1
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Sun Y, Cheng G, Wei D, Luo J, Liu J. Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers. Front Immunol 2024; 15:1493377. [PMID: 39691710 PMCID: PMC11649677 DOI: 10.3389/fimmu.2024.1493377] [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: 09/09/2024] [Accepted: 11/18/2024] [Indexed: 12/19/2024] Open
Abstract
Introduction Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived biomarkers. Methods We analyzed a multicenter public dataset comprising 61 OSCC patients and 61 healthy controls. Plasma metabolomics data were processed to extract 29 numerical and 47 ratio features. The Extra Trees (ET) algorithm was applied for feature selection, and the TabPFN model was used for classification and prediction. Results The model achieved an area under the curve (AUC) of 93% and an overall accuracy of 76.6% when using top-ranked individual biomarkers. Key metabolic features significantly differentiated OSCC patients from healthy controls, providing a detailed metabolic fingerprint of the disease. Discussion Our findings demonstrate the utility of integrating omics data with advanced machine learning techniques to develop accurate, non-invasive diagnostic tools for OSCC. The study highlights actionable metabolic signatures that have potential applications in personalized therapeutics and early intervention strategies.
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Affiliation(s)
- Yilan Sun
- Department of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - Guozhen Cheng
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Dongliang Wei
- Department of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
| | - Jiacheng Luo
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Jiannan Liu
- Department of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
- National Center for Stomatology, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology, Shanghai, China
- Shanghai Research Institute of Stomatology, Shanghai, China
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2
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Dyachenko EI, Bel’skaya LV. Salivary Metabolites in Breast Cancer and Fibroadenomas: Focus on Menopausal Status and BMI. Metabolites 2024; 14:531. [PMID: 39452912 PMCID: PMC11509358 DOI: 10.3390/metabo14100531] [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: 09/04/2024] [Revised: 09/20/2024] [Accepted: 09/30/2024] [Indexed: 10/26/2024] Open
Abstract
This study of the features of the biochemical composition of biological fluids in patients with breast cancer, including saliva, allows us to identify some indicators as metabolic predictors of the presence of the disease. OBJECTIVES to study the influence of the menopause factor and body mass index (BMI) on the biochemical composition of saliva and to evaluate the applicability of metabolic markers of saliva for the diagnosis of breast cancer. METHODS The case-control study involved 1438 people (breast cancer, n = 543; fibroadenomas, n = 597; control, n = 298). A comprehensive study of the biochemical composition of saliva was carried out using 36 parameters. RESULTS When comparing the salivary biochemical composition in breast cancer, fibroadenomas, and controls, it is necessary to take into account the menopausal status, as well as BMI (less than 25 or more) for the group of patients with preserved menstrual function. A complex of biochemical parameters has been identified that change in saliva during breast cancer, regardless of menopause and BMI (total protein, urea, uric acid, NO, α-amino acids, GGT), as well as specific parameters that must be taken into account when analyzing individual subgroups (imidazole compounds, LDH, catalase, α-amylase). During the study of a separate group of patients with leaf-shaped (phyllodes) tumors, we found similarities with breast cancer in the changes in some biochemical parameters that can be attributed to metabolites of malignant growth (protein, α-amino acids, calcium, NO, pyruvate, peroxidase, α-amylase). CONCLUSIONS We demonstrated changes in a wide range of salivary biochemical parameters depending on the presence of fibroadenomas and breast cancer. From the point of view of clinical practice, this may be useful information for monitoring the condition of patients with fibroadenomas, which are difficult to unambiguously classify based on instrumental diagnostics alone.
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Affiliation(s)
| | - Lyudmila V. Bel’skaya
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
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3
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Chohan DP, Biswas S, Wankhede M, Menon P, K A, Basha S, Rodrigues J, Mukunda DC, Mahato KK. Assessing Breast Cancer through Tumor Microenvironment Mapping of Collagen and Other Biomolecule Spectral Fingerprints─A Review. ACS Sens 2024; 9:4364-4379. [PMID: 39175278 PMCID: PMC11443534 DOI: 10.1021/acssensors.4c00585] [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: 03/12/2024] [Revised: 08/06/2024] [Accepted: 08/09/2024] [Indexed: 08/24/2024]
Abstract
Breast cancer is a major challenge in the field of oncology, with around 2.3 million cases and around 670,000 deaths globally based on the GLOBOCAN 2022 data. Despite having advanced technologies, breast cancer remains the major type of cancer among women. This review highlights various collagen signatures and the role of different collagen types in breast tumor development, progression, and metastasis, along with the use of photoacoustic spectroscopy to offer insights into future cancer diagnostic applications without the need for surgery or other invasive techniques. Through mapping of the tumor microenvironment and spotlighting key components and their absorption wavelengths, we emphasize the need for extensive preclinical and clinical investigations.
