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Araúzo-Bravo MJ, Erichsen L, Ott P, Beermann A, Sheikh J, Gerovska D, Thimm C, Bendhack ML, Santourlidis S. Consistent DNA Hypomethylations in Prostate Cancer. Int J Mol Sci 2022; 24:ijms24010386. [PMID: 36613831 PMCID: PMC9820221 DOI: 10.3390/ijms24010386] [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: 11/07/2022] [Revised: 12/14/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
With approximately 1.4 million men annually diagnosed with prostate cancer (PCa) worldwide, PCa remains a dreaded threat to life and source of devastating morbidity. In recent decades, a significant decrease in age-specific PCa mortality has been achieved by increasing prostate-specific antigen (PSA) screening and improving treatments. Nevertheless, upcoming, augmented recommendations against PSA screening underline an escalating disproportion between the benefit and harm of current diagnosis/prognosis and application of radical treatment standards. Undoubtedly, new potent diagnostic and prognostic tools are urgently needed to alleviate this tensed situation. They should allow a more reliable early assessment of the upcoming threat, in order to enable applying timely adjusted and personalized therapy and monitoring. Here, we present a basic study on an epigenetic screening approach by Methylated DNA Immunoprecipitation (MeDIP). We identified genes associated with hypomethylated CpG islands in three PCa sample cohorts. By adjusting our computational biology analyses to focus on single CpG-enriched 60-nucleotide-long DNA probes, we revealed numerous consistently differential methylated DNA segments in PCa. They were associated among other genes with NOTCH3, CDK2AP1, KLK4, and ADAM15. These can be used for early discrimination, and might contribute to a new epigenetic tumor classification system of PCa. Our analysis shows that we can dissect short, differential methylated CpG-rich DNA fragments and combinations of them that are consistently present in all tumors. We name them tumor cell-specific differential methylated CpG dinucleotide signatures (TUMS).
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Affiliation(s)
- Marcos J. Araúzo-Bravo
- Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, 20014 San Sebastián, Spain
- IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of Basque Country (UPV/EHU), 48940 Leioa, Spain
| | - Lars Erichsen
- Epigenetics Core Laboratory, Medical Faculty, Institute of Transplantation Diagnostics and Cell Therapeutics, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Pauline Ott
- Epigenetics Core Laboratory, Medical Faculty, Institute of Transplantation Diagnostics and Cell Therapeutics, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Agnes Beermann
- Epigenetics Core Laboratory, Medical Faculty, Institute of Transplantation Diagnostics and Cell Therapeutics, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jamal Sheikh
- Epigenetics Core Laboratory, Medical Faculty, Institute of Transplantation Diagnostics and Cell Therapeutics, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Daniela Gerovska
- Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, 20014 San Sebastián, Spain
| | - Chantelle Thimm
- Medical Faculty, Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Marcelo L. Bendhack
- Department of Urology, University Hospital, Positivo University, Curitiba 80420-011, Brazil
| | - Simeon Santourlidis
- Epigenetics Core Laboratory, Medical Faculty, Institute of Transplantation Diagnostics and Cell Therapeutics, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Correspondence:
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Cabau-Laporta J, Ascensión AM, Arrospide-Elgarresta M, Gerovska D, Araúzo-Bravo MJ. FOntCell: Fusion of Ontologies of Cells. Front Cell Dev Biol 2021; 9:562908. [PMID: 33644039 PMCID: PMC7905052 DOI: 10.3389/fcell.2021.562908] [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: 05/18/2020] [Accepted: 01/05/2021] [Indexed: 11/25/2022] Open
Abstract
High-throughput cell-data technologies such as single-cell RNA-seq create a demand for algorithms for automatic cell classification and characterization. There exist several cell classification ontologies with complementary information. However, one needs to merge them to synergistically combine their information. The main difficulty in merging is to match the ontologies since they use different naming conventions. Therefore, we developed an algorithm that merges ontologies by integrating the name matching between class label names with the structure mapping between the ontology elements based on graph convolution. Since the structure mapping is a time consuming process, we designed two methods to perform the graph convolution: vectorial structure matching and constraint-based structure matching. To perform the vectorial structure matching, we designed a general method to calculate the similarities between vectors of different lengths for different metrics. Additionally, we adapted the slower Blondel method to work for structure matching. We implemented our algorithms into FOntCell, a software module in Python for efficient automatic parallel-computed merging/fusion of ontologies in the same or similar knowledge domains. FOntCell can unify dispersed knowledge from one domain into a unique ontology in OWL format and iteratively reuse it to continuously adapt ontologies with new data endlessly produced by data-driven classification methods, such as of the Human Cell Atlas. To navigate easily across the merged ontologies, it generates HTML files with tabulated and graphic summaries, and interactive circular Directed Acyclic Graphs. We used FOntCell to merge the CELDA, LifeMap and LungMAP Human Anatomy cell ontologies into a comprehensive cell ontology. We compared FOntCell with tools used for the alignment of mouse and human anatomy ontologies task proposed by the Ontology Alignment Evaluation Initiative (OAEI) and found that the Fβ alignment accuracies of FOntCell are above the geometric mean of the other tools; more importantly, it outperforms significantly the best OAEI tools in cell ontology alignment in terms of Fβ alignment accuracies.
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Affiliation(s)
- Javier Cabau-Laporta
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Alex M Ascensión
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Mikel Arrospide-Elgarresta
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Daniela Gerovska
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastián, Spain.,Computational Biomedicine Data Analysis Platform, Biodonostia Health Research Institute, San Sebastián, Spain
| | - Marcos J Araúzo-Bravo
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastián, Spain.,Computational Biomedicine Data Analysis Platform, Biodonostia Health Research Institute, San Sebastián, Spain.,Basque Foundation for Science (IKERBASQUE), Bilbao, Spain.,Centro de Investigación Biomédica en Red (CIBER) of Frailty and Healthy Aging (CIBERfes), Madrid, Spain.,TransBioNet Thematic Network of Excellence for Transitional Bioinformatics, Barcelona Supercomputing Center, Barcelona, Spain.,Computational Biology and Bioinformatics, Department Cell and Developmental Biology Max Planck Institute for Molecular Biomedicine, Münster, Germany
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