In-depth proteomic analysis of six types of exudative pleural effusions for nonsmall cell lung cancer biomarker discovery

Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion

To identify protein biomarkers capable of early prediction regarding the distinguishing malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung disease. A four-dimensional data independent acquisition (4D-DIA) proteomic was performed to determine the differentially expressed proteins in samples from 20 lung adenocarcinoma MPE and 30 BPE. The significantly differential expressed proteins were selected for Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis. Protein biomarkers with high capability to discriminate MPE from BPE patients were identified by Random Forest (RF) algorithm prediction model, whose diagnostic and prognostic efficacy in primary tumors were further explored in public datasets, and were validated by ELISA experiment. 50 important proteins (30 up-regulated and 20 down-regulated) were selected out as potential markers to distinguish the MPE from BPE group. GO analysis revealed that those proteins involving the most important cell component is extracellular space. KEGG analysis identified the involvement of cellular adhesion molecules pathway. Furthermore, the Area Under Curve (AUC) of these proteins were ranged from 0.717 to 1.000，with excellent diagnostic properties to distinguish the MPE. Finally, significant survival and gene and protein expression analysis demonstrated BPIFB1, DPP4, HPRT1 and ABI3BP had high discriminating values. SIGNIFICANCE: We performed a 4D-DIA proteomics to determine the differentially expressed proteins in pleural effusion samples from MPE and BPE. Some potential protein biomarkers were identified to distinguish the MPE from BPE patients., which may provide helpful diagnostic and therapeutic insights for lung cancer. This is significant because the median survival time of patients with MPE is usually 4-12 months, thus, it is particularly important to diagnose MPE early to start treatments promptly. The most common causes of MPE are lung cancers, while pneumonia and tuberculosis are the main causes of BPE. If more diagnostic markers could be identified periodically, there would be an important significance to clinical diagnose and treatment with drugs in lung cancer patients.

Lipid raft interacting galectin 8 regulates primary ciliogenesis

Primary cilium is a specialized sensory organelle that transmits environmental information into cells. Its length is tightly controlled by various mechanisms such as the frequency or the cargo size of the intraflagellar transport trains which deliver the building materials such as tubulin subunits essential for the growing cilia. Here, we show the sialoglycan interacting galectin 8 regulates the process of primary ciliogenesis. As the epithelia become polarized, there are more galectin 8 being apically secreted and these extracellular galectin 8 molecules apparently bind to a lipid raft enriched domain at the base of the primary cilia through interacting with lipid raft components, such as GD3 ganglioside and scaffold protein caveolin 1. Furthermore, the binding of galectin 8 at this critical region triggers rapid growth of primary cilia by perturbing the barrier function of the transition zone (TZ). Our study also demonstrates the functionality of this barrier depends on intact organization of lipid rafts at the cilia as genetically knockout of Cav1 and pharmacologically inhibition of lipid raft both phenocopy the effect of apical addition of recombinant galectin 8; that is, rapid elongation of primary cilia and redistribution of cilia proteins from TZ to the growing axoneme. Indeed, as cilia elongated, endogenous galectin 8, caveolin 1, and TZ component, TMEM231, also transited from the TZ to the growing axoneme. We also noted that the interaction between caveolin 1 and TMEM231 could be perturbed by exogenous galectin 8. Taken together, we proposed that galectin 8 promoted primary cilia elongation through impeding the barrier function of the TZ by interfering with the interaction between caveolin 1 and TMEM231.

Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort

Simple Summary

Pleural effusion (PE) occurs as a consequence of various pathologies. Malignant effusion due to lung cancer is one of the most frequent causes. A method for accurate differentiation of malignant from benign PE is an unmet clinical need. Proteomics profiling of PE has shown promising results. However, mass spectrometry (MS) analysis typically involves the tedious elimination of abundant proteins before analysis, and clinical annotation of proteomics profiled cohorts is limited. This study compares the proteomes of malignant PE and nonmalignant PE, identifies lung cancer malignant markers in agreement with other studies, and identifies markers strongly associated with patient survival.

Abstract

Background: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed. Methods: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated. Results: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism. Conclusion: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 × 10⁻⁶).

Diagnosing pleural effusions using mass spectrometry-based multiplexed targeted proteomics quantitating mid- to high-abundance markers of cancer, infection/inflammation and tuberculosis

