Literature DB >> 33389402

Cancer stem cell transcriptome landscape reveals biomarkers driving breast carcinoma heterogeneity.

Zhifa Zhang1, Xiaofeng Dai2,3, Xiao Chen4, Jianying Zhang5.   

Abstract

BACKGROUND: Breast carcinomas are heterogeneous diseases with distinct clinical outcomes and cancer stem cell (CSC) percentages. Exploring breast carcinoma stem cell landscape could help understand the heterogeneity of such cancers with profound clinical relevance.
METHODS: We conducted transcriptional profiling of CSCs and non-stem cancer cells isolated from three triple-negative breast carcinoma cell lines, analyzed the CSC transcriptome landscape that drives breast carcinoma heterogeneity through differentially expressed gene identification, gene ontology (GO) and pathway enrichment analyses as well as network construction, and experimentally validated the network hub gene.
RESULTS: We identified a CSC feature panel consisting of 122 and 381 over-represented and under-expressed genes capable of differentiating breast carcinoma subtypes. We also underpinned the prominent roles of the PI3K-AKT pathway in empowering carcinoma cells with uncontrolled proliferative and migrative abilities that ultimately foster cancer stemness, and revealed the potential promotive roles of ATP6V1B1 on breast carcinoma stemness through functional in vitro studies.
CONCLUSIONS: Our study contributes in identifying a CSC feature panel for breast carcinomas that drives breast carcinoma heterogeneity at the transcriptional level, which provides a reservoir for diagnostic marker and/or therapeutic target identification once experimentally validated as demonstrated by ATP6V1B1.

Entities:  

Keywords:  Biomarker; Breast carcinoma; Cancer stem cell; Transcriptome

Mesh:

Substances:

Year:  2021        PMID: 33389402     DOI: 10.1007/s10549-020-06045-y

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  48 in total

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4.  Transcriptome profiles of stem-like cells from primary breast cancers allow identification of ITGA7 as a predictive marker of chemotherapy response.

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Journal:  Br J Cancer       Date:  2021-07-12       Impact factor: 7.640

  4 in total

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