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

Global Proteomics for Identifying the Alteration Pathway of Niemann-Pick Disease Type C Using Hepatic Cell Models

Niemann-Pick disease type C (NPC) is an autosomal recessive disorder with progressive neurodegeneration. Although the causative genes were previously identified, NPC has unclear pathophysiological aspects, and patients with NPC present various symptoms and onset ages. However, various novel biomarkers and metabolic alterations have been investigated; at present, few comprehensive proteomic alterations have been reported in relation to NPC. In this study, we aimed to elucidate proteomic alterations in NPC and perform a global proteomics analysis for NPC model cells. First, we developed two NPC cell models by knocking out NPC1 using CRISPR/Cas9 (KO1 and KO2). Second, we performed a label-free (LF) global proteomics analysis. Using the LF approach, more than 300 proteins, defined as differentially expressed proteins (DEPs), changed in the KO1 and/or KO2 cells, while the two models shared 35 DEPs. As a bioinformatics analysis, the construction of a protein-protein interaction (PPI) network and an enrichment analysis showed that common characteristic pathways such as ferroptosis and mitophagy were identified in the two model cells. There are few reports of the involvement of NPC in ferroptosis, and this study presents ferroptosis as an altered pathway in NPC. On the other hand, many other pathways and DEPs were previously suggested to be associated with NPC, supporting the link between the proteome analyzed here and NPC. Therapeutic research based on these results is expected in the future.

Biomarkers for the Detection and Surveillance of Renal Cancer

Renal cell carcinoma (RCC) is a heterogeneous disease characterized by a broad spectrum of disorders in terms of genetics, molecular and clinical characteristics. There is an urgent need for noninvasive tools to stratify and select patients for treatment accurately. In this review, we analyze serum, urinary, and imaging biomarkers that have the potential to detect malignant tumors in patients with RCC. We discuss the characteristics of these numerous biomarkers and their ability to be used routinely in clinical practice. The development of biomarkers continues to evolve with promising prospects.

Proteomics Characterization of Clear Cell Renal Cell Carcinoma

PURPOSE

To explore the tumor proteome of patients diagnosed with localized clear cell renal cancer (ccRCC) and treated with surgery.

MATERIAL AND METHODS

A total of 165 FFPE tumor samples from patients diagnosed with ccRCC were analyzed using DIA-proteomics. Proteomics ccRCC subtypes were defined using a consensus cluster algorithm (CCA) and characterized by a functional approach using probabilistic graphical models and survival analyses.

RESULTS

We identified and quantified 3091 proteins, including 2026 high-confidence proteins. Two proteomics subtypes of ccRCC (CC1 and CC2) were identified by CC using the high-confidence proteins only. Characterization of molecular differences between CC1 and CC2 was performed in two steps. First, we defined 514 proteins showing differential expression between the two subtypes using a significance analysis of microarrays analysis. Proteins overexpressed in CC1 were mainly related to translation and ribosome, while proteins overexpressed in CC2 were mainly related to focal adhesion and membrane. Second, a functional analysis using probabilistic graphical models was performed. CC1 subtype is characterized by an increased expression of proteins related to glycolysis, mitochondria, translation, adhesion proteins related to cytoskeleton and actin, nucleosome, and spliceosome, while CC2 subtype showed higher expression of proteins involved in focal adhesion, extracellular matrix, and collagen organization.

CONCLUSIONS

ccRCC tumors can be classified in two different proteomics subtypes. CC1 and CC2 present specific proteomics profiles, reflecting alterations of different molecular pathways in each subtype. The knowledge generated in this type of studies could help in the development of new drugs targeting subtype-specific deregulated pathways.

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

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

Diagnostic and Therapeutic Roles of the “Omics” in Hypoxic–Ischemic Encephalopathy in Neonates

Perinatal asphyxia and neonatal encephalopathy remain major causes of neonatal mortality, despite the improved availability of diagnostic and therapeutic tools, contributing to neurological and intellectual disabilities worldwide. An approach using a combination of clinical data, neuroimaging, and biochemical parameters is the current strategy towards the improved diagnosis and prognosis of the outcome in neonatal hypoxic–ischemic encephalopathy (HIE) using bioengineering methods. Traditional biomarkers are of little use in this multifactorial and variable phenotype-presenting clinical condition. Novel systems of biology-based “omics” approaches (genomics, transcriptome proteomics, and metabolomics) may help to identify biomarkers associated with brain and other tissue injuries, predicting the disease severity in HIE. Biomarker studies using omics technologies will likely be a key feature of future neuroprotective treatment methods and will help to assess the successful treatment and long-term efficacy of the intervention. This article reviews the roles of different omics as biomarkers of HIE and outlines the existing knowledge of our current understanding of the clinical use of different omics molecules as novel neonatal brain injury biomarkers, which may lead to improved interventions related to the diagnostic and therapeutic aspects of HIE.

Renal tumor biomarkers (Review)

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

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.

Protein profile of MCF-7 breast cancer cell line treated with lectin delivered by CaCO3NPs revealed changes in molecular chaperones, cytoskeleton, and membrane-associated proteins

The second most predominant cancer in the world and the first among women is breast cancer. We aimed to study the protein abundance profiles induced by lectin purified from the Agaricus bisporus mushroom (ABL) and conjugated with CaCO₃NPs in the MCF-7 breast cancer cell line. Two-dimensional electrophoresis (2-DE) and orbitrap mass spectrometry techniques were used to reveal the protein abundance pattern induced by lectin. Flow cytometric analysis showed the accumulation of ABL-CaCO₃NPs treated cells in the G1 phase than the positive control. Thirteen proteins were found different in their abundance in breast cancer cells after 24 h exposure to lectin conjugated with CaCO₃NPs. Most of the identified proteins were showing a low abundance in ABL-CaCO₃NPs treated cells in comparison to the positive and negative controls, including V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP. Hornerin, tropomyosin alpha-1 chain, annexin A2, and protein disulfide-isomerase were up-regulated in comparison to the positive. Bioinformatic analyses revealed the regulation changes of these proteins mainly affected the pathways of 'Bcl-2-associated athanogene 2 signalling pathway', 'Unfolded protein response', 'Caveolar-mediated endocytosis signalling', 'Clathrin-mediated endocytosis signalling', 'Calcium signalling' and 'Sucrose degradation V', which are associated with breast cancer. We concluded that lectin altered the abundance in molecular chaperones/heat shock proteins, cytoskeletal, and metabolic proteins. Additionally, lectin induced a low abundance of MCF-7 cancer cell proteins in comparison to the positive and negative controls, including; V-set and immunoglobulin domain, serum albumin, actin cytoplasmic 1, triosephosphate isomerase, tropomyosin alpha-4 chain, and endoplasmic reticulum chaperone BiP.

