Exploring the pathogenesis of renal cell carcinoma: pathway and bioinformatics analysis of dysregulated genes and proteins

Quantitative proteomic analysis reveals potential diagnostic markers and pathways involved in pathogenesis of renal cell carcinoma

There are no serum biomarkers for the accurate diagnosis of clear cell renal cell carcinoma (ccRCC). Diagnosis and decision of nephrectomy rely on imaging which is not always accurate. Non-invasive diagnostic biomarkers are urgently required. In this study, we preformed quantitative proteomics analysis on a total of 199 patients including 30 matched pairs of normal kidney and ccRCC using isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify differentially expressed proteins. We found 55 proteins significantly dysregulated in ccRCC compared to normal kidney tissue. 54 were previously reported to play a role in carcinogenesis, and 39 are secreted proteins. Dysregulation of alpha-enolase (ENO1), L-lactate dehydrogenase A chain (LDHA), heat shock protein beta-1 (HSPB1/Hsp27), and 10 kDa heat shock protein, mitochondrial (HSPE1) was confirmed in two independent sets of patients by western blot and immunohistochemistry. Pathway analysis, validated by PCR, showed glucose metabolism is altered in ccRCC compared to normal kidney tissue. In addition, we examined the utility of Hsp27 as biomarker in serum and urine. In ccRCC patients, Hsp27 was elevated in the urine and serum and high serum Hsp27 was associated with high grade (Grade 3-4) tumors. These data together identify potential diagnostic biomarkers for ccRCC and shed new light on the molecular mechanisms that are dysregulated and contribute to the pathogenesis of ccRCC. Hsp27 is a promising diagnostic marker for ccRCC although further large-scale studies are required. Also, molecular profiling may help pave the road to the discovery of new therapies.

Lactate dehydrogenase A is a potential prognostic marker in clear cell renal cell carcinoma

Background

Over 90% of cancer-related deaths in clear cell renal cell carcinoma (RCC) are caused by tumor relapse and metastasis. Thus, there is an urgent need for new molecular markers that can potentiate the efficacy of the current clinical-based models of prognosis assessment. The objective of this study is to evaluate the potential significance of lactate dehydrogenase A (LDHA), assessed by immunohistochemical staining, as a prognostic marker in clear cell renal cell carcinoma in relation to clinicopathological features and clinical outcome.

Methods

We assessed the expression of LDHA at the protein level, by immunohistochemistry, and correlated its expression with multiple clinicopathological features including tumor size, clinical stage, histological grade, disease-free and overall survival in 385 patients with primary clear cell renal cell carcinoma. We also correlated the LDHA expression with overall survival, at mRNA level, in an independent data set of 170 clear cell renal cell carcinoma cases from The Cancer Genome Atlas databases. Cox proportional hazards models adjusted for the potential clinicopathological factors were used to test for associations between the LDHA expression and both disease-free survival and overall survival.

Results

There is statistically significant positive correlation between LDHA level of expression and tumor size, clinical stage and histological grade. Moreover, LDHA expression shows significantly inverse correlation with both disease-free survival and overall survival in patients with clear cell renal cell carcinoma. Our results are validated by examining LDHA expression, at the mRNA level, in the independent data set of clear cell renal cell carcinoma cases from The Cancer Genome Atlas databases which also shows that higher lactate dehydrogenase A expression is associated with significantly shorter overall survival.

Conclusion

Our results indicate that LDHA up-regulation can be a predictor of poor prognosis in clear cell renal cell carcinoma. Thus, it represents a potential prognostic biomarker that can boost the accuracy of other prognostic models in patients with clear cell renal cell carcinoma.

FUT11 as a potential biomarker of clear cell renal cell carcinoma progression based on meta-analysis of gene expression data

In this paper, we provide a comprehensive summary of available clear cell renal cell carcinoma (ccRCC) microarray data in the form of meta-analysis of genes differentially regulated in tumors as compared to healthy tissue, using effect size to measure the strength of a relationship between the disease and gene expression. We identified 725 differentially regulated genes, with a number of interesting targets, such as TMEM213, SMIM5, or ATPases: ATP6V0A4 and ATP6V1G3, of which limited or no information is available in terms of their function in ccRCC pathology. Downregulated genes tended to represent pathways related to tissue remodeling, blood clotting, vasodilation, and energy metabolism, while upregulated genes were classified into pathways generally deregulated in cancers: immune system response, inflammatory response, angiogenesis, and apoptosis. One hundred fifteen deregulated genes were included in network analysis, with EGLN3, AP-2, NR3C1, HIF1A, and EPAS1 (gene encoding HIF2-α) as points of functional convergence, but, interestingly, 610 genes failed to join previously identified molecular networks. Furthermore, we validated the expression of 14 top deregulated genes in independent sample set of 32 ccRCC tumors by qPCR and tested if it could serve as a marker of disease progression. We found a correlation of high fucosyltransferase 11 (FUT11) expression with non-symptomatic course of the disease, which suggests that FUT11's expression might be potentially used as a biomarker of disease progression.

