Oncosis, a non-ATP-dependent active cell death, is usually an adaptive response to some form of injuries such as ischemia, hypoxia, or toxic factors21. Some research suggests that oncosis involving ROS is closely associated with acute lung injury, liver failure, and stroke22. Furthermore, the interaction between malignant tumors and oncosis has been investigated comprehensively in recent years. With the development of bioinformatics, utilizing mRNAs and lncRNAs to construct prognostic signatures for predicting the OS of cancer patients has become a hot topic of research23. We can precisely predict a 5-year survival rate, the efficacy of immunotherapy, and the sensitivity to chemotherapy drugs of patients with malignant tumors by detecting the expression of a few genes or lncRNA, which provides great convenience for individualized clinical medication. In the present study, we divided the LUAD patients into different risk groups and different molecular subtypes based on the expression of orlncRNAs, which is a more accurate prediction than the traditional risk assessment model and is more in line with the philosophy of precision medicine.
In the present study, we collected all orgenes by searching relevant literature, obtained the orlncRNAs by conducting correlation analysis, and screened the orlncRNAs closely related to prognosis by performing univariate Cox analysis for subsequent modeling. Next, we conducted the cluster analysis to differentiate the LUAD into different clusters according to the expression of the orlncRNAs related to prognosis and found that patients in cluster2 usually had a survival advantage, while patients in cluster1 had more abundant stromal cells and immune cells, more active immune pathways, higher expression of PD-1, PD-L1, HAVCR-2, and higher TMB. After randomly assigning LUAD patients into the train group and the test group, we obtained an optimal model by performing the lasso regression, calculated the risk score, performed survival analysis, and assessed the predictive ability of the prognostic signature. The results above were verified by the data of the test group. According to the GO enrichment analysis, we speculated that the prognostic signature was closely related to tumor immunity. After further exploration of the tumor immunity of the risk assessment model, we concluded that low-risk patients had a better survival outcome, higher expression of PD-1, CTLA-4, HAVCR-2, higher abundance of stromal cells and immune cells, and better efficacy for immunotherapy targeting PD-1, CTLA-4, while patients in the high-risk group tended to have a higher TMB. Finally, we performed survival analysis on the algorithm, which indicated that low-risk patients in cluster2 usually had the best prognosis.
In the 11 orlncRNAs included by the lasso regression, several orlncRNAs have been demonstrated to be closely associated with the progression of multiple malignant tumors. CARD8-AS1, a risk lncRNA of glioma, could regulate the metastasis of glioma cell lines24. Ren et al. demonstrated that LINC00941 acted as a sponge to interact with miR-877-3p, suppressed its expression, and promoted angiogenesis and progression of NSCLC in vitro25. In addition, LINC00941 was shown to be involved in the lung metastasis process of colorectal cancer26. Up-regulated LINC01137 enhanced the malignant tendency of oral squamous cell carcinoma cells and promoted the malignant transformation of oral cells27. LINC01116 functioned as a competing endogenous RNA (ceRNA) to bind with miR-744-5p, regulate the downstream CDCA4 expression, and accelerate proliferation, migration, invasion, and cisplatin resistance of LUAD cell lines28–29, which could be blocked by the AKT signaling pathway30. LINC00324 acted as a ceRNA to interact with miR-139-5p and down-regulated its expression, which accelerated the cell proliferation and invasion of NSCLC cell lines31. Pan et al. have also proved that LINC00324 promoted the proliferation and metastasis of LUAD tissues and cells by the miR-615-5p/AKT1 pathway32. However, in the remaining orlncRNAs of 11 orlncRNAs (e.g., AC010980.233–36, AL365203.237–39, AL606489.140,34, AC004687.141–43, HLA-DQB1-AS140,47, AL590226.148), although there are few experiments to explore their relationship with the occurrence and development of malignant tumors, plenty of prognostic signatures have included them to predict the OS of patients with various malignant tumors.
We are the first to establish a novel oncosis-based algorithm and explore the clinical significance of the risk assessment model and molecular subtypes. Based on the expression of orlncRNAs, we classified the LUAD patients twice, namely cluster analysis combined with multivariate Cox regression analysis, which is relatively better than traditional modeling procedures for individualized management on LUAD patients. In addition, we analyzed the survival outcome and tumor immunity of LUAD patients in different risk groups and different clusters, respectively, which could provide solid theoretical support for precision medicine.
However, there are several limitations in our study. Firstly, given that the data was obtained from the open public database, the bias of the profile analyzed should not be neglected. Secondly, we utilized the data from TCGA database to perform internal verification for the constructed risk assessment model, rather than conducting external verification by using data sets other than TCGA database. Thirdly, the novel algorithm ultimately served for clinical treatment. Whether the predictive ability of the algorithm in the clinical treatment of LUAD patients is as accurate as analyzed, this needs be further verified by qRT-PCR.
In conclusion, we established a novel algorithm that combined cluster analysis with multivariate Cox regression analysis. We only needed to detect the 11 lncRNAs expression to distinguish LUAD patients into different molecular subtypes and risk groups, to predict the survival outcomes and related tumor immunity, which could provide individualized management and treatment for LUAD patients.