Public transcriptome database-based selection and validation of reliable reference genes for breast cancer research.
Background: Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is the most sensitive technique for evaluating gene expression levels. Choosing appropriate reference genes (RGs) is critical for normalizing and evaluating changes in the expression of target genes. However, uniform...
| Publicado en: | BioMedical Engineering OnLine Vol. 20; no. 1; pp. 1 - 20 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
12/11/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154083747&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154083747 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 12/11/2021 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 154083747 154083747 NLM34895237 154083747 10.1186/s12938-021-00963-8 NLM34895237 154083747 ppf: 1 ppct: 19 formats: tig: atl: Public transcriptome database-based selection and validation of reliable reference genes for breast cancer research. aug: au: Song, Qiang Dou, Lu Zhang, Wenjin Peng, Yang Huang, Man Wang, Mengyuan affil: Department of Central Laboratory, Chongqing University Three Gorges Hospital, School of Medicine, Chongqing University, 404000, Chongqing, China sug: subj: Breast Neoplasms Gene Expression Profiling Female Resource Databases Algorithms Funding Source Female ab: Background: Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is the most sensitive technique for evaluating gene expression levels. Choosing appropriate reference genes (RGs) is critical for normalizing and evaluating changes in the expression of target genes. However, uniform and reliable RGs for breast cancer research have not been identified, limiting the value of target gene expression studies. Here, we aimed to identify reliable and accurate RGs for breast cancer tissues and cell lines using the RNA-seq dataset.Methods: First, we compiled the transcriptome profiling data from the TCGA database involving 1217 samples to identify novel RGs. Next, ten genes with relatively stable expression levels were chosen as novel candidate RGs, together with six conventional RGs. To determine and validate the optimal RGs we performed qRT-PCR experiments on 87 samples from 11 types of surgically excised breast tumor specimens (n = 66) and seven breast cancer cell lines (n = 21). Five publicly available algorithms (geNorm, NormFinder, ΔCt method, BestKeeper, and ComprFinder) were used to assess the expression stability of each RG across all breast cancer tissues and cell lines.Results: Our results show that RG combinations SF1 + TRA2B + THRAP3 and THRAP3 + RHOA + QRICH1 showed stable expression in breast cancer tissues and cell lines, respectively, and that they displayed good interchangeability. We propose that these combinations are optimal triplet RGs for breast cancer research.Conclusions: In summary, we identified novel and reliable RG combinations for breast cancer research based on a public RNA-seq dataset. Our results lay a solid foundation for the accurate normalization of qRT-PCR results across different breast cancer tissues and cells. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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