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Related Experiment Video

Updated: Mar 21, 2026

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
12:24

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

Published on: September 30, 2021

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Predicting the Impact of ARHGAP33 Gene on Liver Cancer Prognosis Based on Multi-Algorithm Model.

Yaohui Zhao1,2,3, Chenjie Wang1,2,3, Xiaotong Shen1,2,3

  • 1School of Medicine, Shihezi University, Shihezi, 832000, People's Republic of China.

International Journal of General Medicine
|March 20, 2026
PubMed
Summary

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This summary is machine-generated.

Overexpression of Rho GTPase-activating protein 33 (ARHGAP33) and SFPQ significantly worsens hepatocellular carcinoma (HCC) prognosis. ARHGAP33 may serve as a prognostic biomarker for HCC patients, improving treatment strategies.

Area of Science:

  • Oncology
  • Bioinformatics
  • Molecular Biology

Background:

  • Hepatocellular carcinoma (HCC) remains a significant global health challenge.
  • Identifying reliable prognostic biomarkers is crucial for improving patient outcomes.
  • The role of Rho GTPase-activating protein 33 (ARHGAP33) in HCC requires further investigation.

Purpose of the Study:

  • To investigate the prognostic impact of ARHGAP33 in hepatocellular carcinoma (HCC).
  • To explore the synergistic interaction between ARHGAP33 and SFPQ in HCC.
  • To develop a bioinformatics-based prognostic model for HCC.

Main Methods:

  • Analysis of RNA sequencing data from The Cancer Genome Atlas (TCGA) for ARHGAP33 expression in HCC.
  • Weighted Gene Co-expression Network Analysis (WGCNA) to identify co-expressed genes.
Keywords:
ARHGAP33 genebioinformaticshepatocellular carcinomamulti-algorithmprognostic model

Related Experiment Videos

Last Updated: Mar 21, 2026

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
12:24

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

Published on: September 30, 2021

6.3K
  • Gene Set Variation Analysis (GSVA) and construction of a multi-algorithm consensus prognostic model.
  • Kaplan-Meier (K-M) survival analysis and CoxBoost model for risk stratification.

    • Main Results:

    • ARHGAP33 mRNA was significantly overexpressed in HCC tissues compared to normal tissues (P < 0.001).
    • WGCNA identified SFPQ as a gene co-expressed with ARHGAP33.
    • The consensus prognostic model indicated that high-risk patients had significantly shorter overall survival (P < 0.05).

    Conclusions:

    • ARHGAP33 expression levels are associated with HCC prognosis.
    • Synergistic overexpression of ARHGAP33 and SFPQ negatively impacts HCC patient prognosis.
    • ARHGAP33 shows potential as a prognostic biomarker for HCC, offering a basis for improved therapeutic strategies.