Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction

Specific microRNA Profile Associated with Inflammation and Lipid Metabolism for Stratifying Allergic Asthma Severity

The mechanisms underlying severe allergic asthma are complex and unknown, meaning it is a challenge to provide the most appropriate treatment. This study aimed to identify novel biomarkers for stratifying allergic asthmatic patients according to severity, and to uncover the biological mechanisms that lead to the development of the severe uncontrolled phenotype. By using miRNA PCR panels, we analyzed the expression of 752 miRNAs in serum samples from control subjects (n = 15) and mild (n = 11) and severe uncontrolled (n = 10) allergic asthmatic patients. We identified 40 differentially expressed miRNAs between severe uncontrolled and mild allergic asthmatic patients. Functional enrichment analysis revealed signatures related to inflammation, angiogenesis, lipid metabolism and mRNA regulation. A random forest classifier trained with DE miRNAs achieved a high accuracy of 97% for severe uncontrolled patient stratification. Validation of the identified biomarkers was performed on a subset of allergic asthmatic patients from the CAMP cohort at Brigham and Women's Hospital, Harvard Medical School. Four of these miRNAs (hsa-miR-99b-5p, hsa-miR-451a, hsa-miR-326 and hsa-miR-505-3p) were validated, pointing towards their potential as biomarkers for stratifying allergic asthmatic patients by severity and providing insights into severe uncontrolled asthma molecular pathways.

Characterization of the plasma proteomic profile of Fabry disease: Potential sex- and clinical phenotype-specific biomarkers

Fabry disease (FD) is a X-linked rare lysosomal storage disorder caused by deficient α-galactosidase A (α-GalA) activity. Early diagnosis and the prediction of disease course are complicated by the clinical heterogeneity of FD, as well as by the frequently inconclusive biochemical and genetic test results that do not correlate with clinical course. We sought to identify potential biomarkers of FD to better understand the underlying pathophysiology and clinical phenotypes. We compared the plasma proteomes of 50 FD patients and 50 matched healthy controls using DDA and SWATH-MS. The >30 proteins that were differentially expressed between the 2 groups included proteins implicated in processes such as inflammation, heme and haemoglobin metabolism, oxidative stress, coagulation, complement cascade, glucose and lipid metabolism, and glycocalyx formation. Stratification by sex revealed that certain proteins were differentially expressed in a sex-dependent manner. Apolipoprotein A-IV was upregulated in FD patients with complications, especially those with chronic kidney disease, and apolipoprotein C-III and fetuin-A were identified as possible markers of FD with left ventricular hypertrophy. All these proteins had a greater capacity to identify the presence of complications in FD patients than lyso-GB3, with apolipoprotein A-IV standing out as being more sensitive and effective in differentiating the presence and absence of chronic kidney disease in FD patients than renal markers such as creatinine, glomerular filtration rate and microalbuminuria. Identification of these potential biomarkers can help further our understanding of the pathophysiological processes that underlie the heterogeneous clinical manifestations associated with FD.

Identification of plasma proteomic signatures associated with the progression of cardia gastric cancer and precancerous lesions

Objective

Considering that there are no effective biomarkers for the screening of cardia gastric cancer (CGC), we developed a noninvasive diagnostic approach, employing data-independent acquisition (DIA) proteomics to identify candidate protein markers.

Methods

Plasma samples were obtained from 40 subjects, 10 each for CGC, cardia high-grade dysplasia (CHGD), cardia low-grade dysplasia (CLGD), and healthy controls. Proteomic profiles were obtained through liquid chromatography-mass spectrometry (LC-MS/MS-based DIA proteomics. Candidate plasma proteins were identified by weighted gene co-expression network analysis (WGCNA) combined with machine learning and further validated by the Human Protein Atlas (HPA) database. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the biomarker panel.

Results

There was a clear distinction in proteomic features among CGC, CHGD, CLGD, and the healthy controls. According to the WGCNA, we found 42 positively associated and 164 inversely associated proteins related to CGC progression and demonstrated several canonical cancer-associated pathways. Combined with the results from random forests, LASSO regression, and immunohistochemical results from the HPA database, we identified three candidate proteins (GSTP1, CSRP1, and LY6G6F) that could together distinguish CLGD (AUC = 0.91), CHGD (AUC = 0.99) and CGC (AUC = 0.98) from healthy controls with excellent accuracy.

Conclusions

The panel of protein biomarkers showed promising diagnostic potential for CGC and precancerous lesions. Further validation and a larger-scale study are warranted to assess its potential clinical applications, suggesting a potential avenue for CGC prevention in the future.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples

BACKGROUND

Clinical bronchoalveolar lavage fluid (BALF) samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, mass spectrometry (MS)-based proteomic analysis of BALF is challenged by the dynamic range of protein abundance, and potential for interfering contaminants. A robust, MS-based proteomics compatible sample preparation workflow for BALF samples, including those of small and large volume, would be useful for many researchers.

RESULTS

We have developed a workflow that combines high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, that is compatible with either qualitative or quantitative MS-based proteomic analysis. The workflow includes a value-added collection of endogenous peptides for peptidomic analysis of BALF samples, if desired, as well as amenability to offline semi-preparative or microscale fractionation of complex peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We demonstrate the effectiveness of this workflow on BALF samples collected from COPD patients, including for smaller sample volumes of 1-5 mL that are commonly available from the clinic. We also demonstrate the repeatability of the workflow as an indicator of its utility for quantitative proteomic studies.

CONCLUSIONS

Overall, our described workflow consistently provided high quality proteins and tryptic peptides for MS analysis. It should enable researchers to apply MS-based proteomics to a wide-variety of studies focused on BALF clinical specimens.

Bronchoalveolar Lavage Fluid-Isolated Biomarkers for the Diagnostic and Prognostic Assessment of Lung Cancer

Lung cancer is considered one of the most fatal malignant neoplasms because of its late detection. Detecting molecular markers in samples from routine bronchoscopy, including many liquid-based cytology procedures, such as bronchoalveolar lavage fluid (BALF), could serve as a favorable technique to enhance the efficiency of a lung cancer diagnosis. BALF analysis is a promising approach to evaluating the tumor progression microenvironment. BALF's cellular and non-cellular components dictate the inflammatory response in a cancer-proliferating microenvironment. Furthermore, it is an essential material for detecting clinically significant predictive and prognostic biomarkers that may aid in guiding treatment choices and evaluating therapy-induced toxicities in lung cancer. In the present article, we have reviewed recent literature about the utility of BALF analysis for detecting markers in different stages of tumor cell metabolism, employing either specific biomarker assays or broader omics approaches.

