Metastasis is one of the most significant independent risk factors that can negatively affect prostate cancer (PCa) patients. However, the exact mechanisms have not been fully elucidated. To illustrate the mechanisms underlying PCa metastasis, we conducted a series of integrated bioinformatics analyses. The essential genes involved in PCa metastasis were obtained by analyzing differentially expressed genes (DEGs) between metastatic PCa and localized PCa. Gene Ontology and KEGG pathway enrichment analysis were performed for functional annotation. Protein–protein interaction networks were constructed for hub gene selection. Three transcription factor genes (FOS, CENPA, and FOXM1) were identified by integrating the hub genes with human transcription factors from The Human Transcription Factors database. Moreover, expression validation and prognostic analysis of the three transcription factor genes were carried out on GEO, TCGA, GEPIA, and the Human Protein Atlas, respectively. Further verification showed that expression variation of the three transcription factor genes existed between metastatic PCa and localized PCa as well as between localized PCa and normal prostate. In addition, different expressions of the three transcription factor genes were associated with the prognosis of localized PCa. In summary, the three transcription factor genes can serve as potential prognostic biomarkers as well as therapeutic targets for PCa.

Abbreviations: PCa: prostate cancer; DEGs: differentially expressed genes; TFs: transcription factors; GEO: Gene Expression Omnibus; FC: fold change; DAVID: Database for Annotation, Visualization, and Integrated Discovery; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological process; CC: cell component; MF: molecular function; PPI: protein–protein interaction; MCODE: Molecular Complex Detection; GEPIA: Gene Expression Profiling Interactive Analysis; GTEx: Genotype-Tissue Expression; TCGA: The Cancer Genome Atlas Program; MCC: Maximal Clique Centrality; DMNC: Density of Maximum Neighborhood Component; MNC: Maximum neighborhood component; EPC: Edge Percolated component; DFS: disease-free survival; OS: overall survival; MAPK: mitogen-activated protein kinases

转移是可对前列腺癌（PCa）患者产生负面影响的最重要的独立危险因素之一。但是，确切的机制尚未完全阐明。为了说明PCa转移的潜在机制，我们进行了一系列整合的生物信息学分析。通过分析转移性PCa与局部PCa之间的差异表达基因（DEGs），获得参与PCa转移的必需基因。进行基因本体论和KEGG途径富集分析以进行功能注释。蛋白质-蛋白质相互作用网络被构建用于中心基因选择。三个转录因子基因（FOS，CENPA和FOXM1）是通过将集线器基因与人类转录因子数据库中的人类转录因子整合而鉴定的。此外，分别在GEO，TCGA，GEPIA和人类蛋白质图谱上进行了三种转录因子基因的表达验证和预后分析。进一步的验证表明，三种转录因子基因的表达变异存在于转移性PCa与局部PCa之间以及局部PCa与正常前列腺之间。此外，三种转录因子基因的不同表达与局部PCa的预后相关。总之，这三个转录因子基因可以作为PCa的潜在预后生物标志物和治疗靶标。

缩写： PCa：前列腺癌；DEGs：差异表达的基因；TF：转录因子；GEO：基因表达综合；FC：倍数变化；DAVID：用于注释，可视化和集成发现的数据库；GO：基因本体论；KEGG：《京都基因与基因组百科全书》；BP：生物过程；CC：细胞成分；MF：分子功能；PPI：蛋白质间相互作用；MCODE：分子复合物检测；GEPIA：基因表达谱交互分析；GTEx：基因型组织表达；TCGA：癌症基因组图谱计划；MCC：最高集团中央度；DMNC：最大邻域分量的密度；MNC：最大邻域分量；EPC：边缘渗透成分；DFS：无病生存；OS：整体生存率；MAPK：促分裂原激活的蛋白激酶