scholarly journals Serum proteomics identify potential biomarkers for nasopharyngeal carcinoma sensitivity to radiotherapy

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Guangying Zhang ◽  
Kun Zhang ◽  
Chao Li ◽  
Yanyan Li ◽  
Zhanzhan Li ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Serum Proteins ◽  
Sequence Similarity ◽  
Predictive Biomarkers ◽  
Primary Treatment ◽  
Label Free ◽  
Discrimination Ability ◽  
Serum Proteomics ◽  
High Discrimination ◽  
Southern University

Abstract Radiotherapy is the primary treatment option for nasopharyngeal carcinoma (NPC). Local recurrence and metastasis caused by radioresistance become a bottleneck of curative effect for patients with NPC. Currently, serum predictive biomarkers of radioresistance are scare. We enrolled NPC patients, who underwent radiotherapy in the Department of Oncology, Xiangya Hospital, Central Southern University, and analyzed the serum proteins profiles in NPC patients using with quantitative label-free proteomics using ultra-definition MS. Patients were divided into those who were radioresistant and radiosensitive by the overall reduction (≤50% or >50%, respectively) in tumor extent. The MS/MS spectrum database search identified 911 proteins and 809 proteins are quantitatable. Eight proteins significantly up-regulated and 12 serum proteins were significantly down-regulated in the radioresistance group compared with radiosensitivity group (P<0.05). Finally, five proteins entered the optimal models, including secreted protein acidic and cysteine rich (SPARC) (P=0.032), serpin family D member 1S (ERPIND1) (P=0.040), complement C4B (C4B) (P=0.017), peptidylprolyl Isomerase B (PPIB) (P=0.042), and family with sequence similarity 173 member A (FAM173A) (P=0.017). In all patient, the area under the curves (AUC) for SPARC, SERPIND, C4B, PPIB, and FAM173A were 0.716 (95% CI: 0.574–0.881), 0.697 (95% CI: 0.837–0.858), 0.686 (95% CI: 0.522–0.850), 0.668 (95% CI: 0.502–0.834) and 0.657 (95% CI: 0.512–0.825), respectively. The AUC of five selected proteins was 0.968 (95% CI: 0.918–1.000) with the sensitivity of 0.941 and the specificity of 0.926. Our result indicated that a panel including five serum protein (SPARC SERPIND1 C4B PPIB FAM173A) based on serum proteomics provided a high discrimination ability for radiotherapy effects in NPC patients. Studies with larger sample size and longer follow-up outcome are required.

scholarly journals Serum proteomics analysis reveals the thermal fitness of dairy buffalo to chronic heat stress

2019 ◽  
Author(s):  
Liu Ping ◽  
Guo Lulu ◽  
Mao Huaming ◽  
Gu Zhaobing
Keyword(s):  
Heat Stress ◽  
Oxygen Delivery ◽  
Production Efficiency ◽  
Serum Proteins ◽  
Differentially Expressed ◽  
Blood Oxygen ◽  
Label Free ◽  
Serum Proteomics ◽  
Parallel Reaction Monitoring ◽  
Dairy Buffalo

Abstract Background: Chronic heat stress (CHS), aggravated by global warming, reduces the production efficiency of the buffalo dairy industry. CHS changes protein abundance, and low-abundant proteins take important roles in biological processes. Results: The objective of the study was to assess differences in low-abundant serum proteins in dairy buffaloes at thermoneutral (TN) or under chronic heat stress (CHS) conditions with proteomic approaches. Six dairy buffaloes as reference animal raised in TN season, and another six dairy buffaloes raised in CHS to discover the molecular mechanism of thermal fitness in hot season with serum proteomics. After the removal of multiple high-abundant proteins in serum, 344 low-abundant proteins were identified in serum with label-free quantification. Of these, 17 low-abundant differentially expressed serum proteins with known functions were detected, and five of these differentially expressed proteins were validated with parallel reaction monitoring. These five proteins were associated with various aspects of heat stress, including decreased heat production, increased blood oxygen delivery, and enhanced natural disease resistance. Conclusions: Lipase (LPL), glutathione peroxidase 3 (GPX3), cathelicidin-2 (CATHL2), ceruloplasmin (CP), and hemoglobin subunit alpha 1 (HBA1) were shown to play cooperative roles in CHS fitness in dairy buffalo. Dairy buffaloes adapt to CHS and hypoxia with high levels of RBCs, HBA1 and CP increased blood oxygen delivery capacity and thermal fitness.

scholarly journals Patients with early-stage oropharyngeal cancer can be identified with label-free serum proteomics

