ArticleScientific reports2020
Molecular classification of breast cancer using the mRNA expression profiles of immune-related genes.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Unveiling Epigenetic Regulatory Elements Associated with Breast Cancer Development.International journal of molecular sciences · 2025Article
- Diagnostic accuracy of automated breast volume scanning, hand-held ultrasound and molybdenum-target mammography for breast lesions: a systematic review and meta-analysis.Gland surgery · 2025Article
- Identification of modules and key genes associated with breast cancer subtypes through network analysis.Scientific reports · 2024Article
- PD-L1 Gene Polymorphisms rs822336 G>C and rs822337 T>A: Promising Prognostic Markers in Triple Negative Breast Cancer PatientsMedicina (Kaunas, Lithuania) · 2022Article
- Identification of pyroptosis related subtypes and tumor microenvironment infiltration characteristics in breast cancer.Scientific reports · 2022Article
- Development and validation of a novel endoplasmic reticulum stress-related lncRNA prognostic signature and candidate drugs in breast cancer.Frontiers in genetics · 2022Article
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Authors and funding
3 authors.
Funding
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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.
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