Candidate biomarkers in renal cell carcinoma

Knockdown of GA-binding protein subunit β1 inhibits cell proliferation via p21 induction in renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer. In the present study, bioinformatics tools were systematically used to investigate the potential upstream effector involved in the progression of ccRCC. Using the Gene Expression Omnibus database and Library of Integrated Network-based Cellular Signatures L1000 platform, it was identified that GA-binding protein subunit β1 (GABPB1) was a potential effector gene. GABPB1 is a transcription factor subunit and its function in ccRCC is unclear. Elevated expression of GABPB1 mRNA in ccRCC was also observed in other clinical datasets from the Oncomine database. Following reverse transcription-quantitative polymerase chain reaction and western blot analysis, the ccRCC 786-O and A498 cell lines showed higher expression levels of GABPB1 than HK-2, a normal kidney cell line. Knockdown of GABPB1 in the 786-O and A498 cells significantly decreased the ability to form colonies by inducing the expression of p21Waf/Cip1. SurvExpress database analysis indicated that a higher expression of GABPB1 was associated with poor survival outcome in patients with renal cancer. These findings imply that GABPB1 serves an important role in the progression of ccRCC.

LINC00152: A pivotal oncogenic long non-coding RNA in human cancers

In recent years, increasing evidence has shown the potential role of long non‐coding RNAs (lncRNAs) in multiple cancers. Deregulation of lncRNAs was detected being closely associated with many kinds of tumours where they can act as a tumour suppressor or accelerator. LINC00152 was identified as an oncogene involved in many kinds of cancers, such as gastric cancer, hepatocellular carcinoma, colon cancer, gallbladder cancer and renal cell carcinoma. Moreover, inhibition of LINC00152 can suppress proliferation, migration and invasion of the cancer cells. Increasing evidence has showed that LINC00152 may act as a diagnostic and prognostic biomarker for the above‐mentioned cancers. In our review, we summarize the recent research progress of the expression and role of LINC00152 in various kinds of cancers.

PGRMC1 Is a Novel Potential Tumor Biomarker of Human Renal Cell Carcinoma Based on Quantitative Proteomic and Integrative Biological Assessments

Progesterone receptor membrane component 1 (PGRMC1) is widely observed with an elevated level in multiple human cancers. However, the roles of PGRMC1 in renal cancer are not clear and merit further study. In this report, we made a systematic, integrative biological assessment for PGRMC1 in renal cell carcinoma (RCC) by a quantitative proteomic identification, immunohistochemical detection, and its clinic pathologic significance analysis. We found that PGRMC1 abundance is increased by 3.91-fold in RCC tissues compared with its autologous para-cancerous tissues by a quantitative proteome identification. To validate the proteomic result with more confidence, 135 clinic RCC tissues were recruited to measure PGRMC1 abundance by immunohistochemical staining, and 63.7% RCC samples (n = 86) showed a higher abundance of PGRMC1 than the noncancerous counterparts. And the elevated PGRMC1 level was related to the tumor malignancy degree and overall survival of RCC patients. Meanwhile the average serum PGRMC1 concentration for RCC patients (n = 18) was significantly increased by 1.67 fold compared with healthy persons. Moreover an exogenous elevated abundance of PGRMC1 by plasmid transfections significantly enhanced cell proliferation of renal cancer cells in vitro. Our findings demonstrate PGRMC1, which promotes RCC progression phenotypes in vitro and in vivo, is a novel potential biomarker and therapeutic target for RCC.

Identification of genes associated with renal cell carcinoma using gene expression profiling analysis

Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults and accounts for ~80% of all kidney cancer cases. However, the pathogenesis of RCC has not yet been fully elucidated. To interpret the pathogenesis of RCC at the molecular level, gene expression data and bio-informatics methods were used to identify RCC associated genes. Gene expression data was downloaded from Gene Expression Omnibus (GEO) database and identified differentially coexpressed genes (DCGs) and dysfunctional pathways in RCC patients compared with controls. In addition, a regulatory network was constructed using the known regulatory data between transcription factors (TFs) and target genes in the University of California Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu) and the regulatory impact factor of each TF was calculated. A total of 258,0427 pairs of DCGs were identified. The regulatory network contained 1,525 pairs of regulatory associations between 126 TFs and 1,259 target genes and these genes were mainly enriched in cancer pathways, ErbB and MAPK. In the regulatory network, the 10 most strongly associated TFs were FOXC1, GATA3, ESR1, FOXL1, PATZ1, MYB, STAT5A, EGR2, EGR3 and PELP1. GATA3, ERG and MYB serve important roles in RCC while FOXC1, ESR1, FOXL1, PATZ1, STAT5A and PELP1 may be potential genes associated with RCC. In conclusion, the present study constructed a regulatory network and screened out several TFs that may be used as molecular biomarkers of RCC. However, future studies are needed to confirm the findings of the present study.

Molecular and immunologic markers of kidney cancer-potential applications in predictive, preventive and personalized medicine

Kidney cancer is one of the deadliest malignancies due to frequent late diagnosis (33 % or renal cell carcinoma are metastatic at diagnosis) and poor treatment options. There are two major subtypes of kidney cancer: renal cell carcinoma (RCC) and renal pelvis carcinoma. The risk factors for RCC, accounting for more than 90 % of all kidney cancers, are smoking, obesity, hypertension, misuse of pain medication, and some genetic diseases. The most common molecular markers of kidney cancer include mutations and epigenetic inactivation of von Hippel-Lindau (VHL) gene, genes of vascular endothelial growth factor (VEGF) pathway, and carbonic anhydrase IX (CIAX). The role of epigenetic pathways, including DNA methylation and chromatin structure remodeling, was also demonstrated. Immunologic properties of RCC enable this type of tumor to escape immune response effectively. An important role in this process is played by tumor-associated macrophages that demonstrate mixed M1/M2 phenotype. In this review, we discuss molecular and cellular aspects for RCC development and current state of knowledge allowing personalized approaches for diagnostics and prognostic prediction of this disease. A set of macrophage markers is suggested for the analysis of the association of macrophage phenotype and disease prognosis.

Identification of Protein Markers Specific for Papillary Renal Cell Carcinoma Using Imaging Mass Spectrometry

Since the emergence of proteomics methods, many proteins specific for renal cell carcinoma (RCC) have been identified. Despite their usefulness for the specific diagnosis of RCC, such proteins do not provide spatial information on the diseased tissue. Therefore, the identification of cancer-specific proteins that include information on their specific location is needed. Recently, matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) based imaging mass spectrometry (IMS) has emerged as a new tool for the analysis of spatial distribution as well as identification of either proteins or small molecules in tissues. In this report, surgical tissue sections of papillary RCC were analyzed using MALDI-IMS. Statistical analysis revealed several discriminative cancer-specific m/z-species between normal and diseased tissues. Among these m/z-species, two particular proteins, S100A11 and ferritin light chain, which are specific for papillary RCC cancer regions, were successfully identified using LC-MS/MS following protein extraction from independent RCC samples. The expressions of S100A11 and ferritin light chain were further validated by immunohistochemistry of human tissues and tissue microarrays (TMAs) of RCC. In conclusion, MALDI-IMS followed by LC-MS/MS analysis in human tissue identified that S100A11 and ferritin light chain are differentially expressed proteins in papillary RCC cancer regions.

Identification of potential serum proteomic biomarkers for clear cell renal cell carcinoma

Objective

To investigate discriminating protein patterns and serum biomarkers between clear cell renal cell carcinoma (ccRCC) patients and healthy controls, as well as between paired pre- and post-operative ccRCC patients.

Methods

We used magnetic bead-based separation followed by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to identify patients with ccRCC. A total of 162 serum samples were analyzed in this study, among which there were 58 serum samples from ccRCC patients, 40 from additional paired pre- and post-operative ccRCC patients (n = 20), and 64 from healthy volunteers as healthy controls. ClinProTools software identified several distinct markers between ccRCC patients and healthy controls, as well as between pre- and post-operative patients.

