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Li N, Desiderio DM, Zhan X. The use of mass spectrometry in a proteome-centered multiomics study of human pituitary adenomas. MASS SPECTROMETRY REVIEWS 2022; 41:964-1013. [PMID: 34109661 DOI: 10.1002/mas.21710] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
A pituitary adenoma (PA) is a common intracranial neoplasm, and is a complex, chronic, and whole-body disease with multicausing factors, multiprocesses, and multiconsequences. It is very difficult to clarify molecular mechanism and treat PAs from the single-factor strategy model. The rapid development of multiomics and systems biology changed the paradigms from a traditional single-factor strategy to a multiparameter systematic strategy for effective management of PAs. A series of molecular alterations at the genome, transcriptome, proteome, peptidome, metabolome, and radiome levels are involved in pituitary tumorigenesis, and mutually associate into a complex molecular network system. Also, the center of multiomics is moving from structural genomics to phenomics, including proteomics and metabolomics in the medical sciences. Mass spectrometry (MS) has been extensively used in phenomics studies of human PAs to clarify molecular mechanisms, and to discover biomarkers and therapeutic targets/drugs. MS-based proteomics and proteoform studies play central roles in the multiomics strategy of PAs. This article reviews the status of multiomics, multiomics-based molecular pathway networks, molecular pathway network-based pattern biomarkers and therapeutic targets/drugs, and future perspectives for personalized, predeictive, and preventive (3P) medicine in PAs.
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Affiliation(s)
- Na Li
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
| | - Dominic M Desiderio
- The Charles B. Stout Neuroscience Mass Spectrometry Laboratory, Department of Neurology, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Xianquan Zhan
- Shandong Key Laboratory of Radiation Oncology, Cancer Hospital of Shandong First Medical University, Jinan, Shandong, China
- Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, Shandong, China
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Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, Tayefi M, Saffar S, Ferns GA, Ghayour-Mobarhan M. Potential value and impact of data mining and machine learning in clinical diagnostics. Crit Rev Clin Lab Sci 2021; 58:275-296. [PMID: 33739235 DOI: 10.1080/10408363.2020.1857681] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.
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Affiliation(s)
- Maryam Saberi-Karimian
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.,Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Khorasanchi
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamideh Ghazizadeh
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.,Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Maryam Tayefi
- Norwegian Center for e-health Research, University Hospital of North Norway, Tromsø, Norway
| | - Sara Saffar
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Division of Medical Education, Brighton and Sussex Medical School, Falmer, UK
| | - Majid Ghayour-Mobarhan
- International UNESCO Center for Health Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
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Cheng T, Wang Y, Lu M, Zhan X, Zhou T, Li B, Zhan X. Quantitative Analysis of Proteome in Non-functional Pituitary Adenomas: Clinical Relevance and Potential Benefits for the Patients. Front Endocrinol (Lausanne) 2019; 10:854. [PMID: 31920968 PMCID: PMC6915109 DOI: 10.3389/fendo.2019.00854] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 11/21/2019] [Indexed: 12/26/2022] Open
Abstract
Background: Non-functional pituitary adenoma (NFPA) is a common tumor that occurs in the pituitary gland, and generally without any symptoms at its early stage and without clinical elevation of hormones, which is commonly diagnosed when it grows up to compress its surrounding tissues and organs. Currently, the pathogenesis of NFPA has not been clarified yet. It is necessary to investigate molecular alterations in NFPA, and identify reliable biomarkers and drug therapeutic targets for effective treatments. Methods: Tandem mass tags (TMT)-based quantitative proteomics was used to identify and quantify proteins in NFPAs. GO and KEGG enrichment analyses were used to analyze the identified proteins. Differentially expressed genes (DEGs) between NFPA and control tissues were obtained from GEO datasets. These two sets of protein and gene data were analyzed to obtain overlapped molecules (genes; proteins), followed by further GO and KEGG pathway analyses of these overlapped molecules, and molecular network analysis to obtain the hub molecules with Cytoscape. Two hub molecules (SRC and AKT1) were verified with Western blotting. Results: Totally 6076 proteins in NFPA tissues were identified, and 3598 DEGs between NFPA and control tissues were identified from GEO database. Overlapping analysis of 6076 proteins and 3598 DEGs obtained 1088 overlapped molecules (DEGs; proteins). KEGG pathway analysis of 6076 proteins obtained 114 statistically significant pathways, including endocytosis, and spliceosome signaling pathways. KEGG pathway analysis of 1088 overlapped molecules obtained 52 statistically significant pathways, including focal adhesion, cGMP-PKG pathway, and platelet activation signaling pathways. These pathways play important roles in cell energy supply, adhesion, and maintenance of the tumor microenvironment. According to the association degree in Cytoscape, ten hub molecules (DEGs; proteins) were identified, including GAPDH, ALB, ACACA, SRC, ENO2, CALM1, POTEE, HSPA8, DECR1, and AKT1. Western-blotting analysis confirmed the upregulated expressions of SRC and PTMScan experiment confirmed the increased levels of pAKT1, in NFPAs compared to controls. Conclusions: This study established the large-scale quantitative protein profiling of NFPA tissue proteome. It offers a basis for subsequent in-depth proteomics analysis of NFPAs, and insight into the molecular mechanism of NFPAs. It also provided the basic data to discover reliable biomarkers and therapeutic targets for NFPA patients.
