Literature DB >> 31974598

Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis.

Guansheng Zhong1, Weiyang Lou2, Qinyan Shen3, Kun Yu1, Yajuan Zheng1.   

Abstract

Triple‑negative breast cancer (TNBC) accounts for the worst prognosis of all types of breast cancers due to a high risk of recurrence and a lack of targeted therapeutic options. Extensive effort is required to identify novel targets for TNBC. In the present study, a robust rank aggregation (RRA) analysis based on genome‑wide gene expression datasets involving TNBC patients from the Gene Expression Omnibus (GEO) database was performed to identify key genes associated with TNBC. A total of 194 highly ranked differentially expressed genes (DEGs) were identified in TNBC vs. non‑TNBC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was utilized to explore the biological functions of the identified genes. These DEGs were mainly involved in the biological processes termed positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptotic process, response to drug, response to estradiol and negative regulation of cell growth. Genes were mainly involved in the KEGG pathway termed estrogen signaling pathway. The aberrant expression of several randomly selected DEGs were further validated in cell lines, clinical tissues and The Cancer Genome Atlas (TCGA) cohort. Furthermore, all the top‑ranked DEGs underwent survival analysis using TCGA database, of which overexpression of 4 genes (FABP7, ART3, CT83, and TTYH1) were positively correlated to the life expectancy (P<0.05) of TNBC patients. In addition, a model consisting of two genes (FABP7 and CT83) was identified to be significantly associated with the overall survival (OS) of TNBC patients by means of Cox regression, Kaplan‑Meier, and receiver operating characteristic (ROC) analyses. In conclusion, the present study identified a number of key genes as potential biomarkers involved in TNBC, which provide novel insights into the tumorigenesis of TNBC at the gene level and may serve as independent prognostic factors for TNBC prognosis.

Entities:  

Year:  2019        PMID: 31974598     DOI: 10.3892/mmr.2019.10867

Source DB:  PubMed          Journal:  Mol Med Rep        ISSN: 1791-2997            Impact factor:   2.952


  8 in total

1.  Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis.

Authors:  Jiulong Ma; Chen Chen; Shan Liu; Jiahua Ji; Di Wu; Peng Huang; Dexian Wei; Zhimin Fan; Liqun Ren
Journal:  Cancer Gene Ther       Date:  2022-04-26       Impact factor: 5.987

2.  Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies.

Authors:  Md Shahin Alam; Adiba Sultana; Md Selim Reza; Md Amanullah; Syed Rashel Kabir; Md Nurul Haque Mollah
Journal:  PLoS One       Date:  2022-05-26       Impact factor: 3.752

3.  Multiomics analysis reveals CT83 is the most specific gene for triple negative breast cancer and its hypomethylation is oncogenic in breast cancer.

Authors:  Chen Chen; Dan Gao; Jinlong Huo; Rui Qu; Youming Guo; Xiaochi Hu; Libo Luo
Journal:  Sci Rep       Date:  2021-06-09       Impact factor: 4.379

4.  Lift the Veil of Breast Cancers Using 4 or Fewer Critical Genes.

Authors:  Zhengjun Zhang
Journal:  Cancer Inform       Date:  2022-02-14

5.  Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer.

Authors:  Anamika Thalor; Hemant Kumar Joon; Gagandeep Singh; Shikha Roy; Dinesh Gupta
Journal:  Comput Struct Biotechnol J       Date:  2022-03-24       Impact factor: 6.155

Review 6.  Multi-Gene Testing Overview with a Clinical Perspective in Metastatic Triple-Negative Breast Cancer.

Authors:  Martina Dameri; Lorenzo Ferrando; Gabriella Cirmena; Claudio Vernieri; Giancarlo Pruneri; Alberto Ballestrero; Gabriele Zoppoli
Journal:  Int J Mol Sci       Date:  2021-07-01       Impact factor: 5.923

7.  Cloning and Functional Analysis of Rat Tweety-Homolog 1 Gene Promoter.

Authors:  Malgorzata Gorniak-Walas; Karolina Nizinska; Katarzyna Lukasiuk
Journal:  Neurochem Res       Date:  2021-06-26       Impact factor: 3.996

8.  The tweety Gene Family: From Embryo to Disease.

Authors:  Rithvik R Nalamalapu; Michelle Yue; Aaron R Stone; Samantha Murphy; Margaret S Saha
Journal:  Front Mol Neurosci       Date:  2021-06-28       Impact factor: 5.639

  8 in total

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