Results

Patients with ccRCC could be identified with a mean sensitivity of 88.38% and a mean specificity of 91.67%. Of 67 m/z peaks that differed among the ccRCC, healthy controls, pre- and post-operative ccRCC patients, 24 were significantly different (P<0.05). Three candidate peaks, which were upregulated in ccRCC group and showed a tendency to return to healthy control values after surgery, were identified as peptide regions of RNA-binding protein 6 (RBP6), tubulin beta chain (TUBB), and zinc finger protein 3 (ZFP3) with the m/z values of 1466.98, 1618.22, and 5905.23, respectively.

Conclusion

MB-MALDI-TOF-MS method could generate serum peptidome profiles of ccRCC, and provide a new approach to identify potential biomarkers for diagnosis as well as prognosis of this malignancy.

Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms

Background

Clear-cell Renal Cell Carcinoma (ccRCC) is the most- prevalent, chemotherapy resistant and lethal adult kidney cancer. There is a need for novel diagnostic and prognostic biomarkers for ccRCC, due to its heterogeneous molecular profiles and asymptomatic early stage. This study aims to develop classification models to distinguish early stage and late stage of ccRCC based on gene expression profiles. We employed supervised learning algorithms- J48, Random Forest, SMO and Naïve Bayes; with enriched model learning by fast correlation based feature selection to develop classification models trained on sequencing based gene expression data of RNAseq experiments, obtained from The Cancer Genome Atlas.

Results

Different models developed in the study were evaluated on the basis of 10 fold cross validations and independent dataset testing. Random Forest based prediction model performed best amongst the models developed in the study, with a sensitivity of 89%, accuracy of 77% and area under Receivers Operating Curve of 0.8.

Conclusions

We anticipate that the prioritized subset of 62 genes and prediction models developed in this study will aid experimental oncologists to expedite understanding of the molecular mechanisms of stage progression and discovery of prognostic factors for ccRCC tumors.

MALDI imaging mass spectrometry profiling of proteins and lipids in clear cell renal cell carcinoma

Reducing the incidence and mortality rates for clear cell renal cell carcinoma (ccRCC) remains a significant clinical challenge with poor 5-year survival rates. A unique tissue cohort was assembled of matched ccRCC and distal nontumor tissues (n = 20) associated with moderate risk of disease progression, half of these from individuals who progressed to metastatic disease and the other half who remained disease free. These tissues were used for MALDI imaging MS profiling of proteins in the 2-20 kDa range, resulting in a panel of 108 proteins that had potential disease-specific expression patterns. Protein lysates from the same tissues were analyzed by MS/MS, resulting in identification of 56 proteins of less than 20 kDa molecular weight. The same tissues were also used for global lipid profiling analysis by MALDI-FT-ICR MS. From the cumulative protein and lipid expression profile data, a refined panel of 26 proteins and 39 lipid species was identified that could either distinguish tumor from nontumor tissues, or tissues from recurrent disease progressors from nonrecurrent disease individuals. This approach has the potential to not only improve prognostic assessment and enhance postoperative surveillance, but also to inform on the underlying biology of ccRCC progression.

Towards defining biomarkers indicating resistances to targeted therapies

An impressive, but often short objective response was obtained in many tumor patients treated with different targeted therapies, but most of the patients develop resistances against these drugs. So far, a number of distinct mechanisms leading to intrinsic as well as acquired resistances have been identified in tumors of distinct origin. These can arise from genetic alterations, like mutations, truncations, and amplifications or due to deregulated expression of various proteins and signal transduction pathways, but also from cellular heterogeneity within tumors after an initial response. Therefore, biomarkers are urgently needed for cancer prognosis and personalized cancer medicine. The application of "ome"-based technologies including cancer (epi)genomics, next generation sequencing, cDNA microarrays and proteomics might led to the predictive or prognostic stratification of patients to categorize resistance mechanisms and to postulate combinations of treatment strategies. This review discusses the implementation of proteome-based analysis to identify markers of pathway (in)activation in tumors and the resistance mechanisms, which represent major clinical problems as a tool to optimize individually tailored therapies based on targeted drugs. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Proteomic identification of PKM2 and HSPA5 as potential biomarkers for predicting high-risk endometrial carcinoma

AIM

Endometrial carcinoma (EC) is a common gynecologic malignancy. EC has a favorable prognosis because it is usually diagnosed at an early stage. However, the recurrence rate is high and the prognosis is poor for high-risk EC. Identification of new biomarkers for the prediction of high-risk features will help to guide the treatment and improve the prognosis of patients with EC.