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Affiliation(s)
- Diya Pratish Chohan
- Manipal
School of Life Sciences, Manipal Academy
of Higher Education, Karnataka, Manipal 576104, India
| | - Shimul Biswas
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Mrunmayee Wankhede
- Manipal
School of Life Sciences, Manipal Academy
of Higher Education, Karnataka, Manipal 576104, India
| | - Poornima Menon
- Manipal
School of Life Sciences, Manipal Academy
of Higher Education, Karnataka, Manipal 576104, India
| | - Ameera K
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Shaik Basha
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | - Jackson Rodrigues
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
| | | | - Krishna Kishore Mahato
- Department
of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, Manipal 576104, India
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4
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Thodi G, Triantopoulou A, Iliou A, Molou E, Dotsikas Y, Loukas YL. A simplified metabolomic analysis of dried blood spots in breast cancer patients. Scand J Clin Lab Invest 2024; 84:326-335. [PMID: 39225029 DOI: 10.1080/00365513.2024.2392241] [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: 04/30/2024] [Revised: 07/21/2024] [Accepted: 08/11/2024] [Indexed: 09/04/2024]
Abstract
Breast cancer (BC) is among the most commonly diagnosed cancers. Besides mammography, breast ultrasonography and the routinely monitored protein markers, the variations of small molecular metabolites in blood may be of great diagnostic value. This study aimed to quantify specific metabolite markers with potential application in BC detection. The study enrolled 50 participants, 25 BC patients and 25 healthy controls (CTRL). Dried blood spots (DBS) were utilized as biological media and were quantified via a simplified liquid chromatography tandem mass spectrometry (LC-MS/MS) method, used in expanded newborn screening. The targeted metabolomic analysis included 12 amino acids and 32 acylcarnitines. Statistical analysis revealed a significant variation of metabolic profiles between BC patients and CTRL. Among the 44 metabolites, 18 acylcarnitines and 10 amino acids remained significant after Bonferroni correction, showing increase or decrease and enabled classification of BC patients and CTRL. The well-established LC-MS/MS protocol could provide results within few minutes. Therefore, the combination of an easy-to-handle material-DBS and LC-MS/MS protocol could facilitate BC screening/diagnosis and in the next step applied to other cancer patients, as well.
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Affiliation(s)
| | - Aikaterini Triantopoulou
- Laboratory of Pharmaceutical Analysis, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Aikaterini Iliou
- Laboratory of Pharmaceutical Analysis, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Elina Molou
- Neoscreen Diagnostic Laboratory, Athens, Greece
| | - Yannis Dotsikas
- Laboratory of Pharmaceutical Analysis, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Yannis L Loukas
- Laboratory of Pharmaceutical Analysis, Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
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5
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El-Tanani M, Rabbani SA, El-Tanani Y, Matalka II. Metabolic vulnerabilities in cancer: A new therapeutic strategy. Crit Rev Oncol Hematol 2024; 201:104438. [PMID: 38977145 DOI: 10.1016/j.critrevonc.2024.104438] [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: 05/09/2024] [Accepted: 07/02/2024] [Indexed: 07/10/2024] Open
Abstract
Cancer metabolism is now a key area for therapeutic intervention, targeting unique metabolic reprogramming crucial for tumor growth and survival. This article reviews the therapeutic potential of addressing metabolic vulnerabilities through glycolysis and glutaminase inhibitors, which disrupt cancer cell metabolism. Challenges such as tumor heterogeneity and adaptive resistance are discussed, with strategies including personalized medicine and predictive biomarkers to enhance treatment efficacy. Additionally, integrating diet and lifestyle changes with metabolic targeting underscores a holistic approach to improving therapy outcomes. The article also examines the benefits of incorporating these strategies into standard care, highlighting the potential for more tailored, safer treatments. In conclusion, exploiting metabolic vulnerabilities promises a new era in oncology, positioning metabolic targeting at the forefront of personalized cancer therapy and transforming patient care.
