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Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis.

ABSTRACT: Breast cancer is a highly heterogeneous disease that is clinically classified into several subtypes. Among these subtypes, basal-like breast cancer largely overlaps with triple-negative breast cancer (TNBC), and these two groups are generally studied together as a single entity. Differences in the molecular makeup of breast cancers can result in different treatment strategies and prognoses for patients with different breast cancer subtypes. Compared with other subtypes, basal-like and other ER+ breast cancer subtypes exhibit marked differences in etiologic factors, clinical characteristics and therapeutic potential. Anthracycline drugs are typically used as the first-line clinical treatment for basal-like breast cancer subtypes. However, certain patients develop drug resistance following chemotherapy, which can lead to disease relapse and death. Even among patients with basal-like breast cancer, there can be significant molecular differences, and it is difficult to identify specific drug resistance proteins in any given patient using conventional variance testing methods. Therefore, we designed a new method for identifying drug resistance genes. Subgroups, personalized biomarkers, and therapy targets were identified using cluster analysis of differentially expressed genes. We found that basal-like breast cancer could be further divided into at least four distinct subgroups, including two groups at risk for drug resistance and two groups characterized by sensitivity to pharmacotherapy. Based on functional differences among these subgroups, we identified nine biomarkers related to drug resistance: SYK, LCK, GAB2, PAWR, PPARG, MDFI, ZAP70, CIITA and ACTA1. Finally, based on the deviation scores of the examined pathways, 16 pathways were shown to exhibit varying degrees of abnormality in the various subgroups, indicating that patients with different subtypes of basal-like breast cancer can be characterized by differences in the functional status of these pathways. Therefore, these nine differentially expressed genes and their associated functional pathways should provide the basis for novel personalized clinical treatments of basal-like breast cancer.

SUBMITTER: Wu T

PROVIDER: S-EPMC4488356 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis.

Wu Tong T Wang Xudong X Li Jing J Song Xiuzhen X Wang Ying Y Wang Yunfeng Y Zhang Lei L Li Ziyao Z Tian Jiawei J

PloS one 20150630 6

Breast cancer is a highly heterogeneous disease that is clinically classified into several subtypes. Among these subtypes, basal-like breast cancer largely overlaps with triple-negative breast cancer (TNBC), and these two groups are generally studied together as a single entity. Differences in the molecular makeup of breast cancers can result in different treatment strategies and prognoses for patients with different breast cancer subtypes. Compared with other subtypes, basal-like and other ER+ ...[more]

PMID: 26126114

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Project description:BACKGROUND:Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. METHODS:We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein-protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. RESULTS:We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein-protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. CONCLUSIONS:NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

Project description:BackgroundNeoadjuvant chemotherapy (NAC) is an effective therapeutic regimen for patients with breast cancer. However, some individuals cannot benefit from NAC because of drug resistance. To date, valid strategies about enhancing sensitivity of breast cancer to NAC are still scarce. miRNAs have been reported to proverbially be involved in the onset and development of malignancies including drug resistance.MethodsGSE73736 was downloaded from the GEO database. Student's t-test was conducted to acquire differentially expressed-miRNAs (DE-miRNAs). Potential target genes of DE-miRNAs were predicted by miRTarBase. Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for these target genes were performed by database for annotation, visualization, and integrated discovery. Protein-protein interaction network was constructed by STRING database and visualized through Cytoscape software. The hub target gene-miRNA network was also established by Cytoscape software. Next, the expression of potential functional miRNAs in breast cancer cell lines and tissues was determined. Finally, the roles of miR-3617-3p, miR-3136-3p, and miR-520b in modulating breast cancer chemoresistance were further examined.ResultsA total of 123 DE-miRNAs were identified, including 60 upregulated miRNAs and 63 downregulated miRNAs in the chemoresistant breast cancer group when compared with the chemosensitive group. Six hundred and seventeen and 1,146 potential target genes for the top 10 most upregulated and downregulated miRNAs were predicted, respectively. Enrichment analyses revealed that these target genes were enriched in some cancer-associated or chemo-resistance-associated pathways, such as MAPK signaling pathway, wnt signaling pathway, and p53 signaling pathway. MAPK1 and PRDM10 were identified as hub genes in the protein-protein interaction network. The top 25 hub genes were potentially regulated by 16 DE-miRNAs, among which miR-3617-3p and miR-3136-3p were commonly upregulated, whereas miR-520b was downregulated in two chemoresistant breast cancer cells compared with chemosensitive cell. By analyzing TCGA data, we found that expression of miR-3136-3p and miR-520b was increased and decreased in breast cancer tissues, respectively. Moreover, functional experiments demonstrated that miR-3136-3p and miR-3617-3p could reduce chemosensitivity of breast cancer, whereas miR-520b could reverse chemoresistance.ConclusionThe present study, based on bioinformatics analysis and experimental validation, brings to light novel mechanisms of breast cancer NAC resistance.

Project description:BackgroundBasal-like breast cancer (BLBC) is the most aggressive subtype of breast cancer (BC) and links to poor outcomes. As the molecular mechanism of BLBC has not yet been completely discovered, identification of key pathways and hub genes of this disease is an important way for providing new insights into exploring the mechanisms of BLBC initiation and progression.ObjectiveThe aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis.Methods and resultsThe differential expressed genes (DEGs) including 40 up-regulated and 21 down-regulated DEGs were identified between GSE25066 and GSE21422 microarrays, and these DEGs were significantly enriched in the terms related to oncogenic or suppressive roles in BLBC progression. In addition, KEGG pathway and GSEA (Gene Set Enrichment Analysis) enrichment analyses were performed for DEGs between the basal type and non-basal-type breast cancer from GSE25066 microarray. These DEGs were enriched in pathways such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism signaling and TNF signaling pathway. Moreover, the protein-protein interaction (PPI) network was constructed with those 61 DEGs using the Cytoscape software, and the biological significance of putative modules was established using MCODE. The module 1 was found to be closely related with a term of mitosis regulation and enriched in cell cycle pathway, and thus confirmed the pathological characteristic of BLBC with a high mitotic index. Furthermore, prediction values of the top 10 hub genes such as CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A were validated using Oncomine and Kaplan-Meier plotter.ConclusionOur results suggest the intriguing possibility that the hub genes and modules in the PPI network contributed to in-depth knowledge about the molecular mechanism of BLBC, paving a way for more accurate discovery of potential treatment targets for BLBC patients.

Dataset Information

Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis.

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Identification of Personalized Chemoresistance Genes in Subtypes of Basal-Like Breast Cancer Based on Functional Differences Using Pathway Analysis.

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