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Affiliation(s)
- Tingting Cheng
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
| | - Ya Wang
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
| | - Miaolong Lu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaohan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
| | - Tian Zhou
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
| | - Biao Li
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
| | - Xianquan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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Wang Y, Cheng T, Lu M, Mu Y, Li B, Li X, Zhan X. TMT-based quantitative proteomics revealed follicle-stimulating hormone (FSH)-related molecular characterizations for potentially prognostic assessment and personalized treatment of FSH-positive non-functional pituitary adenomas. EPMA J 2019; 10:395-414. [PMID: 31832114 PMCID: PMC6882982 DOI: 10.1007/s13167-019-00187-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 08/14/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Non-functional pituitary adenoma (NFPA) is highly heterogeneous with different hormone expression subtypes. Of them, follicle-stimulating hormone (FSH)-positive expression is an important subtype of NFPAs. It is well-known that FSH exerted its functions through binding its receptor. However, the expression rate of FSH receptor was significantly higher in aggressive pituitary adenomas. This study aimed to investigate the molecular characteristics of FSH-positive NFPAs for effective stratification of patient, target treatment, prognostic assessment, and personalized treatment of FSH-positive NFPAs. METHODS Tandem mass tag (TMT)-based quantitative proteomics was used to investigate differentially expressed proteins (DEPs) between FSH-positive and negative NFPAs. Gene ontology and KEGG pathway enrichment analyses were used to analyze the DEPs. Differentially expressed genes (DEGs) between invasive and non-invasive NFPAs from GEO database were analyzed with pathway enrichment analysis. Protein-protein interaction (PPI) networks were constructed based on DEPs in excetral cellular matrix (ECM)-receptor interaction, focal adhesion, and PI3K-Akt pathways. Cytoscape was used to obtain most significant modules. Western blot was used to validate the expressions of upregulated proteins (ITGA1, ITGA6, and ITGB4), the expression and phosphorylated status of Akt in PI3K-Akt pathway, and the expression of FSH receptors in FSH-positive relative to negative NFPAs. RESULTS A total of 594 DEPs (374 upregulated and 220 downregulated) were identified between FSH-positive and negative NFPAs. Nineteen KEGG pathway networks were identified to involve DEPs, and reveal molecular differences between FSH-positive and negative NFPAs, including three important pathways that were significantly associated with tumor invasiveness and aggressiveness: ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways. Further, focal adhesion pathway was also confirmed with invasiveness-related NFPA DEG data that were derived from GEO database. Moreover, the significantly upregulated DEPs (ITGA1, ITGA6, and ITGB4) that were associated with tumor invasiveness and aggressiveness were confirmed by immunoaffinity analysis in FSH-positive vs. negative NFPAs. Also, the phosphorylation level but not its expression level of AKT in PI3K-AKT signaling was significantly increased, and the expression level of FSH receptor was significantly increased in FSH-positive relative to negative NFPAs. Also, overlapping analysis of 594 DEPs and 898 DEGs revealed 45 invasiveness-related DEPs, including 11 upregulated DEPs (ITGA6, FARP1, PALLD, PPBP, LIMA1, SCD, UACA, BAG3, CLU, PLEC, and GATM) that were also upregulated genes in invasive NFPAs, and 8 downregulated DEPs (ALCAM, HP, FSTL4, IL13RA2, NPTX2, DPP6, CRABP2, and SLC27A2) that were also downregulated genes in invasive NFPAs. CONCLUSIONS FSH-positive expression was an important NFPA subtype. It was the first time for this study to reveal FSH-related proteomic variations and the corresponding molecular network alterations in FSH-positive relative to negative NFPAs. Also, three signaling pathways (ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways) and involved upregulated proteins (ITGA1, ITGA6, ITGB4, pAKT, and FSHR) were significantly associated with tumor invasiveness and aggressiveness, and a set of invasiveness-related DEPs were identified with overlapping analysis of 594 DEPs in FSH-positive vs. negative NFPAs and 898 DEGs in invasive vs. non-invasive NFPAs. These findings offered the scientific evidence to in-depth understand molecular characteristics of FSH-positive NFPAs, and effectively stratify the post-surgery patients for personalized prognostic assessment and targeted treatment of FSH-positive NFPAs.
