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

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.6K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.6K
MicroRNAs01:22

MicroRNAs

3.0K
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
3.0K

You might also read

Related Articles

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

Sort by
Same author

SARS-CoV-2 infection is associated with hypothalamic orexin suppression and persistent cortical NeuN attenuation.

Journal of neuroinflammation·2026
Same author

Ligand-receptor interaction profiling as a predictive biomarker for anti-PD-1 therapy response in melanoma.

Clinical and experimental medicine·2025
Same author

TCR-epiDiff: solving dual challenges of TCR generation and binding prediction.

Bioinformatics (Oxford, England)·2025
Same author

Unraveling the three-dimensional genome structure using machine learning.

BMB reports·2025
Same author

Integrative Analysis of ATAC-Seq and RNA-Seq through Machine Learning Identifies 10 Signature Genes for Breast Cancer Intrinsic Subtypes.

Biology·2024
Same author

Distinct cellular composition between normal surgical margins and tumor tissues in oral squamous cell carcinoma.

Genes & genomics·2023

Related Experiment Video

Updated: Jun 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Identifying microRNAs associated with tumor immunotherapy response using an interpretable machine learning model.

Dong-Yeon Nam1, Je-Keun Rhee2

  • 1Department of Bioinformatics & Life Science, Soongsil University, Seoul, Republic of Korea.

Scientific Reports
|March 15, 2024
PubMed
Summary
This summary is machine-generated.

Predicting immunotherapy response is crucial. This study shows microRNAs (miRNAs) can predict patient response to immune checkpoint blockade therapy, identifying key miRNA biomarkers for better treatment selection.

More Related Videos

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K
Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

4.7K

Related Experiment Videos

Last Updated: Jun 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K
Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

4.7K

Area of Science:

  • Oncology
  • Immunology
  • Genetics

Background:

  • Predicting patient response to tumor immunotherapy, specifically immune checkpoint blockade (ICB), is critical for optimizing treatment efficacy and minimizing adverse effects.
  • Current methods for preselecting patients likely to benefit from ICB therapy remain a significant challenge in clinical oncology.

Purpose of the Study:

  • To investigate the potential of microRNAs (miRNAs) as predictive biomarkers for patient response to tumor immune checkpoint blockade (ICB) therapy.
  • To develop and validate a machine learning model utilizing miRNA expression profiles for predicting ICB treatment outcomes across diverse cancer types.

Main Methods:

  • Construction of random forest models to predict ICB therapy response based on miRNA expression data from 19 cancer types.
  • Application of SHapley Additive exPlanations (SHAP) to interpret model predictions and identify the contribution of individual miRNAs.
  • Analysis of pathways targeted by high-importance miRNAs to understand their role in immune response.

Main Results:

  • Machine learning models using a limited set of high-importance miRNAs achieved predictive performance comparable to models using the entire miRNA expression profile.
  • SHAP analysis identified specific miRNAs crucial for predicting ICB response.
  • Genes targeted by these key miRNAs were significantly associated with tumor-related and immune-related biological pathways.

Conclusions:

  • MicroRNA expression data holds significant potential for predicting patient responses to tumor immunotherapy.
  • Selected informative miRNAs can serve as reliable biomarkers for assessing immunotherapy efficacy, advancing the understanding of underlying therapeutic mechanisms.