Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Decoding the Sphingolipid Landscape of Clear Cell Renal Cell Carcinoma: A Single-Cell-Guided Prognostic Model Built With 101 Machine Learning.

Human mutation - 1 Jan 2026

Huang Jinbang, Wang Shunsheng, Zhou Yaojun, Zhang Hui, Zhang Yanyuan, Huang Shi, Su Haiwei, Zhu Hongling, Lin Yuhan, Deng Gang

Abstract excerpt

Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of kidney cancer and shows marked heterogeneity in progression, metastasis, and therapeutic response. Sphingolipid metabolism has emerged as an important regulator of tumor progression and the tumor microenvironment, but its cell-state-specific role in ccRCC remains unclear. Methods: Public datasets from multiple cohorts...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Carcinoma, Renal Cell
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Expression Regulation, Neoplastic

Share to Kidney Neoplasms

Share to Machine Learning

Share to Single-Cell Analysis

Share to Sphingolipids

Share to Tumor Microenvironment

Source

Open in PubMed Open full text

Identifiers

PMID: 42158467
DOI: 10.1155/humu/3249360
PMCID: PMC13181217

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Decoding the Sphingolipid Landscape of Clear Cell Renal Cell Carcinoma: A Single-Cell-Guided Prognostic Model Built With 101 Machine Learning.PMID 42158467Read ↗

Integrative analysis of bulk and single-cell RNA sequencing reveals sphingolipid metabolism and immune landscape in clear cell renal cell carcinoma.Semantic proximity Read ↗

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progressionSemantic proximity Read ↗

A novel lipid metabolism gene signature for clear cell renal cell carcinoma using integrated bioinformatics analysisSemantic proximity Read ↗

Integrated Proteogenomic Characterization of Clear Cell Renal Cell CarcinomaSemantic proximity Read ↗

NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencingSemantic proximity Read ↗

A spatial multi-omic portrait of survival outcome for clear cell renal cell carcinomaSemantic proximity Read ↗

MDK promotes M2 macrophage polarization to remodel the tumour microenvironment in clear cell renal cell carcinomaSemantic proximity Read ↗

Combining Single-Cell and Bulk RNA Sequencing Data to Create a Reliable Prognostic Model for Predicting Clear-cell Renal Cell Carcinoma ProgressionSemantic proximity Read ↗

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell CarcinomaSemantic proximity Read ↗

Clinical value and in vitro validation of gene and molecular subtype identification in renal clear cell carcinomaSemantic proximity Read ↗

Single-cell disulfidptosis regulator patterns guide intercellular communication of tumor microenvironment that contribute to kidney renal clear cell carcinoma progression and immunotherapySemantic proximity Read ↗

Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinomaSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.