Integrative Transcriptomic Meta-Analysis Reveals Risk Signatures and Immune Infiltration Patterns in High-Grade Serous Ovarian Cancer

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Abstract
Background: High-grade serous ovarian cancer (HGSOC) is a highly aggressive malignancy with poor prognosis due to late-stage diagnosis and limited treatments. Identifying differentially expressed genes (DEGs), and immune cell infiltration patterns may improve prognostic assessment and therapeutic strategies. Methods: We conducted a meta-analysis of gene expression data from the GEO (Gene Expression Omnibus, NCBI). DEGs were identified, functionally enriched, and analyzed for protein-protein interactions. Overlaps with oncogenes and tumor suppressor genes were examined. Cox survival analysis and a gene expression-based risk stratification model were developed. Immune infiltration differences were assessed using deconvolution methods. Results: A total of 11 studies (291 HGSOC, 96 controls) identified 892 DEGs, mainly involved in mitochondrial function, vesicle trafficking, and immune regulation. Key oncogenes (EZH2, PDK1, ERBB2) and tumor suppressor genes (BRCA1, DUSP22) were identified. Survival analysis associated the expression of SEC24B, TGOLN2, TRAK1, and CAST with poor prognosis. Low-risk patients had higher activated dendritic cells and CD4+ memory T cells while high-risk patients were enriched in common lymphoid progenitors and megakaryocyte-erythroid progenitors. Conclusions: This study identifies key DEGs in HGSOC progression and presents a risk stratification model predicting patient outcomes.
Funding Information
  • Instituto Nacional de Cancerología (INC) (Colombia) (ID: 19010300455)

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