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 Integrated Analysis of Circular RNA-Associated ceRNA Network Reveals Potential circRNA Biomarkers in Human Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Han Sheng ◽  
Huan Pan ◽  
Ming Yao ◽  
Longsheng Xu ◽  
Jianju Lu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Correlation Analysis ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Circular Rna ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Cerna Network

Circular RNA (circRNA) is closely related to tumorigenesis and cancer progression. Yet, the roles of cancer-specific circRNAs in the circRNA-related ceRNA network of breast cancer (BRCA) remain unclear. The aim of this study was to construct a ceRNA network associated with circRNA and to explore new therapeutic and prognostic targets and biomarkers for breast cancer. We downloaded the circRNA expression profile of BRCA from Gene Expression Omnibus (GEO) microarray datasets and downloaded the miRNA and mRNA expression profiles of BRCA from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs) were identified, and a competitive endogenous RNA (ceRNA) regulatory network was constructed based on circRNA–miRNA pairs and miRNA–mRNA pairs. Gene ontology and pathway enrichment analyses were performed on mRNAs regulated by circRNAs in ceRNA networks. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network were performed. A total of 72 DEcircRNAs, 158 DEmiRNAs, and 2762 DE mRNAs were identified. The constructed ceRNA network contains 60 circRNA–miRNA pairs and 140 miRNA–mRNA pairs, including 40 circRNAs, 30 miRNAs, and 100 mRNAs. Functional enrichment indicated that DEmRNAs regulated by DEcircRNAs in ceRNA networks were significantly enriched in the PI3K-Akt signaling pathway, microRNAs in cancer, and proteoglycans in cancer. Survival analysis and correlation analysis of all mRNAs and miRNAs in the ceRNA network showed that 13 mRNAs and 6 miRNAs were significantly associated with overall survival, and 48 miRNA–mRNA interaction pairs had a significant negative correlation. A PPI network was established, and 21 hub genes were determined from the network. This study provides an effective bioinformatics basis for further understanding of the molecular mechanisms and predictions of breast cancer. A better understanding of the circRNA-related ceRNA network in BRCA will help identify potential biomarkers for diagnosis and prognosis.

scholarly journals Overexpression of CCNE1 confers a poorer prognostic in triple negative breast cancer identified by bioinformatic analysis

2020 ◽  
Author(s):  
Qianqian Yuan ◽  
Lewei Zheng ◽  
Yiqin Liao ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Cancer Survival ◽  
Process Analysis ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus

Abstract Background. Triple-negative breast cancer (TNBC) is a major subtype of breast cancer. Due to the lack of effective therapeutic targets, the prognosis is poor. In order to find an effective target, despite many efforts, the molecular mechanisms of TNBC are still not well understood which remain to be a profound clinical challenge.Methods. To identify the candidate genes in the carcinogenesis and progression of TNBC, microarray datasets GSE36693 and GSE65216 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional and pathway enrichment analyses were performed using the Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) databases via DAVID. We constructed the protein-protein interaction network (PPI) and the performed the module analysis using STRING and Cytoscape. Then we reanalyzed the selected DEGs genes and the survival analysis was performed using cBioportal.Results. A total of 140 DEGs were identified, consisting of 69 upregulated genes and 71 downregulated genes. Three hub genes were up-regulated among the selected genes from PPI and biological process analysis uncovered the fact that these genes were mainly enriched in p53 pathway and the pathways in cancer. Survival analysis showed that only CCNE1 may be involved in the carcinogenesis, invasion or recurrence of TNBC. Conclusion. CCNE1 could confer a poorer prognostic in TNBC identified by bioinformatic analysis and play key roles in the progression of TNBC which may contribute potential targets for the diagnosis, treatment and prognosis assessment of TNBC.

scholarly journals Identification of Immune-Related Risk Characteristics and Prognostic Value of Immunophenotyping in TNBC

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiarong Yi ◽  
Zeyu Shuang ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differentially Expressed Genes ◽  
Triple Negative Breast Cancer ◽  
Evaluation System ◽  
Triple Negative ◽  
Breast Cancer Subtype ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Evaluation Tool ◽  
Her 2

