Abstract 6300: Global fit normalization for circulating microRNA measured by quantitative PCR

Abstract MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression at the post-transcriptional level and play a key role in cancer biology affecting cancer cells’ proliferation, metastasis, and drug resistance. Cancer cells can release miRNAs into the bloodstream and can potentially be non-invasive markers for cancer diagnosis and prognosis. However, variations in quantitative PCR (qPCR) preparation have limited their clinical utility, and normalization of circulating miRNA qPCR data remains challenging due to low amount of circulating RNA and the lack of universal housekeeping miRNAs. To address these limitations, we have developed Global Fit (GF) normalization, a novel approach that accounts for technical variations from RNA input by fitting a sigmoidal curve to the reverse cumulative distribution of miRNA cycle threshold (Ct) values and subsequently adjusting sample bias through distribution unification. Additionally, we introduce an extended normalization method that employs linear regression (LR) to further correct residual sample bias. The performance of the GF was compared against the commonly used normalization method, Global Mean (GM) using simulated data based on a murine plasma dataset and three public human cancer plasma datasets. In the murine dataset, we demonstrated that sample input bias is reflected by the horizontal shift of the cumulative distribution of Ct values, and GF normalization yielded more aligned distributions compared to GM. Simulation showed that GF outperformed GM by reducing post-normalization variability across scenarios considering different dropout rates and panel sizes. Across multiple simulations of differentially expressed miRNAs, GF showed higher statistical power compared to GM (median 51.9% vs 20.4%), while maintaining similar type I error rates. LR further improved the detection power beyond GF alone. Across three human cancer miRNA datasets, GF consistently demonstrated better correction of circulating miRNA distribution compared to GM. In ANOVA analyses, GF normalization consistently accounted for higher data variability than GM normalization. GF also outperformed other normalizers in stability score (ranked No. 1) as measured by NormFinder. Consequently, GF allowed better detection of biologically meaningful circulating miRNAs in colon and liver cancers, such as miR-210, miR-532-3p and miR-195. These findings support the use of GF normalization in circulating miRNA data analysis to enable more robust biomarker identification for cancer diagnosis and prognosis in clinical settings. Citation Format: Kai Song, Chen Wang, Jordan Wellerstein Kardos, Nandita Sarkar, Yihua Liu, Oh Kyu Yoon. Global fit normalization for circulating microRNA measured by quantitative PCR [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6300.