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Affiliation(s)
- Mohamed El-Tanani
- RAK College of Pharmacy, RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.
| | - Syed Arman Rabbani
- RAK College of Pharmacy, RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.
| | - Yahia El-Tanani
- Medical School, St George's University of London, Cranmer Terrace, Tooting, London, UK
| | - Ismail I Matalka
- RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates; Department of Pathology and Microbiology, Medicine, Jordan University of Science and Technology, Irbid, Jordan.
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6
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Tian Y, Liu X, Wang J, Zhang C, Yang W. Antitumor Effects and the Potential Mechanism of 10-HDA against SU-DHL-2 Cells. Pharmaceuticals (Basel) 2024; 17:1088. [PMID: 39204193 PMCID: PMC11357620 DOI: 10.3390/ph17081088] [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: 07/18/2024] [Revised: 08/13/2024] [Accepted: 08/16/2024] [Indexed: 09/03/2024] Open
Abstract
10-hydroxy-2-decenoic acid (10-HDA), which is a unique bioactive fatty acid of royal jelly synthesized by nurse bees for larvae and adult queen bees, is recognized for its dual utility in medicinal and nutritional applications. Previous research has indicated that 10-HDA exerts antitumor effects on numerous tumor cell lines, including colon cancer cells, A549 human lung cancer cells, and human hepatoma cells. The present study extends this inquiry to lymphoma, specifically evaluating the impact of 10-HDA on the SU-DHL-2 cell line. Our findings revealed dose-dependent suppression of SU-DHL-2 cell survival, with an IC50 of 496.8 μg/mL at a density of 3 × 106 cells/well after 24 h. For normal liver LO2 cells and human fibroblasts (HSFs), the IC50 values were approximately 1000 μg/mL and over 1000 μg/mL, respectively. The results of label-free proteomics revealed 147 upregulated and 347 downregulated differentially expressed proteins that were significantly enriched in the complement and coagulation cascades pathway (adjusted p-value = 0.012), including the differentially expressed proteins prothrombin, plasminogen, plasminogen, carboxypeptidase B2, fibrinogen beta chain, fibrinogen gamma chain, and coagulation factor V. The top three hub proteins, ribosomal protein L5, tumor protein p53, and ribosomal protein L24, were identified via protein-protein interaction (PPI) analysis. This result showed that the complement and coagulation cascade pathways might play a key role in the antitumor process of 10-HDA, suggesting a potential therapeutic avenue for lymphoma treatment. However, the specificity of the effect of 10-HDA on SU-DHL-2 cells warrants further investigation.
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Affiliation(s)
- Yuanyuan Tian
- College of Bee Science and Biomedicine, Fujian Agriculture and Forestry University, Fuzhou 350002, China; (Y.T.); (X.L.); (J.W.); (C.Z.)
- College of JunCao Science and Ecology (College of Carbon Neutrality), Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Xiaoqing Liu
- College of Bee Science and Biomedicine, Fujian Agriculture and Forestry University, Fuzhou 350002, China; (Y.T.); (X.L.); (J.W.); (C.Z.)
| | - Jie Wang
- College of Bee Science and Biomedicine, Fujian Agriculture and Forestry University, Fuzhou 350002, China; (Y.T.); (X.L.); (J.W.); (C.Z.)
| | - Chuang Zhang
- College of Bee Science and Biomedicine, Fujian Agriculture and Forestry University, Fuzhou 350002, China; (Y.T.); (X.L.); (J.W.); (C.Z.)
| | - Wenchao Yang
- College of Bee Science and Biomedicine, Fujian Agriculture and Forestry University, Fuzhou 350002, China; (Y.T.); (X.L.); (J.W.); (C.Z.)
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7
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Bauer BA, Schmidt CM, Ruddy KJ, Olson JE, Meydan C, Schmidt JC, Smith SY, Couch FJ, Earls JC, Price ND, Dudley JT, Mason CE, Zhang B, Phipps SM, Schmidt MA. A Multiomics, Molecular Atlas of Breast Cancer Survivors. Metabolites 2024; 14:396. [PMID: 39057719 PMCID: PMC11279123 DOI: 10.3390/metabo14070396] [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: 06/02/2024] [Revised: 07/09/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.
