Trying to Reveal the Mysteries of Stem Cells Using “Omics” Strategies

Falahzadeh, Khadijeh; Jalalvand, Masumeh; Alavi-Moghadam, Sepideh; Bana, Nikoo; Negahdari, Babak

doi:10.1007/978-3-030-27727-7_1

Cited by 4 publications

(2 citation statements)

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“…Moreover, they play an important role in differentiation, self-renewal and homeostasis. Generally, high glucose levels increase the number of CSCs, while low glucose levels lower the quantity of CSCs ( Falahzadeh et al, 2019 ). CSCs are adaptable cells that can cope with a wide range of situations, including low oxygen levels, insufficient blood vessel development, hyperoxidation, and hypoxia ( Luo and Wicha, 2015 ).…”

Section: The Biology Of Cancer Stem Cellsmentioning

confidence: 99%

Molecular Docking as a Therapeutic Approach for Targeting Cancer Stem Cell Metabolic Processes

et al. 2022

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Cancer stem cells (CSCs) are subpopulation of cells which have been demonstrated in a variety of cancer models and involved in cancer initiation, progression, and development. Indeed, CSCs which seem to form a small percentage of tumor cells, display resembling characteristics to natural stem cells such as self-renewal, survival, differentiation, proliferation, and quiescence. Moreover, they have some characteristics that eventually can demonstrate the heterogeneity of cancer cells and tumor progression. On the other hand, another aspect of CSCs that has been recognized as a central concern facing cancer patients is resistance to mainstays of cancer treatment such as chemotherapy and radiation. Owing to these details and the stated stemness capabilities, these immature progenitors of cancerous cells can constantly persist after different therapies and cause tumor regrowth or metastasis. Further, in both normal development and malignancy, cellular metabolism and stemness are intricately linked and CSCs dominant metabolic phenotype changes across tumor entities, patients, and tumor subclones. Hence, CSCs can be determined as one of the factors that correlate to the failure of common therapeutic approaches in cancer treatment. In this context, researchers are searching out new alternative or complementary therapies such as targeted methods to fight against cancer. Molecular docking is one of the computational modeling methods that has a new promise in cancer cell targeting through drug designing and discovering programs. In a simple definition, molecular docking methods are used to determine the metabolic interaction between two molecules and find the best orientation of a ligand to its molecular target with minimal free energy in the formation of a stable complex. As a comprehensive approach, this computational drug design method can be thought more cost-effective and time-saving compare to other conventional methods in cancer treatment. In addition, increasing productivity and quality in pharmaceutical research can be another advantage of this molecular modeling method. Therefore, in recent years, it can be concluded that molecular docking can be considered as one of the novel strategies at the forefront of the cancer battle via targeting cancer stem cell metabolic processes.

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Section: The Biology Of Cancer Stem Cellsmentioning

confidence: 99%

Molecular Docking as a Therapeutic Approach for Targeting Cancer Stem Cell Metabolic Processes

et al. 2022

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“…But this importance is heightened when genes and related changes are used to trace the pathology of diseases. Therefore, the use of technologies such as gel electrophoresis, blotting, polymerase chain reaction (PCR), DNA microarray, DNA sequencing, and chromatin immunoprecipitation (ChIP) assay can be effective steps in decoding various aspects of diseases (Falahzadeh et al, 2019). If the meaning of genomics is extended to the field of oncology, a new approach called cancer genomics will find meaning.…”

Section: The Integration Of Omics Science With Cancer Researchmentioning

confidence: 99%

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

et al. 2022

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Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

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