MATERIAL AND METHODS

Differentially expressed proteins among high-risk EC, low-risk EC, and normal endometrial tissues were determined by two-dimensional gel electrophoresis (2-DE) and a liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) proteomics approach. Then, the candidate proteins were examined by immunohistochemical analysis.

RESULTS

Thirteen protein spots were differentially expressed between the high- and low-risk groups, and 25 protein spots were differentially expressed between the high-risk and normal endometrium groups. Twenty-two proteins were identified by MS analysis. PKM2 and HSPA5 were elevated in the high-risk EC tissues compared with both the low-risk EC and normal endometrial tissues. The elevated expression of PKM2 and HSPA5 in high-risk EC tissue was confirmed by immunohistochemical analysis.

DISCUSSION

PKM2 and HSPA5 may play an important role in the progression of EC. These two proteins are potential biomarkers to better predict high-risk EC and thereby guide clinical therapy.

Mining protein lists from proteomics studies: applications for drug discovery

IMPORTANCE OF THE FIELD

In recent years, proteomics has become a common technique applied to a wide spectrum of scientific problems, including the identification of diagnostic biomarkers, monitoring the effects of drug treatments or identification of chemical properties of a protein or a drug. Although being significantly different in scientific essence, the ultimate result of the majority of proteomics studies is a protein list. Thousands of independent proteomics studies have reported protein lists in various functional contexts.

AREAS COVERED IN THIS REVIEW

We review here the spectrum of scientific problems where proteomics technology was applied recently to deliver protein lists. The available bioinformatics methods commonly used to understand the properties of the protein lists are compared.

WHAT THE READER WILL GAIN

The types and common functional properties of the reported protein lists are discussed. The range of scientific problems where this knowledge could be potentially helpful with a focus on drug discovery issues is explored.

TAKE HOME MESSAGE

Reported protein lists represent a valuable resource which can be used for a variety of goals, ranging from biomarkers discovery to identification of novel therapeutic implications of known drugs.

Proteomic analysis in clear cell renal cell carcinoma: identification of differentially expressed protein by 2-D DIGE

Renal cell carcinoma (RCC), the most common neoplasm affecting the adult kidney, is characterised by heterogeneity of histological subtypes, drug resistance, and absence of molecular markers. Two-dimensional difference gel electrophoresis (2-D DIGE) technology in combination with mass spectrometry (MS) was applied to detect differentially expressed proteins in 20 pairs of RCC tissues and matched adjacent normal kidney cortex (ANK), in order to search for RCC markers. After gel analysis by DeCyder 6.5 and EDA software, differentially expressed protein spots were excised from Deep Purple stained preparative 2DE gel. A total of 100 proteins were identified by MS out of 2500 spots, 23 and 77 of these were, respectively, over- and down-expressed in RCC. The Principal Component Analysis applied to gels and protein spots exactly separated the two sample classes in two groups: RCC and ANK. Moreover, some spots, including ANXA2, PPIA, FABP7 and LEG1, resulted highly differential. The DIGE data were also confirmed by immunoblotting analysis for these proteins. In conclusion, we suggest that applying 2-D DIGE to RCC may provide the basis for a better molecular characterization and for the discovery of candidate biomarkers.

Stage-related alterations in renal cell carcinoma--comprehensive quantitative analysis by 2D-DIGE and protein network analysis