Exploiting the HSP60/10 chaperonin system as a chemotherapeutic target for colorectal cancer

Over the past few decades, an increasing variety of molecular chaperones have been investigated for their role in tumorigenesis and as potential chemotherapeutic targets; however, the 60 kDa Heat Shock Protein (HSP60), along with its HSP10 co-chaperone, have received little attention in this regard. In the present study, we investigated two series of our previously developed inhibitors of the bacterial homolog of HSP60/10, called GroEL/ES, for their selective cytotoxicity to cancerous over non-cancerous colorectal cells. We further developed a third "hybrid" series of analogs to identify new candidates with superior properties than the two parent scaffolds. Using a series of well-established HSP60/10 biochemical screens and cell-viability assays, we identified 24 inhibitors (14%) that exhibited > 3-fold selectivity for targeting colorectal cancer over non-cancerous cells. Notably, cell viability EC₅₀ results correlated with the relative expression of HSP60 in the mitochondria, suggesting a potential for this HSP60-targeting chemotherapeutic strategy as emerging evidence indicates that HSP60 is up-regulated in colorectal cancer tumors. Further examination of five lead candidates indicated their ability to inhibit the clonogenicity and migration of colorectal cancer cells. These promising results are the most thorough analysis and first reported instance of HSP60/10 inhibitors being able to selectively target colorectal cancer cells and highlight the potential of the HSP60/10 chaperonin system as a viable chemotherapeutic target.

Proteomic comparison between different tissue preservation methods for identification of promising biomarkers of urothelial bladder cancer

Samples in biobanks are generally preserved by formalin-fixation and paraffin-embedding (FFPE) and/or optimal cutting temperature compound (OCT)-embedding and subsequently frozen. Mass spectrometry (MS)-based analysis of these samples is now available via developed protocols, however, the differences in results with respect to preservation methods needs further investigation. Here we use bladder urothelial carcinoma tissue of two different tumor stages (Ta/T1-non-muscle invasive bladder cancer (NMIBC), and T2/T3-muscle invasive bladder cancer (MIBC)) which, upon sampling, were divided and preserved by FFPE and OCT. Samples were parallel processed from the two methods and proteins were analyzed with label-free quantitative MS. Over 700 and 1200 proteins were quantified in FFPE and OCT samples, respectively. Multivariate analysis indicates that the preservation method is the main source of variation, but also tumors of different stages could be differentiated. Proteins involved in mitochondrial function were overrepresented in OCT data but missing in the FFPE data, indicating that these proteins are not well preserved by FFPE. Concordant results for proteins such as HMGCS2 (uniquely quantified in Ta/T1 tumors), and LGALS1, ANXA5 and plastin (upregulated in T2/T3 tumors) were observed in both FFPE and OCT data, which supports the use of MS technology for biobank samples and encourages the further evaluation of these proteins as biomarkers.

Molecular and Metabolic Subtypes in Sporadic and Inherited Clear Cell Renal Cell Carcinoma

The promise of personalized medicine is a therapeutic advance where tumor signatures obtained from different omics platforms, such as genomics, transcriptomics, proteomics, and metabolomics, in addition to environmental factors including metals and metalloids, are used to guide the treatments. Clear cell renal carcinoma (ccRCC), the most common type of kidney cancer, can be sporadic (frequently) or genetic (rare), both characterized by loss of the von Hippel-Lindau (VHL) gene that controls hypoxia inducible factors. Recently, several genomic subtypes were identified with different prognoses. Transcriptomics, proteomics, metabolomics and metallomic data converge on altered metabolism as the principal feature of the disease. However, in view of multiple biochemical alterations and high level of tumor heterogeneity, identification of clearly defined subtypes is necessary for further improvement of treatments. In the future, single-cell combined multi-omics approaches will be the next generation of analyses gaining deeper insights into ccRCC progression and allowing for design of specific signatures, with better prognostic/predictive clinical applications.

Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States

INTRODUCTION

The incidence of kidney cancer is increasing; it could be counteracted with new ways to predict and detect it. We aimed to implement an artificial neural network in order to predict new cases of renal-cell carcinoma (RCC) in the population using population rate, obesity, smoking incidence, uncontrolled hypertension, and life expectancy data in the United States.

PATIENTS AND METHODS

Statistics were collected on US population numbers, life expectancy, obesity, smoking, and hypertension. We used MATLAB R2018 (MathWorks) software to implement an artificial neural network. Data were repeatedly and randomly divided into training (70%) and validation (30%) subsets.

RESULTS

The number of new RCC cases will grow from 44,400 (2020) to 55,400 (2050), an increase of +24.7%. Our data show that preventing hypertension would have the greatest impact on reduction of the incidence, estimated at -775 and -575 cases per year in 2020 and in 2030, respectively. The prevention of obesity and smoking would have a more limited impact, estimated at -64 and -180 cases per year in 2020 and in 2030, respectively, for obesity, and -173 and -21 cases per year in 2020 and in 2030, respectively, for smoking.

CONCLUSIONS

Our predictions underline the need for accurate studies on RCC-related risk factors to reduce the incidence.

