Survival for patients with newly diagnosed cervical cancer has not significantly improved over the past several decades. We sought to identify a clinically relevant set of prognostic genes for squamous cell carcinoma of the cervix (SCCC), the most common cervical cancer subtype. Using RNA-sequencing data and survival data from 203 patients in The Cancer Genome Atlas (TCGA), we conducted a series of analyses using different decile cutoffs for gene expression to identify genes that could indicate large and consistent survival differences across different decile cutoffs of gene expression. Those analyses identified 42 high-risk genes. A patient's survivability could be estimated by simply counting the number of high-risk genes with extremely high expression (above the 90th percentile) or estimating a transcriptomic risk score (TRS) using a machine learning algorithm with 9 of the 42 genes. On multivariate analysis, the significant predictors of mortality included high TRS (HR = 44.8), stage IV (HR = 28.1), intermediate TRS (HR = 4.75), and positive lymph node status (HR = 2.92). Approximately 18% of earlier-stage patients were identified as a poor-prognosis subgroup with high TRS. In patients with SCCC, transcriptomic risk appears to better predict survival than clinical prognostic factors, including stage.
Keywords: Cervical cancer; RNA; TCGA; gene expression; prognostic biomarker; squamous cell carcinoma; transcriptomic risk.
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