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Ma S, He S, Liu J, Zhuang W, Li H, Lin C, Wang L, Feng J, Wang L. Metabolomics unveils the exacerbating role of arachidonic acid metabolism in atherosclerosis. Front Mol Biosci 2024; 11:1297437. [PMID: 38384498 PMCID: PMC10879346 DOI: 10.3389/fmolb.2024.1297437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Atherosclerosis is a complex vascular disorder characterized by the deposition of lipids, inflammatory cascades, and plaque formation in arterial walls. A thorough understanding of its causes and progression is necessary to develop effective diagnostic and therapeutic strategies. Recent breakthroughs in metabolomics have provided valuable insights into the molecular mechanisms and genetic factors involved in atherosclerosis, leading to innovative approaches for preventing and treating the disease. In our study, we analyzed clinical serum samples from both atherosclerosis patients and animal models using laser desorption ionization mass spectrometry. By employing methods such as orthogonal partial least-squares discrimination analysis (OPLS-DA), heatmaps, and volcano plots, we can accurately classify atherosclerosis (AUC = 0.892) and identify key molecules associated with the disease. Specifically, we observed elevated levels of arachidonic acid and its metabolite, leukotriene B4, in atherosclerosis. By inhibiting arachidonic acid and monitoring its downstream metabolites, we discovered the crucial role of this metabolic pathway in regulating atherosclerosis. Metabolomic research provides detailed insights into the metabolic networks involved in atherosclerosis development and reveals the close connection between abnormal metabolism and the disease. These studies offer new possibilities for precise diagnosis, treatment, and monitoring of disease progression, as well as evaluating the effectiveness of therapeutic interventions.
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Affiliation(s)
- Sai Ma
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Songqing He
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Jing Liu
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Wei Zhuang
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Hanqing Li
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Chen Lin
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Lijun Wang
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Jing Feng
- Department of Emergency Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Emergency Medicine, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Lei Wang
- Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- Department of Cardiology, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
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Erlmeier F, Sun N, Shen J, Feuchtinger A, Buck A, Prade VM, Kunzke T, Schraml P, Moch H, Autenrieth M, Weichert W, Hartmann A, Walch A. MALDI Mass Spectrometry Imaging-Prognostic Pathways and Metabolites for Renal Cell Carcinomas. Cancers (Basel) 2022; 14:cancers14071763. [PMID: 35406537 PMCID: PMC8996951 DOI: 10.3390/cancers14071763] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 01/27/2023] Open
Abstract
Simple Summary Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Nevertheless, prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by high mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) for prognostic biomarker detection. This is a suitable method for biomarker detection for several tumor entities. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes. Abstract High mass resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a suitable method for biomarker detection for several tumor entities. Renal cell carcinoma (RCC) is the seventh most common cancer type and accounts for more than 80% of all renal tumors. Prognostic biomarkers for RCC are still missing. Therefore, we analyzed a large, multicenter cohort including the three most common RCC subtypes (clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC)) by MALDI for prognostic biomarker detection. MALDI-Fourier-transform ion cyclotron resonance (FT-ICR)-MSI analysis was performed for renal carcinoma tissue sections from 782 patients. SPACiAL pipeline was integrated for automated co-registration of histological and molecular features. Kaplan–Meier analyses with overall survival as endpoint were executed to determine the metabolic features associated with clinical outcome. We detected several pathways and metabolites with prognostic power for RCC in general and also for different RCC subtypes.
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Affiliation(s)
- Franziska Erlmeier
- Institute of Pathology, University Hospital Erlangen-Nuremberg, 91054 Erlangen, Germany;
- Correspondence: (F.E.); (N.S.)
| | - Na Sun
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
- Correspondence: (F.E.); (N.S.)
| | - Jian Shen
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Achim Buck
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Verena M. Prade
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Thomas Kunzke
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University Hospital Zurich, 8091 Zurich, Switzerland; (P.S.); (H.M.)
