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Expression levels of lncRNAs are prognostic for hepatocellular carcinoma overall survival.

ABSTRACT: Studies have demonstrated that long non-coding RNAs (lncRNAs) play important roles in cancer development and progression. However, associations between the expression patterns and prognostic roles of lncRNAs in hepatocellular carcinoma (HCC) have not been comprehensively described. In this study, we established a prognostic model of lncRNA expression using public datasets of HCC from The Cancer Genome Atlas (TCGA) and adopted the International Cancer Genome Consortium (ICGC) as an independent cohort to validate the stability of our model. Cox regression analysis was used to explore the independent prognostic factor in both training and validation cohorts. Additionally, we explored the functional roles of lncRNAs using bioinformatic analyses. According to lncRNA consensus clusters, we resolved the distribution of molecular and clinical data and observed that individual lncRNA could function as prognostic biomarkers in HCC. Furthermore, the novel lncRNA molecular subtypes were statistically significant for predicting HCC status, which was validated by nested cross-validation. We found that lncRNA subtypes were partially related to gender, histological grade, and mutations within TP53. The lncRNA subtypes were also consistent with mRNA-based subtypes, and pathway enrichment analysis identified the involvement of multiple signaling pathways. In addition, we observed that upregulated DANCR was significantly associated with poor prognosis in HCC patients. In conclusion, our model based on lncRNA expression is statistically significant as a diagnostic and prognostic indicator for patients with HCC.

SUBMITTER: Xue C

PROVIDER: S-EPMC7269992 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNo biomarkers have been identified for the prognosis of lung squamous cell carcinoma (LUSC). Risk models based on m6A-lncRNAs help to predict survival in some cancers. However, very few studies have reported m6A-lncRNA risk models in LUSC. We aimed to construct a prognostic model based on m6A-lncRNAs in LUSC.MethodsThe clinical and RNA-sequencing information of 504 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-lncRNAs were identified by a Pearson correlation analysis and univariate Cox regression analysis. The ConsensusClusterPlus algorithm was used to cluster the prognostic m6A-lncRNAs. The overall survival (OS) and clinicopathological characteristics of the 2 clusters were compared. A gene set enrichment analysis (GSEA) analysis was performed to analyze the genes enriched in the 2 clusters. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct the risk-score model. Two hundred and forty eight patients were randomly chosen from TCGA-LUSC cohort for the training set. The receiver operating characteristic (ROC) curve analysis was used to assess the predictive ability of the model. The clinical characteristics and OS in the high- and low-risk groups were compared. The independent prognostic value of the model was tested by Cox regression analyses.ResultsThirteen m6A-lncRNAs were identified as prognostic lncRNAs and classified into cluster A and cluster B. The OS of patients in cluster A was better than that of patients in cluster B (P<0.001). Patients in cluster B had higher expressions of immune checkpoints. Immune score, stromal score, and ESTIMATE score were higher in cluster B (P<0.001). Seven of the 13 lncRNAs were used to construct the risk-score model. Patients in the high-risk group had a worse OS. ROC curves showed a under the curve (AUC) of 0.639 in the training set and 0.624 in the validation set. A high risk was associated with cluster B, a high immune score, and stage III-IV disease. Patients in the high-risk group had increased expressions of immune checkpoints. The Cox regression analyses showed that the risk-score model had independent prognostic value for OS. The risk-score model retained its prognostic value in different subgroups.ConclusionsThe m6A-lncRNA risk-score model is an independent prognostic factor for OS in LUSC patients. However, the risk-score model need to be further tested clinically.

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Expression levels of lncRNAs are prognostic for hepatocellular carcinoma overall survival.

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