Renal cell carcinoma accounts for about 3% of adult malignancies and 85% of neoplasms arising from the kidney. To identify potential progression markers for kidney cancer we examined non-neoplastic and neoplastic kidney tissue from three groups of patients, which represent different tumor stages (pT1, pT2, pT3) by a fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) approach combined with MALDI-ToF-MS/MS. Delta2D software package was used for gel image based quantification and statistical analysis. Thereby, a comprehensive Principal Component Analysis (PCA) could be performed and allowed a robust quality control of the experiment as well as a classification of the analyzed samples, which correlated with the predicted stages from the pathological examination. Additionally for selected candidate proteins we detected a correlation to the tumor grading as revealed by immunohistochemistry. On the 2D protein map 176 spots out of 989 were detected as at least 2-fold differentially expressed. These spots were analyzed by MALDI-ToF-MS/MS and 187 different proteins were identified. The functional clustering of the identified proteins revealed ten groups. Within these groups we found 86 enzymes, 63 proteins of unknown function, 14 transporter, 8 peptidases and 7 kinases. From the systems biology approach we could map many of these proteins in major pathways involved in remodelling of cytoskeleton, mitochondrial dysfunctions and changes in lipid metabolism. Due to complexity of the highly interconnected pathway network, further expression and functional validation of these proteins might provide new insights in kidney cancer progression to design novel diagnostic and therapeutic strategies.

Identification and characterization of human leukocyte antigen class I ligands in renal cell carcinoma cells

The presentation of tumor antigen-derived peptides by human leukocyte antigen (HLA) class I surface antigens on tumor cells is a key prerequisite to trigger effective T-cell responses in cancer patients. Multiple complementary strategies like cDNA and serological expression cloning, reverse immunology and different 'ome'-based methods have been employed to identify potential T-cell targets. This report focuses on a ligandomic profiling approach leading to the identification of 49 naturally processed HLA class I peptide ligands presented on the cell surface of renal cell carcinoma (RCC) cells. The source proteins of the defined HLA ligands are classified according to their biological function and subcellular localization. Previously established cDNA microarray data of paired tissue specimen of RCC and renal epithelium assessed the transcriptional regulation for 28 source proteins. In addition, HLA-A2-restricted, peptide-specific T cells directed against a HLA ligand derived from sulfiredoxin-1 (SRXN1) were generated, which were able to recognize and lyse ligand-presenting target cells in a HLA class I-restricted manner. Furthermore, tumor-infiltrating T cells isolated from a RCC patient were also able to kill SRXN1 expressing tumor cells. Thus, this experimental strategy might be suited to define potential candidate biomarkers and novel targets for T-cell-based immunotherapies of this disease.

Linkage of microRNA and proteome-based profiling data sets: a perspective for the priorization of candidate biomarkers in renal cell carcinoma?

Despite recent advances in the understanding of the biology of renal cell carcinoma (RCC) and the implementation of novel targeted therapies, the overall 5 years' survival rate for RCC patients remains disappointing. Late presentation, tumor heterogeneity and in particular the lack of molecular biomarkers for early detection and classification represent major obstacles. Global, untargeted comparative analysis of RCC vs tumor adjacent renal epithelium (NN) samples by high throughput analyses both at the transcriptome and proteome level have identified signatures, which might further clarify the molecular differences of RCC subtypes and might allow the identification of suitable therapeutic targets and diagnostic/prognostic biomarkers, but none thereof has yet been implemented in routine clinical use. The increasing knowledge regarding the functional role of noncoding microRNA (miR) in physiological, developmental, and pathophysiological processes by shaping the protein expression profile might provide an important link to improve the definition of disease-relevant regulatory networks. Taking into account that miR profiling of RCC and NN provides robust signatures discriminating between malignant and normal tissues, the concept of evaluating and scoring miR/protein pairs might represent a strategy for the selection and prioritization of potential biomarkers and their translation into practical use.

Warburg phenotype in renal cell carcinoma: high expression of glucose-transporter 1 (GLUT-1) correlates with low CD8(+) T-cell infiltration in the tumor