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Affiliation(s)
- Ya Wang
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Tingting Cheng
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Miaolong Lu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Yun Mu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Biao Li
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Xianquan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 88 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
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Qiao N. A systematic review on machine learning in sellar region diseases: quality and reporting items. Endocr Connect 2019; 8:952-960. [PMID: 31234143 PMCID: PMC6612064 DOI: 10.1530/ec-19-0156] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 06/11/2019] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies. METHODS PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance. RESULTS Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing's disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results. CONCLUSIONS Machine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.
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Affiliation(s)
- Nidan Qiao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Neuroendocrine Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Correspondence should be addressed to N Qiao:
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van Ee TJ, Van Acker HH, van Oorschot TG, Van Tendeloo VF, Smits EL, Bakdash G, Schreibelt G, de Vries IJM. BDCA1+CD14+ Immunosuppressive Cells in Cancer, a Potential Target? Vaccines (Basel) 2018; 6:E65. [PMID: 30235890 PMCID: PMC6161086 DOI: 10.3390/vaccines6030065] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 09/17/2018] [Accepted: 09/18/2018] [Indexed: 12/22/2022] Open
Abstract
Dendritic cell (DC) vaccines show promising effects in cancer immunotherapy. However, their efficacy is affected by a number of factors, including (1) the quality of the DC vaccine and (2) tumor immune evasion. The recently characterized BDCA1+CD14+ immunosuppressive cells combine both aspects; their presence in DC vaccines may directly hamper vaccine efficacy, whereas, in patients, BDCA1+CD14+ cells may suppress the induced immune response in an antigen-specific manner systemically and at the tumor site. We hypothesize that BDCA1+CD14+ cells are present in a broad spectrum of cancers and demand further investigation to reveal treatment opportunities and/or improvement for DC vaccines. In this review, we summarize the findings on BDCA1+CD14+ cells in solid cancers. In addition, we evaluate the presence of BDCA1+CD14+ cells in leukemic cancers. Preliminary results suggest that the presence of BDCA1+CD14+ cells correlates with clinical features of acute and chronic myeloid leukemia. Future research focusing on the differentiation from monocytes towards BDCA1+CD14+ cells could reveal more about their cell biology and clinical significance. Targeting these cells in cancer patients may improve the outcome of cancer immunotherapy.
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Affiliation(s)
- Thomas J van Ee
- Department of Tumor Immunology, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen 6525 GA, The Netherlands.
| | - Heleen H Van Acker
- Laboratory of Experimental Hematology, University of Antwerp, Antwerp 2000, Belgium.
| | - Tom G van Oorschot
- Department of Tumor Immunology, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen 6525 GA, The Netherlands.
| | - Viggo F Van Tendeloo
- Laboratory of Experimental Hematology, University of Antwerp, Antwerp 2000, Belgium.
| | - Evelien L Smits
- Laboratory of Experimental Hematology, University of Antwerp, Antwerp 2000, Belgium.
- Center for Oncological Research, University of Antwerp, Antwerp 2000, Belgium.
| | - Ghaith Bakdash
- Allergic Inflammation Discovery Performance Unit, Respiratory Therapy Area, GlaxoSmithKline, Stevenage SG1 2NY, UK.
| | - Gerty Schreibelt
- Department of Tumor Immunology, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen 6525 GA, The Netherlands.
| | - I Jolanda M de Vries
- Department of Tumor Immunology, Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen 6525 GA, The Netherlands.
