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Isolation of Small Noncoding RNAs from Human Serum

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Published on: June 19, 2014

miRBiT: a rules-based single-sample serum miRNA classifier for pan-cancer detection with multi-cohort validation.

Pandikannan Krishnamoorthy¹, Madhavan Parthasarathy¹, Nilanjana Das¹

¹Department of Biological Sciences, Laboratory of Immunology and Infectious Disease Biology, Indian Institute of Science Education and Research (IISER) Bhopal, Bhopal Bypass Road, Bhauri, Bhopal-462066, Madhya Pradesh, India.

Briefings in Bioinformatics

|April 25, 2026

Summary

This study introduces miRNA binary trend (miRBiT), a novel single-sample classifier for early cancer detection using microRNAs (miRNAs). miRBiT enables personalized, minimally invasive cancer screening with high accuracy across diverse cohorts.

Keywords:

early cancer diagnosis liquid biopsy machine learning classifier microRNAs (miRNAs) biomarkers pan-cancer detection single-sample classifier (SSC)

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Area of Science:

Biochemistry
Molecular Biology
Oncology

Background:

Liquid biopsy presents a minimally invasive method for early cancer diagnosis.
MicroRNAs (miRNAs) are stable, non-coding RNAs with diagnostic potential due to their dysregulation in disease.
Current miRNA-based cancer classifiers often rely on cohort comparisons, limiting clinical application.

Purpose of the Study:

To develop a single-sample miRNA classifier for robust cancer detection.
To enable personalized and minimally invasive cancer screening.
To validate the classifier across diverse datasets and disease cohorts.

Main Methods:

Development of miRNA binary trend (miRBiT), a rules-based single-sample classifier.
Training and testing miRBiT on extensive datasets (16,190 samples) across multiple independent cohorts (9 datasets, 8 disease cohorts).
Utilizing intra-sample miRNA expression signatures for classification.

Main Results:

miRBiT achieved high sensitivity and specificity in classifying 'cancer' versus 'non_cancer' samples, including healthy and other diseases.
The classifier demonstrated robust performance across independent datasets and disease cohorts.
An interactive web application, miRBiT Explorer, was developed for visualizing serum miRNA expression in 46,349 samples.

Conclusions:

miRNAs hold significant potential for accurate, personalized cancer classification.
miRBiT offers a scalable solution for minimally invasive cancer screening and early detection.
The study validates the utility of miRNAs in robust, large-scale cancer diagnostics.