.png){kind=icon}
.png){kind=icon}
Return
Development and validation of a miRNA-based prognostic model for high-grade serous ovarian cancer: a retrospective cohort study
.png){kind=icon}
OA
AI
C
N
G
A
H
D
L
C
J
A
M
DOI：10.1016/j.lana.2026.101527.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Ovarian cancer, particularly high-grade serous ovarian cancer (HGSOC), is the most lethal gynecological malignancy, mainly due to late-stage diagnosis and limited prognostic biomarkers. Current clinical markers, such as CA125, have limited prognostic accuracy for risk stratification. MicroRNAs (miRNAs) have emerged as promising biomarkers due to roles in tumor biology and stability in biofluids. This study aimed to identify and validate prognostic miRNA biomarkers in HGSOC.
Keywords:
miRNA-based prognostic model
High-grade serous ovarian cancer
Machine learning in oncology
Prognostic biomarkers
Explainable AI
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
T
IF:
0
Papers:
174
Citations:
0