Study Flags TBP, PUM1 and RER1 as Top Reference Genes for Breast Tissue qPCR

Every measurement in molecular biology needs a yardstick, and in gene expression studies that yardstick is called a reference gene. A new study from researchers in Morocco, published in Molecular Biology Reports, suggests that the field’s most commonly used yardsticks may be among the least reliable when it comes to breast tissue. The team, led by Fatima-Ezzahrae Oubaqui of Mohammed V University in Rabat and the Moroccan Foundation for Advanced Science, Innovation and Research, systematically compared ten candidate reference genes in breast tumors and the normal tissue adjacent to them, and found that three relatively unheralded genes—TBP, PUM1, and RER1—outperformed the traditional favorites ACTB and GAPDH by a wide margin.

Real-time quantitative polymerase chain reaction, or RT-qPCR, remains one of the most widely used techniques in breast cancer research. It allows scientists to quantify precisely how active particular genes are in a sample, which is critical for identifying biomarkers, validating molecular targets, and understanding how tumors differ from healthy tissue at the transcript level. But the raw output of an RT-qPCR experiment—the cycle threshold, or Ct value at which fluorescence crosses a detection limit—depends not only on the target gene but also on how much starting material was loaded, how efficiently the RNA was extracted, and how well the reverse transcription step worked. Normalization is the mathematical correction that strips away these technical variables, and it is typically done by measuring a reference gene assumed to be expressed at a constant level in every sample.

The trouble is that this assumption of constancy is often wrong. Genes chosen decades ago as ‘housekeeping’ controls because of their essential roles in basic cellular metabolism can vary substantially between tumor and normal tissue, between molecular subtypes, and even between individual patients. If the reference gene itself fluctuates with disease state, the apparent expression changes in the genes under study may be artifacts of the normalization rather than real biology. The MIQE guidelines, published in 2009, formalized the requirement that researchers validate their reference genes for every specific tissue and experimental context rather than borrowing them uncritically from the literature, and a growing body of work has shown that conventionally used controls frequently fail this test in human cancer studies.

To address this gap for breast tissue, the Moroccan team analyzed twenty breast tissue samples—thirteen tumors and seven normal adjacent tissues—collected at the Military Hospital of Instruction Mohammed V in Rabat, with ethical approval from the Ethics Committee for Biomedical Research in Rabat and written informed consent from all participants. They measured the expression of ten candidate reference genes spanning a range of cellular functions: ACTB, which encodes beta-actin, a core component of the cytoskeleton; GAPDH, a central enzyme of glycolysis; GUSB, a lysosomal enzyme; HNRNPL, an RNA-binding protein involved in splicing; PCBP1, another RNA-binding protein; PPIA, a peptidyl-prolyl isomerase; PUM1, a translational regulator; RER1, an endoplasmic reticulum sorting receptor; TBP, the TATA-box binding protein central to transcription initiation; and 18S ribosomal RNA.

The methodological heart of the study lay in how stability was assessed. Rather than relying on a single algorithm, the researchers used the RefFinder tool, which integrates four independent approaches: geNorm, which ranks candidates based on the pairwise variation between them and identifies the optimal number of genes to combine; NormFinder, a model-based method that estimates both intra- and inter-group variance and is particularly good at accounting for differences between tumor and normal samples; BestKeeper, which uses pairwise correlations of raw Ct values to identify candidates that co-vary least with the rest of the panel; and the comparative delta-Ct method, which compares each candidate directly against all others within each sample. Each algorithm produces its own ranking, and RefFinder condenses these into a geometric mean ranking that reflects the consensus across methods.

The results were strikingly consistent. TBP emerged as the most stable reference gene overall, with a RefFinder geometric mean of 2.21, followed closely by PUM1 at 2.34 and RER1 at 2.78. The three genes occupied closely aligned positions across the individual algorithms, which strengthens confidence in the ranking, since agreement between statistically independent methods is a hallmark of genuine stability. At the other end of the spectrum, ACTB and GAPDH—the two genes most deeply entrenched in the qPCR literature—consistently ranked as the least stable candidates across all four algorithms. In other words, the genes most likely to be chosen by default were the ones most likely to introduce systematic error into a breast tissue expression study.

The finding that GAPDH is unstable in breast tumors is biologically plausible rather than merely statistical. Tumor cells undergo a metabolic shift toward aerobic glycolysis, the Warburg effect, and GAPDH sits at the center of that reprogramming, so its expression can rise with tumor aggressiveness and hypoxia. ACTB, meanwhile, is tied to cytoskeletal remodeling, a process intimately involved in invasion and metastasis. TBP, by contrast, is a basal transcription factor whose expression tends to track overall cellular transcriptional activity more evenly, and PUM1 and RER1 have shown stable expression in other cancer contexts, including rectal tumors and cancer cell line panels, lending external support to the new results.

The study also fits into a broader pattern in the normalization literature. Previous work in breast cancer cell lines, including the widely used MCF-7 line, has identified different optimal reference sets than those suited to fresh tissue, and studies in endometrial, ovarian, colorectal, and renal cancers have each concluded that no single reference gene is universally appropriate. Some groups have advocated normalizing to the geometric average of multiple reference genes rather than a single control, an approach pioneered by Vandesompele and colleagues in 2002, and the close agreement among TBP, PUM1, and RER1 in this study suggests that a combination of two or three of them could provide a robust normalization strategy for breast tissue work.

The authors are appropriately measured about the limitations of their work. The sample size of twenty tissues, while sufficient for a candidate-gene validation study of this design, is modest, and the tumor cohort of thirteen samples cannot capture the full heterogeneity of breast cancer, which spans estrogen receptor-positive, HER2-positive, and triple-negative subtypes with distinct molecular landscapes. The team explicitly calls for further validation on a larger scale before the findings are adopted as standard practice. They also note that the datasets generated in the study are available from the corresponding author on reasonable request, and that all summarized data supporting the conclusions are included in the published article, in keeping with transparency norms in the field.

Even with those caveats, the practical implications are immediate for laboratories working on breast cancer gene expression. Any study planning to use RT-qPCR on breast tumors or matched normal adjacent tissue now has a data-driven, multi-algorithm-validated shortlist of candidates—TBP, PUM1, and RER1—and a clear warning against defaulting to ACTB or GAPDH. As RT-qPCR continues to complement immunohistochemistry in molecular subtyping and to support the validation of therapeutic targets such as PIK3CA and BRCA1/2 in the era of precision oncology, the accuracy of the underlying normalization quietly underpins every downstream conclusion. A study like this one, though modest in scale, addresses one of the most consequential and least glamorous sources of error in cancer molecular biology, and it does so with the kind of methodological rigor that the MIQE guidelines were written to encourage.

Subject of Research: Validation of reference genes for RT-qPCR normalization in breast tumors and normal adjacent tissues

Article Title: Promising reference genes for RT-qPCR normalization in breast tumors and normal adjacent tissues

Article References: Oubaqui, F.-E., Oukabli, M., Kouach, J., Bakri, Y., Ameziane El Hassani, R., & Qmichou, Z. (2026). Promising reference genes for RT-qPCR normalization in breast tumors and normal adjacent tissues. Molecular Biology Reports, 53(1), Article 1646. https://doi.org/10.1007/s11033-026-12851-2

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12851-2

Keywords: reference genes, RT-qPCR, breast cancer, gene expression normalization, TBP, PUM1, RER1, GAPDH, ACTB, housekeeping genes, molecular biomarkers, MIQE guidelines