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Valencia I, Nuzzo PV, Francini E, Ravera F, Fanelli GN, Bleve S, Scatena C, Marchionni L, Omar M. Gene Signature for Predicting Metastasis in Prostate Cancer Using Primary Tumor Expression Profiles. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.30.24312735. [PMID: 39252915 PMCID: PMC11383506 DOI: 10.1101/2024.08.30.24312735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Prostate cancer (PCa) is currently the most commonly diagnosed cancer and second leading cause of cancer-related death in men in the United States. The development of metastases is associated with a poor prognosis in PCa patients. Since current clinicopathological classification schemes are unable to accurately prognosticate the risk of metastasis for those diagnosed with localized PCa, there is a pressing need for precise and easily attainable biomarkers of metastatic risk in these patients. Primary tumor samples from 1239 individuals with PCa were divided into development (n=1000) and validation (n=239) cohorts. In the development cohort, we utilized a meta-analysis workflow on retrospective primary tumor gene expression profiles to identify a subset of genes predictive of metastasis. For each gene, we computed Hedges' g effect size and combined their p-values using Fisher's combined probability test. We then adjusted for multiple hypothesis testing using the Benjamini-Hochberg method. Our developed gene signature, termed Meta-Score, achieved a robust performance at predicting metastasis from primary tumor gene expression profiles, with an AUC of 0.72 in the validation cohort. In addition to its robust predictive power, Meta-Score also demonstrated a significant prognostic utility in two independent cohorts. Specifically, patients with a higher risk-score had a significantly worse metastasis-free survival and progression-free survival compared to those with lower score. Multivariate cox proportional hazards model showed that Meta-Score is significantly associated with worse survival even after adjusting for Gleason score. Our findings suggest that our primary tumor transcriptional signature, Meta-Score, could be a valuable tool to assess the risk of metastasis in PCa patients with localized disease, pending validation in large prospective studies. Author Summary Metastasis is the leading cause of death in patients diagnosed with prostate cancer (PCa), underscoring the need for reliable prediction tools to forecast the risk of metastasis at an early stage. Here, we utilize the gene expression profiles of 1,000 unique primary tumors from patients with localized PCa to develop a gene signature capable of predicting metastasis. Our signature, termed Meta-Score, comprises forty-five genes that can accurately distinguish primary tumor with high propensity for metastasis across different patient cohorts. Notably, Meta-Score maintained its robust predictive performance in an internal validation cohort of comprising primary tumor samples from 239 patients. In addition to its robust predictive performance, Meta-Score demonstrates a significant association with survival, independent of Gleason score in two independent patient cohorts, underscoring its prognostic utility. Taken together, Meta-Score is a robust risk-stratification tool that can be leveraged to identify patients at high-risk of metastasis and unfavorable survival using their primary tumor gene expression profiles.
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Luo X, Liu Y, Balck A, Klein C, Fleming RMT. Identification of metabolites reproducibly associated with Parkinson's Disease via meta-analysis and computational modelling. NPJ Parkinsons Dis 2024; 10:126. [PMID: 38951523 PMCID: PMC11217404 DOI: 10.1038/s41531-024-00732-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 05/30/2024] [Indexed: 07/03/2024] Open
Abstract
Many studies have reported metabolomic analysis of different bio-specimens from Parkinson's disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson's disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.
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Affiliation(s)
- Xi Luo
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Yanjun Liu
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Alexander Balck
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Christine Klein
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Ronan M T Fleming
- School of Medicine, University of Galway, University Rd, Galway, Ireland.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
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Lee JH, Gwon MR, Kim JI, Hwang SY, Seong SJ, Yoon YR, Kim M, Kim H. Alterations in Plasma Lipid Profile before and after Surgical Removal of Soft Tissue Sarcoma. Metabolites 2024; 14:250. [PMID: 38786727 PMCID: PMC11123356 DOI: 10.3390/metabo14050250] [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: 03/19/2024] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/25/2024] Open
Abstract
Soft tissue sarcoma (STS) is a relatively rare malignancy, accounting for about 1% of all adult cancers. It is known to have more than 70 subtypes. Its rarity, coupled with its various subtypes, makes early diagnosis challenging. The current standard treatment for STS is surgical removal. To identify the prognosis and pathophysiology of STS, we conducted untargeted metabolic profiling on pre-operative and post-operative plasma samples from 24 STS patients who underwent surgical tumor removal. Profiling was conducted using ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry. Thirty-nine putative metabolites, including phospholipids and acyl-carnitines were identified, indicating changes in lipid metabolism. Phospholipids exhibited an increase in the post-operative samples, while acyl-carnitines showed a decrease. Notably, the levels of pre-operative lysophosphatidylcholine (LPC) O-18:0 and LPC O-16:2 were significantly lower in patients who experienced recurrence after surgery compared to those who did not. Metabolic profiling may identify aggressive tumors that are susceptible to lipid synthase inhibitors. We believe that these findings could contribute to the elucidation of the pathophysiology of STS and the development of further metabolic studies in this rare malignancy.
