Dataset Information

Public transcriptome database-based selection and validation of reliable reference genes for breast cancer research.

ABSTRACT: 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. 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. 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. 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.

SUBMITTER: Song Q

PROVIDER: S-EPMC8665499 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Quantitative real-time polymerase chain reaction (RT-qPCR) using a stable reference gene is widely used for gene expression research. Barnyard millet (Echinochloa spp.) is an ancient crop in Asia and Africa that is widely cultivated for food and fodder. It thrives well under drought, salinity, cold, and heat environmental conditions, besides adapting to any soil type. To date, there are no gene expression studies performed to identify the potential candidate gene responsible for stress response in barnyard millet, due to lack of reference gene. Here, 10 candidate reference genes, Actin (ACT), α-tubulin (α-TUB), β-tubulin (β-TUB), RNA pol II (RP II), elongation factor-1 alpha (EF-1α), adenine phosphoribosyltransferase (APRT), TATA-binding protein-like factor (TLF), ubiquitin-conjugating enzyme 2 (UBC2), ubiquitin-conjugating enzyme E2L5 (UBC5) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), were selected from mRNA sequences of E. crus-galli and E. colona var frumentacea. Five statistical algorithms (geNorm, NormFinder, BestKeeper, ΔCt, and RefFinder) were applied to determine the expression stabilities of these genes in barnyard millet grown under four different abiotic stress (drought, salinity, cold and heat) exposed at different time points. The UBC5 and ɑ-TUB in drought, GAPDH in salinity, GAPDH and APRT in cold, and EF-1α and RP II in heat were the most stable reference genes, whereas ß-TUB was the least stable irrespective of stress conditions applied. Further Vn/Vn + 1 analysis revealed two reference genes were sufficient to normalize gene expression across all sample sets. The suitability of identified reference genes was validated with Cu-ZnSOD (SOD1) in the plants exposed to different abiotic stress conditions. The results revealed that the relative quantification of the SOD1 gene varied according to reference genes and the number of reference genes used, thus highlighting the importance of the choice of a reference gene in such experiments. This study provides a foundational framework for standardizing RT-qPCR analyses, enabling accurate gene expression profiling in barnyard millet.

Dataset Information

Public transcriptome database-based selection and validation of reliable reference genes for breast cancer research.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets