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Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer

  • Gynecologic Oncology: Original Article
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Abstract

Improved insight into the molecular mechanisms of triple-negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study was to identify genes significantly associated with TNBC and further analyze their prognostic significance. The Cancer Genome Atlas (TCGA) TNBC database and gene expression profiles of GSE76275 from Gene Expression Omnibus (GEO) were used to explore differentially co-expressed genes in TNBC compared with those in normal tissues and non-TNBC breast cancer tissues. Differential gene expression and weighted gene co-expression network analyses identified 24 differentially co-expressed genes. Functional annotation suggested that these genes were primarily enriched in processes such as metabolism, membrane, and protein binding. The protein-protein interaction (PPI) network further identified ten hub genes, five of which (MAPT, CBS, SOX11, IL6ST, and MEX3A) were confirmed to be differentially expressed in an independent dataset (GSE38959). Moreover, CBS and MEX3A expression was upregulated, whereas IL6ST expression was downregulated in TNBC tissues compared to that in other breast cancer subtypes. Furthermore, lower expression of IL6ST was associated with worse overall survival in patients with TNBC. Thus, IL6ST might play an important role in TNBC progression and could serve as a tumor suppressor gene for diagnosis and treatment.

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Data Availability

All datasets analyzed in this study can be found at TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/gds), and Oncomine (https://www.oncomine.org/resource/login.html).

Code Availability

Not applicable.

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Funding

This study was supported by the Fund of Youth Innovation of Inner Mongolia Medical University [grant number YKD2020QNCX021].

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Author notes

  1. Rong Jia and Yujie Weng contributed equally to this work.

Authors and Affiliations

  1. College of Computer and Information, Inner Mongolia Medical University, Hohhot, 010110, Inner Mongolia Autonomous Region, China

    Rong Jia, Yujie Weng, Zhongxian Li, Wei Liang, Yucheng Ji, Ying Liang & Pengfei Ning

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  1. Rong Jia
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  2. Yujie Weng
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  3. Zhongxian Li
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  4. Wei Liang
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  5. Yucheng Ji
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  6. Ying Liang
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  7. Pengfei Ning
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Contributions

Conceptualization, R.J. and YJ.W.; methodology, ZX.L.; software, W.L.; formal analysis, YC.J.; resources, Y.L.; writing—original draft preparation, R.J.; writing—review and editing, PF.N. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Pengfei Ning.

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This study is conducted based on TCGA and GEO databases and is not involved in animal or human experiments, so ethical consents are not appropriate in this study.

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The authors declare no competing interests.

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Jia, R., Weng, Y., Li, Z. et al. Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer. Reprod. Sci. 28, 2331–2341 (2021). https://doi.org/10.1007/s43032-021-00509-2

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  • DOI: https://doi.org/10.1007/s43032-021-00509-2

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