Abstract: Objective The aim of this study was to identify characterization of disulfidptosis-related long non-coding RNAs (DRLs) and investigate their prognostic features in patients with hepatocellular carcinoma (HCC). Methods Materials of patients with HCC were retrieve from cancer genome atlas database (TCGA), and feature of DRLs was analyzed by univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression. We establish an HCC prognostic model, and the model's performance was validated. The HCC patients were divided into high-risk and low-risk groups based on the median of the risk score. Molecular subtypes of HCC were identified through cluster analysis based on DRLs characteristics. Survival analysis was conducted based on different risk groups and clustering of molecular subtypes. Results A total of 3002 DRLs were identified, among which 345 DRLs were found to be related to prognosis by univariate COX regression(P＜0.05); further selection by LASSO regression reduced the number of DRLs to 7, and finally, 3 DRLs were selected by multivariate COX regression to be believed to participate in the model construction; the risk score was calculated as follows: risk score=0.9478 × AC026412.3 expression level + 0.5511 × RNF216P1 expression level + 0.5367 × TMCC1-AS1 expression level; the overall survival (OS) in the high-risk group was significantly lower than that in the low-risk group(P＜0.05); the cluster analysis categorized HCC samples into three molecular subtypes: e.g., cluster 1(C1), cluster 2(C2), and cluster 3(C3); survival analysis indicated that patients in group C2 had the best prognosis, followed by group C1, and patients in group C3 had the worst prognosis(P＜0.001). Conclusion The HCC prognostic model based on 3 DRLs could provide guidance for personalized management and treatment in patients with HCC.

Key words: Hepatoma, Long non-coding RNA, Prognostic model, Disulfidptosis, Cancer genome atlas database