Open Access

ARTICLE

Weighted gene co-expression network analysis identifies a novel immune-related gene signature and nomogram to predict the survival and immune infiltration status of breast cancer

JUNXIA LIU¹, KE PANG², FEI HE^2,*

1 Department of Oncology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China
2 Department of Geriatrics, Yongchuan Hospital of Chongqing Medical University, Chongqing, China

* Corresponding Author: FEI HE. Email:

(This article belongs to the Special Issue: Decoding Gene (including circRNA, lincRNA miRNA and mRNA) Expression)

BIOCELL 2022, 46(7), 1661-1673. https://doi.org/10.32604/biocell.2022.018023

Received 26 June 2021; Accepted 16 August 2021; Issue published 17 March 2022

Abstract

Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide. Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients. In the study, we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes. Subsequently, the prognostic immune-related gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis. Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has strong predictive performance. And we developed a nomogram by combing the risk score with multiple clinical characteristics. CIBERSORT and TIMER algorithms confirmed that there are significant differences in tumor-infiltrating immune cells in different risk groups. In addition, gene set enrichment analysis shows 6 pathways that differ between high- and low-risk group. The immune-related gene signature effectively predicts the survival and immune infiltration of breast cancer patients and is expected to provide more effective immunotherapy targets for the prognosis prediction of breast cancer.

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