Liquid Biopsies in Renal Cell Carcinoma-Recent Advances and Promising New Technologies for the Early Detection of Metastatic Disease

Clear cell renal cell carcinoma (ccRCC) displays a highly varying clinical progression, from slow growing localized tumors to very aggressive metastatic disease (mRCC). Almost a third of all patients with ccRCC show metastatic dissemination at presentation while another third develop metastasis during the course of the disease. Survival rates of mRCC patients remain low despite the development of novel targeted treatment regimens. Biomarkers indicating disease progression could help to define its aggressive potential and thus guide patient management. However, molecular markers that can reliably assess metastatic dissemination and disease recurrence in ccRCC have not been recommended for clinical practice to date. Liquid biopsies could provide an attractive and non-invasive method to determine the risk of recurrence or metastatic dissemination during follow-up and thus assist the search for surveillance biomarkers in ccRCC tumors. A wide spectrum of circulating molecules have already shown considerable potential for ccRCC diagnosis and prognostication. In this review, we outline state of the art of the key circulating analytes such as cfDNA, cfRNA, proteins, and exosomes that may serve as biomarkers for the longitudinal monitoring of ccRCC progression to metastasis. Moreover, we address some of the prevailing limitations in the past approaches and present promising adoptable technologies that could help to pursue the implementation of liquid biopsies as a prognostic tool for mRCC.

Small Heat Shock Proteins in Cancers: Functions and Therapeutic Potential for Cancer Therapy

Small heat shock proteins (sHSPs) are ubiquitous ATP-independent chaperones that play essential roles in response to cellular stresses and protein homeostasis. Investigations of sHSPs reveal that sHSPs are ubiquitously expressed in numerous types of tumors, and their expression is closely associated with cancer progression. sHSPs have been suggested to control a diverse range of cancer functions, including tumorigenesis, cell growth, apoptosis, metastasis, and chemoresistance, as well as regulation of cancer stem cell properties. Recent advances in the field indicate that some sHSPs have been validated as a powerful target in cancer therapy. In this review, we present and highlight current understanding, recent progress, and future challenges of sHSPs in cancer development and therapy.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

THE PRESENT AND FUTURE OF THE MASS SPECTROMETRY-BASED INVESTIGATION OF THE EXOSOME LANDSCAPE

Exosomes are critical intercellular messengers released upon the fusion of multivesicular bodies with the cellular plasma membrane that deliver their cargo in the form of extracellular vesicles. Containing numerous nonrandomly packed functional proteins, lipids, and RNAs, exosomes are vital intercellular messengers that contribute to the physiologic processes of the healthy organism. During the post-genome era, exosome-oriented proteomics have garnered great interest. Since its establishment, mass spectrometry (MS) has been indispensable for the field of proteomics research and has advanced rapidly to interrogate biological samples at a higher resolution and sensitivity. Driven by new methodologies and more advanced instrumentation, MS-based approaches have revolutionized our understanding of protein biology. As the access to online proteomics database platforms has blossomed, experimental data processing occurs with more speed and accuracy. Here, we review recent advances in the technological progress of MS-based proteomics and several new detection strategies for MS-based proteomics research. We also summarize the use of integrated online databases for proteomics research in the era of big data. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Amyloid Beta (A4) Precursor Protein: A Potential Biomarker for Recurrent Nasopharyngeal Carcinoma

Background and Aim

Nasopharyngeal carcinoma (NPC) is one of the most common cancers in Southern China, Southeast Asia. Radiotherapy is the main treatment for NPC. Still, about 20% of patients with NPC have a recurrence. No effective serum biomarkers are available for recurrent nasopharyngeal carcinoma (rNPC) to date. This study aimed to explore whether amyloid beta (A4) precursor protein (APP) might serve as a valuable diagnostic and prognostic biomarker for patients with rNPC.

Methods

In a previous study, a tandem mass tag–based proteomic test was performed, which screened 59 differentially expressed proteins (DEPs) between nonrecurrent nasopharyngeal carcinoma (nrNPC) and rNPC. In this study, a protein–protein interaction was conducted to screen the key proteins among the 59 DEPs. APP was validated and evaluated by enzyme-linked immunosorbent assay in 70 serum samples [recurrence (n = 35) and no-recurrence (n = 35)]. Also, the receiver operating characteristic (ROC) curve was plotted to evaluate the predictive value of APP.

Results

The area under the ROC curve was 0.666 (95% CI: 0.514–0.818, P = 0.044). The best cutoff point of the relative expression levels for APP was 1.23 (concentration = 16.95 ng/mL), at which the sensitivity was 55.2% and the specificity was 90.9%.

Conclusion

The findings indicated that APP might be a valuable diagnostic and prognostic biomarker for patients with rNPC.

Dysregulation of Ketone Body Metabolism Is Associated With Poor Prognosis for Clear Cell Renal Cell Carcinoma Patients

Kidney is an important organ for ketone body metabolism. However, the role of abnormal ketone metabolism and its possible function in tumorigenesis of clear cell renal cell carcinoma (ccRCC) have not yet been elucidated. Three differentially expressed key enzymes involved in ketone body metabolism, ACAT1, BDH2, and HMGCL, were screened out between ccRCC and normal kidney tissues using the GEO and TCGA databases.We confirmed that the transcription and protein expression of ACAT1, BDH2, and HMGCL were significantly lower in ccRCC by real-time RT-PCR and IHC assays. Those patients with lower expression of these three genes have a worse outcome. In addition, we demonstrated that ectopic expression of each of these genes inhibited the proliferation of ccRCC cells. The overexpressed ACAT1 and BDH2 genes remarkably impeded the migratory and invasive capacity of ccRCC cells. Furthermore, exogenous β-hydroxybutyrate suppressed the growth of ccRCC cells in vitro in a dose-dependent manner. Our findings suggest that ACAT1, BDH2, and HMGCL are potential tumor suppressor genes, and constitute effective prognostic biomarkers for ccRCC. Ketone body metabolism might thus be a promising target in a process for developing novel therapeutic approaches to treat ccRCC.

