@Article{biocell.2022.018023,
AUTHOR = {JUNXIA LIU, KE PANG, FEI HE},
TITLE = {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},
JOURNAL = {BIOCELL},
VOLUME = {46},
YEAR = {2022},
NUMBER = {7},
PAGES = {1661--1673},
URL = {http://www.techscience.com/biocell/v46n7/47034},
ISSN = {1667-5746},
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.},
DOI = {10.32604/biocell.2022.018023}
}