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KMID : 1143220220650010052
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Obstetrics & Gynecology Science
2022 Volume.65 No. 1 p.52 ~ p.63
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Signature of arylacetamide deacetylase expression is associated with prognosis and immune infiltration in ovarian cancer
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Feng Jianyang
He Hong
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Abstract
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Objective: The role of the protein-coding gene arylacetamide deacetylase (AADAC) in the prognostication of ovarian cancer remains uncertain. We aimed to identify and validate its prognostic value using integrated bioinformatics analyses.
Methods: Gene expression profiles of RNA-sequencing and microarray data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate and multivariate Cox regression models were used to evaluate the prognostic value of gene expression. The predictive accuracy of the gene signature model was evaluated using a time-dependent receiver operating characteristic (ROC) curve. In addition, the correlation between immune infiltration and AADAC was identified. A nomogram of the gene signature with clinical parameters was constructed to estimate the clinical application of the signature for survival prediction in patients with ovarian cancer.
Results: Univariate and multivariate Cox regression analyses in the training and validation cohorts indicated that a high AADAC expression signature was significantly and independently correlated with better survival outcomes in ovarian cancer. AADAC upregulation positively correlated with the infiltration of CD4+ memory T cells. Immunological signature gene sets were significantly enriched in CD4+ T cell regulation pathways. The area under the curve of the time-dependent ROC for overall survival indicated that the constructed nomogram had a moderate predictive ability for prognostic prediction in ovarian cancer.
Conclusion: AADAC expression signature significantly and independently correlated with the survival outcome and CD4+ memory T cell infiltration in ovarian cancer, indicating its potential applicability in the prediction of prognosis and immunotherapy efficacy.
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KEYWORD
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Arylacetamide deacetylase, Ovarian neoplasms, Computational biology, Tumor microenvironment, Nomogram
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