Searching for prognostic biomarkers for small renal masses in the urinary proteome

Renal cell carcinoma (RCC) is frequently diagnosed incidentally as an early-stage small renal mass (SRM; pT1a, ≤4 cm). Overtreatment of patients with benign or clinically indolent SRMs is increasingly common and has resulted in a recent shift in treatment recommendations. There are currently no available biomarkers that can accurately predict clinical behavior. Therefore, we set out to identify early biomarkers of RCC progression. We employed a quantitative label-free liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) proteomics approach and targeted parallel-reaction monitoring to identify and validate early, noninvasive urinary biomarkers for RCC-SRMs. In total, we evaluated 115 urine samples, including 33 renal oncocytoma (≤4 cm) cases, 30 progressive and 26 nonprogressive clear cell RCC (ccRCC)-SRM cases, in addition to 26 healthy controls. We identified six proteins, which displayed significantly elevated expression in clear cell RCC-SRMs (ccRCC-SRMs) relative to healthy controls. Proteins C12ORF49 and EHD4 showed significantly elevated expression in ccRCC-SRMs compared to renal oncocytoma (≤4 cm). Additionally, proteins EPS8L2, CHMP2A, PDCD6IP, CNDP2 and CEACAM1 displayed significantly elevated expression in progressive relative to nonprogressive ccRCC-SRMs. A two-protein signature (EPS8L2 and CCT6A) showed significant discriminatory ability (areas under the curve: 0.81, 95% CI: 0.70-0.93) in distinguishing progressive from nonprogressive ccRCC-SRMs. Patients (Stage I-IV) with EPS8L2 and CCT6A mRNA alterations showed significantly shorter overall survival (p = 1.407 × 10^-6 ) compared to patients with no alterations. Our in-depth proteomic analysis identified novel biomarkers for early-stage RCC-SRMs. Pretreatment characterization of urinary proteins may provide insight into early RCC progression and could potentially help assign patients to appropriate management strategies.

The mRNA Expression Signature and Prognostic Analysis of Multiple Fatty Acid Metabolic Enzymes in Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a metabolic disease, and accumulating evidences indicate significant alterations in the cellular metabolism, especial aerobic glycolysis and glutamine metabolism, in RCC. However, fatty acid (FA) metabolism has received less attention, and the mRNA expression pattern and prognostic role of FA metabolic enzymes in clear cell RCC (ccRCC) have not been carefully examined. In the current study, we first investigated the mRNA expression profiles of multiple FA metabolic enzymes, i.e., ACLY, ACC, FASN, SCD, CPT1A, HADHA, HADHB, and ACAT1, in 42 ccRCC and 33 normal kidney tissues using the Oncomine database, validated their mRNA expression profiles using GEPIA resource, then evaluated and validated the prognostic significance of these metabolic enzymes in 530 ccRCC patients using Kaplan-Meier plotter and GEPIA analyses respectively. The Oncomine and GEPIA confirmed higher ACLY, SCD, and lower ACAT1 mRNA expression in ccRCC than normal tissues (P<0.05). And further prognostic analysis displayed that overexpression of the some FA anabolic enzymes (FASN) was correlated to poor overall survival (OS), while overexpression of the FA catabolic enzymes (CPT1A, HADHA, HADHB, and ACAT1) was correlated to favorable OS in ccRCC patients. In conclusion, multiple FA metabolic enzymes, such as FASN, HADHA, and ACAT1, were potential prognostic markers of ccRCC, which implied alterations in FA metabolism might be involved in ccRCC tumorigenesis and progression.

ACAT1 and Metabolism-Related Pathways Are Essential for the Progression of Clear Cell Renal Cell Carcinoma (ccRCC), as Determined by Co-expression Network Analysis

Kidney cancer ranks as one of the top 10 causes of cancer death; this cancer is difficult to detect, difficult to treat, and poorly understood. The most common subtype of kidney cancer is clear cell renal cell carcinoma (ccRCC) and its progression is influenced by complex gene interactions. Few clinical studies have investigated the molecular markers associated with the progression of ccRCC. In this study, we collected microarray profiles of 72 ccRCCs and matched normal samples to identify differentially expressed genes (DEGs). Then a weighted gene co-expression network analysis (WGCNA) was conducted to identify co-expressed gene modules. By relating all co-expressed modules to clinical features, we found that the brown module and Fuhrman grade had the highest correlation (r = -0.8, p = 1e-09). Thus, the brown module was regarded as a clinically significant module and subsequently analyzed. Functional annotation showed that the brown module focused on metabolism-related biological processes and pathways, such as fatty acid oxidation and amino acid metabolism. We then performed a protein-protein interaction (PPI) network to identify the hub nodes in the brown module. It is worth noting that only one candidate, acetyl-CoA acetyltransferase (ACAT1), was considered to be the final target most relevant to the Fuhrman grade of ccRCC, by applying the intersection of hub genes in the co-expressed network and the PPI network. ACAT1 was subsequently validated using another two external microarray datasets and the TCGA dataset. Intriguingly, validation results indicated that ACAT1 was negatively correlated with four grades of ccRCC, which was also consistent with our results from qRT-PCR analysis and immunohistochemistry staining of clinical samples. Overexpression of ACAT1 inhibited the proliferation and migration of human ccRCC cells in vitro. In addition, the Kaplan-Meier survival curve showed that patients with a lower expression of ACAT1 showed a significantly lower overall survival rate and disease-free survival rate, indicating that ACAT1 could act as a prognostic and recurrence/progression biomarker of ccRCC. In summary, we found and confirmed that ACAT1 might help to identify the progression of ccRCC, which might have important clinical implications for enhancing risk stratification, therapeutic decision, and prognosis prediction in ccRCC patients.

Targeting Heat Shock Protein 27 in Cancer: A Druggable Target for Cancer Treatment?

Heat shock protein 27 (HSP27), induced by heat shock, environmental, and pathophysiological stressors, is a multi-functional protein that acts as a protein chaperone and an antioxidant. HSP27 plays a significant role in the inhibition of apoptosis and actin cytoskeletal remodeling. HSP27 is upregulated in many cancers and is associated with a poor prognosis, as well as treatment resistance, whereby cells are protected from therapeutic agents that normally induce apoptosis. This review highlights the most recent findings and role of HSP27 in cancer, as well as the strategies for using HSP27 inhibitors for therapeutic purposes.

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

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

Key miRNAs and target genes played roles in the development of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (CCRCC) is the most aggressive form of renal cell carcinoma (RCC).

OBJECTIVE

This study was aimed to identify the differentially expressed miRNAs and target genes in CCRCC.

