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

Search research articles

Related Experiment Video

Updated: May 21, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Decoding the Sphingolipid Landscape of Clear Cell Renal Cell Carcinoma: A Single-Cell-Guided Prognostic Model Built

Jinbang Huang¹, Shunsheng Wang², Yaojun Zhou²

¹Department of General Surgery, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China, sysu.edu.cn.

|May 20, 2026

Summary

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

Macrophage-Dependent Intercellular Crosstalk in Multiphenotypic Heart Failure With Preserved Ejection Fraction.

Journal of the American Heart Association·2026

Same author

1-Deoxysphingolipids Require Very-Long-Chain Ceramide Synthesis to Induce ER Stress and Cytotoxicity.

The Journal of biological chemistry·2026

Same author

EAGP: an efficient generative augmentation framework for phage protein classification under severe class imbalance.

Bioinformatics (Oxford, England)·2026

Same author

Milieu-dependent small-cavity diabetic HFpEF in the DIO-STZ model.

American journal of physiology. Heart and circulatory physiology·2026

Same author

Atomic Interlayer Mo-N<sub>4</sub> Sites Enable Rapid Charge Transfer and Efficient CO<sub>2</sub> Photoreduction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Mesoscopic Hydrophobic Microenvironment Engineering on a Single-Atom Catalyst for Steering CO<sub>2</sub> Photoreduction to CH<sub>4</sub>.

Journal of the American Chemical Society·2026

This study reveals the role of sphingolipid metabolism in clear cell renal cell carcinoma (ccRCC) heterogeneity. A 12-gene signature predicts ccRCC patient outcomes and guides therapy selection.

Area of Science:

Oncology
Molecular Biology
Bioinformatics

Background:

Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer subtype, characterized by significant variability in disease progression, metastasis, and treatment response.
Sphingolipid metabolism is increasingly recognized for its role in tumor development and the tumor microenvironment, yet its specific functions within different cell states in ccRCC are not well understood.

Purpose of the Study:

To investigate the cell-state-specific roles of sphingolipid metabolism in ccRCC.
To identify key genes and develop a prognostic model for ccRCC based on sphingolipid metabolism.

Main Methods:

Integrated public datasets including single-cell RNA sequencing and bulk transcriptomic/clinical data.
Employed nonnegative matrix factorization to analyze cellular heterogeneity and identify sphingolipid metabolism-related genes.

Keywords:

clear cell renal cell carcinoma immunotherapy machine learning nonnegative matrix factorization (NMF)prognosis prediction prognostic model single-cell analysis sphingolipid metabolism sphingolipid metabolism–related genes tumor microenvironment

Related Experiment Videos

Last Updated: May 21, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Developed and validated a 12-gene prognostic signature using machine learning models (Lasso+SuperPC).

Main Results:

Identified 23 cell clusters within ccRCC, revealing sphingolipid metabolism-related subclusters in immune and stromal cells.
Established a 12-gene prognostic signature with robust performance across independent cohorts, outperforming clinical variables.
The signature correlated with immune characteristics, predicted therapeutic vulnerabilities, and genomic alterations.

Conclusions:

Developed a single-cell-guided approach to understand ccRCC microenvironment heterogeneity related to sphingolipid metabolism.
The 12-gene prognostic signature offers potential for improved ccRCC risk stratification and therapeutic strategy development.