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Affiliation(s)
- Jae-Hwa Lee
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea
| | - Mi-Ri Gwon
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- Clinical Omics Institute, School of Medicine, Kyungpook National University, Daegu 41405, Republic of Korea
| | - Jeung-Il Kim
- Department of Orthopaedic Surgery and Biomedical Research Institute, School of Medicine, Pusan National University, Busan 49241, Republic of Korea;
| | - Seung-young Hwang
- Pharmacokinetics Laboratory, Clinical Trial Center, Pusan National University Hospital, Busan 49241, Republic of Korea;
| | - Sook-Jin Seong
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea
- Clinical Omics Institute, School of Medicine, Kyungpook National University, Daegu 41405, Republic of Korea
- Department of Clinical Pharmacology and Therapeutics, Kyungpook National University Hospital, Daegu 41944, Republic of Korea
| | - Young-Ran Yoon
- Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (J.-H.L.); (M.-R.G.); (S.-J.S.); (Y.-R.Y.)
- BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea
- Clinical Omics Institute, School of Medicine, Kyungpook National University, Daegu 41405, Republic of Korea
- Department of Clinical Pharmacology and Therapeutics, Kyungpook National University Hospital, Daegu 41944, Republic of Korea
| | - Myungsoo Kim
- Department of Neurosurgery, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea;
| | - Hyojeong Kim
- Department of Internal Medicine, Division of Hemato-Oncology, Maryknoll Hospital, Busan 48972, Republic of Korea
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Wong EY, Chu TN, Ladi-Seyedian SS. Genomics and Artificial Intelligence: Prostate Cancer. Urol Clin North Am 2024; 51:27-33. [PMID: 37945100 DOI: 10.1016/j.ucl.2023.06.006] [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] [Indexed: 11/12/2023]
Abstract
Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be missed by traditional methods. These AI-driven insights can lead to the discovery of novel biomarkers, enhance the accuracy of diagnosis, and predict disease progression and treatment response. As such, AI is becoming an indispensable tool in the pursuit of personalized medicine for prostate cancer.
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Affiliation(s)
- Elyssa Y Wong
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Timothy N Chu
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Seyedeh-Sanam Ladi-Seyedian
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
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Oliveira MF, de Albuquerque Neto MC, Leite TS, Alves PAA, Lima SVC, Silva RO. Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics. Metabolomics 2023; 20:8. [PMID: 38127222 DOI: 10.1007/s11306-023-02067-x] [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: 06/25/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023]
Abstract
INTRODUCTION In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue. OBJECTIVES Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis. METHODS We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled. RESULTS Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy. CONCLUSION The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.
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Affiliation(s)
- Márcio Felipe Oliveira
- Metabonomics and Chemometrics Laboratory, Fundamental Chemistry Department, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife, Pernambuco, Brazil.
- Fundamental Chemistry Department, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife, Pernambuco, Brazil.
| | - Moacir Cavalcante de Albuquerque Neto
- Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil
| | - Thiago Siqueira Leite
- Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil
| | - Paulo André Araújo Alves
- Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil
| | - Salvador Vilar Correia Lima
- Surgery Department, Clinics Hospital, Urology Clinic, Universidade Federal de Pernambuco, Av. Professor Luis Freire, s/n. Cidade Universitária, Recife, Pernambuco, Brazil
| | - Ricardo Oliveira Silva
- Metabonomics and Chemometrics Laboratory, Fundamental Chemistry Department, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife, Pernambuco, Brazil
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Godlewski A, Czajkowski M, Mojsak P, Pienkowski T, Gosk W, Lyson T, Mariak Z, Reszec J, Kondraciuk M, Kaminski K, Kretowski M, Moniuszko M, Kretowski A, Ciborowski M. A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors. Sci Rep 2023; 13:11044. [PMID: 37422554 PMCID: PMC10329700 DOI: 10.1038/s41598-023-38243-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/05/2023] [Indexed: 07/10/2023] Open
Abstract
Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.
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Affiliation(s)
- Adrian Godlewski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Marcin Czajkowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Patrycja Mojsak
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Pienkowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Wioleta Gosk
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
| | - Tomasz Lyson
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Zenon Mariak
- Department of Neurosurgery, Medical University of Bialystok, Białystok, Poland
| | - Joanna Reszec
- Department of Medical Pathomorphology, Medical University of Bialystok, Białystok, Poland
| | - Marcin Kondraciuk
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Karol Kaminski
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Marek Kretowski
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Marcin Moniuszko
- Department of Regenerative Medicine and Immune Regulation, Medical University of Bialystok, Białystok, Poland
- Department of Allergology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Białystok, Poland
| | - Michal Ciborowski
- Clinical Research Centre, Medical University of Bialystok, M. Sklodowskiej-Curie 24a, 15-276, Białystok, Poland.
