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Kim K, Ku CR, Lee EJ. Multiomics Approach to Acromegaly: Unveiling Translational Insights for Precision Medicine. Endocrinol Metab (Seoul) 2023; 38:463-471. [PMID: 37828709 PMCID: PMC10613768 DOI: 10.3803/enm.2023.1820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 09/24/2023] [Accepted: 10/04/2023] [Indexed: 10/14/2023] Open
Abstract
The clinical characteristics and prognoses of acromegaly vary among patients. Assessment of current and novel predictors can lead to multilevel categorization of patients, allowing integration into new clinical guidelines and a reduction in the increased morbidity and mortality associated with acromegaly. Despite advances in the diagnosis and treatment of acromegaly, its pathophysiology remains unclear. Recent advancements in multiomics technologies, including genomics, transcriptomics, proteomics, metabolomics, and radiomics, have offered new opportunities to unravel the complex pathophysiology of acromegaly. This review comprehensively explores the emerging role of multiomics approaches in elucidating the molecular landscape of acromegaly. We discuss the potential implications of multiomics data integration in the development of novel diagnostic tools, identification of therapeutic targets, and the prospects of precision medicine in acromegaly management. By integrating diverse omics datasets, these approaches can provide valuable insights into disease mechanisms, facilitate the identification of diagnostic biomarkers, and identify potential therapeutic targets for precision medicine in the management of acromegaly.
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Affiliation(s)
- Kyungwon Kim
- Endocrinology, Institute of Endocrine Research, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Cheol Ryong Ku
- Endocrinology, Institute of Endocrine Research, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Eun Jig Lee
- Endocrinology, Institute of Endocrine Research, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
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Pînzariu O, Georgescu CE. Metabolomics in acromegaly: a systematic review. J Investig Med 2023:10815589231169452. [PMID: 37139720 DOI: 10.1177/10815589231169452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
The therapeutic response heterogeneity in acromegaly persists, despite the medical-surgical advances of recent years. Thus, personalized medicine implementation, which focuses on each patient, is justified. Metabolomics would decipher the molecular mechanisms underlying the therapeutic response heterogeneity. Identification of altered metabolic pathways would open new horizons in the therapeutic management of acromegaly. This research aimed to evaluate the metabolomic profile in acromegaly and metabolomics' contributions to understanding disease pathogenesis. A systematic review was carried out by querying four electronic databases and evaluating patients with acromegaly through metabolomic techniques. In all, 21 studies containing 362 patients were eligible. Choline, the ubiquitous metabolite identified in growth hormone (GH)-secreting pituitary adenomas (Pas) by in vivo magnetic resonance spectroscopy (MRS), negatively correlated with somatostatin receptors type 2 expression and positively correlated with magnetic resonance imaging T2 signal and Ki-67 index. Moreover, elevated choline and choline/creatine ratio differentiated between sparsely and densely granulated GH-secreting PAs. MRS detected low hepatic lipid content in active acromegaly, which increased after disease control. The panel of metabolites of acromegaly deciphered by mass spectrometry (MS)-based techniques mainly included amino acids (especially branched-chain amino acids and taurine), glyceric acid, and lipids. The most altered pathways in acromegaly were the metabolism of glucose (particularly the downregulation of the pentose phosphate pathway), linoleic acid, sphingolipids, glycerophospholipids, arginine/proline, and taurine/hypotaurine. Matrix-assisted laser desorption/ionization coupled with MS imaging confirmed the functional nature of GH-secreting PAs and accurately discriminated PAs from healthy pituitary tissue.
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Affiliation(s)
- Oana Pînzariu
- Department of Endocrinology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Carmen Emanuela Georgescu
- Department of Endocrinology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- Endocrinology Clinic, Cluj County Emergency Clinical Hospital, Cluj-Napoca, Romania
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Batchu S, Diaz MJ, Lin K, Arya N, Patel K, Lucke-Wold B. Single Cell Metabolic Landscape of Pituitary Neuroendocrine Tumor Subgroups and Lineages. OBM NEUROBIOLOGY 2023; 7:10. [PMID: 37007673 PMCID: PMC10062196 DOI: 10.21926/obm.neurobiol.2301157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Pituitary neuroendocrine tumors (PitNETs) are common intracranial tumors comprising numerous subtypes whose metabolic profiles have yet to be fully examined. The present in silico study analyzed single-cell expression profiles from 2311 PitNET cells from various lineages and subtypes to elucidate differences in metabolic activities. Gonadotroph tumors exhibited high activities with histidine metabolism, whose activity is low in lactotroph tumors. Somatotroph tumors enriched for sulfur and tyrosine metabolism, while lactotroph tumors were enriched metabolism of nitrogen, ascorbate, and aldarate. PIT-1 lineage tumors exhibited high sulfur and thiamine metabolism. These results set precedence for further translational studies for subgroup/lineage specific targeted therapies.
