Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis - PubMed (original) (raw)
Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis
Guo-Jie Qiao et al. PeerJ. 2019.
Abstract
Background: Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC.
Methods: Bioinformatics analysis including Cox's regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database.
Results: We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years, n = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test P < 0.001) and in the test dataset (median 2.68 vs. 4.24 years, log-rank test P = 0.004, n = 221, GSE14520). Further multivariate Cox regression analysis showed that the signature was an independent prognostic factor for patients with HCC. Compared with TNM stage and another reported three-gene model, the signature displayed improved survival prediction power in entire dataset (AUC signature = 0.66 vs. AUC TNM = 0.64 vs. AUC gene model = 0.60, n = 553). Stratification analysis shows that it can be used as an auxiliary marker for many traditional staging models.
Conclusions: We constructed an eight-gene signature that can be a novel prognostic marker to predict the survival of HCC patients.
Keywords: Gene signature; Hepatocellular carcinoma; Overall survival; Prognostic marker.
Conflict of interest statement
The authors declare there are no competing interests.
Figures
Figure 1. Identification of the prognostic PCG signature in the training dataset.
(A) Volcano plot of the survival associated PCGs in univariate cox regression analysis. (B) According to important score to filter genes which were calculated by random survival forest analysis, the twelve genes with highest accuracies (k = 1, 2…12, k represents the gene number) are shown in the plot. (C) After calculating the AUC of 4,095 signatures, the prognostic PCG-lncRNA signature with biggest prediction power (n = 8) was screen out. (D) Validating the expression of the selected eight genes in six cell lines.
Figure 2. The PCG signature predicts overall survival of patients with HCC in the training set and test set.
(A, B) Kaplan–Meier survival curves classify patients into high- and low-risk groups by the PCG signature in the training and test dataset. P Values were calculated by log-rank test. (C, D) Risk score distribution, survival status and gene expression patterns for patients in high- and low-risk groups by the PCG signature in the training and test dataset.
Figure 3. ROC analysis for comparing survival prediction power between the PCG signature and TNM stage in the training (A) and entire dataset (B) and time-dependent ROC analysis of the signature and TNM stage in the training (C, D) and entire dataset (E, F).
Figure 4. Comparing the survival prediction power of PCG signature with three-gene signature by Kaplan–Meier (A, B) and ROC (C) analysis and time-dependent ROC analysis of the three-gene signature in the entire dataset (n = 553).
Figure 5. Stratified analysis of TNM (A, B) and T (C, D) low/high stage of the signature in the entire group and BCLC staging (E) and AFP (F) in the GEO dataset.
Figure 6. Functional enrichment of the co-expressed protein-coding genes with prognostic eight PCGs.
Significantly enriched GO terms (A) and KEGG pathways (B) of the co-expressed protein-coding genes with the eight prognostic PCGs.
References
- Bhutiani N, Egger ME, Ajkay N, Scoggins CR, Martin 2nd RC, McMasters KM. Multigene signature panels and breast cancer therapy: patterns of use and impact on clinical decision making. Journal of the American College of Surgeons. 2018;226:406–412. doi: 10.1016/j.jamcollsurg.2017.12.043. - DOI - PubMed
- Brodeur J, Theriault C, Lessard-Beaudoin M, Marcil A, Dahan S, Lavoie C. LDLR-related protein 10 (LRP10) regulates amyloid precursor protein (APP) trafficking and processing: evidence for a role in Alzheimer’s disease. Molecular Neurodegeneration. 2012;7:31. doi: 10.1186/1750-1326-7-31. - DOI - PMC - PubMed
- Calderaro J, Couchy G, Imbeaud S, Amaddeo G, Letouze E, Blanc JF, Laurent C, Hajji Y, Azoulay D, Bioulac-Sage P, Nault JC, Zucman-Rossi J. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. Journal of Hepatology. 2017;67:727–738. doi: 10.1016/j.jhep.2017.05.014. - DOI - PubMed
LinkOut - more resources
Full Text Sources
Miscellaneous