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Benedetti E, Liu EM, Tang C, Kuo F, Buyukozkan M, Park T, Park J, Correa F, Hakimi AA, Intlekofer AM, Krumsiek J, Reznik E. A multimodal atlas of tumour metabolism reveals the architecture of gene-metabolite covariation. Nat Metab 2023; 5:1029-1044. [PMID: 37337120 PMCID: PMC10290959 DOI: 10.1038/s42255-023-00817-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/09/2023] [Indexed: 06/21/2023]
Abstract
Tumour metabolism is controlled by coordinated changes in metabolite abundance and gene expression, but simultaneous quantification of metabolites and transcripts in primary tissue is rare. To overcome this limitation and to study gene-metabolite covariation in cancer, we assemble the Cancer Atlas of Metabolic Profiles of metabolomic and transcriptomic data from 988 tumour and control specimens spanning 11 cancer types in published and newly generated datasets. Meta-analysis of the Cancer Atlas of Metabolic Profiles reveals two classes of gene-metabolite covariation that transcend cancer types. The first corresponds to gene-metabolite pairs engaged in direct enzyme-substrate interactions, identifying putative genes controlling metabolite pool sizes. A second class of gene-metabolite covariation represents a small number of hub metabolites, including quinolinate and nicotinamide adenine dinucleotide, which correlate to many genes specifically expressed in immune cell populations. These results provide evidence that gene-metabolite covariation in cellularly heterogeneous tissue arises, in part, from both mechanistic interactions between genes and metabolites, and from remodelling of the bulk metabolome in specific immune microenvironments.
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Affiliation(s)
- Elisa Benedetti
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Eric Minwei Liu
- Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Cerise Tang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Fengshen Kuo
- Department of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Tricia Park
- Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jinsung Park
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Fabian Correa
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - A Ari Hakimi
- Department of Surgery, Urology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew M Intlekofer
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jan Krumsiek
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
| | - Ed Reznik
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA.
- Computational Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Wang Y, Qian H, Shao X, Zhang H, Liu S, Pan J, Xue W. Multimodal convolutional neural networks based on the Raman spectra of serum and clinical features for the early diagnosis of prostate cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 293:122426. [PMID: 36787677 DOI: 10.1016/j.saa.2023.122426] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/25/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
We collected surface-enhanced Raman spectroscopy (SERS) data from the serum of 729 patients with prostate cancer or benign prostatic hyperplasia (BPH), corresponding to their pathological results, and built an artificial intelligence-assisted diagnosis model based on a convolutional neural network (CNN). We then evaluated its value in diagnosing prostate cancer and predicting the Gleason score (GS) using a simple cross-validation method. Our CNN model based on the spectral data for prostate cancer diagnosis revealed an accuracy of 85.14 ± 0.39%. After adjusting the model with patient age and prostate specific antigen (PSA), the accuracy of the multimodal CNN was up to 88.55 ± 0.66%. Our multimodal CNN for distinguishing low-GS/high-GS and GS = 3 + 3/GS = 3 + 4 revealed accuracies of 68 ± 0.58% and 77 ± 0.52%, respectively.
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Affiliation(s)
- Yan Wang
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Hongyang Qian
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xiaoguang Shao
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Heng Zhang
- Shanghai Institute for Advanced Communication and Data Science, Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Shupeng Liu
- Shanghai Institute for Advanced Communication and Data Science, Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Jiahua Pan
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Wei Xue
- Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
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Innocenti L, Ortenzi V, Scarpitta R, Montemurro N, Pasqualetti F, Asseri R, Lazzi S, Szumera-Cieckiewicz A, De Ieso K, Perrini P, Naccarato AG, Scatena C, Fanelli GN. The Prognostic Impact of Gender, Therapeutic Strategies, Molecular Background, and Tumor-Infiltrating Lymphocytes in Glioblastoma: A Still Unsolved Jigsaw. Genes (Basel) 2023; 14:501. [PMID: 36833428 PMCID: PMC9956148 DOI: 10.3390/genes14020501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/21/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023] Open
Abstract
Despite the adoption of novel therapeutical approaches, the outcomes for glioblastoma (GBM) patients remain poor. In the present study, we investigated the prognostic impact of several clinico-pathological and molecular features as well as the role of the cellular immune response in a series of 59 GBM. CD4+ and CD8+ tumor-infiltrating lymphocytes (TILs) were digitally assessed on tissue microarray cores and their prognostic role was investigated. Moreover, the impact of other clinico-pathological features was evaluated. The number of CD4+ and CD8+ is higher in GBM tissue compared to normal brain tissue (p < 0.0001 and p = 0.0005 respectively). A positive correlation between CD4+ and CD8+ in GBM is present (rs = 0.417-p = 0.001). CD4+ TILs are inversely related to overall survival (OS) (HR = 1.79, 95% CI 1.1-3.1, p = 0.035). The presence of low CD4+ TILs combined with low CD8+ TILs is an independent predictor of longer OS (HR 0.38, 95% CI 0.18-0.79, p = 0.014). Female sex is independently related to longer OS (HR 0.42, 95% CI 0.22-0.77, p = 0.006). Adjuvant treatment, methylguanine methyltransferase (MGMT) promoter methylation, and age remain important prognostic factors but are influenced by other features. Adaptive cell-mediated immunity can affect the outcomes of GBM patients. Further studies are needed to elucidate the commitment of the CD4+ cells and the effects of different TILs subpopulations in GBM.