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Affiliation(s)
| | | | - Keldon Lin
- Mayo Clinic Alix School of Medicine, Scottsdale, AZ, United States
| | - Namrata Arya
- Mayo Clinic Alix School of Medicine, Scottsdale, AZ, United States
| | | | - Brandon Lucke-Wold
- Department of Neurosurgery, University of Florida, Gainesville, United States
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Jiang X, Xiao J, Zhang Q, Wang L, Jiang J, Lan K. Improved U-Net based on cross-layer connection for pituitary adenoma MRI image segmentation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:34-51. [PMID: 36650756 DOI: 10.3934/mbe.2023003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Pituitary adenoma is a common neuroendocrine neoplasm, and most of its MR images are characterized by blurred edges, high noise and similar to surrounding normal tissues. Therefore, it is extremely difficult to accurately locate and outline the lesion of pituitary adenoma. To sovle these limitations, we design a novel deep learning framework for pituitary adenoma MRI image segmentation. Under the framework of U-Net, a newly cross-layer connection is introduced to capture richer multi-scale features and contextual information. At the same time, full-scale skip structure can reasonably utilize the above information obtained by different layers. In addition, an improved inception-dense block is designed to replace the classical convolution layer, which can enlarge the effectiveness of the receiving field and increase the depth of our network. Finally, a novel loss function based on binary cross-entropy and Jaccard losses is utilized to eliminate the problem of small samples and unbalanced data. The sample data were collected from 30 patients in Quzhou People's Hospital, with a total of 500 lesion images. Experimental results show that although the amount of patient sample is small, the proposed method has better performance in pituitary adenoma image compared with existing algorithms, and its Dice, Intersection over Union (IoU), Matthews correlation coefficient (Mcc) and precision reach 88.87, 80.67, 88.91 and 97.63%, respectively.
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Affiliation(s)
- Xiaoliang Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Junjian Xiao
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Qile Zhang
- Department of Rehabilitation, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China
| | - Lihui Wang
- Department of Science and Education, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China
| | - Jinyun Jiang
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
| | - Kun Lan
- College of Mechanical Engineering, Quzhou University, Quzhou 324000, China
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Abooshahab R, Ardalani H, Zarkesh M, Hooshmand K, Bakhshi A, Dass CR, Hedayati M. Metabolomics-A Tool to Find Metabolism of Endocrine Cancer. Metabolites 2022; 12:1154. [PMID: 36422294 PMCID: PMC9698703 DOI: 10.3390/metabo12111154] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 05/18/2024] Open
Abstract
Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.
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Affiliation(s)
- Raziyeh Abooshahab
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 19395-4763, Iran
- Curtin Medical School, Curtin University, Bentley 6102, Australia
| | - Hamidreza Ardalani
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maryam Zarkesh
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 19395-4763, Iran
| | - Koroush Hooshmand
- System Medicine, Steno Diabetes Center Copenhagen, 2730 Herlev, Denmark
| | - Ali Bakhshi
- Department of Clinical Biochemistry, School of Medicine, Shahid Sadoughi University of Medical Sciences and Health Services, Yazd P.O. Box 8915173160, Iran
| | - Crispin R. Dass
- Curtin Medical School, Curtin University, Bentley 6102, Australia
- Curtin Health Innovation Research Institute, Curtin University, Bentley 6102, Australia
| | - Mehdi Hedayati
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 19395-4763, Iran
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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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Wen S, Li C, Zhan X. Muti-omics integration analysis revealed molecular network alterations in human nonfunctional pituitary neuroendocrine tumors in the framework of 3P medicine. EPMA J 2022; 13:9-37. [PMID: 35273657 PMCID: PMC8897533 DOI: 10.1007/s13167-022-00274-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 02/09/2022] [Indexed: 12/12/2022]
Abstract
Nonfuctional pituitary neuroendocrine tumor (NF-PitNET) is highly heterogeneous and generally considered a common intracranial tumor. A series of molecules are involved in NF-PitNET pathogenesis that alter in multiple levels of genome, transcriptome, proteome, and metabolome, and those molecules mutually interact to form dynamically associated molecular-network systems. This article reviewed signaling pathway alterations in NF-PitNET based on the analyses of the genome, transcriptome, proteome, and metabolome, and emphasized signaling pathway network alterations based on the integrative omics, including calcium signaling pathway, cGMP-PKG signaling pathway, mTOR signaling pathway, PI3K/AKT signaling pathway, MAPK (mitogen-activated protein kinase) signaling pathway, oxidative stress response, mitochondrial dysfunction, and cell cycle dysregulation, and those signaling pathway networks are important for NF-PitNET formation and progression. Especially, this review article emphasized the altered signaling pathways and their key molecules related to NF-PitNET invasiveness and aggressiveness that are challenging clinical problems. Furthermore, the currently used medication and potential therapeutic agents that target these important signaling pathway networks are also summarized. These signaling pathway network changes offer important resources for insights into molecular mechanisms, discovery of effective biomarkers, and therapeutic targets for patient stratification, predictive diagnosis, prognostic assessment, and targeted therapy of NF-PitNET.