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital Zurich, 8091 Zurich, Switzerland; (P.S.); (H.M.)
| | - Michael Autenrieth
- Department of Urology, Rechts der Isar Medical Center, Technical University of Munich, 81675 Munich, Germany;
| | - Wilko Weichert
- Institute of Pathology, Technical University Munich, 81675 Munich, Germany;
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen-Nuremberg, 91054 Erlangen, Germany;
| | - Axel Walch
- Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, 85764 Neuherberg, Germany; (J.S.); (A.F.); (A.B.); (V.M.P.); (T.K.); (A.W.)
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Zheng H, Ji J, Zhao L, Chen M, Shi A, Pan L, Huang Y, Zhang H, Dong B, Gao H. Prediction and diagnosis of renal cell carcinoma using nuclear magnetic resonance-based serum metabolomics and self-organizing maps. Oncotarget 2018; 7:59189-59198. [PMID: 27463020 PMCID: PMC5312304 DOI: 10.18632/oncotarget.10830] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 07/09/2016] [Indexed: 12/19/2022] Open
Abstract
Diagnosis of renal cell carcinoma (RCC) at an early stage is challenging, but it provides the best chance for cure. We aimed to develop a predictive diagnostic method for early-stage RCC based on a biomarker cluster using nuclear magnetic resonance (NMR)-based serum metabolomics and self-organizing maps (SOMs). We trained and validated the SOM model using serum metabolome data from 104 participants, including healthy individuals and early-stage RCC patients. To assess the predictive capability of the model, we analyzed an independent cohort of 22 subjects. We then used our method to evaluate changes in the metabolic patterns of 23 RCC patients before and after nephrectomy. A biomarker cluster of 7 metabolites (alanine, creatine, choline, isoleucine, lactate, leucine, and valine) was identified for the early diagnosis of RCC. The trained SOM model using a biomarker cluster was able to classify 22 test subjects into the appropriate categories. Following nephrectomy, all RCC patients were classified as healthy, which was indicative of metabolic recovery. But using a diagnostic criterion of 0.80, only 3 of the 23 subjects could not be confidently assessed as metabolically recovered after nephrectomy. We successfully followed-up 17 RCC patients for 8 years post-nephrectomy. Eleven of these patients who diagnosed as metabolic recovery remained healthy after 8 years. Our data suggest that a SOM model using a biomarker cluster from serum metabolome can accurately predict early RCC diagnosis and can be used to evaluate postoperative metabolic recovery.
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Affiliation(s)
- Hong Zheng
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China
| | - Jiansong Ji
- Lishui Central Hospital, The Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, 323000, China
| | - Liangcai Zhao
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China
| | - Minjiang Chen
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China.,Lishui Central Hospital, The Fifth Affiliated Hospital, Wenzhou Medical University, Lishui, 323000, China
| | - An Shi
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Linlin Pan
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China
| | - Yiran Huang
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Huajie Zhang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China
| | - Baijun Dong
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Hongchang Gao
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, China
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Challenges in biomarker discovery with MALDI-TOF MS. Clin Chim Acta 2016; 458:84-98. [PMID: 27134187 DOI: 10.1016/j.cca.2016.04.033] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 04/21/2016] [Accepted: 04/27/2016] [Indexed: 12/30/2022]
Abstract
MALDI-TOF MS technique is commonly used in system biology and clinical studies to search for new potential markers associated with pathological conditions. Despite numerous concerns regarding a sample preparation or processing of complex data, this strategy is still recognized as a popular tool and its awareness has risen in the proteomic community over the last decade. In this review, we present comprehensive application of MALDI mass spectrometry with special focus on profiling research. We also discuss major advantages and disadvantages of universal sample preparation methods such as micro-SPE columns, immunodepletion or magnetic beads, and we show the potential of nanostructured materials in capturing low molecular weight subproteomes. Furthermore, as the general protocol considerably affects spectra quality and interpretation, an alternative solution for improved ion detection, including hydrophobic constituents, data processing and statistical analysis is being considered in up-to-date profiling pattern. In conclusion, many reports involving MALDI-TOF MS indicated highly abundant proteins as valuable indicators, and at the same time showed the inaccuracy of available methods in the detection of low abundant proteome that is the most interesting from the clinical perspective. Therefore, the analytical aspects of sample preparation methods should be standardized to provide a reproducible, low sample handling and credible procedure.
