Pilot study of plasma miRNA signature panel for differentiating single vs multiglandular parathyroid disease.

Publication Title

The Journal of clinical endocrinology and metabolism

Authors

Melanie Goldfarb, Providence St. John's Health Center, Santa Monica, CAFollow
Matias A Bustos, Department of Translational Molecular Medicine, John Wayne Cancer Institute (JWCI) at Providence Saint John's Health Center, Santa MonicaFollow
Jamie Moon, Saint John's Cancer Institute (SJCI), Providence Saint John's Health Center (SJHC), Santa Monica, CA 90404, USAFollow
Katherine M Jackson, Department of Surgical Oncology, Providence Saint John's Cancer Institute, Santa Monica, CA, USAFollow
Frederick R Singer, Center for Endocrine Tumors and Disorders, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), Santa Monica, CA, USFollow
Dave Hoon, Department of Translational Molecular Medicine, Saint John's Cancer Institute (SJCI) at Providence Saint John's Health Center (SJHC), Santa Monica, CA, USA, Department of Genomic Sequencing Center, SJCI at Providence SJHC, Santa Monica, CA, USAFollow

Document Type

Article

Publication Date

8-20-2024

Keywords

california; sjci; santa monica; genomics

Abstract

CONTEXT: The ability to differentiate sporadic primary hyperparathyroidism (sPHPT) caused by a single parathyroid adenoma (PTA) from multiglandular disease (MGD) pre-operatively, as well as definitely diagnose sPHPT in difficult patients, would enhance surgical decision making.

OBJECTIVE: Identify miRNA (miR) signatures for MGD, single- and double-PTA, as well as cell-free miRNA (cfmiR) in plasma samples from patients with single-PTAs to use as biomarkers.

DESIGN/SETTING/PATIENTS: 47 patients with sPHPT (single-PTA n=32, double-PTA n=12, MGD n=9). Pre-operative plasma samples from 16 single-PTA and 29 normal healthy donors (NHD).

INTERVENTION: All specimens were processed and analyzed for 2,083 miRs using HTG EdgeSeq miR whole transcriptome assay and normalized using DESeq2 to identify differentially expressed (DE) miRs. MiR classifiers were identified using Random Forest.

MAIN OUTCOME MEASURES: ROC curves and AUC.

RESULTS: MiR signatures distinguished normal parathyroid from MGD and PTA as well as MGD from PTA in tissue samples. Common miRs were found in the single-PTA and double-PTAs. Data integration identified a 27-miR signature in single-PTA tissue samples compared to the rest of the tissue samples. In plasma samples analysis, significant cfmiRs were DE in single-PTA patients compared to NHD. Of those, only 9 miRNAs/cfmiRs were found DE in both tissue and plasma samples from patients diagnosed with a single-PTA (AUC=76%).

CONCLUSIONS: Twenty-seven miRs were consistently found DE in single-PTA tissue and plasma samples. Data integration showed a 9-cfmiR signature with potential clinical utility to pre-operatively diagnose sPHPT caused by a single-PTA, which could decrease more invasive parathyroid explorations.

Area of Special Interest

Cancer

Specialty/Research Institute

Endocrinology

Specialty/Research Institute

Oncology

DOI

10.1210/clinem/dgae577