2018 ◽  
Vol 119 (2) ◽  
pp. 200-212 ◽  
Author(s):  
Anna Tuhkuri ◽  
Mayank Saraswat ◽  
Antti Mäkitie ◽  
Petri Mattila ◽  
Robert Silén ◽  
...  
Keyword(s):  
Oropharyngeal Cancer ◽  
Early Stage ◽  
Label Free ◽  
Serum Proteomics ◽  
Free Serum

scholarly journals Deep serum proteomics reveal biomarkers and causal candidates for type 2 diabetes

2019 ◽  
Author(s):  
Valborg Gudmundsdottir ◽  
Valur Emilsson ◽  
Thor Aspelund ◽  
Marjan Ilkov ◽  
Elias F Gudmundsson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Serum Proteins ◽  
Protein Profile ◽  
Disease Onset ◽  
Serum Levels ◽  
The Body ◽  
Protein Biomarkers ◽  
Clinical Value ◽  
Serum Proteomics

AbstractThe prevalence of type 2 diabetes mellitus (T2DM) is expected to increase rapidly in the next decades, posing a major challenge to societies worldwide. The emerging era of precision medicine calls for the discovery of biomarkers of clinical value for prediction of disease onset, where causal biomarkers can furthermore provide actionable targets. Blood-based factors like serum proteins are in contact with every organ in the body to mediate global homeostasis and may thus directly regulate complex processes such as aging and the development of common chronic diseases. We applied a data-driven proteomics approach measuring serum levels of 4,137 proteins in 5,438 Icelanders to discover novel biomarkers for incident T2DM and describe the serum protein profile of prevalent T2DM. We identified 536 proteins associated with incident or prevalent T2DM. Through LASSO penalized logistic regression analysis combined with bootstrap resampling, a panel of 20 protein biomarkers that accurately predicted incident T2DM was identified with a significant incremental improvement over traditional risk factors. Finally, a Mendelian randomization analysis provided support for a causal role of 48 proteins in the development of T2DM, which could be of particular interest as novel therapeutic targets.

scholarly journals Proteomics Investigations of Potential Protein Biomarkers in Sera of Rabbits Infected With Schistosoma japonicum

2021 ◽  
Vol 11 ◽  
Author(s):  
Nian-Nian Bi ◽  
Song Zhao ◽  
Jian-Feng Zhang ◽  
Ying Cheng ◽  
Chen-Yang Zuo ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Schistosoma Japonicum ◽  
Serum Proteins ◽  
Public Health Problem ◽  
Temporal Analysis ◽  
Host Protein ◽  
Label Free ◽  
Protein Biomarkers ◽  
Depth Analysis ◽  
Potential Biomarkers

Schistosomiasis is a chronic parasitic disease that continues to be a pressing public health problem in many developing countries. The primary pathological damage from the disease is granuloma and fibrosis caused by egg aggregation, and early treatment can effectively prevent the occurrence of liver fibrosis. Therefore, it is very important to identify biomarkers that can be used for early diagnosis of Schistosoma japonicum infection. In this study, a label-free proteomics method was performed to observe the alteration of proteins before infection, 1 and 6 weeks after infection, and 5 and 7 weeks after treatment. A total of 10 proteins derived from S. japonicum and 242 host-derived proteins were identified and quantified as significantly changed. Temporal analysis was carried out to further analyze potential biomarkers with coherent changes during infection and treatment. The results revealed biological process changes in serum proteins compared to infection and treatment groups, which implicated receptor-mediated endocytosis, inflammatory response, and acute-phase response such as mannan-binding lectin serine peptidase 1, immunoglobulin, and collagen. These findings offer guidance for the in-depth analysis of potential biomarkers of schistosomiasis, host protein, and early diagnosis of S. japonicum and its pathogenesis. Data are available via ProteomeXchange with identifier PXD029635.

scholarly journals Proteomics in Melanoma Biomarker Discovery: Great Potential, Many Obstacles

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Michael S. Sabel ◽  
Yashu Liu ◽  
David M. Lubman
Keyword(s):  
Biomarker Discovery ◽  
Predictive Biomarkers ◽  
Clinical Staging ◽  
Fresh Tissue ◽  
Primary Tumors ◽  
Unique Challenge ◽  
Advantages And Disadvantages ◽  
Serum Proteomics ◽  
Therapeutic Decisions ◽  
Large Populations

The present clinical staging of melanoma stratifies patients into heterogeneous groups, resulting in the application of aggressive therapies to large populations, diluting impact and increasing toxicity. To move to a new era of therapeutic decisions based on highly specific tumor profiling, the discovery and validation of new prognostic and predictive biomarkers in melanoma is critical. Genomic profiling, which is showing promise in other solid tumors, requires fresh tissue from a large number of primary tumors, and thus faces a unique challenge in melanoma. For this and other reasons, proteomics appears to be an ideal choice for the discovery of new melanoma biomarkers. Several approaches to proteomics have been utilized in the search for clinically relevant biomarkers, but to date the results have been relatively limited. This article will review the present work using both tissue and serum proteomics in the search for melanoma biomarkers, highlighting both the relative advantages and disadvantages of each approach. In addition, we review several of the major obstacles that need to be overcome in order to advance the field.

