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In this paper, we introduce a domain-based random forest of decision trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces cerevisiae dataset demonstrate that our approach can predict protein-protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared with that of the maximum likelihood approach. Furthermore, our model can be used to infer interactions not only for single-domain pairs but also for multiple domain pairs.

Liver metastases develop in more than half of patients with colorectal cancer (CRC) and are associated with a poor prognosis. The factors influencing liver metastasis of CRC are poorly characterized, but this information is urgently needed. We have now discovered that small extracellular vesicles (sEVs, exosomes) derived from CRC can be specifically targeted to liver tissue and induce liver macrophage polarization toward an interleukin-6 (IL-6)-secreting pro-inflammatory phenotype. More importantly, we found that microRNA-21-5p (miR-21) was highly enriched in CRC-derived sEVs and was essential for creating a liver pro-inflammatory phenotype and liver metastasis of CRC. Silencing either miR-21 in CRC-sEVs or Toll-like receptor 7 (TLR7) in macrophages, to which miR-21 binds, abolished CRC-sEVs' induction of pro-inflammatory macrophages. Furthermore, miR-21 expression in plasma-derived sEVs was positively correlated with liver metastasis in CRC patients. Collectively, our data demonstrate a pivotal role of CRC-sEVs in promoting liver metastasis by inducing an inflammatory pre-metastatic niche through the miR-21-TLR7-IL6 axis. Thus, sEVs-miR-21 represents a potential prognostic marker and therapeutic target for CRC patients with liver metastasis.

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