scholarly journals Integrated analysis of differentially expressed genes in breast cancer pathogenesis

2015 ◽  
Vol 9 (6) ◽  
pp. 2560-2566 ◽  
Author(s):  
DAOBAO CHEN ◽  
HONGJIAN YANG
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Cancer Pathogenesis

scholarly journals Integrated Analysis of Key Differentially Expressed Genes Identifies DBN1 as a Predictive Marker of Response to Endocrine Therapy in Luminal Breast Cancer

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1549 ◽  
Author(s):  
Lutfi H. Alfarsi ◽  
Rokaya El Ansari ◽  
Brendah K. Masisi ◽  
Ruth Parks ◽  
Omar J Mohammed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Endocrine Therapy ◽  
Prognostic Marker ◽  
Differentially Expressed Genes ◽  
Personalised Medicine ◽  
Differentially Expressed ◽  
Endocrine Treatment ◽  
Integrated Analysis ◽  
Luminal Breast Cancer ◽  
Potential Biomarker

Endocrine therapy is the mainstay of adjuvant treatment for patients with luminal breast cancer. Despite ongoing advances in endocrine therapy to date, a proportion of patients ultimately develop endocrine resistance, resulting in failure of therapy and poor prognosis. Therefore, as part of the growing concept of personalised medicine, the need for identification of predictive markers of endocrine therapy response at an early stage, is recognised. The METABRIC series was used to identify differentially expressed genes (DEGs) in term of response to adjuvant endocrine therapy. Drebrin 1 (DBN1) was identified as a key DEG associated with response to hormone treatment. Next, large, well-characterised cohorts of primary luminal breast cancer with long-term follow-up were assessed at the mRNA and protein levels for the value of DBN1 as a prognostic marker in luminal breast cancer, as well as its potential for predicting the benefit of endocrine therapy. DBN1 positivity was associated with aggressive clinicopathological variables and poor patient outcomes. Importantly, high DBN1 expression predicted relapse patients who were subject to adjuvant endocrine treatment. Our results further demonstrate that DBN1 is an independent prognostic marker in luminal breast cancer. Its association with the response to endocrine therapy and outcome provides evidence for DBN1 as a potential biomarker in luminal breast cancer, particularly for the benefit of endocrine treatment. Further functional investigations into the mechanisms underlying sensitivity to endocrine therapy is required.

scholarly journals Investigation of Candidate Genes and Pathways in Basal/TNBC Patients by Integrated Analysis

2021 ◽  
Vol 20 ◽  
pp. 153303382110195
Author(s):  
Qi Liu ◽  
Xiang Song ◽  
Zhaoyun Liu ◽  
Zhiyong Yu
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Differentially Expressed ◽  
Breast Cancer Cell Lines ◽  
Integrated Analysis ◽  
Transcription Activator ◽  
Luminal Type

Purpose: This study aims to identify the key pathway and related genes and to further explore the potential molecular mechanisms of triple negative breast cancer (TNBC). Methods: The transcriptome data and clinical information of breast cancer patients were downloaded from the TCGA database, including 94 cases of paracancerous tissue, 225 cases of Basal like type, 151 cases of Her2 type, 318 cases of Luminal type A, 281 cases of Luminal type B, and 89 cases of Normal Like type. The differentially expressed genes (DEGs) were identified based on the criteria of |logFC|≥1.5 and adjust P < 0.001.Their functions were annotated by gene ontology (GO) analysis and Kyoto Encyclopedia of differentially expressed genes & Genomes (KEGG) pathway analysis. Cox regression univariate analysis and Kaplan-Meier survival curves (Log-rank method) were used for survival analysis. FOXD1, DLL3 and LY6D were silenced in breast cancer cell lines, and cell viability was assessed by CCK-8 assay. Further, the expression of FOXD1, DLL3 and LY6D were explored by immunohistochemistry on triple negative breast tumor tissue and normal breast tissue. Results: A total of 533 DEGs were identified. Functional annotation showed that DEGs were significantly enriched in intermediate filament cytoskeleton, DNA−binding transcription activator activity, epidermis development, and Neuroactive ligand−receptor interaction. Survival analysis found that FOXD1, DLL3, and LY6D showed significant correlation with the prognosis of patients with the Basal-like type ( P < 0.05). CCK-8 assay showed that compared with Doxorubicin alone group, the cytotoxicity of Doxorubicin combined with siRNA-knockdown of FOXD1, DLL3, or LY6D was much significant. Conclusion: The DEGs and their enriched functions and pathways identified in this study contribute to the understanding of the molecular mechanisms of TNBC. In addition, FOXD1, DLL3, and LY6D may be defined as the prognostic markers and potential therapeutic targets for TNBC patients.

