Metastasis remains the key issue impacting cancer patient survival and failure or success of cancer therapies. Metastatic spread is a complex process including dissemination of single cells or collective cell migration, penetration of the blood or lymphatic vessels and seeding at a distant organ site. Hundreds of genes involved in metastasis have been identified in studies across numerous cancer types. Here, we analyzed how the metastasis-associated gene MACC1 cooperates with other genes in metastatic spread and how these coactions could be exploited by combination therapies: We performed (i) a MACC1 correlation analysis across 33 cancer types in the mRNA expression data of TCGA and (ii) a comprehensive literature search on reported MACC1 combinations and regulation mechanisms. The key genes MET, HGF and MMP7 reported together with MACC1 showed significant positive correlations with MACC1 in more than half of the cancer types included in the big data analysis. However, ten other genes also reported together with MACC1 in the literature showed significant positive correlations with MACC1 in only a minority of 5 to 15 cancer types. To uncover transcriptional regulation mechanisms that are activated simultaneously with MACC1, we isolated pan-cancer consensus lists of 1306 positively and 590 negatively MACC1-correlating genes from the TCGA data and analyzed each of these lists for sharing transcription factor binding motifs in the promotor region. In these lists, binding sites for the transcription factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1 and NFE2L2 were significantly enriched, but none of them except SP1 was reported in combination with MACC1 in the literature. Thus, while some of the results of the big data analysis were in line with the reported experimental results, hypotheses on new genes involved in MACC1-driven metastasis formation could be generated and warrant experimental validation. Furthermore, the results of the big data analysis can help to prioritize cancer types for experimental studies and testing of combination therapies.

转移仍然是影响癌症患者生存，癌症治疗失败或成功的关键问题。转移扩散是一个复杂的过程，包括散布单细胞或集体细胞迁移，血液或淋巴管的渗透以及在遥远器官部位的播种。在涉及多种癌症类型的研究中，已经确定了涉及转移的数百种基因。在这里，我们分析了转移相关基因MACC1如何与其他基因在转移扩散中协同作用，以及如何通过联合疗法利用这些相互作用：我们（i）在TCGA和mRNA的mRNA表达数据中对33种癌症类型进行了MACC1相关性分析（ii）对报道的MACC1组合和调控机制进行全面的文献检索。关键基因MET 大数据分析中超过一半的癌症类型中，HGF和MMP7与MACC1一起报告显示出与MACC1显着正相关。然而，在文献中还与MACC1一起报道的另外10个基因在5至15种癌症中只有少数与MACC1呈显着正相关。为了揭示与MACC1同时激活的转录调控机制，我们从TCGA数据中分离了1306个正相关和590个负相关的MACC1相关基因的泛癌共有列表，并分析了这些列表中的每一个以共享启动子区域中的转录因子结合基序。在这些列表中，转录因子TELF1，ETS2，ETV4，TEAD1，FOXO4，NFE2L1，ELK1，SP1和NFE2L2的结合位点显着富集，但文献中除SP1以外，均未与MACC1结合使用。因此，尽管大数据分析的某些结果与已报道的实验结果相符，但可以产生关于参与MACC1驱动的转移形成的新基因的假设，并需要进行实验验证。此外，大数据分析的结果可以帮助确定癌症类型的优先级，以进行实验研究和联合疗法的测试。