Background: Since ovarian cancer leads to the poor prognosis in women all over the world, we aim to construct an immune-related lncRNAs signature to improve the survival of ovarian cancer patients.
Methods: Normal and cancer patient samples and corresponding clinical data of ovarian were obtained from The Genotype-Tissue Expression (GTEx) portal and The Cancer Genome Atlas (TCGA) database. The predictive signature was constructed by the lasso penalty Cox proportional hazard regression model. The division of different risk groups was accounting for the optimal critical value of the time-dependent Receiver Operating Characteristic (ROC) curve. Finally, we validated and evaluated the application of this prognostic signature based on the clinical factors, chemo-sensitivity and immune status of different risk groups.
Results: The signature was established from 145 DEirlncRNAs and can be shown as an independent prognostic risk factor with accurate prediction on overall survival in ovarian cancer patients. Further analysis on the application of the prognostic signature showed that patients with low-risk had a better sensitivity to chemotherapy and a higher immunogenicity.
Conclusion: We constructed and verified an effective signature based on DEirlncRNA pairs, which could predict the prognosis, drug sensitivity and immune status of ovarian cancer patients and promote the prognostic estimation and individualized treatment.
Keywords: Chemo-sensitivity; Immune infiltration; Long non-coding RNA; Ovarian cancer; Signature.
© 2021. The Author(s).