A Systemic Pipeline of Identifying lncRNA-Disease Associations to the Prognosis and Treatment of Hepatocellular Carcinoma

Exploring disease mechanisms at the lncRNA level provides valuable guidance for disease prognosis and treatment. Recently, there has been a surge of interest in exploring disease mechanisms via computational methods to overcome the challenge of tremendous manpower and material resources in biological experiments. However, current computational methods suffer from two main limitations: simple data structures that do not consider the close association between multiple types of data, and the lack of a systematic pathogenesis analysis that identified disease-associated lncRNAs are not applied to the downstream disease prognosis and therapeutic analysis from the perspective of data analysis. In this end, we present a systemic pipeline including disease-associated lncRNAs identification and downstream pathogenesis analysis on how the predicted lncRNAs are involved in the disease prognosis and therapy. Due to the importance of identifying disease-associated lncRNAs and the weak interpretability of existing computational identification methods, we propose a novel approach named iLncDA-PT to identify disease-associated lncRNAs considering the interactions between various bio-entities outperforming the other state-of-the-art methods, and then we conduct a systematically subsequent analysis on prognosis and therapy for a specific disease, hepatocellular carcinoma (HCC), as an example. Finally, we reveal a significant association between immune checkpoint expression, tumor microenvironment, and drug treatment.