Many tumor cells are characterized by a dysregulated glucose metabolism associated with increased glycolysis in the presence of oxygen ("Warburg Effect"). Here, we analyzed for the first time a possible link between glucose metabolism and immune cell infiltration in renal cell carcinoma (RCC). RCC specimens revealed a highly significant increase in the expression of lactate dehydrogenase A (LDHA) and glucose-transporter 1 (GLUT-1) compared to the corresponding normal kidney tissue on mRNA level. Accordingly, tumor cell lines of different origin such as RCC, melanoma and hepatocellular carcinoma strongly expressed LDHA and GLUT-1 compared to their nonmalignant counterparts. In line with this finding, tumor cells secreted high amounts of lactate. High expression of GLUT-1 and LDH5, a tetramer of 4 LDHA subunits, was confirmed by tissue microarray analysis of 249 RCC specimens. Overall, 55/79 (69.6%) and 46/71 (64.7%) cases of clear cell carcinoma showed a constitutive, but heterogeneous expression of GLUT-1 and LDH5, respectively. The number of CD3(+), CD8(+) and FOXP3(+) T cells was significantly elevated in RCC lesions compared to normal kidney epithelium, but effector molecules such as granzyme B and perforin were decreased in tumor infiltrating T cells. Of interest, further analysis revealed an inverse correlation between GLUT-1 expression and the number of CD8(+) T cells in RCC lesions. Together, our data suggest that an accelerated glucose metabolism in RCC tissue is associated with a low infiltration of CD8(+) effector T cells. Targeting the glucose metabolism may represent an interesting tool to improve the efficacy of specific immunotherapeutic approaches in RCC.

14-3-3 protein beta/alpha as a urinary biomarker for renal cell carcinoma: proteomic analysis of cyst fluid

Although various samples, including tissue, cells, serum, and urine, from patients with renal cell carcinoma (RCC) have been analyzed, biomarkers with diagnostic value have yet to be identified. We used a proteomics approach to analyze cyst fluid in cases of cyst-associated RCC to identify accessible and abundant proteins that are overexpressed and/or secreted by RCC cells. Proteins in the cyst fluid were separated by reverse-phase high-performance liquid chromatography and agarose two-dimensional gel electrophoresis and were identified by tandem mass spectrometry. We conducted a National Center for Biotechnology Information search and a MEDLINE search to predict the function of these identified proteins and to select a tumor-marker candidate protein. Our search resulted in the identification and selection of the differentially regulated protein known as 14-3-3 protein beta/alpha, which was overexpressed in cyst fluid from cyst-associated RCC but has not been previously associated with RCC. We then measured its incidence through Western blotting of various normal and RCC samples (serum, urine, tissue, and cyst fluid). The expression levels of 14-3-3 protein beta/alpha were higher in urine samples from patients with RCC than in samples from healthy volunteers. Receiver operating characteristic (ROC) curve analyses were performed to assess this potential biomarker; these data (area under the ROC curve value was 0.8813) indicate a high degree of accuracy for this screening method. 14-3-3 Protein beta/alpha may be a diagnostically useful biomarker for early diagnosis of RCC.

Profilin 1 overexpression in renal cell carcinoma

OBJECTIVES

To gain information about overexpressed antigens in renal cell carcinoma (RCC) by using a chemical proteomics approach.

METHODS

RCC cell line 769P was cultured and proteome analysis was subsequently carried out in the culture supernatants. By using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and tandem mass spectrometry (LC-MS/MS), proteins in the culture supernatants were searched. A MEDLINE search to define the functions of the identified proteins was carried out.

RESULTS

Four differentially regulated proteins (profilin 1, amyloid beta A4 protein [APP], proprotein convertase subtilisin/kexin type 1 inhibitor [ProSAAS], galectin-3-binding protein [LGALS3BP]) were selected. These were not overexpressed in normal kidney tissue or reported in RCC. Their levels were measured through western blotting of normal kidney and RCC tissues. No differences were observed in the expression levels of APP, ProSAAS or LGALS3BP between RCC and normal kidney tissues. Profilin 1 was overexpressed in RCC tissue. On the basis of this observation, an immunohistochemical analysis of profilin 1 in normal kidney and RCC tissues was carried out. In normal tissues, tubules that were sources of RCC stained positive for profilin 1. In RCC tissue, in contrast, the stromal cells in the tumors stained positive.

CONCLUSIONS

Profilin 1 can be a key element in the pathological processes of RCC, such as tumorigenesis and/or tumor growth. Thus, it has the potential to serve as a diagnostic or progression biomarker and therapeutic target in RCC.

Urinary biomarkers for the early diagnosis of kidney cancer

OBJECTIVE

To test the hypothesis that increased tumor expression of proteins such as aquaporin-1 (AQP1) and adipophilin (ADFP) in patients with renal cancer would result in increased urine AQP1 and ADFP excretion.

