Compound kushen injection is an effective traditional Chinese medicine for the treatment of lung cancer. However, its influence on the survival and prognosis of patients with lung adenocarcinoma patients was less studied; especially its pharmacological mechanism remains to be further elucidated. In the present study, we adopted a network pharmacology (NP)-based approach to screening effective compounds, screening and predicting target genes, analyzing biological functions and pathways, constructing a regulatory network and protein interaction network, and screening the key targets. Moreover, mass survival analysis and molecular docking were conducted. In the end, 35 key compounds and four possible central target genes were screened out, which could be used for the treatment of lung adenocarcinoma and affected the survival and prognosis of patients with lung adenocarcinoma. In addition, their key compounds had good docking affinity. Enrichment analysis showed that CKI might affect the treatment and prognosis of lung adenocarcinoma patients by regulating the PI3K–Akt signaling pathway, TNF signaling pathway, non-small cell lung cancer, Hepatitis C, etc. We discussed the pharmacological mechanisms and potential therapeutic targets of CKI in the treatment of lung adenocarcinoma, which verified the effect of CKI on the prognosis and survival of patients. The present study might promote the further clinical application of CKI and provide a theoretical basis for further experimental studies.

Background: This study aimed to construct a risk signature with predictive power based on immunerelated RNA-binding proteins (RBPs) for lung adenocarcinoma (LUAD) patients. Methods:The Cancer Genome Atlas (TCGA) database was used as the data source. Immune genes (IGs)were obtained from the Immunology Database and Analysis Portal (immPort) database. Differentially expressed RBPs and IGs between tumor and normal tissues were screened. For external validation, an independent cohort from the Gene-Expression Omnibus (GEO) database was used. The accuracy of the risk signature prediction was evaluated using Cox regression analysis and the receiver operating characteristic (ROC) curve. Results:The risk signature was constructed from four immune-related and prognostic RBPs (OAS3, PCF11, TLR7, and EXO1). The patients were divided into the low-and high-risk groups, with the low-risk group having a higher survival rate than the high-risk group. The risk signature outperformed other clinical parameters, with a multivariable hazard ratio of 1.862 (95% confidence interval: 1.292-2.683). The tumor immune microenvironment, stemness index, immune checkpoint, immune infiltration, and proportion of immune cells were significantly different between the low-and high-risk groups (all P<0.05). Conclusions:The risk signature of immune-related RBPs can provide the basis for clinical decisions regarding diagnosis, prognosis, and immunotherapy in LUAD patients.