scholarly journals Gene and lncRNA co-expression network analysis reveals novel ceRNA network for triple-negative breast cancer

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kehao Le ◽  
Hui Guo ◽  
Qiulei Zhang ◽  
Xiaojuan Huang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Protein Coding ◽  
Cerna Network

Abstract Breast cancer is the most frequently diagnosed malignancy among women, and triple-negative breast cancer (TNBC) is a highly aggressive subtype. Increasing evidence has shown that lncRNAs are involved in tumor growth, cell-cycle, and apoptosis through interactions with miRNAs or mRNAs. However, there is still limited data on ceRNAs involved in the molecular mechanisms underlying TNBC. In this study, we applied the weighted gene co-expression network analysis to the existing microarray mRNA and lncRNA expression data obtained from the breast tissues of TNBC patients to find the hub genes and lncRNAs involved in TNBC. Functional enrichment was performed on the module that correlated with Ki-67 status the most (Turquoise module). The hub genes in the Turquoise module were found to be associated with DNA repair, cell proliferation, and the p53 signaling pathway. We performed co-expression analysis of the protein-coding and lncRNA hub genes in the Turquoise module. Analysis of the RNA-seq data obtained from The Cancer Genome Atlas database revealed that the protein-coding genes and lncRNAs that were co-expressed were also differentially expressed in the TNBC tissues compared with the normal mammary tissues. On the basis of establishing the ceRNA network, two mRNAs (RAD51AP1 and TYMS) were found to be correlated with overall survival in TNBC. These results suggest that TNBC-specific mRNA and lncRNAs may participate in a complex ceRNA network, which represents a potential therapeutic target for the treatment of TNBC.

scholarly journals Identification of Metastasis-Associated Genes in Triple-Negative Breast Cancer Using Weighted Gene Co-expression Network Analysis

2020 ◽  
Vol 16 ◽  
pp. 117693432095486
Author(s):  
Wenting Xie ◽  
Zhongshi Du ◽  
Yijie Chen ◽  
Naxiang Liu ◽  
Zhaoming Zhong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Candidate Genes ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Characteristic Curve ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Diagnosis And Prognosis

Triple-negative breast cancer (TNBC) is the most aggressive and fatal sub-type of breast cancer. This study aimed to identify metastasis-associated genes that could serve as biomarkers for TNBC diagnosis and prognosis. RNA-seq data and clinical information on TNBC from the Cancer Genome Atlas were used to conduct analyses. Expression data were used to establish co-expression modules using average linkage hierarchical clustering. We used weighted gene co-expression network analysis to explore the associations between gene sets and clinical features and to identify metastasis-associated candidate biomarkers. The K-M plotter website was used to explore the association between the expression of candidate biomarkers and patient survival. In addition, receiver operating characteristic curve analysis was used to illustrate the diagnostic performance of candidate genes. The pale turquoise module was significantly associated with the occurrence of metastasis. In this module, 64 genes were identified, and its functional enrichment analysis revealed that they were mainly associated with transcriptional misregulation in cancer, microRNAs in cancer, and negative regulation of angiogenesis. Further, 4 genes, IGSF10, RUNX1T1, XIST, and TSHZ2, which were negatively associated with relapse-free survival and have seldom been reported before in TNBC, were selected. In addition, the mRNA expression levels of the 4 candidate genes were significantly lower in TNBC tumor tissues compared with healthy tissues. Based on the K-M plotter, these 4 genes were correlated with poor prognosis of TNBC. The area under the curve of IGSF10, RUNX1T1, TSHZ2, and XIST was 0.918, 0.957, 0.977, and 0.749. These findings provide new insight into TNBC metastasis. IGSF10, RUNX1T1, TSHZ2, and XIST could be used as candidate biomarkers for the diagnosis and prognosis of TNBC metastasis.

scholarly journals A Novel Platelet-Related Gene Signature for Predicting the Prognosis of Triple-Negative Breast Cancer

2022 ◽  
Vol 9 ◽  
Author(s):  
Jindong Xie ◽  
Yutian Zou ◽  
Feng Ye ◽  
Wanzhen Zhao ◽  
Xinhua Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinical Prognosis

