Random Forest regression was also performed to predict concentration of immunoproteomic biomarkers based on microbiome and metabolome profiles, demonstrating strong predictive strength for several targets, including proinflammatory cytokines and chemokines (IL-1β, IL-6, IL-8, MIF, MIP-1β), the anti-inflammatory cytokine IL-10, growth factors (HGF, SCF, TGF-α,) apoptosis-related proteins (sFAS, TRAIL), the hormone prolactin, the cytokeratin CYFRA21-1, and other cancer biomarkers (AFP, sCD40L, CEA) (S5 Fig). This evidence concerns the gene CCL4 and cancer.