Dataset Information

Identification of ribosomal protein family in triple-negative breast cancer by bioinformatics analysis.

ABSTRACT: Triple-negative breast cancer (TNBC) accounts for ?20% of all breast cancer (BC) cases. The management of TNBC represents a challenge due to its worse prognosis, heterogeneity and lack of targeted therapy. Moreover, its mechanisms are not fully clear. The aim of the study is to identify crucial genes between TNBC and non-TNBC for underlying targets for diagnostic and therapeutic methods of TNBC. The differentially expressed genes (DEGs) between TNBC and non-TNBC were selected from the Gene Expression Omnibus (GEO) database after the integrated analysis of two datasets (GSE65194 and GSE76124). Then Gene ontology (GO) and KEGG analysis were performed by DAVID database, protein-protein interaction (PPI) of DEGs was constructed by Search Tool for the Retrieval of Reciprocity Genes (STRING) database. Furthermore, centrality analysis and module analysis were carried out by Cytoscape to analyze the TNBC-related PPI. Subsequently, overall survival (OS) analysis was performed by GEPIA. Finally, the expressions of these key genes in TNBC and non-TNBC tissues were tested by qRT-PCR. The results showed that 955 DEGs were obtained, which were mainly enriched in ribosome, ribosomal subunit, and so on. Moreover, 19 candidate genes were focused on by centrality analysis and module analysis. Furthermore, we found the low expressions of ribosomal protein S9 (RPS9), ribosomal protein S14 (RPS14), ribosomal protein S27 (RPS27), ribosomal protein L11 (RPL11) and ribosomal protein L14 (RPL14) were related to a poor OS in BC patients. Additionally, qRT-PCR results suggested that these five genes were notably down-regulated in TNBC tissues. In summary, the present study suggests that ribosomal proteins are related to TNBC, and they may play an important role in the diagnosis, treatment and prognosis of TNBC.

SUBMITTER: Lin Z

PROVIDER: S-EPMC7789804 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Identification of ribosomal protein family in triple-negative breast cancer by bioinformatics analysis.

Lin Ziyue Z Peng Rui R Sun Yan Y Zhang Luyu L Zhang Zheng Z

Bioscience reports 20210101 1

Triple-negative breast cancer (TNBC) accounts for ∼20% of all breast cancer (BC) cases. The management of TNBC represents a challenge due to its worse prognosis, heterogeneity and lack of targeted therapy. Moreover, its mechanisms are not fully clear. The aim of the study is to identify crucial genes between TNBC and non-TNBC for underlying targets for diagnostic and therapeutic methods of TNBC. The differentially expressed genes (DEGs) between TNBC and non-TNBC were selected from the Gene Expre ...[more]

PMID: 33305312

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Project description:Purpose:Triple-negative breast cancer refers to breast cancer that does not express estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (Her2). This study aimed to identify the key pathways and genes and find the potential initiation and progression mechanism of triple-negative breast cancer (TNBC). Methods:We downloaded the gene expression profiles of GSE76275 from Gene Expression Omnibus (GEO) datasets. This microarray Super-Series sets are composed of gene expression data from 265 samples which included 67 non-TNBC and 198 TNBC. Next, all the differentially expressed genes (DEGs) with p<0.01 and fold change ?1.5 or ?-1.5 were identified. Result:56 upregulated and 151 downregulated genes were listed and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. These significantly changed genes were mainly involved in the biological process termed prostate gland morphogenesis, inner ear morphogenesis, cell maturation, digestive tract morphogenesis, autonomic nervous system development, monovalent inorganic anion homeostasis, neural crest cell development, regulation of dendrite extension and glial cell proliferation, immune system process termed T cell differentiation, regulation of immune response, and macrophage activation. Genes are mainly involved in the KEGG pathway termed Oocyte meiosis. All DEGs underwent survival analysis using datasets from The Cancer Genome Atlas (TCGA) integrated by cBioPortal, of which amplification of SRY-related HMG-box 8 (SOX8), androgen receptor (AR), and Chromosome 9 Open Reading Frame 152 (C9orf152) were significantly negative while Nik Related Kinase (NRK) and RAS oncogene family 30 (RAB30) were positively correlated to the life expectancy (p<0.05). Conclusions:In conclusion, these pathways and genes identified could help understanding the mechanism of development of TNBC. Besides, SOX8, AR, C9orf152, NRK and RAB30, and other key genes and pathways might be promising targets for the TNBC treatment.

