An integrated bioinformatics and machine learning-based approach to depict key immunological players associated with candidemia during immunodeficiency.

It is evident that a robust immune system keeps Candida albicans infection in check, but weakened immunity opens the door for shifting from a benign yeast form to an invasive hyphal form which leads to systemic candidiasis with high mortality rate. However, the crucial players contributing to the increased susceptibility of immune-deficient individuals to Candida infection remain obscure. To uncover the molecular differences between these conditions, blood-associated proteins from the NDEx database and differentially expressed genes from GEO datasets of immunocompetent and immune-deficient individuals infected with C. albicans were analysed. We focused on deregulated proteins exhibiting inverse expression patterns i.e. upregulated in one group and downregulated in the other and identified 539 proteins. Mapping them onto protein-protein interaction network reconstructed with blood- associated proteins, revealed that they exhibit in 45 hubs, 31 network nodes forming 29 intermodular complexes, and 69 clustered into 11 immunologically relevant MCODE modules. Amongst them 13 key host molecules emerging as key player based on their network topological properties. Furthermore, a machine learning model was developed with a precision of 85 %, recall of 92 %, F1-score of 89 %, and accuracy of 81 % which substantiates the robust association of 11 out of 13 proteins with fungal co-infections in immune-deficient individuals. These findings underscore key host proteins maintaining immune balance in healthy individuals while their disruption in immune-deficient conditions may weaken defense mechanisms and promote fungal infections. Identification of crucial proteins promoting T-reg cells proliferation and M2 macrophage polarization in immune-deficient conditions offers promising therapeutic targets following experimental validation.