Identification of hub genes to regulate breast cancer metastasis to brain by bioinformatics analyses

2018 ◽  
Vol 120 (6) ◽  
pp. 9522-9531 ◽  
Author(s):  
Dongyang Tang ◽  
Xin Zhao ◽  
Li Zhang ◽  
Zhiwei Wang ◽  
Cheng Wang
Keyword(s):  
Breast Cancer ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Hub Genes ◽  
Bioinformatics Analyses

scholarly journals Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

Hereditas ◽  
2019 ◽  
Vol 156 (1) ◽  
Author(s):  
Yun Cai ◽  
Jie Mei ◽  
Zhuang Xiao ◽  
Bujie Xu ◽  
Xiaozheng Jiang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
In Silico ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Hub Genes

scholarly journals Machine Learning Model for Lymph Node Metastasis Prediction in Breast Cancer Using Random Forest Algorithm and Mitochondrial Metabolism Hub Genes

2021 ◽  
Vol 11 (7) ◽  
pp. 2897
Author(s):  
Byung-Chul Kim ◽  
Jingyu Kim ◽  
Ilhan Lim ◽  
Dong Ho Kim ◽  
Sang Moo Lim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Set ◽  
Machine Learning Model

Breast cancer metastasis can have a fatal outcome, with the prediction of metastasis being critical for establishing effective treatment strategies. RNA-sequencing (RNA-seq) is a good tool for identifying genes that promote and support metastasis development. The hub gene analysis method is a bioinformatics method that can effectively analyze RNA sequencing results. This can be used to specify the set of genes most relevant to the function of the cell involved in metastasis. Herein, a new machine learning model based on RNA-seq data using the random forest algorithm and hub genes to estimate the accuracy of breast cancer metastasis prediction. Single-cell breast cancer samples (56 metastatic and 38 non-metastatic samples) were obtained from the Gene Expression Omnibus database, and the Weighted Gene Correlation Network Analysis package was used for the selection of gene modules and hub genes (function in mitochondrial metabolism). A machine learning prediction model using the hub gene set was devised and its accuracy was evaluated. A prediction model comprising 54-functional-gene modules and the hub gene set (NDUFA9, NDUFB5, and NDUFB3) showed an accuracy of 0.769 ± 0.02, 0.782 ± 0.012, and 0.945 ± 0.016, respectively. The test accuracy of the hub gene set was over 93% and that of the prediction model with random forest and hub genes was over 91%. A breast cancer metastasis dataset from The Cancer Genome Atlas was used for external validation, showing an accuracy of over 91%. The hub gene assay can be used to predict breast cancer metastasis by machine learning.

scholarly journals Comprehensive Analysis of Aberrantly Expressed Profiles of ncRNAs Revealed lncRNA AGPAT4-IT1 is a Potential Prognostic Marker for Breast Cancer Metastasis

2020 ◽  
Author(s):  
Feng chun Zhang ◽  
ning ning yan ◽  
Ming jun Li ◽  
Ying chun Xu ◽  
Xing ya Li
Keyword(s):  
Breast Cancer ◽  
Prognostic Marker ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Bioinformatics Analyses ◽  
Functional Roles ◽  
Kaplan Meier ◽  
Non Coding Rnas ◽  
Microarray Analyses ◽  
The Relationship

Abstract Aims: we investigated the relationship between long non-coding RNAs (lncRNAs) and breast cancer lung metastasis (BCLM). Methods: We performed lncRNA microarray analyses to establish the lncRNA profile of BCLM. Bioinformatics analyses were carried out to analyzed functional roles of identified lncRNAs. Kaplan-meier analysis was conducted to determine the relation between lncRNA AGPAT4-IT1 and prognosis of breast cancer. Results: We found 317 upregulated and 166 downregulated lncRNAs in BCLM group. We showed AGPAT4-IT1 was positively correlated with its parental gene APGAT4. Furthermore, we suggested AGPAT4-IT1 were highly expressed in higher tumour grade and predicted poorer prognosis. Conclusions: These findings provide evidence for exploring the mechanisms of BCLM and indicate AGPAT4-IT1 is a prospective prognostic marker for breast cancer metastasis.

Identification of Seven Hub Genes as the Novel Biomarkers in Triple-negative Breast Cancer and Breast Cancer Metastasis

2020 ◽  
Author(s):  
Huanxian Wu ◽  
Huining Lian ◽  
Qianqing Chen ◽  
Jinlamao Yang ◽  
Baofang Ou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Protein Interactions ◽  
Cancer Metastasis ◽  
Triple Negative ◽  
Malignant Tumors ◽  
Underlying Disease ◽  
Breast Cancer Metastasis ◽  
Hub Genes ◽  
Cancer Subtypes

Abstract Background: Breast cancer is one of the most common malignant tumors with the highest morbidity and mortality among women. Compared with the other breast cancer subtypes, Triple-negative breast cancer (TNBC) has a higher probability of recurrence and is prone to distant metastasis. To reveal the underlying disease mechanisms and identify more effective biomarkers for TNBC and breast cancer metastasis. Methods: Gene Ontology and KEGG pathway analysis were used for investigating the role of overlapping differentially expressed genes (DEGs). Hub genes among these DEGs were determined by the protein-protein interactions network analysis and CytoHubba. Oncomine databases were used for verifying the clinical relevance of hub genes. Furthermore, the differences in the expression of these genes in cancer and normal tissues were validated in the cellular, animal and human tissue.Results: Seven hub genes, including TTK, KIF11, SPAG5, RRM2, BUB1, CDCA8 and CDC25C, were identified that might be associated with TNBC and breast cancer metastasis. Meanwhile, these genes have been verified highly expressed in tumor cells and tumor tissues, and patients with higher expression of these genes have a poorer prognosis. Conclusions: Seven hub genes were potential biomarkers for the diagnosis and therapy of TNBC and breast cancer metastasis.

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