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.

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).

diaPASEF Proteomics and Feature Selection for the Description of Sputum Proteome Profiles in a Cohort of Different Subtypes of Lung Cancer Patients and Controls

The high mortality, the presence of an initial asymptomatic stage and the fact that diagnosis in early stages reduces mortality justify the implementation of screening programs in the populations at risk of lung cancer. It is imperative to develop less aggressive methods that can complement existing diagnosis technologies. In this study, we aimed to identify lung cancer protein biomarkers and pathways affected in sputum samples, using the recently developed diaPASEF mass spectrometry (MS) acquisition mode. The sputum proteome of lung cancer cases and controls was analyzed through nano-HPLC-MS using the diaPASEF mode. For functional analysis, the results from differential expression analysis were further analyzed in the STRING platform, and feature selection was performed using sparse partial least squares discriminant analysis (sPLS-DA). Our results showed an activation of inflammation, with an alteration of pathways and processes related to acute-phase, complement, and immune responses. The resulting sPLS-DA model separated between case and control groups with high levels of sensitivity and specificity. In conclusion, we showed how new-generation proteomics can be used to detect potential biomarkers in sputum samples, and ultimately to discriminate patients from controls and even to help to differentiate between different cancer subtypes.

SWATH-MS-based proteomics reveals functional biomarkers of Th1/Th2 responses of tropomyosin allergy in mouse models

Type-I food allergies are hypersensitive reactions compromising the immune organs and epithelial barriers. To investigate the organ-specific proteomic alterations of the allergy responses, the spleen and intestine of mice sensitized with high (shrimp and clam) and weak (fish) allergenic tropomyosins were analyzed using sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS)-based proteomics. The results showed that Th1 and Th2 tropomyosin-induced responses in the spleen are characterized by the unique upregulation of innate (cochlin) and adaptive (Ig κ chain V-III region PC 7175) immune regulators, respectively. In the intestine, tropomyosin allergy concurred with the downregulation of 35 differentiating proteins featuring the overall impairment of metabolic pathways, absorption processes and ammonium ion responses. These data provide new functional biomarkers of tropomyosin-induced immune responses as well as candidate targets for intervention.

A practical technique for rapid characterisation of ent-kaurane diterpenoids in Isodon serra (Maxim.) Hara by UHPLC-Q-TOF-MS/MS

INTRODUCTION

The diterpenoids are the most important active constituents that contribute to the pharmacological efficacy of Isodon serra (Maxim.) Hara. Clinical studies have revealed that diterpenoids possess multiple features, e.g. antitumour, antitubercular and anti-ischemic activities. Therefore, the identification and detection of diterpenoids may be equally important for understanding the pharmacological basis of diterpenoids and enhancing the product quality control of I. serra.

OBJECTIVES

The purpose of this study was to develop a practical analysis approach of rapid characterisation using ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) for the structure characterisation of the ent-kaurane diterpenoids from I. serra.

METHODOLOGY

The analytical strategy was as follows: first, ent-kaurane diterpenoids were detected by a novel on-line data acquisition approach, i.e. sequential window acquisition of all theoretical fragment-ion spectra (SWATH). Second, the MS of eight ent-kaurane diterpenoids was explored, and their mass spectrum cleavage pathways were summarised and determined. Finally, the methanol extract of I. serra was studied using SWATH and identified by extracted ion chromatography (XIC).

RESULTS

Compared to the traditional information-dependent acquisition (IDA) method, SWATH significantly improved the hit rate of ent-kaurane diterpenoids. With support from UHPLC separation and specific detection by tandem mass spectrometry (MS/MS), 48 ent-kaurane diterpenoids were successfully characterised and classified as ent-kaurane diterpenoids from a complex matrix.

CONCLUSIONS

These combined qualitative methods were used to provide a potential approach for the characterisation of traditional Chinese medicine (TCM) and its preparations. Meanwhile, the SWATH provided a novel and reliable method for the structural characterisation of ent-kaurane diterpenoids from other complicated TCMs.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Alteration of EIF2 Signaling, Glycolysis, and Dopamine Secretion in Form-Deprived Myopia in Response to 1% Atropine Treatment: Evidence From Interactive iTRAQ-MS and SWATH-MS Proteomics Using a Guinea Pig Model

Purpose: Atropine, a non-selective muscarinic antagonist, effectively slows down myopia progression in human adolescents and several animal models. However, the underlying molecular mechanism is unclear. The current study investigated retinal protein changes of form-deprived myopic (FDM) guinea pigs in response to topical administration of 1% atropine gel (10 g/L).

Methods: At the first stage, the differentially expressed proteins were screened using fractionated isobaric tags for a relative and absolute quantification (iTRAQ) approach, coupled with nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) (n = 24, 48 eyes) using a sample pooling technique. At the second stage, retinal tissues from another cohort with the same treatment (n = 12, 24 eyes) with significant ocular changes were subjected to label-free sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics for orthogonal protein target confirmation. The localization of Alpha-synuclein was verified using immunohistochemistry and confocal imaging.

Results: A total of 1,695 proteins (8,875 peptides) were identified with 479 regulated proteins (FC ≥ 1.5 or ≤0.67) found from FDM eyes and atropine-treated eyes receiving 4-weeks drug treatment using iTRAQ-MS proteomics. Combining the iTRAQ-MS and SWATH-MS datasets, a total of 29 confident proteins at 1% FDR were consistently quantified and matched, comprising 12 up-regulated and 17 down-regulated proteins which differed between FDM eyes and atropine treated eyes (iTRAQ: FC ≥ 1.5 or ≤0.67, SWATH: FC ≥ 1.4 or ≤0.71, p-value of ≤0.05). Bioinformatics analysis using IPA and STRING databases of these commonly regulated proteins revealed the involvement of the three commonly significant pathways: EIF2 signaling; glycolysis; and dopamine secretion. Additionally, the most significantly regulated proteins were closely connected to Alpha-synuclein (SNCA). Using immunostaining (n = 3), SNCA was further confirmed in the inner margin of the inner nuclear layer (INL) and spread throughout the inner plexiform layer (IPL) of the retina of guinea pigs.

Conclusion: The molecular evidence using next-generation proteomics (NGP) revealed that retinal EIF2 signaling, glycolysis, and dopamine secretion through SNCA are implicated in atropine treatment of myopia in the FDM-induced guinea pig model.

