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Severcan F, Ozyurt I, Dogan A, Severcan M, Gurbanov R, Kucukcankurt F, Elibol B, Tiftikcioglu I, Gursoy E, Yangin MN, Zorlu Y. Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning. Sci Rep 2024; 14:19316. [PMID: 39164310 PMCID: PMC11336246 DOI: 10.1038/s41598-024-66501-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 07/02/2024] [Indexed: 08/22/2024] Open
Abstract
Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time-consuming, burden patients financially, and sometimes all test results can be negative. Therefore, rapid, cost-effective novel methods are essential for the early accurate diagnosis of MG. Here, we aimed to determine MG-induced spectral biomarkers from blood serum using infrared spectroscopy. Furthermore, infrared spectroscopy coupled with multivariate analysis methods e.g., principal component analysis (PCA), support vector machine (SVM), discriminant analysis and Neural Network Classifier were used for rapid MG diagnosis. The detailed spectral characterization studies revealed significant increases in lipid peroxidation; saturated lipid, protein, and DNA concentrations; protein phosphorylation; PO2-asym + sym /protein and PO2-sym/lipid ratios; as well as structural changes in protein with a significant decrease in lipid dynamics. All these spectral parameters can be used as biomarkers for MG diagnosis and also in MG therapy. Furthermore, MG was diagnosed with 100% accuracy, sensitivity and specificity values by infrared spectroscopy coupled with multivariate analysis methods. In conclusion, FTIR spectroscopy coupled with machine learning technology is advancing towards clinical translation as a rapid, low-cost, sensitive novel approach for MG diagnosis.
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Affiliation(s)
- Feride Severcan
- Department of Biophysics, Faculty of Medicine, Altinbas University, Istanbul, Türkiye.
| | - Ipek Ozyurt
- Department of Biophysics, Faculty of Medicine, Altinbas University, Istanbul, Türkiye
| | - Ayca Dogan
- Department of Physiology, Faculty of Medicine, Altinbas University, Istanbul, Türkiye
| | - Mete Severcan
- Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Türkiye
| | - Rafig Gurbanov
- Department of Bioengineering, Faculty of Engineering, Bilecik Seyh Edebali University, Bilecik, Türkiye
| | - Fulya Kucukcankurt
- Department of Medical Biology, Faculty of Medicine, Altinbas University, Istanbul, Türkiye
| | - Birsen Elibol
- Department of Medical Biology, Faculty of Medicine, Istanbul Medeniyet University, Istanbul, Türkiye
- Department of Medical Biology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Türkiye
| | - Irem Tiftikcioglu
- Cigli Training and Research Hospital, Neurology Clinic, Bakircay University, İzmir, Türkiye
| | - Esra Gursoy
- Department of Neurology, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Türkiye
- Basaksehir Cam and Sakura City Hospital, Neurology Clinics, Istanbul, Türkiye
| | - Melike Nur Yangin
- Biomedical Sciences Graduate Program, Institute of Graduate Studies, Altinbas University, Istanbul, Türkiye
| | - Yasar Zorlu
- Tepecik Educational and Training Hospital, Neurology Department, University of Health Sciences, Izmir, Türkiye
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Eissa T, Leonardo C, Kepesidis KV, Fleischmann F, Linkohr B, Meyer D, Zoka V, Huber M, Voronina L, Richter L, Peters A, Žigman M. Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening. Cell Rep Med 2024; 5:101625. [PMID: 38944038 PMCID: PMC11293328 DOI: 10.1016/j.xcrm.2024.101625] [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: 10/11/2023] [Revised: 04/19/2024] [Accepted: 06/07/2024] [Indexed: 07/01/2024]
Abstract
Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.
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Affiliation(s)
- Tarek Eissa
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany.
| | - Cristina Leonardo
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany
| | - Kosmas V Kepesidis
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary
| | - Frank Fleischmann
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany
| | - Birgit Linkohr
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Daniel Meyer
- Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary
| | - Viola Zoka
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Center for Molecular Fingerprinting (CMF), Budapest, Hungary
| | - Marinus Huber
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany
| | - Liudmila Voronina
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany
| | - Lothar Richter
- School of Computation, Information and Technology, Technical University of Munich (TUM), Garching, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany; School of Public Health, Institute for Medical Information Processing, Biometry, and Epidemiology, Pettenkofer, Ludwig Maximilian University of Munich (LMU), Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Munich, Munich, Germany
| | - Mihaela Žigman
- Department of Laser Physics, Ludwig Maximilian University of Munich (LMU), Garching, Germany; Laboratory for Attosecond Physics, Max Planck Institute of Quantum Optics (MPQ), Garching, Germany.
