Figure 7
Overall workflow. We used all HCCs in TCGA as a training set and 50% of HCCs as a test set. In the training set, we performed the limma analysis to identify DE-mRNAs, DE-lncRNAs, and DE-miRNAs. Chi-square analysis was used to screen abnormal CNV genes. The high-frequency SNPs (Top SNPs) in HCC were selected for further research. The univariate Cox regression analysis, LASSO Cox analysis, and backward stepwise Cox proportional hazard analysis were used to identify critical markers. We constructed five single-omic models (mRNA, lncRNA, miRNA, CNV, and SNP model) through LASSO Cox analysis or stepwise Cox. The multi-omics model was constructed based on the five single-omic models through multiple Cox regression analysis. These models were evaluated and verified in the training set and test set, respectively. Moreover, we externally validated the mRNA and SNP models in the LIRI-JP, GSE1898, and LICA-FR, respectively. HCC hepatocellular carcinoma, TCGA The Genome Cancer Atlas, LASSO Least absolute shrinkage and selection operator, OS overall survival, DE-mRNAs Differentially expressed mRNAs, DE-lncRNAs, differently expressed lncRNAs, DE-miRNAs differentially expressed miRNA, CNV copy number variation, SNP single nucleotide polymorphism.