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Kriegsmann J, Kriegsmann M, Casadonte R. MALDI TOF imaging mass spectrometry in clinical pathology: a valuable tool for cancer diagnostics (review). Int J Oncol 2014; 46:893-906. [PMID: 25482502 DOI: 10.3892/ijo.2014.2788] [Citation(s) in RCA: 114] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2014] [Accepted: 11/04/2014] [Indexed: 11/06/2022] Open
Abstract
Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) is an evolving technique in cancer diagnostics and combines the advantages of mass spectrometry (proteomics), detection of numerous molecules, and spatial resolution in histological tissue sections and cytological preparations. This method allows the detection of proteins, peptides, lipids, carbohydrates or glycoconjugates and small molecules.Formalin-fixed paraffin-embedded tissue can also be investigated by IMS, thus, this method seems to be an ideal tool for cancer diagnostics and biomarker discovery. It may add information to the identification of tumor margins and tumor heterogeneity. The technique allows tumor typing, especially identification of the tumor of origin in metastatic tissue, as well as grading and may provide prognostic information. IMS is a valuable method for the identification of biomarkers and can complement histology, immunohistology and molecular pathology in various fields of histopathological diagnostics, especially with regard to identification and grading of tumors.
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Affiliation(s)
- Jörg Kriegsmann
- MVZ for Histology, Cytology and Molecular Diagnostics, Trier, Germany
| | - Mark Kriegsmann
- Institute for Pathology, University of Heidelberg, Heidelberg, Germany
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Urinary signatures of Renal Cell Carcinoma investigated by peptidomic approaches. PLoS One 2014; 9:e106684. [PMID: 25202906 PMCID: PMC4159280 DOI: 10.1371/journal.pone.0106684] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Accepted: 07/31/2014] [Indexed: 01/16/2023] Open
Abstract
Renal Cell Carcinoma (RCC) is typically asymptomatic and surgery usually increases patient's lifespan only for early stage tumours. Moreover, solid renal masses cannot be confidently differentiated from RCC. Therefore, markers to distinguish malignant kidney tumours and for their detection are needed. Two different peptide signatures were obtained by a MALDI-TOF profiling approach based on urine pre-purification by C8 magnetic beads. One cluster of 12 signals could differentiate malignant tumours (n = 137) from benign renal masses and controls (n = 153) with sensitivity of 76% and specificity of 87% in the validation set. A second cluster of 12 signals distinguished clear cell RCC (n = 118) from controls (n = 137) with sensitivity and specificity values of 84% and 91%, respectively. Most of the peptide signals used in the two models were observed at higher abundance in patient urines and could be identified as fragments of proteins involved in tumour pathogenesis and progression. Among them: the Meprin 1α with a pro-angiogenic activity, the Probable G-protein coupled receptor 162, belonging to the GPCRs family and known to be associated with several key functions in cancer, the Osteopontin that strongly correlates to tumour stages and invasiveness, the Phosphorylase b kinase regulatory subunit alpha and the SeCreted and TransMembrane protein 1.
