Construction of Novel Immune-Related LncRNA Signature and its Potential Prediction of Immune Status in Hepatocellular Carcinoma

Abstract Background: The accuracy of the existing biomarkers in predicting the prognosis of hepatocellular carcinoma (HCC) is not satisfactory. It is necessary to explore biomarkers that can accurately predict the prognosis of HCC.Materials and methods: In this study, the original transcriptome data was downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related long non-coding ribonucleic acids (irlncRNAs) were identified by co-expression analysis, and different expression irlncRNA (DEirlncRNA) pairs were distinguished by univariate analysis. Besides, the least absolute shrinkage and selection operator (LASSO) penalized regression was modified. Next, the cut-off point was determined based on the area under the curve (AUC) and Akaike information criterion (AIC) values of the 5-year receiver operating characteristic curve (ROC) to establish an optimal model for identifying high-risk and low-risk groups in HCC patients. The model was then reassessed in terms of clinicopathological features, survival rate, tumor-infiltrating immune cells, immunosuppressive markers, and chemotherapy efficacy.Results: A total of 1009 pairs of DEirlncRNA were recognized in this study, 30 of which were included in the Cox regression model for subsequent analysis. After regrouping according to the cut-off point, we can more effectively identify factors such as aggressive clinicopathological features, poor survival outcomes, specific tumor immune infiltration status, the high expression level of immunosuppressive biomarkers, and low sensitivity to chemotherapy drugs in HCC patients.Conclusions: The non-specific expression level signature involved in irlncRNAs shows a promising clinical value in predicting the prognosis of HCC patients.