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Yu H, Li X, Shu J, Wu X, Wang Y, Zhang C, Wang J, Li Z. Evaluation of salivary glycopatterns based diagnostic models for prediction of diabetic vascular complications. Int J Biol Macromol 2024; 264:129763. [PMID: 38281526 DOI: 10.1016/j.ijbiomac.2024.129763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/08/2024] [Accepted: 01/24/2024] [Indexed: 01/30/2024]
Abstract
Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC. In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort. Our study provided a cost-effective and non-invasive method for auxiliary diagnosis DVC based on the combination of salivary glycopatterns and machine learning algorithms.
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Affiliation(s)
- Hanjie Yu
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China; School of Medicine, Faculty of Life Science & Medicine, Northwest University, Xi'an, China
| | - Xia Li
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Jian Shu
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Xin Wu
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Yuzi Wang
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Chen Zhang
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Junhong Wang
- Department of Endocrine, Shanghai Gongli Hospital of Pudong New Area, Shanghai, China.
| | - Zheng Li
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China.
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Koopaie M, Ghafourian M, Manifar S, Younespour S, Davoudi M, Kolahdooz S, Shirkhoda M. Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls. BMC Cancer 2022; 22:473. [PMID: 35488257 PMCID: PMC9055774 DOI: 10.1186/s12885-022-09570-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 04/19/2022] [Indexed: 01/07/2023] Open
Abstract
Background Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally, with late diagnosis, low survival rate, and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods. Methods Demographic data, clinical characteristics, and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins was evaluated. To construct diagnostic algorithms, we used the machine learning method. Results The mean salivary expression of CSTB in GC patients was significantly lower (115.55 ± 7.06, p = 0.001), and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88 ± 39.67, p = 0.002) than the control. Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R2 = 0.20, p < 0.001). Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC were 83.87 and 70.97%, respectively. The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity = 80.65% and specificity = 64.52%). The area under the ROC curve was up to 0.741. As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics, and food intake habits was 0.95. The machine learning model’s sensitivity, specificity, and accuracy were 100, 70.8, and 80.5%, respectively. Conclusion Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical, and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.
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Affiliation(s)
- Maryam Koopaie
- Department of Oral Medicine, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran
| | - Marjan Ghafourian
- Department of Oral Medicine, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheila Manifar
- Department of Oral Medicine, Imam Khomeini Hospital, Tehran University of Medical Sciences, North Kargar St, P.O.Box:14395-433, Tehran, 14399-55991, Iran.
| | - Shima Younespour
- Dentistry Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mansour Davoudi
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Sajad Kolahdooz
- Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Mohammad Shirkhoda
- Department of General Oncology, Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, Iran
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Dunphy K, O’Mahoney K, Dowling P, O’Gorman P, Bazou D. Clinical Proteomics of Biofluids in Haematological Malignancies. Int J Mol Sci 2021; 22:ijms22158021. [PMID: 34360786 PMCID: PMC8348619 DOI: 10.3390/ijms22158021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 12/25/2022] Open
Abstract
Since the emergence of high-throughput proteomic techniques and advances in clinical technologies, there has been a steady rise in the number of cancer-associated diagnostic, prognostic, and predictive biomarkers being identified and translated into clinical use. The characterisation of biofluids has become a core objective for many proteomic researchers in order to detect disease-associated protein biomarkers in a minimally invasive manner. The proteomes of biofluids, including serum, saliva, cerebrospinal fluid, and urine, are highly dynamic with protein abundance fluctuating depending on the physiological and/or pathophysiological context. Improvements in mass-spectrometric technologies have facilitated the in-depth characterisation of biofluid proteomes which are now considered hosts of a wide array of clinically relevant biomarkers. Promising efforts are being made in the field of biomarker diagnostics for haematologic malignancies. Several serum and urine-based biomarkers such as free light chains, β-microglobulin, and lactate dehydrogenase are quantified as part of the clinical assessment of haematological malignancies. However, novel, minimally invasive proteomic markers are required to aid diagnosis and prognosis and to monitor therapeutic response and minimal residual disease. This review focuses on biofluids as a promising source of proteomic biomarkers in haematologic malignancies and a key component of future diagnostic, prognostic, and disease-monitoring applications.