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Deng BG, Yao JH, Liu QY, Feng XJ, Liu D, Zhao L, Tu B, Yang F. Comparative serum proteomic analysis of serum diagnosis proteins of colorectal cancer based on magnetic bead separation and maldi-tof mass spectrometry. Asian Pac J Cancer Prev 2014; 14:6069-75. [PMID: 24289627 DOI: 10.7314/apjcp.2013.14.10.6069] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND At present, the diagnosis of colorectal cancer (CRC) requires a colorectal biopsy which is an invasive procedure. We undertook this pilot study to develop an alternative method and potential new biomarkers for diagnosis, and validated a set of well-integrated tools called ClinProt to investigate the serum peptidome in CRC patients. METHODS Fasting blood samples from 67 patients diagnosed with CRC by histological diagnosis, 55 patients diagnosed with colorectal adenoma by biopsy, and 65 healthy volunteers were collected. Division was into a model construction group and an external validation group randomly. The present work focused on serum proteomic analysis of model construction group by ClinProt Kit combined with mass spectrometry. This approach allowed construction of a peptide pattern able to differentiate the studied populations. An external validation group was used to verify the diagnostic capability of the peptidome pattern blindly. An immunoassay method was used to determine serum CEA of CRC and controls. RESULTS The results showed 59 differential peptide peaks in CRC, colorectal adenoma and health volunteers. A genetic algorithm was used to set up the classification models. Four of the identified peaks at m/z 797, 810, 4078 and 5343 were used to construct peptidome patterns, achieving an accuracy of 100% (> CEA, P < 0. 05). Furthermore, the peptidome patterns could differentiate the validation group with high accuracy close to 100%. CONCLUSIONS Our results showed that proteomic analysis of serum with MALDI-TOF MS is a fast and reproducible approach, which may provide a novel approach to screening for CRC.
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Affiliation(s)
- Bao-Guo Deng
- Department of Microbiology, Xinxiang Medical University, Xinxiang, China E-mail :
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Identification of kininogen-1 as a serum biomarker for the early detection of advanced colorectal adenoma and colorectal cancer. PLoS One 2013; 8:e70519. [PMID: 23894665 PMCID: PMC3720899 DOI: 10.1371/journal.pone.0070519] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2012] [Accepted: 06/25/2013] [Indexed: 02/06/2023] Open
Abstract
Background Serum markers represent potential tools for the detection of colorectal cancer (CRC). The aim of this study was to obtain proteomic expression profiles and identify serum markers for the early detection of CRC. Methods Proteomic profiles of serum samples collected from 35 healthy volunteers, 35 patients with advanced colorectal adenoma (ACA), and 40 patients with CRC were compared using Clinprot technology. Using enzyme-linked immunosorbent assays (ELISAs), 366 sera samples were additionally analyzed, and immunohistochemistry studies of 400 tissues were used to verify the expression of kininogen-1 and its value in the early detection of CRC. Results Predicting models were established among the three groups, and kininogen-1 was identified as a potential marker for CRC using Clinprot technology. ELISAs also detected significantly higher serum kininogen-1 levels in ACA and CRC patients compared to controls (P<0.05). Furthermore, the area under the receiver operating characteristic curve (AUC) for serum kininogen-1 in the diagnosis of ACA was 0.635 (P = 0.003), and for serum carcinoembryonic antigen (CEA) was 0.453 (P = 0.358). The sensitivity, specificity, and accuracy of serum kininogen-1 for diagnosing Duke’s stage A and B CRC was 70.13%, 65.88%, and 67.90%, respectively, whereas serum CEA was 38.96%, 85.88%, and 63.58%, respectively. Moreover, immunohistochemistry showed that expression of kininogen-1 was significantly higher in CRC and ACA tissues than in normal mucosa (48.39% vs. 15.58% vs. 0%, P<0.05). Conclusions These results suggest that Clinprot technology provides a useful tool for the diagnosis of CRC, and kininogen-1 is a potential serum biomarker for the early detection of advanced colorectal adenoma and CRC.
