Identification of HOXC Gene Family as Prognostic and Immune-Related Biomarkers in Breast Cancer Through mRNA Transcriptional Profile and Experimental Validation.
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引用次数: 0
Abstract
Breast cancer (BC) is the most common malignancy in women worldwide, and more effective biomarkers are urgently needed for the prevention and treatment of BC. Our study aimed to investigate the role of the HOXC gene family (HOXCs) and its relationship with the immune response in BC. The differential expression of HOXCs and its clinical prognostic significance in BC were explored using bioinformatics analysis, and the cBioPortal database was used to evaluate the genetic mutation profile of the HOXCs in BC. The results indicated that the expression levels of HOXC4, 10, 11, 12, and 13 were significantly increased in BC tissues compared with the normal tissues, and expressions of these genes were closely associated with BC stage, among them, high expression levels of HOXC10 and HOXC13 predicted poor outcome in BC patients. In addition, to elucidate the essential role of HOXCs in the tumor microenvironment and immunotherapeutic response of BC, the impact of HOXCs on the regulation of immune infiltration in BC was comprehensively assessed. The result showed that HOXC10 and HOXC13 expressions were significantly positively linked with the infiltration levels of CD8+T cell and M1 macrophage, while they were negatively related to Mast and Natural killer cells, suggesting the important influence of HOXCs on regulating tumor immunity in BC patients. Lastly, the RT-qPCR assay was employed to validate HOXCs expression in samples of BC patients. In conclusion, HOXCs may be a promising prognostic indicator and could regulate the immune infiltration in BC patients, thus being a promising targeted immunotherapy for BC.
期刊介绍:
Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses.
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