J 2021

OCaMIR-A Noninvasive, Diagnostic Signature for Early-Stage Ovarian Cancer: A Multi-cohort Retrospective and Prospective Study

KANDIMALLA, R., W. WANG, F. YU, N. X. ZHOU, F. GAO et. al.

Basic information

Original name

OCaMIR-A Noninvasive, Diagnostic Signature for Early-Stage Ovarian Cancer: A Multi-cohort Retrospective and Prospective Study

Authors

KANDIMALLA, R., W. WANG, F. YU, N. X. ZHOU, F. GAO, M. SPILLMAN, Lucie MOUKOVÁ (203 Czech Republic), Ondřej SLABÝ (203 Czech Republic, belonging to the institution), B. SALHIA, S. T. ZHOU, X. WANG (guarantor) and A. GOEL

Edition

Clinical cancer research, Philadelphia, AMER ASSOC CANCER RESEARCH, 2021, 1078-0432

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30204 Oncology

Country of publisher

Slovenia

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 13.801

RIV identification code

RIV/00216224:14110/21:00124276

Organization unit

Faculty of Medicine

DOI

UT WoS

000680860800017

Keywords in English

OCaMIR; Early-Stage Ovarian Cancer

Tags

Tags

International impact, Reviewed
Změněno: 28/2/2022 07:26, Mgr. Tereza Miškechová

Abstract

V originále

Purpose Due to the lack of effective screening approaches and early detection biomarkers, ovarian cancer has the highest mortality rates among gynecologic cancers. Herein, we undertook a systematic biomarker discovery and validation approach to identify microRN A (mi RNA) biomarkers for the early detection of ovarian cancer. Experimental Design: During the discovery phase, we performed small RNA sequencing in stage I high-grade serous ovarian cancer (n = 31), which was subsequently validated in multiple, independent data sets (TCGA, n = 543; GSE65819, n = 87). Subsequently, we performed multivariate logistic regressionbased training in a serum data set (GSE106817, n = 640), followed by its independent validation in three retrospective data sets (GSE31568, n = 85; GSE113486, n = 140; Czech Republic cohort, n = 192) and one prospective serum cohort (n = 95). In addition, we evaluated the specificity of OCaMIR, by comparing its performance in several other cancers (GSE31568 cohort, n = 369). Results: The OCalvIIR demonstrated a robust diagnostic accuracy in the stage I high-grade serous ovarian cancer patients in the discovery cohort (AUC = 0.99), which was consistently reproducible in both stage ] (AUC = 0.96) and all stage patients (AUC - 0.89) in the TCGA cohort. Logistic regression-based training and validation of OCaMIR achieved AUC values of 0.89 (GSE106817), 0.85 (GSE31568), 0.86 (GSE113486), and 0.82 (Czech Republic cohort) in the retrospective serum validation cohorts, as well as prospective validation cohort (AUC = 0.92). More importantly, OCaMIR demonstrated a significantly superior diagnostic performance compared with CA125 levels, even in stage I patients, and was more cost-effective, highlighting its potential role for screening and early detection of ovarian cancer. Conclusions: Small RNA sequencing identified a robust noninvasive miRNA signature for early-stage serous ovarian cancer detection.