Increased α2,3-sialyl N-glycosylated prostate-specific membrane antigen (PSMA) in post-DRE urine is associated with high grade group prostate cancer.

INTRODUCTION: Aberrant glycosylation of proteins is an important hallmark in multiple cancers. Prostate-specific membrane antigen (PSMA), a highly glycosylated protein with 10 N-linked glycosylation sites, is an Food and Drug Administration approved theranostic for prostate cancer. However, glycosylation changes in PSMA that are associated with prostate cancer disease progression have not been fully characterized. METHODS: We investigated whether urinary PSMA sialylation correlate with high-grade prostate cancer. Urine samples were collected from men after digital rectal examination (DRE) before prostate biopsy. Lectin-antibody enzyme-linked immunoassay was used to quantify α2,3-sialyl PSMA in post-DRE urine samples from subjects with benign prostate tumors, Grade Group 1 prostate cancer and those with Grade Group ≥2 disease. RESULTS: There are significant increases in α2,3-sialylated PSMA in patients with Grade Group ≥2 disease compared to benign (p = 0.0009) and those with Grade Group 1 disease (p = 0.0063). There were no significant differences in α2,3-sialyl PSMA levels between Grade Group 1 and benign prostate tumors (p = 0.7947). CONCLUSIONS: Our study shows that there are significant differences in the abundance of α2,3-sialylated PSMA in post-DRE urines from disease stratified prostate cancer patients, and the increase is correlated with progression and disease severity. The detection of increased PSMA sialyation in post-DRE urines from patients with higher Grade Group ≥2 disease states provides novel untapped potential for the development of prognostic biomarkers for prostate cancer. Specifically, quantitation of α2,3-sialylated PSMA shows potential for discriminating between benign to intermediate grade disease, which is a significant clinical challenge in staging and risk stratification of prostate cancer.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine.

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Prostate-specific membrane antigen (PSMA) glycoforms in prostate cancer patients seminal plasma.

INTRODUCTION: Prostate-specific membrane antigen (PSMA) is a US Food and Drug Administration-approved theranostic target for prostate cancer (PCa). Although PSMA is known to be glycosylated, the composition and functional roles of its N-linked glycoforms have not been fully characterized. METHODS: PSMA was isolated from pooled seminal plasma from low-risk grade Groups 1 and 2 PCa patients. Intact glycopeptides were analyzed by mass spectrometry to identify site-specific glycoforms. RESULTS: We observed a rich distribution of PSMA glycoforms in seminal plasma from low and low-intermediate-risk PCa patients. Some interesting generalities can be drawn based on the predicted topology of PSMA on the plasma membrane. The glycoforms at ASN-459, ASN-476, and ASN-638 residues that are located at the basal domain facing the plasma membrane in cells, are predominantly high mannose glycans. ASN-76 which is located in the interdomain region adjacent to the apical domain of the protein shows a mixture of high mannose glycans and complex glycans, whereas ASN-121, ASN-195 and ASN-336 that are located and are exposed at the apical domain of the protein predominantly possess complex sialylated and fucosylated N-linked glycans. These highly accessible glycosites display the greatest diversity in isoforms across the patient samples. CONCLUSIONS: Our study provides novel qualitative insights into PSMA glycoforms that are present in the seminal fluid of PCa patients. The presence of a rich diversity of glycoforms in seminal plasma provides untapped potential for glycoprotein biomarker discovery and as a clinical sample for noninvasive diagnostics of male urological disorders and diseases including PCa. Specifically, our glycomics approach will be critical in uncovering PSMA glycoforms with utility in staging and risk stratification of PCa.

MDA-9/Syntenin in the tumor and microenvironment defines prostate cancer bone metastasis.

Bone metastasis is a frequent and incurable consequence of advanced prostate cancer (PC). An interplay between disseminated tumor cells and heterogeneous bone resident cells in the metastatic niche initiates this process. Melanoma differentiation associated gene-9 (mda-9/Syntenin/syndecan binding protein) is a prometastatic gene expressed in multiple organs, including bone marrow-derived mesenchymal stromal cells (BM-MSCs), under both physiological and pathological conditions. We demonstrate that PDGF-AA secreted by tumor cells induces CXCL5 expression in BM-MSCs by suppressing MDA-9-dependent YAP/MST signaling. CXCL5-derived tumor cell proliferation and immune suppression are consequences of the MDA-9/CXCL5 signaling axis, promoting PC disease progression. mda-9 knockout tumor cells express less PDGF-AA and do not develop bone metastases. Our data document a previously undefined role of MDA-9/Syntenin in the tumor and microenvironment in regulating PC bone metastasis. This study provides a framework for translational strategies to ameliorate health complications and morbidity associated with advanced PC.

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes.

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Differential Activation of NRF2 Signaling Pathway in Renal-Cell Carcinoma Caki Cell Lines.

