Establishment of an endoplasmic reticulum stress-associated lncRNAs model to predict prognosis and immunological characteristics in hepatocellular carcinoma

Abstract

Background The endoplasmic reticulum stress (ERS) and unfolded protein response (UPR) pathways play an essential role in the pathophysiology of hepatocellular carcinoma (HCC), and activation of the UPR pathway is strongly associated with tumor growth. However, the function of ERS-associated long non-coding RNAs (lncRNAs) in HCC is less recognized. Methods We have used TCGA (The Cancer Genome Atlas) to obtain clinical and transcriptome data for HCC patients and the GSEA (Gene Set Enrichment Analysis) molecular signature database to get the ERS gene. ERS-associated prognostic lncRNA was determined using univariate Cox regression study. Then, least absolute shrinkage and selection operator and multivariate Cox regression study were used to construct ERS-associated lncRNAs risk model. Next, we use Kaplan-Meier (KM) survival study, time-dependent receiver operating characteristic (ROC) curve, univariate and multivariate Cox regression study to validate and evaluate the risk model. GSEA reveals the underlying molecular mechanism of the risk model. In addition, differences in Immune cell Infiltration Study, half-maximal inhibitory concentration (IC50) and immune checkpoints blockade (ICB) treatment between high and low risk groups were analyzed. Results We constructed a risk model consisting of 6 ERS-associated lncRNAS (containingMKLN1-AS, LINC01224, AL590705.3, AC008622.2, AC145207.5, and AC026412.3). The KM survival study showed that the prognosis of HCC patients in low-risk group was better than that in high-risk group. ROC study, univariate and multivariate Cox regression study showed that the risk model had good predictive power for HCC patients. Our verification sample verified the aforesaid findings. GSEA suggests that several tumor- and metabolism-related signaling pathways are associated with risk groups. Simultaneously, we discovered that the risk models may help in the treatment of ICB and the selection of chemotherapeutic drugs. Conclusions In this article, we created an ERS-associated lncRNAs risk model to help prognostic diagnosis and personalized therapy in HCC.