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Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis

A Correction to this article was published on 01 July 2022

This article has been updated


Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the hub genes and signal pathways involved in the progress of TNBC to provide reliable biomarkers for the diagnosis and treatment of TNBC. Download the raw data of four TNBC-related datasets from the Gene Expression Omnibus (GEO) database and use them for bioinformatics analysis. GEO2R tool was used to analyze and identify differentially expressed (DE) mRNAs. DAVID database was used to carry out gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome Pathways (KEGG) signal pathway enrichment analysis for DE mRNAs. STRING database and Cytoscape were used to build DE mRNAs protein-protein interaction (PPI) network diagram and visualize PPI network, respectively. Through cytoHubba, cBioPortal database, Kaplan-Meier mapper database, Gene Expression Profiling Interactive Analysis (GEPIA) Database, UALCAN Database, The Cancer Genome Atlas (TCGA) database, Tumor Immunity Estimation Resource identify hub genes. Perform qRT-PCR, Human Protein Atlas analysis, mutation analysis, survival analysis, clinical-pathological characteristics, and infiltrating immune cell analysis. 22 DE mRNAs were identified from the four datasets, including 16 upregulated DE mRNAs and six downregulated DE mRNAs. Enrichment analysis of the KEGG showed that DE mRNAs were principally enriched in pathways in cancer, mismatch repair, cell cycle, platinum drug resistance, breast cancer. Six hub genes were screened based on the PPI network diagram of DE mRNAs. Survival analysis found that TOP2A, CCNA2, PCNA, MSH2, CDK6 are related to the prognosis of TNBC. In addition, mutations, clinical indicators, and immune infiltration analysis show that these five hub genes play an important role in the progress of TNBC and immune monitoring. Compared with MCF-10A, MCF-7, and SKBR-3 cells, TOP2A, PCNA, MSH2, and CDK6 were significantly upregulated in MDA-MB-321 cells. Compared with normal, luminal, and Her-2 positive tissues, CCNA2, MSH2, and CDK6 were significantly upregulated in TNBC. Through comparative analysis of GEO datasets related to colorectal cancer and lung adenocarcinoma, it was determined that these five hub genes were unique differentially expressed genes of TNBC. At last, the hub genes related to the progression, prognosis, and immunity of TNBC have been successfully screened. They are indeed specific to TNBC as prognostic features. They can be used as potential markers for the prognosis of TNBC and provide potential therapeutic targets.

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Fig. 1
Fig. 2: Identification of differentially expressed genes in TNBC from GEO datasets.
Fig. 3: Functional enrichment analysis for DE mRNAs.
Fig. 4: Identification and analysis of hub genes.
Fig. 5: Genetic alterations linked to hub genes in TNBC in the TCGA.
Fig. 6: Survival analysis of five hub genes and comparison of expression of five hub genes between tumor and peri-tumor.
Fig. 7: Expression of five hub genes in TNBC subgroups stratified by clinical parameters in the UALCAN database.
Fig. 8: qRT-PCR results and representative immunohistochemistry staining of hub genes and volcano plots of DE mRNAs in the LUAD and CRC datasets.
Fig. 9: Correlation of expression of the five hub genes with immune infiltration in TNBC.
Fig. 10: Significant pathways and genes obtained by GSEA.

Data availability

Additional data pertaining to this work can be obtained from the corresponding author upon request.

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JM, ZF and LR conceived and designed experiments. JM performed experiments. CC and SL carried out data analysis. DW, JJ and PH, DW participated in the preparation of reagents/materials/analysis tools. JM wrote the manuscript. ZF and LR supervised the manuscript. All data were generated in-house, and no paper mill was used. All authors agree to be accountable for all aspects of this work including the integrity and accuracy of the data presented.

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Correspondence to Zhimin Fan or Liqun Ren.

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Ma, J., Chen, C., Liu, S. et al. Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis. Cancer Gene Ther 29, 1578–1589 (2022).

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