Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Search research articles

Home
Research Domains
Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Comprehensive Analysis Of Single-cell And Bulk Rna Sequencing Reveals Postoperative Progression Markers For Non-muscle Invasive Bladder Cancer And Predicts Responses To Immunotherapy.

Home
Research Domains
Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Comprehensive Analysis Of Single-cell And Bulk Rna Sequencing Reveals Postoperative Progression Markers For Non-muscle Invasive Bladder Cancer And Predicts Responses To Immunotherapy.

Related Experiment Video

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Published on: November 19, 2019

Comprehensive analysis of single-cell and bulk RNA sequencing reveals postoperative progression markers for non-muscle invasive bladder cancer and predicts responses to immunotherapy.

Zhiliang Xiao¹, Xin Liu¹, Yuan Wang²

¹Department of Urology, The Affiliated Rehabilitation Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330000, Jiangxi, China.

Discover Oncology

|November 12, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Keywords:

Biomarkers Bladder cancer Machine learning Single-cell RNA sequencing

More Related Videos

Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors

Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors

Published on: August 16, 2024

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Related Experiment Videos

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Published on: November 19, 2019

Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors

Author Spotlight: Exploring Strategies for Successful Immune Response Against Tumors

Published on: August 16, 2024

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

New biomarkers AURKB, GINS2, and UHRF1 show promise for non-muscle-invasive bladder cancer (NMIBC) treatment. These genes can improve postoperative management and predict immunotherapy response in NMIBC patients.

Area of Science:

Genomics and Molecular Biology
Oncology
Bioinformatics

Background:

Non-muscle-invasive bladder cancer (NMIBC) presents challenges due to high recurrence rates and potential for invasiveness.
Effective risk assessment and novel therapeutic targets are crucial for improving postoperative management and patient prognosis in NMIBC.

Purpose of the Study:

To identify novel differentially expressed genes (DEGs) associated with NMIBC progression and prognosis.
To investigate the potential of identified genes as biomarkers for risk stratification and predicting immunotherapy response in NMIBC patients.
To explore the therapeutic potential of targeting key identified genes in bladder cancer treatment.

Main Methods:

Differential gene expression analysis across multiple Gene Expression Omnibus (GEO) datasets.

Prognostic gene selection using Kaplan-Meier (KM) and Cox regression analyses, followed by Boruta algorithm screening.

Immune microenvironment analysis using ssGSEA, and predictive modeling with machine learning (ML) and Receiver Operating Characteristic (ROC) analyses.

Main Results:

AURKB, GINS2, and UHRF1 were identified as core differentially expressed genes (DEGs) associated with bladder cancer progression.
These core genes showed a positive correlation with specific immune cell types (activated CD4 T cells, Type 2 helper T cells) and predicted immunotherapy response.
An online nomogram tool was developed for predicting postoperative progression risk in NMIBC patients, and GINS2 was experimentally validated for its pro-tumor effects.

Conclusions:

AURKB, GINS2, and UHRF1 are promising biomarkers for enhancing postoperative management of NMIBC patients.
These genes demonstrate potential in predicting immunotherapy response, highlighting them as valuable therapeutic targets.
The developed online tool aids in prognostic prediction for NMIBC patients undergoing transurethral resection of bladder tumor (TURBT).