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Affiliation(s)
- Katie Dunphy
- Department of Biology, National University of Ireland, W23 F2K8 Maynooth, Ireland; (K.D.); (P.D.)
| | - Kelly O’Mahoney
- Department of Haematology, Mater Misericordiae University Hospital, D07 WKW8 Dublin, Ireland; (K.O.); (P.O.)
| | - Paul Dowling
- Department of Biology, National University of Ireland, W23 F2K8 Maynooth, Ireland; (K.D.); (P.D.)
| | - Peter O’Gorman
- Department of Haematology, Mater Misericordiae University Hospital, D07 WKW8 Dublin, Ireland; (K.O.); (P.O.)
| | - Despina Bazou
- Department of Haematology, Mater Misericordiae University Hospital, D07 WKW8 Dublin, Ireland; (K.O.); (P.O.)
- Correspondence:
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Bel’skaya LV, Sarf EA, Kosenok VK. Analysis of Saliva Lipids in Breast and Prostate Cancer by IR Spectroscopy. Diagnostics (Basel) 2021; 11:1325. [PMID: 34441260 PMCID: PMC8394871 DOI: 10.3390/diagnostics11081325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 07/20/2021] [Accepted: 07/22/2021] [Indexed: 12/26/2022] Open
Abstract
We have developed a method for studying the lipid profile of saliva, combining preliminary extraction and IR spectroscopic detection. The case-control study involved patients with a histologically verified diagnosis of breast and prostate cancer and healthy volunteers. The comparison group included patients with non-malignant pathologies of the breast (fibroadenomas) and prostate gland (prostatic intraepithelial neoplasia). Saliva was used as a material for biochemical studies. It has been shown that the lipid profile of saliva depends on gender, and for males it also depends on the age group. In cancer pathologies, the lipid profile changes significantly and also depends on gender and age characteristics. The ratio of 1458/1396 cm-1 for both breast and prostate cancer has a potential diagnostic value. In both cases, this ratio decreases compared to healthy controls. For prostate cancer, the ratio of 2923/2957 cm-1 is also potentially informative, which grows against the background of prostate pathologies. It is noted that, in all cases, changes in the proposed ratios are more pronounced in the early stages of diseases, which increases the relevance of their study in biomedical applications.
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Affiliation(s)
- Lyudmila V. Bel’skaya
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Elena A. Sarf
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Victor K. Kosenok
- Department of Oncology, Omsk State Medical University, 644099 Omsk, Russia;
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Tierney C, Bazou D, Majumder MM, Anttila P, Silvennoinen R, Heckman CA, Dowling P, O'Gorman P. Next generation proteomics with drug sensitivity screening identifies sub-clones informing therapeutic and drug development strategies for multiple myeloma patients. Sci Rep 2021; 11:12866. [PMID: 34145309 PMCID: PMC8213739 DOI: 10.1038/s41598-021-90149-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 04/07/2021] [Indexed: 12/14/2022] Open
Abstract
With the introduction of novel therapeutic agents, survival in Multiple Myeloma (MM) has increased in recent years. However, drug-resistant clones inevitably arise and lead to disease progression and death. The current International Myeloma Working Group response criteria are broad and make it difficult to clearly designate resistant and responsive patients thereby hampering proteo-genomic analysis for informative biomarkers for sensitivity. In this proof-of-concept study we addressed these challenges by combining an ex-vivo drug sensitivity testing platform with state-of-the-art proteomics analysis. 35 CD138-purified MM samples were taken from patients with newly diagnosed or relapsed MM and exposed to therapeutic agents from five therapeutic drug classes including Bortezomib, Quizinostat, Lenalidomide, Navitoclax and PF-04691502. Comparative proteomic analysis using liquid chromatography-mass spectrometry objectively determined the most and least sensitive patient groups. Using this approach several proteins of biological significance were identified in each drug class. In three of the five classes focal adhesion-related proteins predicted low sensitivity, suggesting that targeting this pathway could modulate cell adhesion mediated drug resistance. Using Receiver Operating Characteristic curve analysis, strong predictive power for the specificity and sensitivity of these potential biomarkers was identified. This approach has the potential to yield predictive theranostic protein panels that can inform therapeutic decision making.