Identification and validation of dysregulated metabolic pathways in metastatic renal cell carcinoma

Metastatic renal cell carcinoma (mRCC) is a devastating disease with a 5-year survival rate of approximately 9 % and low response to chemotherapy and radiotherapy. Targeted therapies have slightly improved patient survival, but are only effective in a small subset of patients, who eventually develop resistance. A better understanding of pathways contributing to tumor progression and metastasis will allow for the development of novel targeted therapies and accurate prognostic markers. We performed extensive bioinformatics coupled with experimental validation on proteins dysregulated in mRCC. Gene ontology analysis showed that many proteins are involved in oxidation reduction, metabolic processes, and signal transduction. Pathway analysis showed metabolic pathways are altered in mRCC including glycolysis and pyruvate metabolism, the citric acid cycle, and the pentose phosphate pathway. RT-qPCR analysis showed that genes involved in the citric acid cycle were downregulated in metastatic RCC while genes of the pentose phosphate pathway were overexpressed. Protein-protein interaction analysis showed that most of the 198 proteins altered in mRCC clustered together and many were involved in glycolysis and pyruvate metabolism. We identified 29 reported regions of chromosomal aberrations in metastatic disease that correlate with the direction of protein dysregulation in mRCC. Furthermore, 36 proteins dysregulated in mRCC are predicted to be targets of metastasis-related miRNAs. A more comprehensive understanding of the pathways dysregulated in metastasis can be useful for the development of new therapies and novel prognostic markers. Also, multileveled analyses provide a unique "snapshot" of the molecular "environment" in RCC with prognostic and therapeutic implications.

Quantitative proteomic analysis in metastatic renal cell carcinoma reveals a unique set of proteins with potential prognostic significance

Metastatic renal cell carcinoma (RCC) is one of the most treatment-resistant malignancies, and patients have a dismal prognosis, with a <10% five-year survival rate. The identification of markers that can predict the potential for metastases will have a great effect in improving patient outcomes. In this study, we used differential proteomics with isobaric tags for relative and absolute quantitation (iTRAQ) labeling and LC-MS/MS analysis to identify proteins that are differentially expressed in metastatic and primary RCC. We identified 1256 non-redundant proteins, and 456 of these were quantified. Further analysis identified 29 proteins that were differentially expressed (12 overexpressed and 17 underexpressed) in metastatic and primary RCC. Dysregulated protein expressions of profilin-1 (Pfn1), 14-3-3 zeta/delta (14-3-3ζ), and galectin-1 (Gal-1) were verified on two independent sets of tissues by means of Western blot and immunohistochemical analysis. Hierarchical clustering analysis showed that the protein expression profile specific for metastatic RCC can distinguish between aggressive and non-aggressive RCC. Pathway analysis showed that dysregulated proteins are involved in cellular processes related to tumor progression and metastasis. Furthermore, preliminary analysis using a small set of tumors showed that increased expression of Pfn1 is associated with poor outcome and is a potential prognostic marker in RCC. In addition, 14-3-3ζ and Gal-1 also showed higher expression in tumors with poor prognosis than in those with good prognosis. Dysregulated proteins in metastatic RCC represent potential prognostic markers for kidney cancer patients, and a greater understanding of their involved biological pathways can serve as the foundation of the development of novel targeted therapies for metastatic RCC.

Dynamic tumor modeling of the dose-response relationship for everolimus in metastatic renal cell carcinoma using data from the phase 3 RECORD-1 trial

Background

The phase 3 RECORD-1 trial (NCT00410124) established the efficacy and safety of everolimus in patients with metastatic renal cell carcinoma (mRCC) who progress on sunitinib or sorafenib. In RECORD-1, patients received 10 mg everolimus daily, with dose reduction to 5 mg daily allowed for toxicity. We have developed a model of tumor growth dynamics utilizing serial measurements of the sum of the longest tumor diameters (SLD) from individual RECORD-1 patients to define the dose–response relationship of everolimus.

Results

The model predicts that after 1 year of continuous dosing, the change in SLD of target lesions will be +142.1% ± 98.3%, +22.4% ± 17.2%, and –15.7% ± 11.5% in the average patient treated with placebo, 5 mg everolimus, and 10 mg everolimus, respectively. This nonlinear, mixed-effects modeling approach can be used to describe the dynamics of each individual patient, as well as the overall population. This allows evaluation of how an actual dosing history and individual covariates impact on the observed drug effect, and offers the possibility of predicting clinical observations as a function of time.

