Guoyu Mu1, Hong Ji2, Hui He3, Hongjiang Wang4. 1. Breast Surgery Department, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China. 2. Gynecology and Obstetrics Department, The Second Affiliated Hospital of Dalian Medical University, Zhongshan Road 467, Dalian, 116023, Liaoning, China. 3. Department of Laparoscopic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China. 4. Breast Surgery Department, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China. wanghongjiang_dmu@163.com.
Abstract
BACKGROUND: Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis. METHODS: In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed. RESULTS: We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed. CONCLUSIONS: Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.
BACKGROUND:Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis. METHODS: In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed. RESULTS: We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed. CONCLUSIONS: Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.
Entities:
Keywords:
Breast cancer; Immunotherapy; TCGA database
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