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2000
Volume 20, Issue 5
  • ISSN: 1574-8928
  • E-ISSN: 2212-3970

Abstract

Background

Ovarian cancer is one of the most common gynecological malignancies globally, and immunotherapy has emerged as a promising treatment strategy in recent years. However, the effectiveness of immunotherapy is often limited by immune escape mechanisms.

Objective

To unravel the immune response mechanisms in ovarian cancer, this study aimed to employ integrated Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-cell sequencing analysis to systematically investigate immune infiltration-related molecular features in ovarian cancer patients and experimentally validate the molecular mechanisms of the immune response. This research may provide a new theoretical foundation and treatment strategy for immune-based therapies in ovarian cancer.

Methods

Relevant ovarian cancer datasets were collected from public databases. The ConsensusClusterPlus and ggplot2 R packages were used to perform dimensionality reduction and clustering analysis of immune infiltration-related genes. Various algorithms were employed to select the best ovarian cancer prognostic model with OC consistency. The prognostic value of angiogenesis and immune-related gene expression was evaluated through Kaplan-Meier survival analysis, and the impact of immune infiltration on immune function in ovarian cancer patients was assessed. Functional pathways were identified using the Gene Set Enrichment Analysis (GSEA) method, and the infiltration abundance of immune and stromal components was inferred using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The influence of angiogenesis on the cellular level of Ovarian Cancer (OC) was explored in single-cell sequencing data, followed by cell experiments for further validation. The effect of the angiogenesis model on OC was evaluated through the above-mentioned research and experiments, aiming to investigate the mechanism of targeted therapy strategies in ovarian cancer.

Results

Immune-related data were collected from ovarian cancer patients in this study. Through WGCNA analysis, the MEturquoise module was identified, and a total of 1018 hub genes were determined. A prediction model was constructed using machine learning, with CoxBoost+StepCox selected as the best model, leading to the identification of 10 genes associated with ovarian cancer. Patients with high AIDPS had shorter survival time, and GSEA analysis revealed enrichment in immune-related pathways. Single-sample gene set enrichment analysis demonstrated increased immune cell infiltration and malignant stromal changes in the high AIDPS group. Results from cell experiments showed that silencing RPL31 inhibited the proliferation and migration of ovarian cancer cells while enhancing immune response capability.

Conclusion

AIDPS holds significant clinical significance in Ovarian Cancer (OC) with poor prognosis observed in patients with high AIDPS. These patients exhibit more significant genomic variations, denser immune cell infiltration, and greater tolerance toward immune therapy. Importantly, inhibiting the expression of RPL31, a key component of AIDPS, can significantly suppress the proliferation, migration, and invasive properties of ovarian cancer cells, while stimulating the cytotoxicity of effector T cells and promoting immune response, thus slowing down the progression of ovarian cancer.

© 2025 Bentham Science Publishers
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2024-07-18
2025-12-05

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/content/journals/pra/10.2174/0115748928297769240611055258
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Identification of Immune Infiltration-related Molecular Features in Ovarian Cancer Patients and Experimental Validation of Immune Response Molecular Mechanisms through Integrated WGCNA, Machine Learning, and Single-cell Sequencing Analysis
Recent Patents on Anti-Cancer Drug Discovery 20, 710 (2025); https://doi.org/10.2174/0115748928297769240611055258
/content/journals/pra/10.2174/0115748928297769240611055258
/content/journals/pra/10.2174/0115748928297769240611055258
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  • Article Type:     Research Article
Keyword(s): immune response; machine learning; Ovarian cancer; RPL31; single-cell analysis; WGCNA analysis

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