Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Spatial Transcriptomics and Bulk RNA-Seq Analysis Revealed Molecular Classification of Invasive Lobular Carcinoma.

Cancer science·2026
Same author

LIMCH1-enriched extracellular vesicles promote vascular permeability in early-onset preeclampsia.

Science advances·2026
Same author

In-Tumor CRISPR-Cas9 Knockout Screening and Novel Therapy Development for Malignant Transformation of Ovarian Teratoma.

Cancer science·2026
Same author

Creating a general-purpose generative model for healthcare data based on multiple clinical studies.

PLOS digital health·2025
Same author

Label-free imaging of intracellular structures in living mammalian cells via external apodization phase-contrast microscopy.

The FEBS journal·2025
Same author

Overcoming platinum-resistant ovarian cancer targeting the activated JAK-STAT pathways via extracellular vesicles.

Communications biology·2025

Related Experiment Video

Updated: Oct 2, 2025

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
08:14

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

15.8K

Multiple cancer type classification by small RNA expression profiles with plasma samples from multiple facilities.

Kuno Suzuki1, Hideyoshi Igata2, Motoki Abe2

  • 1Healthcare Business Department, PFDeNA, Tokyo, Japan.

Cancer Science
|February 26, 2022
PubMed
Summary

Small RNA liquid biopsies show promise for multi-cancer screening. However, inter-facility variations, like hemolysis, impact classification accuracy, necessitating methods to reduce bias for reliable, multi-center testing.

Keywords:
NGSliquid biopsymachine learningmultiple cancer type classificationmultiple facilitiessmall RNA

More Related Videos

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer
08:12

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer

Published on: March 14, 2019

5.6K
Exosomal miRNA Analysis in Non-small Cell Lung Cancer NSCLC Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool
08:49

Exosomal miRNA Analysis in Non-small Cell Lung Cancer NSCLC Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool

Published on: May 27, 2016

11.3K

Related Experiment Videos

Last Updated: Oct 2, 2025

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
08:14

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening

Published on: October 26, 2017

15.8K
Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer
08:12

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer

Published on: March 14, 2019

5.6K
Exosomal miRNA Analysis in Non-small Cell Lung Cancer NSCLC Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool
08:49

Exosomal miRNA Analysis in Non-small Cell Lung Cancer NSCLC Patients' Plasma Through qPCR: A Feasible Liquid Biopsy Tool

Published on: May 27, 2016

11.3K

Area of Science:

  • Biomarkers
  • Molecular Diagnostics
  • Oncology

Background:

  • Liquid biopsy offers a minimally invasive approach for cancer screening and detection of multiple cancer types.
  • Small RNA detection in blood has emerged as a key area of research for cancer diagnostics.
  • Standardizing methods across multiple facilities is crucial for the clinical adoption of small RNA-based cancer tests.

Purpose of the Study:

  • To evaluate the performance of small RNA expression profiles for classifying multiple cancer types across different facilities.
  • To identify and address factors contributing to inter-facility variability in small RNA-based cancer detection.
  • To develop a robust classification model for multi-cancer screening using samples from diverse clinical settings.

Main Methods:

  • Collected 2,475 cancer samples and 496 healthy control samples from 20 facilities using a standardized protocol.
  • Generated small RNA expression profiles and constructed initial classification models for single-facility analysis.
  • Utilized principal component analysis (PCA) to investigate facility-specific characteristics and their impact on expression profiles.
  • Developed a refined classification model to mitigate biases from hemolysis and data acquisition periods.

Main Results:

  • Single-facility classification models achieved areas under the receiver curve (AUC) ranging from 0.67 to 0.89.
  • Principal component analysis revealed that hemolysis and data acquisition period significantly influenced small RNA expression profiles.
  • The refined classification model, accounting for inter-facility biases, demonstrated an AUC of 0.76.
  • Inter-facility biases were identified as a significant challenge for multi-facility cancer classification.

Conclusions:

  • Small RNA expression profiles are effective for cancer type classification within a single facility.
  • Inter-facility biases, particularly those related to sample handling and data acquisition, pose a challenge for multi-facility small RNA-based cancer screening.
  • Further development is needed to improve the performance and standardization of small RNA expression profiling for multi-cancer detection across multiple institutions.