Abstract Objective: To construct a prognostic risk model for predicting bone metastasis in prostate cancer (PCa) based on bioinformatics approach, and to investigate the effect of risk score on prognosis and immune infiltration. Methods: Clinical information and microarray expression data of PCa patients were downloaded from the Gene Expression Omnibus (GEO), and genes related to the bone morphogenetic protein (BMP) pathway were downloaded from the Molecular Signature Database (MSigDB). The differential expression genes (DEGs) of PCa bone metastasis samples and primary PCa samples were screened by differential analysis, and the intersection with BMP pathway-related genes was taken to obtain BMP-related DEGs, and the prognostic risk model was constructed by screening risk genes through LASSO regression analysis, and independent prognostic factors of PCa were screened by multifactorial regression analysis. The risk scores of patients were calculated according to the risk model and subsequently divided into two groups of high and low risk scores by corresponding score median values, and DEGs were identified for functional enrichment analysis to compare the differences in immune infiltration between high and low risk groups. Results: After the intersection of DEGs and BMP-related gene sets in 3 055 differentially expressed genes of PCa bone metastasis samples, 13 BMP-related DEGs were obtained. Four gene were screened by LASSO regression to construct a prognostic risk model, in which secreted frizzled-related proteins 2 (SFRP2), endoglin (ENG), and follistatin-like protein 1 (FSTL1) were risk genes and chordin like 1 (CHRDL1) was a protective gene. Kaplan-Meier analysis showed that metastasis-free survival and biochemical recurrence-free survival of patients in the high-risk group were significantly lower than that of patients in the low-risk group. Multifactorial regression analysis identified risk score as an independent prognostic factor for PCa bone metastasis, and patients in the high-risk group were more likely to develop bone metastasis and biochemical recurrence, and the risk score was positively correlated with the PSA value, the Gleason score, and the T stage. The DEGs of high-risk group were mainly involved in the activation of the WNT/BMP signaling pathway, and were significantly enriched in the proliferation of epithelial cells and extracellular matrix bioprocesses. GSEA analysis showed that, the differentially expressed genes were upregulated in the gene sets of mesenchymal transition, inflammatory response, angiogenesis, and multiple tumor metastasis gene sets. Immune infiltration analysis showed that PCa bone metastasis samples and patients in the high-risk group possessed a higher degree of immune cell infiltration, cancer associated fibroblasts (CAFs), macrophages, Estimate scores, stromal scores, and immune scores than the lowrisk group, and lower tumor purity than the low-risk group. Conclusion: The prognostic risk model of BMP-related DEGs constructed based on the LASSO regression analysis can effectively predict the occurrence of PCa bone metastasis, and the high-risk score is closely related to the prognosis and immune infiltration of PCa.
［Key words］prostate cancer; bone metastasis; BMP signaling pathway; prognostic risk model; immune invasion
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Received: 01 March 2024
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