Conclusions

In this pharmacodynamic model of tumor response, everolimus more effectively shrinks target lesions in mRCC when dosed 10 mg daily versus 5 mg daily, although a 5-mg dose still shows an antitumor effect. These data support earlier studies that established 10 mg daily as the preferred clinical dose of everolimus, and improve our understanding of the everolimus dose–response relationship.

Microvascular density as an independent predictor of clinical outcome in renal cell carcinoma: an automated image analysis study

Tumor microvascular density (MVD) has been shown to correlate with the aggressiveness of several cancers. With the introduction of targeted anti-angiogenic therapy, assessment of MVD has the potential not only as a prognostic but also as a therapeutic marker. The significance of tumor vascularity in clear cell renal cell carcinoma (ccRCC) has been debated, with studies showing contradictory results. Previous studies were limited by manual quantification of MVD within a small area of tumor. Since then, the validity of this method has been questioned. To avoid the inaccuracies of manual quantification, we employed a computerized image analysis, which allowed assessment of large areas of tumor and adjacent normal tissue. The latter was used as an internal reference for normalization. MVD and vascular endothelial growth factor (VEGF) were assessed in 57 cases of ccRCC. Sections were immunostained for CD34 and VEGF. Areas of ccRCC and normal kidney medulla were analyzed within scanned images using software that counted CD34-positive vessels and measured the intensity of VEGF staining. We obtained unadjusted values from tumoral areas and calculated adjusted values as tumor/normal ratios. Unadjusted MVD had no association with clinical outcome. However, similarly to tumor stage, higher adjusted MVD was associated with shorter disease-free survival (log-rank P=0.037, Cox P=0.02). This was significant in univariate and multivariate analyses. MVD did not correlate with tumor stage, pointing to its independent prognostic value. As expected due to the known molecular abnormalities in ccRCC, most tumors showed higher VEGF expression than normal tissue. Higher adjusted VEGF was associated with high tumor grade (P=0.049). The finding of increased MVD as an independent marker of tumor aggressiveness may prove useful in the development of new tests for prognostic and therapeutic guidance. Digital techniques can provide more accurate assessment of immunomarkers and may reveal less obvious associations.

Personalized medicine: marking a new epoch in cancer patient management

Personalized medicine (PM) is defined as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." The promise of PM has been on us for years. The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate the "one size fits all" model of medicine, which has centered on reaction to disease based on average responses to care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also shifting the emphasis of patient management from primary patient care to prevention and early intervention for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis, and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is clear that PM will gradually continue to be incorporated into cancer patient management and will have a significant impact on our health care in the future.

The miR-17-92 cluster is over expressed in and has an oncogenic effect on renal cell carcinoma

PURPOSE

miRNAs are small, nonprotein coding RNAs that are differentially expressed in many malignancies. We previously identified 80 miRNAs that are dysregulated in clear cell renal cell carcinoma. In this study we validated over expression of the miR-17-92 cluster in clear cell renal cell carcinoma and tested the effect of 2 members of this cluster (miR-17-5p and miR-20a) on tumor proliferation. We also elucidated the role of miRNA in clear cell renal cell carcinoma pathogenesis with bioinformatics.

MATERIALS AND METHODS

miRNA expression was validated by quantitative reverse transcriptase-polymerase chain reaction. The cell proliferation effect of miR-17-5p and miR-20a was tested in a renal adenocarcinoma cell line model. Multiple in silico analyses were done of dysregulated miRNAs.

RESULTS

We validated miR-71-92 cluster over expression in clear cell renal cell carcinoma by quantitative reverse transcriptase-polymerase chain reaction. Transfection of miR-20a inhibitor significantly decreased cell proliferation in a dose dependent manner. Transfection of miR-17-5p, which is not endogenously expressed in the ACHN cell line, led to increased cell proliferation compared to control values. This effect was suppressed by miR-17-5p inhibitor. Bioinformatics analysis identified 10 clusters of miRNAs dysregulated in clear cell renal cell carcinoma that followed the same expression patterns. We also identified matching patterns between reported chromosomal aberration in clear cell renal cell carcinoma and miRNA dysregulation for 37.5% of the miRNAs. Target prediction analysis was done using multiple algorithms. Many key molecules in clear cell renal cell carcinoma pathogenesis, including HIFs, mTOR, VEGF and VHL, were potential targets for dysregulated miRNAs.

CONCLUSIONS

A significant number of dysregulated proteins in clear cell renal cell carcinoma are potential miRNA targets. Also, many clear cell renal cell carcinoma dysregulated miRNAs are phylogenetically conserved.