Project description:Triple-negative breast cancer (TNBC) is a subtype with high rates of metastasis, poor prognosis and limited therapeutic options. The present study aimed to identify the potential pivotal genes for prognosis and treatment in TNBC. A total of two microarray expression datasets, GSE38959 and GSE65212, were downloaded from the Gene Expression Omnibus database, and RNA-sequencing data of breast cancer from The Cancer Genome Atlas database were analyzed to screen out differentially expressed genes (DEGs) between TNBC tissues and normal tissues. The intersection of DEGs was submitted to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A protein-protein interaction (PPI) network was constructed and visualized using Cytoscape software. Furthermore, module, centrality and survival analyses were performed to identify the potential hub genes. Reverse transcription-quantitative (RT-q)PCR analysis was performed to detect the expression levels of key genes in TNBC samples, and 377 DEGs were identified. Functional analysis revealed that the DEGs were significantly involved in cell cycle process, nuclear division and the p53 signaling pathway. A PPI network was constructed with these DEGs, and 66 core genes with high centrality features in module 1 were selected. Relapse-free survival analysis confirmed that high expression levels of five genes [cyclin B1 (CCNB1), GINS complex subunit 2, non-SMC condensin I complex subunit G (NCAPG), minichromosome maintenance 4 (MCM4) and ribonucleotide reductase regulatory subunit M2 (RRM2)] were significantly associated with poor prognosis in TNBC. RT-qPCR analysis demonstrated that CCNB1, NCAPG, MCM4 and RRM2 were significantly upregulated in 25 TNBC tissues compared with adjacent normal breast tissues. Furthermore, gene set enrichment analysis revealed that CCNB1, NCAPG, MCM4 and RRM2 were closely associated with tumor proliferation. Taken together, these results suggest that CCNB1, NCAPG, MCM4 and RRM2 are associated with tumorigenesis and TNBC progression, and thus may act as promising prognostic biomarkers and therapeutic targets for TNBC.

Project description:PurposeThis study aimed to investigate the diagnostic and prognostic values of neuropilin-1 (NRP-1) in triple-negative breast cancer (TNBC) and analyze its immune function in the tumor microenvironment.MethodsBased on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, Genotype Tissue Expression, Immune Cell Abundance Identifier (ImmuCellAI), Reactome, and Genomics of Drug Sensitivity in Cancer databases, the cancer tissues from 50 patients with TNBC and corresponding adjacent noncancerous tissues from 10 patients (tissue microarrays were purchased from Shanghai Xinchao Biotechnology Co., Ltd.) were collected for validation. Bioinformatics combined with immunohistochemistry was used to analyze the relationship among NRP-1 expression, prognosis, tumor immune cell infiltration, immune genes, and drug resistance so as to investigate the role of NRP-1 in the development of TNBC.ResultsA significant difference in NRP-1 gene expression was found between the cancerous and noncancerous tissues (p-value < 0.05); NRP-1 expression was high in carcinoma. No significant correlation was found between NRP-1 protein expression levels and each stage in the TCGA database. Prognostic expression survival analysis showed that the survival probability of patients with high NRP-1 expression was significantly lower than that of patients with low NRP-1 expression (p-value < 0.05), suggesting that the gene might be a pro-oncogene. The data from 50 clinical samples also confirmed that the NRP-1 expression was significantly higher in triple-negative breast cancer (TNBC) tissues than in adjacent noncancerous tissues. The NRP-1 expression significantly correlated with the tumor diameter and pathological grade (p-value < 0.05), but not with age, stage, and ki67 (p-value > 0.05). The Kaplan-Meier survival curves suggested that the median overall survival was significantly shorter in patients with high NRP-1 expression than in those with low NRP-1 expression (13.6 months vs 15.2 months, p-value < 0.05). The 300 genes most significantly positively associated with this gene were selected for Gene Ontology (including Biological Process, Molecular Function, and Cellular Component groups) and Kyoto Encyclopedia of Genes and Genomics enrichment analysis. The findings showed that NRP-1 was involved in immune regulation in TNBC. In addition, the NRP-1 expression in TNBC positively correlated with a variety of immune cells and checkpoints.ConclusionNRP-1 can be used as a potential biomarker and therapeutic target in TNBC.

Project description:BackgroundTriple-negative breast cancer (TNBC) is a heterogeneous disease with a worse prognosis. However, current therapies have rarely improved the outcome of patients with TNBC. Here we sought to identify novel biomarkers or targets for TNBC.Materials and methodsPatients GSE76275 clinic traits and their corresponding mRNA profiles for 198 TNBC and 67 non-TNBC were obtained from the GEO database. Weighted gene co-expression network analysis (WGCNA) of the GSE76275 keyed out hub genes, and the differentially expressed genes (DEGs) were identified with the cut-off of adjusted P (adj. P) <0.01 and |log2 fold-change (FC)| > 1.5. The hub - DEGs overlapping genes, as key genes, were considered for further study using Kaplan-Meier plotter online analysis. Subsequently, Breast Cancer Gene-Expression Miner v4.0 and tissue microarray analysis were applied to determine the transcriptional and translational levels of every key gene. Following plasmid transfection for overexpression, the proliferation of TNBC cells was determined by CCK8 and colony formation assay. Moreover, xenograft tumor models were canvassed to investigate their effect upon in vivo tumor growth.ResultsFour genes (SIDT1, ANKRD30A, GPR160, and CA12) were found to be associated with relapse-free survival (RFS) in TNBC through WGCNA and DEGs integrated analysis. Patients with a higher level of SIDT1 had significantly better RFS compared to those with lower levels. The transcriptional and translational levels of SIDT1 were validated as downregulated in patients with triple-negative status, negative estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Furthermore, SIDT1 inhibited proliferation of breast cancer cells (MDA-MB-231 and MDA-MB-468) and xenograft studies demonstrated that SIDT1 can suppress tumor growth in vivo.ConclusionThis study suggests that SIDT1 may play a crucial role in TNBC progression and has the potential as a prognostic biomarker of TNBC.