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Kino S, Kanamori M, Shimoda Y, Niizuma K, Endo H, Matsuura Y. Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation. BMC Cancer 2024; 24:222. [PMID: 38365669 PMCID: PMC10870484 DOI: 10.1186/s12885-024-11970-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/06/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Glioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation. METHODS Plasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations. RESULTS The derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of β-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas. CONCLUSIONS The plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of β-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.
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Affiliation(s)
- Saiko Kino
- Graduate School of Biomedical Engineering, Tohoku University, 6-6-05, Aza-Aoba, Aramaki, Aoba, Sendai City, 980-8579, Miyagi Prefecture, Japan
| | - Masayuki Kanamori
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Yoshiteru Shimoda
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Kuniyasu Niizuma
- Department of Neurosurgical Engineering and Translational Neuroscience, Graduate School of Biomedical Engineering, Tohoku University, Seiryo 2-1, Aoba, Sendai City, 980-8575, Miyagi Prefecture, Japan
- Department of Neurosurgical Engineering and Translational Neuroscience, Tohoku University Graduate School of Medicine, 980-8575 Seiryo 2-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Hidenori Endo
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Yuji Matsuura
- Graduate School of Biomedical Engineering, Tohoku University, 6-6-05, Aza-Aoba, Aramaki, Aoba, Sendai City, 980-8579, Miyagi Prefecture, Japan.
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Voronina L, Fleischmann F, Šimunović J, Ludwig C, Novokmet M, Žigman M. Probing Blood Plasma Protein Glycosylation with Infrared Spectroscopy. Anal Chem 2024. [PMID: 38324652 PMCID: PMC10882574 DOI: 10.1021/acs.analchem.3c03589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
The health state of an individual is closely linked to the glycosylation patterns of his or her blood plasma proteins. However, obtaining this information requires cost- and time-efficient analytical methods. We put forward infrared spectroscopy, which allows label-free analysis of protein glycosylation but so far has only been applied to analysis of individual proteins. Although spectral information does not directly provide the molecular structure of the glycans, it is sensitive to changes therein and covers all types of glycosidic linkages. Combining single-step ion exchange chromatography with infrared spectroscopy, we developed a workflow that enables the separation and analysis of major protein classes in blood plasma. Our results demonstrate that infrared spectroscopy can identify different patterns and global levels of glycosylation of intact plasma proteins. To showcase the strengths and limitations of the proposed approach, we compare the glycoforms of human and bovine alpha-1-acid glycoproteins, which exhibit highly variable global levels of glycosylation. To independently evaluate our conclusions, the glycan moieties of human alpha-1-acid glycoprotein were further analyzed using an established glycomics workflow. Importantly, the chromatographic separation of blood plasma improves the detection of aberrant glycoforms of a given protein as compared to infrared spectroscopy of bulk plasma. The presented approach allows a time-efficient comparison of glycosylation patterns of multiple plasma proteins, opening new avenues for biomedical probing.
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Affiliation(s)
- Liudmila Voronina
- Ludwig Maximilian University of Munich, Garching 85748, Germany
- Max Planck Institute of Quantum Optics, Garching 85748, Germany
| | - Frank Fleischmann
- Ludwig Maximilian University of Munich, Garching 85748, Germany
- Max Planck Institute of Quantum Optics, Garching 85748, Germany
| | - Jelena Šimunović
- Glycoscience Research Laboratory, Genos Ltd., Zagreb 10000, Croatia
| | - Christina Ludwig
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich (TUM), Freising 85354, Germany
| | - Mislav Novokmet
- Glycoscience Research Laboratory, Genos Ltd., Zagreb 10000, Croatia
| | - Mihaela Žigman
- Ludwig Maximilian University of Munich, Garching 85748, Germany
- Max Planck Institute of Quantum Optics, Garching 85748, Germany
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5
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Shi L, Li Y, Li Z. Early cancer detection by SERS spectroscopy and machine learning. LIGHT, SCIENCE & APPLICATIONS 2023; 12:234. [PMID: 37714845 PMCID: PMC10504315 DOI: 10.1038/s41377-023-01271-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
A new approach for early detection of multiple cancers is presented by integrating SERS spectroscopy of serum molecular fingerprints and machine learning.