Renal-cell carcinoma (RCC) is a heterogeneous disease consisting of several subtypes based on specific genomic profiles and histological and clinical characteristics. The subtype with the highest prevalence is clear-cell RCC (ccRCC), next is papillary RCC (pRCC), and then chromophobe RCC (chRCC). The ccRCC cell lines are further subdivided into prognostic expression-based subtypes ccA or ccB. This heterogeneity necessitates the development, availability, and utilization of cell line models with the correct disease phenotypic characteristics for RCC research. In this study, we focused on characterizing proteomic differences between the Caki-1 and Caki-2 cell lines that are commonly used in ccRCC research. Both cells are primarily defined as human ccRCC cell lines. Caki-1 cell lines are metastatic, harboring wild-type VHL, whereas Caki-2 are considered as the primary ccRCC cell lines expressing wild-type von Hippel-Lindau protein (pVHL). Here, we performed a comprehensive comparative proteomic analysis of Caki-1 and Caki-2 cells using tandem mass-tag reagents together with liquid chromatography mass spectrometry (LC/MS) for the identification and quantitation of proteins in the two cell lines. Differential regulation of a subset of the proteins identified was validated using orthogonal methods including western blot, q-PCR, and immunofluorescence assays. Integrative bioinformatic analysis identifies the activation/inhibition of specific molecular pathways, upstream regulators, and causal networks that are uniquely regulated and associated with the two cell lines and RCC subtypes, and potentially the disease stage. Altogether, we have identified multiple molecular pathways, including NRF2 signaling, which is the most significantly activated pathway in Caki-2 versus Caki-1 cells. Some of the differentially regulated molecules and signaling pathways could serve as potential diagnostic and prognostic biomarkers and therapeutic targets amongst ccRCC subtypes.

promor: a comprehensive R package for label-free proteomics data analysis and predictive modeling.

Summary: We present promor, a comprehensive, user-friendly R package that streamlines label-free quantification proteomics data analysis and building machine learning-based predictive models with top protein candidates. Availability and implementation: promor is freely available as an open source R package on the Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=promor) and distributed under the Lesser General Public License (version 2.1 or later). Development version of promor is maintained on GitHub (https://github.com/caranathunge/promor) and additional documentation and tutorials are provided on the package website (https://caranathunge.github.io/promor/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

NEFL is overexpressed and it modulates invasion and migration in neuroendocrine-like PC3-ML2 prostate cancer cells.

Prostate cancer clinical outcomes are varied, from non-aggressive asymptomatic to lethal aggressive neuroendocrine forms which represent a critical challenge in the management of the disease. The neurofilament light ( NEFL ) is proposed to be a tumor suppressor gene. Studies have shown that expression of the gene is decreased in various cancers. We have used quantitative RT-PCR, immunoblotting, methylation specific PCR, siRNA knockdown followed by migration/invasion assays to determine associations between NEFL expression and disease phenotype in a panel of prostate cells. We demonstrate that NEFL is overexpressed and it modulates invasion and migration in PC3-ML2 prostate cancers cells which have an aggressive neuroendocrine-like phenotype.

Site-Specific Intact N-Linked Glycopeptide Characterization of Prostate-Specific Membrane Antigen from Metastatic Prostate Cancer Cells.

The composition of N-linked glycans that are conjugated to the prostate-specific membrane antigen (PSMA) and their functional significance in prostate cancer progression have not been fully characterized. PSMA was isolated from two metastatic prostate cancer cell lines, LNCaP and MDAPCa2b, which have different tissue tropism and localization. Isolated PSMA was trypsin-digested, and intact glycopeptides were subjected to LC-HCD-EThcD-MS/MS analysis on a Tribrid Orbitrap Fusion Lumos mass spectrometer. Differential qualitative and quantitative analysis of site-specific N-glycopeptides was performed using Byonic and Byologic software. Comparative quantitative analysis demonstrates that multiple glycopeptides at asparagine residues 51, 76, 121, 195, 336, 459, 476, and 638 were in significantly different abundance in the two cell lines (p < 0.05). Biochemical analysis using endoglycosidase treatment and lectin capture confirm the MS and site occupancy data. The data demonstrate the effectiveness of the strategy for comprehensive analysis of PSMA glycopeptides. This approach will form the basis of ongoing experiments to identify site-specific glycan changes in PSMA isolated from disease-stratified clinical samples to uncover targets that may be associated with disease progression and metastatic phenotypes.

Optimization of small extracellular vesicle isolation from expressed prostatic secretions in urine for in-depth proteomic analysis.

