Tumour necrosis factor receptor-associated factor-1 (TRAF-1) expression is increased in renal cell carcinoma patient serum but decreased in cancer tissue compared with normal: potential biomarker significance

TRAF1 improves cisplatin-induced acute kidney injury via inhibition of inflammation and metabolic disorders

BACKGROUND

Cisplatin-induced acute kidney injury (AKI) is a severe clinical complication with no satisfactory therapies in the clinic. Tumor necrosis factor receptor (TNFR)-associated factor 1 (TRAF1) plays a vital role in both inflammation and metabolism. However, the TRAF1 effect in cisplatin induced AKI needs to be evaluated.

METHODS

We observed the role of TRAF1 in eight-week-old male mice and mouse proximal tubular cells both treated with cisplatin by examining the indicators associated with kidney injury, apoptosis, inflammation, and metabolism.

RESULTS

TRAF1 expression was decreased in cisplatin-treated mice and mouse proximal tubular cells (mPTCs), suggesting a potential role of TRAF1 in cisplatin-associated kidney injury. TRAF1 overexpression significantly alleviated cisplatin-triggered AKI and renal tubular injury, as demonstrated by reduced serum creatinine (Scr) and urea nitrogen (BUN) levels, as well as the ameliorated histological damage and inhibited upregulation of NGAL and KIM-1. Moreover, the NF-κB activation and inflammatory cytokine production enhanced by cisplatin were significantly blunted by TRAF1. Meanwhile, the increased number of apoptotic cells and enhanced expression of BAX and cleaved Caspase-3 were markedly decreased by TRAF1 overexpression both in vivo and vitro. Additionally, a significant correction of the metabolic disturbance, including perturbations in energy generation and lipid and amino acid metabolism, was observed in the cisplatin-treated mice kidneys.

CONCLUSION

TRAF1 overexpression obviously attenuated cisplatin-induced nephrotoxicity, possibly by correcting the impaired metabolism, inhibiting inflammation, and blocking apoptosis in renal tubular cells.

GENERAL SIGNIFICANCE

These observations emphasize the novel mechanisms associated to metabolism and inflammation of TRAF1 in cisplatin-induced kidney injury.

Phosphorylated Proteins from Serum: A Promising Potential Diagnostic Biomarker of Cancer

Cancer is a fatal disease worldwide. Each year ten million people are diagnosed around the world, and more than half of patients eventually die from it in many countries. A majority of cancer remains asymptomatic in the earlier stages, with specific symptoms appearing in the advanced stages when the chances of adequate treatment are low. Cancer screening is generally executed by different imaging techniques like ultrasonography (USG), mammography, CT-scan, and magnetic resonance imaging (MRI). Imaging techniques, however, fail to distinguish between cancerous and non-cancerous cells for early diagnosis. To confirm the imaging result, solid and liquid biopsies are done which have certain limitations such as invasive (in case of solid biopsy) or missed early diagnosis due to extremely low concentrations of circulating tumor DNA (in case of liquid biopsy). Therefore, it is essential to detect certain biomarkers by a noninvasive approach. One approach is a proteomic or glycoproteomic study which mostly identifies proteins and glycoproteins present in tissues and serum. Some of these studies are approved by the Food and Drug Administration (FDA). Another non-expensive and comparatively easier method to detect glycoprotein biomarkers is by ELISA, which uses lectins of diverse specificities. Several of the FDA approved proteins used as cancer biomarkers do not show optimal sensitivities for precise diagnosis of the diseases. In this regard, expression of phosphoproteins is associated with a more specific stage of a particular disease with high sensitivity and specificity. In this review, we discuss the expression of different serum phosphoproteins in various cancers. These phosphoproteins are detected either by phosphoprotein enrichment by immunoprecipitation using phosphospecific antibody and metal oxide affinity chromatography followed by LC-MS/MS or by 2D gel electrophoresis followed by MALDI-ToF/MS analysis. The updated knowledge on phosphorylated proteins in clinical samples from various cancer patients would help to develop these serum phophoproteins as potential diagnostic/prognostic biomarkers of cancer.

Renal tumor biomarkers (Review)

One of the most common types of cancer worldwide (9th most commonly diagnosed) is renal cell carcinoma (RCC). It is more common in developed countries and it usually develops in individuals between 60 and 70 years of age. The earlier the disease is identified, the lower the morbidity. Therefore molecular markers that exist in blood and urine may be used for earlier detection and diagnosis but also for the follow-up of the patient after treatment, whether surgical or oncological. The trend is to analyze the gene and protein expression as they constitute a source for new biomarkers. These markers are promising but in clinical practice regarding disease management, they are rarely used. Biological markers can be employed in many tumors because they can identify the prognostic value for individual treatment. However, markers for RCC are not validated, and their analysis is currently under investigation. Previous findings have demonstrated that the metastatic potential of RCC can be predicted using the biological features of the tumor cell. It is believed that the transformation from epithelial to mesenchymal phenotype gives the tumor cell the ability to metastasize. The purpose of this review was to identify the most valuable tumor markers that can be clinically used for the prognosis, treatment and follow-up of patients with renal tumors.

Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma

Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers of ccRCC. Between January 2016 and August 2018, we collected urine samples from 87 patients who had pathologically diagnosed ccRCC and from 60 controls who were patients with benign urological conditions. Metabolite concentrations in urine samples were investigated using liquid chromatography‐mass spectrometry with an internal standard and adjustment based on urinary creatinine levels. We analyzed the association between metabolite concentration and predictability of diagnosis and of malignant status by multiple logistic regression and receiver operating characteristic (ROC) curves to establish ccRCC predictive models. Of the 47 metabolites identified in our previous study, we quantified 33 metabolites in the urine samples. Multiple logistic regression analysis revealed 5 metabolites (l‐glutamic acid, lactate, d‐sedoheptulose 7‐phosphate, 2‐hydroxyglutarate, and myoinositol) for a diagnostic predictive model and 4 metabolites (l‐kynurenine, l‐glutamine, fructose 6‐phosphate, and butyrylcarnitine) for a predictive model for clinical stage III/IV. The sensitivity and specificity of the diagnostic predictive model were 93.1% and 95.0%, respectively, yielding an area under the ROC curve (AUC) of 0.966. The sensitivity and specificity of the predictive model for clinical stage were 88.5% and 75.4%, respectively, with an AUC of 0.837. In conclusion, quantitative analysis of urinary metabolites yielded predictive models for diagnosis and malignant status of ccRCC. Urinary metabolites have the potential to be clinically useful noninvasive biomarkers of ccRCC to improve patient outcomes.

Value of Ferritin Heavy Chain (FTH1) Expression in Diagnosis and Prognosis of Renal Cell Carcinoma

BACKGROUND Serum ferritin is a useful tumor marker for renal cell carcinoma (RCC). However, the expression of ferritin heavy chain (FTH1), the main subunit of ferritin, is unclear in primary RCC tissues. In this study, we investigated FTH1 mRNA expression and its diagnostic and prognostic value in RCC. MATERIAL AND METHODS The mRNA expression of FTH1 was analyzed using including Oncomine, Gene Expression Omnibus, and Cancer Genome Atlas datasets, while the protein level of FTH1 was analyzed using the Human Protein Atlas database. The associations between FTH1 and clinicopathologic characteristics and survival time and Cox multivariate survival analysis were analyzed using SPSS 22.0 software. A meta-analysis was performed to assess consistency of FTH1 expression. GO, KEGG, and PPI analyses were used to predict biological functions. RESULTS According to TCGA data, overexpression of FTH1 was detected in 890 RCC tissues (15.2904±0.63157) compared to 129 normal kidney tissues (14.4502±0.51523, p<0.001). Among the clinicopathological characteristics evaluated, patients with increased pathologic T staging, lymph node metastasis, and distant metastasis were significantly associated with higher expression of FTH1. Elevated FTH1 mRNA levels were correlated with worse prognosis of RCC patients. Cox multivariate survival analysis indicated that age, stage, and M stage were predictors of poor prognosis in patients with RCC. CONCLUSIONS Our data suggest that FTH1 expression is an effective prognostic and diagnosis biomarker for RCC.

Renal cell carcinoma: a critical analysis of metabolomic biomarkers emerging from current model systems

Metabolomics, an emerging field of "omics" sciences, has caught wide scientific attention in the area of biomarker research for cancers in which early diagnostic biomarkers have the potential to greatly improve patient outcome, such as renal cell carcinoma (RCC). Metabolomic approaches have been successfully applied to various human RCC model systems, mostly ex vivo neoplastic renal tissues and biofluids (urine and serum) from patients with RCC. Importantly, in contrast to other cancers, only a few studies have addressed the RCC metabolome using cancer cell culture-based in vitro models. Herein, we first carried out a comprehensive review of current metabolomic data in RCC, with emphasis on metabolite disturbances and dysregulated metabolic pathways identified in each of these experimental models. We then critically analyzed the consistency of evidence in this field and whether metabolites found altered in tumor cell and tissue microenvironment are reflected in biofluids, which constitute the rationale underlying the translation of discovered metabolic biomarkers into noninvasive diagnostic tools. Finally, dominant metabolic pathways and promising metabolites as biomarkers for diagnosis and prognosis of RCC are outlined.

Serum and urine biomarkers for human renal cell carcinoma

Renal cell carcinoma (RCC) diagnosis is mostly achieved incidentally by imaging provided for unrelated clinical reasons. The surgical management of localized tumors has reported excellent results. The therapy of advanced RCC has evolved considerably over recent years with the widespread use of the so-called “targeted therapies.” The identification of molecular markers in body fluids (e.g., sera and urine), which can be used for screening, diagnosis, follow-up, and monitoring of drug-based therapy in RCC patients, is one of the most ambitious challenges in oncologic research. Although there are some promising reports about potential biomarkers in sera, there is limited available data regarding urine markers for RCC. The following review reports some of the most promising biomarkers identified in the biological fluids of RCC patients.