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Craven RA, Vasudev NS, Banks RE. Proteomics and the search for biomarkers for renal cancer. Clin Biochem 2013; 46:456-65. [DOI: 10.1016/j.clinbiochem.2012.11.029] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2012] [Revised: 11/28/2012] [Accepted: 11/29/2012] [Indexed: 12/25/2022]
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Silvestre DD, Zoppis I, Brambilla F, Bellettato V, Mauri G, Mauri P. Availability of MudPIT data for classification of biological samples. J Clin Bioinforma 2013; 3:1. [PMID: 23317455 PMCID: PMC3563498 DOI: 10.1186/2043-9113-3-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2012] [Accepted: 01/07/2013] [Indexed: 01/18/2023] Open
Abstract
Background Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins. Results Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both collection 1 and 2). These results indicate that sequencing of peptides and proteins reduces the experimental noise affecting the raw mass spectra, and allows the extraction of more informative features available for the effective classification of samples. In addition, proteins and peptides features selected by SVM matched for 80% with the differentially expressed proteins identified by the MAProMa software. Conclusions These findings confirm the availability of the most label-free quantitative methods based on processing of spectral count and SEQUEST-based SCORE values. On the other hand, it stresses the usefulness of MudPIT data for a correct grouping of sample phenotypes, by applying both supervised and unsupervised learning algorithms. This capacity permit the evaluation of actual samples and it is a good starting point to translate proteomic methodology to clinical application.
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Affiliation(s)
- Dario Di Silvestre
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
| | - Italo Zoppis
- Department of Informatics, Systems and Communication, Viale Sarca 336, University of Milano-Bicocca, Milan, Italy
| | - Francesca Brambilla
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
| | - Valeria Bellettato
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, Viale Sarca 336, University of Milano-Bicocca, Milan, Italy
| | - Pierluigi Mauri
- , Institute for Biomedical Technologies (ITB-CNR), via F.lli Cervi 93, Segrate (Milan), Italy
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Gianazza E, Chinello C, Mainini V, Cazzaniga M, Squeo V, Albo G, Signorini S, Di Pierro SS, Ferrero S, Nicolardi S, van der Burgt YE, Deelder AM, Magni F. Alterations of the serum peptidome in renal cell carcinoma discriminating benign and malignant kidney tumors. J Proteomics 2012; 76 Spec No.:125-40. [DOI: 10.1016/j.jprot.2012.07.032] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2012] [Revised: 07/16/2012] [Accepted: 07/19/2012] [Indexed: 01/21/2023]
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Ornellas P, Ornellas AA, Chinello C, Gianazza E, Mainini V, Cazzaniga M, Pereira DA, Sandim V, Cypriano AS, Koifman L, Silva PCBD, Alves G, Magni F. Downregulation of C3 and C4A/B complement factor fragments in plasma from patients with squamous cell carcinoma of the penis. Int Braz J Urol 2012; 38:739-49. [DOI: 10.1590/1677-553820133806739] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2012] [Indexed: 12/22/2022] Open
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Savino R, Paduano S, Preianò M, Terracciano R. The proteomics big challenge for biomarkers and new drug-targets discovery. Int J Mol Sci 2012. [PMID: 23203042 PMCID: PMC3509558 DOI: 10.3390/ijms131113926] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.
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Affiliation(s)
- Rocco Savino
- Department of Health Sciences, Laboratory of Mass Spectrometry and Proteomics, University "Magna Græcia", Catanzaro, University Campus, Europa Avenue, 88100 Catanzaro, Italy.
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Preianò M, Pasqua L, Gallelli L, Galasso O, Gasparini G, Savino R, Terracciano R. Simultaneous extraction and rapid visualization of peptidomic and lipidomic body fluids fingerprints using mesoporous aluminosilicate and MALDI-TOF MS. Proteomics 2012; 12:3286-94. [PMID: 22997056 DOI: 10.1002/pmic.201200204] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2012] [Revised: 08/01/2012] [Accepted: 09/05/2012] [Indexed: 01/15/2023]
Abstract
Herein we report the use of mesoporous aluminosilicate (MPAS) for the simultaneous extraction of peptides and lipids from complex body fluids such as human plasma and synovial fluid. We show that MPAS particles, given their mesostructural features with nanometric pore size and high surface area, are an efficient device for simultaneous extraction of peptidome and lipidome from as little as a few microliters of body fluids. The peptides and the lipids, selected and enriched by MPAS particles and rapidly visualized by MALDI-TOF MS, could form part of a diagnostic profile of the "peptidome" and the "lipidome" of healthy versus diseased subjects in comparative studies. The ability of this approach to rapidly reveal the overall pattern of changes in both lipidome and peptidome signatures of complex biofluids could be of valuable interest for handling large numbers of samples required in -omics studies for the purpose of finding novel biomarkers.
