Published online Jan 14, 2019. doi: 10.3748/wjg.v25.i2.220
Peer-review started: October 12, 2018
First decision: November 15, 2018
Revised: December 5, 2018
Accepted: December 19, 2018
Article in press: December 20, 2018
Published online: January 14, 2019
Hepatocellular carcinoma (HCC) is the most common type of liver cancer which remains a severe health issue worldwide. In recent years, genetic markers and predictive models have been put forward for improving the management of HCC. Meanwhile, many statistical techniques have been used for data mining in a series of large public databases involving the high-throughput genetic data of cancers. With the help of the most advanced clinic-practical methods, more accurate and robust prognostic models can be constructed for HCC.
Researchers have tried to constitute a prognostic model based on molecular biomarkers for HCC over these years. Long non-coding RNAs (lncRNAs) are novel predictive indicators. Although a few attempts have been made to construct lncRNA-based models for HCC, more are needed for further really significant findings.
By analyzing data from two databases, Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we wanted to identify a prognostic signature for HCC which is comprised of the potential functional lncRNAs.
The latest statistical algorithm, the least absolute shrinkage and selection operator (LASSO), was utilized to constitute our predictive model. This method was performed based on the significant lncRNAs screened based on the lncRNA expression profiles from the GEO database. The expression values of the candidate lncRNAs were also examined in the HCC and normal liver tissues. The robustness of this model was validated using TCGA dataset. The suitable patients and other clinical applicability of the lncRNA-signature were explored as well.
The risk score system for predicting the recurrence of HCC was constructed based on the six lncRNAs (MSC-AS1, POLR2J4, EIF3J-AS1, SERHL, RMST, and PVT1) using LASSO. All six lncRNAs were aberrantly expressed in HCC and non-tumor tissue and they were significantly enriched in TGF-β signaling pathway and cellular apoptosis-related pathways. The best candidates we identified were younger early-staged male patients with HBV infection and family history in better physical condition but with higher preoperative AFP. To broaden the application scope of the model, a nomogram involving the lncRNA signature and other clinicopathological characteristics was formulated.
The six-lncRNA signature showed great predictive ability in prognostic evaluation of HCC patients. This tool may help perform risk stratification and provide more individualized clinical advice for each patient.
Our study offered extra evidence that lncRNAs are potential functional regulators in HCC progression. Finding effective molecular biomarkers and predictive signatures of HCC prognosis are future direction calling urgently for groundbreaking attempts.