Candidate Proteomic Biomarkers Research Articles

Enrichr in silico analysis of MS-based extracted candidate proteomic biomarkers highlights pathogenic pathways in systemic sclerosis

Systemic sclerosis (SSc) is a rheumatic disease characterised by vasculopathy, inflammation and fibrosis. Its aetiopathogenesis is still unknown, and the pathways/mechanisms of the disease are not clarified. This study aimed to perform in silico analysis of the already Mass Spectrometry (MS)-based discovered biomarkers of SSc to extract possible pathways/mechanisms implicated in the disease. We recorded all published candidate MS-based found biomarkers related to SSc. We then selected a number of the candidate biomarkers using specific criteria and performed pathway and cellular component analyses using Enrichr. We used PANTHER and STRING to assess the biological processes and the interactions of the recorded proteins, respectively. Pathway analysis extracted several pathways that are associated with the three different stages of SSc pathogenesis. Some of these pathways are also related to other diseases, including autoimmune diseases. We observe that these biomarkers are located in several cellular components and implicated in many biological processes. STRING analysis showed that some proteins interact, creating significant clusters, while others do not display any evidence of an interaction. All these data highlight the complexity of SSc, and further investigation of the extracted pathways/biological processes and interactions may help study the disease from a different angle.

Candidate proteomic biomarkers in systemic sclerosis discovered using mass-spectrometry: an update of a systematic review (2014–2020)

Abstract Background. Systemic sclerosis (Ssc) is an autoimmune disease characterized by graduate cutaneous and tissue fibrosis development and irreversible fibroproliferative vascular changes. The aim of the current systematic review was to update the list of proteomic candidate biomarkers identified from Ssc samples with mass spectrometry techniques. Methods. Medline and Scopus databases were searched on 1st September 2020. Relevant articles were searched from March 2014 until September 2020. Two independent reviewers evaluated the retrieved articles. Results. From a total of 97 articles, 9 articles were included in the final analysis summarizing 539 candidate proteomic biomarkers from various samples from Ssc patients (a larger number compared to the previous systematic review). Most biomarkers were identified from cutaneous biopsies. Only 5 articles included a validation step of the findings with only 13 biomarkers being validated. Conclusions. Although many candidate biomarkers were additionally identified, independent validation studies are needed in order to evaluate the importance of these biomarkers for Ssc patients.

S100A6, Calumenin and Cytohesin 2 as Biomarkers for Cutaneous Involvement in Systemic Sclerosis Patients: A Case Control Study.

Background: Systemic sclerosis (Ssc) is an autoimmune disease with incomplete known physiopathology. There is a high number of candidate proteomic biomarkers for Ssc that have not yet been confirmed on independent Ssc cohorts. The aim of the study was to confirm circulating S100A6, calumenin, and cytohesin 2 as biomarkers for Ssc. Methods: 53 Ssc patients and 26 age- and gender-matched controls were included. Serum S100A6, calumenin, and cytohesin 2 were evaluated with commercial ELISA kits. Associations between serum expression and clinical Ssc characteristics were evaluated. Results: Serum calumenin, S100A6, and cytohesin 2 were higher in Ssc patients compared to controls. Calumenin associated with extensive cutaneous fibrosis, frequency of Raynaud phenomenon, and low complement level, and had a tendency to be higher in Ssc patients with pulmonary fibrosis. S100A6 correlated with the number of active digital ulcers. Serum cytohesin 2 levels were higher in patients with teleangiectasia and associated with pulmonary artery pressure. Conclusions: Serum calumenin, S100A6, and cytohesin 2 were confirmed as biomarkers on an independent group of Ssc patients. Calumenin had the best predictive capacity for cutaneous Ssc manifestations. Future studies are needed to evaluate the prognostic value of these biomarkers and evaluate them as possible therapeutic targets.