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Serum peptidome patterns of colorectal cancer based on magnetic bead separation and MALDI-TOF mass spectrometry analysis. J Biomed Biotechnol 2012; 2012:985020. [PMID: 23091368 PMCID: PMC3469310 DOI: 10.1155/2012/985020] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Accepted: 06/14/2012] [Indexed: 12/27/2022] Open
Abstract
Background. Colorectal cancer (CRC) is one of the most common cancers in the world, identification of biomarkers for early detection of CRC represents a relevant target. The present study aims to determine serum peptidome patterns for CRC diagnosis.
Methods. The present work focused on serum proteomic analysis of 32 health volunteers and 38 CRC by ClinProt Kit combined with mass spectrometry. This approach allowed the construction of a peptide patterns able to differentiate the studied populations. An independent group of serum (including 33 health volunteers, 34 CRC, 16 colorectal adenoma, 36 esophageal carcinoma, and 31 gastric carcinoma samples) was used to verify the diagnostic and differential diagnostic capability of the peptidome patterns blindly. An immunoassay method was used to determine serum CEA of CRC and controls. Results. A quick classifier algorithm was used to construct the peptidome patterns for identification of CRC from controls. Two of the identified peaks at m/z 741 and 7772 were used to construct peptidome patterns, achieving an accuracy close to 100% (>CEA, P < 0.05). Furthermore, the peptidome patterns could differentiate validation group with high accuracy.
Conclusions. These results suggest that the ClinProt Kit combined with mass spectrometry yields significantly higher accuracy for the diagnosis and differential diagnosis of CRC.
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Morgan TM, Seeley EH, Fadare O, Caprioli RM, Clark PE. Imaging the clear cell renal cell carcinoma proteome. J Urol 2012; 189:1097-103. [PMID: 23009866 DOI: 10.1016/j.juro.2012.09.074] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2012] [Indexed: 11/19/2022]
Abstract
PURPOSE A key barrier to identifying tissue biomarkers of clear cell renal cell carcinoma is the heterogeneity of protein expression in tissue. However, by providing spectra for every 0.05 mm(2) area of tissue, imaging mass spectrometry reveals the spatial distribution of peptides. We determined whether this approach could be used to identify and map protein signatures of clear cell renal cell carcinoma. MATERIALS AND METHODS We constructed 2 tissue microarrays with 2 cores each of matched tumor and normal tissue from the nephrectomy specimens of 70 patients with clear cell renal cell carcinoma. Samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. In each tissue microarray peptide signatures were identified that differentiated cancer from normal tissue. The signatures were then cross validated. Mass spectrometry/mass spectrometry sequencing was performed to determine the identity of select, differentially expressed peptides. Immunohistochemistry was used for validation. RESULTS In each tissue microarray peptide signatures were identified that had 94.7% to 98.5% classification accuracy for each 0.05 mm(2) spot (spectrum) and 96.9% to 100% accuracy for each tissue core. Cross validation across tissue microarrays revealed a classification accuracy of 82.6% to 84.7% for each spot and 88.9% to 92.4% for each core. We identified vimentin, histone 2A.X and α-enolase as proteins with greater expression in cancer tissue. This was validated by immunohistochemistry. CONCLUSIONS Imaging mass spectrometry identified and mapped specific peptides that accurately distinguished malignant from normal renal tissue. This demonstrates its potential as a novel, high throughput approach to clear cell renal cell carcinoma biomarker discovery. Given the multiple pathways and known heterogeneity involved in tumors such as clear cell renal cell carcinoma, multiple peptide signatures that maintain their spatial relationships may outperform traditional protein biomarkers.
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Affiliation(s)
- Todd M Morgan
- Department of Urologic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.
