Objectives: To predict and characterize the three-dimensional (3D) structure of protein arginine methyltransferase 2 (PRMT2) using homology modeling, besides, the identification of potent inhibitors for enhanced comprehension of the biological function of this protein arginine methyltransferase (PRMT) family protein in carcinogenesis.
Materials And Methods: An method was employed to predict and characterize the three-dimensional structure. The bulk of PRMTs in the PDB shares just a structurally conserved catalytic core domain.
The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I-IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44).
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