Metabolism-related enzyme alterations identified by proteomic analysis in human renal cell carcinoma

Absolute quantitative proteomics using the total protein approach to identify novel clinical immunohistochemical markers in renal neoplasms

BACKGROUND

Renal neoplasms encompass a variety of malignant and benign tumors, including many with shared characteristics. The diagnosis of these renal neoplasms remains challenging with currently available tools. In this work, we demonstrate the total protein approach (TPA) based on high-resolution mass spectrometry (MS) as a tool to improve the accuracy of renal neoplasm diagnosis.

METHODS

Frozen tissue biopsies of human renal tissues [clear cell renal cell carcinoma (n = 7), papillary renal cell carcinoma (n = 5), chromophobe renal cell carcinoma (n = 5), and renal oncocytoma (n = 5)] were collected for proteome analysis. Normal adjacent renal tissue (NAT, n = 5) was used as a control. Proteins were extracted and digested using trypsin, and the digested proteomes were analyzed by label-free high-resolution MS (nanoLC-ESI-HR-MS/MS). Quantitative analysis was performed by comparison between protein abundances of tumors and NAT specimens, and the label-free and standard-free TPA was used to obtain absolute protein concentrations.

RESULTS

A total of 205 differentially expressed proteins with the potential to distinguish the renal neoplasms were found. Of these proteins, a TPA-based panel of 24, including known and new biomarkers, was selected as the best candidates to differentiate the neoplasms. As proof of concept, the diagnostic potential of PLIN2, TUBB3, LAMP1, and HK1 was validated using semi-quantitative immunohistochemistry with a total of 128 samples assessed on tissue micro-arrays.

CONCLUSIONS

We demonstrate the utility of combining high-resolution MS and the TPA as potential new diagnostic tool in the pathology of renal neoplasms. A similar TPA approach may be implemented in any cancer study with solid biopsies.

Analysis of the Metabolic Characteristics of Serum Samples in Patients With Multiple Myeloma

Aims: This study aimed to identify potential, non-invasive biomarkers for diagnosis and monitoring of the progress in multiple myeloma (MM) patients. Methods: MM patients and age-matched healthy controls (HC) were recruited in Discovery phase and Validation phase, respectively. MM patients were segregated into active group (AG) and responding group (RG). Serum samples were collected were conducted to non-targeted metabolomics analyses. Metabolites which were significantly changed (SCMs) among groups were identified in Discovery phase and was validated in Validation phase. The signaling pathways of these SCMs were enriched. The ability of SCMs to discriminate among groups in Validation phase was analyzed through receiver operating characteristic curve. The correlations between SCMs and clinical features, between SCMs and survival period of MM patients were analyzed. Results: Total of 23 SCMs were identified in AG compared with HC both in Discovery phase and Validation phase. Those SCMs were significantly enriched in arginine and proline metabolism and glycerophospholipid metabolism. 4 SCMs had the discriminatory ability between MM patients and healthy controls in Validation phase. Moreover, 12 SCMs had the ability to discriminate between the AG patients and RG patients in Validation phase. 10 out of 12 SCMs correlated with advanced features of MM. Moreover, 8 out of 12 SCMs had the negative impact on the survival of MM. 5'-Methylthioadenosine may be the only independent prognostic factor in survival period of MM. Conclusion: 10 SCMs identified in our study, which correlated with advanced features of MM, could be potential, novel, non-invasive biomarkers for active disease in MM.

Identification of metabolism-associated genes and pathways involved in different stages of clear cell renal cell carcinoma

The lack of early diagnostic markers and novel therapeutic targets for clear cell renal cell carcinoma (ccRCC) negatively affects patient prognosis. Cancer metabolism is an attractive area for the understanding of the molecular mechanism of carcinogenesis. The present study attempted to identify metabolic changes from the view of the expression of metabolism-associated genes between control samples and those of ccRCC at different disease stages. Data concerning ccRCC gene expression obtained by RNA-sequencing was obtained from The Cancer Genome Atlas and data on metabolism-associated genes were extracted using the Recon2 model. Following analysis of differential gene expression, multiple differentially expressed metabolic genes at each tumor-node-metastasis disease stage were identified, compared with control non-disease samples: Metabolic genes (305) were differentially expressed in stage I disease, 323 in stage II disease, 355 in stage III disease and 363 in stage IV disease. Following enrichment analysis for differential metabolic genes, 22 metabolic pathways were identified to be dysregulated in multiple stages of ccRCC. Abnormalities in hormone, vitamin, glucose and lipid metabolism were present in the early stages of the disease, with dysregulation to reactive oxygen species detoxification and amino acid metabolism pathways occurring with advanced disease stages, particularly to valine, leucine, and isoleucine metabolism, which was substantially dysregulated in stage IV disease. The xenobiotic metabolism pathway, associated with multiple cytochrome P450 family genes, was dysregulated in each stage of the disease. This pathway is worthy of substantial attention since it may aid understanding of drug resistance in ccRCC. The results of the present study offer information to aid further research into early diagnostic biomarkers and therapeutic targets of ccRCC.

