Identification of Novel Endogenous Controls for qPCR Normalization in SK-BR-3 Breast Cancer Cell Line

Advanced breast cancer diagnosis: Multiplex RT-qPCR for precise typing and angiogenesis profiling

Breast cancer (BC) remains the foremost cause of cancer-related mortality, with an estimated 2.3 million new cases anticipated globally. The timely diagnosis of BC is pivotal for effective treatment. Currently, BC diagnosis predominantly relies on Immunohistochemistry (IHC), a method known for its sluggishness, expense, and dependence on proficient pathologists for confident cancer typing. In this study, we introduce a novel approach to enhance the accuracy, speed, and cost-effectiveness of BC diagnosis. We employ multiplex Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) with touch-down methods, which consistently yield significantly lower Cycle Threshold (CT) values. The study evaluates gene expression profiles of HER2, PGR, ESR, and Ki67 genes across 61 samples representing four BC subtypes, using RPL13A as the endogenous control gene. The results demonstrate that our method offers remarkable precision, nearly equivalent to IHC, in detecting gene expressions vital for BC diagnosis and subtyping. Moreover, we explore the gene expression of Hif1A, ANG, and VEGFR genes involved in angiogenesis, shedding light on the metastatic potential of the tested BC tumours. Notably, numerous samples exhibit elevated levels of Hif1A and VEGFR, indicating their potential as valuable biomarkers for assessing metastatic status. Collectively, our RT-qPCR methodology emerges as a powerful diagnostic tool for swiftly identifying BC subtypes and can be complemented with other essential tumorigenic biomarker assessments, such as angiogenesis, to further refine cancer characterisation and inform personalised therapeutic strategies for BC patients. This innovation holds the promise of revolutionising BC diagnosis and treatment, offering expedited and reliable insights for improved patient care.

Selection of reference genes in liproxstatin-1-treated K562 Leukemia cells via RT-qPCR and RNA sequencing

BACKGROUND

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) can accurately detect relative gene expression levels in biological samples. However, widely used reference genes exhibit unstable expression under certain conditions.

METHODS AND RESULTS

Here, we compared the expression stability of eight reference genes (RPLP0, RPS18, RPL13, EEF1A1, β-actin, GAPDH, HPRT1, and TUBB) commonly used in liproxstatin-1 (Lip-1)-treated K562 cells using RNA-sequencing and RT-qPCR. The expression of EEF1A1, ACTB, GAPDH, HPRT1, and TUBB was considerably lower in cells treated with 20 μM Lip-1 than in the control, and GAPDH also showed significant downregulation in the 10 μM Lip-1 group. Meanwhile, when we used geNorm, NormFinder, and BestKeeper to compare expression stability, we found that GAPDH and HPRT1 were the most unstable reference genes among all those tested. Stability analysis yielded very similar results when geNorm or BestKeeper was used but not when NormFinder was used. Specifically, geNorm and BestKeeper identified RPL13 and RPLP0 as the most stable genes under 20 μM Lip-1 treatment, whereas RPL13, EEF1A1, and TUBB were the most stable under 10 μM Lip-1 treatment. TUBB and EEF1A1 were the most stable genes in both treatment groups according to the results obtained using NormFinder. An assumed most stable gene was incorporated into each software to validate the accuracy. The results suggest that NormFinder is not an appropriate algorithm for this study.

CONCLUSIONS

Stable reference genes were recognized using geNorm and BestKeeper but not NormFinder. Overall, RPL13 and RPLP0 were the most stable reference genes under 20 μM Lip-1 treatment, whereas RPL13, EEF1A1, and TUBB were the most stable genes under 10 μM Lip-1 treatment.