Patent AU-2021104371-A4 (AU)
Title: Method for constructing model for predicting survival period of hepatocellular carcinoma based on RNA binding protein
Inventor: YUAN XIANGLIN
IPC: G16B30/00
Filed: 2021-07-21
Granted: 2021-09-16
Abstract: The invention discloses a method for constructing model for predicting survival period of 
hepatocellular carcinoma based on RNA binding protein, which comprises that following steps 
of: Acquiring mRNA sequencing data of a hepatocellular carcinoma tissue and a normal liver 
tissue, and identifying a differentially expressed RBP gene based on a human RBP gene 
between the mRNA sequencing data of the hepatocellular carcinoma tissue and the normal 
liver tissue; Constructing a protein interaction network, and performing GO and KEGG 
analysis on modules related to the differentially expressed RBP genes in the protein interaction 
network; Based on the results of GO and KEGG analyses, screen out the RBP genes that related 
to survival period prediction by univariate Cox regression analysis, and perform further 
screening by Least Absolute Shrinkage And Selection Operator Regression(Lasso Regression), 
and use stepwise multiple regression analysis to obtain key RBP genes that related to survival 
period prediction, and construct a model for predicting survival period of hepatocellular 
carcinoma based on key RBP genes. The invention can quickly and accurately predict the 
survival period of patients with hepatocellular carcinoma.
1/1 
FIGURES 
Obtain mRNA sequencing data of a plurality of hepatocellular carcinoma 
tissues and a plurality of normal liver tissues, and obtain a plurality of S1 
human RBP genes of RNA binding protein; 
Identify a plurality of differentially expressed RBP genes between mRNA 
sequencing data of a plurality of hepatocellular carcinoma tissues and a S2 
plurality ofnormal liver tissues based on the human RBP genes 
Lb 
Construct a protein interaction network based on a plurality of differentially 
expressed RBP genes, and perform GO and KEGG functional enrichment analysis 
on modules that related to the differentially expressed RBP genes in the protein S3 
interaction network to obtain biological functions and signal transduction pathways 
of the differentially expressed RBP genes in the protein interaction network; 
.U 
Based on the obtained biological functions and the signal transduction pathway of the RBP 
genes differentially expressed in the protein interaction network, screen out the RBP genes S4 
that related to the survival period prediction by adopting univariate Cox regression analysis: 
L3 
Further screen out the selected RBP genes related to the survival period prediction by 
adopting the Least Absolute Shrinkage and Selection Operator Regression(Lasso ..  
Regression), and obtain key RBP genes that related to the survival period prediction by 
adopting a stepwise multiple regression analysis: 
Construct a model for predicting survival period of hepatocellular 
carcinoma based on the obtained key RBP genes that related to survival S6 
period prediction, wherein the model for predicting survival period of 
hepatocellular carcinoma is used for survival period prediction of patients 
with hepatocellular carcinoma.  
Figure 1