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Affiliation(s)
- Lingyan Shi
- Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA.
| | - Yajuan Li
- Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA
| | - Zhi Li
- Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA, 92093, USA
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6
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Rotem O, Zer A, Yosef L, Beery E, Goldvaser H, Gutkin A, Levin R, Dudnik E, Berger T, Feinmesser M, Levy-Barda A, Lahav M, Raanani P, Uziel O. Blood-Derived Exosomal hTERT mRNA in Patients with Lung Cancer: Characterization and Correlation with Response to Therapy. Biomedicines 2023; 11:1730. [PMID: 37371825 DOI: 10.3390/biomedicines11061730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/12/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Background: Telomerase (human telomerase reverse transcriptase (hTERT) is considered a hallmark of cancer, being active in cancer cells but repressed in human somatic cells. As such, it has the potential to serve as a valid cancer biomarker. Exosomal hTERT mRNA can be detected in the serum of patients with solid malignancies but not in healthy individuals. We sought to evaluate the feasibility of measuring serum exosomal hTERT transcripts levels in patients with lung cancer. Methods: A prospective analysis of exosomal hTERT mRNA levels was determined in serum-derived exosomes from 76 patients with stage III-IV lung cancer (11 SCLC and 65 NSCLC). An hTERT level above RQ = 1.2 was considered "detectable" according to a previous receiver operating characteristic curve (ROC) curve. Sequential measurements were obtained in 33 patients. Demographic and clinical data were collected retrospectively from patients' charts. Data on response to systemic therapy (chemotherapy, immunotherapy, and tyrosine kinase inhibitors) were collected by the treating physicians. Results: hTERT was detected in 53% (40/76) of patients with lung cancer (89% of SCLC and 46% of NSLCC). The mean hTERT levels were 3.7 in all 76 patients, 5.87 in SCLC patients, and 3.62 in NSCLC patients. In total, 25 of 43 patients with sequential measurements had detectable levels of hTERT. The sequential exosomal hTERT mRNA levels reflected the clinical course in 23 of them. Decreases in hTERT levels were detected in 17 and 5 patients with partial and complete response, respectively. Eleven patients with a progressive disease had an increase in the level of exosomal hTERT, and seven with stable disease presented increases in its exosomal levels. Another patient who progressed on the first line of treatment and had a partial response to the second line of treatment exhibited an increase in exosomal hTERT mRNA levels during the progression and a decrease during the response. Conclusions: Exosomal hTERT mRNA levels are elevated in over half of patients with lung cancer. The potential association between hTERT levels and response to therapy suggests its utility as a promising cancer biomarker for response to therapy. This issue should be further explored in future studies.
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Affiliation(s)
- Ofer Rotem
- Davidoff Cancer Center, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Alona Zer
- Davidoff Cancer Center, Rabin Medical Center, Petah Tikva 49100, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Lilach Yosef
- Davidoff Cancer Center, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Einat Beery
- The Felsenstein Medical Research Center, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Hadar Goldvaser
- Shaare Zedek Medical Center, Faculty of Medicine, Hebrew University, Rehovot 7612001, Israel
| | - Anna Gutkin
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- The Felsenstein Medical Research Center, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Ron Levin
- Sheba Medical Center, Ramat Gan 5262000, Israel
| | - Elizabeth Dudnik
- Davidoff Cancer Center, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Tamar Berger
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Institute of Hematology, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Meora Feinmesser
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Biobank, Department of Pathology, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Adva Levy-Barda
- Biobank, Department of Pathology, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Meir Lahav
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- The Felsenstein Medical Research Center, Rabin Medical Center, Petah Tikva 49100, Israel
- Institute of Hematology, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Pia Raanani
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- The Felsenstein Medical Research Center, Rabin Medical Center, Petah Tikva 49100, Israel
- Institute of Hematology, Rabin Medical Center, Petah Tikva 49100, Israel
| | - Orit Uziel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- The Felsenstein Medical Research Center, Rabin Medical Center, Petah Tikva 49100, Israel
- Institute of Hematology, Rabin Medical Center, Petah Tikva 49100, Israel
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7
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Maiti KS. Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review. Molecules 2023; 28:2320. [PMID: 36903576 PMCID: PMC10005715 DOI: 10.3390/molecules28052320] [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: 01/12/2023] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/06/2023] Open
Abstract
Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.
