Identification and Validation of a Prognostic lncRNA Signature for Hepatocellular Carcinoma

Abstract

Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P-value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238-5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.

Keywords: TCGA; hepatocellular carcinoma; least absolute shrinkage and selection operator; long non-coding RNAs; prognosis analysis.

Figure 1

Overall study design.

Figure 2

Volcano plot and heatmap. (A) Volcano plot depicting the DElncRNAs; the X-axis represents the log-transformed values of false discovery rates, and the Y-axis indicates the average differences in lncRNA expression. Red and green dots indicate the up- and downregulated lncRNAs in tumor, and black dots indicate DElncRNA with nonsignificant differences. (B) Heatmaps demonstrate the DElncRNAs; the X-axis shows the sample category, and the Y-axis represents the DElncRNAs. Green and red indicate down- and up-regulation, respectively.

Figure 3

Regression coefficient diagram based on LASSO regression. (A) LASSO coefficient profiles for some significant lncRNAs in univariate Cox regression analysis. Coefficient profiles decrease with larger lambda values. (B) Cross-validation for selecting the tuning parameters for the LASSO model. The vertical lines are plotted based on the optimal data according to the minimum criteria and 1-standard error criterion. The left vertical line represents the 11 lncRNAs finally identified. (C) Forest plots showing the relationships of various lncRNA subsets with OS in training cohort. The unadjusted HRs are presented with 95% CIs. (D) Differential gene expression of model lncRNA in TCGA and GTEx database. ***P < 0.001, **P < 0.01, and *P < 0.05.

Figure 4

Verification of the lncRNA signature for predicting HCC prognosis in the training group. (A) LncRNA expression in the high- and low-risk groups. (B) Distribution of lncRNA risk score. (C) Survival status together with OS. (D) Kaplan–Meier curve showing OS in the low- and high-risk groups classified based on the median risk score. (E) The ROC curve of survival discriminated by the lncRNA signature. (F) Univariate Cox regression analyses of OS. (G) Multivariate Cox regression analyses of OS. (H) LncRNA expression grouped by pathological stage. (I) Risk score significantly increased with more advanced stage.

Figure 5

Further verification of the lncRNA signature for HCC prognosis in the validation group and the entire cohort. (A–E) are validation group results that are consistent with the training cohort results (Figure 4). Cox regression results. (F) Univariate results in the validation group. (G) Multivariate results in the validation group. (H) Univariate results for the entire cohort. (I) Multivariate results for the entire cohort. (J) LncRNA expression grouped by pathological stage in the validation group. (K) Risk score significantly increased for advanced stage cases in the validation group.

Figure 6

Validation of the lncRNA signature in the Gene Expression Omnibus cohort. (A) Distribution of lncRNA risk score. (B) Survival status together with OS. (C) Kaplan–Meier curves of overall survival. (D) Time-dependent receiver operating characteristic curves. (E) Univariate and (F) multivariate Cox regression analysis further confirmed the signature as an independent factor.

Figure 7

The clinical significance of GACAT3 in HCC and in vitro study. (A) GACAT3 are overexpressed in HCC tissues, and higher GACAT3 level predicts poor prognosis (B). (C) Transfection efficiency was verified after transfection of GACAT3 or negative control siRNA. (D) Transwell assays were used to detect HCC invasion and migration. Representative experiments are shown. (E) Images were recorded 0 and 24 h after scratching the cell surface; representative images are shown; (F) HCC cell viability was evaluated with CCK-8 assays at 0, 24, 48, and 72 h post-transfection. **P < 0.001. (G) The number of HCC cell colonies was reduced after GACAT3 knockdown.

Figure 8

GSEA delineation of the biological pathways related to the risk score values of the lncRNA model using the gene set “c2.cp.kegg.v6.2.symbols”.