A Novel Diagnostic Biomarker, PZP, for Detecting Colorectal Cancer in Type 2 Diabetes Mellitus Patients Identified by Serum-Based Mass Spectrometry

Background: Growing evidence has confirmed that populations with type 2 diabetes mellitus (T2DM) have an increasing risk of developing colorectal cancer (CRC). Thus, convenient and effective screening strategies for CRC should be developed for the T2DM population to increase the detection rate of CRC.

Methods: Twenty serum samples extracted from five healthy participants, five T2DM patients, five CRC patients and five T2DM patients with CRC (T2DM + CRC) were submitted to data-independent acquisition mass spectrometry (DIA-MS) analysis to discover unique differentially altered proteins (DAPs) for CRC in patients with T2DM. Then, the diagnostic value of pregnancy zone protein (PZP) was validated by ELISA analysis in the validated cohort.

Results: Based on DIA-MS analysis, we found eight unique proteins specific to T2DM patients with CRC. Among these proteins, four proteins showed different expression between the T2DM + CRC and T2DM groups, and PZP exhibited the largest difference. Next, the diagnostic value of serum PZP was validated by ELISA analysis with an AUC of 0.713. Moreover, the combination of PZP, CA199 and CEA exhibited encouraging diagnostic value, and the AUC reached 0.916.

Conclusion: Overall, our current research implied that PZP could be regarded as a newfound serum biomarker for CRC medical diagnosis in T2DM patients.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Integrated tissue proteome and metabolome reveal key elements and regulatory pathways in cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (CSCC) is a widespread malignancy but has a very low long-term survival rate for patients at the metastatic stage. Therefore, it is urgent to identify prognostic biomarkers for CSCC and improve our understanding of disease progression. Here we took advantage of a data-independent acquisition (DIA)-based nano liquid chromatography equipped with an orbitrap mass spectrometry (nLC-MS/MS) and ultraperformance LC coupled to a time-of-flight tandem MS (UPLC-TOF-MS/MS) technique to analyze cancer and corresponding noncancerous tissues from 20 CSCC patients for integrated proteomic and metabolomic analyses. Overall, 6241 tissue proteins were detected, while 136 proteins were significantly expressed in CSCC tissues. Further functional analysis revealed that various biological processes were highly enriched and participated in the pathogenesis of CSCC, especially DNA damage responses. Moreover, 641 named metabolites in total were identified, among which 181 were significantly changed in CSCC tissues. A total of 101 pathways were significantly enriched including apoptosis, autophagy, PI3K-Akt and mTOR signaling pathways. Interestingly, two pathways, protein digestion & absorption and platelet activation were both enriched in proteomic and metabolomic studies involving 5 proteins and 11 metabolites. Accordingly, a four-metabolite panel consisting of arachidonate, glutamine, glutamic acid, and proline (all area under the curve (AUC) values more than 0.9) was developed with a high accuracy (0.971) to distinguish the 20 detected cancer tissues from their noncancerous tissues. Collectively, our work highlighted the key elements and regulatory pathways involved in the pathogenesis of CSCC. More importantly, the present study not only provided potential biomarkers for the early diagnosis of CSCC, but also expanded our knowledge of the physiopathology of the disease. SIGNIFICANCE: CSCC is the second most common human cancer but has few treatment options and few sensitive biomarkers for diagnosis. Here we comprehensively revealed its molecular characteristics by performing integrated tissue proteomic and metabolomic analyses. Significantly distinct profiles and certain enriched pathways including DNA damage responses were identified as associated with CSCC. Moreover, protein digestion & absorption and platelet activation were both enriched in the proteome and metabolome. These identified molecular changes probably play significant roles in CSCC development. Finally, we developed a four-metabolite panel to distinguish CSCC with high accuracy. Overall, our data not only provided potential diagnostic biomarkers, but also extended knowledge on the pathogenesis of CSCC.

Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials

TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient's ability to perform a variety of activities. However, the quality of present algorithms to assess PS is limited and based on qualitative clinician assessment. Plasma samples from patients enrolled into TARGET were analysed using the mass spectrometry (MS) technique: sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. SWATH-MS was used on a discovery cohort of 55 patients to differentiate patients into either a good or poor prognosis by creation of a Wellness Score (WS) that showed stronger prediction of overall survival (p = 0.000551) compared to PS (p = 0.001). WS was then tested against a validation cohort of 77 patients showing significant (p = 0.000451) prediction of overall survival. WS in both sets had receiver operating characteristic curve area under the curve (AUC) values of 0.76 (p = 0.002) and 0.67 (p = 0.011): AUC of PS was 0.70 (p = 0.117) and 0.55 (p = 0.548). These signatures can now be evaluated further in larger patient populations to assess their utility in a clinical setting.

Clinical Utility of microRNAs in Exhaled Breath Condensate as Biomarkers for Lung Cancer

This study represents a novel proof of concept of the clinical utility of miRNAs from exhaled breath condensate (EBC) as biomarkers of lung cancer (LC). Genome-wide miRNA profiling and machine learning analysis were performed on EBC from 21 healthy volunteers and 21 LC patients. The levels of 12 miRNAs were significantly altered in EBC from LC patients where a specific signature of miR-4507, miR-6777-5p and miR-451a distinguished these patients with high accuracy. Besides, a distinctive miRNA profile between LC adenocarcinoma and squamous cell carcinoma was observed, where a combined panel of miR-4529-3p, miR-8075 and miR-7704 enabling discrimination between them. EBC levels of miR-6777-5p, 6780a-5p and miR-877-5p predicted clinical outcome at 500 days. Two additional miRNA signatures were also associated with other clinical features such as stage and invasion status. Dysregulated EBC miRNAs showed potential target genes related to LC pathogenesis, including CDKN2B, PTEN, TP53, BCL2, KRAS and EGFR. We conclude that EBC miRNAs might allow the identification, stratification and monitorization of LC, which could lead to the development of precision medicine in this and other respiratory diseases.

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.