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Affiliation(s)
- Lorenzo Innocenti
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
| | - Valerio Ortenzi
- Department of Laboratory Medicine, Pisa University Hospital, 56126 Pisa, Italy
| | - Rosa Scarpitta
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
| | - Nicola Montemurro
- Department of Neurosurgery, Pisa University Hospital, 56126 Pisa, Italy
| | - Francesco Pasqualetti
- Department of Radiation Oncology, Pisa University Hospital, 56126 Pisa, Italy
- Department of Oncology, Oxford University, Oxford OX1 4BH, UK
| | - Roberta Asseri
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
| | - Stefano Lazzi
- Anatomic Pathology Unit, Department of Medical Biotechnology, University of Siena, 53100 Siena, Italy
| | - Anna Szumera-Cieckiewicz
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
- Department of Diagnostic Hematology, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
| | - Katia De Ieso
- Department of Laboratory Medicine, Pisa University Hospital, 56126 Pisa, Italy
| | - Paolo Perrini
- Department of Neurosurgery, Pisa University Hospital, 56126 Pisa, Italy
| | - Antonio Giuseppe Naccarato
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
- Department of Laboratory Medicine, Pisa University Hospital, 56126 Pisa, Italy
| | - Cristian Scatena
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
- Department of Laboratory Medicine, Pisa University Hospital, 56126 Pisa, Italy
| | - Giuseppe Nicolò Fanelli
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy
- Department of Laboratory Medicine, Pisa University Hospital, 56126 Pisa, Italy
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
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10
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Pejčić T, Todorović Z, Đurašević S, Popović L. Mechanisms of Prostate Cancer Cells Survival and Their Therapeutic Targeting. Int J Mol Sci 2023; 24:ijms24032939. [PMID: 36769263 PMCID: PMC9917912 DOI: 10.3390/ijms24032939] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 01/29/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Prostate cancer (PCa) is today the second most common cancer in the world, with almost 400,000 deaths annually. Multiple factors are involved in the etiology of PCa, such as older age, genetic mutations, ethnicity, diet, or inflammation. Modern treatment of PCa involves radical surgical treatment or radiation therapy in the stages when the tumor is limited to the prostate. When metastases develop, the standard procedure is androgen deprivation therapy, which aims to reduce the level of circulating testosterone, which is achieved by surgical or medical castration. However, when the level of testosterone decreases to the castration level, the tumor cells adapt to the new conditions through different mechanisms, which enable their unhindered growth and survival, despite the therapy. New knowledge about the biology of the so-called of castration-resistant PCa and the way it adapts to therapy will enable the development of new drugs, whose goal is to prolong the survival of patients with this stage of the disease, which will be discussed in this review.
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Affiliation(s)
- Tomislav Pejčić
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
- Clinic of Urology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
- Correspondence: ; Tel.: +381-641281844
| | - Zoran Todorović
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
- University Medical Centre “Bežanijska kosa”, University of Belgrade, 11000 Belgrade, Serbia
| | - Siniša Đurašević
- Faculty of Biology, University of Belgrade, 11000 Belgrade, Serbia
| | - Lazar Popović
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
- Medical Oncology Department, Oncology Institute of Vojvodina, 21000 Novi Sad, Serbia
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11
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Esquivel Tunubala LA, Garcia‐Perdomo HA. Should
Gleason
score six (
GS6
) tumours be labelled as non‐cancer? INTERNATIONAL JOURNAL OF UROLOGICAL NURSING 2023. [DOI: 10.1111/ijun.12343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
| | - Herney Andres Garcia‐Perdomo
- UROGIV Research Group, School of Medicine Universidad del Valle Cali Colombia
- Division of Urology/Urooncology, Department of Surgery, School of Medicine Universidad del Valle Cali Colombia
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12
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Lasorsa F, di Meo NA, Rutigliano M, Ferro M, Terracciano D, Tataru OS, Battaglia M, Ditonno P, Lucarelli G. Emerging Hallmarks of Metabolic Reprogramming in Prostate Cancer. Int J Mol Sci 2023; 24:ijms24020910. [PMID: 36674430 PMCID: PMC9863674 DOI: 10.3390/ijms24020910] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 12/30/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023] Open
Abstract
Prostate cancer (PCa) is the most common male malignancy and the fifth leading cause of cancer death in men worldwide. Prostate cancer cells are characterized by a hybrid glycolytic/oxidative phosphorylation phenotype determined by androgen receptor signaling. An increased lipogenesis and cholesterogenesis have been described in PCa cells. Many studies have shown that enzymes involved in these pathways are overexpressed in PCa. Glutamine becomes an essential amino acid for PCa cells, and its metabolism is thought to become an attractive therapeutic target. A crosstalk between cancer and stromal cells occurs in the tumor microenvironment because of the release of different cytokines and growth factors and due to changes in the extracellular matrix. A deeper insight into the metabolic changes may be obtained by a multi-omic approach integrating genomics, transcriptomics, metabolomics, lipidomics, and radiomics data.
