Background: The accuracy of existing biomarkers for predicting the prognosis of hepatocellular carcinoma (HCC) is not satisfactory. It is necessary to explore biomarkers that can accurately predict the prognosis of HCC.
Methods: In this study, original transcriptome data were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related long noncoding ribonucleic acids (irlncRNAs) were identified by coexpression analysis, and differentially expressed irlncRNA (DEirlncRNA) pairs were distinguished by univariate analysis. In addition, the least absolute shrinkage and selection operator (LASSO) penalized regression was modified. Next, the cutoff point was determined based on the area under the curve (AUC) and Akaike information criterion (AIC) values of the 5-year receiver operating characteristic (ROC) curve to establish an optimal model for identifying high-risk and low-risk groups of 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 DEirlncRNAs were recognized in this study, 30 of these pairs were included in the Cox regression model for subsequent analysis. After regrouping according to the cutoff point, we could more effectively identify factors such as aggressive clinicopathological features, poor survival outcomes, specific immune cell infiltration status of tumors, high expression level of immunosuppressive biomarkers, and low sensitivity to chemotherapy drugs in HCC patients.
Conclusions: The nonspecific expression level signature involved with irlncRNAs shows promising clinical value in predicting the prognosis of HCC patients.
Keywords: Bioinformatics; Hepatocellular carcinoma; Immune-related lncRNA; Prognosis.
© 2021. The Author(s).