Pleural effusion (PE) is excess fluid in the pleural cavity that stems from lung cancer, other diseases like extra-pulmonary tuberculosis (TB) and pneumonia, or from a variety of benign conditions. Diagnosing its cause is often a clinical challenge and we have applied targeted proteomic methods with the aim of aiding the determination of PE etiology. We developed a mass spectrometry (MS)-based multiple reaction monitoring (MRM)-protein-panel assay to precisely quantitate 53 established cancer-markers, TB-markers, and infection/inflammation-markers currently assessed individually in the clinic, as well as potential biomarkers suggested in the literature for PE classification. Since MS-based proteomic assays are on the cusp of entering clinical use, we assessed the merits of such an approach and this marker panel based on a single-center 209 patient cohort with established etiology. We observed groups of infection/inflammation markers (ADA2, WARS, CXCL10, S100A9, VIM, APCS, LGALS1, CRP, MMP9, and LDHA) that specifically discriminate TB-PEs and other-infectious-PEs, and a number of cancer markers (CDH1, MUC1/CA-15-3, THBS4, MSLN, HPX, SVEP1, SPINT1, CK-18, and CK-8) that discriminate cancerous-PEs. Some previously suggested potential biomarkers did not show any significant difference. Using a Decision Tree/Multiclass classification method, we show a very good discrimination ability for classifying PEs into one of four types: cancerous-PEs (AUC: 0.863), tuberculous-PEs (AUC of 0.859), other-infectious-PEs (AUC of 0.863), and benign-PEs (AUC: 0.842). This type of approach and the indicated markers have the potential to assist in clinical diagnosis in the future, and help with the difficult decision on therapy guidance.

Prognostic utility of the ovarian cancer secretome: a systematic investigation

BACKGROUND

Ovarian cancer is usually detected at an advanced stage with frequent recurrence. The recurrence-free survival and overall survival is influenced by the age at diagnosis, tumor stage and histological subtype. Nonetheless, quantifiable prognostic biomarkers are needed for early identification of the high-risk patients and for personalized medicine. Several studies link tumor-specific dysregulated expression of certain proteins with ovarian cancer prognosis. However, careful investigation of presence of these prognostically relevant proteins in ovarian cancer secretome is lacking.

OBJECTIVE

To critically analyze the recent published data on prognostically relevant proteins for ovarian cancer and to carefully search how many of them are reported in the published ovarian cancer secretome datasets.

DESIGN

A search for relevant studies in the past 2 years was conducted in PubMed and a comprehensive list of proteins associated with the ovarian cancer prognosis was prepared. These were cross-referred to the published ovarian cancer secretome profiles. The proteins identified in the secretome were further shortlisted based on a scoring strategy employing stringent criteria.

RESULTS

A panel of seven promising secretory biomarkers associated with ovarian cancer prognosis is proposed.

CONCLUSION

Scanning the ovarian cancer secretome datasets provides the opportunity to identify if tumor-specific biomarkers could be tested as secretory biomarkers. Detecting their levels in the body fluid would be more advantageous than evaluating the expression in the tissue, since it could be monitored multiple times over the course of the disease to have a better judgment of the prognosis and response to therapy.

Discovery and Pharmacological Evaluation of STEAP4 as a Novel Target for HER2 Overexpressing Breast Cancer

Breast cancer (BC) is a highly heterogeneous disease encompassing multiple subtypes with different molecular and histopathological features, disease prognosis, and therapeutic responses. Among these, the Triple Negative BC form (TNBC) is an aggressive subtype with poor prognosis and therapeutic outcome. With respect to HER2 overexpressing BC, although advanced targeted therapies have improved the survival of patients, disease relapse and metastasis remains a challenge for therapeutic efficacy. In this study the aim was to identify key membrane-associated proteins which are overexpressed in these aggressive BC subtypes and can serve as potential biomarkers or drug targets. We leveraged on the development of a membrane enrichment protocol in combination with the global profiling GeLC-MS/MS technique, and compared the proteomic profiles of a HER2 overexpressing (HCC-1954) and a TNBC (MDA-MB-231) cell line with that of a benign control breast cell line (MCF-10A). An average of 2300 proteins were identified from each cell line, of which approximately 600 were membrane-associated proteins. Our global proteomic methodology in tandem with invigoration by Western blot and Immunofluorescence analysis, readily detected several previously-established BC receptors like HER2 and EPHA2, but importantly STEAP4 and CD97 emerged as novel potential candidate markers. This is the first time that the mitochondrial iron reductase STEAP4 protein up-regulation is linked to BC (HER2+ subtype), while for CD97, its role in BC has been previously described, but never before by a global proteomic technology in TNBC. STEAP4 was selected for further detailed evaluation by the employment of Immunohistochemical analysis of BC xenografts and clinical tissue microarray studies. Results showed that STEAP4 expression was evident only in malignant breast tissues whereas all the benign breast cases had no detectable levels. A functional role of STEAP4 intervention was established in HER2 overexpressing BC by pharmacological studies, where blockage of the STEAP4 pathway with an iron chelator (Deferiprone) in combination with the HER2 inhibitor Lapatinib led to a significant reduction in cell growth in vitro. Furthermore, siRNA mediated knockdown of STEAP4 also suppressed cell proliferation and enhanced the inhibition of Lapatinib in HER2 overexpressing BC, confirming its potential oncogenic role in BC. In conclusion, STEAP4 may represent a novel BC related biomarker and a potential pharmacological target for the treatment of HER2 overexpressing BC.

A study of the proteomic expression in patients with complicated parapneumonic pleural effusion

Introduction

The present study aimed to investigate the differences in the proteomic expression between uncomplicated parapneumonic pleural effusion (UPPE) and complicated parapneumonic pleural effusion (CPPE).

