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  • 1
    In: Journal of Korean Medical Science, XMLink, Vol. 24, No. 2 ( 2009), p. 232-
    Type of Medium: Online Resource
    ISSN: 1011-8934
    Language: English
    Publisher: XMLink
    Publication Date: 2009
    detail.hit.zdb_id: 2056822-8
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  • 2
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
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  • 3
    In: Endocrinology and Metabolism, Korean Endocrine Society, Vol. 36, No. 5 ( 2021-10-31), p. 1131-1141
    Abstract: Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids.Methods: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors.Results: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT.Conclusion: The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.
    Type of Medium: Online Resource
    ISSN: 2093-596X , 2093-5978
    Language: English
    Publisher: Korean Endocrine Society
    Publication Date: 2021
    detail.hit.zdb_id: 2802452-7
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  • 4
    Online Resource
    Online Resource
    The Endocrine Society ; 2021
    In:  Journal of the Endocrine Society Vol. 5, No. Supplement_1 ( 2021-05-03), p. A1031-A1031
    In: Journal of the Endocrine Society, The Endocrine Society, Vol. 5, No. Supplement_1 ( 2021-05-03), p. A1031-A1031
    Abstract: Parathyroid carcinomas are rare endocrine tumors derived from the parathyroid glands with poor prognosis. Moreover, parathyroid carcinomas are resistant to radiation or drug therapy, and surgical resection is the only treatment option. Understanding the molecular pathogenesis of parathyroid carcinomas may pave the way for early diagnostic biomarkers and therapeutic targets. Therefore, we aimed to elucidate the protein signatures for parathyroid carcinomas through quantitative proteomic analyses. We performed liquid chromatography with tandem mass spectrometry (LC-MS/MS) technique with formalin-fixed paraffin-embedded (FFPE) samples and reached a quantitative depth of more than 5,000 proteins per sample. For the analyses, 23 parathyroid carcinoma and 15 adenoma samples were collected from five tertiary hospitals in Korea. Patients’ mean age was 52 years, and 24 (63%) were female. Patients with parathyroid carcinoma had higher parathyroid hormone (PTH) and serum calcium level than adenomas (PTH, 1077.6 ± 760.7, 181.8 ± 139.8 pg/mL; calcium 13.0 ± 2.8, 11.3 ± 1.0 mg/dL, respectively). From the proteomic expression profiling, there were 137 differentially expressed proteins with the cutoff of both p & lt;0.05 and fold change & gt; 1.5. Using the Ingenuity Pathway Analysis (IPA), top enriched canonical pathways in parathyroid carcinomas included glycoprotein-6 signaling related to the coagulation pathway, acute phase response, mTOR, and clathrin-/caveolar-mediated endocytosis signaling. In transcription factor analysis, TGFβ and TP53 were activated in carcinoma, and these factors were up-regulators of CD44 antigen and Annexin A2 (ANXA2) proteins. In network analysis, α-1-acid glycoprotein 1 (ORM1), laminin subunit β-2 (LAMB2), and Serpin family (SERPIN) proteins were derived as essential proteins and correlated to the AKT complex. Also, with the support vector machine (SVM)-based classification method, we derived a set of proteins that can discriminate carcinomas from adenomas, which consists of Carbonic anhydrase 4 (CA4), α/β hydrolase domain-containing protein 14B (ABHD14B), CD44, LAMB2, phosphatidylinositol transfer protein β isoform (PITPNB), and ORM1, with the lowest error rate of 11.1%. In conclusion, from the proteomics analyses of parathyroid neoplasms, newly recognized pathways - signaling related to coagulation, acute phase response, and endocytosis - were enriched in parathyroid carcinoma in addition to the known mTOR signaling pathway. The proteins such as α-1-acid glycoprotein and laminin subunit β-2 from SVM classification and network analyses could be the distinctive signature of carcinoma and may provide insights into the therapeutic target.
    Type of Medium: Online Resource
    ISSN: 2472-1972
    Language: English
    Publisher: The Endocrine Society
    Publication Date: 2021
    detail.hit.zdb_id: 2881023-5
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  • 5
    Online Resource
    Online Resource
    Elsevier BV ; 2015
    In:  Osteoporosis and Sarcopenia Vol. 1, No. 2 ( 2015-12), p. 150-
    In: Osteoporosis and Sarcopenia, Elsevier BV, Vol. 1, No. 2 ( 2015-12), p. 150-
    Type of Medium: Online Resource
    ISSN: 2405-5255
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 2874910-8
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  European Journal of Endocrinology Vol. 188, No. 4 ( 2023-04-05), p. 385-394
    In: European Journal of Endocrinology, Oxford University Press (OUP), Vol. 188, No. 4 ( 2023-04-05), p. 385-394
    Abstract: Diagnosing parathyroid carcinoma (PC) is complicated and controversial that early diagnosis and intervention are often difficult. Therefore, we aimed to elucidate the protein signatures of PC through quantitative proteomic analyses to aid in the early and accurate diagnosis of PC. Design We conducted a retrospective cohort study. Methods We performed liquid chromatography with tandem mass spectrometry using formalin-fixed paraffin-embedded samples. For the analyses, 23 PC and 15 parathyroid adenoma (PA) tissues were collected from 6 tertiary hospitals in South Korea. Results The mean age of the patients was 52 years, and 63% were women. Proteomic expression profiling revealed 304 differentially expressed proteins (DEPs) with a cut-off of P & lt; .05 and fold change & gt;1.5. Among DEPs, we identified a set of 5 proteins that can discriminate PC from PA: carbonic anhydrase 4 (CA4), alpha/beta hydrolase domain-containing protein 14B (ABHD14B), laminin subunit beta-2 (LAMB2), CD44 antigen (CD44), and alpha-1-acid glycoprotein 1 (ORM1) that exhibited the highest area under the curve of 0.991 in neural network model. The nuclear percentage of CA4 and LAMB2 in immunohistochemistry was significantly lower in PC tissue than in the PA (CA4: 2.77 ± 1.96%, 26.2 ± 3.45%, P & lt; .001; LAMB2: 6.86 ± 3.46%, 38.54 ± 4.13%, P & lt; .001). The most enriched canonical pathways in PC included glycoprotein-6 signaling and mammalian target of rapamycin (mTOR). Conclusions We identified key proteins differentially expressed between PC and PA using proteomic analyses of parathyroid neoplasms. These findings may help to diagnose PC accurately and elucidate potential therapeutic targets.
    Type of Medium: Online Resource
    ISSN: 0804-4643 , 1479-683X
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1485160-X
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