Objective     To investigate the biological characteristics and clinical significance of cuproptosis-related genes in lung adenocarcinoma (LUAD) based on the multi-omics data from The Cancer Genome Atlas. Methods     The cuproptosis-related genes were obtained from a study published in Science in March 2022. The whole genome data were used to reveal the mutation spectrum and copy number variation landscape of cuproptosis-related genes in LUAD and analyze its effects on transcriptome expression. Cuproptosis-related genes were annotated using Metascape analysis to further understand the pathways or functions in which these genes were involved. Subsequent univariate Cox analysis and Kaplan-Meier methods determined the prognosis of these genes in LUAD patients, and CellMiner analysis were used to identify those potential anticancer drugs for potentially targeting cuproptosis-related genes. Results     Cuproptosis-related genes were less frequently mutated in LUAD, and the effect of gene mutations on transcriptomic expression may depend on the type of mutation. Gene copy number variation was an important factor resulting in the disordered expression of cuproptosis-related genes. The 16 cuproptosis-related genes were mainly involved in glyoxylate metabolism and glycine degradation, copper ion entry, proteolitidylation, cellular amino acid catabolism process, oxidative stress response, etc. Among them, 6 genes (DLD, FDX1, DLAT, DLST, PDHA1, CDKN2A) were prognostic risk genes in LUAD. The CellMiner analysis suggested that 13 drugs were associated with 7 cuproptosis-related genes and they might be potential  anticancer drugs for potentially targeting cuproptosis. Conclusion     This study reveals the biological characteristics and clinical significance of cuproptosis-related genes in LUAD, and provides some reference and theoretical basis for the subsequent research of cuproptosis in cancer.

Objective To construct and validate a predictive model for the risk of bone metastasis in patients with primary liver cancer. Methods The research was generally divided into two parts: model establishment and model validation. A total of 197 patients with primary liver cancer from January 2018 to June 2018 were selected to be included in the study when building the model, and the nomogram prediction model based on Cox regression was used in the case-control study method. The validation process continued to select 238 patients with primary liver cancer (no bone metastasis) in our hospital (from July 2018 to December 2018) and followed up for 3 years. The information of the prognosis of bone metastasis during the follow-up period was observed and collected to complete the validation. SPSS statistical software and R software were used to complete the data analysis. Results The results of regression analysis at the stage of building the model showed that age, family history of malignant tumor, previous history of hepatitis B, tumor stage, primary focus surgery and tumor differentiation were independent factors affecting the prognosis of patients with bone metastasis (P <0.05). The nomogram clinical prediction model was established by using R software. The prediction model finally included four factors: age, previous history of hepatitis B, primary surgery, and degree of differentiation. The AUC of ROC curve for predicting the risk of bone metastasis was 0.758. Conclusion The nomogram model constructed in this study has a medium to high degree of predictive calibration for predicting the risk of bone metastasis in patients with primary liver cancer within 3 years and is worthy of clinical auxiliary use.