METHODS

The miRNA and mRNA next-generation sequencing data were downloaded from The Cancer Genome Atlas (TCGA) dataset. Differential expression analysis was performed, followed by correlation analysis of miRNA-mRNA. Functional enrichment and survival analysis was also performed.

RESULTS

Seven hundred and eighty-seven patients with CCRCC from TCGA data portal were included in this study. A total of 52 differentially expressed miRNAs were identified in CCRCC. Then 2361 differentially expressed genes (DEGs) were identified. Prediction analysis and correlation analysis revealed that 89 miRNA-mRNA pairs were not only predicted by algorithms but also had a significant inverse relationship. Several differentially expressed miRNAs such as hsa-mir-501 and their target genes including AK1, SLC25A15 and PCDHGC3 had a significant prognostic value for CCRCC patients.

CONCLUSIONS

Alterations of differentially expressed miRNAs and target genes may be involved in the development of CCRCC and can be considered as the prognostic markers for CCRCC.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Fast quantitative urinary proteomic profiling workflow for biomarker discovery in kidney cancer

Background

Urine has evolved as a promising body fluids in clinical proteomics because it can be easily and noninvasively obtained and can reflect physiological and pathological status of the human body. Many efforts have been made to characterize more urinary proteins in recent years, but few have focused on the analysis throughput and detection reproducibility. Increasing the urine proteomic profiling throughput and reproducibility is urgently needed for discovering potential biomarker in large cohorts.

Methods

In this study, we developed a fast and robust workflow for streamlined urinary proteome analysis. The workflow integrate highly efficient sample preparation technique and urinary specific data-independent acquisition (DIA) approach. The performance of the workflow was systematically evaluated and the workflow was subsequently applied in a proof-of-concept urine proteome study of 21 kidney cancer (KC) patients and 22 healthy controls.

Results

With this workflow, the entire sample preparation process takes less than 3 h and allows multiplexing on standard centrifuges. Without pre-fractionation, our newly developed DIA method allows quantitative analysis of ~ 1000 proteins within 80 min of MS time (~ 15 samples/day). The quantitation accuracy of the whole workflow was excellent with median CV of 9.1%. The preliminary study on KC identified 125 significantly changed proteins.

Conclusions

The result suggested the feasibility of applying the high throughput workflow in extensive urinary proteome profiling and clinical relevant biomarker discovery.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9220-2) contains supplementary material, which is available to authorized users.

Urinary proteomic biomarkers in oncology: ready for implementation?

Introduction: Biomarkers are expected to improve the management of cancer patients by enabling early detection and prediction of therapeutic response. Proteins reflect a molecular phenotype, have high potential as biomarkers, and also are key targets for intervention. Given the ease of collection and proximity to certain tumors, the urinary proteome is a rich source of biomarkers and several proteins have been already implemented. Areas covered: We examined the literature on urine proteins and proteome analysis in oncology from reports published during the last 5 years to generate an overview on the status of urine protein and peptide biomarkers, with emphasis on their actual clinical value. Expert commentary: A few studies report on biomarkers that are ready to be implemented in patient management, among others in bladder cancer and cholangiocarcinoma. These reports are based on multi-marker approaches. A high number of biomarkers, though, has been described in studies with low statistical power. In fact, several of them have been consistently reported across different studies. The latter should be the focus of attention and be tested in properly designed confirmatory and ultimately, prospective investigations. It is expected that multi-marker classifiers for a specific context-of-use, will be the preferred path toward clinical implementation.

iTRAQ-based quantitative proteomic analysis of differentially expressed proteins in chemoresistant nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a highly prevalent disease in Southeast Asia. The disease is typically diagnosed in the later stages, and chemotherapy resistance often causes treatment failure. To investigate the underlying mechanisms of drug resistance, we searched for chemoresistant-associated proteins in NPC and drug-resistant NPC cell lines using isobaric tags for relative and absolute quantitation combined with nano liquid chromatography-tandem mass spectrometry. The chemoresistant NPC cell lines CNE1DDP and CNE2DDP were resistant to 1 mg/L cisplatin, had resistant indexes of 4.58 and 2.63, respectively, and clearly grew more slowly than the NPC cell lines CNE1 and CNE2. Using three technical replicates, we identified 690 nonredundant proteins, 56 of which were differentially expressed in both groups of cell lines (CNE1 vs. CNE1DDP and CNE2 vs. CNE2DDP). Gene Ontology, KEGG pathway, and miRNA analyses and protein-protein interactions of differentially expressed proteins showed that proteins TRIM29, HSPB1, CLIC1, ANXA1, and STMN1, among others, may play a role in the mechanisms of chemoresistance in clinical therapy. The chemotherapy-resistant proteomic profiles obtained may allow the identification of novel biomarkers for early detection of chemoresistance in NPC and other cancers.

Evaluation and prognostic significance of manganese superoxide dismutase in clear cell renal cell carcinoma

The antioxidant enzyme manganese superoxide dismutase (MnSOD) is up-regulated in renal cell carcinoma (RCC) and has been implicated in multiple stages of RCC tumorigenesis and progression. However, the prognostic significance of MnSOD in RCC has not been fully elucidated. This study aimed to investigate the expression profile of MnSOD in clear cell RCC (ccRCC) tissues and evaluate the clinical significance of this enzyme in ccRCC patients. MnSOD mRNA was assessed in 42 ccRCC and 33 normal kidney tissues using the Oncomine database, and its protein was detected in 145 ccRCCs and 3 normal tissues by immunohistochemistry staining. The Oncomine database confirmed higher MnSOD mRNA expression in ccRCC than in normal tissues, and immunohistochemistry analysis revealed that MnSOD protein expression was inversely associated with pathologic grade, clinical stage, tumor size, M status, and cancer-specific survival. In addition, univariate survival analysis demonstrated that high-grade, late-stage, large tumors, stage M₁, and low MnSOD expression were associated with a poorer prognosis for cancer-specific survival, and further multivariate analysis revealed that tumor grade, stage, M₁ stage, and MnSOD were identified as independent prognostic factors for cancer-specific survival in patients with ccRCC. Collectively, these findings imply that MnSOD is a promising prognostic marker in ccRCC and implies that oxidative stress might be involved in the tumorigenesis and progression of ccRCC.

Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB)

Background

Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits.

Results

We have developed the Cancer Systems Biology Database (CancerSysDB), a resource for highly flexible queries and analysis of cancer-related data across multiple data types and multiple studies. The CancerSysDB can be adopted by any center for the organization of their locally acquired data and its integration with publicly available data from multiple studies. A publicly available main instance of the CancerSysDB can be used to obtain highly flexible queries across multiple data types as shown by highly relevant use cases. In addition, we demonstrate how the CancerSysDB can be used for predictive cancer classification based on whole-exome data from 9091 patients in The Cancer Genome Atlas (TCGA) research network.

Conclusions

Our database bears the potential to be used for large-scale integrative queries and predictive analytics of clinically relevant traits.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2157-7) contains supplementary material, which is available to authorized users.

MiR-34b-3 and miR-449a inhibit malignant progression of nasopharyngeal carcinoma by targeting lactate dehydrogenase A

MicroRNA expression profiling assays have shown that miR-34b/c and miR-449a are down-regulated in nasopharyngeal carcinoma (NPC); however, the targets and functions of miR-34b/c and miR-449a in the pathologenesis of NPC remain elusive. In this study, we verified miR-34b/c and miR-449a were significantly reduced with the advance of NPC. Overexpression of miR-34b-3 and miR-449a suppressed the growth of NPC cells in culture and mouse tumor xenografts. Using tandem mass tags for quantitative labeling and LC-MS/MS analysis to investigate protein changes after restoring expression of miR-34b-3, 251 proteins were found to be down-regulated after miR-34b-3 transfection. Through 3 replicate experiments, we found that miR-34b-3 regulated the expression of 15 potential targeted genes mainly clustered in the key enzymes of glycolysis metabolism, including lactate dehydrogenase A (LDHA). Further investigation revealed that miR-34b-3 and miR-449a negatively regulated LDHA by binding to the 3′ untranslated regions of LDHA. Furthermore, LDHA overexpression rescued the miR-34b-3 and miR-449a induced tumor inhibition effect in CNE2 cells. In addition, miR-34b-3 and miR-449a suppressed LDH activity and reduced LD content, which were directly induced by downregulation of the LDHA. Our findings suggest that miR-34b-3 and miR-449a suppress the development of NPC through regulation of glycolysis via targeting LDHA and may be potential therapeutic targets for the treatment of NPC.

Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps

Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.

Downregulation of HSP60 disrupts mitochondrial proteostasis to promote tumorigenesis and progression in clear cell renal cell carcinoma

In the present study, we demonstrate that HSP60 is unequivocally downregulated in clear cell renal cell carcinoma (ccRCC) tissues compared to pericarcinous tissues. Overexpression of HSP60 in ccRCC cancer cells suppresses cell growth. HSP60 knockdown increases cell growth and proliferation in both cell culture and nude mice xenografts, and drives cells to undergo epithelial to mesenchymal transition (EMT). Our results propose that HSP60 silencing disrupts the integrity of the respiratory complex I and triggers the excessive ROS production, which promotes tumor progression in the following aspects: (1) ROS activates the AMPK pathway that promotes acquisition of the Warburg phenotype in HSP60-KN cells; (2) ROS generated by HSP60 knockdown or by rotenone inhibition drives cells to undergo EMT; and (3) the high level of ROS may also fragment the Fe-S clusters that up regulates ADHFe1 expression and the 2-hydroxygluterate (2-HG) production leading to changes in DNA methylation. These results suggest that the high level of ROS is needed for tumorigenesis and progression in tumors with the low HSP60 expression and HSP60 is a potential diagnostic biomarker as well as a therapeutic target in ccRCC.

G6PD promotes renal cell carcinoma proliferation through positive feedback regulation of p-STAT3

Ectopic Glucose 6-phosphate dehydrogenase (G6PD) expression plays important role in tumor cell metabolic reprogramming and results in poor prognosis of multiple malignancies. Our previous study indicated that G6PD is overexpressed in clear cell renal cell carcinoma (ccRCC), the most common subtype of RCC. However, its role in RCC is still unclear. Here, we demonstrate that G6PD is not only up-regulated in all types of RCC specimens but also displays higher activities in RCC cell lines. G6PD overexpression promoted RCC cell proliferation, altered cell cycle distribution, and enhanced xenografted RCC development. G6PD up-regulated ROS generation by facilitating NADPH-dependent NOX4 activation, which led to increased expression of p-STAT3 and CyclinD1. Enhanced ROS generation rescued the p-STAT3 and CyclinD1 expression reduction in G6PD-knockdown cells, while ROS scavengers reversed the up-regulated p-STAT3 and CyclinD1 expression in G6PD-overexpressing cells. Furthermore, p-STAT3 activated G6PD gene expression via binding to the G6PD promoter, demonstrating that p-STAT3 forms a positive feedback regulatory loop for G6PD overexpression. G6PD expression was up or down-regulated in response to the impact of p-STAT3 activators or inhibitors. Therefore, G6PD may be an effective RCC therapeutic target.

Proteome profiling of clear cell renal cell carcinoma in von Hippel-Lindau patients highlights upregulation of Xaa-Pro aminopeptidase-1, an anti-proliferative and anti-migratory exoprotease

Patients of the von Hippel-Lindau (VHL) disease frequently develop clear cell renal cell carcinoma (ccRCC). Using archived, formalin-fixed, paraffin-embedded (FFPE) samples, we sought to determine global proteome alterations that distinguish ccRCC tissue from adjacent, non-malignant kidney tissue in VHL-patients. Our quantitative proteomic analysis clearly discriminated tumor and non-malignant tissue. Significantly dysregulated proteins were distinguished using the linear models for microarray data algorithm. In the ccRCC tissue, we noticed a predominant under-representation of proteins involved in the tricarboxylic acid cycle and an increase in proteins involved in glycolysis. This profile possibly represents a proteomic fingerprint of the "Warburg effect", which is a molecular hallmark of ccRCC. Furthermore, we observed an increase in proteins involved in extracellular matrix organization. We also noticed differential expression of many exoproteases in the ccRCC tissue. Of particular note were opposing alterations of Xaa-Pro Aminopeptidases-1 and -2 (XPNPEP-1 and -2): a strong decrease of XPNPEP-2 in ccRCC was accompanied by abundant presence of the related protease XPNPEP-1. In both cases, we corroborated the proteomic results by immunohistochemical analysis of ccRCC and adjacent, non-malignant kidney tissue of VHL patients. To functionally investigate the role of XPNPEP-1 in ccRCC, we performed small-hairpin RNA mediated XPNPEP-1 expression silencing in 786-O ccRCC cells harboring a mutated VHL gene. We found that XPNPEP-1 expression dampens cellular proliferation and migration. These results suggest that XPNPEP-1 is likely an anti-target in ccRCC. Methodologically, our work further validates the robustness of using FFPE material for quantitative proteomics.

