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Identification of Genes with Prognostic Value in the Breast Cancer Microenvironment Using Bioinformatics Analysis.

ABSTRACT: BACKGROUND Stromal and immune cells play essential roles in the development of breast cancer (BC). This study was conducted to identify prognosis-related genes from the tumor microenvironment. MATERIAL AND METHODS The gene expression profiles of 622 BC samples were downloaded from TCGA (The Cancer Genome Atlas) database. Stromal and immune scores were calculated by using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumours using Expression data) algorithm. Then, differentially expressed genes (DEGs) between the high score group and the low score group were screened. The intersecting DEGs were selected through Venn diagrams, and survival analysis was conducted. Functional and pathway enrichment analyses were performed using the DAVID (Database for Annotation, Visualization and Integrated Discovery), and a protein-protein interaction (PPI) network was constructed with the STRING database and Cytoscape. These genes were validated for prognostic value by use of the KM (Kaplan-Meier) plotter tool. RESULTS The low immune score group was associated with a poor prognosis. However, there was no difference in the prognosis between the high and low stromal score groups. A total of 248 intersecting DEGs were found in BC, and 61 genes were significantly associated with the prognosis of BC patients in the TCGA database. These genes were enriched in the immune response, components of the plasma membrane, and receptor activity. Furthermore, in the validation group, 31 of 61 genes were significantly associated with prognosis. CONCLUSIONS Our bioinformatics analysis identified 31 tumor microenvironment-related genes as potential prognostic predictors for breast cancer patients. Some of these genes that have not been widely investigated previously, such as CXCL9, GPR18, S1PR4, SASH3, and PYH1N1, might be additional predictive factors for BC patients.

SUBMITTER: Ren H

PROVIDER: S-EPMC7160604 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Identification of Genes with Prognostic Value in the Breast Cancer Microenvironment Using Bioinformatics Analysis.

Ren Haoyu H Hu Daixing D Mao Yu Y Su Xinliang X

Medical science monitor : international medical journal of experimental and clinical research 20200406

BACKGROUND Stromal and immune cells play essential roles in the development of breast cancer (BC). This study was conducted to identify prognosis-related genes from the tumor microenvironment. MATERIAL AND METHODS The gene expression profiles of 622 BC samples were downloaded from TCGA (The Cancer Genome Atlas) database. Stromal and immune scores were calculated by using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumours using Expression data) algorithm. Then, differential ...[more]

PMID: 32251269

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Project description:BackgroundBreast cancer is one of the most frequently diagnosed cancers among women worldwide. Alterations in the tumor microenvironment (TME) have been increasingly recognized as key in the development and progression of breast cancer in recent years. To deeply comprehend the gene expression profiling of the TME and identify immunological targets, as well as determine the relationship between gene expression and different prognoses is highly critical.MethodsThe stromal/immune scores of breast cancer patients from The Cancer Genome Atlas (TCGA) were employed to comprehensively evaluate the TME. Then, TME characteristics were assessed, overlapping genes of the top 3 Gene Ontology (GO) terms and upregulated differentially expressed genes (DEGs) were analyzed. Finally, through combined analyses of overall survival, time-dependent receiver operating characteristic (ROC), and protein-protein interaction (PPI) network, novel immune related genes with good prognosis were screened and validated in both TCGA and GEO database.ResultsAlthough the TME did not correlate with the stages of breast cancer, it was closely associated with the subtypes of breast cancer and gene mutations (CDH1, TP53 and PTEN), and had immunological characteristics. Based on GO functional enrichment analysis, the upregulated genes from the high vs low immune score groups were mainly involved in T cell activation, the external side of the plasma membrane, and receptor ligand activity. The top GO terms of the upregulated DEGs from the high vs low immune score groups exhibited better prognosis in breast cancer; 15 of them were related to good prognosis in breast cancer, especially CD226 and KLRC4-KLRK1.ConclusionsHigh CD226 and KLRC4-KLRK1 expression levels were identified and validated to correlate with better overall survival in specific stages or subtypes of breast cancer. CD226, KLRC4-KLRK1 and other new targets seem to be promising avenues for promoting antitumor targeted immunotherapy in breast cancer.

Dataset Information

Identification of Genes with Prognostic Value in the Breast Cancer Microenvironment Using Bioinformatics Analysis.

Publications

Identification of Genes with Prognostic Value in the Breast Cancer Microenvironment Using Bioinformatics Analysis.

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