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High expression of fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer.


ABSTRACT: BACKGROUND:High-grade serous ovarian cancer (HGSOC) is a fatal form of ovarian cancer. Previous studies indicated some potential biomarkers for clinical evaluation of HGSOC prognosis. However, there is a lack of systematic analysis of different expression genes (DEGs) to screen and detect significant biomarkers of HGSOC. METHODS:TCGA database was conducted to analyze relevant genes expression in HGSOC. Outcomes of candidate genes expression, including overall survival (OS) and progression-free survival (PFS), were calculated by Cox regression analysis for hazard rates (HR). Histopathological investigation of the identified genes was carried out in 151 Chinese HGSOC patients to validate gene expression in different stages of HGSOC. RESULTS:Of all 57,331 genes that were analyzed, FAP was identified as the only novel gene that significantly contributed to both OS and PFS of HGSOC. In addition, FAP had a consistent expression profile between carcinoma-paracarcinoma and early-advanced stages of HGSOC. Immunological tests in paraffin section also confirmed that up-regulation of FAP was present in advanced stage HGSOC patients. Prediction of FAP network association suggested that FN1 could be a potential downstream gene which further influenced HGSOC survival. CONCLUSIONS:High-level expression of FAP was associated with poor prognosis of HGSOC via FN1 pathway.

SUBMITTER: Li M 

PROVIDER: S-EPMC7590670 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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High expression of fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer.

Li Min M   Cheng Xue X   Rong Rong R   Gao Yan Y   Tang Xiuwu X   Chen Youguo Y  

BMC cancer 20201027 1


<h4>Background</h4>High-grade serous ovarian cancer (HGSOC) is a fatal form of ovarian cancer. Previous studies indicated some potential biomarkers for clinical evaluation of HGSOC prognosis. However, there is a lack of systematic analysis of different expression genes (DEGs) to screen and detect significant biomarkers of HGSOC.<h4>Methods</h4>TCGA database was conducted to analyze relevant genes expression in HGSOC. Outcomes of candidate genes expression, including overall survival (OS) and pro  ...[more]

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