Selecting optimal reference genes for breast cancer research

Abstract

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 provide a novel approach for mining RGs by using the RNA-seq dataset to identify reliable and accurate RGs that can be applied to different types of breast cancer tissues and cell lines. Methods First, we compiled the transcriptome profiling data from the TCGA database involving 1217 samples to identify novel RGs and then ten genes (SF1, TARDBP, THRAP3, QRICH1, TRA2B, SRSF3, YY1, DNAJC8, RNF10, and RHOA) with relatively stable expression levels were chosen as novel candidate RGs. Additionally, six conventional RGs (ACTB, TUBA1A, RPL13A, B2M, GAPDH, and GUSB) were also selected. To determine and validate the optimal RGs we performed qRT-PCR experiments on 87 samples from 5 types of surgically excised breast tumor specimens including HR+HER2-, HR+HER2+, HR-HER2-, HR-HER2+, breast cancer after neoadjuvant chemotherapy (NAC) and their matched para-carcinoma tissues, furthermore, we also included a benign breast tumor sample. Six biological replicates were included for each tissue. Moreover, we assessed 7 breast cancer cell lines (MCF-10A, MCF-7, T-47D, MDA-MB-231, MDA-MB-468, as well as MDA-MB-231 with either CNR2 knockdown or overexpression; 3 biological replicates for each line). Five statistical algorithms (geNorm, NormFinder, ΔCt method, BestKeeper, and ComprFinder) were used to assess the stability of expression 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 these two combinations displayed good interchangeability. Therefore, we propose that the above two 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 which lays a solid foundation for accurate normalization of qRT-PCR results across different breast cancer tissues and cells.