Material and methods

There were 10 patients with UPPE and 10 patients with CPPE. These patients were combined due to the complication of pleural effusion and further divided into group A and group B. An LC-MS analysis was conducted with the extraction of high-abundance proteins, and proteins with 1.5-fold or higher difference multiples were identified as differential proteins. Then, gene ontology (GO) and KEGG analyses were conducted on the differential proteins between the groups.

Results

Compared with the UPPE group, there were 38 upregulated proteins and 29 downregulated proteins in the CPPE group. The GO analysis revealed that the CPPE group had enhanced expressions in monosaccharide biosynthesis, glucose catabolism, fructose-6-phosphate glycolysis, glucose-6-phosphate glycolysis, and NADH regeneration as well as reduced expressions in fibrinogen complexes, protein polymerization, and coagulation. Moreover, the KEGG analysis showed that the CPPE group had enhanced expressions in amino acid synthesis, the HIF-1 signalling pathway, and glycolysis/glycoisogenesis and decreased expressions in platelet activation and complement activation.

Conclusions

In pleural effusion in patients with CPPE, there are enhanced expressions of proteins concerning glucose and amino acid metabolism, NADH regeneration, and HIF-1 signalling pathways together with decreased expressions of proteins concerning protein polymerization, blood coagulation, platelet activation, and complement activation.

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics

Background

Although asthma is one of the most common chronic, noncommunicable diseases worldwide, the pathogenesis of childhood asthma is not yet clear. Genetic factors and environmental factors may lead to airway immune-inflammation responses and an imbalance of airway nerve regulation. The aim of the present study was to determine which serum proteins are differentially expressed between children with or without asthma and to ascertain the potential roles that these differentially expressed proteins (DEPs) may play in the pathogenesis of childhood asthma.

Methods

Serum samples derived from four children with asthma and four children without asthma were collected. The DEPs were identified by using isobaric tags for relative and absolute quantitation (iTRAQ) combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses. Using biological information technology, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Cluster of Orthologous Groups of Proteins (COG) databases and analyses, we determined the biological processes associated with these DEPs. Key protein glucose-6-phosphate dehydrogenase (G6PD) was verified by enzyme linked immunosorbent assay (ELISA).

Results

We found 46 DEPs in serum samples of children with asthma vs. children without asthma. Among these DEPs, 12 proteins were significantly (>1.5 fold change) upregulated and 34 proteins were downregulated. The results of GO analyses showed that the DEPs were mainly involved in binding, the immune system, or responding to stimuli or were part of a cellular anatomical entity. In the KEGG signaling pathway analysis, most of the downregulated DEPs were associated with cardiomyopathy, phagosomes, viral infections, and regulation of the actin cytoskeleton. The results of a COG analysis showed that the DEPs were primarily involved in signal transduction mechanisms and posttranslational modifications. These DEPs were associated with and may play important roles in the immune response, the inflammatory response, extracellular matrix degradation, and the nervous system. The downregulated of G6PD in the asthma group was confirmed using ELISA experiment.

Conclusion

After bioinformatics analyses, we found numerous DEPs that may play important roles in the pathogenesis of childhood asthma. Those proteins may be novel biomarkers of childhood asthma and may provide new clues for the early clinical diagnosis and treatment of childhood asthma.

Metabolomic profiling of parapneumonic effusion reveals a regulatory role of dipeptides in interleukin-8 production in neutrophil-like cells

Bacterial pneumonia is a lethal condition, and approximately 40% of bacterial pneumonia patients experience parapneumonic effusion (PPE). Based on the severity of inflammation, PPEs can be categorized as early-stage uncomplicated PPE (UPPE), advanced-stage complicated PPE (CPPE) and, most seriously, thoracic empyema. Appropriate antibiotic treatment at the early stage of PPE can prevent PPE progression and reduce mortality, indicating that understanding PPE generation and components can help researchers develop corresponding treatment strategies for PPE. To this end, metabolomes of 73 PPE (38 UPPE and 35 CPPE samples) and 30 malignant pleural effusion (MPE) samples were profiled with differential ¹²C₂-/¹³C₂-isotope dansylation labeling-based mass spectrometry. We found that PPE is characterized by elevated levels of dipeptides, especially for PPEs at advanced stages. Furthermore, with integrated proteomic and transcriptomic analyses of PPEs, the levels of dipeptides were strongly associated with the production of interleukin-8 (IL-8), an inflammation-associated cytokine. The production of IL-8 indeed increased upon the treatment of HL-60-derived neutrophilic cells with dipeptides, Gly-Val and Gly-Tyr. Our findings help to elucidate the metabolic perturbations present in PPE and indicate for the first time that dipeptides may be involved in the immune regulation observed during PPE progression.

Integrative Omics Analysis Reveals Soluble Cadherin-3 as a Survival Predictor and an Early Monitoring Marker of EGFR Tyrosine Kinase Inhibitor Therapy in Lung Cancer

PURPOSE

EGFR tyrosine kinase inhibitors (EGFR-TKI) benefit patients with advanced lung adenocarcinoma (ADC) harboring activating EGFR mutations. We aimed to identify biomarkers to monitor and predict the progression of patients receiving EGFR-TKIs via a comprehensive omic analysis.

EXPERIMENTAL DESIGN

We applied quantitative proteomics to generate the TKI resistance-associated pleural effusion (PE) proteome from patients with ADC with or without EGFR-TKI resistance. Candidates were selected from integrated genomic and proteomic datasets. The PE (n = 33) and serum (n = 329) levels of potential biomarkers were validated with ELISAs. Western blotting was applied to detect protein expression in tissues, PEs, and a cell line. Gene knockdown, TKI treatment, and proliferation assays were used to determine EGFR-TKI sensitivity. Progression-free survival (PFS) and overall survival (OS) were assessed to evaluate the prognostic values of the potential biomarkers.

RESULTS

Fifteen proteins were identified as potential biomarkers of EGFR-TKI resistance. Cadherin-3 (CDH3) was overexpressed in ADC tissues compared with normal tissues. CDH3 knockdown enhanced EGFR-TKI sensitivity in ADC cells. The PE level of soluble CDH3 (sCDH3) was increased in patients with resistance. The altered sCDH3 serum level reflected the efficacy of EGFR-TKI after 1 month of treatment (n = 43). Baseline sCDH3 was significantly associated with PFS and OS in patients with ADC after EGFR-TKI therapy (n = 76). Moreover, sCDH3 was positively associated with tumor stage in non-small cell lung cancer (n = 272).

CONCLUSIONS

We provide useful marker candidates for drug resistance studies. sCDH3 is a survival predictor and real-time indicator of treatment efficacy in patients with ADC treated with EGFR-TKIs.

Discovery of a putative blood-based protein signature associated with response to ALK tyrosine kinase inhibition

Background

ALK tyrosine kinase inhibition has become a mainstay in the clinical management of ALK fusion positive NSCLC patients. Although ALK mutations can reliably predict the likelihood of response to ALK tyrosine kinase inhibitors (TKIs) such as crizotinib, they cannot reliably predict response duration or intrinsic/extrinsic therapeutic resistance. To further refine the application of personalized medicine in this indication, this study aimed to identify prognostic proteomic biomarkers in ALK fusion positive NSCLC patients to crizotinib.

Methods

Twenty-four patients with advanced NSCLC harboring ALK fusion were administered crizotinib in a phase IV trial which included blood sampling prior to treatment. Targeted proteomics of 327 proteins using MRM-MS was used to measure plasma levels at baseline (including pre-treatment and early treatment blood samples) and assess potential clinical association.

Results

Patients were categorized by duration of response: long-term responders [PFS ≥ 24 months (n = 7)], normal responders [3 < PFS < 24 months (n = 10)] and poor responders [PFS ≤ 3 months (n = 5)]. Several proteins were identified as differentially expressed between long-term responders and poor responders, including DPP4, KIT and LUM. Next, using machine learning algorithms, we evaluated the classification potential of 40 proteins. Finally, by integrating the different analytic methods, we selected 22 proteins as potential candidates for a blood-based prognostic signature of response to crizotinib in NSCLC patients harboring ALK fusion.

Conclusion

In conjunction with ALK mutation, the expression of this proteomic signature may represent a liquid biopsy-based marker of long-term response to crizotinib in NSCLC. Expanding the utility of prognostic biomarkers of response duration could influence choice of therapy, therapeutic sequencing, and potentially the need for alternative or combination therapy.Trial registration ClinicalTrials.gov, NCT02041468. Registered 22 January 2014, https://clinicaltrials.gov/ct2/show/NCT02041468?term=NCT02041468&rank=1.

Glucose intake hampers PKA-regulated HSP90 chaperone activity

Aging is an intricate phenomenon associated with the gradual loss of physiological functions, and both nutrient sensing and proteostasis control lifespan. Although multiple approaches have facilitated the identification of candidate genes that govern longevity, the molecular mechanisms that link aging pathways are still elusive. Here, we conducted a quantitative mass spectrometry screen and identified all phosphorylation/dephosphorylation sites on yeast proteins that significantly responded to calorie restriction, a well-established approach to extend lifespan. Functional screening of 135 potential regulators uncovered that Ids2 is activated by PP2C under CR and inactivated by PKA under glucose intake. ids2Δ or ids2 phosphomimetic cells displayed heat sensitivity and lifespan shortening. Ids2 serves as a co-chaperone to form a complex with Hsc82 or the redundant Hsp82, and phosphorylation impedes its association with chaperone HSP90. Thus, PP2C and PKA may orchestrate glucose sensing and protein folding to enable cells to maintain protein quality for sustained longevity.

Prioritizing predictive biomarkers for gene essentiality in cancer cells with mRNA expression data and DNA copy number profile

Motivation

Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed.

Results

We designed an effective feature selection method and used Support Vector Machines (SVM) to predict the essentiality of the potential driver genes in cancer cell lines with only 10 genes as features. The accuracy of our predictions was the highest in the Broad-DREAM Gene Essentiality Prediction Challenge. We also found a set of genes whose essentiality could be predicted much more accurately than others, which we called Accurately Predicted (AP) genes. Our method can serve as a new way of assessing the essentiality of genes in cancer cells.

Availability and implementation

The raw data that support the findings of this study are available at Synapse. https://www.synapse.org/#! Synapse: syn2384331/wiki/62825. Source code is available at GitHub. https://github.com/GuanLab/DREAM-Gene-Essentiality-Challenge.

Supplementary information

Supplementary data are available at Bioinformatics online.

Discerning the Primary Carcinoma in Malignant Peritoneal and Pleural Effusions Using Imaging Mass Spectrometry-A Feasibility Study

PURPOSE

Malignant effusions challenge diagnostic accuracy due to cytomorphologic overlaps between various malignant primaries. Workup of this material to establish a correct diagnosis is time consuming and limited by the sparsity of material. In order to circumvent these drawbacks, the use of MALDI imaging MS (IMS) as a diagnostic platform has been explored.

EXPERIMENTAL DESIGN

Cytology cell blocks from malignant effusions (serous ovarian carcinoma and several non-ovarian carcinomas including gastric adenocarcinoma) containing at least 30% neoplastic cells are selected for generation of cytology microarrays (CMA). CMA sections are transferred to conductive glass slides, subjected to on-tissue tryptic digestion, and matrix application for MALDI-IMS analysis.

RESULTS

Supervised classification analysis identifies serous ovarian carcinomas as the source of malignant effusions with a sensitivity of 85.7% when compared to samples from all other included primary sites. When compared to gastric adenocarcinoma, serous ovarian carcinoma samples can be delineated with a sensitivity of 97.3%.

CONCLUSION AND CLINICAL RELEVANCE

These preliminary results highlight that MALDI-IMS allows subtyping of malignant effusions to identify the precise origin of neoplastic cells. While achieving similar results compared to classical approaches such as immunocytology, more material is conserved that will be available for further tests.

Quantitative Global Proteome and Lysine Succinylome Analyses Reveal the Effects of Energy Metabolism in Renal Cell Carcinoma

In light of the increasing incidence of renal cell carcinoma (RCC), its molecular mechanisms have been comprehensively explored in numerous recent studies. However, few studies focus on the influence of multi-factor interactions during the occurrence and development of RCC. This study aims to investigate the quantitative global proteome and the changes in lysine succinylation in related proteins, seeking to facilitate a better understanding of the molecular mechanisms underlying RCC. LC-MS/MS combined with bioinformatics analysis are used to quantitatively detect the perspectives at the global protein level. IP and WB analysis were conducted to further verify the alternations of related proteins and lysine succinylation. A total of 3,217 proteins and 1,238 lysine succinylation sites are quantified in RCC tissues, and 668 differentially expressed proteins and 161 differentially expressed lysine succinylation sites are identified. Besides, expressions of PGK1 and PKM2 at protein and lysine, succinylation levels are significantly altered in RCC tissues. Bioinformatics analysis indicates that the glycolysis pathway is a potential mechanism of RCC progression and lysine succinylation may plays a potential role in energy metabolism. These results can provide a new direction for exploring the molecular mechanism of RCC tumorigenesis.

Integrated semi-targeted metabolomics analysis reveals distinct metabolic dysregulation in pleural effusion caused by tuberculosis and malignancy

BACKGROUND

Tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) are the 2 most frequent causes of exudative pleural effusions (PEs). However, the clinical differentiation is challenging.

METHODS

Metabolic signatures in pleural effusion from 156 patients were profiled. An integrated semi-targeted metabolomics platform was incorporated for high throughput metabolite identification and quantitation. In this platform, orbitrap based mass spectrometry with data dependent MS/MS acquisition was applied in the analysis. In-house database containing ~1000MS/MS spectra were established and "MetaInt" was developed for metabolite alignment.

RESULTS

Using this strategy, lower levels of amino acids, citric acid cycle intermediates and free fatty acids accompanied with elevated acyl-carnitines and bile acids were observed, demonstrating increased energy expenditure caused by TPE. Kynurenine pathway from tryptophan was significantly enhanced in TPE. The ratio of tryptophan/kynurenine exhibited decent performance in differentiating TPE from MPE with sensitivity of 92.7% and specificity of 86.1%. After two further independent validations, it turns out that the ratio of tryptophan/kynurenine can be applied confidently as a potential biomarker together with adenosine deaminase (ADA) for clinical diagnosis of TPE.

CONCLUSIONS

Conclusively, the integrated in-house platform for high throughput semi-targeted metabolomics analysis reliably identified great potential of tryptophan/kynurenine ratio as a novel diagnostic biomarker to distinguish pleural effusion caused by tuberculosis and malignancy.

Proteome profiling reveals novel biomarkers to identify complicated parapneumonic effusions

Patients with pneumonia and parapneumonic effusion (PPE) have elevated mortality and a poor prognosis. The aim of this study was to discover novel biomarkers to help distinguish between uncomplicated PPE (UPPE) and complicated PPE (CPPE). Using an iTRAQ-based quantitative proteomics, we identified 766 proteins in pleural effusions from PPE patients. In total, 45 of these proteins were quantified as upregulated proteins in CPPE. Four novel upregulated candidates (BPI, NGAL, AZU1, and calprotectin) were selected and further verified using enzyme-linked immunosorbent assays (ELISAs) on 220 patients with pleural effusions due to different causes. The pleural fluid levels of BPI, NGAL, AZU1, and calprotectin were significantly elevated in patients with CPPE. Among these four biomarkers, BPI had the best diagnostic value for CPPE, with an AUC value of 0.966, a sensitivity of 97%, and a specificity of 91.4%. A logistic regression analysis demonstrated a strong association between BPI levels > 10 ng/ml and CPPE (odds ratio = 341.3). Furthermore, the combination of pleural fluid BPI levels with LDH levels improved the sensitivity and specificity to 100% and 91.4%, respectively. Thus, our findings provided a comprehensive effusion proteome data set for PPE biomarker discovery and revealed novel biomarkers for the diagnosis of CPPE.

Quantitative proteomics in lung cancer

Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.

Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer, COPD, and asthma

INTRODUCTION

Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges. Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma. Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.

The Exploration of Peptide Biomarkers in Malignant Pleural Effusion of Lung Cancer Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Background. Diagnoses of malignant pleural effusion (MPE) are a crucial problem in clinics. In our study, we compared the peptide profiles of MPE and tuberculosis pleural effusion (TPE) to investigate the value of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in diagnosis of MPE. Material and Methods. The 46 MPE and 32 TPE were randomly assigned to training set and validation set. Peptides were isolated by weak cation exchange magnetic beads and peaks in the m/z range of 800–10000 Da were analyzed. Comparing the peptide profile between 30 MPE and 22 TPE samples in training set by ClinProTools software, we screened the specific biomarkers and established a MALDI-TOF-MS classification of MPE. Finally, the other 16 MPE and 10 TPE were included to verify the model. We additionally determined carcinoembryonic antigen (CEA) in MPE and TPE samples using electrochemiluminescent immunoassay method. Results. Five peptide peaks (917.37 Da, 4469.39 Da, 1466.5 Da, 4585.21 Da, and 3216.87 Da) were selected to separate MPE and TPE by MALDI-TOF-MS. The sensitivity, specificity, and accuracy of the classification were 93.75%, 100%, and 96.15%, respectively, after blinded test. The sensitivity of CEA was significantly lower than MALDI-TOF-MS classification (P = 0.035). Conclusions. The results indicate MALDI-TOF-MS is a potential method for diagnosing MPE.

The expression of proline-specific enzymes in the human lung

The pathophysiology of lung diseases is very complex and proteolytic enzymes may play a role or could be used as biomarkers. In this review, the literature was searched to make an overview of what is known on the expression of the proline-specific peptidases dipeptidyl peptidase (DPP) 4, 8, 9, prolyl oligopeptidase (PREP) and fibroblast activation protein α (FAP) in the healthy and diseased lung. Search terms included asthma, chronic obstructive pulmonary disease (COPD), lung cancer, fibrosis, ischemia reperfusion injury and pneumonia. Knowledge on the loss or gain of protein expression and activity during disease might tie these enzymes to certain cell types, substrates or interaction partners that are involved in the pathophysiology of the disease, ultimately leading to the elucidation of their functional roles and a potential therapeutic target. Most data could be found on DPP4, while the other enzymes are less explored. Published data however often appear to be conflicting, the applied methods divers and the specificity of the assays used questionable. In conclusion, information on the expression of the proline-specific peptidases in the healthy and diseased lung is lacking, begging for further well-designed research.

Quantitative global proteome and lysine succinylome analyses provide insights into metabolic regulation and lymph node metastasis in gastric cancer

With the rapid development of high-throughput quantitative proteomic and transcriptomic approaches, the molecular mechanisms of cancers have been comprehensively explored. However, cancer is a multi-dimensional disease with sophisticated regulations, and few studies focus on the crosstalk among multiomics. In order to explore the molecular mechanisms of gastric cancer (GC), particularly in the process of lymph node metastasis (LNM), we investigated dynamic profiling changes as well as crosstalk between long non-coding RNAs (lncRNAs), the proteome, and the lysine succinylome. Our study reports the first qualitative and quantitative profile of lysine succinylation in GC. We identified a novel mechanism through which the TCA cycle and pentose phosphate pathway might be regulated through lysine succinylation in their core enzymes. We then examined the potential of using lysine succinylation as a biomarker for GC and successfully developed a succinylation-dependent antibody for the K569 site in Caldesmon as putative biomarker. Finally, we investigated the relationship between the lysine succinylome and lncRNAs, identifying potential crosstalks between two lncRNAs and one succinylation site. These results expand our understanding of the mechanisms of tumorigenesis and provide new information for the diagnosis and prognosis of GC.

