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Molecular classification of breast cancer using the mRNA expression profiles of immune-related genes.

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.

SUBMITTER: Mei J

PROVIDER: S-EPMC7075995 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Molecular classification of breast cancer using the mRNA expression profiles of immune-related genes.

Mei Juan J Zhao Ji J Fu Yi Y

Scientific reports 20200316 1

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 significa ...[more]

PMID: 32179831

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Project description:In Taiwan, a combination of hepatitis B and C infection, economic boom-related food and alcohol overconsumption, and Chinese medicine prescriptions has led to a high rate of hepatocellular carcinoma (HCC). However, the causative factors and underlying tumor biology for this unique HCC environment have not been identified. Wnt and Hippo signaling pathways play an important regulatory role in HCC development, and their functions are generally considered as positive and negative regulators of cell proliferation, respectively. In this study, we characterized the molecular features of HCC using a newly developed classification system based on the expression of the Wnt-Hippo signaling pathway-related genes. RNA sequencing (RNA-Seq) was performed on liver tumor tissues from 100 patients with liver cancer. RNA-Seq data for 272 previously characterized Wnt-Hippo signaling pathway-related genes were used for hierarchical clustering. We analyzed the data in terms of prognostic value, transcriptome features, immune infiltration, and clinical characteristics, and compared the resulting subclasses with previously published classifications. Four subclasses of HCC (HCCW1-4) were identified. Subclass HCCW1 displayed the highest PCDHB4 expression. Subclass HCCW2 displayed lower Edmondson-Steiner grades (I and II) and CTNNB1 mutation frequencies. Subclass HCCW3 was associated with a good prognosis, the highest PCDHGB7 expression, high CD8+ naïve T cells abundance, and relatively low TP53 mutation rates. Subclass HCCW4 was associated with a poor prognosis, the highest PCDHB2 and PCDHB6 expression, a relatively high abundance of Th1 cells, NKT and class-switched memory B cells, relatively low enrichment of cDC, iDC, and CD4+ memory T cells, and high Edmondson-Steiner grades (III and IV). We also identified Wnt-Hippo signaling pathway-related genes that may influence immune cell infiltration. We developed a panel of 272 Wnt-Hippo signaling pathway-related genes to classify HCC into four groups based on Taiwanese HCC and The Cancer Genome Atlas Liver Hepatocellular Carcinoma datasets. This novel molecular classification system may aid the treatment of HCC.

Dataset Information

Molecular classification of breast cancer using the mRNA expression profiles of immune-related genes.

Publications

Molecular classification of breast cancer using the mRNA expression profiles of immune-related genes.

Similar Datasets

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