The invention discloses a
hepatocellular carcinoma prognosis and immune response prediction method based on multi-
omics machine learning, and relates to the technical field of
biomedicine and
artificial intelligence crossing, and the method comprises the following steps: S1, constructing a multi-
omics data set for model training and
verification; s2, performing feature integration and clustering analysis on the multi-
omics data to obtain corresponding
hepatocellular carcinoma molecular subtype distribution; s3, constructing a
hepatocellular carcinoma prognosis model based on Cox regression combined with a random survival forest, identifying 11 core immune genes and corresponding weight coefficients by training the model, and constructing an
immunotherapy response index IMLIRI
score; and S4, carrying out clinical application on the IMLIRI
score. According to the method, through multi-
omics data integration and multi-
queue external
verification, the influence of data deviation and
queue heterogeneity on the model performance is reduced, so that the method shows stable prediction performance in hepatocellular
carcinoma queues with different sources and different
pathogenesis backgrounds, and the reliability and generalizability of the model in clinical application are improved.