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Affiliation(s)
- Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Nicola Antonio di Meo
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Monica Rutigliano
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Matteo Ferro
- Division of Urology, European Institute of Oncology, IRCCS, 20141 Milan, Italy
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Octavian Sabin Tataru
- The Institution Organizing University Doctoral Studies (I.O.S.U.D.), George Emil Palade University of Medicine, Pharmacy, Sciences and Technology, 540142 Târgu Mureș, Romania
| | - Michele Battaglia
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Pasquale Ditonno
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy
- Correspondence: or
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13
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Andagalu B, Lu P, Onyango I, Bergmann-Leitner E, Wasuna R, Odhiambo G, Chebon-Bore LJ, Ingasia LA, Juma DW, Opot B, Cheruiyot A, Yeda R, Okudo C, Okoth R, Chemwor G, Campo J, Wallqvist A, Akala HM, Ochiel D, Ogutu B, Chaudhury S, Kamau E. Age-dependent antibody profiles to plasmodium antigens are differentially associated with two artemisinin combination therapy outcomes in high transmission setting. Front Med (Lausanne) 2022; 9:991807. [PMID: 36314027 PMCID: PMC9606348 DOI: 10.3389/fmed.2022.991807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/27/2022] [Indexed: 11/28/2022] Open
Abstract
The impact of pre-existing immunity on the efficacy of artemisinin combination therapy is largely unknown. We performed in-depth profiling of serological responses in a therapeutic efficacy study [comparing artesunate-mefloquine (ASMQ) and artemether-lumefantrine (AL)] using a proteomic microarray. Responses to over 200 Plasmodium antigens were significantly associated with ASMQ treatment outcome but not AL. We used machine learning to develop predictive models of treatment outcome based on the immunoprofile data. The models predict treatment outcome for ASMQ with high (72–85%) accuracy, but could not predict treatment outcome for AL. This divergent treatment outcome suggests that humoral immunity may synergize with the longer mefloquine half-life to provide a prophylactic effect at 28–42 days post-treatment, which was further supported by simulated pharmacokinetic profiling. Our computational approach and modeling revealed the synergistic effect of pre-existing immunity in patients with drug combination that has an extended efficacy on providing long term treatment efficacy of ASMQ.
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Affiliation(s)
- Ben Andagalu
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Pinyi Lu
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States,Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, United States
| | - Irene Onyango
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Elke Bergmann-Leitner
- Biologics Research and Development, Walter Reed Army Institute of Research, Silver Spring, MD, United States
| | - Ruth Wasuna
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Geoffrey Odhiambo
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Lorna J. Chebon-Bore
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Luicer A. Ingasia
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Dennis W. Juma
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Benjamin Opot
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Agnes Cheruiyot
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Redemptah Yeda
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Charles Okudo
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Raphael Okoth
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Gladys Chemwor
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Joseph Campo
- Antigen Discovery Inc., Irvine, CA, United States
| | - Anders Wallqvist
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
| | - Hoseah M. Akala
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Daniel Ochiel
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | | | - Sidhartha Chaudhury
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States,Center for Enabling Capabilities, Walter Reed Army Institute of Research, Silver Spring, MD, United States
| | - Edwin Kamau
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya,U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States,*Correspondence: Edwin Kamau, ,
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14
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Circulating Cell-Free DNA in Renal Cell Carcinoma: The New Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14184359. [PMID: 36139519 PMCID: PMC9497114 DOI: 10.3390/cancers14184359] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/03/2022] [Accepted: 09/05/2022] [Indexed: 12/01/2022] Open
Abstract
Simple Summary Early diagnosis of renal cell carcinoma (RCC) is challenging and typically incidental. Currently, several therapeutic strategies are used for the treatment; however, no established predictive biomarker has been established yet, and the optimal treatment choice and sequence of use remain unclear. Moreover, the recurrence occurs in about one-third of patients after tumor resection. Although several prognostic classification systems have been proposed, most of them showed only limited potential in recurrence prediction. Therefore, identifying simple, reliable, and easily accessible biomarkers to anticipate the diagnosis, effectively evaluate the risk of relapse, and predict the response to the therapeutic regimens is an unmet clinical need. Circulating cell-free DNA (cfDNA), released from cancer cells into the bloodstream, was shown to be a non-invasive, viable, inexpensive method to diagnose and monitor several solid malignancies, designed as a potential blood RCC biomarker. This review aims to summarize the state of the art of the current genetic and epigenetic techniques of plasma and serum cfDNA detection and outline the potential application of liquid biopsy in RCC. Abstract Tumor biopsy is still the gold standard for diagnosing and prognosis renal cell carcinoma (RCC). However, its invasiveness, costs, and inability to accurately picture tumor heterogeneity represent major limitations to this procedure. Analysis of circulating cell-free DNA (cfDNA) is a non-invasive cost-effective technique that has the potential to ease cancer detection and prognosis. In particular, a growing body of evidence suggests that cfDNA could be a complementary tool to identify and prognosticate RCC while providing contemporary mutational profiling of the tumor. Further, recent research highlighted the role of cfDNA methylation profiling as a novel method for cancer detection and tissue-origin identification. This review synthesizes current knowledge on the diagnostic, prognostic, and predictive applications of cfDNA in RCC, with a specific focus on the potential role of cell-free methylated DNA (cfMeDNA).
