Objective To establish a prognostic model for ovarian cancer (OC) based on sialylation-related long non-coding RNA (lncRNA), and to analyze prognostic immune response of patients and sensitivity to anticancer drugs.

Methods The Cancer Genome Atlas (TCGA) database was utilized to acquire gene expression data and clinical data for OC; correlation analysis was used to screen for sialylation-related lncRNA; the Lasso and Cox regression analyses were used to screen for ovarian cancer survival-related salivation-related lncRNA (OCSS lncRNA), and a prognostic model was established; the efficiency of the evaluation model was assessed by survival analysis and receiver operating characteristic (ROC) curve; single-factor and multi-factor Cox regression analyses were applied to screen for independent prognostic factors for OC, and a Nomogram was drawn; the CIBERSORT algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) score were used to evaluate immune cell infiltration and immunotherapy benefits in OC patients; the drug sensitivity analysis was used to obtain potential therapeutic drugs.

Results A prognostic model for OC comprising 7 OCSS lncRNAs was established; the survival analysis revealed that the overall survival rate (OS) of high-risk patients was significantly lower than that of low-risk patients in the total dataset, training set and validation set (P < 0.05), and the progression-free survival OS of high-risk patients was also significantly lower than that of low-risk patients in the total dataset (P < 0.05). The ROC curves of the OC prognosis model at 1 year, 3, and 5 years demonstrated that the efficiency of model was excellent and better than other clinical characteristics; the single-factor and multi-factor Cox regression confirmed that age and risk score were independent prognostic factors (P < 0.05); the Nomogram combined with calibration curve demonstrated that the predicted OS of patients was basically consistent with the actual OS. Immune cell infiltration analysis showed significant differences in the contents of γδT cells and M1 macrophages between high-risk group and low-risk group (P < 0.05); immunotherapy sensitivity analysis revealed a significant difference in TIDE score between high-risk group and low-risk group (P < 0.05). Drug sensitivity analysis identified 9 drugs with higher therapeutic value for high-risk OC patients (P < 0.01).

Conclusion The prognostic model for OC constructed with 7 OCSS lncRNAs can help predict the prognosis of OC patients, and provide reference for clinical application, immunotherapy and drug treatment of OC.