INTEGRATING MACHINE LEARNING MODELS WITH MULTI-OMICS ANALYSIS TO DECIPHER THE PROGNOSTIC SIGNIFICANCE OF MITOTIC CATASTROPHE HETEROGENEITY IN BLADDER CANCER

Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer

Abstract Background Mitotic catastrophe is well-known as a major pathway of endogenous tumor death, but the prognostic significance of its heterogeneity regarding bladder cancer (BLCA) remains unclear.Methods Our study focused on digging deeper into the TCGA and GEO databases.Through differential expression analysis as well as Weighted Gene Co-expr

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Spermatogenic cell-specific type 1 hexokinase (HK1S) is essential for capacitation-associated increase in tyrosine phosphorylation and male fertility in mice.

Hexokinase (HK) catalyzes the first irreversible rate-limiting step in glycolysis that converts glucose to glucose-6-phosphate.HK1 is ubiquitously expressed in the brain, erythrocytes, and other tissues where glycolysis serves as the major source of ATP production.Spermatogenic cell-specific type 1 hexokinase (HK1S) is expressed in sperm but its ph

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