Identification and Validation of a Potent Multi-mRNA Signature for the Prediction of Early-Relapse in Hepatocellular Carcinoma
34 Pages Posted: 4 Dec 2018More...
Early recurrence of hepatocellular carcinoma (HCC) is implicated in poor patient survival and is the major obstacle to improving prognosis. The current staging systems are insufficient for accurate prediction of early recurrence, suggesting that additional indicators for early recurrence are needed. Here, by analyzing the gene expression profiles of 12 GEO datasets (N = 1533), we identified 257 differentially expressed genes (DEGs) between HCC and non-tumor tissues. Least absolute shrinkage and selection operator (LASSO) regression model was used to identify a 24-mRNA-based signature in discovery cohort GSE14520. With specific risk score formula, patients were divided into high-risk group and low-risk group. Recurrence-free survival within 2 years (early-RFS) was significantly different between these two groups in GSE14520 [hazard ratio (HR): 7.954, 95% confidence interval (CI): 4.596-13.767, p < 0.001] and validation cohort TCGA (HR: 5.982, 95% CI: 3.414-10.480, p < 0.001). Multivariable and sub-group analyses revealed that the 24-mRNA-based classifier was an independent prognostic factor for predicting early-relapse of patients with HCC. We further developed a nomogram integrating the 24-mRNA-based signature and clinicopathological risk factors to predict the early-RFS. The 24-mRNA signature integrated nomogram showed good discrimination (C-index: 0.883, 95% CI: 0.836-0.929), and calibration. Decision curve analysis demonstrated that the 24-mRNA signature integrated nomogram was clinically useful. In conclusion, our 24-mRNA signature is an accurate tool for early-relapse prediction and will facilitate individual management of HCC patients.
Funding Statement: This work was supported by the National Natural Science Foundation of China (81670562 and 81873582 to X Kong, 81670598 to Q Xia, 31671453 and 31870905 to H. Wu).
Declaration of Interests: The authors have no conflicts of interest to declare.
Keywords: hepatocellular carcinoma; early relapse; Gene Expression Omnibus database; least absolute shrinkage and selection operator model; mRNA signature; nomogram
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