Clinical and Translational Research
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Sep 26, 2023; 11(27): 6344-6362
Published online Sep 26, 2023. doi: 10.12998/wjcc.v11.i27.6344
Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus
Xiong Zhang, Zhi-Hui Mi
Xiong Zhang, Department of Pathology, HuLunBuir Peoples’s Hospital, HuLunBuir 010018, Nei Monggol Autonomous Region, China
Zhi-Hui Mi, Department of Research and Marketing, Inner Mongolia Di An Feng Xin Medical Technology Co., LTD, Huhhot 010010, Nei Monggol Autonomous Region, China
Author contributions: Zhang X designed and directed the research; Mi ZH collected data and wrote the manuscript; all authors have read and approved the final manuscript.
Supported by the Natural Science Foundation of Inner Mongolia, No. 2021GG0298.
Institutional review board statement: The data for the study came from public databases and did not involve blood or tissue samples from humans or animals. Therefore, there were no ethical issues involved in the study.
Informed consent statement: The data for the study came from public databases and did not involve blood or tissue samples from humans or animals. Therefore, the study did not involve any informed consent issues.
Conflict-of-interest statement: All the authors declare that they have no competing interests.
Data sharing statement: The original datasets during the current study are available in the Gene Expression Omnibus (GEO), further inquiries can be directed to the following links (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36765, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10810, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20086).
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhi-Hui Mi, MD, Senior Researcher, Department of Research and Marketing, Inner Mongolia Di An Feng Xin Medical Technology Co., LTD, Ru Yi Development Zone, Huhhot 010010, Nei Monggol Autonomous Region, China. zhihui_mi@sina.com
Received: June 24, 2023
Peer-review started: June 24, 2023
First decision: August 9, 2023
Revised: August 18, 2023
Accepted: August 31, 2023
Article in press: August 31, 2023
Published online: September 26, 2023
Processing time: 88 Days and 4.2 Hours
ARTICLE HIGHLIGHTS
Research background

Breast cancer is widely recognized as a highly malignant neoplasm in women, posing a significant risk to their overall health. The diagnostic challenges of breast cancer arise from the heterogeneity of samples and the limitations of conventional techniques. Consequently, the identification of more stable biomarkers assumes paramount importance in facilitating early breast cancer screening. In this context, bioinformatics methods have been employed to detect differentially expressed genes associated with proliferation, invasion, apoptosis, and overall survival in breast cancer.

Research motivation

To ascertain fundamental prognostic biomarkers in breast cancer, three databases were queried for genes associated with breast cancer as tumor markers.

Research objectives

Bioinformatics analysis of the molecular mechanism involved in breast cancer revealed that seven differentially expressed genes (DEGs) [calreticulin (CALR), heat shock protein family B member 1 (HSPB1), insulin-like growth Factor 1 (IGF1), interleukin-1 receptor 1 (IL1R1), Krüppel-like factor 4 (KLF4), suppressor of cytokine signaling 3 (SOCS3), and triosephosphate isomerase 1 (TPI1)] play critical roles in the progression of breast cancer. Bioinformatics were used to identify hub genes and enrichment pathways in breast cancer, illustrating a biological relationship between the pathways and gene expression in breast cancer.

Research methods

Microarray data information, data processing of differentially expressed genes, protein-protein interaction network and module analysis, and survival analysis were used to mine potential biomarkers. In addition, pathway enrichment analysis was also conducted to elaborate the pathogenesis of disease.

Research results

Three Gene Expression Omnibus datasets that included breast cancer tissues and normal tissues were analyzed; 231 DEGs were identified. 7 potential biomarkers (CALR, HSPB1, IGF1, IL1R1, KLF4, SOCS3, and TPI1) were closely related to the occurrence and progression of breast cancer. The discrimination of potential markers for the model is also relatively perfect, and the discrimination for cancer group and normal group is 100%.

Research conclusions

Through the utilization of bioinformatics analysis, the molecular mechanism of breast cancer was investigated, revealing the significant involvement of seven differentially expressed genes (CALR, HSPB1, IGF1, IL1R1, KLF4, SOCS3, and TPI1) in the advancement of breast cancer. These findings hold promise in enhancing our understanding of breast cancer pathogenesis, as well as in the identification of novel biomarkers and potential drug targets, thereby facilitating advancements in breast cancer diagnosis and therapeutics.

Research perspectives

Bioinformatics was employed to identify hub genes and significant pathways in breast cancer, thereby establishing a biological association between the pathways and gene expression potentially implicated in the advancement of breast cancer. The utilization of bioinformatics analysis revealed the relevant genes and cellular pathways implicated in the genesis and progression of breast cancer.