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Updated: Sep 19, 2025

In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression
Published on: March 29, 2019
Refining housekeeping genes and demonstrating their potential for clinical and experimental applications
Kyung Won Hwang1, Jae Won Yun2, Ye Ji Shin1
1Department of Biological Sciences, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Background:
Housekeeping genes (HKGs) are crucial for maintaining basic cellular functions and are consistently expressed across various tissues and cell types, making them essential for normalizing gene expression. Their application is crucial in both basic research and clinical settings, such as breast cancer, where they help in accurate gene expression measurement and tumor subtype classification such as the PAM50 system. However, HKGs are often used without thorough assessment of their variability across different conditions, which may affect the reliability of normalization.
Methods And Findings:
We identified 16 candidate HKGs in breast tissue using TCGA RNA-seq data. These genes, along with previously known HKGs such as GAPDH, were evaluated across several breast cancer cell lines and experimental conditions that mimic clinical cancer treatment using quantitative real-time PCR (qPCR). The candidate HKGs were further validated using additional bulk and single cell RNA-seq datasets from the Gene Expression Omnibus (GEO) and by performing droplet digital PCR (ddPCR). We finally concluded that our candidate HKGs, especially EIF4H, GHITM, ATP5F1B, BRK1, and OS9, demonstrated greater stability than GAPDH and RPLP0. These genes were subsequently tested within the PAM50 breast cancer subtyping system, where they improved normalization performance over GAPDH.
Conclusion:
We have identified novel HKGs useful in breast cancer research and proved that they exhibit more stable expression compared to previously known HKGs. These findings may offer researchers and clinicians a more reliable normalization standard for gene expression analysis, potentially enhancing the accuracy of breast cancer diagnosis and the selection of personalized treatments.
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