Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer

Zhilin Chen; Ruifa Feng; Ulf Dietrich Kahlert; Zhitong Chen; Luz Angela Torres-Dela Roche; Amr Soliman; Chen Miao; Rudy Leon De Wilde; Wenjie Shi

doi:10.3389/fonc.2022.883197

Construction of ceRNA Networks Associated With CD8 T Cells in Breast Cancer

Front Oncol. 2022 Jun 9:12:883197. doi: 10.3389/fonc.2022.883197. eCollection 2022.

Authors

Zhilin Chen^{1

2}, Ruifa Feng³, Ulf Dietrich Kahlert⁴, Zhitong Chen², Luz Angela Torres-Dela Roche², Amr Soliman², Chen Miao⁵, Rudy Leon De Wilde², Wenjie Shi^{2

4}

Affiliations

¹ Department of Breast and Thoracic Oncological Surgery, The First Affiliated Hospital of Hainan Medical University, Haikou, China.
² University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany.
³ Breast Center of The Second Affiliated Hospital of Guilin Medical University, Guilin, China.
⁴ Molecular and Experimental Surgery, University Clinic for General-, Visceral- and Vascular Surgery, University Medicine Magdeburg and Otto-von Guericke University, Magdeburg, Germany.
⁵ Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Abstract

Background: The infiltration of CD8 T cells is usually linked to a favorable prognosis and may predict the therapeutic response of breast cancer patients to immunotherapy. The purpose of this research is to investigate the competing endogenous RNA (ceRNA) network correlated with the infiltration of CD8 T cells.

Methods: Based on expression profiles, CD8 T cell abundances for each breast cancer (BC) patient were inferred using the bioinformatic method by immune markers and expression profiles. We were able to extract the differentially expressed RNAs (DEmRNAs, DEmiRNAs, and DElncRNAs) between low and high CD8 T-cell samples. The ceRNA network was constructed using Cytoscape. Machine learning models were built by lncRNAs to predict CD8 T-cell abundances. The lncRNAs were used to develop a prognostic model that could predict the survival rates of BC patients. The expression of selected lncRNA (XIST) was validated by quantitative real-time PCR (qRT-PCR).

Results: A total of 1,599 DElncRNAs, 89 DEmiRNAs, and 1,794 DEmRNAs between high and low CD8 T-cell groups were obtained. Two ceRNA networks that have positive or negative correlations with CD8 T cells were built. Among the two ceRNA networks, nine lncRNAs (MIR29B2CHG, NEAT1, MALAT1, LINC00943, LINC01146, AC092718.4, AC005332.4, NORAD, and XIST) were selected for model construction. Among six prevalent machine learning models, artificial neural networks performed best, with an area under the curve (AUC) of 0.855. Patients from the high-risk category with BC had a lower survival rate compared to those from the low-risk group. The qRT-PCR results revealed significantly reduced XIST expression in normal breast samples, which was consistent with our integrated analysis.

Conclusion: These results potentially provide insights into the ceRNA networks linked with T-cell infiltration and provide accurate models for T-cell prediction.

Keywords: T cell; breast cancer; ceRNA; lncRNA; target.