Differences in serum miRNA profiles by race, ethnicity, & socioeconomic status: Implications for developing an equitable ovarian cancer screening test.
Stephanie AlimenaBriana Joy K StephensonJames W WebberLaura WollbornChad B SussmanDaniel George PackardMarta M WilliamsCameron Elizabeth ComrieJoyce Y WangTahireh MarkertJulia SpiegelCarmen B RodriguezMaya LightfootAmia GrayeSean O'ConnorKevin Meyer EliasPublished in: Cancer prevention research (Philadelphia, Pa.) (2024)
Serum miRNAs are promising biomarkers for several clinical conditions, including ovarian cancer. To inform equitable implementation of these tests, we investigated the effects of race, ethnicity, and socioeconomic status on serum miRNA profiles. Serum samples from a large institutional biobank were analyzed using a custom panel of 179 miRNA species highly expressed in human serum, measured using the Abcam Fireplex® assay via flow cytometry. Data were log-transformed prior to analysis. Differences in miRNA by race and ethnicity were assessed using logistic regression. Pairwise t-tests analyzed racial and ethnic differences among eight miRNAs previously associated with ovarian cancer risk. Pearson's correlations determined the relationship between mean miRNA expression and the social deprivation index (SDI) for Massachusetts residents. Of 1586 patients (76.9% white, non-Hispanic), compared to white, non-Hispanic patients, those from other racial and ethnic groups were younger (41.9 years ± 13.2 vs 51.3 ± 15.1, p<0.01) and had fewer comorbidities (3.5 comorbidities ± 2.7 vs 4.6 ± 2.8, p<0.01). On logistic regression, miRNAs predicted race and ethnicity at an AUC of 0.69 (95% CI 0.66-0.72), which remained consistent when stratified by most comorbidities. Among eight miRNAs previously associated with ovarian cancer risk, seven significantly varied by race and ethnicity (all p<0.01). There were no significant differences in SDI for any of these eight miRNAs. miRNA expression is significantly influenced by race and ethnicity, which remained consistent after controlling for confounders. Understanding baseline differences in biomarker test characteristics prior to clinical implementation is essential to ensure instruments perform comparably across diverse populations.