Application of Proteomics in Sarcoidosis

Sarcoidosis is a multisystem disease with heterogeneity in manifestations and outcomes. System-level studies leveraging "omics" technologies are expected to define mechanisms contributing to sarcoidosis heterogeneous manifestations and course. With improvements in mass spectrometry (MS) and bioinformatics, it is possible to study protein abundance for a large number of proteins simultaneously. Contemporary fast-scanning MS enables the acquisition of spectral data for deep coverage of the proteins with data-dependent or data-independent acquisition MS modes. Studies leveraging MS-based proteomics in sarcoidosis have characterized BAL fluid (BALF), alveolar macrophages, plasma, and exosomes. These studies identified several differentially expressed proteins, including protocadherin-2 precursor, annexin A2, pulmonary surfactant A2, complement factors C3, vitamin-D-binding protein, cystatin B, and amyloid P, comparing subjects with sarcoidosis with control subjects. Other studies identified ceruloplasmin, complement factors B, C3, and 1, and others with differential abundance in sarcoidosis compared with other interstitial lung diseases. Using quantitative proteomics, most recent studies found differences in PI3K/Akt/mTOR, MAP kinase, pluripotency-associated transcriptional factor, and hypoxia response pathways. Other studies identified increased clathrin-mediated endocytosis and Fcγ receptor-mediated phagocytosis pathways in sarcoidosis alveolar macrophages. Although studies in mixed BAL and blood cells or plasma are limited, some of the changes in lung compartment are detected in the blood cells and plasma. We review proteomics for sarcoidosis with a focus on the existing MS data acquisition strategies, bioinformatics for spectral data analysis to infer protein identity and quantity, unique aspects about biospecimen collection and processing for lung-related proteomics, and proteomics studies conducted to date in sarcoidosis.

Is the Proteome of Bronchoalveolar Lavage Extracellular Vesicles a Marker of Advanced Lung Cancer?

Simple Summary

Bronchoalveolar lavage is routinely collected during bronchoscopy for cytology analysis in the diagnostic of lung cancer. Due to low sensitivity of this method, early-stage cancers are undetected, lowering the treatment success. In this study, we analyzed extracellular vesicles isolated from bronchoalveolar lavage of lung cancer suspects by mass spectrometry-based proteomics. The protein composition of bronchoalveolar lavage extracellular vesicles of late-stage cancer showed a higher proteome complexity associated with mortality within the two year follow-up period. We identified a potential therapeutic target DNMT3B complex which was significantly expressed in bronchoalveolar lavage extracellular vesicles as well as in tumor tissue. Bronchoalveolar lavage extracellular vesicles proteome analysis of immune markers indicates the presence of markers of innate immune and fibroblast cells.

Abstract

Acellular bronchoalveolar lavage (BAL) proteomics can partially separate lung cancer from non-lung cancer patients based on principal component analysis and multivariate analysis. Furthermore, the variance in the proteomics data sets is correlated mainly with lung cancer status and, to a lesser extent, smoking status and gender. Despite these advances BAL small and large extracellular vehicles (EVs) proteomes reveal aberrant protein expression in paracrine signaling mechanisms in cancer initiation and progression. We consequently present a case-control study of 24 bronchoalveolar lavage extracellular vesicle samples which were analyzed by state-of-the-art liquid chromatography-mass spectrometry (LC-MS). We obtained evidence that BAL EVs proteome complexity correlated with lung cancer stage 4 and mortality within two years´ follow-up (p value = 0.006). The potential therapeutic target DNMT3B complex is significantly up-regulated in tumor tissue and BAL EVs. The computational analysis of the immune and fibroblast cell markers in EVs suggests that patients who deceased within the follow-up period display higher marker expression indicative of innate immune and fibroblast cells (four out of five cases). This study provides insights into the proteome content of BAL EVs and their correlation to clinical outcomes.

A novel system for the comprehensive collection of nonvolatile molecules from human exhaled breath

Characterization of nonvolatile molecules in exhaled breath particles can be used for respiratory disease monitoring and diagnosis. Conventional methods for the collection of nonvolatile molecules in breath heavily rely on the physical properties of exhaled breath particles. Strategies taking advantage of their chemical properties have not yet been explored. In the present study, we developed a column system in which the surface chemistry between organic nonvolatile molecules and octadecyl carbon chain was exploited for the comprehensive collection of metabolites, lipids, and proteins. We demonstrated that the collection system had the capture efficiency of 99% and the capacity to capture representative nonvolatile molecules. The collection system was further evaluated using human subjects and proteins collected from human exhaled breath were characterized and identified using gel electrophoresis and bottom-up proteomics. The identified 303 proteins from mass spectrometry were further searched against reported bronchoalveolar lavage fluid proteomes and it was shown that 60 proteins have the tissue origin of lower respiratory airways. In summary, we demonstrate that our collection system can collect nonvolatile molecules from human exhaled breath in an efficient and comprehensive manner and has the potential to be used for the study of respiratory diseases.

Identification of the proteins of Borrelia garinii interacting with human brain microvascular endothelial cells

Lyme borreliosis is one of the major tick-borne diseases in Europe. Events of the translocation of Borrelia across the blood-brain barrier (BBB) involve multiple interactions between borrelial surface proteins and receptors on the brain microvascular endothelial cells (hBMECs). In this study, we aimed to identify proteins of Borrelia that plausibly interact with hBMECs. The surface proteome of live Borrelia (a neuroinvasive strain of B. garinii) was crosslinked with biotin prior to its incubation with hBMECs. The interacting proteins were recovered by affinity purification, followed by SWATH-MS. Twenty-four interacting candidates were grouped into outer membrane proteins (n = 12) and inner membrane proteins (n = 12) based on the subcellular location as per the predictions of LocateP. Other algorithms like TMHMM 2.0 and LipoP, ontology search and literature review were subsequently applied to each of the identified protein candidates to shortlist the most probable interactors. Six proteins namely, LysM domain protein, BESBP-5, Antigen S1, CRASP-1 (Bg071), Erp23 protein and Mlp family Lipoprotein were selected to produce their recombinant forms and experimentally validate their interaction with hBMECs. All the recombinant proteins interacted with hBMECs, in ELISA and immunocytochemistry. We present here a high-throughput approach of generating a dataset of plausible borrelial ligands followed by a systematic bioinformatic pipeline to categorize the proteins for experimental validation.

