The invention discloses a combined predictive
gene composition for evaluating the prognosis of
urothelial carcinoma and predicting the
immunotherapy effect. The combined predictive
gene composition comprises 12 genes, namely NRP2, SOD2, NCBP1,
FKBP5, DEGS1, ME2, LAP3, CCDC88A, SLC16A1, ANXA5, ASAP1 and HSPA13. The invention discloses a comprehensive construction method and
system of a combined predictive
gene composition based on
machine learning. The method comprises the following steps: analyzing depletion CD8 + T immune infiltration related genes based on a specific BLCA
data set; carrying out cross analysis on the exhaustion CD8 + T immune infiltration
related gene and an SLC16A1 expression related pathway gene to obtain M genes; through single-variable Cox analysis, identifying N prognosis genes from the M genes; a CD8 immune-related prognostic spectrum based on the SLC16A1 is determined based on a
machine learning
ensemble learning algorithm, and a combined predictive gene composition is determined based on the CD8 immune-related prognostic spectrum based on the SLC16A1. The invention discloses a combined predictive gene composition detection kit based on SLC16A1 related exhaustive CD8 +
T cell polygene characteristics and application of the detection kit in predicting the lifetime of a patient with
urothelial carcinoma and predicting the
immunotherapy effect of the patient with
urothelial carcinoma.