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Affiliation(s)
- Ciara Tierney
- Department of Biology, National University of Ireland, Maynooth, Ireland
| | - Despina Bazou
- Department of Hematology, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Muntasir M Majumder
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Pekka Anttila
- Department of Hematology, Helsinki University Hospital and Comprehensive Cancer Center, Helsinki, Finland
| | - Raija Silvennoinen
- Department of Hematology, Helsinki University Hospital and Comprehensive Cancer Center, Helsinki, Finland
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Paul Dowling
- Department of Biology, National University of Ireland, Maynooth, Ireland
| | - Peter O'Gorman
- Department of Hematology, Mater Misericordiae University Hospital, Dublin, Ireland.
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Figura M, Sitkiewicz E, Świderska B, Milanowski Ł, Szlufik S, Koziorowski D, Friedman A. Proteomic Profile of Saliva in Parkinson's Disease Patients: A Proof of Concept Study. Brain Sci 2021; 11:brainsci11050661. [PMID: 34070185 PMCID: PMC8158489 DOI: 10.3390/brainsci11050661] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 05/11/2021] [Accepted: 05/13/2021] [Indexed: 12/23/2022] Open
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder. It affects many organs. Lewy bodies—a histopathological “hallmark” of PD—are detected in about 75% of PD submandibular gland samples. We hypothesize that saliva can be a source of biomarkers of PD. The aim of the study was to evaluate and compare the salivary proteome of PD patients and healthy controls (HC). Salivary samples from 39 subjects (24 PD patients, mean age 61.6 ± 8.2; 15 HC, mean age 60.9 ± 6.7) were collected. Saliva was collected using RNA-Pro-Sal kits. Label-free LC-MS/MS mass spectrometry was performed to characterize the proteome of the saliva. IPA analysis of upstream inhibitors was performed. A total of 530 proteins and peptides were identified. We observed lower concentrations of S100-A16, ARP2/3, and VPS4B in PD group when compared to HC. We conclude that the salivary proteome composition of PD patients is different than that of healthy controls. We observed a lower concentration of proteins involved in inflammatory processes, exosome formation, and adipose tissue formation. The variability of expression of proteins between the two groups needs to be considered.
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Affiliation(s)
- Monika Figura
- Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, 03-242 Warsaw, Poland; (Ł.M.); (S.S.); (D.K.); (A.F.)
- Correspondence:
| | - Ewa Sitkiewicz
- Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics Polish Academy of Sciences, 02-106 Warsaw, Poland; (E.S.); (B.Ś.)
| | - Bianka Świderska
- Mass Spectrometry Laboratory, Institute of Biochemistry and Biophysics Polish Academy of Sciences, 02-106 Warsaw, Poland; (E.S.); (B.Ś.)
| | - Łukasz Milanowski
- Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, 03-242 Warsaw, Poland; (Ł.M.); (S.S.); (D.K.); (A.F.)
| | - Stanisław Szlufik
- Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, 03-242 Warsaw, Poland; (Ł.M.); (S.S.); (D.K.); (A.F.)
| | - Dariusz Koziorowski
- Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, 03-242 Warsaw, Poland; (Ł.M.); (S.S.); (D.K.); (A.F.)
| | - Andrzej Friedman
- Department of Neurology, Faculty of Health Sciences, Medical University of Warsaw, 03-242 Warsaw, Poland; (Ł.M.); (S.S.); (D.K.); (A.F.)
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