Identification of a 7-gene signature that predicts relapse and survival for early stage patients with cervical carcinoma

Abstract

There is no gene signature for predicting relapse and survival of cervical cancer with early stage currently. In this study, we investigate whether gene expression profiling of cervical cancer could be used to predict the prognosis of patient. A series of 100 primary cervical cancer patients who underwent radical hysterectomy between January 2001 and October 2006 were analyzed for gene expression profiles by using a custom oligonucleotide microarray containing probes for 1440 human tumor-related gene transcripts. Supervised analysis of gene expression data identified 19 genes that exhibited differential expression between cervical cancer and normal cervix. Then, all 100 patients were divided into the training (n=50) and testing sets (n=50). Using Cox regression and risk-score analysis, we identified a 7-gene (UBL3, FGF3, BMI1, PDGFRA, PTPRF, RFC4, and NOL7) signature for predicting relapse of patient in the training set. The 7-gene signature was validated by the testing set (sensitivity, 84.6%; specificity, 91.9%; positive predictive value, 78.6%; negative predictive value, 94.4%). Patients with high-risk 7-gene signature had poor relapse-free survivals (RFS) than patients with low-risk 7-gene signature in both training set (P=0.026) and testing set (P=0.042). Multivariate analysis showed that the FIGO stage and 7-gene signature are independent prognostic factors associated with RFS of cervical cancer patients. The 7-gene signature can predict cancer recurrence and survival of cervical cancer patients. This may have prognostic or therapeutic implications for the future management of cervical cancer patients.

GeneExpressScore Signature: a robust prognostic and predictive classifier in gastric cancer

“…Several signatures have been successfully constructed based on candidate biomarkers that are differentially expressed between tumor and adjacent normal tissues (Huang et al ., ; Leung et al ., ; Zhang et al ., ). These mainly included a 35 miRNA‐based signature that could predict the prognosis of patients with stage II colon cancer and a seven‐gene signature that could predict the relapse and survival for early‐stage cervical carcinoma (Huang et al ., ; Zhang et al ., ).…”

“…Several signatures have been successfully constructed based on candidate biomarkers that are differentially expressed between tumor and adjacent normal tissues (Huang et al, 2012;Leung et al, 2004;Zhang et al, 2013). These mainly included a 35 miRNA-based signature that could predict the prognosis of patients with stage II colon cancer and a seven-gene signature that could predict the relapse and survival for earlystage cervical carcinoma (Huang et al, 2012;Zhang et al, 2013). These studies indicated that some differentially expressed biomarkers in tumor and adjacent normal tissues might not only contribute to the development of cancer but also correlate with cancer prognosis.…”

The Complexity of Human Ribosome Biogenesis Revealed by Systematic Nucleolar Screening of Pre-rRNA Processing Factors

“…Identification of Human Pre-rRNA Processing Factors encoding a p53-regulated nuclear RNA-binding dual specificity phosphatase, and SF3B14 were recently studied in the context of chronic inflammatory disease (Caprara et al, 2009;Deshpande et al, 1999; Hä sler et al, 2011; Yuan et al, 1998). NOL7 is a cervical carcinoma biomarker (Hasina et al, 2006;Huang et al, 2012). THYN1 has been linked to the Jacobsen haploinsufficiency syndrome (Ji et al, 2010).…”

A novel four-gene prognostic signature as a risk biomarker in cervical cancer

“…Several studies have examined the potential of multi-mRNA signatures for clinical research on CC. Huang et al provided a prediction model of CC recurrence based on the expression patterns of seven genes [14], while Ding et al developed a prediction model of CC survival based on the expression of three genes [15]. Although they both used a high-throughput molecular identi cation method, both studies considered only a single dataset.…”