PSMD12 promotes non-small cell lung cancer progression through activating the Nrf2/TrxR1 pathway.

BACKGROUND: Non-small cell lung cancer (NSCLC) contributes to the vast majority of cancer-related deaths. Proteasome 26S subunit, non-ATPase 12 (PSMD12), a subunit of 26S proteasome complex, is known to play the tumor-promoting role in several types of cancer but its function in NSCLC remains elusive. OBJECTIVE: To explore the role and underlying mechanisms of PSMD12 in NSCLC. METHODS: The PSMD12 expression in human normal lung epithelial cell line (BEAS-2B) and four NSCLC cell lines (A549, NCI-H1299, NCI-H1975, Calu-1) were determined by qRT-PCR and western blot. Malignant phenotypes of NSCLC cells were detected by CCK-8, EdU staining, immunofluorescence staining for E-cadherin, flow cytometry, and Transwell assays to assess cell viability, proliferation, epithelial-mesenchymal transition (EMT), apoptosis, migration and invasion. Dual luciferase assay was used to verify the regulatory role of transcription factor on the promoter. RESULTS: We identified the upregulation of PSMD12 in NSCLC tissues based on the GEO datasets, which further verified in NSCLC and BEAS-2B cell lines. PSMD12 knockdown significantly suppressed malignant behaviors of NSCLC cells, including cell growth, invasion, and migration, while PSMD12 overexpression presented the opposite effects. Interestingly, we found that PSMD12 upregulated the tumor-promoting factor TrxR1 mRNA expression. For its potential mechanisms, we demonstrated that PSMD12 elevated transcription factor Nrf2 protein level and promoted Nrf2 nuclear translocation. And Nrf2 further increased TrxR1 promoter activity and enhanced TrxR1 transcription. Meanwhile, we proved that TrxR1 overexpression erased the inhibitory effect of PSMD12 knockdown. CONCLUSION: PSMD12 promotes NSCLC progression by activating the Nrf2/TrxR1 pathway, providing a novel prognostic and therapeutic target for NSCLC treatment.

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests.

BACKGROUND: The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial. OBJECTIVE: This study aimed to construct a model to predict cancer risk in patients with type 2 diabetes. METHODS: This study collected clinical data from a total of 5198 patients. A cancer risk prediction model was established by analyzing 261 items from routine laboratory tests. We screened 107 risk factors from 261 clinical tests based on the importance of the characteristic variables, significance of differences between groups (P< 0.05), and minimum description length algorithm. RESULTS: Compared with 16 machine learning classifiers, five classifiers based on the decision tree algorithm (CatBoost, light gradient boosting, random forest, XGBoost, and gradient boosting) had an area under the receiver operating characteristic curve (AUC) of > 0.80. The AUC for CatBoost was 0.852 (sensitivity: 79.6%; specificity: 83.2%). CONCLUSION: The constructed model can predict the risk of cancer in patients with type 2 diabetes based on tumor biomarkers and routine tests using machine learning algorithms. This is helpful for early cancer risk screening and prevention to improve patient outcomes.