Dataset Information

A novel prognostic signature and therapy guidance for hepatocellular carcinoma based on STEAP family.

ABSTRACT:

Background

The six-transmembrane epithelial antigen of prostate (STEAP) family members are known to be involved in various tumor-related biological processes and showed its huge potential role in tumor immunotherapy.

Methods

Biological differences were investigated through Gene set enrichment analysis (GSEA) and tumor microenvironment analysis by CIBERSORT. Tumor mutation burden (TMB), immunotherapy response and chemotherapeutic drugs sensitivity were estimated in R.

Results

We established a prognostic signature with the formula: risk score = STEAP1 × 0.3994 + STEAP4 × (- 0.7596), which had a favorable concordance with the prediction. The high-risk group were enriched in cell cycle and RNA and protein synthesis related pathways, while the low-risk group were enriched in complement and metabolic related pathways. And the risk score was significantly correlated with immune cell infiltration. Most notably, the patients in the low-risk group were characterized with increased TMB and decreased tumor immune dysfunction and exclusion (TIDE) score, indicating that these patients showed better immune checkpoint blockade response. Meanwhile, we found the patients with high-risk were more sensitive to some drugs related to cell cycle and apoptosis.

Conclusions

The novel signature based on STEAPs may be effective indicators for predicting prognosis, and provides corresponding clinical treatment recommendations for HCC patients based on this classification.

SUBMITTER: Fu D

PROVIDER: S-EPMC10775544 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

A novel prognostic signature and therapy guidance for hepatocellular carcinoma based on STEAP family.

Fu Dongxue D Zhang Xian X Zhou Yi Y Hu Shanshan S

BMC medical genomics 20240108 1

<h4>Background</h4>The six-transmembrane epithelial antigen of prostate (STEAP) family members are known to be involved in various tumor-related biological processes and showed its huge potential role in tumor immunotherapy.<h4>Methods</h4>Biological differences were investigated through Gene set enrichment analysis (GSEA) and tumor microenvironment analysis by CIBERSORT. Tumor mutation burden (TMB), immunotherapy response and chemotherapeutic drugs sensitivity were estimated in R.<h4>Results</h ...[more]

PMID: 38191397

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Project description:BackgroundNumerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear.MethodsMulti-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays.ResultsA total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes.ConclusionsCollectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC.

Project description:Background: Immunity and ferroptosis often play a synergistic role in the progression and treatment of hepatocellular carcinoma (HCC). However, few studies have focused on identifying immune-related ferroptosis gene biomarkers. Methods: We performed weighted gene co-expression network analysis (WGCNA) and random forest to identify prognostic differentially expressed immune-related genes (PR-DE-IRGs) highly related to HCC and characteristic prognostic differentially expressed ferroptosis-related genes (PR-DE-FRGs) respectively to run co-expression analysis for prognostic differentially expressed immune-related ferroptosis characteristic genes (PR-DE-IRFeCGs). Lasso regression finally identified 3 PR-DE-IRFeCGs for us to construct a prognostic predictive model. Differential expression and prognostic analysis based on shared data from multiple sources and experimental means were performed to further verify the 3 modeled genes' biological value in HCC. We ran various performance testing methods to test the model's performance and compare it with other similar signatures. Finally, we integrated composite factors to construct a comprehensive quantitative nomogram for accurate prognostic prediction and evaluated its performance. Results: 17 PR-DE-IRFeCGs were identified based on co-expression analysis between the screened 17 PR-DE-FRGs and 34 PR-DE-IRGs. Multi-source sequencing data, QRT-PCR, immunohistochemical staining and testing methods fully confirmed the upregulation and significant prognostic influence of the three PR-DE-IRFeCGs in HCC. The model performed well in the performance tests of multiple methods based on the 5 cohorts. Furthermore, our model outperformed other related models in various performance tests. The immunotherapy and chemotherapy guiding value of our signature and the comprehensive nomogram's excellent performance have also stood the test. Conclusion: We identified a novel PR-DE-IRFeCGs signature with excellent prognostic prediction and clinical guidance value in HCC.

Dataset Information

A novel prognostic signature and therapy guidance for hepatocellular carcinoma based on STEAP family.

Background

Methods

Results

Conclusions

Publications

A novel prognostic signature and therapy guidance for hepatocellular carcinoma based on STEAP family.

Similar Datasets

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