Genomics, microRNA, epigenetics, and proteomics for future diagnosis, treatment and monitoring response in upper GI

Björn L D M Brücher1,2,3,4,5,6, Yan Li7, Philipp Schnabel8

  • 1Theodor-Billroth-Academy®, Munich, Germany. b-bruecher@gmx.de.

Insights

Integrating patient and tumor data guides personalized cancer therapy. This approach aims to tailor treatments for better outcomes and spare patients unnecessary morbidity.

Area of Science:

  • Oncology and Biotechnology
  • Translational Cancer Research

Background:

  • Cancer treatment is evolving towards integrating patient and tumor variables for outcome-oriented strategies.
  • Multimodal therapy settings can enable personalized and individualized treatment approaches.
  • Precision medicine aims to improve cancer classification by integrating translational molecular data.

Purpose of the Study:

  • To explore key biotechnology issues for personalized and individualized cancer diagnosis and multimodal treatment.
  • To provide an overview of translational data in cancer, focusing on upper gastrointestinal cancers.
  • To assess the pros and cons of methodologies in genomics, microRNA, epigenetics, and proteomics for cancer care.

Main Methods:

  • Review of current biotechnologies and translational data in cancer research.
  • Analysis of methodologies for molecular data acquisition (genomics, microRNA, epigenetics, proteomics).
  • Focus on upper gastrointestinal cancers to illustrate diagnostic and therapeutic challenges.

Main Results:

  • Challenges exist in integrating diverse molecular data due to varied methods and tumor types.
  • Genomics, microRNA, epigenetics, and proteomics offer potential for improved cancer diagnosis and prognosis.
  • Understanding technological limitations is crucial for advancing personalized cancer treatment.

Conclusions:

  • Personalized and individualized cancer therapies require integrating patient and tumor data.
  • Biotechnology advancements are key to refining cancer classification and treatment strategies.
  • Further research is needed to overcome challenges in translational data integration for precision oncology.

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