Background: Triple-negative breast cancer (TNBC) is not sensitive to targeted therapy with HER-2 monoclonal antibody and endocrine therapy due to lack of ER, PR, and HER-2 receptors. TNBC is a breast cancer subtype with the worst prognosis and the highest mortality rate compared with other subtypes.Materials and Methods: Breast cancer-related data were retrieved from The Cancer Genome Atlas (TCGA) database, and 116 cases of triple-negative breast cancer were identified from the data. GSE31519 dataset was retrieved from Gene Expression Omnibus (GEO) database, comprising a total of 68 cases with TNBC. Survival analysis was performed based on immune score, infiltration score and mutation score to explore differences in prognosis of different immune types. Analysis of differentially expressed genes was conducted and GSEA analysis based on these genes was conducted to explore the potential mechanism.Results: The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that MMP9, CXCL9, CXCL10, CXCL11 and CD7 are key genes that may affect immune typing of TNBC patients and play an important role in prediction of prognosis in TNBC patients.Conclusion: The current study presents an evaluation system based on immunophenotyping, which provides a more accurate prognostic evaluation tool for TNBC patients. Differentially expressed genes can be targeted to improve treatment of TNBC.

scholarly journals Comparative adipose transcriptome analysis digs out genes related to fat deposition in two pig breeds

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kai Xing ◽  
Kejun Wang ◽  
Hong Ao ◽  
Shaokang Chen ◽  
Zhen Tan ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Signaling Pathway ◽  
Meat Quality ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Mapk Signaling ◽  
Fat Deposition ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Pig Breeds

Abstract Fatness traits are important in pigs because of their implications for fattening efficiency, meat quality, reproductive performance and immunity. Songliao black pigs and Landrace pigs show important differences in production and meat quality traits, including fatness and muscle growth. Therefore, we used a high-throughput massively parallel RNA-seq approach to identify genes differentially expressed in backfat tissue between these two breeds (six pigs in each). An average of 37.87 million reads were obtained from the 12 samples. After statistical analysis of gene expression data by edgeR, a total of 877 differentially expressed genes were detected between the two pig breeds, 205 with higher expression and 672 with lower expression in Songliao pigs. Candidate genes (LCN2, CES3, DGKB, OLR1, LEP, PGM1, PCK1, ACACB, FADS1, FADS2, MOGAT2, SREBF1, PPARGC1B) with known effects on fatness traits were included among the DEGs. A total of 1071 lncRNAs were identified, and 85 of these lncRNAs were differentially expressed, including 53 up-regulated and 32 down-regulated lncRNAs, respectively. The differentially expressed genes and lncRNAs involved in glucagon signaling pathway, glycolysis/gluconeogenesis, insulin signaling pathway, MAPK signaling pathway and so on. Integrated analysis potential trans-regulating or cis-regulating relation between DEGs and DE lncRNAs, suggested lncRNA MSTRG.2479.1 might regulate the expressed level of VLDLR affecting porcine fat metabolism. These results provide a number of candidate genes and lncRNAs potentially involved in porcine fat deposition and provide a basis for future research on the molecular mechanisms underlying in fat deposition.

scholarly journals Overexpression of CCNE1 confers a poorer prognosis in triple-negative breast cancer identified by bioinformatic analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qianqian Yuan ◽  
Lewei Zheng ◽  
Yiqin Liao ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Cancer Survival ◽  
Process Analysis ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus

Abstract Background Triple-negative breast cancer (TNBC) is a major subtype of breast cancer. Due to the lack of effective therapeutic targets, the prognosis is poor. In order to find an effective target, despite many efforts, the molecular mechanisms of TNBC are still not well understood which remain to be a profound clinical challenge. Methods To identify the candidate genes in the carcinogenesis and progression of TNBC, microarray datasets GSE36693 and GSE65216 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional and pathway enrichment analyses were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases via DAVID. We constructed the protein-protein interaction network (PPI) and performed the module analysis using STRING and Cytoscape. Then, we reanalyzed the selected DEG genes, and the survival analysis was performed using cBioportal. Results A total of 140 DEGs were identified, consisting of 69 upregulated genes and 71 downregulated genes. Three hub genes were upregulated among the selected genes from PPI, and biological process analysis uncovered the fact that these genes were mainly enriched in p53 pathway and the pathways in cancer. Survival analysis showed that only CCNE1 may be involved in the carcinogenesis, invasion, or recurrence of TNBC. The expression levels of CCNE1 were significantly higher in TNBC cells than non-TNBC cells that were detected by qRT-PCR (P < 0.05). Conclusion CCNE1 could confer a poorer prognosis in TNBC identified by bioinformatic analysis and plays key roles in the progression of TNBC which may contribute potential targets for the diagnosis, treatment, and prognosis assessment of TNBC.

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.

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