- Department of Medical Oncology; Radboud Institute for Molecular Life Sciences, Radboudumc, Nijmegen 6525 GA, The Netherlands.
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Zhan X, Wang X, Cheng T. Human Pituitary Adenoma Proteomics: New Progresses and Perspectives. Front Endocrinol (Lausanne) 2016; 7:54. [PMID: 27303365 PMCID: PMC4885873 DOI: 10.3389/fendo.2016.00054] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 05/17/2016] [Indexed: 11/13/2022] Open
Abstract
Pituitary adenoma (PA) is a common intracranial neoplasm that impacts on human health through interfering hypothalamus-pituitary-target organ axis systems. The development of proteomics gives great promises in the clarification of molecular mechanisms of a PA and discovery of effective biomarkers for prediction, prevention, early-stage diagnosis, and treatment for a PA. A great progress in the field of PA proteomics has been made in the past 10 years, including (i) the use of laser-capture microdissection, (ii) proteomics analyses of functional PAs (such as prolactinoma), invasive and non-invasive non-functional pituitary adenomas (NFPAs), protein post-translational modifications such as phosphorylation and tyrosine nitration, NFPA heterogeneity, and hormone isoforms, (iii) the use of protein antibody array, (iv) serum proteomics and peptidomics, (v) the integration of proteomics and other omics data, and (vi) the proposal of multi-parameter systematic strategy for a PA. This review will summarize these progresses of proteomics in PAs, point out the existing drawbacks, propose the future research directions, and address the clinical relevance of PA proteomics data, in order to achieve our long-term goal that is use of proteomics to clarify molecular mechanisms, construct molecular networks, and discover effective biomarkers.
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Affiliation(s)
- Xianquan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
- The State Key Laboratory of Medical Genetics, Central South University, Changsha, China
- *Correspondence: Xianquan Zhan,
| | - Xiaowei Wang
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
| | - Tingting Cheng
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, Changsha, China
- State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, Changsha, China
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Wang X, Guo T, Peng F, Long Y, Mu Y, Yang H, Ye N, Li X, Zhan X. Proteomic and functional profiles of a follicle-stimulating hormone positive human nonfunctional pituitary adenoma. Electrophoresis 2015; 36:1289-304. [PMID: 25809007 DOI: 10.1002/elps.201500006] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 02/26/2015] [Accepted: 02/26/2015] [Indexed: 12/23/2022]
Affiliation(s)
- Xiaowei Wang
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Hunan Engineering Laboratory for Structural Biology and Drug Design; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Local Joint Engineering Laboratory for Anticancer Drugs; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
| | - Tianyao Guo
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Hunan Engineering Laboratory for Structural Biology and Drug Design; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Local Joint Engineering Laboratory for Anticancer Drugs; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
| | - Fang Peng
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Hunan Engineering Laboratory for Structural Biology and Drug Design; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Local Joint Engineering Laboratory for Anticancer Drugs; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
| | - Ying Long
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Hunan Engineering Laboratory for Structural Biology and Drug Design; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Local Joint Engineering Laboratory for Anticancer Drugs; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
| | - Yun Mu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Hunan Engineering Laboratory for Structural Biology and Drug Design; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Local Joint Engineering Laboratory for Anticancer Drugs; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
| | - Haiyan Yang
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Hunan Engineering Laboratory for Structural Biology and Drug Design; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Local Joint Engineering Laboratory for Anticancer Drugs; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Department of Lung Cancer and Gastroenterology; Hunan Cancer Hospital; Changsha Hunan P. R. China
| | - Ningrong Ye
- Department of Neurosurgery; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
| | - Xuejun Li
- Department of Neurosurgery; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
| | - Xianquan Zhan
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- Hunan Engineering Laboratory for Structural Biology and Drug Design; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Local Joint Engineering Laboratory for Anticancer Drugs; Xiangya Hospital; Central South University; Changsha Hunan P. R. China