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Affiliation(s)
| | - Caleb M. Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis and Human Performance Group, Boulder, CO 80302, USA
| | | | | | - Cem Meydan
- Thorne Research, Inc., Summerville, SC 29483, USA
| | - Julian C. Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis and Human Performance Group, Boulder, CO 80302, USA
| | | | | | | | - Nathan D. Price
- Thorne Research, Inc., Summerville, SC 29483, USA
- Buck Institute for Research on Aging, Novato, CA 94945, USA
| | | | | | - Bodi Zhang
- Thorne Research, Inc., Summerville, SC 29483, USA
| | | | - Michael A. Schmidt
- Sovaris Aerospace, Boulder, CO 80302, USA
- Advanced Pattern Analysis and Human Performance Group, Boulder, CO 80302, USA
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8
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Xavier JB. Machine learning of cellular metabolic rewiring. Biol Methods Protoc 2024; 9:bpae048. [PMID: 39011352 PMCID: PMC11249387 DOI: 10.1093/biomethods/bpae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/14/2024] [Accepted: 07/01/2024] [Indexed: 07/17/2024] Open
Abstract
Metabolic rewiring allows cells to adapt their metabolism in response to evolving environmental conditions. Traditional metabolomics techniques, whether targeted or untargeted, often struggle to interpret these adaptive shifts. Here, we introduce MetaboLiteLearner, a lightweight machine learning framework that harnesses the detailed fragmentation patterns from electron ionization (EI) collected in scan mode during gas chromatography/mass spectrometry to predict changes in the metabolite composition of metabolically adapted cells. When tested on breast cancer cells with different preferences to metastasize to specific organs, MetaboLiteLearner predicted the impact of metabolic rewiring on metabolites withheld from the training dataset using only the EI spectra, without metabolite identification or pre-existing knowledge of metabolic networks. Despite its simplicity, the model learned captured shared and unique metabolomic shifts between brain- and lung-homing metastatic lineages, suggesting cellular adaptations associated with metastasis to specific organs. Integrating machine learning and metabolomics paves the way for new insights into complex cellular adaptations.
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Affiliation(s)
- Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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9
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Mota AM, Mendes J, Matela N. Breast Cancer Molecular Subtype Prediction: A Mammography-Based AI Approach. Biomedicines 2024; 12:1371. [PMID: 38927578 PMCID: PMC11201998 DOI: 10.3390/biomedicines12061371] [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: 06/05/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Breast cancer remains a leading cause of mortality among women, with molecular subtypes significantly influencing prognosis and treatment strategies. Currently, identifying the molecular subtype of cancer requires a biopsy-a specialized, expensive, and time-consuming procedure, often yielding to results that must be supported with additional biopsies due to technique errors or tumor heterogeneity. This study introduces a novel approach for predicting breast cancer molecular subtypes using mammography images and advanced artificial intelligence (AI) methodologies. Using the OPTIMAM imaging database, 1397 images from 660 patients were selected. The pretrained deep learning model ResNet-101 was employed to classify tumors into five subtypes: Luminal A, Luminal B1, Luminal B2, HER2, and Triple Negative. Various classification strategies were studied: binary classifications (one vs. all others, specific combinations) and multi-class classification (evaluating all subtypes simultaneously). To address imbalanced data, strategies like oversampling, undersampling, and data augmentation were explored. Performance was evaluated using accuracy and area under the receiver operating characteristic curve (AUC). Binary classification results showed a maximum average accuracy and AUC of 79.02% and 64.69%, respectively, while multi-class classification achieved an average AUC of 60.62% with oversampling and data augmentation. The most notable binary classification was HER2 vs. non-HER2, with an accuracy of 89.79% and an AUC of 73.31%. Binary classification for specific combinations of subtypes revealed an accuracy of 76.42% for HER2 vs. Luminal A and an AUC of 73.04% for HER2 vs. Luminal B1. These findings highlight the potential of mammography-based AI for non-invasive breast cancer subtype prediction, offering a promising alternative to biopsies and paving the way for personalized treatment plans.
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Affiliation(s)
- Ana M. Mota
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal; (J.M.); (N.M.)
| | - João Mendes
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal; (J.M.); (N.M.)
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
| | - Nuno Matela
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal; (J.M.); (N.M.)