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Affiliation(s)
- Kiran Sankar Maiti
- Max–Planck–Institut für Quantenoptik, Hans-Kopfermann-Straße 1, 85748 Garching, Germany; ; Tel.: +49-289-14054
- Lehrstuhl für Experimental Physik, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748 Garching, Germany
- Laser-Forschungslabor, Klinikum der Universität München, Fraunhoferstrasse 20, 82152 Planegg, Germany
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8
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Pabico LJ, Jaron JN, Mosqueda ME, Wu JJ, Tiongco RE, Albano PM. Diagnostic Efficiency of Serum-Based Infrared Spectroscopy in Detecting Breast Cancer: A Meta-Analysis. Lab Med 2023; 54:98-105. [PMID: 35960788 DOI: 10.1093/labmed/lmac068] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND The advancement of Fourier transform infrared (FTIR) spectroscopy as a potential diagnostic tool in the clinical setting has been studied over the years, particularly its application in cancer diagnostics. OBJECTIVE To summarize previous research on FTIR spectroscopy in detecting breast cancer using serum specimens. METHODS Related literature was searched and screened from various databases. Relevant data were then extracted, tabulated, and analyzed using Meta-DiSc 1.4 software. RESULTS Sensitivity and specificity rates were 90% to 100% and 80% to 95%, respectively. The area under the receiver operating characteristic curve was at 0.9729, indicating that serum analysis via FTIR spectroscopy can accurately discriminate between healthy individuals and patients with breast cancer. CONCLUSION Overall, FTIR spectroscopy for breast cancer diagnosis using serum specimens shows promising results. However, further studies are still needed to validate these claims.
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Affiliation(s)
- Louise Julie Pabico
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Jennica Naiomi Jaron
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Marc Erickson Mosqueda
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Jorge Jaesen Wu
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines
| | - Raphael Enrique Tiongco
- Department of Medical Technology, College of Allied Medical Professions, Angeles University Foundation, Angeles City, Philippines
| | - Pia Marie Albano
- Department of Biological Sciences, College of Science, University of Santo Tomas, Manila, Philippines.,Research Center for the Natural and Applied Sciences, University of Santo Tomas, Manila, Philippines
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9
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Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers. Biomolecules 2022; 12:biom12121815. [PMID: 36551243 PMCID: PMC9775374 DOI: 10.3390/biom12121815] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.
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10
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Iwamura H, Mizuno K, Akamatsu S, Hatakeyama S, Tobisawa Y, Narita S, Narita T, Yamashita S, Kawamura S, Sakurai T, Fujita N, Kodama H, Noro D, Kakizaki I, Nakaji S, Itoh K, Tsuchiya N, Ito A, Habuchi T, Ohyama C, Yoneyama T. Machine learning diagnosis by immunoglobulin N-glycan signature for precision diagnosis of urological diseases. Cancer Sci 2022; 113:2434-2445. [PMID: 35524940 PMCID: PMC9277255 DOI: 10.1111/cas.15395] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/11/2022] [Accepted: 04/27/2022] [Indexed: 11/28/2022] Open
Abstract
Early diagnosis of urological diseases is often difficult due to the lack of specific biomarkers. More powerful and less invasive biomarkers that can be used simultaneously to identify urological diseases could improve patient outcomes. The aim of this study was to evaluate a urological disease‐specific scoring system established with a machine learning (ML) approach using Ig N‐glycan signatures. Immunoglobulin N‐glycan signatures were analyzed by capillary electrophoresis from 1312 serum subjects with hormone‐sensitive prostate cancer (n = 234), castration‐resistant prostate cancer (n = 94), renal cell carcinoma (n = 100), upper urinary tract urothelial cancer (n = 105), bladder cancer (n = 176), germ cell tumors (n = 73), benign prostatic hyperplasia (n = 95), urosepsis (n = 145), and urinary tract infection (n = 21) as well as healthy volunteers (n = 269). Immunoglobulin N‐glycan signature data were used in a supervised‐ML model to establish a scoring system that gave the probability of the presence of a urological disease. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). The supervised‐ML urologic disease‐specific scores clearly discriminated the urological diseases (AUC 0.78–1.00) and found a distinct N‐glycan pattern that contributed to detect each disease. Limitations included the retrospective and limited pathological information regarding urological diseases. The supervised‐ML urological disease‐specific scoring system based on Ig N‐glycan signatures showed excellent diagnostic ability for nine urological diseases using a one‐time serum collection and could be a promising approach for the diagnosis of urological diseases.
