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 Li et al. Prognostic lncRNA Signature for HCC 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.

Construction of an Immune-Related Six-lncRNA Signature to Predict the Outcomes, Immune Cell Infiltration, and Immunotherapy Response in Patients With Hepatocellular Carcinoma

“…Evidence has recently indicated that lncRNAs can regulate immune cell differentiation and function, such as dendritic cell activity, T cell ratio, and metabolism (6), and thus, are potential targets for cancer therapeutics and possess predictive value for survival prognosis (4). Through mining the transcriptome sequencing data via the bioinformatics analysis, many studies have already established the lncRNA signature for predicting the prognosis of cancers, including thyroid cancer (19), breast cancer (20), renal cell carcinoma (21), bladder cancer ( 22), gastric cancer (23), as well as HCC (24). In this study, we aimed to construct an immune-related lncRNAs signature in HCC.…”

Identification of Ten-LncRNA Signatures Predict Disease-Free and Overall Survival for Hepatocellular Carcinoma

“…Several lncRNA-based prognostic models have been used for many malignancies, such as carcinoma of lung, bladder and breast(26-30), etc. Several researches have constructed lncRNA-based risk models of HCC, but the existing risk models mostly based on the overall survival rather than disease free survival (31)(32)(33)(34). In this study, we screened both DFS-associated and OSassociated DElncRNAs in the TCGA databases by univariate Cox and LASSO regression, then established two lncRNA-based signatures focusing on DFS and OS respectively, both of the prognostic models were validated and showed good performance.…”

miR-9-5p Expression is Associated With Vascular Invasion and Prognosis in Hepatocellular Carcinoma

“…The present study focused on identifying miRNAs related to VI in HCC. Unlike other database-based studies [41][42][43], all the study subjects were HCC patients, to more accurately screen for differentially expressed miRNAs related to VI. Patients who met the inclusion criteria were divided into two groups based on whether or not they had VI, and the differences in miRNA expression levels analyzed.…”