RESUMEN
Breast cancer is one of the most human malignant diseases and the leading cause of cancer-related death in the world. However, the prognostic and therapeutic benefits of breast cancer patients cannot be predicted accurately by the current stratifying system. In this study, an immune-related prognostic score was established in 22 breast cancer cohorts with a total of 6415 samples. An extensive immunogenomic analysis was conducted to explore the relationships between immune score, prognostic significance, infiltrating immune cells, cancer genotypes and potential immune escape mechanisms. Our analysis revealed that this immune score was a promising biomarker for estimating overall survival in breast cancer. This immune score was associated with important immunophenotypic factors, such as immune escape and mutation load. Further analysis revealed that patients with high immune scores exhibited therapeutic benefits from chemotherapy and immunotherapy. Based on these results, we can conclude that this immune score may be a useful tool for overall survival prediction and treatment guidance for patients with breast cancer.
Asunto(s)
Neoplasias de la Mama/inmunología , Biomarcadores de Tumor , Neoplasias de la Mama/genética , Estudios de Cohortes , Femenino , Perfilación de la Expresión Génica , Genotipo , Humanos , Pronóstico , Escape del Tumor , Microambiente Tumoral/genética , Microambiente Tumoral/inmunologíaRESUMEN
Breast cancer is one of the most common types of cancers and the leading cause of death from malignancy among women worldwide. Tumor-infiltrating lymphocytes are a source of important prognostic biomarkers for breast cancer patients. In this study, based on the tumor-infiltrating lymphocytes in the tumor immune microenvironment, a risk score prognostic model was developed in the training cohort for risk stratification and prognosis prediction in breast cancer patients. The prognostic value of this risk score prognostic model was also verified in the two testing cohorts and the TCGA pan cancer cohort. Nomograms were also established in the training and testing cohorts to validate the clinical use of this model. Relationships between the risk score, intrinsic molecular subtypes, immune checkpoints, tumor-infiltrating immune cell abundances and the response to chemotherapy and immunotherapy were also evaluated. Based on these results, we can conclude that this risk score model could serve as a robust prognostic biomarker, provide therapeutic benefits for the development of novel chemotherapy and immunotherapy, and may be helpful for clinical decision making in breast cancer patients.
Asunto(s)
Biomarcadores de Tumor , Neoplasias de la Mama , Perfilación de la Expresión Génica , Regulación Neoplásica de la Expresión Génica , Modelos Inmunológicos , Microambiente Tumoral , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/inmunología , Neoplasias de la Mama/diagnóstico , Neoplasias de la Mama/genética , Neoplasias de la Mama/inmunología , Neoplasias de la Mama/terapia , Femenino , Humanos , Linfocitos Infiltrantes de Tumor/inmunología , Valor Predictivo de las Pruebas , Pronóstico , Microambiente Tumoral/genética , Microambiente Tumoral/inmunologíaRESUMEN
AIMS: Because of the high affinity of these animal neurotoxin proteins for some special target site, they were usually used as pharmacological tools and therapeutic agents in medicine to gain deep insights into the function of the nervous system. BACKGROUND AND OBJECTIVE: The animal neurotoxin proteins are one of the most common functional groups among the animal toxin proteins. Thus, it was very important to characterize and predict the animal neurotoxin proteins. METHODS: In this study, the differences between the animal neurotoxin proteins and non-toxin proteins were analyzed. RESULT: Significant differences were found between them. In addition, the support vector machine was proposed to predict the animal neurotoxin proteins. The predictive results of our classifier achieved the overall accuracy of 96.46%. Furthermore, the random forest and k-nearest neighbors were applied to predict the animal neurotoxin proteins. CONCLUSION: The compared results indicated that the predictive performances of our classifier were better than other two algorithms.
Asunto(s)
Aminoácidos/análisis , Aprendizaje Automático , Neurotoxinas/química , Animales , Neurotoxinas/clasificación , Proteínas/química , Proteínas/clasificaciónRESUMEN
Breast cancer is the most common cancer and the leading cause of cancer death among women in the world. Tumour-infiltrating lymphocytes were defined as the white blood cells left in the vasculature and localized in tumours. Recently, tumour-infiltrating lymphocytes were found to be associated with good prognosis and response to immunotherapy in tumours. In this study, to examine the influence of FLI1 in immune system in breast cancer, we interrogated the relationship between the FLI1 expression levels with infiltration levels of 28 immune cell types. By splitting the breast cancer samples into high and low expression FLI1 subtypes, we found that the high expression FLI1 subtype was enriched in many immune cell types, and the up-regulated differentially expressed genes between them were enriched in immune system processes, immune-related KEGG pathways and biological processes. In addition, many important immune-related features were found to be positively correlated with the FLI1 expression level. Furthermore, we found that the FLI1 was correlated with the immune-related genes. Our findings may provide useful help for recognizing the relationship between tumour immune microenvironment and FLI1, and may unravel clinical outcomes and immunotherapy utility for FLI1 in breast cancer.