The highly heterogeneous symptomatology and unpredictable progress of COVID‑19 triggered unprecedented intensive biomedical research and a number of clinical research projects. Although the pathophysiology of the disease is being progressively clarified, its complexity remains vast. Moreover, some extremely infrequent cases of thrombotic thrombocytopenia following vaccination against SARS‑CoV‑2 infection have been observed. The present study aimed to map the signaling pathways of thrombocytopenia implicated in COVID‑19, as well as in vaccine‑induced thrombotic thrombocytopenia (VITT). The biomedical literature database, MEDLINE/PubMed, was thoroughly searched using artificial intelligence techniques for the semantic relations among the top 50 similar words (>0.9) implicated in COVID‑19‑mediated human infection or VITT. Additionally, STRING, a database of primary and predicted associations among genes and proteins (collected from diverse resources, such as documented pathway knowledge, high‑throughput experimental studies, cross‑species extrapolated information, automated text mining results, computationally predicted interactions, etc.), was employed, with the confidence threshold set at 0.7. In addition, two interactomes were constructed: i) A network including 119 and 56 nodes relevant to COVID‑19 and thrombocytopenia, respectively; and ii) a second network containing 60 nodes relevant to VITT. Although thrombocytopenia is a dominant morbidity in both entities, three nodes were observed that corresponded to genes (AURKA, CD46 and CD19) expressed only in VITT, whilst ADAM10, CDC20, SHC1 and STXBP2 are silenced in VITT, but are commonly expressed in both COVID‑19 and thrombocytopenia. The calculated average node degree was immense (11.9 in COVID‑19 and 6.43 in VITT), illustrating the complexity of COVID‑19 and VITT pathologies and confirming the importance of cytokines, as well as of pathways activated following hypoxic events. In addition, PYCARD, NLP3 and P2RX7 are key potential therapeutic targets for all three morbid entities, meriting further research. This interactome was based on wild‑type genes, revealing the predisposition of the body to hypoxia‑induced thrombosis, leading to the acute COVID‑19 phenotype, the 'long‑COVID syndrome', and/or VITT. Thus, common nodes appear to be key players in illness prevention, progression and treatment.
Keywords: COVID‑19; HLA system; SARS‑CoV‑2; artificial intelligence; autoimmunity; cytokine storm; enzymes; interactions network; lymphocytes; natural language processing; thrombocytopenia; vaccine‑induced thrombotic thrombocytopenia.