- State Key Laboratory of Medical Genetics; Central South University; Changsha Hunan P. R. China
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Zhan X, Wang X, Long Y, Desiderio DM. Heterogeneity analysis of the proteomes in clinically nonfunctional pituitary adenomas. BMC Med Genomics 2014; 7:69. [PMID: 25539738 PMCID: PMC4302698 DOI: 10.1186/s12920-014-0069-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2014] [Accepted: 12/11/2014] [Indexed: 12/28/2022] Open
Abstract
Background Clinically nonfunctional pituitary adenomas (NFPAs) without any clinical elevation of hormone and with a difficulty in its early-stage diagnosis are highly heterogeneous with different hormone expressions in NFPA tissues, including luteinizing hormone (LH)-positive, follicle-stimulating hormone (FSH)-positive, LH/FSH-positive, and negative (NF). Elucidation of molecular mechanisms and discovery of biomarkers common and specific to those different subtypes of NFPAs will benefit NFPA patients in early-stage diagnosis and individualized treatment. Methods Two-dimensional gel electrophoresis (2DGE) and PDQuest image analyses were used to compare proteomes of different NFPA subtypes (NF-, LH-, FSH-, and LH/FSH-positive) relative to control pituitaries (Con). Differentially expressed proteins (DEPs) were characterized with mass spectrometry (MS). Each set of DEPs in four NFPA subtypes was evaluated with overlap analysis and signaling pathway network analysis with comparison to determine any DEP and pathway network that are common and specific to each NFPA subtype. Results A total of 93 differential protein-spots were determined with comparison of each NFPA type (NF-, LH-, FSH-, and LH/FSH-positive) versus control pituitaries. A total of 76 protein-spots were MS-identified (59 DEPs in NF vs. Con; 65 DEPs in LH vs. Con; 63 DEPs in FSH vs. Con; and 55 DEPs in LH/FSH vs. Con). A set of DEPs and pathway network data were common and specific to each NFPA subtype. Four important common pathway systems included MAPK-signaling abnormality, oxidative stress, mitochondrial dysfunction, and cell-cycle dysregulation. However, these pathway systems were, in fact, different among four NFPA subtypes with different protein-expression levels of most of nodes, different protein profiles, and different pathway network profiles. Conclusions These result data demonstrate that common and specific DEPs and pathway networks exist in four NFPA subtypes, and clarify proteome heterogeneity of four NFPA subtypes. Those findings will help to elucidate molecular mechanisms of NFPAs, and discover protein biomarkers to effectively manage NFPA patients towards personalized medicine. Electronic supplementary material The online version of this article (doi:10.1186/s12920-014-0069-6) contains supplementary material, which is available to authorized users.
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Di Ieva A, Butz H, Niamah M, Rotondo F, De Rosa S, Sav A, Yousef GM, Kovacs K, Cusimano MD. MicroRNAs as biomarkers in pituitary tumors. Neurosurgery 2014; 75:181-9; discussion 188-9. [PMID: 24739366 DOI: 10.1227/neu.0000000000000369] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The use of extracellular microRNAs (miRNAs) as circulating biomarkers is currently leading to relevant advances in the diagnosis and assessment of prognosis of several diseases. Specific miRNAs have also been shown to play a role in the pathophysiology of many neoplastic and non-neoplastic diseases. A number of studies have demonstrated that miRNAs show differential expression in various tumors, such as in the prostate, ovary, lung, breast, brain, and pituitary. Recent findings have built connections between miRNAs that are deregulated within the tumor and their presence in peripheral blood. MiRNAs have been shown to be stable in the blood where they are present in either free and/or uncomplexed form, as well as packed in microvesicles, exosomes, and apoptotic bodies, or bound to different proteins. Because the pituitary is a highly vascularized organ that releases hormones into the circulation, miRNAs would be useful biomarkers for the diagnosis of pituitary tumors, as well as for predicting or detecting recurrence after surgery. Here we review the biological significance of miRNAs in pituitary tumors and the potential value of circulating miRNAs as biomarkers.
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Affiliation(s)
- Antonio Di Ieva
- ‡Division of Neurosurgery, Department of Surgery, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada; §Department of Laboratory Medicine, Division of Pathology, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada; ¶Division of Cardiology, Magna Graecia University, Catanzaro, Italy; ‖Department of Pathology, Acıbadem University, School of Medicine, Maltepe, Istanbul, Turkey
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Cámara Gómez R. Tumores hipofisarios no funcionantes: actualización 2012. ACTA ACUST UNITED AC 2014; 61:160-70. [DOI: 10.1016/j.endonu.2013.04.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2012] [Revised: 03/27/2013] [Accepted: 04/02/2013] [Indexed: 01/10/2023]
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