PATIENTS AND METHODS

Prenephrectomy and postnephrectomy (pseudocontrol) urine samples were collected from 42 patients with an incidental radiographically discovered renal mass and presurgical presumptive diagnosis of kidney cancer from July 8, 2008, through March 10, 2009. Also enrolled were 15 control patients who underwent nonrenal surgery and 19 healthy volunteers. Urine AQP1 and ADFP concentrations normalized to urine creatinine were determined by sensitive and specific Western blot assays.

RESULTS

Mean +/- SD preexcision urine AQP1 and ADFP concentrations (76+/-29 and 117+/-74 arbitrary units, respectively) in patients with a pathologic diagnosis of clear cell (n=22) or papillary (n=10) cancer were significantly greater than in patients with renal cancer of nonproximal tubule origin, control surgical patients, and healthy volunteers (combined values of 0.1+/-0.1 and 1.0+/-1.6 arbitrary units, respectively; n=44; P<.001). The AQP1 and ADFP concentrations decreased 88% to 97% in the 25 patients with clear cell or papillary cancer who provided postnephrectomy follow-up urine samples. In patients with clear cell and papillary carcinoma, a linear correlation (Spearman) was found between tumor size and preexcision urine AQP1 or ADFP concentration (r=0.82 and 0.76, respectively; P<.001 for each).

CONCLUSION

Urine AQP1 and ADFP concentrations appear to be sensitive and specific biomarkers of kidney cancers of proximal tubule origin. These biomarkers may be useful to diagnose an imaged renal mass and screen for kidney cancer at an early stage.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00851994.

Systematic comparative protein expression profiling of clear cell renal cell carcinoma: a pilot study based on the separation of tissue specimens by two-dimensional gel electrophoresis

Proteome-based technologies represent powerful tools for the analysis of protein expression profiles, including the identification of potential cancer candidate biomarkers. Thus, here we provide a comprehensive protein expression map for clear cell renal cell carcinoma established by systematic comparative two-dimensional gel electrophoresis-based protein expression profiling of 16 paired tissue systems comprising clear cell renal cell carcinoma lesions and corresponding tumor-adjacent renal epithelium using overlapping narrow pH gradients. This approach led to the mapping of 348 distinct spots corresponding to 248 different protein identities. By implementing restriction criteria concerning their detection frequency and overall regulation mode, 28 up- and 56 down-regulated single target spots were considered as potential candidate biomarkers. Based on their gene ontology information, these differentially expressed proteins were classified into distinct functional groups and according to their cellular distribution. Moreover, three representative members of this group, namely calbindin, gelsolin, and heart fatty acid-binding protein, were selected, and their expression pattern was analyzed by immunohistochemistry using tissue microarrays. Thus, this pilot study provides a significant update of the current renal cell carcinoma map and defines a number of differentially expressed proteins, but both their potential as candidate biomarkers and clinical relevance has to be further explored in tissues and for body fluids like serum and urine.

PLIPS, an automatically collected database of protein lists reported by proteomics studies

The spectrum of problems covered by proteomics studies range from the discovery of compartment specific cell proteomes to clinical applications, including the identification of diagnostic markers and monitoring the effects of drug treatments. In most cases, the ultimate results of a proteomics study are lists of proteins found to be present (or differentially present) at cell physiological conditions under study. Normally, the results are published directly in the article in one or several tables. In many cases, this type of information remains disseminated in hundreds of proteomics publications. We have developed a Web mining tool which allows the collection of this information by searching through full text papers and automatically selecting tables, which report a list of protein identifiers. By searching through major proteomics journals, we have collected approximately 800 independent studies published recently, which reported about 1000 different protein lists. On the basis of this data, we developed a computational tool PLIPS (Protein Lists Identified in Proteomics Studies). PLIPS accepts as input a list of protein/gene identifiers. With the use of statistical analyses, PLIPS infers recently published proteomics studies, which report protein lists that significantly intersect with a query list. PLIPS is a freely available Web-based tool ( http://mips.helmholtz-muenchen.de/proj/plips ).