Identification and Validation of Novel Metastasis-Related Immune Gene Signature in Breast Cancer

Abstract

Shen Ma,1,* Ran Hao,2,* Yi-Wei Lu,1 Hui-Po Wang,1 Jie Hu,2,3 Yi-Xin Qi1 1Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050035, People’s Republic of China; 2Institutes of Health Research, Hebei Medical University, Shijiazhuang, Hebei, 050017, People’s Republic of China; 3Department of Science and Technology, Hebei Medical University, Shijiazhuang, Hebei, 050017, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi-Xin Qi; Jie Hu, Tel [phone], Email [email protected]; [email protected]: Distant metastasis remains the leading cause of death among patients with breast cancer (BRCA). The process of cancer metastasis involves multiple mechanisms, including compromised immune system. However, not all genes involved in immune function have been comprehensively identified.Methods: Firstly 1623 BRCA samples, including transcriptome sequencing and clinical information, were acquired from Gene Expression Omnibus (GSE102818, GSE45255, GSE86166) and The Cancer Genome Atlas-BRCA (TCGA-BRCA) dataset. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed using the GSE102818 dataset to identify the most relevant module to the metastasis of BRCA. Besides, ConsensusClusterPlus was applied to divide TCGA-BRCA patients into two subgroups (G1 and G2). In the meantime, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a metastasis-related immune genes (MRIGs)_score to predict the metastasis and progression of cancer. Importantly, the expression of vital genes was validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC).Results: The expression pattern of 76 MRIGs screened by WGCNA divided TCGA-BRCA patients into two subgroups (G1 and G2), and the prognosis of G1 group was worse. Also, G1 exhibited a higher mRNA expression level based on stemness index score and Tumor Immune Dysfunction and Exclusion score. In addition, higher MRIGs_score represented the higher probability of progression in BRCA patients. It was worth mentioning that the patients in the G1 group had a high MRIGs_score than those in the G2 group. Importantly, the results of RT-qPCR and IHC demonstrated that fasciculation and elongation protein zeta 1 (FEZ1) and insulin-like growth factor 2 receptor (IGF2R) were risk factors, while interleukin (IL)-1 receptor antagonist (IL1RN) was a protective factor.Conclusion: Our study revealed a prognostic model composed of eight immune related genes that could predict the metastasis and progression of BRCA. Higher score represented higher metastasis probability. Besides, the consistency of key genes in BRCA tissue and bioinformatics analysis results from mRNA and protein levels was verified.Keywords: breast cancer, metastasis, immune genes, weighted gene co-expression network analysis, prognostic mode

Keywords

• breast cancer
• metastasis
• immune genes
• weighted gene co-expression network analysis
• prognostic mode