Early urine proteome changes in the Walker-256 tail-vein injection rat model

Detection of cancer at its early stage is important for treatment. Urine, which is not regulated by homeostatic mechanisms, reflects early systemic changes throughout the whole body and can be used for the early detection of cancer. In this study, the Walker-256 tail-vein injection rat model was established to find whether the urine proteome could reflect early changes if tumor grown in lung. Urine samples from the control group (n = 7) and Walker-256 tail-vein injection group (n = 7) on days 2, 4, 6 and 9 were analyzed by label-free proteomic quantitative methods. On day 2, when lung tumor nodules did not appear, 62 differential proteins were identified. They were associated with epithelial cell differentiation, regulation of immune system processes and the classical complement activation pathway. On day 4, when lung tumor nodules appeared, 72 differential proteins were identified. They were associated with the innate immune response and positive regulation of phagocytosis. On day 6, when body weight began to decrease, 117 differential proteins were identified. On day 9, the identified 125 differential proteins were associated with the B cell receptor signaling pathway and the positive regulation of B cell activation. Our results indicate that (1) the urine proteome changed even on the second day after tail-vein injection of Walker-256 cells and that (2) compared to previous studies, the urine proteomes were different when the same cancer cells were grown in different organs.

Proteomic Analysis in Morquio A Cells Treated with Immobilized Enzymatic Replacement Therapy on Nanostructured Lipid Systems

Morquio A syndrome, or mucopolysaccharidosis type IVA (MPS IVA), is a lysosomal storage disease due to mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. Systemic skeletal dysplasia and the related clinical features of MPS IVA are due to disruption of cartilage and its extracellular matrix, leading to an imbalance of growth. Enzyme replacement therapy (ERT) with recombinant human GALNS, alpha elosulfase, provides a systemic treatment. However, this therapy has a limited impact on skeletal dysplasia because the infused enzyme cannot penetrate cartilage and bone. Therefore, an alternative therapeutic approach to reach the cartilage is an unmet challenge. We have developed a new drug delivery system based on a nanostructure lipid carrier with the capacity to immobilize enzymes used for ERT and to target the lysosomes. This study aimed to assess the effect of the encapsulated enzyme in this new delivery system, using in vitro proteomic technology. We found a greater internalization of the enzyme carried by nanoparticles inside the cells and an improvement of cellular protein routes previously impaired by the disease, compared with conventional ERT. This is the first qualitative and quantitative proteomic assay that demonstrates the advantages of a new delivery system to improve the MPS IVA ERT.

OLFM4 Expression in Ductal Carcinoma In Situ and in Invasive Breast Cancer Cohorts by a SWATH-Based Proteomic Approach

Human olfactomedin-4 (OLFM4) is a secreted protein involved in a variety of cellular functions including proliferation, differentiation, apoptosis, and cell adhesion. OLFM4 expression has been studied in several tumor types including gastric, colorectal, lung, and endometrioid cancers where it has been suggested to be an independent favorable or unfavorable prognostic marker. For breast cancer, the clinical significance of OLFM4 is still unclear. In the present study, SWATH-MS is used as a tool for the robust identification and quantification of breast tissue proteins. SWATH-MS data show that OLFM4 expression is higher in DCIS than in invasive breast cancer. In-depth analysis of the breast tumor proteome show that OLFM4 is a favorable pronostic marker. Serum OLFM4 levels in peripheral blood are also analyzed by ELISA in 825 cases, including 94 cases of healthy individuals, 61 cases of non-invasive breast tumor (DCIS) and 670 cases of breast cancer (BC). It is found that serum OLFM4 levels are significantly higher in the DCIS cohort and in the breast cancer cohort compared with the healthy controls. This result suggests that circulating OLFM4 could be an interesting biomarker of early breast cancer. Data are available via ProteomeXchange with identifier PXD014194.

Identification of potential metabolic biomarkers of polycystic ovary syndrome in follicular fluid by SWATH mass spectrometry

BACKGROUND

Polycystic ovary syndrome (PCOS) is a complex disorder associated with multiple metabolic disturbance, including defective glucose metabolism and insulin resistance. The altered metabolites caused by the related metabolic disturbance may affect ovarian follicles, which can be reflected in follicular fluid composition. The aim of this study is to investigate follicular fluid metabolic profiles in women with PCOS using an advanced sequential window acquisition of all theoretical fragment-ion spectra (SWATH) mass spectrometry.

MATERIALS AND METHODS

Nineteen women with PCOS and twenty-one healthy controls undergoing IVF/ET were recruited, and their follicular fluid samples were collected for metabolomic study. Follicular fluid metabolic profiles, including steroid hormones, free fatty acids, bioactive lipids, and amino acids were analyzed using the principal component analysis (PCA) and partial least squares to latent structure-discriminant analysis (PLS-DA) model.

RESULTS

Levels of free fatty acids, 3-hydroxynonanoyl carnitine and eicosapentaenoic acid were significantly increased (P < 0.05), whereas those of bioactive lipids, lysophosphatidylcholines (LysoPC) (16:0), phytosphingosine, LysoPC (14:0) and LysoPC (18:0) were significantly decreased in women with PCOS (P < 0.05). Additionally, levels of steroid hormone deoxycorticosterone and two amino acids, phenylalanine and leucine were higher in the PCOS patients (P < 0.05).

CONCLUSION

Women with PCOS display unique metabolic profiles in their follicular fluid, and this data may provide us with important biochemical information and metabolic signatures that enable a better understanding of the pathogenesis of PCOS.

In-Depth Proteomic Analysis of Human Bronchoalveolar Lavage Fluid toward the Biomarker Discovery for Lung Cancers

PURPOSE

Lung cancer is among the most common cancers. Bronchoalveolar lavage fluid (BALF) can be easily obtained from patients with lung cancers. The aim is to develop a novel proteomic method of the molecule-based sensitive detection of biomarkers from BALF.

EXPERIMENTAL DESIGN

BALF samples are collected from segmental bronchus of 14 patients with lung cancers from Kyung Hee University Hospital. First, BALF proteome is depleted using a depletion column, and then peptides are prepared from the enriched low abundant proteins and fractionated by high pH reverse phase liquid chromatography prior to LC-MS/MS. Data are available via ProteomeXchange with identifier PXD012645.

RESULTS

A novel method is developed for in-depth proteomic analysis of BALF by combining antibody-based depletion of high abundant proteins from BALF with high pH peptide fractionation. Peptides are analyzed on a high resolution Orbitrap Fusion mass spectrometer. MaxQuant search result in the identification of 4615 protein groups mapped to 4534 genes.

CONCLUSIONS AND CLINICAL RELEVANCE

It is found that the method outperformed conventional BALF proteomic methods and it is believed that this method will facilitate the biomarker research for lung cancer. In addition, it is shown that BALF will be a great source of biomarkers of lung diseases.

Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method

BACKGROUND

SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification.

METHODS

The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1.

RESULTS

Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally.

CONCLUSION

This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer?

Lung cancer is one of the most lethal malignancies worldwide, mainly due to its late diagnoses. The detection of molecular markers on samples provided from routine bronchoscopy including several liquid-based cytology tests (e.g., bronchoaspirate, bronchoalveolar lavage) and/or on easily obtained specimens such as sputum could represent a new approach to improve the sensitivity in lung cancer diagnoses. Recently growing interest has been reported for "noninvasive" liquid biopsy as a valuable source for molecular profiling. Unfortunately, a biomarker and/or composition of biomarkers capable of detecting early-stage lung cancer has yet to be discovered even if in the last few years there has been, through the use of revolutionary new technologies, an explosion of lung cancer biomarkers. Assay sensitivity and specificity need to be improved particularly when new approaches and/or tools are used. We have focused on the most important markers detected in tissue, and on several cytological specimens and liquid biopsies overall.

Extracellular Vesicles Mediate Mesenchymal Stromal Cell-Dependent Regulation of B Cell PI3K-AKT Signaling Pathway and Actin Cytoskeleton

Mesenchymal stromal cells (MSCs) are adult, multipotent cells of mesodermal origin representing the progenitors of all stromal tissues. MSCs possess significant and broad immunomodulatory functions affecting both adaptive and innate immune responses once MSCs are primed by the inflammatory microenvironment. Recently, the role of extracellular vesicles (EVs) in mediating the therapeutic effects of MSCs has been recognized. Nevertheless, the molecular mechanisms responsible for the immunomodulatory properties of MSC-derived EVs (MSC-EVs) are still poorly characterized. Therefore, we carried out a molecular characterization of MSC-EV content by high-throughput approaches. We analyzed miRNA and protein expression profile in cellular and vesicular compartments both in normal and inflammatory conditions. We found several proteins and miRNAs involved in immunological processes, such as MOES, LG3BP, PTX3, and S10A6 proteins, miR-155-5p, and miR-497-5p. Different in silico approaches were also performed to correlate miRNA and protein expression profile and then to evaluate the putative molecules or pathways involved in immunoregulatory properties mediated by MSC-EVs. PI3K-AKT signaling pathway and the regulation of actin cytoskeleton were identified and functionally validated in vitro as key mediators of MSC/B cell communication mediated by MSC-EVs. In conclusion, we identified different molecules and pathways responsible for immunoregulatory properties mediated by MSC-EVs, thus identifying novel therapeutic targets as safer and more useful alternatives to cell or EV-based therapeutic approaches.

Data-Independent Acquisition Mass Spectrometry To Quantify Protein Levels in FFPE Tumor Biopsies for Molecular Diagnostics

Mass spectrometry-based protein quantitation is currently used to measure therapeutically relevant protein biomarkers in CAP/CLIA setting to predict likely responses of known therapies. Selected reaction monitoring (SRM) is the method of choice due to its outstanding analytical performance. However, data-independent acquisition (DIA) is now emerging as a proteome-scale clinical assay. We evaluated the ability of DIA to profile the patient-specific proteomes of sample-limited tumor biopsies and to quantify proteins of interest in a targeted fashion using formalin-fixed, paraffin-embedded (FFPE) tumor biopsies ( n = 12) selected from our clinical laboratory. DIA analysis on the tumor biopsies provided 3713 quantifiable proteins including actionable biomarkers currently in clinical use, successfully separated two gastric cancers from colorectal cancer specimen solely on the basis of global proteomic profiles, and identified subtype-specific proteins with prognostic or diagnostic value. We demonstrate the potential use of DIA-based quantitation to inform therapeutic decision-making using TUBB3, for which clinical cutoff expression levels have been established by SRM. Comparative analysis of DIA-based proteomic profiles and mRNA expression levels found positively and negatively correlated protein-gene pairs, a finding consistent with previously reported results from fresh-frozen tumor tissues.

Deciphering the Interactome of Neisseria meningitidis With Human Brain Microvascular Endothelial Cells

Neisseria meningitidis is able to translocate the blood-brain barrier and cause meningitis. Bacterial translocation is a crucial step in the onset of neuroinvasion that involves interactions between pathogen surface proteins and host cells receptors. In this study, we applied a systematic workflow to recover and identify proteins of N. meningitidis that may interact with human brain microvascular endothelial cells (hBMECs). Biotinylated proteome of N. meningitidis was incubated with hBMECs, interacting proteins were recovered by affinity purification and identified by SWATH-MS. Interactome of N. meningitidis comprised of 41 potentially surface exposed proteins. These were assigned into groups based on their probability to interact with hBMECs: high priority candidates (21 outer membrane proteins), medium priority candidates (14 inner membrane proteins) and low priority candidates (six secretory proteins). Ontology analysis provided information for 17 out of 41 surface proteins. Based on the series of bioinformatic analyses and literature review, five surface proteins (adhesin MafA1, major outer membrane protein P.IB, putative adhesin/invasion, putative lipoprotein and membrane lipoprotein) were selected and their recombinant forms were produced for experimental validation of interaction with hBMECs by ELISA and immunocytochemistry. All candidates showed interaction with hBMECs. In this study, we present a high-throughput approach to generate a dataset of plausible meningococcal ligands followed by systematic bioinformatic pipeline to categorize the proteins for experimental validation.

Identification of ceRNA network based on a RNA-seq shows prognostic lncRNA biomarkers in human lung adenocarcinoma

Previous studies have emphasized the significant functions of long non-coding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in tumor biology. However, the functions of certain cancer lncRNAs in the lncRNA-related ceRNA network in lung adenocarcinoma (LUAD) are unknown. A systematic and integrative survey of RNA-seq data from The Cancer Genome Atlas (TCGA) was performed to identify candidate lncRNAs for the prognosis of LUAD. In total, 20,502 genes that contain 181 lncRNAs were evaluated in a cohort of 570 LUAD cases. Initially, 6,280 differentially expressed genes (fold-change >2, P<0.05) were obtained using R package, which includes 75 lncRNAs. Next, by univariate regression and multivariate Cox proportional hazards analysis, 32 genes were associated with survival in LUAD. Using these 29 mRNAs and 3 lncRNAs, a prognosis index (PI) was calculated to accurately estimate the survival in LUAD: PI=∑exp_{risk gene} × HRrisk gene. Furthermore, the 32-gene signature was an independent prognostic indicator for LUAD (HR >1; P<0.05, by multivariate analysis). Weighted gene co-expression network analysis (WGCNA) of three risk lncRNAs-FAM138B, NHEG1 and TLX1NB-was performed, based on the P-values of the associated genes, and the top 27 miRNAs that bound to these lncRNAs were predicted by Miranda as target miRNAs. Next, these target miRNAs were transferred to the TarBase, miRTarBase, miRecards and starBase v2.0 databases to obtain their target genes. According to the previous miRNA-mRNA and miRNA-lncRNA data, three lncRNA-miRNA-mRNA ceRNA networks were established, based on the 29 prognostic mRNAs, forming a regulatory network in LUAD. The present study provided insight into the lncRNA-related ceRNA network in LUAD and has identified potential diagnostic and prognostic biomarkers.