Urinary metabolites in patients undergoing coronary catheterization via the radial versus femoral artery approach

The transradial approach (TRA) for coronary angiography and percutaneous coronary intervention is associated with lower rates of vascular complications and acute kidney injury when compared to the transfemoral approach (TFA). Urine metabolites and proteins may be useful in identifying the dynamic changes at the vascular endothelial cell level. We attempted to explore the changes in the measurable signals of endothelial and nephron injury within 60 to 90 minutes after catheterization among those with the TRA and TFA approaches. Consecutive patients of a single interventionist who underwent coronary angiography between June 2015 and May 2016 were included. Of the 60 patients included in the analysis, the baseline characteristics were similar between those with a TRA (n = 30) and TFA (n = 30) approach. The values of the biomarkers were natural log transformed for the analysis. We found that the mean values of heat shock protein 27, taurine, and sulfuric acid did not significantly change after the procedure. However, the median value of thioredoxin decreased (P = 0.002) and that of talose increased (P = 0.01) after the procedure. None of the patients in our cohort experienced vascular complications or acute kidney injury. No differences in the values of urinary metabolites (pre, post, and delta) were found between TRA and TFA except for postprocedural thioredoxin. In conclusion, this exploratory study showed no difference in the patterns of acute vascular/renal injury metabolic markers before and after catheterization irrespective of the arterial access site.

Identification of CALM as the potential serum biomarker for predicting the recurrence of nasopharyngeal carcinoma using a mass spectrometry-based comparative proteomic approach

To date, there are no serum biomarkers available for the prediction of recurrent nasopharyngeal carcinoma (rNPC). The diagnosis of rNPC mostly depends on imaging and biopsy of diseased tissue; however, both of these methods work mostly if the target tumor is at an advanced stage. Therefore, the identificaqtion of recurrent biomarkers is urgently required. In the present study, we used tandem mass tag (TMT) labeling and high performance liquid chromatography (HPLC) fractionation followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify differentially expressed proteins. Serum was collected from 40 patients with NPC [recurrence (n=20) and no recurrence (n=20)]. Compared to non-recurrent NPC (nrNPC), we found 59 proteins to be significantly dysregulated in rNPC; most of these have been previously reported to play a role in carcinogenesis. The dysregulation of calmodulin (CALM) was confirmed in 74 new patients [recurrence (n=32) and no recurrence (n=42)] by ELISA. Moreover, we performed a preliminary pathway analysis which revealed that oxidative phosphorylation was altered in the patients with rNPC compared to those with nrNPC. Taken together, these data identify a potential diagnostic biomarker for rNPC and elucidate the potential molecular mechanisms that are dysregulated and contribute to the pathogenesis of rNPC.

AHNAK2 is a potential prognostic biomarker in patients with PDAC

BACKGROUND

AHNAK nucleoprotein 2 (AHNAK2) belongs to the AHNAK protein family. The studies of AHNAK2 are limited. A recent study reported that AHNAK2 might be a biomarker for pancreatic ductal adenocarcinoma (PDAC); however, tissue-based experiments have not been conducted. The aim of this study was to determine the tissue expression of AHNAK2 and to find the correlation between AHNAK2 and overall survival rate in PDAC.

RESULTS

AHNAK2 is highly expressed in PDAC (n=79) compared with adjacent normal tissues (n=64, P<0.001). Overexpression of AHNAK2 showed a significant relationship with a lower overall survival rate (P=0.033) in PDAC patients. The predictive value of increased expression of AHNAK2 remains relevant in patients with AJCC grade above II (n=43, P=0.006) or lymph node metastasis (n=32, P=0.004). Cox regression analysis showed that AHNAK2 expression (P=0.003) and pathology grade (P<0.001) are independent prognostic factors for PDAC. The nomogram model was performed to predict the 1- and 3-year survival rates based on Cox regression. The C-index was 0.61. The calibration curves were also made to show the association between the observed and predicted probability of the overall survival rates.

MATERIALS AND METHODS

AHNAK2 expression was performed in tissue microarrays by immunohistochemistry. The overall survival rate analysis was performed using the Kaplan-Meier method, Cox proportional hazards regression, and a nomogram model.

CONCLUSIONS

AHNAK2 is overexpressed in PDAC tissues and is an independent prognostic factor in patients with PDAC.

Liquid biopsy: a step forward towards precision medicine in urologic malignancies

There is a growing trend towards exploring the use of a minimally invasive "liquid biopsy" to identify biomarkers in a number of cancers, including urologic malignancies. Multiple aspects can be assessed in circulating cell-free DNA, including cell-free DNA levels, integrity, methylation and mutations. Other prospective liquid biopsy markers include circulating tumor cells, circulating RNAs (miRNA, lncRNAs and mRNAs), cell-free proteins, peptides and exosomes have also emerged as non-invasive cancer biomarkers. These circulating molecules can be detected in various biological fluids, including blood, urine, saliva and seminal plasma. Liquid biopsies hold great promise for personalized medicine due to their ability to provide multiple non-invasive global snapshots of the primary and metastatic tumors. Molecular profiling of circulating molecules has been a stepping-stone to the successful introduction of several non-invasive multi-marker tests into the clinic. In this review, we provide an overview of the current state of cell-free DNA-based kidney, prostate and bladder cancer biomarker research and discuss the potential utility other circulating molecules. We will also discuss the challenges and limitations facing non-invasive cancer biomarker discovery and the benefits of this growing area of translational research.