Project description:BackgroundBreast cancer is the most common malignancy in women. Triple-negative breast cancer (TNBC) refers to a special subtype that is deficient in the expression of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER-2). In this study, a variety of bioinformatics analysis tools were used to screen Hub genes related to the occurrence and development of triple negative breast cancer, and their biological functions were analyzed.MethodsGene Expression Omnibus (GEO) breast cancer microarray data GSE62931 was selected as the research object. The differentially expressed genes (DEGs) were screened and the protein-protein interaction (PPI) network of DEGs was constructed using bioinformatics tools. The Hub genes were also screened. The Gene Ontology (GO) knowledgebase and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for biological enrichment analysis. The Gene Expression Profiling Interactive Analysis (GEPIA) online tool was used to verify the expression of the screened genes and patient survival. The effects of polo-like kinase 1 (PLK1) on the proliferation, invasion, migration, and dryness of breast cancer cells were verified using cell counting kit 8 (CCK-8), transwell migration assays, scratch tests, and clone formation tests. An animal model of subcutaneous xenotransplantation of breast cancer was established to evaluate the effect of PLK1 on the proliferation of breast cancer.ResultsA total of 824 DEGs were screened by GSE62931 microarray data; 405 of which were up-regulated and 419 of which were down-regulated. Functional enrichment analysis showed that these DEGs were mainly enriched in cancer-related pathways and were primarily involved in biological processes (BP) such as cell and mitotic division. From the Hub gene screening, PLK1 was further identified as the Hub gene associated with TNBC. Cell and animal experiments indicated that PLK1 promotes the proliferation, invasion, migration, and clone formation of breast cancer cells.ConclusionsGene chip combined with bioinformatics methods can effectively analyze the DEGs related to the occurrence and development of breast cancer, and the screening of PLK1 can provide theoretical guidance for further research on the molecular mechanism of breast cancer and the screening of molecular markers.

Project description:BackgroundMetaplastic breast carcinoma (MBC) is a rare breast cancer subtype; most cases are triple-negative breast cancers (TNBCs) and are poorly responsive to conventional systemic therapy. Few potential diagnostic and prognostic markers for distinguishing between metaplastic TNBC and nonmetaplastic TNBC have been discovered. We performed bioinformatic analysis to explore the underlying mechanism by which metaplastic TNBC differs from nonmetaplastic TNBC and provides potential pathogenic genes of metaplastic TNBC.MethodsDifferentially expressed genes (DEGs) in metaplastic tumors and nonmetaplastic tumors from TNBC patients were screened using GSE165407. The GSE76275 data set and The Cancer Genome Atlas (TCGA) database were used to screen DEGs in TNBC and non-TNBC. Metascape and DAVID were used for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Ontology (GO) analysis of DEGs. Online databases, including UALCAN, GEPIA, HPA, Breast Cancer Gene-Expression Miner, and quantitative PCR and western blot, were used to examine KLK5 messenger RNA and protein expression in breast cancer. Analysis of KLK5‑associated genes was performed with TCGA data, and the LinkedOmics database was used to detect the genes co-expressed with KLK5. STRING (Search Tool for the Retrieval of Interacting Genes) and Cytoscape were used to screen for hub genes. Kaplan‑Meier plotter was used for survival analysis.ResultsKLK5 was identified among the DEGs in nonmetaplastic TNBC and metaplastic TNBC. The KLK5 gene was overexpressed in nonmetaplastic TNBC but downregulated in metaplastic TNBC. KEGG and GO analyses revealed that epithelial-to-mesenchymal transition was a pathogenic mechanism in metaplastic TNBC and an important pathway by which KLK5 and its associated genes DSG1 and DSG3 influence metaplastic TNBC progression. Prognosis analysis showed that only low expression of KLK5 in metaplastic TNBC had clinical significance.ConclusionOur research indicated that KLK5 may be a pivotal molecule with a key role in the mechanism of tumorigenesis in metaplastic TNBC.

Dataset Information

Identification of ribosomal protein family in triple-negative breast cancer by bioinformatics analysis.

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Identification of ribosomal protein family in triple-negative breast cancer by bioinformatics analysis.

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