Highly Sensitive Marker Panel for Guidance in Lung Cancer Rapid Diagnostic Units

While evidence for lung cancer screening implementation in Europe is awaited, Rapid Diagnostic Units have been established in many hospitals to accelerate the early diagnosis of lung cancer. We seek to develop an algorithm to detect lung cancer in a symptomatic population attending such unit, based on a sensitive serum marker panel. Serum concentrations of Epidermal Growth Factor, sCD26, Calprotectin, Matrix Metalloproteinases -1, -7, -9, CEA and CYFRA 21.1 were determined in 140 patients with respiratory symptoms (lung cancer and controls with/without benign pathology). Logistic Lasso regression was performed to derive a lung cancer prediction model, and the resulting algorithm was tested in a validation set. A classification rule based on EGF, sCD26, Calprotectin and CEA was established, able to reasonably discriminate lung cancer with 97% sensitivity and 43% specificity in the training set, and 91.7% sensitivity and 45.4% specificity in the validation set. Overall, the panel identified with high sensitivity stage I non-small cell lung cancer (94.7%) and 100% small-cell lung cancers. Our study provides a sensitive 4-marker classification algorithm for lung cancer detection to aid in the management of suspicious lung cancer patients in the context of Rapid Diagnostic Units.

Interactome analysis of the EV71 5' untranslated region in differentiated neuronal cells SH-SY5Y and regulatory role of FBP3 in viral replication

Enterovirus 71 (EV71), a single-stranded RNA virus, is one of the most serious neurotropic pathogens in the Asia-Pacific region. Through interactions with host proteins, the 5' untranslated region (5'UTR) of EV71 is important for viral replication. To gain a protein profile that interact with the EV71 5'UTR in neuronal cells, we performed a biotinylated RNA-protein pull-down assay in conjunction with LC-MS/MS analysis. A total of 109 proteins were detected and subjected to Database for Annotation, Visualization and Integrated Discovery (DAVID) analyses. These proteins were found to be highly correlated with biological processes including RNA processing/splicing, epidermal cell differentiation, and protein folding. A protein-protein interaction network was constructed using the STRING online database to illustrate the interactions of those proteins that are mainly involved in RNA processing/splicing or protein folding. Moreover, we confirmed that the far-upstream element binding protein 3 (FBP3) was able to bind to the EV71 5'UTR. The redistribution of FBP3 in subcellular compartments was observed after EV71 infection, and the decreased expression of FBP3 in host neuronal cells markedly inhibited viral replication. Our results reveal various host proteins that potentially interact with the EV71 5'UTR in neuronal cells, and we found that FBP3 could serve as a positive regulator in host cells.

A Multiplexed Cytokeratin Analysis Using Targeted Mass Spectrometry Reveals Specific Profiles in Cancer-Related Pleural Effusions

Pleural effusion (PE), excess fluid in the pleural space, is often observed in lung cancer patients and also forms due to many benign ailments. Classifying it quickly is critical, but this remains an analytical challenge often lengthening the diagnosis process or exposing patients to unnecessary risky invasive procedures. We tested the analysis of PE using a multiplexed cytokeratin (CK) panel with targeted mass spectrometry–based quantitation for its rapid classification. CK markers are often assessed in pathological examinations for cancer diagnosis and guiding treatment course. We developed methods to simultaneously quantify 33 CKs in PE using peptide standards for increased analytical specificity and a simple CK enrichment method to detect their low amounts. Analyzing 121 PEs associated with a variety of lung cancers and noncancerous causes, we show that abundance levels of 10 CKs can be related to PE etiology. CK-6, CK-7, CK-8, CK-18, and CK-19 were found at significantly higher levels in cancer-related PEs. Additionally, elevated levels of vimentin and actin differentiated PEs associated with bacterial infections. A classifier algorithm effectively grouped PEs into cancer-related or benign PEs with 81% sensitivity and 79% specificity. A set of undiagnosed PEs showed that our method has potential to shorten PE diagnosis time. For the first time, we show that a cancer-relevant panel of simple-epithelial CK markers currently used in clinical assessment can also be quantitated in PEs. Additionally, while requiring less invasive sampling, our methodology demonstrated a significant ability to identify cancer-related PEs in clinical samples and thus could improve patient care in the future.