[Quantitative and comparative proteomics analysis in clear cell renal cell carcinoma and adjacent noncancerous tissues by 2-D DIGE]

OBJECTIVE

To identify specific protein markers for renal cell carcinoma detection and diagnosis, as well as develop new potential therapeutic targets of the disease.

METHODS

We used two-dimensional difference in-gel electrophoresis (2-D DIGE) technique conjunction with mass spectrometry (MS) for the identification of significant differentially expressed proteins between 15cases of paired clear cell renal cell carcinoma (ccRCC) and adjacent normal renal tissues. The protein spots were considered as differentially expressed if a 1.5-fold altered expression level was observed (Student's t test, P value<0.05).

RESULTS

Of the 27 differentially expressed protein spots, 26 proteins were successfully identified. 11 proteins up-regulated in renal cell carcinoma,15 proteins down-regulated. Among them Short/branched chain specific acyl-CoA dehydrogenase, mitochondrial (ACDSB), Aldose 1-epimerase (GALM), Peroxiredoxin-4 (PRDX4), Macrophage-capping protein (CAPG), Beta-defensin 107 (D107A), Microfibril-associated glycoprotein 4 (MFAP4) were first time screening as new differential expressed proteins by protomic study in renal cell carcinoma.

CONCLUSIONS

2-D DIGE is a useful technique for screening and analysis differential expressed proteins in renal cell carcinoma. These new differently expressed proteins may be useful for development new molecular markers for the tumor.

[Quantitative and comparative proteomics analysis in clear cell renal cell carcinoma and adjacent noncancerous tissues by 2-D DIGE]

OBJECTIVE

To identify specific protein markers for renal cell carcinoma detection and diagnosis, as well as develop new potential therapeutic targets of the disease.

METHODS

We used two-dimensional difference in-gel electrophoresis (2-D DIGE) technique conjunction with mass spectrometry (MS) for the identification of significant differentially expressed proteins between 15cases of paired clear cell renal cell carcinoma (ccRCC) and adjacent normal renal tissues. The protein spots were considered as differentially expressed if a 1.5-fold altered expression level was observed (Student's t test, P value<0.05).

RESULTS

Of the 27 differentially expressed protein spots, 26 proteins were successfully identified. 11 proteins up-regulated in renal cell carcinoma,15 proteins down-regulated. Among them Short/branched chain specific acyl-CoA dehydrogenase, mitochondrial (ACDSB), Aldose 1-epimerase (GALM), Peroxiredoxin-4 (PRDX4), Macrophage-capping protein (CAPG), Beta-defensin 107 (D107A), Microfibril-associated glycoprotein 4 (MFAP4) were first time screening as new differential expressed proteins by protomic study in renal cell carcinoma.

CONCLUSIONS

2-D DIGE is a useful technique for screening and analysis differential expressed proteins in renal cell carcinoma. These new differently expressed proteins may be useful for development new molecular markers for the tumor.

The proteomic landscape of renal tumors

INTRODUCTION

Renal cell carcinoma (RCC) is the most fatal of the common urologic cancers, with approximately 35% of patients dying within 5 years following diagnosis. Therefore, there is a need for non-invasive markers that are capable of detecting and determining the severity of small renal masses at an early stage in order to tailor treatment and follow-up. Proteomic studies have proved to be very useful in the study of tumors. Areas covered: In this review, we will detail the current knowledge obtained by the different proteomic approaches, focusing on MS-based strategies, used to investigate RCC biology in order to identify diagnostic, prognostic and predictive biomarkers on tissue, cultured cells and biological fluids. Expert commentary: Currently, no reliable biomarkers or targets for RCC have been translated into the clinical setting. Moreover, despite the efforts of proteomics and other -omics disciplines, only a small number of them have been observed as shared targets between the different analytical platforms and biological specimens. The difficulty to define a specific molecular pattern for RCC and its subtypes highlights a peculiar profile and a heterogeneity that must be taken into account in future studies.