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10
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Zhu C, Zhang C, Wang S, Xun Z, Zhang D, Lan Z, Zhang L, Chao J, Liang Y, Pu Z, Ning C, Sang X, Yang X, Wang H, Jiang X, Zhao H. Characterizations of multi-kingdom gut microbiota in immune checkpoint inhibitor-treated hepatocellular carcinoma. J Immunother Cancer 2024; 12:e008686. [PMID: 38844407 PMCID: PMC11163665 DOI: 10.1136/jitc-2023-008686] [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] [Accepted: 05/17/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND The association between gut bacteria and the response to immune checkpoint inhibitors (ICI) in hepatocellular carcinoma (HCC) has been studied; however, multi-kingdom gut microbiome alterations and interactions in ICI-treated HCC cohorts are not fully understood. METHODS From November 2018 to April 2022, patients receiving ICI treatment for advanced HCC were prospectively enrolled. Herein, we investigated the multi-kingdom microbiota characterization of the gut microbiome, mycobiome, and metabolome using metagenomic, ITS2, and metabolomic data sets of 80 patients with ICI-treated HCC. RESULTS Our findings demonstrated that bacteria and metabolites differed significantly between the durable clinical benefit (DCB) and non-durable clinical benefit (NDB) groups, whereas the differences were smaller for fungi. The overall diversity of bacteria and fungi before treatment was higher in the DCB group than in the NDB group, and the difference in diversity began to change with the use of immunotherapy after 6-8 weeks. We also explored the alterations of gut microbes in the DCB and NDB groups, established 18 bacterial species models as predictive biomarkers for predicting whether immunotherapy is of sustained benefit (area under the curve=75.63%), and screened two species of bacteria (Actinomyces_sp_ICM47, and Senegalimassilia_anaerobia) and one metabolite (galanthaminone) as prognostic biomarkers for predicting survival in patients with HCC treated with ICI. CONCLUSIONS In this study, the status and characterization of the multi-kingdom microbiota, including gut bacteria, fungi, and their metabolites, were described by multiomics sequencing for the first time in patients with HCC treated with ICI. Our findings demonstrate the potential of bacterial taxa as predictive biomarkers of ICI clinical efficacy, and bacteria and their metabolites as prognostic biomarkers.
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Affiliation(s)
- Chengpei Zhu
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
- Department of General Surgery Center, Beijing Youan Hospital, Clinical Center for Liver Cancer, Capital Medical University, Beijing, China
| | - Chenchen Zhang
- Microbiome Research Center, Moon (Guangzhou) Biotech Ltd, Guangzhou, China
| | - Shanshan Wang
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Ziyu Xun
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Dongya Zhang
- Microbiome Research Center, Moon (Guangzhou) Biotech Ltd, Guangzhou, China
| | - Zhou Lan
- Microbiome Research Center, Moon (Guangzhou) Biotech Ltd, Guangzhou, China
| | - Longhao Zhang
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Jiashuo Chao
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Yajun Liang
- Microbiome Research Center, Moon (Guangzhou) Biotech Ltd, Guangzhou, China
| | - Zilun Pu
- Microbiome Research Center, Moon (Guangzhou) Biotech Ltd, Guangzhou, China
| | - Cong Ning
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Xinting Sang
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Xiaobo Yang
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Hanping Wang
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC), Beijing, China
| | - Xianzhi Jiang
- Microbiome Research Center, Moon (Guangzhou) Biotech Ltd, Guangzhou, China
| | - Haitao Zhao
- Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China
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11
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Alvarez-Frutos L, Barriuso D, Duran M, Infante M, Kroemer G, Palacios-Ramirez R, Senovilla L. Multiomics insights on the onset, progression, and metastatic evolution of breast cancer. Front Oncol 2023; 13:1292046. [PMID: 38169859 PMCID: PMC10758476 DOI: 10.3389/fonc.2023.1292046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/23/2023] [Indexed: 01/05/2024] Open
Abstract
Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.
