A Noninvasive and Screening miRNA Signature for Gastrointestinal Cancer

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

ACTIVE_NOT_RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-06-21

Study Completion Date

2026-06-18

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Gastrointestinal (GI) cancers remain a major global health burden, largely due to the lack of effective and accessible early screening strategies. Current diagnostic approaches-including endoscopy, computed tomography (CT), and magnetic resonance imaging (MRI)-are either invasive, resource-intensive, or insufficiently sensitive for detecting early-stage disease, and are therefore not suitable for population-wide screening or for simultaneously identifying multiple GI tumor types. As a result, many patients are diagnosed at advanced stages, when therapeutic options are limited and prognosis is poor.

Circulating microRNAs (miRNAs) offer a promising alternative, as they are stable in peripheral blood and reflect tumor-related molecular alterations. In this study, the investigators aim to develop and validate a robust, noninvasive miRNA-based signature capable of distinguishing GI cancers from non-malignant controls. By integrating multi-cohort datasets and applying machine learning-based feature selection and predictive modeling, the investigators will construct a screening panel optimized for reproducibility, scalability, and early-stage detection. This noninvasive miRNA signature has the potential to support accessible, cost-effective, and clinically practical population-level screening for GI cancers, ultimately facilitating earlier diagnosis and improving outcomes for participants.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

This study will establish a comprehensive, retrospective, international multi-center cohort consisting of peripheral blood samples from participants with major gastrointestinal cancers-including hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), pancreatic ductal adenocarcinoma (PDAC), esophageal squamous cell carcinoma (ESCC), gastric cancer (GC), and colorectal cancer (CRC)-as well as non-malignant controls. Small RNA sequencing will be performed to generate high-resolution circulating miRNA expression profiles.

During the discovery phase, the investigators will conduct rigorous preprocessing, normalization, batch effect correction, and differential expression analyses to identify circulating miRNAs associated with malignant transformation across GI cancer types. Machine learning-based feature selection (e.g., LASSO, mRMR, ensemble methods) and classifier development (e.g., SVM, Random Forest, XGBoost) will then be used to derive a minimal yet robust miRNA panel capable of optimally distinguishing cancer from non-cancer.

During the modeling and evaluation phase, the identified miRNA signature will undergo multi-center training and validation across international cohorts to ensure robustness across geographic regions, sequencing platforms, and clinical demographics. Beyond binary classification, the investigators will assess the panel's ability to discriminate among specific GI cancer subtypes, thereby supporting differential diagnosis and tumor-origin inference. Model performance will be evaluated using AUROC, sensitivity at clinically meaningful specificity thresholds, early-stage detection capability, and calibration in independent validation cohorts.

Through this sequential discovery → modeling → multi-center validation framework, the investigators aim to develop a noninvasive circulating miRNA panel that (1) accurately distinguishes cancer from non-cancer individuals and (2) differentiates among multiple gastrointestinal cancer types, thereby providing a clinically scalable solution for early cancer detection and population-level screening.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Hepatocellular Carcinoma (HCC) Cholangiocarcinoma Pancreatic Ductal Adenocarcinoma (PDAC) Esophageal Squamous Cell Carcinoma (ESCC) Gastric Cancer (GC) Colorectal Cancer Screening

Keywords

Explore important study keywords that can help with search, categorization, and topic discovery.

Noninvasive screening Circulating miRNA Machine learning Gastrointestinal cancer Blood-based cancer detection

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Hepatocellular Carcinoma cohort

Patients diagnosed with hepatocellular carcinoma (HCC) confirmed by clinical, imaging, and/or histopathological criteria. Blood samples collected retrospectively from multiple international centers.