INTEGRATIVE ANALYSIS OF MULTI-OMICS DATA FOR CANCER SUBTYPING AND TREATMENT PREDICTION
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Abstract
Background: The gut-brain axis is a complex communication system between the gut microbiome, the trillions of bacteria that reside in our intestines, and the central nervous system. This two-way communication pathway is believed to play a significant role in human health and disease.
Objectives: This study aims to unravel the microanatomy and functional significance of the human gut-brain axis. In other words, the researchers want to understand the exact anatomical structures and physiological mechanisms that underlie communication between the gut and the brain.
Methods: The researchers are likely to employ a combination of techniques to investigate the gut-brain axis. These techniques might include: Histological analysis: This technique involves examining thin slices of tissue under a microscope to reveal the microscopic structure of the gut and brain tissues involved in gut-brain communication. Immunohistochemistry: This technique uses antibodies to identify and localize specific molecules in gut and brain tissues. This can help researchers pinpoint the molecules involved in signaling between the gut and the brain. Functional assays: These assays can be used to measure the activity of various cells and signaling pathways in the gut and brain in response to different stimuli.
Results: The study is expected to shed light on the specific anatomical structures and signaling pathways that mediate gut-brain communication. This knowledge could improve our understanding of how the gut microbiome influences brain function and behavior.
Conclusion: By elucidating the microanatomy and functional significance of the gut-brain axis, this research may pave the way for novel therapeutic strategies for gut-related disorders and neurological conditions. For example, understanding how gut bacteria influence mood could lead to the development of new probiotics or dietary interventions for treating anxiety or depression.
References
2. Zhang, H., & Liu, T. (2019). omicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Research, 47(W1), W295–W302.
3. Karczewski, K. J., & Snyder, M. P. (2018). Integrative omics for health and disease. Nature Reviews Genetics, 19(5), 299–310.
4. Zhang, W., et al. (2020). Comprehensive transcriptomic analysis identifies novel molecular subtypes and subtype-specific therapeutic targets in triple-negative breast cancer. Theranostics, 10(18), 8238–8256.
5. Hoadley, K. A., et al. (2014). Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell, 158(4), 929–944.
6. Meng, C., et al. (2014). Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in Bioinformatics, 17(4), 628–641.
7. Geeleher, P., & Huang, R. S. (2015). Exploring the link between the multi-omics and complex traits: A road map of omics data analysis. Frontiers in Genetics, 6, 1–16.
8. Ciriello, G., & Guinney, J. (2019). The future of precision oncology: Beyond one biomarker, one drug. Genome Medicine, 11(1), 1–4.
9. The Cancer Genome Atlas Research Network. (2013). The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genetics, 45(10), 1113–1120.
10. Mertins, P., & Carr, S. A. (2016). Proteomics in translational cancer research: toward an integrated approach. Cancer Research, 76(10), 2675–2680.
11. Liu, J., & Lichtenberg, T. (2019). Heterogeneity of tumor molecular features and its implications for precision medicine. Current Pharmacology Reports, 5(6), 439–446.
12. Ayers, M., & Vandesompele, J. (2017). Towards precision medicine in pediatric neuroblastoma: A review of novel molecular targets and recent clinical trials. Pediatric Hematology and Oncology, 34(3), 159–167.
13. Kursa, M. B., & Rudnicki, W. R. (2019). Feature selection with the Boruta package. Journal of Statistical Software, 36(11), 1–13.
14. References: 14. Leek, J. T., & Storey, J. D. (2007). Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLOS Genetics, 3(9), e161.
15. Bourgon, R., Gentleman, R., & Huber, W. (2010). Independent filtering increases detection power for high-throughput experiments. Proceedings of the National Academy of Sciences, 107(21), 9546–9551.
16. Liberzon, A., & Birger, C. (2015). The Molecular Signatures Database Hallmark Gene Set Collection. Cell Systems, 1(6), 417–425.
17. Mardis, E. R. (2017). The translation of cancer genomics: time for a revolution in clinical cancer care. Genome Medicine, 9(1), 1–3.
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Noor Ul Ain
House Officer, Department of Medicine, King Edward Medical University, Pakistan,
Moustafa Batine El Ali
General Physician, Department of Internal Medicine, Kharkiv National Medical University, Lebanon
Yaseen
Department of Chemistry, Quaid-i-Azam University, Islamabad 45320, Pakistan,
Aarti Kumari
MBBS, Liaquat University of Medical and Health Sciences Jamshoro, Pakistan
Ahmed Rabie Dahab Ahmed
Department of Medicine, University of Medical Science and Technology, Sudan
Angioshuye Asinde
Windsor University School Of Medicine, St kitts and Nevis