Related Experiment Video
Updated: May 3, 2026
MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017
MicroRNA Classifier Based on EUS-FNA for the Early Detection of Occult Liver Metastasis in Pancreatic Ductal
Xiulin Hu, Yuting Xie1, Shimin Wang2
1Department of Gastroenterology, Changhai Hospital, National Key Laboratory of Immunity and Inflammation, Naval Medical University, Shanghai, China.
Objective:
This study aimed to investigate whether microRNAs (miRNAs) quantified from primary pancreatic ductal adenocarcinoma (PDAC) samples could serve as potential biomarkers for identifying PDAC with occult liver metastasis.
Summary Background Data:
Undetectable occult liver metastasis impair the survival of PDAC. Novel biomarkers are immediately required to detect occult liver metastasis in PDAC.
Methods:
Primary PDAC samples were collected using endoscopic ultrasound-guided fine needle aspiration (EUS-FNA). In the discovery stage, miRNA profiles were assessed in tissues from non-metastatic PDACs and PDACs with liver metastasis (n=10 each) using small RNA sequencing. Liver metastasis-associated miRNA signatures were assessed in a training cohort (n=114) and validated in two independent cohorts (n=116), including one nested case-control cohort.
Results:
Between October 01, 2020, and July 01, 2023, 250 patients were recruited. Data from the discovery cohort consisted of 34 miRNAs that were significantly deregulated in PDACs with liver metastasis, compared with non-metastatic PDACs. One miRNA classifier (Cmi) comprising three miRNAs (miR-483-5p, miR-6734-5p, and miR-548q) was established in the training cohort and evaluated in two validation cohorts for the predictive efficacy of liver metastasis. In the nested case-control cohort, Cmi identified occult liver metastasis with high efficacy based on EUS-FNA. Additionally, considering the variation in cancer antigen 125 (CA125) between groups and its association with metastasis, Cmi was combined with CA125. The area under the receiver operating characteristic curve for Cmi ranged from 0.798 to 0.814, which was superior to the AUCs of CA125 alone (P<0.05). It demonstrated comparable efficacy to the combined Cmi+CA125 model (P>0.05 across all cohorts).
Conclusions:
The proposed miRNA-based model holds promise in detecting occult liver metastasis and guiding clinical stratification for optimizing PDAC treatment.
Insights
MicroRNAs (miRNAs) from pancreatic ductal adenocarcinoma (PDAC) samples can identify occult liver metastasis. A novel miRNA classifier (Cmi) shows high efficacy in detecting metastasis, aiding PDAC treatment strategies.
Area of Science:
- Oncology
- Molecular Biology
- Biomarker Discovery
Background:
- Occult liver metastasis significantly impairs survival in pancreatic ductal adenocarcinoma (PDAC).
- There is an urgent need for novel biomarkers to detect occult liver metastasis in PDAC patients.
Purpose of the Study:
- To investigate the potential of microRNAs (miRNAs) from primary PDAC samples as biomarkers for identifying PDAC with occult liver metastasis.
- To develop and validate a miRNA-based classifier for detecting liver metastasis in PDAC.
Main Methods:
- Primary PDAC samples were collected via endoscopic ultrasound-guided fine needle aspiration (EUS-FNA).
- Small RNA sequencing was used to profile miRNAs in discovery cohorts.
- A miRNA classifier (Cmi) was developed in a training cohort and validated in two independent cohorts.
Main Results:
- 34 miRNAs were significantly deregulated in PDACs with liver metastasis compared to non-metastatic PDACs.
- A three-miRNA classifier (Cmi) demonstrated high efficacy in identifying occult liver metastasis in validation cohorts, with an AUC ranging from 0.798 to 0.814.
- Cmi showed superior performance to CA125 alone and comparable efficacy to a combined Cmi+CA125 model.
Conclusions:
- The developed miRNA-based model (Cmi) shows significant promise for detecting occult liver metastasis in PDAC.
- This biomarker can aid in clinical stratification for optimizing treatment strategies in PDAC patients.
