Open AccessArticle
Integrative Transcriptomic Meta-Analysis Reveals Risk Signatures and Immune Infiltration Patterns in High-Grade Serous Ovarian Cancer
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Paula D. Morales-Suárez, Yina T. Zambrano-O, Alejandro Mejía-Garcia, Hsuan Megan Tsao, Liliana Lopez-Kleine, Diego A. Bonilla, Alba L. Combita, Rafel Parra-Medina, Patricia Lopez-Correa, Silvia J. Serrano-G, Juliana L. Rodriguez and Carlos A. Orozco
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
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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.
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