MicroRNA Profiling as a Methodology to Diagnose Ménière's Disease: Potential Application of Machine Learning. (February 2021)

• Record Type: Journal Article
• Authors: Shew, Matthew; Wichova, Helena; Bur, Andres; Koestler, Devin C.; St Peter, Madeleine; Warnecke, Athanasia; Staecker, Hinrich
• Is Part Of: Otolaryngology--head and neck surgery. Volume 164:Number 2(2021)
• Page Start: 399
• Page End: 406
• Publication Date: 2021-02
• Subjects: miRNA -- Ménière's disease -- machine learning -- perilymph sample; Head -- Surgery -- Periodicals; Neck -- Surgery -- Periodicals; Otolaryngology -- Periodicals; 617.51
• DOI: 10.1177/0194599820940649
• Languages: English
• ISSNs: 0194-5998

Abstract

Objective: Diagnosis and treatment of Ménière's disease remains a significant challenge because of our inability to understand what is occurring on a molecular level. MicroRNA (miRNA) perilymph profiling is a safe methodology and may serve as a "liquid biopsy" equivalent. We used machine learning (ML) to evaluate miRNA expression profiles of various inner ear pathologies to predict diagnosis of Ménière's disease. Study Design: Prospective cohort study. Setting: Tertiary academic hospital. Subjects and Methods: Perilymph was collected during labyrinthectomy (Ménière's disease, n = 5), stapedotomy (otosclerosis, n = 5), and cochlear implantation (sensorineural hearing loss [SNHL], n = 9). miRNA was isolated and analyzed with the Affymetrix miRNA 4.0 array. Various ML classification models were evaluated with an 80/20 train/test split and cross-validation. Permutation feature importance was performed to understand miRNAs that were critical to the classification models. Results: In terms of miRNA profiles for conductive hearing loss versus Ménière's, 4 models were able to differentiate and identify the 2 disease classes with 100% accuracy. The top-performing models used the same miRNAs in their decision classification model but with different weighted values. All candidate models for SNHL versus Ménière's performed significantly worse, with the best models achieving 66% accuracy. Ménière's models showed unique features distinct from SNHL. Conclusions: We can use ML to build Ménière's-specific prediction models using miRNA profile alone. However, ML models were less accurate in predicting SNHL from Ménière's, likely from overlap of miRNA biomarkers. The power of this technique is that it identifies biomarkers without knowledge of the pathophysiology, potentially leading to identification of novel biomarkers and diagnostic tests.