MDM2 provides TOP2 poison resistance by promoting proteolysis of TOP2ßcc in a p53-independent manner.

DNA topoisomerase II (TOP2) is an enzyme that performs a critical function in manipulating DNA topology during replication, transcription, and chromosomal compaction by forming a vital intermediate known as the TOP2-DNA cleavage complex (TOP2cc). Although the TOP2cc is often transient, stabilization can be achieved by TOP2 poisons, a family of anti-cancer chemotherapeutic agents targeting TOP2, such as etoposide (VP-16), and then induce double-strand breaks (DSBs) in cellular DNA. TOP2cc first needs to be proteolyzed before it can be processed by TDP2 for the removal of these protein adducts and to produce clean DNA ends necessary for proper repair. However, the mechanism by which TOP2ßcc is proteolyzed has not been thoroughly studied. In this study, we report that after exposure to VP-16, MDM2, a RING-type E3 ubiquitin ligase, attaches to TOP2ß and initiates polyubiquitination and proteasomal degradation. Mechanistically, during exposure to VP-16, TOP2ß binds to DNA to form TOP2ßcc, which promotes MDM2 binding and subsequent TOP2ß ubiquitination and degradation, and results in a decrease in TOP2ßcc levels. Biologically, MDM2 inactivation abrogates TOP2ß degradation, stabilizes TOP2ßcc, and subsequently increases the number of TOP2ß-concealed DSBs, resulting in the rapid death of cancer cells via the apoptotic process. Furthermore, we demonstrate the combination activity of VP-16 and RG7112, an MDM2 inhibitor, in the xenograft tumor model and in situ lung cancer mouse model. Taken together, the results of our research reveal an underlying mechanism by which MDM2 promotes cancer cell survival in the presence of TOP2 poisons by activating proteolysis of TOP2ßcc in a p53-independent manner, and provides a rationale for the combination of MDM2 inhibitors with TOP2 poisons for cancer therapy.

Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis.

Background: Lung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively. Materials and methods: LUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay. Results: A total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues. Conclusion: Immune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.