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15
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Milligan K, Deng X, Ali-Adeeb R, Shreeves P, Punch S, Costie N, Crook JM, Brolo AG, Lum JJ, Andrews JL, Jirasek A. Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study. Sci Rep 2022; 12:15104. [PMID: 36068275 PMCID: PMC9448740 DOI: 10.1038/s41598-022-19446-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/29/2022] [Indexed: 11/09/2022] Open
Abstract
This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.
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Affiliation(s)
- Kirsty Milligan
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Xinchen Deng
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Ramie Ali-Adeeb
- Department of Physics, University of British Columbia, Kelowna, BC, Canada
| | - Phillip Shreeves
- Department of Statistics, University of British Columbia, Kelowna, Canada
| | - Samantha Punch
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada
| | - Nathalie Costie
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada
| | - Juanita M Crook
- Department of Radiation Oncology, University of British Columbia, Kelowna, BC, Canada
| | - Alexandre G Brolo
- Department of Chemistry, University of Victoria, British Columbia, Canada
| | - Julian J Lum
- Trev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
| | - Jeffrey L Andrews
- Department of Statistics, University of British Columbia, Kelowna, Canada
| | - Andrew Jirasek
- Department of Physics, University of British Columbia, Kelowna, BC, Canada.
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16
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Berchuck JE, Adib E, Abou Alaiwi S, Dash AK, Shin JN, Lowder D, McColl C, Castro P, Carelli R, Benedetti E, Deng J, Robertson M, Baca SC, Bell C, McClure HM, El Zarif T, Davidsohn MP, Lakshminarayanan G, Rizwan K, Skapura DG, Grimm SL, Davis CM, Ehli EA, Kelleher KM, Seo JH, Mitsiades N, Coarfa C, Pomerantz MM, Loda M, Ittmann M, Freedman ML, Kaochar S. The Prostate Cancer Androgen Receptor Cistrome in African American Men Associates with Upregulation of Lipid Metabolism and Immune Response. Cancer Res 2022; 82:2848-2859. [PMID: 35731919 PMCID: PMC9379363 DOI: 10.1158/0008-5472.can-21-3552] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/03/2022] [Accepted: 06/14/2022] [Indexed: 11/16/2022]
Abstract
African-American (AA) men are more likely to be diagnosed with and die from prostate cancer than European American (EA) men. Despite the central role of the androgen receptor (AR) transcription factor in prostate cancer, little is known about the contribution of epigenetics to observed racial disparities. We performed AR chromatin immunoprecipitation sequencing on primary prostate tumors from AA and EA men, finding that sites with greater AR binding intensity in AA relative to EA prostate cancer are enriched for lipid metabolism and immune response genes. Integration with transcriptomic and metabolomic data demonstrated coinciding upregulation of lipid metabolism gene expression and increased lipid levels in AA prostate cancer. In a metastatic prostate cancer cohort, upregulated lipid metabolism associated with poor prognosis. These findings offer the first insights into ancestry-specific differences in the prostate cancer AR cistrome. The data suggest a model whereby increased androgen signaling may contribute to higher levels of lipid metabolism, immune response, and cytokine signaling in AA prostate tumors. Given the association of upregulated lipogenesis with prostate cancer progression, our study provides a plausible biological explanation for the higher incidence and aggressiveness of prostate cancer observed in AA men. SIGNIFICANCE With immunotherapies and inhibitors of metabolic enzymes in clinical development, the altered lipid metabolism and immune response in African-American men provides potential therapeutic opportunities to attenuate racial disparities in prostate cancer.