Toward Candidate Proteomic Biomarkers in Clinical Monitoring of Acute Promyelocytic Leukemia Treatment with Arsenic Trioxide

The introduction of arsenic trioxide (ATO) in treatment of acute promyelocytic leukemia (APL) has resulted in high clinical complete remission (CR) rates over 90%. On the contrary, the risk for early death (ED) in APL patients treated with ATO continues to have a negative impact for optimization of APL therapeutics. There is an urgent need for precision medicine and biomarkers in clinical monitoring of ATO toxicity in APL, and ED in particular. This retrospective case series cohort proteomics study was conducted as a hypothesis generation effort and provides here several potential molecular leads on serum peptides expressed at different times after treatment with ATO in patients with APL. In 12 patients with a de novo APL diagnosis, and treated with single-agent ATO as frontline remission induction therapy, serum peptides were fractionated by weak cation exchange magnetic beads and analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Ten peptides (m/z 2075.5, 2084.2, 2203.0, 2265.2, 2872.8, 2916.6, 3145.2, 3153.4, 3953.4, and 3964.8) were significantly downregulated in serum after ATO treatment. Among them, four peptides were identified as (1) Immunoglobulin heavy chain V-III region BUT, (2) RRP15-like protein, (3) filaggrin, and (4) protein SON isoform F. To the best of our knowledge, this is the first clinical oncology proteomic biomarker study with a view to future rational therapeutic monitoring of patients with APL in the course of single-agent ATO treatment and hematological CR.

Biomarkers of systemic lupus erythematosus identified using mass spectrometry-based proteomics: a systematic review.

Advances in mass spectrometry technologies have created new opportunities for discovering novel protein biomarkers in systemic lupus erythematosus (SLE). We performed a systematic review of published reports on proteomic biomarkers identified in SLE patients using mass spectrometry‐based proteomics and highlight their potential disease association and clinical utility. Two electronic databases, MEDLINE and EMBASE, were systematically searched up to July 2015. The methodological quality of studies included in the review was performed according to Preferred Reporting Items for Systematic Reviews and Meta‐analyses guidelines. Twenty‐five studies were included in the review, identifying 241 SLE candidate proteomic biomarkers related to various aspects of the disease including disease diagnosis and activity or pinpointing specific organ involvement. Furthermore, 13 of the 25 studies validated their results for a selected number of biomarkers in an independent cohort, resulting in the validation of 28 candidate biomarkers. It is noteworthy that 11 candidate biomarkers were identified in more than one study. A significant number of potential proteomic biomarkers that are related to a number of aspects of SLE have been identified using mass spectrometry proteomic approaches. However, further studies are required to assess the utility of these biomarkers in routine clinical practice.

Candidate proteomic biomarkers for three genogroups of the swine pathogen Streptococcus suis serotype 2.

BackgroundStreptococcus suis, more specifically serotype 2, is a major swine pathogen and an emerging zoonotic agent that causes severe infections such as meningitis, endocarditis, and septicemia. In this study, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI) was used to investigate the protein expression profiles of 45 strains of S. suis serotype 2 that had previously been clustered by multilocus sequence typing (MLST) into three sequence types (ST1, ST25, and ST28) (n = 15 for each ST).ResultsThe SELDI data were analyzed using the univariate Mann–Whitney and Kruskal-Wallis tests and multivariate statistical methods (heatmap/hierarchical clustering). The heatmap identified 136 cell proteins, and hierarchical clustering provided a 100% correct classification of all fifteen ST1 and ST25 strains and thirteen of the fifteen ST28 strains (87% correct). The univariate statistical analyses of the SELDI protein expression profiles identified nine significant proteins that discriminated the strains of the three STs of S. suis. Of these proteins, two were overexpressed in ST1 (5958 Da and 10249 Da), four in ST25 (5989 Da, 6646 Da, 7421 Da, and 9825 Da), and three in ST28 (4516 Da, 7833 Da, and 9342 Da). Two of the proteins associated with the ST28 strains (p4516 and p9342) were purified and were identified as a putative ABC transporter and a nucleoid-DNA-binding protein, respectively.ConclusionsSELDI analysis of 45 strains of S. suis allowed to identify nine statistically significant proteins that can be specifically correlated with either ST1, ST25 or ST28. The possible involvement of the overexpressed proteins in the pathology of S. suis infections will require further investigation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-015-0401-0) contains supplementary material, which is available to authorized users.

