Dataset Information

Functional estrogen receptor signaling pathway activity in high-grade serous ovarian carcinoma as compared to estrogen receptor protein expression by immunohistochemistry.

ABSTRACT:

Purpose

Anti-estrogen therapy may be used as a palliative treatment option in high-grade serous ovarian carcinomas (HGSC). However, clinical implementation is limited as the use of estrogen receptor (ER) protein expression by immunohistochemistry remains insufficient in predicting therapy response. To determine the accuracy of ER protein expression as a marker for ER signaling pathway activity, we aimed to correlate ER protein expression to functional ER signaling pathway activity in HGSC.

Methods

Immunohistochemical ER protein expression was visually scored using total percentages of stained tumor cells and histoscores. Subsequently, mRNA was extracted, and RT-qPCR analysis was performed. Functional ER pathway activity was assessed by a computational Bayesian model inferring ER signaling pathway activity from mRNA levels of ER-specific target genes.

Results

Our analysis of 29 HGSCs shows that neither total percentage of ER protein expression, nor ER histoscores are significantly correlated to ER signaling pathway activity (respectively, p = 0.473 and p = 0.606). Classification of HGSC into three groups based on ER histoscores 0-100 (n = 6), 101-200 (n = 15) and 201-300 (n = 8) resulted in comparable mean ER signaling pathway activity among the groups (p = 0.356). Several samples in the higher ER histoscore groups had low ER signaling pathway activity, indicating that nuclear ER protein expression is not sufficient to describe transcriptional ER activation.

Conclusion

Positive immunohistochemical ER staining is not always indicative of an active ER signaling pathway and is, therefore, a poor predictor of anti-estrogen response. Further research is needed to prove the predictive value of ER signaling pathway activity regarding anti-estrogen sensitivity in HGSC patients.

SUBMITTER: van der Ploeg P

PROVIDER: S-EPMC8338831 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHigh-grade serous ovarian carcinoma (HGSOC) is a pathologic subtype of ovarian cancer (OC) with a more lethal prognosis. Extensive heterogeneity results in HGSOC being more susceptible to treatment resistance and adverse treatment effects. Revealing the heterogeneity involved is crucial.MethodsWe downloaded the single-cell RNA-seq (scRNA) data from GEO database and performed a scRNA analysis for cell landscape of HGSOC by using the Seurat package. The highly expressed genes were uploaded into the DAVID and KEGG database for enrichment analysis, and the AUCell package was used to calculate cancer-associated hallmark score. The SCENIC analysis was used for key regulons, the estrogen response enrichment scores in TCGA-OV RNA-seq dataset were calculated by using the GSVA package. Besides, the expression of STRA6 and IRF1 and the cell invasion and migration in si-STRA6 OC cells were detected by using the quantitative reverse transcription (qRT)-PCR method and Transwell assay respectively.ResultsWe successfully constructed a single-cell atlas of HGSOC and delineated the heterogeneity of epithelial cells therein. There were five epithelial cell subpopulations, GLDC + Epithelial cells, PEG3+ leydig cells, STRA6+ granulosa cells, POLE2+ Epithelial cells, and AURKA + Epithelial cells. STRA6+ granulosa cells have the potential to promote tumor growth as well as the highest estrogen response early activity through the biological pathways analysis of highly expressed genes and estrogen response score of ssGSEA. We found that IRF1 and STRA6 expression was remarkably upregulated in the OC cancer cell line HEY. Silencing of STRA6 markedly decreased the invasion and migration ability of the OC cancer cell line HEY.ConclusionThere is extreme heterogeneity of epithelial cells in HGSOC, and STRA6+ granulosa cells may be able to promote cancer progression. Our findings are benefit to the heterogeneity identification of HGSOC and develop targeted therapy strategy for HGSOC patients.