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Affiliation(s)
- Jacob E. Berchuck
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Elio Adib
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Sarah Abou Alaiwi
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Amit K. Dash
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Jin Na Shin
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Dallin Lowder
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Collin McColl
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Patricia Castro
- Department of Pathology, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Ryan Carelli
- Avera Institute for Human Genetics, Sioux Falls, South Dakota
| | - Elisa Benedetti
- Avera Institute for Human Genetics, Sioux Falls, South Dakota
| | - Jenny Deng
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Matthew Robertson
- Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Sylvan C. Baca
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Connor Bell
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Heather M. McClure
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Talal El Zarif
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Matthew P. Davidsohn
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Gitanjali Lakshminarayanan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Kinza Rizwan
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | | | - Sandra L. Grimm
- Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Christel M. Davis
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Erik A. Ehli
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - Kaitlin M. Kelleher
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ji-Heui Seo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Nicholas Mitsiades
- Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas
| | - Cristian Coarfa
- Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas
| | - Mark M. Pomerantz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Massimo Loda
- Avera Institute for Human Genetics, Sioux Falls, South Dakota
| | - Michael Ittmann
- Department of Pathology, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Matthew L. Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Salma Kaochar
- Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas
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17
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Relevance of Emerging Metabolomics-Based Biomarkers of Prostate Cancer: A Systematic Review. Expert Rev Mol Med 2022; 24:e25. [PMID: 35730322 DOI: 10.1017/erm.2022.20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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18
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Buszewska-Forajta M, Raczak-Gutknecht J, Struck-Lewicka W, Nizioł M, Artymowicz M, Markuszewski M, Kordalewska M, Matuszewski M, Markuszewski MJ. Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer. Front Mol Biosci 2022; 9:849966. [PMID: 35309505 PMCID: PMC8931686 DOI: 10.3389/fmolb.2022.849966] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/11/2022] [Indexed: 01/16/2023] Open
Abstract
The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.
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Affiliation(s)
- Magdalena Buszewska-Forajta
- Institute of Veterinary Medicine, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University in Toruń, Torun, Poland
- Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
- *Correspondence: Magdalena Buszewska-Forajta,
| | - Joanna Raczak-Gutknecht
- Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
| | - Wiktoria Struck-Lewicka
- Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
| | - Magdalena Nizioł
- Department of Pharmaceutical and Biopharmaceutical Analysis, Faculty of Pharmacy, Medical University of Białystok, Białystok, Poland
| | - Małgorzata Artymowicz
- Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
| | - Marcin Markuszewski
- Department of Urology, Faculty of Medicine, Medical University of Gdańsk, Gdańsk, Poland
| | - Marta Kordalewska
- Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
| | - Marcin Matuszewski
- Department of Urology, Faculty of Medicine, Medical University of Gdańsk, Gdańsk, Poland
| | - Michał J. Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
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Kumar D, Bansal N, Gupta A, Mandhani A, Lal H, Kumar M, Sankhwar SN. Metabolomics of prostate cancer: Knock-in versus knock-out prostate. J Pharm Biomed Anal 2021; 205:114333. [PMID: 34461489 DOI: 10.1016/j.jpba.2021.114333] [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: 06/24/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 10/20/2022]
Abstract
Several metabolomics-derived biomarkers of prostate cancer (PC) have been reported with pre-radical prostatectomy (RP) (knock-in PC) conditions; however, uncontested PC biomarkers panel appraisal and investigation of correlative evidence of these measures is lacking through post-RP (knock-out PC). We sought to explore patients' filtered serum-based metabolomics derived signature measures in knock-in PC (n = 90) using nuclear magnetic resonance spectroscopy and multiple rigorous statistical analyses, and to develop the correlative evidence of these measures through knock-out PC (n = 90) follow-up on the 15th and 30th days. The glutamate, citrate and glycine were observed as hallmarks of PC. Observed trends revealed; augmented glutamate level in knock-in PC following a sudden drop and subsequently upside of glutamate at 15th and 30th days of knock-out PC, reduction of citrate in knock-in PC subsequently gradual increase of citrate in knock-out PC, and glycine lessening in knock-in PC following augmentation on 30th day of knock-out PC. This study-based evidence clears the doubts regarding the discovery of metabolomics-derived PC biomarkers.
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Affiliation(s)
- Deepak Kumar
- Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India
| | - Navneeta Bansal
- Department of Urology, King George's Medical University, Lucknow, India
| | - Ashish Gupta
- Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India.
| | - Anil Mandhani
- Department of Urology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Hira Lal
- Department of Radiodiagnosis, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Manoj Kumar
- Department of Urology, King George's Medical University, Lucknow, India
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Sena LA, Denmeade SR. Fatty Acid Synthesis in Prostate Cancer: Vulnerability or Epiphenomenon? Cancer Res 2021; 81:4385-4393. [PMID: 34145040 PMCID: PMC8416800 DOI: 10.1158/0008-5472.can-21-1392] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/28/2021] [Accepted: 06/15/2021] [Indexed: 01/07/2023]
Abstract
Tumor metabolism supports the energetic and biosynthetic needs of rapidly proliferating cancer cells and modifies intra- and intercellular signaling to enhance cancer cell invasion, metastasis, and immune evasion. Prostate cancer exhibits unique metabolism with high rates of de novo fatty acid synthesis driven by activation of the androgen receptor (AR). Increasing evidence suggests that activation of this pathway is functionally important to promote prostate cancer aggressiveness. However, the mechanisms by which fatty acid synthesis are beneficial to prostate cancer have not been well defined. In this review, we summarize evidence indicating that fatty acid synthesis drives progression of prostate cancer. We also explore explanations for this phenomenon and discuss future directions for targeting this pathway for patient benefit.
