Advances in algorithms for normalizer gene selection in qRT-PCR: implications for cancer biology and precision medicine

Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) plays a significant role in gene expression analysis in cancer research and precision medicine. It allows precise quantification of gene expression variation which is necessary for understanding tumor biology, identifying predictive biomarkers and developing therapeutics interventions. However the accuracy and stability of qRT-PCR data heavily rely on finding stable reference genes. The gene stability refers to minimal variation in expression levels of a candidate reference gene across different biological conditions, sample groups and technical replicates. Traditionally, housekeeping genes such as β-actin, GAPDH and 18S rRNA have been used for normalization but consistency and variation can vary under different experimental settings. Over time, mathematical and statistical tools such as geNorm, NormFinder, BestKeeper and gQuant have been developed to find most stable reference genes. These algorithms have become essential in ensuring accurate and reproducible data in cancer research, where gene expression profiles can vary significantly across different tumor types, stages and individual patients. This review focuses on the progression and advancements of traditional and advanced reference gene selection methods, applications in cancer research and their significant role in precision medicine. It presents an overview of the commonly employed normalizers, outlining their respective advantages and limitations, and includes a concise discussion on the assessment of gene stability across diverse experimental contexts. Additionally, it emphasizes their use in cancer research and their importance in enhancing the accuracy and consistency of gene expression normalization, particularly within precision medicine.