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Neagu AN, Whitham D, Bruno P, Arshad A, Seymour L, Morrissiey H, Hukovic AI, Darie CC. Onco-Breastomics: An Eco-Evo-Devo Holistic Approach. Int J Mol Sci 2024; 25:1628. [PMID: 38338903 PMCID: PMC10855488 DOI: 10.3390/ijms25031628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.
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Affiliation(s)
- Anca-Narcisa Neagu
- Laboratory of Animal Histology, Faculty of Biology, “Alexandru Ioan Cuza” University of Iași, Carol I bvd. 20A, 700505 Iasi, Romania
| | - Danielle Whitham
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Pathea Bruno
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Aneeta Arshad
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Logan Seymour
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Hailey Morrissiey
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Angiolina I. Hukovic
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
| | - Costel C. Darie
- Biochemistry & Proteomics Laboratories, Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699-5810, USA; (D.W.); (P.B.); (A.A.); (L.S.); (H.M.); (A.I.H.)
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Goddard TR, Brookes KJ, Sharma R, Moemeni A, Rajkumar AP. Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science. Cells 2024; 13:223. [PMID: 38334615 PMCID: PMC10854541 DOI: 10.3390/cells13030223] [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: 12/14/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.
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Affiliation(s)
- Thomas R. Goddard
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
| | - Keeley J. Brookes
- Department of Biosciences, School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
| | - Riddhi Sharma
- Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
- UK Health Security Agency, Radiation Effects Department, Radiation Protection Science Division, Harwell Science Campus, Didcot, Oxfordshire OX11 0RQ, UK
| | - Armaghan Moemeni
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
| | - Anto P. Rajkumar
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
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Jethalia M, Jani SP, Ceccarelli M, Mall R. Pancancer network analysis reveals key master regulators for cancer invasiveness. J Transl Med 2023; 21:558. [PMID: 37599366 PMCID: PMC10440887 DOI: 10.1186/s12967-023-04435-6] [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: 03/23/2023] [Accepted: 08/12/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness. MATERIALS AND METHODS We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository. RESULTS Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis. CONCLUSION Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.
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Affiliation(s)
- Mahesh Jethalia
- Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
| | - Siddhi P Jani
- Centre of Brain Research, Indian Institute of Sciences, Bangalore, Karnataka, India
- Institute of Science, Nirma University, Ahmedabad, India
| | - Michele Ceccarelli
- Department of Public Health Sciences, University of Miami, Miami, FL, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Raghvendra Mall
- St. Jude Children's Hospital, Memphis, TN, USA.
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates.
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Li J, Li L, You P, Wei Y, Xu B. Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer. Semin Cancer Biol 2023; 91:35-49. [PMID: 36868394 DOI: 10.1016/j.semcancer.2023.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/05/2023]
Abstract
Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.
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Affiliation(s)
- Junyu Li
- Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang 330029, Jiangxi, China; Jiangxi Health Committee Key (JHCK) Laboratory of Tumor Metastasis, Jiangxi Cancer Hospital, Nanchang 330029, Jiangxi, China
| | - Lin Li
- Department of Thoracic Oncology, Jiangxi Cancer Hospital, Nanchang 330029, Jiangxi, China
| | - Peimeng You
- Nanchang University, Department of Radiation Oncology, Jiangxi Cancer Hospital, Nanchang 330029, Jiangxi, China
| | - Yiping Wei
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, China.
| | - Bin Xu
- Jiangxi Health Committee Key (JHCK) Laboratory of Tumor Metastasis, Jiangxi Cancer Hospital, Nanchang 330029, Jiangxi, China.
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Dong Z, Liao Z, He Y, Wu C, Meng Z, Qin B, Xu G, Li Z, Sun T, Wen Y, Li G. Advances in the Biological Functions and Mechanisms of miRNAs in the Development of Osteosarcoma. Technol Cancer Res Treat 2022; 21:15330338221117386. [PMID: 35950243 PMCID: PMC9379803 DOI: 10.1177/15330338221117386] [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] [Indexed: 11/16/2022] Open
Abstract
Osteosarcoma is one of the most common primary malignant bone tumors, mainly
occurring in children and adolescents, and is characterized by high morbidity
and poor prognosis. MicroRNAs, a class of noncoding RNAs consisting of 19 to 25
nucleotides, are involved in cell proliferation, invasion, metastasis, and
apoptosis to regulate the development and progression of osteosarcoma. Studies
have found that microRNAs are closely related to the diagnosis, treatment, and
prognosis of osteosarcoma patients and have an important role in improving drug
resistance in osteosarcoma. This paper reviews the role of microRNAs in the
pathogenesis of osteosarcoma and their clinical value, aiming to provide a new
research direction for diagnosing and treating osteosarcoma and achieving a
better prognosis.
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Affiliation(s)
- Zihe Dong
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Zhipeng Liao
- The Second School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Yonglin He
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Chengye Wu
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Zixiang Meng
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Baolong Qin
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Ge Xu
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Zeyang Li
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Tianxin Sun
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Yuyan Wen
- The First School of Clinical Medicine, 12426Lanzhou University, Lanzhou, Gansu, China
| | - Guangjie Li
- The First Hospital of Lanzhou University, Lanzhou, Gansu, China
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New Trends in Esophageal Cancer Management. Cancers (Basel) 2021; 13:cancers13123030. [PMID: 34204314 PMCID: PMC8235022 DOI: 10.3390/cancers13123030] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/10/2021] [Accepted: 06/13/2021] [Indexed: 12/08/2022] Open
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