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Related Experiment Video

Updated: Jul 6, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

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

Exploring and Validating Prognostic Biomarkers Related to Sphingolipid Metabolism in Gastric Cancer through Machine

Jian Chai1,2, Ce Guo2, Houze Wang2

  • 1Chengde Medical College, Chengde City, Hebei Province, China.

Endocrine, Metabolic & Immune Disorders Drug Targets
|March 28, 2025
PubMed
Summary

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This study identifies sphingolipid metabolism genes as potential prognostic biomarkers for gastric cancer (GC). ELOVL4 expression in tumors correlates with poor prognosis, offering new insights for GC prediction and treatment.

Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Sphingolipid metabolism (SM) is linked to gastric cancer (GC) progression.
  • The prognostic value of SM in GC is not well understood.
  • This research explores SM's potential as a prognostic biomarker in GC.

Purpose of the Study:

  • To investigate the feasibility of using sphingolipid metabolism to predict GC prognosis.
  • To identify key sphingolipid metabolism-related genes (SMRGs) associated with GC survival.
  • To develop and validate a prognostic model for GC based on SMRGs.

Main Methods:

  • Extracted SMRGs and identified differentially expressed genes (DEGs) from TCGA-STAD and GSE84437 datasets.
  • Employed univariate Cox, Lasso-Cox, and random survival forest analyses to identify key survival genes.
Keywords:
Bioinformaticsgastric cancerimmunohistochemistry.machine learningprognosissphingolipid metabolism

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Last Updated: Jul 6, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

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Published on: April 18, 2025

  • Developed a prognostic model (SMscore) using multivariate Cox regression and validated hub gene expression via immunohistochemistry (IHC).
  • Main Results:

    • Identified ELOVL4, NOS3, and ABCA2 as key prognostic SMRGs.
    • Developed and validated a robust prognostic model with strong predictive performance.
    • Observed increased ELOVL4 and NOS3 expression in tumor tissues, significantly correlating with poor prognosis.

    Conclusions:

    • Bioinformatics analysis and IHC validation suggest ELOVL4 as a potential prognostic biomarker for GC.
    • Findings offer new insights for GC prognosis prediction.
    • ELOVL4 may represent a novel therapeutic target for gastric cancer.