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Affiliation(s)
- Lucia Alvarez-Frutos
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Daniel Barriuso
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Mercedes Duran
- Laboratory of Molecular Genetics of Hereditary Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Mar Infante
- Laboratory of Molecular Genetics of Hereditary Cancer, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France
- Department of Biology, Institut du Cancer Paris CARPEM, Hôpital Européen Georges Pompidou, Paris, France
| | - Roberto Palacios-Ramirez
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
| | - Laura Senovilla
- Laboratory of Cell Stress and Immunosurveillance, Unidad de Excelencia Instituto de Biomedicina y Genética Molecular (IBGM), Universidad de Valladolid – Centro Superior de Investigaciones Cientificas (CSIC), Valladolid, Spain
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université Paris Cité, Sorbonne Université, Inserm U1138, Institut Universitaire de France, Paris, France
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France
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12
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Winz C, Zong WX, Suh N. Endocrine-disrupting compounds and metabolomic reprogramming in breast cancer. J Biochem Mol Toxicol 2023; 37:e23506. [PMID: 37598318 PMCID: PMC10840637 DOI: 10.1002/jbt.23506] [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: 03/08/2023] [Revised: 06/23/2023] [Accepted: 08/11/2023] [Indexed: 08/21/2023]
Abstract
Endocrine-disrupting chemicals pose a growing threat to human health through their increasing presence in the environment and their potential interactions with the mammalian endocrine systems. Due to their structural similarity to hormones like estrogen, these chemicals can interfere with endocrine signaling, leading to many deleterious effects. Exposure to estrogenic endocrine-disrupting compounds (EDC) is a suggested risk factor for the development of breast cancer, one of the most frequently diagnosed cancers in women. However, the mechanisms through which EDCs contribute to breast cancer development remain elusive. To rapidly proliferate, cancer cells undertake distinct metabolic programs to utilize existing nutrients in the tumor microenvironment and synthesize macromolecules de novo. EDCs are known to dysregulate cell signaling pathways related to cellular metabolism, which may be an important mechanism through which they exert their cancer-promoting effects. These altered pathways can be studied via metabolomic analysis, a new advancement in -omics technologies that can interrogate molecular pathways that favor cancer development and progression. This review will summarize recent discoveries regarding EDCs and the metabolic reprogramming that they may induce to facilitate the development of breast cancer.
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Affiliation(s)
- Cassandra Winz
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Pharmacology and Toxicology, Environmental and Occupational Health Sciences Institute, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Wei-Xing Zong
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Nanjoo Suh
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
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13
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Bel’skaya LV, Gundyrev IA, Solomatin DV. The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review. Curr Issues Mol Biol 2023; 45:7513-7537. [PMID: 37754258 PMCID: PMC10527988 DOI: 10.3390/cimb45090474] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/28/2023] Open
Abstract
This review summarizes the role of amino acids in the diagnosis, risk assessment, imaging, and treatment of breast cancer. It was shown that the content of individual amino acids changes in breast cancer by an average of 10-15% compared with healthy controls. For some amino acids (Thr, Arg, Met, and Ser), an increase in concentration is more often observed in breast cancer, and for others, a decrease is observed (Asp, Pro, Trp, and His). The accuracy of diagnostics using individual amino acids is low and increases when a number of amino acids are combined with each other or with other metabolites. Gln/Glu, Asp, Arg, Leu/Ile, Lys, and Orn have the greatest significance in assessing the risk of breast cancer. The variability in the amino acid composition of biological fluids was shown to depend on the breast cancer phenotype, as well as the age, race, and menopausal status of patients. In general, the analysis of changes in the amino acid metabolism in breast cancer is a promising strategy not only for diagnosis, but also for developing new therapeutic agents, monitoring the treatment process, correcting complications after treatment, and evaluating survival rates.
