iTRAQ-based quantitative proteomic analysis of Yamanaka factors reprogrammed breast cancer cells

Kun Wang1, Zhiyan Shan1,2, Lian Duan1,3

  • 1Department of Histology and Embryology, Harbin Medical University, Harbin, China.

Oncotarget
|April 21, 2017
PubMed

Insights

Induced pluripotent stem cells (iPSCs) technology reprogrammed cancer cells. This study analyzed genome-wide proteomic changes, identifying mitochondria, ribosome, and tumor suppressor proteins as key regulators in tumor reprogramming.

Area of Science:

  • Biochemistry
  • Molecular Biology
  • Cancer Research

Background:

  • Induced pluripotent stem cells (iPSCs) technology offers a method to reprogram cancer cells into a stem-like state.
  • Understanding the genome-wide mechanisms of tumor differentiation and dedifferentiation is crucial for cancer research.

Purpose of the Study:

  • To analyze the proteomic changes during the reprogramming of breast cancer cells (MCFs) into induced pluripotent stem cells (Mcfips).
  • To identify key proteins and pathways involved in tumor reprogramming using a genome-wide approach.

Main Methods:

  • Reprogramming of MCF cells using OCT4, SOX2, C-MYC, and KLF4 transcription factors to generate Mcfips.
  • Proteomic analysis using LC-MS/MS iTRAQ technology to compare protein expression between MCFs, Mcfips, and human induced pluripotent stem cells (Hips).
  • Gene Ontology (GO) functional classification and KEGG pathway analysis for differentially expressed proteins and protein interaction network analysis.

Main Results:

  • Identified 4,616 proteins, with 247 differentially expressed in Mcfips compared to Hips and 142 compared to MCFs.
  • Discovered 35 co-upregulated and 10 co-downregulated proteins.
  • Identified key protein regulators including mitochondria, ribosome, and tumor suppressor proteins involved in tumor reprogramming.

Conclusions:

  • Mitochondria, ribosome, and tumor suppressor proteins are core regulators of tumor reprogramming.
  • This study provides valuable proteomic data for understanding tumor reprogramming mechanisms.
  • Findings may contribute to exploring strategies for normalizing malignant phenotypes in cancer cells.

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