Regarded as the most invasive subtype, triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2) proteins. Platelets have recently been shown to be associated with metastasis of malignant tumors. Nevertheless, the status of platelet-related genes in TNBC and their correlation with patient prognosis remain unknown. In this study, the expression and variation levels of platelet-related genes were identified and patients with TNBC were divided into three subtypes. We collected cohorts from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, we constructed a seven-gene signature which classified the two cohorts of patients with TNBC into low- or high-risk groups. Patients in the high-risk group were more likely to have lower survival rates than those in the low-risk group. The risk score, incorporated with the clinical features, was confirmed as an independent factor for predicting the overall survival (OS) time. Functional enrichment analyses revealed the involvement of a variety of vital biological processes and classical cancer-related pathways that could be important to the ultimate prognosis of TNBC. We then built a nomogram that performed well. Moreover, we tested the model in other cohorts and obtained positive outcomes. In conclusion, platelet-related genes were closely related to TNBC, and this novel signature could serve as a tool for the assessment of clinical prognosis.

scholarly journals Characterization of ceRNA network to reveal potential prognostic biomarkers in triple-negative breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7522 ◽  
Author(s):  
Xiang Song ◽  
Chao Zhang ◽  
Zhaoyun Liu ◽  
Qi Liu ◽  
Kewen He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Quantitative Polymerase Chain Reaction ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Non Coding Rnas

Triple-negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Previous studies have demonstrated that some abnormal expression of non-coding RNAs including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) were closely related to tumor cell proliferation, apoptosis, invasion, migration and drug sensitivity. However, the role of non-coding RNAs in the pathogenesis of TNBC is still unclear. In order to characterize the molecular mechanism of non-coding RNAs in TNBC, we downloaded RNA data and miRNA data from the cancer genome atlas database. We successfully identified 686 message RNAs (mRNAs), 26 miRNAs and 50 lncRNAs as key molecules for high risk of TNBC. Then, we hypothesized that the lncRNA–miRNA–mRNA regulatory axis positively correlates with TNBC and constructed a competitive endogenous RNA (ceRNA) network of TNBC. Our series of analyses has shown that five molecules (TERT, TRIML2, PHBP4, mir-1-3p, mir-133a-3p) were significantly associated with the prognosis of TNBC, and there is a prognostic ceRNA sub-network between those molecules. We mapped the Kaplan–Meier curve of RNA on the sub-network and also suggested that the expression level of the selected RNA is related to the survival rate of breast cancer. Reverse transcription-quantitative polymerase chain reaction showed that the expression level of TRIML2 in TNBC cells was higher than normal. In general, our findings have implications for predicting metastasis, predicting prognosis and discovering new therapeutic targets for TNBC.

scholarly journals Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiarong Yi ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
Xiazi Zouxu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

scholarly journals Exploring specific prognostic biomarkers in triple-negative breast cancer

2019 ◽  
Vol 10 (11) ◽  
Author(s):  
Chang Bao ◽  
Yunkun Lu ◽  
Jishun Chen ◽  
Danni Chen ◽  
Weiyang Lou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Target Genes ◽  
Triple Negative ◽  
Therapeutic Targets ◽  
Good Prognosis ◽  
The Cancer Genome Atlas ◽  
Prognostic Biomarkers ◽  
Luciferase Reporter ◽  
Hub Genes

Abstract Lacking of both prognostic biomarkers and therapeutic targets, triple-negative breast cancer (TNBC) underscores pivotal needs to uncover novel biomarkers and viable therapies. MicroRNAs have broad biological functions in cancers and may serve as ideal biomarkers. In this study, by data mining of the Cancer Genome Atlas database, we screened out 4 differentially-expressed microRNAs (DEmiRNAs) between TNBC and normal samples: miR-135b-5p, miR-9-3p, miR-135b-3p and miR-455-5p. They were specially correlated with the prognosis of TNBC but not non-TNBC. The weighted correlation network analysis (WGCNA) for potential target genes of 3 good prognosis-related DEmiRNAs (miR-135b-5p, miR-9-3p, miR-135b-3p) identified 4 hub genes with highly positive correlation with TNBC subtype: FOXC1, BCL11A, FAM171A1 and RGMA. The targeting relationships between miR-9-3p and FOXC1/FAM171A1, miR-135b-3p and RGMA were validated by dual-luciferase reporter assays. Importantly, the regulatory functions of 4 DEmiRNAs and 3 verified target genes on cell proliferation and migration were explored in TNBC cell lines. In conclusion, we shed lights on these 4 DEmiRNAs (miR-135b-5p, miR-9-3p, miR-135b-3p, miR-455-5p) and 3 hub genes (FOXC1, FAM171A1, RGMA) as specific prognostic biomarkers and promising therapeutic targets for TNBC.

