e20522 Background: Bone metastasis (BM) occurs in about 30% of patients with advanced lung cancer, and the prognosis of BM patients is very poor, with a median survival of 6-10 months. Therefore, we attempted to use machine learning methods based on gene expression profiles to construct a model to predict BM. Methods: Gene Expression Omnibus (GEO) database were searched, then these datasets (GSE10096, GSE29367, GSE29391, GSE76194, GSE175601) were included for analysis. Batch effects were removed using the ComBat function from the R package sva (v3.44.0). edgeR was used for differential expression analysis, and genes that had an absolute log2 fold change > 1 and adjusted p-value < 0.05, were selected as differentially expressed genes (DEGs) for modeling. Random forest (RF) modeling was performed using the R package random Forest (v4.7-1.1), and support vector machine (SVM) modeling and SVM Recursive Feature Elimination (SVM-RFE) were performed using the R package e1071 (v1.7-13). Survival curves were generated using the GEPIA2 R packages. Models performance was evaluated using the area under the receiver operating characteristics curve (AUC). Results: A total of 48 qualified cases were subjected to analysis, including 22 BM and 26 non-BM patients. Differential expression analysis revealed that a total of 327 DEGs were identified in the BM group. We divided the sample dataset into a training set and a test set. In the first approach, we attempted to use all 327 DEGs features to train the models. Among the evaluated models, the RF algorithm performed best with an AUC of 0.753, while the SVM algorithm had an AUC of 0.938. The second approach, we applied the SVM-RFE algorithm for feature selection, which ranked the features according to their importance, and the top 10 genes/features (TMC5, S100A4, EEF1A2, CLDN1, RALY, MYH10, MAGEC2, ALDH1A1, PI3, SALL1) were used to build a classification model (in the test set, sensitivity was100%, specificity: 81.8%, AUC:0.909). In addition, TCGA survival analysis of 10 genes was performed and found that the expression levels of five genes, including ALDH1A1, MYH10, RALY, CLDN1 and MAGEC2, were significantly associated with prognosis (p < 0.1). Conclusions: Our study suggests that we have identified ten key genes associated with BM of lung cancer which could be potential therapeutic targets.