Construction of an algorithm based on oncosis‐related LncRNAs comprising the molecular subtypes and a risk assessment model in lung adenocarcinoma

Abstract

Background As an important non‐apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few research reported the relationship between oncosis and lung cancer. Methods In this study, we established an oncosis‐based algorithm comprised of cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with lung adenocarcinomas (LUAD). We selected 11 oncosis‐related lncRNAs associated with the prognosis (CARD8‐AS1, LINC00941, LINC01137, LINC01116, AC010980.2, LINC00324, AL365203.2, AL606489.1, AC004687.1, HLA‐DQB1‐AS1, and AL590226.1) to divide the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low‐risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low‐risk patients tended to have a higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD‐1, CTLA‐4, HAVCR2, and were more sensitive to immune checkpoint inhibitors (ICIs), including PD‐1/CTLA‐4 inhibitors. The risk score had a significantly positive correlation with tumor mutation burden (TMB). The survival curve of the novel oncosis‐based algorithm suggested that low‐risk patients in cluster2 have the most obvious survival advantage. Conclusion The novel oncosis‐based algorithm investigated the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.

A novel LUAD prognosis prediction model based on immune checkpoint-related lncRNAs

“…As for lung cancer, Liu et al (2020) have proved that the expression of SLC16A1-AS1 was significantly lower in NSCLC tissue than that in adjacent tissue, and SLC16A1-AS1 over-expression could block the cell cycle and promote cell apoptosis in vitro , suggesting that it might act as a potential biomarker for patients with NSCLC. Then, when it comes to lncRNA AL606489.1, some investigations have proved that it was associated with ferroptosis in LUAD ( Guo et al, 2021 ; Song et al, 2021 ; Wu et al, 2021 ) as well as oncosis ( Chen et al, 2022 ), which all demonstrating a relationship between non-apoptotic cell death and LUAD and provide important predictive value for the prognosis of LUAD as well as potential clinical therapeutic targets. Similarly, AC026355.2, a vital immune-associated lncRNA, also showed its prognostic value for identifying immune and necroptosis characteristics in LUAD patients ( He et al, 2021 ; Lu et al, 2022 ).…”