Identifying Thoracic Malignancies Through Pleural Fluid Biomarkers: A Predictive Multivariate Model

The diagnosis of malignant pleural effusions may be challenging when cytological examination of aspirated pleural fluid is equivocal or noncontributory. The purpose of this study was to identify protein candidate biomarkers differentially expressed in the pleural fluid of patients with mesothelioma, lung adenocarcinoma, lymphoma, and tuberculosis (TB).A multiplex protein biochip comprising 120 biomarkers was used to determine the pleural fluid protein profile of 29 mesotheliomas, 29 lung adenocarcinomas, 12 lymphomas, and 35 tuberculosis. The relative abundance of these predetermined biomarkers among groups served to establish the differential diagnosis of: malignant versus benign (TB) effusions, lung adenocarcinoma versus mesothelioma, and lymphoma versus TB. The selected putative markers were validated using widely available commercial techniques in an independent sample of 102 patients.Significant differences were found in the protein expressions of metalloproteinase-9 (MMP-9), cathepsin-B, C-reactive protein, and chondroitin sulfate between malignant and TB effusions. When integrated into a scoring model, these proteins yielded 85% sensitivity, 100% specificity, and an area under the curve (AUC) of 0.98 for labeling malignancy in the verification sample. For lung adenocarcinoma-mesothelioma discrimination, combining CA19-9, CA15-3, and kallikrein-12 had maximal discriminatory capacity (65% sensitivity, 100% specificity, AUC 0.94); figures which also refer to the validation set. Last, cathepsin-B in isolation was only moderately useful (sensitivity 89%, specificity 62%, AUC 0.75) in separating lymphomatous and TB effusions. However, this last differentiation improved significantly when cathepsin-B was used with respect to the patient's age (sensitivity 72%, specificity 100%, AUC 0.94).In conclusion, panels of 4 (i.e., MMP-9, cathepsin-B, C-reactive protein, chondroitin sulfate), or 3 (i.e., CA19-9, CA15-3, kallikrein-12) different protein biomarkers on pleural fluid samples are highly discriminative for signaling a malignant versus tuberculous effusion, or lung adenocarcinoma versus mesothelioma, respectively. Cathepsin-B could also be helpful in establishing the presence of a lymphomatous effusion versus that of TB, if the patient's age is simultaneously taken into consideration.

Application of proteomics in non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is a heterogeneous disease with diverse pathological features. Clinical proteomics allows the discovery of molecular markers and new therapeutic targets for this most prevalent type of lung cancer. Some of them may be used to detect early lung cancer, while others may serve as predictive markers of resistance to different therapies. Therapeutic targets and prognostic markers in NSCLC have also been discovered. These proteomics biomarkers may help to pair the individual NSCLC patient with the best treatment option. Despite the fact that implementation of these biomarkers in the clinic appears to be scarce, the recently launched Precision Medicine Initiative may encourage their translation into clinical practice.

Saliva proteome profiling reveals potential salivary biomarkers for detection of oral cavity squamous cell carcinoma

Oral cavity squamous cell carcinoma (OSCC), which is frequently associated with poor prognosis and mortality, is a leading cause of cancer-related death worldwide. Discovery of body fluid accessible biomarkers is needed to improve OSCC screening. To this end, we profiled proteomes of saliva from the healthy volunteers, the individuals with oral potentially malignant disorders (OPMD), and the OSCC patients by means of SDS-PAGE coupled with LC-MS/MS. In the control, the OPMD, and the OSCC groups, 958, 845, and 1030 salivary proteins were detected, respectively. With spectral counting-based label-free quantification, 22 overexpressed salivary proteins were identified in the OSCC group compared with the healthy controls and the OPMD individuals. Among them, resistin (RETN) was subjected to further validation with an independent cohort using ELISA. The data confirmed that the salivary RETN levels in the OSCC patients were significantly higher than that in the healthy or in the OPMD group. Moreover, the elevated levels of salivary RETN were highly correlated with late-stage primary tumors, advanced overall stage, and lymph-node metastasis. Our results not only reveal that profiling of saliva proteome is feasible for discovery of OSCC biomarkers, but also identify RETN as a potential salivary biomarker for OSCC detection.

Lung Cancer Biomarkers

Lung cancer is the most frequently occurring cancer in the world and continually leads in mortality among cancers. The overall 5-year survival rate for lung cancer has risen only 4% (from 12% to 16%) over the past 4 decades, and late diagnosis is a major obstacle in improving lung cancer prognosis. Survival of patients undergoing lung resection is greater than 80%, suggesting that early detection and diagnosis of cancers before they become inoperable and lethal will greatly improve mortality. Lung cancer biomarkers can be used for screening, detection, diagnosis, prognosis, prediction, stratification, therapy response monitoring, and so on. This review focuses on noninvasive diagnostic and prognostic biomarkers. For that purpose, our discussion in this review will focus on biological fluid-based biomarkers. The body fluids include blood (serum or plasma), sputum, saliva, BAL, pleural effusion, and VOC. Since it is rich in different cellular and molecular elements and is one of the most convenient and routine clinical procedures, serum or plasma is the main source for the development and validation of many noninvasive biomarkers. In terms of molecular aspects, the most widely validated ones are proteins, some of which are used in the clinical sector, though in limited accessory purposes. We will also discuss the lung cancer (protein) biomarkers in clinical trials and currently in the validation phase with hundreds of samples. After proteins, we will discuss microRNAs, methylated DNA, and circulating tumor cells, which are being vigorously developed and validated as potential lung cancer biomarkers. The main aim of this review is to provide researchers and clinicians with an understanding of the potential noninvasive lung cancer biomarkers in biological fluids that have recently been discovered.