The isolation and subsequent molecular analysis of extracellular vesicles (EVs) derived from patient samples is a widely used strategy to understand vesicle biology and to facilitate biomarker discovery. Expressed prostatic secretions in urine are a tumor proximal fluid that has received significant attention as a source of potential prostate cancer (PCa) biomarkers for use in liquid biopsy protocols. Standard EV isolation methods like differential ultracentrifugation (dUC) co-isolate protein contaminants that mask lower-abundance proteins in typical mass spectrometry (MS) protocols. Further complicating the analysis of expressed prostatic secretions, uromodulin, also known as Tamm-Horsfall protein (THP), is present at high concentrations in urine. THP can form polymers that entrap EVs during purification, reducing yield. Disruption of THP polymer networks with dithiothreitol (DTT) can release trapped EVs, but smaller THP fibres co-isolate with EVs during subsequent ultracentrifugation. To resolve these challenges, we describe here a dUC method that incorporates THP polymer reduction and alkaline washing to improve EV isolation and deplete both THP and other common protein contaminants. When applied to human expressed prostatic secretions in urine, we achieved relative enrichment of known prostate and prostate cancer-associated EV-resident proteins. Our approach provides a promising strategy for global proteomic analyses of urinary EVs.

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

Direct N-Glycosylation Profiling of Urine and Prostatic Fluid Glycoproteins and Extracellular Vesicles.

Expressed prostatic secretions (EPS), also called post digital rectal exam urines, are proximal fluids of the prostate that are widely used for diagnostic and prognostic assays for prostate cancer. These fluids contain an abundant number of glycoproteins and extracellular vesicles secreted by the prostate gland, and the ability to detect changes in their N-glycans composition as a reflection of disease state represents potential new biomarker candidates. Methods to characterize these N-glycan constituents directly from clinical samples in a timely manner and with minimal sample processing requirements are not currently available. In this report, an approach is described to directly profile the N-glycan constituents of EPS urine samples, prostatic fluids and urine using imaging mass spectrometry for detection. An amine reactive slide is used to immobilize glycoproteins from a few microliters of spotted samples, followed by peptide N-glycosidase digestion. Over 100 N-glycan compositions can be detected with this method, and it works with urine, urine EPS, prostatic fluids, and urine EPS-derived extracellular vesicles. A comparison of the N-glycans detected from the fluids with tissue N-glycans from prostate cancer tissues was done, indicating a subset of N-glycans present in fluids derived from the gland lumens. The developed N-glycan profiling is amenable to analysis of larger clinical cohorts and adaptable to other biofluids.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry.

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

Targeted Mass Spectrometry of a Clinically Relevant PSA Variant from Post-DRE Urines for Quantitation and Genotype Determination.

PURPOSE: The rs17632542 single nucleotide polymorphism (SNP) results in lower serum prostate specific antigen (PSA) levels which may further mitigate against its clinical utility as a prostate cancer biomarker. Post-digital rectal exam (post-DRE) urine is a minimally invasive fluid that is currently utilized in prostate cancer diagnosis. To detect and quantitate the variant protein in urine. EXPERIMENTAL DESIGN: Fifty-three post-DRE urines from rs17632542 genotyped individuals processed and analyzed by liquid chromatography/mass spectrometry (LC-MS) in a double-blinded randomized study. The ability to distinguish between homozygous wild-type, heterozygous, or homozygous variant is examined before unblinding. RESULTS: Stable-isotope labeled peptides are used in the detection and quantitation of three peptides of interest in each sample using parallel reaction monitoring (PRM). Using these data, groupings are predicted using hierarchical clustering in R. Accuracy of the predictions show 100% concordance across the 53 samples, including individuals homozygous and heterozygous for the SNP. CONCLUSIONS AND CLINICAL RELEVANCE: The study demonstrates that MS based peptide variant quantitation in urine could be useful in determining patient genotype expression. This assay provides a tool to evaluate the utility of PSA variant (rs17632542) in parallel with current and forthcoming urine biomarker panels.

Proteomic and Transcriptional Profiles of Human Stem Cell-Derived ß Cells Following Enteroviral Challenge.

Enteroviral infections are implicated in islet autoimmunity and type 1 diabetes (T1D) pathogenesis. Significant ß-cell stress and damage occur with viral infection, leading to cells that are dysfunctional and vulnerable to destruction. Human stem cell-derived ß (SC-ß) cells are insulin-producing cell clusters that closely resemble native ß cells. To better understand the events precipitated by enteroviral infection of ß cells, we investigated transcriptional and proteomic changes in SC-ß cells challenged with coxsackie B virus (CVB). We confirmed infection by demonstrating that viral protein colocalized with insulin-positive SC-ß cells by immunostaining. Transcriptome analysis showed a decrease in insulin gene expression following infection, and combined transcriptional and proteomic analysis revealed activation of innate immune pathways, including type I interferon (IFN), IFN-stimulated genes, nuclear factor-kappa B (NF-κB) and downstream inflammatory cytokines, and major histocompatibility complex (MHC) class I. Finally, insulin release by CVB4-infected SC-ß cells was impaired. These transcriptional, proteomic, and functional findings are in agreement with responses in primary human islets infected with CVB ex vivo. Human SC-ß cells may serve as a surrogate for primary human islets in virus-induced diabetes models. Because human SC-ß cells are more genetically tractable and accessible than primary islets, they may provide a preferred platform for investigating T1D pathogenesis and developing new treatments.