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Affiliation(s)
- Laura A Sena
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Samuel R Denmeade
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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21
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Lin X, Lécuyer L, Liu X, Triba MN, Deschasaux-Tanguy M, Demidem A, Liu Z, Palama T, Rossary A, Vasson MP, Hercberg S, Galan P, Savarin P, Xu G, Touvier M. Plasma Metabolomics for Discovery of Early Metabolic Markers of Prostate Cancer Based on Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry. Cancers (Basel) 2021; 13:3140. [PMID: 34201735 PMCID: PMC8268247 DOI: 10.3390/cancers13133140] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa. METHODS Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). RESULTS Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower. CONCLUSION Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa.
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Affiliation(s)
- Xiangping Lin
- Sorbonne Paris Nord University, Chemistry Structures Properties of Biomaterials and Therapeutic Agents Laboratory (CSPBAT), Nanomédecine Biomarqueurs Détection Team (NBD), The National Center for Scientific Research (CNRS), UMR 7244, 74 Rue Marcel
Cachin, CEDEX, 93017 Bobigny, France; (X.L.); (M.N.T.); (T.P.)
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; (X.L.); (G.X.)
| | - Lucie Lécuyer
- Sorbonne Paris Nord University, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center Inserm U1153, Inrae U1125, Cnam, University of Paris (CRESS), 74 Rue Marcel Cachin, CEDEX, 93017 Bobigny, France; (L.L.); (S.H.); (P.G.); (M.T.)
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; (X.L.); (G.X.)
| | - Mohamed N. Triba
- Sorbonne Paris Nord University, Chemistry Structures Properties of Biomaterials and Therapeutic Agents Laboratory (CSPBAT), Nanomédecine Biomarqueurs Détection Team (NBD), The National Center for Scientific Research (CNRS), UMR 7244, 74 Rue Marcel
Cachin, CEDEX, 93017 Bobigny, France; (X.L.); (M.N.T.); (T.P.)
| | - Mélanie Deschasaux-Tanguy
- Sorbonne Paris Nord University, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center Inserm U1153, Inrae U1125, Cnam, University of Paris (CRESS), 74 Rue Marcel Cachin, CEDEX, 93017 Bobigny, France; (L.L.); (S.H.); (P.G.); (M.T.)
| | - Aïcha Demidem
- Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Human Nutrition Unit (UNH), Clermont Auvergne University, INRAE, UMR 1019, CRNH Auvergne, 63000 Clermont-Ferrand, France; (A.D.); (A.R.); (M.-P.V.)
| | - Zhicheng Liu
- School of Pharmacy, Anhui Medical University, Hefei 230032, China;
| | - Tony Palama
- Sorbonne Paris Nord University, Chemistry Structures Properties of Biomaterials and Therapeutic Agents Laboratory (CSPBAT), Nanomédecine Biomarqueurs Détection Team (NBD), The National Center for Scientific Research (CNRS), UMR 7244, 74 Rue Marcel
Cachin, CEDEX, 93017 Bobigny, France; (X.L.); (M.N.T.); (T.P.)
| | - Adrien Rossary
- Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Human Nutrition Unit (UNH), Clermont Auvergne University, INRAE, UMR 1019, CRNH Auvergne, 63000 Clermont-Ferrand, France; (A.D.); (A.R.); (M.-P.V.)
| | - Marie-Paule Vasson
- Cellular Micro-Environment, Immunomodulation and Nutrition (ECREIN), Human Nutrition Unit (UNH), Clermont Auvergne University, INRAE, UMR 1019, CRNH Auvergne, 63000 Clermont-Ferrand, France; (A.D.); (A.R.); (M.-P.V.)
- Anticancer Center Jean-Perrin, CHU Clermont-Ferrand, CEDEX, 63011 Clermont-Ferrand, France
| | - Serge Hercberg
- Sorbonne Paris Nord University, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center Inserm U1153, Inrae U1125, Cnam, University of Paris (CRESS), 74 Rue Marcel Cachin, CEDEX, 93017 Bobigny, France; (L.L.); (S.H.); (P.G.); (M.T.)
| | - Pilar Galan
- Sorbonne Paris Nord University, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center Inserm U1153, Inrae U1125, Cnam, University of Paris (CRESS), 74 Rue Marcel Cachin, CEDEX, 93017 Bobigny, France; (L.L.); (S.H.); (P.G.); (M.T.)
| | - Philippe Savarin
- Sorbonne Paris Nord University, Chemistry Structures Properties of Biomaterials and Therapeutic Agents Laboratory (CSPBAT), Nanomédecine Biomarqueurs Détection Team (NBD), The National Center for Scientific Research (CNRS), UMR 7244, 74 Rue Marcel
Cachin, CEDEX, 93017 Bobigny, France; (X.L.); (M.N.T.); (T.P.)
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; (X.L.); (G.X.)
| | - Mathilde Touvier
- Sorbonne Paris Nord University, Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center Inserm U1153, Inrae U1125, Cnam, University of Paris (CRESS), 74 Rue Marcel Cachin, CEDEX, 93017 Bobigny, France; (L.L.); (S.H.); (P.G.); (M.T.)
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