Abstract
Breast cancer is the most lethal cancer in women and displaying a broad range of heterogeneity in terms of clinical, molecular behavior and response to therapy. Increasing evidence demonstrated that immune-related genes were an important source of prognostic information for several types of tumors. In this study, the k-mean clustering was applied to gene expression data from the immune-related genes, two molecular clusters were identified for 1980 breast cancer patients. The prognostic significance of the immune-related genes based classification was confirmed in the log-rank test. These clusters were also associated with immune checkpoints, immune-related features and tumor infiltrating levels. In addition, we used the shrunken centroid algorithm to predict the cluster of a given breast cancer sample, and good predictive results were obtained by this algorithm. These results indicated that the proposed classification method is a promising method, and we hope that this method may improve the treatment stratification of breast cancer in the future.
Highlights
Breast cancer is the most lethal cancer in women and displaying a broad range of heterogeneity in terms of clinical, molecular behavior and response to therapy
The shrunken centroid algorithm was used to classify the clusters by the gene expression profiles of immune-related genes with favorable prognosis as the input parameters, and good predictive results were obtained in this study
The breast cancer patients were classified into two equal groups by using the median the single sample gene set enrichment analysis (ssGSEA) score as the cutoff point
Summary
Breast cancer is the most lethal cancer in women and displaying a broad range of heterogeneity in terms of clinical, molecular behavior and response to therapy. The prognostic significance of the immune-related genes based classification was confirmed in the log-rank test These clusters were associated with immune checkpoints, immune-related features and tumor infiltrating levels. The gene expression profiles in breast cancer patients had been investigated by many studies, and found that this cancer was composed of distinct molecular subtypes[2,4,5,6,7]. By using the gene expression profiles of immune-related genes with favorable prognosis, the k-means clustering was applied on the breast cancer samples to establish a robust molecular classification. The shrunken centroid algorithm was used to classify the clusters by the gene expression profiles of immune-related genes with favorable prognosis as the input parameters, and good predictive results were obtained in this study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