Biomarcadores proteómicos en la enfermedad de Parkinson

Parkinson's disease (PD) is a neurodegenerative disorder that affects movement and is caused by the death of the dopaminergic neurons in the compact part of the substantia nigra. Its diagnosis is essentially clinical, but although the signs and symptoms of PD are well known, the rate of diagnostic error is relatively high. It is estimated that 10-30% of patients initially diagnosed with PD are later reclassified. This disease has a high prevalence beyond the age of 60, and one of its biggest problems is that it is diagnosed when the degenerative process is already at a very advanced stage. Therefore, it is necessary to look for other biomarkers that make it possible to carry out an early diagnosis of PD, follow up its development, distinguish it from other related pathologies (parkinsonisms) and help monitor the effect of novel therapies. The fact that there are mutations that lead to PD, as well as polygenetic combinations that can act as risk factors, suggests the possibility of measuring the proteins resulting from the expression of these genes in peripheral tissues. And once their sensitivity and specificity have been proved they could be used as biomarkers for PD, even in the early phases of the disease. The aim of this work is to focus on a detailed review of the main candidate proteomic biomarkers researched to date by discussing the most recent literature.

Graphical Identification of Cancer-Associated Gene Subnetworks Based on Small Proteomics Data Sets

Proteomics is a rapidly emerging frontier in post-genomics medicine and biology, but the quantitative analysis and validation of proteomic data are in need of further improvements. Before selecting potential candidate proteomic biomarkers, it is important to understand the broader context of how biological processes are regulated under different conditions or in different phenotypes. The enrichment of proteomic data consists of extracting as much biological meaning as possible from curated, pathway-based, functional protein interaction networks. Currently, most of the enrichment tools are intended for microarray data and require parametric data, whereas proteomic data are often nonparametric. In this study, we aimed to select a suite of interactive tools that can enrich proteomic results with a graphical overview. This facilitated diagnosis and interpretation prior to further analysis. From a list of proteins, a network was constructed using a map of the most severely disrupted biological process, and the disease entity was then identified on the basis of clinical data. Taken together, this graphical and interactive method ranks potential proteins via functional analysis in order to improve the choice of biomarkers for validation with the following advantages: 1) It adds neighbor proteins that are not selected by mass spectrometry analysis, but could in fact be key proteins; 2) pinpoints the biological process most often involved; and 3) predicts the most likely disease on the basis of clinical data.

Proteomic analysis of venous thromboembolism: an update

Venous thromboembolism is a complex, multifactorial disorder, the pathogenesis of which typically involves a variety of inherited or acquired factors. The multifactorial etiology of this disease and the partial correlation between genotype and prothrombotic phenotype limit greatly the value of genetic analysis in assessing thrombotic risk. The integration of several new ‘omics’ techniques enables a multifaceted and holistic approach to the study of venous thrombotic processes and pave the way to the search and identification of novel blood biomarkers and/or effectors of thrombus formation that can also be the possible future target of new anticoagulant and thrombolytic therapies for more personalized medicine. This review provides a comprehensive overview of the latest candidate proteomic biomarkers of venous thrombosis and of the proteomics studies relevant to its pathophysiology, some of which seem to confirm the existence of a common physiopathological basis for venous thromboembolism and atherothrombosis.

Proteomic Analysis of Human Saliva From Lung Cancer Patients Using Two-Dimensional Difference Gel Electrophoresis and Mass Spectrometry

Lung cancer is often asymptomatic or causes only nonspecific symptoms in its early stages. Early detection represents one of the most promising approaches to reduce the growing lung cancer burden. Human saliva is an attractive diagnostic fluid because its collection is less invasive than that of tissue or blood. Profiling of proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of oral or systematic diseases, which may be used extensively in future medical diagnostics. There were 72 subjects enrolled in this study for saliva sample collection according to the approved protocol. Two-dimensional difference gel electrophoresis combined with MS was the platform for salivary proteome separation, quantification, and identification from two pooled samples. Candidate proteomic biomarkers were verified and prevalidated by using immunoassay methods. There were 16 candidate protein biomarkers discovered by two-dimensional difference gel electrophoresis and MS. Three proteins were further verified in the discovery sample set, prevalidation sample set, and lung cancer cell lines. The discriminatory power of these candidate biomarkers in lung cancer patients and healthy control subjects can reach 88.5% sensitivity and 92.3% specificity with AUC = 0.90. This preliminary data report demonstrates that proteomic biomarkers are present in human saliva when people develop lung cancer. The discriminatory power of these candidate biomarkers indicate that a simple saliva test might be established for lung cancer clinical screening and detection.