Next Article in Journal
Genome-Wide Analysis of the PYL Gene Family and Identification of PYL Genes That Respond to Abiotic Stress in Brassica napus
Previous Article in Journal
Genome-Wide Transcriptome Analysis Reveals the Comprehensive Response of Two Susceptible Poplar Sections to Marssonina brunnea Infection
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Genes 2018, 9(3), 155; https://doi.org/10.3390/genes9030155

Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms

1
Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
2
Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
3
School of Life Sciences, Shanghai University, Shanghai 200444, China
4
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 3 January 2018 / Revised: 3 March 2018 / Accepted: 6 March 2018 / Published: 12 March 2018
(This article belongs to the Section Technologies and Resources for Genetics)
Full-Text   |   PDF [1096 KB, uploaded 12 March 2018]   |  

Abstract

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer. View Full-Text
Keywords: Monte Carlo feature selection; breast cancer; random forest; patient-derived tumor xenograft Monte Carlo feature selection; breast cancer; random forest; patient-derived tumor xenograft
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, D.; Li, J.-R.; Zhang, Y.-H.; Chen, L.; Huang, T.; Cai, Y.-D. Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms. Genes 2018, 9, 155.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Genes EISSN 2073-4425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top