scholarly journals Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12383
Author(s):  
Peng Su ◽  
Ziqi Peng ◽  
Boyang Xu ◽  
Bowen Yang ◽  
Feng Jin
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
The Cancer Genome Atlas ◽  
Classification Method ◽  
Separate Analysis ◽  
Hub Genes ◽  
Chemotherapy Drugs ◽  
Gemcitabine Resistance ◽  
Cluster A

Background Recently, researchers have classified highly heterogeneous triple negative breast cancer (TNBC) into different subtypes from different perspectives and investigated the characteristics of different subtypes to pursue individualized treatment. With the increase of immunotherapy and its preliminary application in TNBC treatment, the value of immune-related strategies in the treatment of TNBC has been initially reflected. Based thereon, this study plans to classify and further explore TNBC from the perspective of immune cell infiltration. Method The fractions of immune cells of TNBC patients were assessed by six immune component analysis methods in The Cancer Genome Atlas (TCGA) database. Hub genes significantly related to poor prognosis were verified by weighted gene co-expression network analysis (WGCNA) analysis, Lasso analysis, and univariate KM analysis. Two cohorts of TNBC patients with complete prognosis information were collected for validation analysis. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was adopted to ascertain the sensitivity differences of different populations based on hub-gene grouping to different chemotherapy drugs. Results Five hub genes (CD79A, CXCL13, IGLL5, LHFPL2, and PLEKHF1) of the key co-expression gene module could divide TNBC patients into two groups (Cluster A and Cluster B) based on consistency cluster analysis. The patients with Cluster A were responsible for significantly worse prognosis than the patients with Cluster B (P = 0.023). In addition, another classification method, PCoA, and two other datasets (GSE103091 and GSE76124), were used to obtain consistent results with previous findings, which verified the stability of the classification method and dataset in this study. The grouping criteria based on the previous results were developed and the accuracy of the cut-off values was validated. A prognosis model of TNBC patients was then constructed based on the grouping results of five hub genes and N staging as prognostic factors. The results of ROC and decision curve analyses showed that this model had high prediction accuracy and patients could benefit therefrom. Finally, GDSC database analysis proved that patients in Cluster A were more sensitive to Vinorelbine. Separate analysis of the sensitivity of patients in Cluster A to Gemcitabine and Vinorelbine showed that the patients in Cluster A exhibited higher sensitivity to Vinorelbine. We hypothesized that these five genes were related to gemcitabine resistance and they could serve as biomarkers for clinical drug decision-making after anthracene resistance and taxane resistance in patients with advanced TNBC. Conclusion This study found five hub prognostic genes associated with macrophages, and a prognostic model was established to predict the survival of TNBC patients. Finally, these five genes were related to gemcitabine resistance in TNBC patients.

scholarly journals CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kelly E. Craven ◽  
Yesim Gökmen-Polar ◽  
Sunil S. Badve
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Immune Cell ◽  
Molecular Taxonomy ◽  
The Cancer Genome Atlas ◽  
Activated T Cells ◽  
Cancer Immunoediting

AbstractStudies have shown that the presence of tumor infiltrating lymphocytes (TILs) in Triple Negative Breast Cancer (TNBC) is associated with better prognosis. However, the molecular mechanisms underlying these immune cell differences are not well delineated. In this study, analysis of hematoxylin and eosin images from The Cancer Genome Atlas (TCGA) breast cancer cohort failed to show a prognostic benefit of TILs in TNBC, whereas CIBERSORT analysis, which quantifies the proportion of each immune cell type, demonstrated improved overall survival in TCGA TNBC samples with increased CD8 T cells or CD8 plus CD4 memory activated T cells and in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC samples with increased gamma delta T cells. Twenty-five genes showed mutational frequency differences between the TCGA high and low T cell groups, and many play important roles in inflammation or immune evasion (ATG2B, HIST1H2BC, PKD1, PIKFYVE, TLR3, NOTCH3, GOLGB1, CREBBP). Identification of these mutations suggests novel mechanisms by which the cancer cells attract immune cells and by which they evade or dampen the immune system during the cancer immunoediting process. This study suggests that integration of mutations with CIBERSORT analysis could provide better prediction of outcomes and novel therapeutic targets in TNBC cases.