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Affiliation(s)
- Hiromichi Iwamura
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Kei Mizuno
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shusuke Akamatsu
- Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shingo Hatakeyama
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.,Department of Advanced blood purification therapy, Hirosaki University Graduate School of Medicine, 036-8562, Hirosaki, Japan
| | - Yuki Tobisawa
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shintaro Narita
- Department of Urology, Akita University Graduate School of Medicine, Akita, Japan
| | - Takuma Narita
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shinichi Yamashita
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | | | - Toshihiko Sakurai
- Department of Urology, Yamagata University Graduate School of Medicine, Yamagata, Japan
| | - Naoki Fujita
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Hirotake Kodama
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Daisuke Noro
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Ikuko Kakizaki
- Department of Glycotechnology, Center for Advanced Medical Research, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shigeyuki Nakaji
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Ken Itoh
- Department of Glycotechnology, Center for Advanced Medical Research, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.,Department of Stress Response Science, Center for Advanced Medical Research, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Norihiko Tsuchiya
- Department of Urology, Yamagata University Graduate School of Medicine, Yamagata, Japan
| | - Akihiro Ito
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomonori Habuchi
- Department of Urology, Akita University Graduate School of Medicine, Akita, Japan
| | - Chikara Ohyama
- Department of Urology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.,Department of Advanced blood purification therapy, Hirosaki University Graduate School of Medicine, 036-8562, Hirosaki, Japan
| | - Tohru Yoneyama
- Department of Glycotechnology, Center for Advanced Medical Research, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
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Li W, Liu JB, Hou LK, Yu F, Zhang J, Wu W, Tang XM, Sun F, Lu HM, Deng J, Bai J, Li J, Wu CY, Lin QL, Lv ZW, Wang GR, Jiang GX, Ma YS, Fu D. Liquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring. Mol Cancer 2022; 21:25. [PMID: 35057806 PMCID: PMC8772097 DOI: 10.1186/s12943-022-01505-z] [Citation(s) in RCA: 123] [Impact Index Per Article: 61.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/10/2022] [Indexed: 12/14/2022] Open
Abstract
Primary lung cancer is one of the most common malignant tumors in China. Approximately 60% of lung cancer patients have distant metastasis at the initial diagnosis, so it is necessary to find new tumor markers for early diagnosis and individualized treatment. Tumor markers contribute to the early diagnosis of lung cancer and play important roles in early detection and treatment, as well as in precision medicine, efficacy monitoring, and prognosis prediction. The pathological diagnosis of lung cancer in small biopsy specimens determines whether there are tumor cells in the biopsy and tumor type. Because biopsy is traumatic and the compliance of patients with multiple biopsies is poor, liquid biopsy has become a hot research direction. Liquid biopsies are advantageous because they are nontraumatic, easy to obtain, reflect the overall state of the tumor, and allow for real-time monitoring. At present, liquid biopsies mainly include circulating tumor cells, circulating tumor DNA, exosomes, microRNA, circulating RNA, tumor platelets, and tumor endothelial cells. This review introduces the research progress and clinical application prospect of liquid biopsy technology for lung cancer.
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