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Affiliation(s)
- Lyudmila V. Bel’skaya
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Ivan A. Gundyrev
- Biochemistry Research Laboratory, Omsk State Pedagogical University, 644099 Omsk, Russia;
| | - Denis V. Solomatin
- Department of Mathematics and Mathematics Teaching Methods, Omsk State Pedagogical University, 644043 Omsk, Russia;
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14
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Dunphy K, Bazou D, Henry M, Meleady P, Miettinen JJ, Heckman CA, Dowling P, O’Gorman P. Proteomic and Metabolomic Analysis of Bone Marrow and Plasma from Patients with Extramedullary Multiple Myeloma Identifies Distinct Protein and Metabolite Signatures. Cancers (Basel) 2023; 15:3764. [PMID: 37568580 PMCID: PMC10417544 DOI: 10.3390/cancers15153764] [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: 07/05/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/13/2023] Open
Abstract
Multiple myeloma (MM) is an incurable haematological malignancy of plasma cells in the bone marrow. In rare cases, an aggressive form of MM called extramedullary multiple myeloma (EMM) develops, where myeloma cells enter the bloodstream and colonise distal organs or soft tissues. This variant is associated with refractoriness to conventional therapies and a short overall survival. The molecular mechanisms associated with EMM are not yet fully understood. Here, we analysed the proteome of bone marrow mononuclear cells and blood plasma from eight patients (one serial sample) with EMM and eight patients without extramedullary spread. The patients with EMM had a significantly reduced overall survival with a median survival of 19 months. Label-free mass spectrometry revealed 225 proteins with a significant differential abundance between bone marrow mononuclear cells (BMNCs) isolated from patients with MM and EMM. This plasma proteomics analysis identified 22 proteins with a significant differential abundance. Three proteins, namely vascular cell adhesion molecule 1 (VCAM1), pigment epithelium derived factor (PEDF), and hepatocyte growth factor activator (HGFA), were verified as the promising markers of EMM, with the combined protein panel showing excellent accuracy in distinguishing EMM patients from MM patients. Metabolomic analysis revealed a distinct metabolite signature in EMM patient plasma compared to MM patient plasma. The results provide much needed insight into the phenotypic profile of EMM and in identifying promising plasma-derived markers of EMM that may inform novel drug development strategies.
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Affiliation(s)
- Katie Dunphy
- Department of Biology, Maynooth University, W23 F2K8 Kildare, Ireland;
| | - Despina Bazou
- Department of Haematology, Mater Misericordiae University Hospital, D07 AX57 Dublin, Ireland; (D.B.); (P.O.)
| | - Michael Henry
- National Institute for Cellular Biotechnology, Dublin City University, D09 NR58 Dublin, Ireland; (M.H.); (P.M.)
| | - Paula Meleady
- National Institute for Cellular Biotechnology, Dublin City University, D09 NR58 Dublin, Ireland; (M.H.); (P.M.)
| | - Juho J. Miettinen
- Institute for Molecular Medicine Finland-FIMM, HiLIFE–Helsinki Institute of Life Science, iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, 00290 Helsinki, Finland; (J.J.M.); (C.A.H.)
| | - Caroline A. Heckman
- Institute for Molecular Medicine Finland-FIMM, HiLIFE–Helsinki Institute of Life Science, iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, 00290 Helsinki, Finland; (J.J.M.); (C.A.H.)
| | - Paul Dowling
- Department of Biology, Maynooth University, W23 F2K8 Kildare, Ireland;
| | - Peter O’Gorman
- Department of Haematology, Mater Misericordiae University Hospital, D07 AX57 Dublin, Ireland; (D.B.); (P.O.)
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15
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Xu R, Wang J, Zhu Q, Zou C, Wei Z, Wang H, Ding Z, Meng M, Wei H, Xia S, Wei D, Deng L, Zhang S. Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer. Biomark Res 2023; 11:71. [PMID: 37475010 PMCID: PMC10360339 DOI: 10.1186/s40364-023-00497-2] [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: 03/29/2023] [Accepted: 05/05/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme. METHODS TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set. RESULTS Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%. CONCLUSIONS Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.
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Affiliation(s)
- Runhao Xu
- Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- Department of Clinical Laboratory, Renji Hospital, Shanghai, 200001, China
| | - Jiongran Wang
- Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qingqing Zhu
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Chen Zou
- Department of Clinical Laboratory, Children's Hospital of Shanghai, Shanghai, 200040, China
| | - Zehao Wei
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Hao Wang
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Zian Ding
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Minjie Meng
- School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Huimin Wei
- Shanghai Cellsolution Biotech Co.,Ltd, Shanghai, 200444, China
| | - Shijin Xia
- Department of Geriatrics, Huadong Hospital, Shanghai Institute of Geriatrics, Fudan University, Shanghai, 200040, China
| | - Dongqing Wei
- Department of Bioinformatics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Nanyang, 473006, Henan, China
| | - Li Deng
- Shanghai Cellsolution Biotech Co.,Ltd, Shanghai, 200444, China.
| | - Shulin Zhang
- Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Nanyang, 473006, Henan, China.
