Reviewing cancer’s biology: an eclectic approach

Abstract Background Cancer refers to a group of some of the worldwide most diagnosed and deadliest pathophysiological conditions that conquered researchers’ attention for decades and yet begs for more questions for a full comprehension of its complex cellular and molecular pathology. Main body The d...

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Bibliographic Details
Main Authors: Ibrahim Diori Karidio, Senay Hamarat Sanlier
Format: article
Language:EN
Published: SpringerOpen 2021
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Online Access:https://doaj.org/article/ac62069bbfac42a1bd1f257bdf7e2a8d
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Summary:Abstract Background Cancer refers to a group of some of the worldwide most diagnosed and deadliest pathophysiological conditions that conquered researchers’ attention for decades and yet begs for more questions for a full comprehension of its complex cellular and molecular pathology. Main body The disease conditions are commonly characterized by unrestricted cell proliferation and dysfunctional replicative senescence pathways. In fact, the cell cycle operates under the rigorous control of complex signaling pathways involving cyclins and cyclin-dependent kinases assumed to be specific to each phase of the cycle. At each of these checkpoints, the cell is checked essentially for its DNA integrity. Genetic defects observed in these molecules (i.e., cyclins, cyclin-dependent kinases) are common features of cancer cells. Nevertheless, each cancer is different concerning its molecular and cellular etiology. These could range from the genetic defects mechanisms and/or the environmental conditions favoring epigenetically harbored homeostasis driving tumorigenesis alongside with the intratumoral heterogeneity with respect to the model that the tumor follows. Conclusions This review is not meant to be an exhaustive interpretation of carcinogenesis but to summarize some basic features of the molecular etiology of cancer and the intratumoral heterogeneity models that eventually bolster anticancer drug resistance for a more efficient design of drug targeting the pitfalls of the models.