Development and Validation of a Mutational Burden-Associated LncRNA Signature for Improving the Clinical Outcome of Hepatocellular Carcinoma

Construction and validation of somatic mutation-derived long non-coding RNAs signatures of genomic instability to predict prognosis of hepatocellular carcinoma

BACKGROUND

Long non-coding RNAs (LncRNAs) have been found to be a potential prognostic factor for cancers, including hepatocellular carcinoma (HCC). Some LncRNAs have been confirmed as potential indicators to quantify genomic instability (GI). Nevertheless, GI-LncRNAs remain largely unexplored. This study established a GI-derived LncRNA signature (GILncSig) that can predict the prognosis of HCC patients.

AIM

To establish a GILncSig that can predict the prognosis of HCC patients.

METHODS

Identification of GI-LncRNAs was conducted by combining LncRNA expression and somatic mutation profiles. The GI-LncRNAs were then analyzed for functional enrichment. The GILncSig was established in the training set by Cox regression analysis, and its predictive ability was verified in the testing set and TCGA set. In addition, we explored the effects of the GILncSig and TP53 on prognosis.

RESULTS

A total of 88 GI-LncRNAs were found, and functional enrichment analysis showed that their functions were mainly involved in small molecule metabolism and GI. The GILncSig was constructed by 5 LncRNAs (miR210HG, AC016735.1, AC116351.1, AC010643.1, LUCAT1). In the training set, the prognosis of high-risk patients was significantly worse than that of low-risk patients, and similar results were verified in the testing set and TCGA set. Multivariate Cox regression analysis and stratified analysis confirmed that the GILncSig could be used as an independent prognostic factor. Receiver operating characteristic curve analysis of the GILncSig showed that the area under the curve (0.773) was higher than the two LncRNA signatures published recently. Furthermore, the GILncSig may have a better predictive performance than TP53 mutation status alone.

CONCLUSION

We established a GILncSig that can predict the prognosis of HCC patients, which will help to guide prognostic evaluation and treatment decisions.

Identification and Validation of Genomic Subtypes and a Prognostic Model Based on Antigen-Presenting Cells and Tumor Microenvironment Infiltration Characteristics in Hepatocellular Carcinoma

Currently, the prognosis of hepatocellular carcinoma (HCC) is poor, and there is a lack of effective targeted therapy. As key mediators of the immune response, the prognostic value of antigen-presenting cells (APCs) in HCC still remains unclear. In this study, we aimed to identify APC-related genomic subtypes and develop a novel prognostic model in HCC. Our results indicated that overall survival (OS) and the level of immune infiltration significantly differed between different APC clusters. By analyzing the gene expression profile between APC clusters, APC-related genomic subtypes were identified. There was a significant difference in OS and tumor microenvironment infiltration in HCC patients with different genomic subtypes. With the aid of genomic subtypes, significantly differentially expressed genes were screened to generate a novel prognostic model. The risk score of the model had a significant positive correlation with APCs and was associated with immune checkpoint expressions. Through the clinical cohort collected from the First Affiliated Hospital of Wenzhou Medical University, the prognostic value of the risk score was further validated. Moreover, after the risk score and clinical characteristics were combined, a nomogram was constructed to evaluate the prognosis for HCC patients. In conclusion, we mainly identified the APC-related genomic subtypes and generated a novel prognostic model to improve the prognostic prediction and targeted therapy for HCC patients.