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Velstra B, van der Burgt YEM, Mertens BJ, Mesker WE, Deelder AM, Tollenaar RAEM. Improved classification of breast cancer peptide and protein profiles by combining two serum workup procedures. J Cancer Res Clin Oncol 2012; 138:1983-92. [PMID: 22763645 PMCID: PMC3491194 DOI: 10.1007/s00432-012-1273-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2012] [Accepted: 06/15/2012] [Indexed: 12/22/2022]
Abstract
Purpose Detection of breast cancer at early stage increases patient’s survival. Mass spectrometry-based protein analysis of serum samples is a promising approach to obtain biomarker profiles for early detection. A combination of commonly applied solid-phase extraction procedures for clean-up may increase the number of detectable peptides and proteins. In this study, we have evaluated whether the classification performance of breast cancer profiles improves by using two serum workup procedures. Methods Serum samples from 105 breast cancer patients and 202 healthy volunteers were processed according to a standardized protocol implemented on a high-end liquid-handling robot. Peptide and protein enrichments were carried out using weak-cation exchange (WCX) and reversed-phase (RP) C18 magnetic beads. Profiles were acquired on a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer. In this way, two different biomarker profiles were obtained for each serum sample, yielding a WCX- and RPC18-dataset. Results The profiles were statistically evaluated with double cross-validation. Classification results of WCX- and RPC18-datasets were determined for each set separately and for the combination of both sets. Sensitivity and specificity were 82 and 87 % (WCX) and 73 and 93 % (RPC18) for the individual workup procedures. These values increased up to 84 and 95 %, respectively, upon combining the data. Conclusion It was found that MALDI-TOF peptide and protein profiles can be used for classification of breast cancer with high sensitivity and specificity. The classification performance even improved when two workup procedures were applied, since these provide a greater number of features (proteins). Electronic supplementary material The online version of this article (doi:10.1007/s00432-012-1273-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Berit Velstra
- Department of Surgery, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
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Fan NJ, Gao CF, Zhao G, Wang XL, Qiao L. Serum peptidome patterns for early screening of esophageal squamous cell carcinoma. Biotechnol Appl Biochem 2012; 59:276-82. [PMID: 23586861 DOI: 10.1002/bab.1024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2012] [Accepted: 05/16/2012] [Indexed: 12/29/2022]
Abstract
Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers in the world. Early diagnosis is critical for guiding the therapeutic management of ESCC. The present study aims to determine serum peptidome patterns for diagnosing ESCC. To identify novel peptidome patterns for diagnosing ESCC, sera from 31 healthy volunteers and 32 ESCC patients were subjected to a comparative proteomic analysis using a ClinProt™ Kit combined with mass spectrometry (MS). This approach enables the determination of peptidome patterns that can differentiate between ESCC sera and sera from healthy volunteers. For further validation, the diagnostic and differential diagnostic capabilities of the peptidome patterns were verified blindly by using an independent group of sera, consisting of sera from 31 ESCC patients, 33 healthy volunteers, 38 colorectal patients, and 36 gastric cancer patients. A Quick Classifier Algorithm was used to construct the peptidome patterns for the identification of ESCC from the control samples. Five of the identified peaks at mass to charge ratios 759, 786, 1,866, 3,316, and 6,634 were used to construct the peptidome patterns with almost 100% accuracy. Furthermore, the peptidome patterns could also differentiate the validation group with high accuracy. These results suggest that the ClinProt™ Kit combined with MS achieves significantly high accuracy for ESCC diagnosis and differential diagnosis.
