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Comertpay et al. J Transl Genet Genom 2022;6:84-94 Journal of Translational
DOI: 10.20517/jtgg.2021.44
Genetics and Genomics

Original Article Open Access

Identification of molecular signatures and pathways
of obese breast cancer gene expression data by a

machine learning algorithm

Betul Comertpay, Esra Gov
Department of Bioengineering, Adana Alparslan Türkeş Science and Technology University, Adana 01250, Turkey.

Correspondence to: Assoc. Prof. Esra Gov, Department of Bioengineering, Adana Alparslan Türkeş Science and Technology
University, Adana 01250, Turkey. E-mail: egov@atu.edu.tr

How to cite this article: Comertpay B, Gov E. Identification of molecular signatures and pathways of obese breast cancer gene
expression data by a machine learning algorithm. J Transl Genet Genom 2022;6:84-94. https://dx.doi.org/10.20517/jtgg.2021.44

Received: 30 Aug 2021 First Decision: 9 Nov 2021 Revised: 29 Nov 2021 Accepted: 27 Dec 2021 Published: 20 Jan 2022
Academic Editors: Sanjay Gupta, Nathan A. Berger Copy Editor: Yue-Yue Zhang Production Editor: Yue-Yue Zhang

Abstract
Aim: Currently, the obesity epidemic is one of the biggest problems for human health. Obesity is impacted on
survival in patients with breast cancer. However, key biomarkers of obesity-related breast cancer risk are still not
well known. Thus, using machine learning to identify the most appropriate features in obesity-associated breast
cancer patients may improve the predictive accuracy and interpretability of regression models.

Methods: In the present study, we identified 23 differentially expressed genes (DEGs) from the GSE24185
transcriptome dataset. Seed genes were identified from DEGs, the co-expression network genes and hub genes of
the protein-protein interaction network. Pathway enrichment analysis was performed for DEGs. The Ridge penalty
regression model was executed by using P-values of enriched pathways and seed gene pathway association score
to obtain the most relevant molecular signatures. The model was performed using 10-fold cross-validation to fit the
penalized models.

Results: Angiotensin II receptor type 1 (AGTR1), cyclin D1 (CCND1), glutamate ionotropic receptor AMPA type
subunit 2 (GRIA2), interleukin-6 cytokine family signal transducer (IL6ST), matrix metallopeptidase 9 (MMP9),
and protein kinase CAMP-dependent type II regulatory subunit beta (PRKAR2B) were considered as candidate
molecular signatures of obese patients with breast cancer. In addition, RAF-independent MAPK1/3 activation,
collagen degradation, bladder cancer, drug metabolism-cytochrome P450, and signaling by Hedgehog pathways in
cancer were primarily associated with obesity-associated breast cancer.

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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