A prognostic model of autophagy gene in hepatocellular carcinoma based on multidatabase
10.3760/cma.j.cn113884-20200316-00140
- VernacularTitle:联合多数据库构建肝细胞癌自噬基因预后模型
- Author:
Rongqi LI
;
Yawen CAO
;
Ke DING
;
Yuechun SHEN
;
Jun LI
- From:
Chinese Journal of Hepatobiliary Surgery
2021;27(2):101-105
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a prognostic model of hepatocellular carcinoma (HCC) with differential expression of autophagy genes.Method:Autophagy genes expression data of HCC and normal liver tissues were obtained from The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) database respectively. The gene expression data from different platforms is normalized into log 2(FPKM value + 1). Differentially expressed autophagy-related genes of HCC were identified by using R program limma package from the TCGA-GTEx combined data set, the criteria of |logFC| > 1 and FDR < 0.05 was deemed to be of statistically significance. The Gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by using R program clusterProfiler package, as criteria of P<0.05. Univariate and multivariate Cox proportional hazards regression analyses were performed by using R program survival package to identify the HCC potential prognostic differentially expressed autophagy-related genes. Furthermore, the statistically significant ( P<0.05) autophagy genes in the univariate Cox regression analysis were included in the multivariate Cox regression analysis, and the expression of each differentially expressed autophagy gene and the corresponding regression coefficient coef value based on this, the autophagy gene prognosis model of HCC was constructed: expmRNA1×βmRNA1+ expmRNA2×βmRNA2+ …+ expmRNAn×βmRNAn (exp: gene expression level; β: regression coefficient coef of multivariate Cox regression analysis). Draw the receiver operating characteristic (ROC) curve of the predictive model and calculate the area under curve (AUC) to evaluate the predictive value of the model. Results:The genes expression data and clinical information of 374 HCC samples and 160 normal liver tissue samples were obtained from TCGA and GTEx databases. Total 205 autophagy genes expression data was obtained from the TCGA-GTEx combined sequence. Among them, SPNS1, DIRAS3, TMEM74, NRG2, NRG1, IRGM, IKBKE, NKX2-3, BIRC5, CDKN2A, TP73 are differentially expressed autophagy genes that meet the screening criteria. GO analysis mainly enriched in "regulation of protein serine/threonine kinase activity" , "ErbB 2 signaling pathway" , "protein kinase regulator activity" and "kinase regulator activity" ; KEGG analysis enriched frequently in "EGFR tyrosine kinase inhibitor resistance" , "Hippo signaling pathway" . After integrating and deleting samples with missing survival information, a total of 418 sample expressions were included in the Cox regression analysis. After univariate and multivariate Cox risk regression analysis, the two autophagy genes NRG1 ( HR=1.5565, 95% CI: 1.1793-2.0543) and IKBKE ( HR=1.7502, 95% CI: 1.2093-2.5330) were screened out and a prognostic prediction model was established: (0.44247 × NRG1 expression level) + (0.55977 × IKBKE expression level). The ROC of the prognosis model shows that the AUC of the overall seven-year survival is 0.711. Conclusion:The prognosis model of HCC based on NRG1 and IKBKE has high predictive value for the long-term survival rate of hepatocellular carcinoma patients.
