Identification of Differentially Expressed Genes as Potential Therapeutic and Prognostic Targets for Breast Cancer Based on Bioinformatics

被引:1
|
作者
Ma, Yan [1 ]
Peng, Yingge [2 ]
Li, Xin [3 ]
Huo, Xiaoguang [1 ]
Xu, Wenzhe [1 ]
Zhao, Wei [1 ]
Wang, Yingnan [4 ]
机构
[1] Zibo Cent Hosp, Dept Ultrasound, Zibo 255000, Shandong, Peoples R China
[2] Zibo Tradit Chinese Med Hosp, Hlth Management Ctr, Zibo 255000, Shandong, Peoples R China
[3] Zibo Cent Hosp, Dept Clin Lab, Zibo 255000, Shandong, Peoples R China
[4] Zibo Tradit Chinese Med Hosp, Dept Surg, Zibo 255000, Shandong, Peoples R China
关键词
breast cancer; hub gene; biomarker; prognosis;
D O I
10.23812/j.biol.regul.homeost.agents.20233711.605
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Breast cancer (BC) is one of the most common malignant tumors in women. There is an urgent need to explore the key genes responsible for the prognosis of BC. The aim of this study was to screen out differentially expressed genes (DEGs) in BC and to provide reliable prognosis prediction. DEGs related to prognosis of BC were identified and verified as markers for predicting BC prognosis.Methods: Two data sets GSE22820 and GSE29431 were selected from the Gene Expression Omnibus (GEO) database and the DEGs were screened by the online tool of GEO2R and visualized by the volcano maps. The bioinformatics software of database for annotation, visualization, and integrated discovery (DAVID) was used to annotate associated functions of DEGs. Search tool for the retrieval of interacting genes/proteins (STRING) was used to construct the protein-protein interaction (PPI) networks which were visualized by Cytoscape software. Afterward, the data from The Cancer Genome Atlas (TCGA) were used for multivari-ate Cox regression analysis and Kaplan-Meier (K-M) survival analysis was used to identify prognosis-related key genes. The immortalized breast epithelial cell line MCF10A and BC cell line MCF7 were purchased and exploited to verify the differential expression of selected hub genes through real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and western blot. The expressions of prognostic-related genes were regulated through transfecting MCF7 cells, followed by exploring the effects of prognosis-related genes. The cell proliferation was determined by assays of colony formation. The cell counting kit (CCK)-8 and the cell migration were evaluated by the tests of transwell and cell scratch. The flow cytometry of Annexin V-PE and 7-AAD double stained cells was used for measuring cell apoptosis. Results: The bioinformatics analysis indicated that 969 DEGs were found in GSE22820, out of which 421 genes were over-expressed and 548 genes were under-expressed. In GSE29431, 724 DEGs were found, out of which 109 genes were over-expressed and 615 genes were under-expressed. GSE22820, GSE29431, and Co-DEGs are involved in mitotic nuclear division, regulation of angiogenesis, connective tissue development, muscle organ development, glucose metabolic process, and hexose metabolic process. Genes of peroxisome proliferator-activated receptor alpha (PPARA), peroxisome proliferator-activated receptor gamma (PPARG), lipoprotein lipase (LPL), leptin (LEP), insulin like growth factor 1 (IGF1), type I collagen alpha 1 (COL1A1), Fibronectin (FN)1, and anillin (ANLN) were identified as hub genes, and ANLN, PPARA, PPARG, and LPL were verified for having effect on prognosis of BC. The upregulation of PPARA and PPARG, and the downregulation of ANLN and LPL restrained both proliferation and migration and induced apoptosis of BC cells.Conclusions: PPARA, PPARG, LPL, LEP, IGF1, COL1A1, FN1, and ANLN were identified as hub genes and ANLN, PPARA, PPARG, and LPL could affect the development of BC by inhibiting proliferation and migration and promoting apoptosis of cancer cells.
引用
收藏
页码:6375 / 6388
页数:14
相关论文
共 50 条
  • [41] Determination of Potential Therapeutic Targets and Prognostic Markers of Ovarian Cancer by Bioinformatics Analysis
    Zhang, Jing
    Huang, Shouguo
    Quan, Lini
    Meng, Qiu
    Wang, Haiyan
    Wang, Jie
    Chen, Jin
    BIOMED RESEARCH INTERNATIONAL, 2021, 2021
  • [42] IDENTIFICATION OF KEY DIFFERENTIALLY EXPRESSED GENES AND MIRNAS IN DIABETES FOOT ULCER BASED ON BIOINFORMATICS
    Zheng, Jihang
    Zou, Enmiao
    Pan, Hao
    Gao, Zimian
    JOURNAL OF INVESTIGATIVE MEDICINE, 2023, 71 : 44 - 45
  • [43] Screening of differentially expressed genes and identification of AMACR as a prognostic marker in prostate cancer
    Fu, Ping
    Bu, Chunying
    Cui, Bin
    Li, Na
    Wu, Jifeng
    ANDROLOGIA, 2021, 53 (06)
  • [44] A bioinformatics analysis of differentially expressed genes associated with liver cancer
    白文萱
    China Medical Abstracts(Internal Medicine), 2017, 34 (03) : 174 - 175
  • [45] Identification of Differentially Expressed Plasma lncRNAs As Potential Biomarkers for Breast Cancer
    Wang, Minghui
    Liu, Huilin
    Wu, Wenyao
    Zhao, Jinxia
    Song, Guanghui
    Chen, Xi
    Wang, Rong
    Shao, Changfeng
    Li, Jing
    Wang, Haiyan
    Wang, Qing
    Feng, Xiaodong
    CLINICAL BREAST CANCER, 2022, 22 (02) : E135 - E141
  • [46] Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
    Xiao-hong Mao
    Qiang Ye
    Guo-bing Zhang
    Jin-ying Jiang
    Hong-ying Zhao
    Yan-fei Shao
    Zi-qi Ye
    Zi-xue Xuan
    Ping Huang
    World Journal of Surgical Oncology, 19
  • [47] Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
    Mao, Xiao-hong
    Ye, Qiang
    Zhang, Guo-bing
    Jiang, Jin-ying
    Zhao, Hong-ying
    Shao, Yan-fei
    Ye, Zi-qi
    Xuan, Zi-xue
    Huang, Ping
    WORLD JOURNAL OF SURGICAL ONCOLOGY, 2021, 19 (01)
  • [48] Systematic identification and molecular characterization of genes differentially expressed in breast and ovarian cancer
    Dahl, E
    Sadr-Nabavi, A
    Klopocki, E
    Betz, B
    Grube, S
    Kreutzfeld, R
    Himmelfarb, M
    An, HX
    Gelling, S
    Klaman, I
    Hinzmann, B
    Kristiansen, G
    Grützmann, R
    Kuner, R
    Petschke, B
    Rhiem, K
    Wiechen, K
    Sers, C
    Wiestler, O
    Schneider, A
    Höfler, H
    Nährig, J
    Dietel, M
    Schäfer, R
    Rosenthal, A
    Schmutzler, R
    Dürst, M
    Meindl, A
    Niederacher, D
    JOURNAL OF PATHOLOGY, 2005, 205 (01): : 21 - 28
  • [50] Identification of potential therapeutic targets for gliomas by bioinformatics analysis
    Ma, Ke
    Cheng, Zhihua
    Sun, Liqun
    Li, Haibo
    ONCOLOGY LETTERS, 2017, 14 (05) : 5203 - 5210