scholarly journals MarkerDB: an online database of molecular biomarkers

2020 ◽  
Vol 49 (D1) ◽  
pp. D1259-D1267
Author(s):  
David S Wishart ◽  
Brendan Bartok ◽  
Eponine Oler ◽  
Kevin Y H Liang ◽  
Zachary Budinski ◽  
...  
Keyword(s):  
Genetic Markers ◽  
Sequence Similarity ◽  
Relevant Literature ◽  
Predictive Biomarkers ◽  
Roc Curves ◽  
Molecular Biomarkers ◽  
Molecular Structures ◽  
Sequence Information ◽  
Chemical Markers ◽  
Protein Markers

Abstract MarkerDB is a freely available electronic database that attempts to consolidate information on all known clinical and a selected set of pre-clinical molecular biomarkers into a single resource. The database includes four major types of molecular biomarkers (chemical, protein, DNA [genetic] and karyotypic) and four biomarker categories (diagnostic, predictive, prognostic and exposure). MarkerDB provides information such as: biomarker names and synonyms, associated conditions or pathologies, detailed disease descriptions, detailed biomarker descriptions, biomarker specificity, sensitivity and ROC curves, standard reference values (for protein and chemical markers), variants (for SNP or genetic markers), sequence information (for genetic and protein markers), molecular structures (for protein and chemical markers), tissue or biofluid sources (for protein and chemical markers), chromosomal location and structure (for genetic and karyotype markers), clinical approval status and relevant literature references. Users can browse the data by conditions, condition categories, biomarker types, biomarker categories or search by sequence similarity through the advanced search function. Currently, the database contains 142 protein biomarkers, 1089 chemical biomarkers, 154 karyotype biomarkers and 26 374 genetic markers. These are categorized into 25 560 diagnostic biomarkers, 102 prognostic biomarkers, 265 exposure biomarkers and 6746 predictive biomarkers or biomarker panels. Collectively, these markers can be used to detect, monitor or predict 670 specific human conditions which are grouped into 27 broad condition categories. MarkerDB is available at https://markerdb.ca.

scholarly journals Proteomic Profiles and Biological Processes of Relapsed vs. Non-Relapsed Pediatric Hodgkin Lymphoma

2020 ◽  
Vol 21 (6) ◽  
pp. 2185
Author(s):  
Ombretta Repetto ◽  
Valli De Re ◽  
Lara Mussolin ◽  
Massimo Tedeschi ◽  
Caterina Elia ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Gel Electrophoresis ◽  
Predictive Biomarkers ◽  
Biological Processes ◽  
Label Free ◽  
Pediatric Hodgkin Lymphoma ◽  
Difference Gel Electrophoresis ◽  
Thrombospondin 1 ◽  
Serum Paraoxonase ◽  
Differentially Abundant Proteins

The identification of circulating proteins associated with relapse in pediatric Hodgkin lymphoma (HL) may help develop predictive biomarkers. We previously identified a set of predictive biomarkers by difference gel electrophoresis. Here we used label-free quantitative liquid chromatography-mass spectrometry (LC-MS/MS) on plasma collected at diagnosis from 12 children (age 12–16 years) with nodular sclerosis HL, including six in whom the disease relapsed within 5 years of treatment in the LH2004 trial. Plasma proteins were pooled in groups of three, separately for non-relapsing and relapsing HL, and differentially abundant proteins between the two disease states were identified by LC-MS/MS in an explorative and validation design. Proteins with a fold change in abundance >1.2 or ≤0.8 were considered “differentially abundant”. LC-MS/MS identified 60 and 32 proteins that were more abundant in non-relapsing and relapsing HL plasma, respectively, in the explorative phase; these numbers were 39 and 34 in the validation phase. In both analyses, 11 proteins were more abundant in non-relapsing HL (e.g., angiotensinogen, serum paraoxonase/arylesterase 1, transthyretin), including two previously identified by difference gel electrophoresis (antithrombin III and α-1-antitrypsin); seven proteins were more abundant in relapsing HL (e.g., fibronectin and thrombospondin-1), including two previously identified proteins (fibrinogen β and γ chains). The differentially abundant proteins participated in numerous biological processes, which were manually grouped into 10 biological classes and 11 biological regulatory subclasses. The biological class Lipid metabolism, and its regulatory subclass, included angiotensinogen and serum paraoxonase/arylesterase 1 (more abundant in non-relapsing HL). The biological classes Immune system and Cell and extracellular matrix architecture included fibronectin and thrombospondin-1 (more abundant in relapsing HL). These findings deepen our understanding of the molecular scenario underlying responses to therapy and provide new evidence about these proteins as possible biomarkers of relapse in pediatric HL.

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