Purpose: Hepatocellular carcinoma (HCC) is a prevalent form of cancer that is distributed globally. Disulfidptosis, characterized by the fragility of the actin cytoskeleton, represents a distinct type of cell death and holds promise for novel cancer therapies. Nevertheless, the connection among disulfidptosis-associated long non-coding RNAs (lncRNAs) and HCC is still unexplored. This study uses an in silico approach to provide the novel biomarkers of disulfidptosis-associated lncRNAs for predicting the immune response and prognosis with HCC. Methods: In order to address this gap, we integrated transcriptomic data of HCC from The Cancer Genome Atlas (TCGA) and identified genes that exhibit differential expression with disulfidptosis and lncRNAs. Through co-expression analysis, we identified disulfidptosis-related lncRNAs. Afterwards, by employing univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO), a model for disulfidptosis-associated lncRNA was constructed. The risk model underwent assessment through the utilization of diverse analytical methodologies, including functional enrichment annotation, Kaplan-Meier analysis, principal component analysis (PCA), immune infiltration and immune status analysis, as well as tumor mutation analysis. Furthermore, we discussed the implications of the model in predicting drug sensitivity. Results: Our study culminated in the construction of a disulfidptosis-related lncRNA model comprising four prognostic disulfidptosisrelated lncRNAs (ACYTOR, NRAV, AL080248.1, and AC069307.1). This model demonstrates exceptional diagnostic value for HCC patients and holds practical implications for guiding clinicians in personalizing immunotherapy and drug selection based on individual variations. Conclusion: In summary, our research introduces a novel predictive tool utilizing disulfidptosis-related lncRNAs, offering potential guidance for the therapeutic management of HCC.