Literature DB >> 32305967

RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer.

Fei Chen1, Yuancheng Li2, Na Qin2, Fengliang Wang3, Jiangbo Du2, Cheng Wang2, Fangzhi Du4, Tao Jiang2, Yue Jiang2, Juncheng Dai2, Zhibin Hu2, Cheng Lu3, Hongbing Shen2.   

Abstract

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer that currently lacks effective biomarkers and therapeutic targets required to investigate the diagnosis and treatment of TNBC. Here we performed a comprehensive differential analysis of 165 TNBC samples by integrating RNA-seq data of breast tumor tissues and adjacent normal tissues from both our cohort and The Cancer Genome Atlas (TCGA). Pathway enrichment analysis was conducted to evaluate the biological function of TNBC-specific expressed genes. Further multivariate Cox proportional hazard regression was performed to evaluate the effect of these genes on TNBC prognosis. In this report, we identified a total of 148 TNBC-specific expressed genes that were primarily enriched in mammary gland morphogenesis and hormone levels related pathways, suggesting that mammary gland morphogenesis might play a unique role in TNBC patients differing from other breast cancer types. Further survival analysis revealed that nine genes ( FSIP1, ADCY5, FSD1, HMSD, CMTM5, AFF3, CYP2A7, ATP1A2, and C11orf86) were significantly associated with the prognosis of TNBC patients, while three of them ( ADCY5, CYP2A7, and ATP1A2) were involved in the hormone-related pathways. These findings indicated the vital role of the hormone-related genes in TNBC tumorigenesis and may provide some independent prognostic markers as well as novel therapeutic targets for TNBC.

Entities:  

Keywords:  RNA-seq; differential expression; prognosis; triple negative breast cancer

Year:  2020        PMID: 32305967     DOI: 10.7555/JBR.34.20190111

Source DB:  PubMed          Journal:  J Biomed Res        ISSN: 1674-8301


  5 in total

1.  AFF3 is a novel prognostic biomarker and a potential target for immunotherapy in gastric cancer.

Authors:  Yuling Zeng; Xueping Zhang; Fazhan Li; Ying Wang; Ming Wei
Journal:  J Clin Lab Anal       Date:  2022-04-27       Impact factor: 3.124

2.  FSIP1 Is Associated with Poor Prognosis and Can Be Used to Construct a Prognostic Model in Gastric Cancer.

Authors:  Xiuchun Yan; Junzhu Dai; Ying Han; Qi You; Yao Liu
Journal:  Dis Markers       Date:  2022-06-03       Impact factor: 3.464

Review 3.  Oncogenic and Tumor Suppressive Components of the Cell Cycle in Breast Cancer Progression and Prognosis.

Authors:  Dharambir Kashyap; Vivek Kumar Garg; Elise N Sandberg; Neelam Goel; Anupam Bishayee
Journal:  Pharmaceutics       Date:  2021-04-17       Impact factor: 6.321

4.  Construction and Validation of a Prognostic Risk Model for Triple-Negative Breast Cancer Based on Autophagy-Related Genes.

Authors:  Cheng Yan; Qingling Liu; Ruoling Jia
Journal:  Front Oncol       Date:  2022-02-04       Impact factor: 6.244

5.  A supervised machine learning approach identifies gene-regulating factor-mediated competing endogenous RNA networks in hormone-dependent cancers.

Authors:  Dulari K Jayarathna; Miguel E Rentería; Jyotsna Batra; Neha S Gandhi
Journal:  J Cell Biochem       Date:  2022-06-27       Impact factor: 4.480

  5 in total

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