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Mechanisms of Gastrointestinal Carcinogenesis

  • Frank I. Scott
  • John P. LynchEmail author
Chapter
Part of the Molecular Pathology Library book series (MPLB, volume 7)

Abstract

Gastrointestinal malignancies represent a significant disease burden worldwide, with over 3.8 million cases per year. Given this impact, much research into the underlying pathogenesis of neoplastic transformation has occurred. This chapter focuses on our current understanding of these processes.

The chapter begins with an explanation of the basic concepts of neoplastic transformation, including a discussion of the multiple steps that occur in carcinogenesis, the role that evolutionary pressures play in this process, and the biological factors involved, such as heredity, sporadic carcinogenesis, the role of carcinogens, and chronic inflammation’s contribution to promoting malignancy.

The focus then moves deeper, into a discussion of the critical molecular features of malignancies. First, the molecular mechanisms initially described by Hanahan and Weinberg in 2000 are discussed. These mechanisms include self-sufficiency in proliferation, silencing of tumor suppressors, apoptosis evasion, telomere modification, and angiogenesis. The role of the inflammasome is also examined. Second, the role of genetic instability is explored, with a description of processes such as microsatellite instability (MSI), chromosomal instability (CIN), dysfunction of base excision repair (BER) pathways, and lastly the role of epigenetic modification and its interplay with those mechanisms.

The chapter concludes with an examination of two burgeoning new fields in cancer research. First, the role of cancer stem cells in malignancy is explored. Lastly, the topic of microRNAs and their role in malignancy is introduced.

Keywords

Pancreatic Cancer Cancer Stem Cell Familial Adenomatous Polyposis Esophageal Adenocarcinoma Base Excision Repair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to acknowledge the ­assistance of Stephanie Icken, who contributed to the design of the figures used in this chapter.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Division of GastroenterologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of Medicine, Division of GastroenterologyHospital of the University of PennsylvaniaPhiladelphiaUSA

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