Signature of arylacetamide deacetylase expression is associated with prognosis and immune infiltration in ovarian cancer
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Objective: The role of the protein-coding gene arylacetamide deacetylase (AADAC) in the prognostication of ovarian cancerremains 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 andGene Expression Omnibus. Univariate and multivariate Cox regression models were used to evaluate the prognosticvalue of gene expression. The predictive accuracy of the gene signature model was evaluated using a time-dependentreceiver operating characteristic (ROC) curve. In addition, the correlation between immune infiltration and AADACwas identified. A nomogram of the gene signature with clinical parameters was constructed to estimate the clinicalapplication 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 highAADAC expression signature was significantly and independently correlated with better survival outcomes in ovariancancer. AADAC upregulation positively correlated with the infiltration of CD4+ memory T cells. Immunologicalsignature gene sets were significantly enriched in CD4+ T cell regulation pathways. The area under the curve of thetime-dependent ROC for overall survival indicated that the constructed nomogram had a moderate predictive abilityfor 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 andimmunotherapy efficacy.
Abstract
Objective: The role of the protein-coding gene arylacetamide deacetylase (AADAC) in the prognostication of ovarian cancerremains 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 andGene Expression Omnibus. Univariate and multivariate Cox regression models were used to evaluate the prognosticvalue of gene expression. The predictive accuracy of the gene signature model was evaluated using a time-dependentreceiver operating characteristic (ROC) curve. In addition, the correlation between immune infiltration and AADACwas identified. A nomogram of the gene signature with clinical parameters was constructed to estimate the clinicalapplication 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 highAADAC expression signature was significantly and independently correlated with better survival outcomes in ovariancancer. AADAC upregulation positively correlated with the infiltration of CD4+ memory T cells. Immunologicalsignature gene sets were significantly enriched in CD4+ T cell regulation pathways. The area under the curve of thetime-dependent ROC for overall survival indicated that the constructed nomogram had a moderate predictive abilityfor 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 andimmunotherapy efficacy.
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