Molecular characterization of lung adenocarcinoma: A potential four-long noncoding RNA prognostic signature

BACKGROUND

Lung adenocarcinoma (LUAD), mainly originated in lung glandular cells, is the most frequent pathological type of lung cancer and the 5-year survival rate of LUAD patients is still very low. Therefore, we aim to identify a long noncoding RNA (lncRNA)-related signature as the sensitive and novel prognostic biomarkers.

METHODS

The associations between survival outcome and the intersection of lncRNAs were obtained from The Cancer Genome Atlas (TCGA) database. By the univariate and multivariate Cox analyses, key lncRNAs were identified to construct the prognostic model. The model was estimated by survival analysis and receiver operating characteristic curve, and verified by the Kaplan-Meier (K-M) plotter and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Functional enrichment analysis was also performed.

RESULTS

A four-lncRNA signature (CEBPA-AS1, GVINP1, MIR31HG, and RAET1K) was developed after Cox analysis. The power of the four-lncRNA prognostic signature was effective in the TCGA database. The results from by the K-M plotter and qRT-PCR validation were consistent with our TCGA bioinformatics results. Furthermore, Gene Ontology and pathway analysis revealed the tumorigenic and prognostic function of the four lncRNAs.

CONCLUSIONS

By mining the TCGA data, we built a four-lncRNA signature, which could effectively predict prognosis of LUAD. In the future, an independent cohort is needed to validate our findings.

IMPACT

The four-lncRNA signature could become potential prognostic indicator of LUAD in the future.

Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.

Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics

Mass spectrometry-based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label-based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling-based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

Exploring the Potential of Data-Independent Acquisition Proteomics Using Untargeted All-Ion Quantitation: Application to Tumor Subtype Diagnosis

Maximizing the recovery of meaningful biological information can facilitate proteomics-guided early detection and precise treatment of diseases. However, the conventional protein and peptide level targeted quantification of untargeted data independent acquisition (DIA) such as sequential window acquisition of all theoretical spectra (SWATH) is not necessarily descriptive of all information. Untargeted all-ion quantification theoretically could retrieve more features in SWATH digital maps by circumventing the initial identification process but is intrinsically susceptible to errors because of the extreme complexity of proteome samples and the poor selectivity of a single ion. In this study, we optimized and applied the untargeted all-ion quantification of SWATH data to differentiate tumor subtypes. Large peptides and low abundant peptides benefited more from untargeted all-ion quantification. Top-ranked significant ions were linked to their corresponding ion envelops, where multiple correlated ions were used for measurement and only ion envelopes containing at least three ions with consistent intensity ratio were kept as refined differentiating features. Multivariate statistical analysis revealed that for the tested data set, the refined markers discovered by untargeted SWATH analysis showed comparable diagnostic power to protein and peptide markers. Limitations and benefits of the approach are further discussed.

Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection

A typical clinical manifestation of growth hormone deficiency (GHD) is a short stature resulting from delayed growth, but GHD affects bone health, cardiovascular function and metabolic profile and therefore quality of life. Although early GH treatment during childhood has been shown to improve outcomes, no single biochemical parameter is currently available for the accurate diagnosis of GHD in children. There is hence a need for non-invasive biomarkers. In this study, the relative abundance of serum proteins from GHD children and healthy controls was measured by next-generation proteomics SWATH-MS technology. The data generated was analysed by machine-learning feature-selection algorithms in order to discover the minimum number of protein biomarkers that best discriminate between both groups. The analysis of serum proteins by a SWATH-MS approach yielded a useful method for discovering potential biomarkers of GHD in children. A total of 263 proteins were confidently detected and quantified in each sample. Pathway analysis indicated an effect on tissue/organ structure and morphogenesis. The top ten serum protein biomarker candidates were identified after applying feature-selection data analysis. The combination of three proteins - apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein - demonstrated the best classification performance for our data. In addition, the apolipoprotein group resulted in strong over-representation, thus highlighting these proteins as an additional promising biomarker panel.

SIGNIFICANCE

Currently there is no single biochemical parameter available for the accurate diagnosis of growth hormone (GH) deficiency (GHD) in children. Simple GH measurements are not an option: because GH is released in a pulsatile action, its blood levels fluctuate throughout the day and remain nearly undetectable for most of that time. This makes measurements of GH in a single blood sample useless for assessing GH deficiency. Actually, the diagnosis of GHD includes a combination of direct and indirect non-accurate measurements, such as taking several body measurements, testing GH levels in multiple blood samples after provocative tests (GH peak <7.3ng/mL, using radioimmunoassay), and conducting magnetic resonance imaging (MRI), among others. Therefore, there is a need for simple, non-invasive, accurate and cost-effective biomarkers. Here we report a case-control study, where relative abundance of serum proteins were measured by next-generation proteomics SWATH-MS technology in 15 GHD children and 15healthy controls matched by age, sex, and not receiving any treatment. Data generated was analysed by machine learning feature selection algorithms. 263 proteins could be confidently detected and quantified on each sample. The top 10 serum protein biomarker candidates could be identified after applying a feature selection data analysis. The combination of three proteins, apolipoprotein A-IV, complement factor H-related protein 4 and platelet basic protein, showed the best classification performance for our data. In addition, the fact that the pathway and GO analysis we performed pointed to the apolipoproteins as over-represented highlights this protein group as an additional promising biomarker panel for the diagnosis of GHD and for treatment evaluation.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

Advances in endometrial cancer protein biomarkers for use in the clinic

INTRODUCTION

Endometrial cancer (EC) is the fourth most common cancer in women in developed countries. The identification of sensitive and specific biomarkers to improve early detection of EC is crucial for an appropriate management of this disease, in which 30% of patients are diagnosed only at advanced stages, which is associated with high levels of morbidity and mortality. Despite major efforts and investments made to identify EC biomarkers, no protein has yet reached the stage of clinical application. Areas covered: This review gathers the numerous candidate biomarkers for EC diagnosis proposed in proteomic studies published from 1978 to 2017. Additionally, we summarize limitations associated with the proteomic technologies and study designs employed in those articles. Finally, we address new perspectives in EC biomarker research, including the comprehensive knowledge of previously suggested candidate biomarkers in conjunction with novel mass spectrometry-based proteomic technologies with enhanced sensitivity and specificity not yet applied to EC studies and a directed clinical perspective in the study design. Expert commentary: These ingredients could be the recipe to accelerate the application of protein biomarkers in the clinic.

Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results

Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8121 when including the 382 proteins that were identified based on a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1412 proteins that were identified based on a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1-barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.

Multiplex and Label-Free Relative Quantification Approach for Studying Protein Abundance of Drug Metabolizing Enzymes in Human Liver Microsomes Using SWATH-MS

We describe a sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) based method for label-free, simultaneous, relative quantification of drug metabolism enzymes in human liver microsomes (HLM; n = 78). In-solution tryptic digestion was aided by a pressure cycling method, which allowed a 90 min incubation time, a significant reduction over classical protocols (12-18 h). Digested peptides were separated on an Acquity UHPLC Peptide BEH C18 column using a 60 min gradient method at a flow rate of 0.100 mL/min. The quadrupole-time-of-flight mass spectrometer (ESI-QTOFMS) was operated in positive electrospray ionization mode, and data were acquired by data-dependent acquisition (DDA) and SWATH-MS^ALL mode. A pooled HLM sample was used as a quality control to evaluate variability in digestion and quantification among different batches, and inter-batch %CV for various proteins was between 3.1 and 7.8%. Spectral library generated from the DDA data identified 1855 distinct proteins and 25 681 distinct peptides at a 1% global false discovery rate (FDR). SWATH data were queried and analyzed for 10 major cytochrome P450 (CYP) enzymes using Skyline, a targeted data extraction software. Further, correlation analysis was performed between functional activity, protein, and mRNA expression for ten CYP enzymes. Pearson correlation coefficient (r) between protein and activity for CYPs ranged from 0.314 (CYP2C19) to 0.767 (CYP2A6). A strong correlation was found between CYP3A4 and CYP3A5 abundance and activity determined using midazolam and testosterone (r > 0.600, p < 0.001). Protein-to-activity correlation was moderate (r > 0.400-0.600, p < 0.001) for CYP1A2, CYP2A6, CYP2B6, CYP2C9, and CYP2E1 and significant but poor (r < 0.400, p < 0.05) for CYP2C8, CYP2C19, and CYP2D6. The findings suggest the suitability of SWATH-MS based method as a valuable and relatively fast analytical technique for relative quantification of proteins in complex biological samples. We also show that protein abundance is a better surrogate than mRNA to predict the activity of CYP activity.

Exhaled breath condensate biomarkers for the early diagnosis of lung cancer using proteomics

We explored whether the proteomic analysis of exhaled breath condensate (EBC) may provide biomarkers for noninvasive screening for the early detection of lung cancer (LC). EBC was collected from 192 individuals [49 control (C), 49 risk factor-smoking (S), 46 chronic obstructive pulmonary disease (COPD) and 48 LC]. With the use of liquid chromatography and tandem mass spectrometry, 348 different proteins with a different pattern among the four groups were identified in EBC samples. Significantly more proteins were identified in the EBC from LC compared with other groups (C: 12.4 ± 1.3; S: 15.3 ± 1; COPD: 14 ± 1.6; LC: 24.2 ± 3.6; P = 0.0001). Furthermore, the average number of proteins identified per sample was significantly higher in LC patients, and receiver operating characteristic curve (ROC) analysis showed an area under the curve of 0.8, indicating diagnostic value. Proteins frequently detected in EBC, such as dermcidin and hornerin, along with others much less frequently detected, such as hemoglobin and histones, were identified. Cytokeratins (KRTs) were the most abundant proteins in EBC samples, and levels of KRT6A, KRT6B, and KRT6C isoforms were significantly higher in samples from LC patients ( P = 0.0031, 0.0011, and 0.0009, respectively). Moreover, the amount of most KRTs in EBC samples from LC patients showed a significant positive correlation with tumor size. Finally, we used a random forest algorithm to generate a robust model using EBC protein data for the diagnosis of patients with LC where the area under the ROC curve obtained indicated a good classification (82%). Thus this study demonstrates that the proteomic analysis of EBC samples is an appropriated approach to develop biomarkers for the diagnosis of lung cancer.

Identification and Validation of a Salivary Protein Panel to Detect Heart Failure Early

BACKGROUND

Over 26 million people suffer from heart failure (HF) globally. Current diagnosis of HF relies on clinical evaluation, blood assays and imaging techniques. Our aim is to develop a diagnostic assay to detect HF in at risk individuals within the community using human saliva as a medium, potentially leading to a simple, safe early warning system.

METHODS

Saliva samples were collected from healthy controls (n=36) and HF patients (n=75). Salivary proteome profiles were analysed by Sequential Window Acquisition of All Theoretical fragment ion spectra - Mass Spectrometry (SWATH-MS). A total of 738 proteins were quantified and 177 proteins demonstrated significant differences between HF patients and healthy controls. Candidate biomarkers were chosen based on their abundance and difference between the two cohorts. A multi-protein panel was developed using logistic regression analysis. The diagnostic performance of the multi-protein panel was assessed using receiver operative characteristic curves. The candidate proteins were further confirmed, using western blot analysis, and validated technically, using an independent biological cohort.

RESULTS

A group of six proteins were chosen in the discovery phase as potential candidates based on their differences in the abundance between the two cohorts. During the validation phase, two of the proteins were not detected with western blotting and as such were removed. The final panel consists of four proteins with sensitivity of 83.3%, specificity of 62.5% with an area under ROC curve of 0.78 in discriminating healthy controls from NYHA class I/II HF patients, and was validated in a second independent cohort study.

CONCLUSION

Analysis of salivary proteome using SWATH-MS revealed novel HF-specific protein candidates yielding high diagnostic performance. A multi-centre longitudinal clinical trial will be the next step before clinical implementation of this panel.