scholarly journals Key Genes and Prognostic Analysis in HER2+ Breast Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382098329
Author(s):  
Yujie Weng ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Zhongxian Li ◽  
Rong Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Differentially Expressed ◽  
Protein Kinase Activity ◽  
Protein Protein Interaction ◽  
Risk Of Death ◽  
Her2 Breast Cancer ◽  
Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

scholarly journals The role of ferroptosis in breast cancer patients: a comprehensive analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Hong Yu ◽  
Hua-Dong Li
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Function Analysis ◽  
Cancer Prognosis ◽  
Differentially Expressed ◽  
Immune Checkpoints ◽  
Cell Functions

AbstractBreast cancer (BC) affects the breast tissue and is the second most common cause of mortalities among women. Ferroptosis is an iron-dependent cell death mode that is characterized by intracellular accumulation of reactive oxygen species (ROS). We constructed a prognostic multigene signature based on ferroptosis-associated differentially expressed genes (DEGs). Moreover, we comprehensively analyzed the role of ferroptosis-associated miRNAs, lncRNAs, and immune responses. A total of 259 ferroptosis-related genes were extracted. KEGG function analysis of these genes revealed that they were mainly enriched in the HIF-1 signaling pathway, NOD-like receptor signaling pathway, central carbon metabolism in cancer, and PPAR signaling pathway. Fifteen differentially expressed genes (ALOX15, ALOX15B, ANO6, BRD4, CISD1, DRD5, FLT3, G6PD, IFNG, NGB, NOS2, PROM2, SLC1A4, SLC38A1, and TP63) were selected as independent prognostic factors for BC patients. Moreover, T cell functions, including the CCR score, immune checkpoint, cytolytic activity, HLA, inflammation promotion, para-inflammation, T cell co-stimulation, T cell co-inhibition, and type II INF responses were significantly different between the low-risk and high-risk groups of the TCGA cohort. Immune checkpoints between the two groups revealed that the expressions of PDCD-1 (PD-1), CTLA4, LAG3, TNFSF4/14, TNFRSF4/8/9/14/18/25, and IDO1/2 among others were significantly different. A total of 1185 ferroptosis-related lncRNAs and 219 ferroptosis-related miRNAs were also included in this study. From the online database, we identified novel ferroptosis-related biomarkers for breast cancer prognosis. The findings of this study provide new insights into the development of new reliable and accurate cancer treatment options.

scholarly journals Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

2020 ◽  
Author(s):  
Rong Jia ◽  
Zhongxian Li ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Yujie Weng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Survival Data ◽  
Breast Cancer Subtypes ◽  
Differentially Expressed ◽  
Expression Data ◽  
Luminal A ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Basal Like Breast Cancer

Abstract 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 1,114 differentially expressed genes in luminal A breast cancer and 1,042 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.

scholarly journals Regulatory Crosstalk of Doxorubicin, Estradiol and TNFα Combined Treatment in Breast Cancer-derived Cell Lines

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Isar Nassiri ◽  
Alberto Inga ◽  
Erna Marija Meškytė ◽  
Federica Alessandrini ◽  
Yari Ciribilli ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Lines ◽  
Differentially Expressed Genes ◽  
Combination Treatment ◽  
Combined Treatment ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Transcriptional Responses ◽  
Predisposing Factor ◽  
Inflammatory Microenvironment

Abstract We present a new model of ESR1 network regulation based on analysis of Doxorubicin, Estradiol, and TNFα combination treatment in MCF-7. We used Doxorubicin as a therapeutic agent, TNFα as marker and mediator of an inflammatory microenvironment and 17β-Estradiol (E2) as an agonist of Estrogen Receptors, known predisposing factor for hormone-driven breast cancer, whose pharmacological inhibition reduces the risk of breast cancer recurrence. Based on the results of transcriptomics analysis, we found 71 differentially expressed genes that are specific for the combination treatment with Doxorubicin + Estradiol + TNFα in comparison with single or double treatments. The responsiveness to the triple treatment was examined for seven genes by qPCR, of which six were validated, and then extended to four additional cell lines differing for p53 and/or ER status. The results of differential regulation enrichment analysis highlight the role of the ESR1 network that included 36 of 71 specific differentially expressed genes. We propose that the combined activation of p53 and NF-kB transcription factors significantly influences ligand-dependent, ER-driven transcriptional responses, also of the ESR1 gene itself. These results provide a model of coordinated interaction of TFs to explain the Doxorubicin, E2 and TNFα induced repression mechanisms.

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