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
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16
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Vahid F, Hajizadeghan K, Khodabakhshi A. Nutritional Metabolomics in Diet-Breast Cancer Relations: Current Research, Challenges, and Future Directions-A Review. Biomedicines 2023; 11:1845. [PMID: 37509485 PMCID: PMC10377267 DOI: 10.3390/biomedicines11071845] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/21/2023] [Accepted: 06/24/2023] [Indexed: 07/30/2023] Open
Abstract
Breast cancer is one of the most common types of cancer in women worldwide, and its incidence is increasing. Diet has been identified as a modifiable risk factor for breast cancer, but the complex interplay between diet, metabolism, and cancer development is not fully understood. Nutritional metabolomics is a rapidly evolving field that can provide insights into the metabolic changes associated with dietary factors and their impact on breast cancer risk. The review's objective is to provide a comprehensive overview of the current research on the application of nutritional metabolomics in understanding the relationship between diet and breast cancer. The search strategy involved querying several electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. The search terms included combinations of relevant keywords such as "nutritional metabolomics", "diet", "breast cancer", "metabolites", and "biomarkers". In this review, both in vivo and in vitro studies were included, and we summarize the current state of knowledge on the role of nutritional metabolomics in understanding the diet-breast cancer relationship, including identifying specific metabolites and metabolic pathways associated with breast cancer risk. We also discuss the challenges associated with nutritional metabolomics research, including standardization of analytical methods, interpretation of complex data, and integration of multiple-omics approaches. Finally, we highlight future directions for nutritional metabolomics research in studying diet-breast cancer relations, including investigating the role of gut microbiota and integrating multiple-omics approaches. The application of nutritional metabolomics in the study of diet-breast cancer relations, including 2-amino-4-cyano butanoic acid, piperine, caprate, rosten-3β,17β-diol-monosulfate, and γ-carboxyethyl hydrochroman, among others, holds great promise for advancing our understanding of the role of diet in breast cancer development and identifying personalized dietary recommendations for breast cancer prevention, control, and treatment.
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Affiliation(s)
- Farhad Vahid
- Nutrition and Health Research Group, Precision Health Department, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
| | - Kimia Hajizadeghan
- Department of Nutrition, Faculty of Public Health, Kerman University of Medical Sciences, Kerman 7616913555, Iran
| | - Adeleh Khodabakhshi
- Department of Nutrition, Faculty of Public Health, Kerman University of Medical Sciences, Kerman 7616913555, Iran
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17
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Neagu AN, Whitham D, Bruno P, Morrissiey H, Darie CA, Darie CC. Omics-Based Investigations of Breast Cancer. Molecules 2023; 28:4768. [PMID: 37375323 DOI: 10.3390/molecules28124768] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/08/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, "Alexandru Ioan Cuza" University of Iasi, Carol I Bvd, No. 20A, 700505 Iasi, Romania
| | - Danielle Whitham
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Hailey Morrissiey
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Celeste A Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
| | - Costel C Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA
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18
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Qiu S, Cai Y, Yao H, Lin C, Xie Y, Tang S, Zhang A. Small molecule metabolites: discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 2023; 8:132. [PMID: 36941259 PMCID: PMC10026263 DOI: 10.1038/s41392-023-01399-3] [Citation(s) in RCA: 173] [Impact Index Per Article: 173.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/22/2023] Open
Abstract
Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.
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Affiliation(s)
- Shi Qiu
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China
| | - Ying Cai
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China
| | - Hong Yao
- First Affiliated Hospital, Harbin Medical University, Harbin, 150081, China
| | - Chunsheng Lin
- Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150001, China
| | - Yiqiang Xie
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Songqi Tang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
| | - Aihua Zhang
- International Advanced Functional Omics Platform, Scientific Experiment Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), College of Chinese Medicine, Hainan Medical University, Xueyuan Road 3, Haikou, 571199, China.
- Graduate School, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
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19
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Carrillo-Rodriguez P, Selheim F, Hernandez-Valladares M. Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps. Cancers (Basel) 2023; 15:555. [PMID: 36672506 PMCID: PMC9856946 DOI: 10.3390/cancers15020555] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.
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Affiliation(s)
- Paula Carrillo-Rodriguez
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
- Vall d’Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Frode Selheim
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
| | - Maria Hernandez-Valladares
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
- Department of Physical Chemistry, University of Granada, Avenida de la Fuente Nueva S/N, 18071 Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
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