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Affiliation(s)
- Siqi Wen
- Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China ,Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, 6699 Qingdao Road, Jinan, Shandong 250117 People’s Republic of China ,Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Chunling Li
- Department of Anesthesiology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Xianquan Zhan
- Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China ,Medical Science and Technology Innovation Center, Shandong First Medical University, Jinan, 6699 Qingdao Road, Jinan, Shandong 250117 People’s Republic of China ,Gastroenterology Research Institute and Clinical Center, Shandong First Medical University, 38 Wuying Shan Road, Jinan, Shandong 250031 People’s Republic of China
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Lin K, Zhang J, Lin Y, Pei Z, Wang S. Metabolic Characteristics and M2 Macrophage Infiltrates in Invasive Nonfunctioning Pituitary Adenomas. Front Endocrinol (Lausanne) 2022; 13:901884. [PMID: 35898456 PMCID: PMC9309300 DOI: 10.3389/fendo.2022.901884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/09/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE The aim of this study was to investigate the metabolic differences between invasive and non-invasive nonfunctioning pituitary adenomas (NFPAs), determine the expression of an M2 macrophage marker in NFPAs, and analyze the effects of metabolic changes in invasive NFPAs on M2 macrophage infiltrates. METHODS Tissue samples of NFPAs from patients who underwent transsphenoidal or craniotomy surgery from January 2021 to August 2021 were collected. NFPA tissues were analyzed based on a gas chromatography-mass spectrometry non-targeted metabolomics platform, and immunohistochemical staining for M2 macrophage marker CD206 was performed. RESULTS We evaluated 15 invasive and 21 non-invasive NFPAs. A total of 22 metabolites were identified through non-targeted metabolomics analysis. Among them, the expression of 1-octadecanol, inosine 5'-monophosphate, adenosine 5'-monophosphate, guanosine 5'-monophosphate, creatinine, desmosterol, taurine, hypotaurine, lactic acid, and succinic acid was upregulated in invasive NFPAs, while that of 1-oleoylglycerol, arachidonic acid, cis-11-eicosenoic acid, docosahexaenoic acid, glyceric acid, hypoxanthine, linoleic acid, lysine, oleic acid, uracil, valine, and xanthine was downregulated. Immunohistochemical analysis suggested that the number of CD206-positive cells was higher in invasive NFPAs than in non-invasive NFPAs. CONCLUSION Invasive and non-invasive NFPAs showed distinct metabolite profiles. The levels of succinic acid and lactic acid were higher in invasive NFPAs, and the high expression of the M2 macrophage marker was verified in invasive NFPAs.
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Affiliation(s)
- Kunzhe Lin
- Department of Neurosurgery, Affiliated Fuzhou First Hospital of Fujian Medical University, Fuzhou, China
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Jianping Zhang
- Department of Urology, 910th Hospital of Joint Logistics Support Force, Quanzhou, China
| | - Yinghong Lin
- College of Integrated Chinese and Western Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Zhijie Pei
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Shousen Wang
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Neurosurgery, 900th Hospital, Fuzhou, China
- *Correspondence: Shousen Wang,
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Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models. J Pers Med 2021; 11:jpm11060496. [PMID: 34205912 PMCID: PMC8229374 DOI: 10.3390/jpm11060496] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/25/2021] [Accepted: 05/28/2021] [Indexed: 12/18/2022] Open
Abstract
Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.
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