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Identification and validation of an m6A-related gene signature to predict prognosis and evaluate immune features of breast cancer

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Abstract

Breast cancer is the most prevalent cancer, and it is accompanied by high heterogeneity. N6-methyladenosine (m6A) modification significantly contributes to breast cancer tumorigenesis and progression. However, how m6A-related genes affect the clinical outcomes and tumor immune microenvironment (TIME) of breast cancer is largely unknown. Our study developed an m6A-related gene signature on the basis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The m6A-related gene signature was constructed using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Breast cancer patients were classified into low- and high-risk groups depending on the median risk score. The reliability and efficiency of the signature were validated using Kaplan–Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). The risk score was validated as an independent indicator associated with overall survival, and a nomogram model was created to estimate the overall survival of patients with breast cancer. Functional annotation suggested that the risk score had a strong relationship with immune-related pathways. Different proportions of immune cell infiltration between the two groups were evaluated using various algorithms. The high-risk group had higher immune checkpoint expression levels. We discovered that one of the 6 prognostic genes, TMEM71, was downregulated in breast cancer tissues. In vitro experiments indicated that overexpression of TMEM71 suppressed breast cancer cell proliferation and migration. In conclusion, the m6A-related gene signature may be a sensitive biomarker for overall survival prediction and guide the individualized treatment for breast cancer patients.

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Data availability statement

The RNA-seq data and clinical data of breast cancer used for the training and validation cohorts were downloaded from the UCSC Cancer Browser (https://xenabrowser.net/datapages/) and GEO (https://www.ncbi.nlm.nih.gov/gds/). The experimental datasets used and analyzed in this study are available from the corresponding authors upon reasonable request.

Abbreviations

AJCC:

The American Joint Committee on Cancer

AUC:

Areas under curve

DC:

Dendritic cell

DEG:

Differentially expressed gene

EMT:

Epithelial–mesenchymal transition

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

GSEA:

Gene Set Enrichment Analysis

HUC:

Human urothelial carcinoma

ICB:

Immune checkpoint blockade

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LASSO:

Least absolute shrinkage and selection operator

m6A:

N6-methyladenosine

NSCLC:

Non-small cell lung cancer

PCA:

Principal component analysis

PRCC:

Papillary renal cell carcinoma

PVDF:

Polyvinylidene fluoride

RIPA:

Radio immunoprecipitation assay

ROC:

Receiver operating characteristic

ssGSEA:

Single-sample gene set enrichment analysis

TAM:

Tumor-associated macrophages

TCGA:

The Cancer Genome Atlas

TIME:

Tumor immune microenvironment

TMEM:

Transmembrane

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Acknowledgements

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Funding

This work was supported by National Key Research and Development Program (No. 2020YFA0712400), Special Foundation for Taishan Scholars (No. ts20190971), National Natural Science Foundation of China (No. 81874119; No. 82072912; No. 82004122), China Postdoctoral Science Foundation (No. 2020M682199), Shandong Provincial Natural Science Foundation, China (No. ZR2020QH335, No. ZR2019LZL003), Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH121001-2021003), 2021 Shandong Medical Association Clinical Research Fund—Qilu Special Project (YXH2022ZX02160), Foundation from Clinical Research Center of Shandong University (No.2020SDUCRCA015), Qilu Hospital Clinical New Technology Developing Foundation (No. 2019-3).

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Author notes

  1. Wenhao Li and Xiaolong Wang have contributed equally to this work.

Authors and Affiliations

  1. Department of Breast Surgery, General Surgery, Cheeloo College of Medicine, Qilu Hospital of Shandong University, Wenhua Xi Road No. 107, Jinan, 250012, Shandong, China

    Wenhao Li, Xiaolong Wang, Chen Li, Tong Chen, Xianyong Zhou, Zheng Li & Qifeng Yang

  2. Department of Breast Surgery, Binzhou People’s Hospital, Binzhou, Shandong, China

    Xianyong Zhou

  3. Department of Pathology Tissue Bank, Cheeloo College of Medicine, Qilu Hospital of Shandong University, Wenhua Xi Road No. 107, Jinan, 250012, Shandong, China

    Qifeng Yang

  4. Research Institute of Breast Cancer, Shandong University, Wenhua Xi Road No. 107, JinanShandong, 250012, China

    Qifeng Yang

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  1. Wenhao Li
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Contributions

Conceived and designed the study: WL and XW. Data collection: CL. Data analysis: ZL. Generated figures: TC. Wrote the manuscript: XZ. Revised and edited the manuscript: QY. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qifeng Yang.

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The authors declare no conflicts of interest.

Ethical approval

The study was approved by the Ethics Committee on Scientific Research of Shandong University, Qilu Hospital (approval number: KYLL-2016-350), and informed consents were provided by all patients.

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Li, W., Wang, X., Li, C. et al. Identification and validation of an m6A-related gene signature to predict prognosis and evaluate immune features of breast cancer. Human Cell 36, 393–408 (2023). https://doi.org/10.1007/s13577-022-00826-x

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