Englerin A induces an acute inflammatory response and reveals lipid metabolism and ER stress as targetable vulnerabilities in renal cell carcinoma

Renal cell carcinoma (RCC) is among the top ten most common forms of cancer and is the most common malignancy of the kidney. Clear cell renal carcinoma (cc-RCC), the most common type of RCC, is one of the most refractory cancers with an incidence that is on the rise. Screening of plant extracts in search of new anti-cancer agents resulted in the discovery of englerin A, a guaiane sesquiterpene with potent cytotoxicity against renal cancer cells and a small subset of other cancer cells. Though a few cellular targets have been identified for englerin A, it is still not clear what mechanisms account for the cytotoxicity of englerin A in RCC, which occurs at concentrations well below those used to engage the targets previously identified. Unlike any prior study, the current study used a systems biology approach to explore the mechanism(s) of action of englerin A. Metabolomics analyses indicated that englerin A profoundly altered lipid metabolism by 24 h in cc-RCC cell lines and generated significant levels of ceramides that were highly toxic to these cells. Microarray analyses determined that englerin A induced ER stress signaling and an acute inflammatory response, which was confirmed by quantitative PCR and Western Blot analyses. Additionally, fluorescence confocal microscopy revealed that englerin A at 25 nM disrupted the morphology of the ER confirming the deleterious effect of englerin A on the ER. Collectively, our findings suggest that cc-RCC is highly sensitive to disruptions in lipid metabolism and ER stress and that these vulnerabilities can be targeted for the treatment of cc-RCC and possibly other lipid storing cancers. Furthermore, our results suggest that ceramides may be a mediator of some of the actions of englerin A. Lastly, the acute inflammatory response induced by englerin A may mediate anti-tumor immunity.

Serum Proteome Alterations in Patients with Cognitive Impairment after Traumatic Brain Injury Revealed by iTRAQ-Based Quantitative Proteomics

Background. Cognitive impairment is the leading cause of traumatic brain injury- (TBI-) related disability; however, the underlying pathogenesis of this dysfunction is not completely understood. Methods. Using an isobaric tagging for relative and absolute quantitation- (iTRAQ-) based quantitative proteomic approach, serum samples from healthy control subjects, TBI patients with cognitive impairment, and TBI patients without cognitive impairment were analysed to identify differentially expressed proteins (DEPs) related to post-TBI cognitive impairment. In addition, DEPs were further analysed using bioinformatic platforms and validated using enzyme-linked immunosorbent assays (ELISA). Results. A total of 56 DEPs were identified that were specifically related to TBI-induced cognitive impairment. Bioinformatic analysis revealed that a wide variety of cellular and metabolic processes and some signaling pathways were involved in the pathophysiology of cognitive deficits following TBI. Five randomly selected DEPs were validated using ELISA in an additional 105 cases, and the results also supported the experimental findings. Conclusions. Despite limitations, our findings will facilitate further studies of the pathological mechanisms underlying TBI-induced cognitive impairment and provide new methods for the research and development of neuroprotective agents. However, further investigation on a large cohort is warranted.

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.

Quantitative score modulation of HSP90 and HSP27 in clear cell renal cell carcinoma

We sought to evaluate the expression of HSP27 and HSP90 chaperones in renal cell carcinomas as a target for cancer therapeutics.A total of 127 clear cell renal cell carcinomas stratified according to the Mayo Clinic SSIGN (size, staging, grading, and necrosis) risk groups (good, 1; poor, 5) and 20 cases with metastases, were available. Immunostaining for both HSP27 and HSP90 was performed on tissue microarrays. Results were detailed per scorable arrays per SSIGN risk groups.Immunolabelling for HSP90 and HSP27 was seen in 109 of 127 (86%) and 114 of 127 (89%) cases, respectively. HSP90 scored 4.9 in 32 cases risked SSIGN 1, 3.5 in 41 cases SSIGN 2, 4.8 in 11 cases SSIGN 3, 4.2 in 22 cases SSIGN 4, and 5.0 in three cases SSIGN 5. HSP27 scored 4.6 in 33 risked SSIGN 1, 3.1 in 43 SSIGN 2, 2.6 in 11 SSIGN 3, 3.6 in 24 SSIGN 4, and 2.7 in three SSIGN 5. Metastases ranged from 2.9-5.0. A trend of increasing value for HSP90 was observed when comparing SSIGN 1-2 versus SSIGN 3-5 risk groups (4.2 versus 4.6 mean values; p = 0.06); no difference has been observed for HSP27 (3.8 to 3.9; p = 0.08).A score modulation of HSPs is observed in renal cell carcinoma and may affect the efficacy of targeted therapy.

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.

Comparative proteomic profiling identifies potential prognostic factors for human clear cell renal cell carcinoma

The identification of markers for disease diagnostic, prognostic, or predictive purposes will have a great effect in improving patient management. Proteomic‑based approaches for biomarker discovery are promising strategies used in cancer research. In this study, we performed quantitative proteomic analysis on four patients including clear cell renal cell carcinoma (ccRCC) and paired adjacent non‑cancerous renal tissues using label‑free quantitative proteomics and liquid chromatography‑tandem mass spectrometry (LC‑MS/MS) to identify differentially expressed proteins. Among 3,061 identified non‑redundant proteins, we found that 210 proteins were differentially expressed (83 overexpressed and 127 underexpressed) in ccRCC tissue when compared with normal kidney tissues. Two most significantly dysregulated proteins (PCK1 and SNRPF) were chosen to be confirmed by western blotting. Pathway analysis of 210 differentially expressed proteins showed that dysregulated proteins are related to many cancer‑related biological processes such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways. Online survival analysis indicated the prognostic value of these dysregulated proteins. In conclusion, we identified some potential diagnostic biomarkers for ccRCC and an in‑depth understanding of their involved biological pathways may help pave the way to discover new therapeutic strategies for ccRCC.

Integrative analysis to select cancer candidate biomarkers to targeted validation

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.