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Affiliation(s)
- Nai-Jun Fan
- Institute of Anal-Colorectal Surgery, No. 150 Central Hospital of PLA, Luoyang, People's Republic of China
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Fan NJ, Gao CF, Zhao G, Wang XL, Liu QY. Serum peptidome patterns of breast cancer based on magnetic bead separation and mass spectrometry analysis. Diagn Pathol 2012; 7:45. [PMID: 22521044 PMCID: PMC3584670 DOI: 10.1186/1746-1596-7-45] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2012] [Accepted: 04/20/2012] [Indexed: 12/13/2022] Open
Abstract
Background Breast cancer is one of the most common cancers in the world, and the
identification of biomarkers for the early detection of breast cancer is a
relevant target. The present study aims to determine serum peptidome patterns for
screening of breast cancer. Methods The present work focused on the serum proteomic analysis of 36 healthy volunteers
and 37 breast cancer patients using a ClinProt Kit combined with mass spectrometry
(MS). This approach allows the determination of peptidome patterns that are able
to differentiate the studied populations. An independent group of sera (36 healthy
volunteers and 37 breast cancer patients) was used to verify the diagnostic
capabilities of the peptidome patterns blindly. An immunoassay method was used to
determine the serum mucin 1 (CA15-3) of validation group samples. Results Support Vector Machine (SVM) Algorithm was used to construct the peptidome
patterns for the identification of breast cancer from the healthy volunteers.
Three of the identified peaks at m/z 698, 720 and 1866 were used to construct the
peptidome patterns with 91.78% accuracy. Furthermore, the peptidome patterns could
differentiate the validation group achieving a sensitivity of 91.89% (34/37) and a
specitivity of 91.67% (33/36) (> CA 15–3,
P < 0.05). Conclusions These results suggest that the ClinProt Kit combined with MS shows great
potentiality for the diagnosis of breast cancer. Virtual slides The virtual slide(s) for this article can be found here:
http://www.diagnosticpathology.diagnomx.eu/vs/1501556838687844
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Affiliation(s)
- Nai-Jun Fan
- Institute of Anal-colorectal Surgery, No, 150 Central Hospital of PLA, No, 2, Huaxiaxi Road, 471000, Luoyang, China
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Mainini V, Gianazza E, Chinello C, Bilo G, Revera M, Giuliano A, Caldara G, Lombardi C, Piperno A, Magni F, Parati G. Modulation of urinary peptidome in humans exposed to high altitude hypoxia. ACTA ACUST UNITED AC 2012; 8:959-66. [DOI: 10.1039/c1mb05377a] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Analysis of synovial fluid in knee joint of osteoarthritis:5 proteome patterns of joint inflammation based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. INTERNATIONAL ORTHOPAEDICS 2011; 36:57-64. [PMID: 21509578 DOI: 10.1007/s00264-011-1258-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2010] [Accepted: 03/26/2011] [Indexed: 10/18/2022]
Abstract
PURPOSE The purpose of this study was to use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in osteoarthritis research. Our aim was to find differentially expressed disease-related and condition-specific peptide in synovial fluid in the knee joint of patients suffering from osteoarthritis (OA), and to develop and validate the peptide classification model for OA diagnosis. METHODS Based on the American College of Rheumatology criteria, 30 OA cases and ten healthy donors were enrolled and underwent analysis. Magnetic beads-based weak cation exchange chromatography (MB-WCX) was performed for sample processing, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was conducted for peptide profile. ClinProt software 2.2 was used for data analysis and a genetic algorithm was created for class prediction. RESULTS Two peptide peaks were found which may be characterised as the potential diagnostic markers for OA. Two other significantly different peptide peaks were found in OA patients at a medium stage compared to the early and late stages. A genetic algorithm (GA) was used to establish differential diagnosis models of OA. As a result, the algorithm models marked 100% of OA, and of 97.92% of medium-stage OA. CONCLUSION This study demonstrated that use of proteomics methods to identify potential biomarkers of OA is possible, and the identified potential biomarkers may be potential markers for diagnosis and monitoring the progression of OA.
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Magni F, Van Der Burgt YEM, Chinello C, Mainini V, Gianazza E, Squeo V, Deelder AM, Kienle MG. Biomarkers discovery by peptide and protein profiling in biological fluids based on functionalized magnetic beads purification and mass spectrometry. BLOOD TRANSFUSION = TRASFUSIONE DEL SANGUE 2010; 8 Suppl 3:s92-7. [PMID: 20606758 PMCID: PMC2897205 DOI: 10.2450/2010.015s] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Fulvio Magni
- Department of Experimental Medicine, University of Milano-Bicocca, Monza, Italy.
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