Identification of stable reference genes for relative quantification of long RNA expression in urinary extracellular vesicles

Author:

Zhu Xiao‐Xiao1,Shen An‐Ran1,Li Ning1,Feng Song‐Tao1,Tang Tao‐Tao1ORCID,Zhang Yue1,Jing Jing1,Zhong Xin1,Xie Li‐Jun1,Huang Sheng‐Lin2,Liu Bi‐Cheng1,Lv Lin‐Li1ORCID

Affiliation:

1. Institute of Nephrology, Zhong Da Hospital Southeast University School of Medicine Nanjing China

2. Fudan University Shanghai Cancer Center and Institutes of Biomedical Sciences Shanghai Medical College Fudan University Shanghai China

Abstract

AbstractUrinary extracellular vesicles (uEVs) are rich in valuable biomolecule information which are increasingly recognized as potential biomarkers for various diseases. uEV long RNAs are among the critical cargos capable of providing unique transcriptome information of the source cells. However, consensus regarding ideal reference genes for relative long RNAs quantification in uEVs is not available as of date. Here we explored stable reference genes through profiling the long RNA expression by RNA‐seq following unsupervised analysis and validation studies. Candidate reference genes were identified using four algorithms: NormFinder, GeNorm, BestKeeper and the Delta Ct method, followed by validation. RNA profile showed uEVs contained abundant long RNAs information and the core transcriptome was related to cellular structures, especially ribosome which functions mainly as translation, protein and RNA binding molecules. Analysis of RNA‐seq data identified RPL18A, RPL11, RPL27, RACK1, RPSA, RPL41, H1‐2, RPL4, GAPDH, RPS27A as candidate reference genes. RT‐qPCR validation revealed that RPL41, RPSA and RPL18A were reliable reference genes for long RNA quantification in uEVs from patients with diabetes mellitus (DM), diabetic nephropathy (DN), IgA nephropathy (IgAN) and prostate cancer (PCA). Interestingly, RPL41 also outperformed traditional reference genes in renal tissues of DN and IgAN, as well as in plasma EVs of several types of cancers. The stable reference genes identified in this study may facilitate development of uEVs as novel biomarkers and increase the accuracy and comparability of biomarker studies.

Publisher

Wiley

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