scholarly journals CENPL, ISG20L2, LSM4 and MRPL3 Are Four Novel Hub Genes Involved in Breast Cancer Development and Progression

2020 ◽  
Author(s):  
Jinbao Yin ◽  
Chen Lin ◽  
Meng jiang ◽  
Xinbing Tang ◽  
Danlin Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Candidate Genes ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Rank Aggregation ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Ppi Networks ◽  
Kaplan Meier

Abstract BackgroundAs a highly prevalent tumor disease worldwide, Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remain an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil its mechanism and provide novel diagnostic and prognostic markers for breast cancer.MethodsIn this study, we systematically integrated multiple bioinformatic methods, including robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), ROC and Kaplan-Meier analyses, DNA methylation analyses and genomic mutation analyses, GSEA and GSVA, based on ten mRNA datasets to identify and investigate novel hub genes involved in breast cancer. In parallel, RNA in situ detection technology was applied to validate those novel hub gene.ResultsEZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4 and MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among those five hub genes, many studies on EZH2 gene in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These novel four hub genes were up-regulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. The preliminary analysis revealed those novel hub genes are four potentially candidate genes for further exploring the molecular mechanism of breast cancer.ConclusionWe identify four novel hub genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. Those hub genes are four potentially candidate genes served as promising candidate diagnostic biomarkers and prognosis predictors for breast cancer, and their exact functional mechanisms in breast cancer deserve further in-depth study.

scholarly journals Comprehensive Analysis of Differentially Expressed Profiles of lncRNAs/mRNAs and miRNAs with Associated ceRNA Networks in Triple-Negative Breast Cancer

2018 ◽  
Vol 50 (2) ◽  
pp. 473-488 ◽  
Author(s):  
Rui Yang ◽  
Lei Xing ◽  
Min Wang ◽  
Hong Chi ◽  
Luyu Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Large Scale ◽  
Triple Negative ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Cerna Network ◽  
Malignant Breast ◽  
Underlying Mechanisms

Background/Aims: Triple-negative breast cancer (TNBC) is a subtype of highly malignant breast cancer with poor prognosis. Growing evidence indicates that Long noncoding RNAs (lncRNAs) play important regulatory roles in the development and progression of a variety of cancers including breast cancer. However, the underlying mechanisms remain largely unknown. Methods: Here, we compared the expression profiles of mRNAs, lncRNAs and miRNAs between 111 TNBC tissues and 104 non-cancerous tissues utilizing RNA-Seq Data from The Cancer Genome Atlas (TCGA). Gene Ontology and KEGG pathway enrichment analyses were executed to investigate the principal functions of the significantly dysregulated mRNAs. Moreover, Kaplan-Meier survival analyses were performed to determine the effects of differentially expressed lncRNAs/mRNAs/miRNAs on overall survival. Subsequently, we constructed a competing endogenous RNA (ceRNA) network, which included 66 dysregulated lncRNAs, 24 miRNAs and 55 mRNAs. The four dysregulated lncRNAs, three aberrantly expressed miRNAs and four mRNAs were confirmed in the ceRNA network by qRT-PCR in 30 pairs of samples, respectively. Results: A total of 1441 lncRNAs, 114 miRNA and 2501 mRNAs were found to be differentially expressed in TNBC tissues compared with controls. 109 lncRNAs and 124 mRNAs might serve as prognostic signature for patients with TNBC according to the survival analysis. Functional analysis revealed that 19 mRNAs in the ceRNA network were enriched in 17 cancer-related pathways. Conclusion: Taken together, we identified novel lncRNAs/miRNAs which may serve as potential biomarkers to predict the survival and therapeutic targets for TNBC patients based on a large-scale sample. More importantly, we constructed the ceRNA network of TNBC, which provides valuable information to further explore the molecular mechanism underlying tumorigenesis and development of TNBC.

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