expression data from 39 normal kidney samples and 39 kidney tumors, leading us to identify 310 differentially expressed genes (DEGs) that were linked to kidney cancer in all three analyzed datasets. Of these genes, 133 and 177 were up- and down-regulated, respectively, in cancer samples. We then incorporated these DEGs into a protein-protein interaction network with the STRING and Cytoscape tools, and we were able to identify signaling pathways significantly enriched for these DEGs. The relationship between DEG expression and ccRCC patient survival was further evaluated using a Kaplan-Meier approach, leading us to identify TIMP1 as an independent prognostic factor in ccRCC patients. When TIMP1 expression was disrupted in ccRCC cell lines, this impaired their migratory and invasive capabilities. In summary, we employed an integrative bioinformatics approach to identify ccRCC-related DEGs and associated signaling pathways. Together these findings offer novel insight into the mechanistic basis for ccRCC, potentially helping to identify novel therapeutic targets for the treatment of this deadly disease.

Clear cell renal cell carcinoma (ccRCC) is one of the most common cancers worldwide. Despite intense efforts to elucidate its pathogenesis, the molecular mechanisms and genetic characteristics of this cancer remain unknown. In this study, three expression profile data sets (GSE15641, GSE16441 and GSE66270) were integrated to identify candidate genes that could elucidate functional pathways in ccRCC. Expression data from 63 ccRCC tumors and 54 normal samples were pooled and analyzed. The GSE profiles shared 379 differentially expressed genes (DEGs), including 249 upregulated genes, and 130 downregulated genes. A protein-protein interaction network (PPI) was constructed and analyzed using STRING and Cytoscape. Functional and signaling pathways of the shared DEGs with significant p values were identified. Kaplan-Meier plots of integrated expression scores were used to analyze survival outcomes. These suggested that FN1, ICAM1, CXCR4, TYROBP, EGF, CAV1, CCND1 and PECAM1/CD31 were independent prognostic factors in ccRCC. Finally, to investigate early events in renal cancer, we screened for the hub genes CCND1 and PECAM1/CD31. In summary, integrated bioinformatics analysis identified candidate DEGs and pathways in ccRCC that could improve our understanding of the causes and underlying molecular events of ccRCC. These candidate genes and pathways could be therapeutic targets for ccRCC.

BACKGROUND The serine peptidase inhibitor Kazal type 13 (SPINK13) gene has tumor suppressor activity, but its role in renal cell carcinoma (RCC) remains unknown. This study aimed to investigate mRNA expression of SPINK13 in clear cell renal cell carcinoma (CCRCC) in human tissue and to use bioinformatics data to investigate the role of SPINK13 expression as a clinicopathological and prognostic biomarker for patients with CCRCC. MATERIAL AND METHODS Patients with CCRCC (N=533) with available RNA sequence data from The Cancer Genome Atlas (TCGA)-CCRCC database were analyzed with patients who had a tissue diagnosis of CCRCC (N=305) at the Fudan University Shanghai Cancer Center (FUSCC). Differential transcriptional and proteome expression profiles were obtained from the ONCOMINE cancer microarray database, TCGA, and the Human Protein Atlas (HPA) database. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) measured SPINK13 mRNA expression in 305 samples of CCRCC tissue from the FUSCC. The effects of clinicopathological parameters on progression-free survival (PFS) and overall survival (OS) were analyzed using the Kaplan-Meier and log-rank test. RESULTS Transcriptional and proteome expression of SPINK13 were significantly increased CCRCC tissue samples. Increased SPINK13 mRNA expression was significantly associated with reduced PFS and OS in 838 patients with CCRCC patients from the two independent cohorts, the FUSCC and the TCGA-CCRCC cohorts (p

Necroptosis is a form of programmed, caspase-independent cell death that is involved in various pathologic disorders such as ischemia/reperfusion injury, acute kidney injury and inflammatory bowel diseases. Identification of necroptosis inhibitors has great therapeutic potential for the treatment of necroptosis-associated diseases. In this study, we identified that the Bcr-Abl inhibitor GNF-7 was a potent inhibitor of necroptosis. GNF-7 inhibited necroptosis in both human and mouse cells, while not protecting cells from apoptosis. Drug affinity responsive target stability assay (DARTS) demonstrated that it binded with RIPK1 and RIPK3. GNF-7 inhibited RIPK1 and RIPK3 kinase activities and thus disrupted RIPK1-RIPK3 necrosome complex formation. In vivo, GNF-7 ameliorated both cisplatin- and ischemia/reperfusion-induced AKI. Orally administration of GNF-7 attenuated renal cell necrosis and reduced pro-inflammatory responses in mouse models of AKI. Taken together, our study shows that GNF-7 is a novel necroptosis inhibitor and has great potential for the treatment of acute renal inflammatory disorders by targeting both RIPK1 and RIPK3 kinases.

Cisplatin (cis-dichloro-diammine platinum, CDDP) is a well-known chemotherapeutic drug against a broad spectrum of human malignancies. However, the clinical utility of this effective chemotherapy agent is dose limited by its toxic side effects such as nephrotoxicity and ototoxicity. Necroptosis is a form of programmed necrotic cell death that is mediated by serine/threonine kinases, RIPK1 and RIPK3, together with MLKL. In this study, we identified that the multitargeted kinase inhibitor KW-2449 inhibited cisplatin-induced necroptosis, while potentiated cisplatin-induced apoptosis in cancer cells. Mechanistic studies indicated that KW-2449 directly inhibited RIPK1 kinase activity to block necroptosis. Oral administration of KW-2449 attenuated renal cell necrosis and reduced pro-inflammatory responses in mouse models of cisplatin-induced nephrotoxicity. Taken together, our study shows that KW-2449 is a novel necroptosis inhibitor by targeting RIPK1 kinase activity and has great clinic potential for the treatment of cisplatin-induced nephrotoxicity.

Additional file 3: Table S1. Clinicopathological characteristics baseline in relation to AQP9 expression status in TCGA cohort. Table S2. Univariate Cox logistic regression analysis of PFS in TCGA and FUSCC cohort (PFS: progression-free survival; TCGA: the Cancer Genome Atlas; FUSCC: Fudan university shanghai cancer center). Table S3. Univariate Cox logistic regression analysis of OS in TCGA and FUSCC cohort (OS: overall survival; TCGA: the Cancer Genome Atlas; FUSCC: Fudan university shanghai cancer center). Table S4. Correlation analysis between AQP9 and immune cell infiltrations in ccRCC samples using TIMER.