Abstract
High-grade serous ovarian cancer (HGSOC) is a heterogeneous disease with diverse clinical outcomes, highlighting a need for prognostic biomarker identification. Here, we combined tumor microenvironment (TME) scores with HGSOC characteristics to identify immune-related prognostic genes through analysis of gene expression profiles and clinical patient data from The Cancer Genome Atlas and the International Cancer Genome Consortium public cohorts. We found that high TME scores (TMEscores) based on the fractions of immune cell types correlated with better overall survival. Furthermore, differential expression analysis revealed 329 differentially expressed genes between patients with high vs. low TMEscores. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that these genes participated mainly in immune-related functions and, among them, 48 TME-related genes predicted overall survival in HGSOC. Seven of those genes were associated with prognosis in an independent HGSOC database. Finally, the two genes with the lowest p-values in the prognostic analysis (GBP1, ETV7) were verified through in vitro experiments. These findings reveal specific TME-related genes that could serve as effective prognostic biomarkers for HGSOC.
Highlights
Among gynecologic malignancies, ovarian cancer (OC) has a poor prognosis
We assessed the prognostic value of the TMEscore and attempted to identify TMErelated differentially expressed genes (DEGs) that contribute to High-grade serous ovarian cancer (HGSOC) overall survival in the The Cancer Genome Atlas (TCGA) database
DEGs were further validated in the International Cancer Genome Consortium (ICGC), a separate HGSOC database
Summary
High-grade serous ovarian cancer (HGSOC), the most common epithelial OC, is usually diagnosed in an advanced stage [1, 2], and patients have widely different outcomes in spite or showing equivalent clinical or pathologic characteristics. Conventional clinical features, such as CA-125 levels, do not accurately predict HGSOC prognosis [3]. Accumulated evidence indicates that the tumor microenvironment (TME) can help predict survival outcomes and assess therapeutic efficacy [7,8,9,10], genetic and epigenetic changes may contribute to progression and recurrence in different cancer types.
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