Oncotarget

Research Papers:

Gene expression profiling as a potential predictor between normal and cancer samples in gastrointestinal carcinoma

Panagiotis Apostolou, Aggelos C. Iliopoulos, Panagiotis Parsonidis and Ioannis Papasotiriou

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Oncotarget. 2019; 10:3328-3338. https://doi.org/10.18632/oncotarget.26913

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Abstract

Panagiotis Apostolou1, Aggelos C. Iliopoulos1, Panagiotis Parsonidis1 and Ioannis Papasotiriou1

1 Research & Development Department, Research Genetic Cancer Centre S.A., Florina, Greece

Correspondence to:

Ioannis Papasotiriou,email: papasotiriou.ioannis@rgcc-genlab.com,
office@rgcc-genlab.com

Keywords: clustering analysis; gastrointestinal cancer; peripheral blood mononuclear cells; qRT-PCR

Abbreviations: PBMCs: Peripheral blood mononuclear cells; CTCs: Circulating tumor cells

Received: January 29, 2019     Accepted: April 03, 2019     Published: May 21, 2019

ABSTRACT

Analysis and comparison of gene expression profile among molecules, correlated with essential and crucial biological processes, is of primary importance in cancer research, since it provides significant info regarding the resistance to chemo/radiotherapy, risk for relapse or prediction of metastasis etc. In this study, gene expression profile is used for discriminating efficiently colon cancer cell lines from normal cells and cancer cells in blood samples of colon cancer patients and categorizing different types of gastrointestinal cancer. In particular, blood samples were collected from normal donors as well as from colon cancer patients. Peripheral blood mononuclear cells were isolated and gene expression analysis was performed for more than fifty genes. The same assays were performed for commercial cancer cell lines representing different types of gastrointestinal cancer. In order to examine whether the comparison of gene expression profile can lead to a thorough discrimination between cancer and normal states as well as between different cancer types, we performed clustering analysis based on hierarchical, and k-means algorithms. The clustering analysis efficiently separated: a) colon cancer cell lines from colon patients’ samples, b) normal from the colon cancer samples, c) gastric and pancreatic cancer from liver and colon types based. The exploitation of gene expression profile can be successfully used for the discrimination between normal vs cancer samples and/or for categorizing various types of cancer. This of course has important implications in cancer management since it enables the quick discrimination based on cells, isolated from bloodstream, needless of tissue examination or protocols requiring specialized equipment.


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