Coxsackievirus-Induced Proteomic Alterations in Primary Human Islets Provide Insights for the Etiology of Diabetes.

Enteroviral infections have been associated with the development of type 1 diabetes (T1D), a chronic inflammatory disease characterized by autoimmune destruction of insulin-producing pancreatic beta cells. Cultured human islets, including the insulin-producing beta cells, can be infected with coxsackievirus B4 (CVB4) and thus are useful for understanding cellular responses to infection. We performed quantitative mass spectrometry analysis on cultured primary human islets infected with CVB4 to identify molecules and pathways altered upon infection. Corresponding uninfected controls were included in the study for comparative protein expression analyses. Proteins were significantly and differentially regulated in human islets challenged with virus compared with their uninfected counterparts. Complementary analyses of gene transcripts in CVB4-infected primary islets over a time course validated the induction of RNA transcripts for many of the proteins that were increased in the proteomics studies. Notably, infection with CVB4 results in a considerable decrease in insulin. Genes/proteins modulated during CVB4 infection also include those involved in activation of immune responses, including type I interferon pathways linked to T1D pathogenesis and with antiviral, cell repair, and inflammatory properties. Our study applies proteomics analyses to cultured human islets challenged with virus and identifies target proteins that could be useful in T1D interventions.

Comparative quantitative proteomic analysis of disease stratified laser captured microdissected human islets identifies proteins and pathways potentially related to type 1 diabetes.

Type 1 diabetes (T1D) is a chronic inflammatory disease that is characterized by autoimmune destruction of insulin-producing pancreatic beta cells. The goal of this study was to identify novel protein signatures that distinguish Islets from patients with T1D, patients who are autoantibody positive without symptoms of diabetes, and from individuals with no evidence of disease. High resolution high mass accuracy label free quantitative mass spectrometry analysis was applied to islets isolated by laser capture microdissection from disease stratified human pancreata from the Network for Pancreatic Organ Donors with Diabetes (nPOD), these included donors without diabetes, donors with T1D-associated autoantibodies in the absence of diabetes, and donors with T1D. Thirty-nine proteins were found to be differentially regulated in autoantibody positive cases compared to the no-disease group, with 25 upregulated and 14 downregulated proteins. For the T1D cases, 63 proteins were differentially expressed, with 24 upregulated and 39 downregulated, compared to the no disease controls. We have identified functional annotated enriched gene families and multiple protein-protein interaction clusters of proteins are involved in biological and molecular processes that may have a role in T1D. The proteins that are upregulated in T1D cases include S100A9, S100A8, REG1B, REG3A and C9 amongst others. These proteins have important biological functions, such as inflammation, metabolic regulation, and autoimmunity, all of which are pathways linked to the pathogenesis of T1D. The identified proteins may be involved in T1D development and pathogenesis. Our findings of novel proteins uniquely upregulated in T1D pancreas provides impetus for further investigations focusing on their expression profiles in beta cells/ islets to evaluate their role in the disease pathogenesis. Some of these molecules may be novel therapeutic targets T1D.

Mitochondria Biogenesis and Bioenergetics Gene Profiles in Isogenic Prostate Cells with Different Malignant Phenotypes.

Background. The most significant hallmarks of cancer are directly or indirectly linked to deregulated mitochondria. In this study, we sought to profile mitochondria associated genes in isogenic prostate cell lines with different tumorigenic phenotypes from the same patient. Results. Two isogenic human prostate cell lines RC77N/E (nonmalignant cells) and RC77T/E (malignant cells) were profiled for expression of mitochondrial biogenesis and energy metabolism genes by qRT-PCR using the Human Mitochondria and the Mitochondrial Energy Metabolism RT(2) PCR arrays. Forty-seven genes were differentially regulated between the two cell lines. The interaction and regulatory networks of these genes were generated by Ingenuity Pathway Analysis. UCP2 was the most significantly upregulated gene in primary adenocarcinoma cells in the current study. The overexpression of UCP2 upon malignant transformation was further validated using human prostatectomy clinical specimens. Conclusions. This study demonstrates the overexpression of multiple genes that are involved in mitochondria biogenesis, bioenergetics, and modulation of apoptosis. These genes may play a role in malignant transformation and disease progression. The upregulation of some of these genes in clinical samples indicates that some of the differentially transcribed genes could be the potential targets for therapeutic interventions.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer.

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.

Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma.

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.).