Integrated Multi-Omics and Machine Learning Identify PTTG1 as a Prognostic Biomarker and Therapeutic Target in Endometrial Cancer
PTTG1 has been identified as a potential prognostic biomarker in endometrial cancer, influencing tumor growth and immune responses.
Where it sits
this study against the rest of the glutathione (gsh) corpusSummary and findings
This study aimed to identify PTTG1 as a prognostic biomarker in endometrial cancer (UCEC) by integrating multi-omics data. The findings revealed significant overexpression of PTTG1 at both mRNA and protein levels. Mechanistically, PTTG1 was shown to influence malignant progression and immune-related transcriptional networks.
Abstract
<title>Abstract</title> <p>Background Endometrial carcinoma (UCEC) exhibits high molecular heterogeneity, and the lack of precise prognostic biomarkers and therapeutic targets severely limits the clinical management of high-risk patients. This study aimed to identify a robust multi-omics biomarker and elucidate its intrinsic oncogenic mechanisms and extrinsic immune-remodeling roles in the UCEC microenvironment. Methods We systematically integrated transcriptomic data from TCGA and GEO cohorts. WGCNA coupled with four machine learning algorithms (LASSO, SVM-RFE, XGBoost, Boruta) were employed for feature selection. Single-cell RNA sequencing and spatial transcriptomics were integrated to map the cellular landscape. In silico virtual knockout, virtual overexpression, and pharmacogenomic analyses were conducted. Computational predictions were experimentally validated using Western blot, qRT-PCR, and immunohistochemistry in paired clinical specimens, and corroborated by CPTAC proteomic analysis. Results PTTG1 was identified as an independent prognostic biomarker, with significant overexpression confirmed at both mRNA and protein levels and predominant nuclear localization in tumor epithelium. Mechanistically, virtual knockout revealed PTTG1 drives malignant progression through hyperactivation of cell cycle, ribosome biogenesis, and glutathione metabolism, whereas virtual overexpression activated immune-related transcriptional networks including MHC class II antigen presentation. Integrated scRNA-seq and spatial transcriptomics demonstrated PTTG1 enrichment in malignant epithelial cells and tumor-associated macrophages. Drug sensitivity predictions identified selective vulnerability to MEK inhibitors, including selumetinib and trametinib. Conclusion PTTG1 serves as a multidimensional oncogenic driver and independent prognostic biomarker in UCEC, intrinsically fueling tumor proliferation while extrinsically orchestrating an immunosuppressive microenvironment. Bidirectional in silico perturbation revealed distinct transcriptional consequences, substantiated by multi-level experimental validation. Drug sensitivity predictions identified selective MEK inhibitor vulnerabilities, providing a rationale for PTTG1-guided prognostic stratification and targeted therapeutic strategies in high-risk UCEC.</p>
Background
Endometrial carcinoma (UCEC) presents significant molecular heterogeneity, complicating the identification of reliable prognostic biomarkers and therapeutic targets. Prior research has highlighted the need for improved stratification methods for high-risk patients. This study addresses these gaps by integrating multi-omics approaches to identify PTTG1 as a potential biomarker and therapeutic target.
Methods
The study utilized transcriptomic data from TCGA and GEO cohorts, applying WGCNA and four machine learning algorithms (LASSO, SVM-RFE, XGBoost, Boruta) for feature selection. Single-cell RNA sequencing and spatial transcriptomics were employed to map the cellular landscape. Experimental validation included Western blot, qRT-PCR, and immunohistochemistry in paired clinical specimens.
Results
PTTG1 was confirmed as an independent prognostic biomarker, with significant overexpression at both mRNA and protein levels. The study demonstrated that PTTG1 drives malignant progression through hyperactivation of cell cycle and glutathione metabolism. Drug sensitivity predictions indicated selective vulnerability to MEK inhibitors.
Interpretation
The identification of PTTG1 as a prognostic biomarker aligns with previous literature on its role in tumor biology. However, the clinical significance of these findings remains uncertain, as the effect sizes and translational implications are not fully established. Limitations include reliance on computational models and potential confounding factors in the tumor microenvironment.
Key findings
- PTTG1 was identified as an independent prognostic biomarker with significant overexpression at both mRNA and protein levels.
- Virtual knockout revealed PTTG1 drives malignant progression through hyperactivation of cell cycle, ribosome biogenesis, and glutathione metabolism.
- Drug sensitivity predictions identified selective vulnerability to MEK inhibitors, including selumetinib and trametinib.
Limitations
- Not reported in abstract.
- Reliance on computational predictions.
- Experimental validation may not fully translate to clinical practice.
- Potential confounding factors in the tumor microenvironment.