ATIC inhibits autophagy in hepatocellular cancer through the AKT/FOXO3 pathway and serves as a prognostic signature for modeling patient survival

Abstract

Background: Autophagy regulates many cell functions related to cancer, ranging from cell proliferation and angiogenesis to metabolism. Due to the close relationship between autophagy and tumors, we investigated the predictive value of autophagy-related genes. Methods: Data from patients with hepatocellular carcinoma were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. A regression analysis of differentially expressed genes was performed. Based on a prognostic model, patients were divided into a high-risk or low-risk group. Kaplan-Meier survival analyses of patients were conducted. The immune landscapes, as determined using single-sample gene set enrichment analysis (ssGSEA), exhibited different patterns in the two groups. The prognostic model was verified using the ICGC database and clinical data from patients collected at Zhongnan Hospital. Based on the results of multivariate Cox regression analysis, 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/inosine monophosphate (IMP) cyclohydrolase (ATIC) had the largest hazard ratio, and thus we studied the effect of ATIC on autophagy and tumor progression by performing in vitro and in vivo experiments. Results: Fifty-eight autophagy-related genes were differentially expressed (false discovery rate (FDR)<0.05, log₂ fold change (logFC)>1); 23 genes were related to the prognosis of patients. A prognostic model based on 12 genes (ATG10, ATIC, BIRC5, CAPN10, FKBP1A, GAPDH, HDAC1, PRKCD, RHEB, SPNS1, SQSTM1 and TMEM74) was constructed. A significant difference in survival rate was observed between the high-risk group and low-risk group distinguished by the model (P<0.001). The model had good predictive power (area under the curve (AUC)>0.7). Risk-related genes were related to the terms type II IFN response, MHC class I (P<0.001) and HLA (P<0.05). ATIC was confirmed to inhibit autophagy and promote the proliferation, invasion and metastasis of liver cancer cells through the AKT/Forkhead box subgroup O3 (FOXO3) signaling pathway in vitro and in vivo. Conclusions: The prediction model effectively predicts the survival time of patients with liver cancer. The risk score reflects the immune cell features and immune status of patients. ATIC inhibits autophagy and promotes the progression of liver cancer through the AKT/FOXO3 signaling pathway.

Keywords: ATIC; autophagy-related genes; hepatocellular carcinoma; immune cell infiltration; prognosis.

Figure 1

Differentially expressed and prognosis-related genes. (A) Flow chart. Heat map (B) and volcano plot (C) of differentially expressed genes (DEGs). (D) Prognosis-related genes. (E) Venn diagram. (F) Heat map of overlapping genes. (G) Hazard ratios of overlapping genes. (H) Protein interaction network. (I) Protein coexpression network. Red, high expression; green, low expression.

Figure 2

Construction and verification of the prognostic models. (A) Cross validation plot for the penalty term in the LASSO regression analysis. (B) LASSO regression coefficients for different values of the penalty parameter. (C) Hazard ratios of genes in the multivariate Cox regression analysis. Survival curves for the high-risk group and the low-risk group of patients in TCGA (D) and ICGC cohorts (E) based on the LASSO regression model. Survival curves for TCGA (F) and ICGC cohorts (G) based on the multivariate Cox regression model. ROC curves of TCGA (H) and ICGC cohorts (I) categorized with the LASSO regression model. ROC curves of TCGA (J) and ICGC cohorts (K) categorized with the multivariate Cox regression model. Distribution of risk scores (L and N) and survival statuses (M and O) in TCGA (L and M) and ICGC cohorts (N and O). PCA (P) and t-SNE analysis (Q) of patients in TCGA cohort. PCA (R) and t-SNE analysis (S) of patients in the ICGC cohort.

Figure 3

Analysis of the independent prognostic value of the risk score and its association with immune cell infiltration. (A) Univariate Cox regression analysis of TCGA cohort. (B) Multivariate Cox regression analysis of TCGA cohort. (C) Univariate Cox regression analysis of the ICGC cohort. (D) Multivariate Cox regression analysis of the ICGC cohort. GO functional enrichment analysis of TCGA (E) and ICGC cohorts (F). KEGG pathway enrichment analysis of TCGA (G) and ICGC cohorts (H). (I) The relationship between immune cell infiltration and risk score in TCGA cohort. (J) The relationship between immune function and risk score in TCGA cohort. (K) The relationship between immune cell infiltration and risk score in the ICGC cohort. (L) The relationship between immune function and risk score in the ICGC cohort. (M) The relationship between immune cell infiltration and ATIC expression in TCGA cohort. (N) The relationship between immune function and ATIC expression in TCGA cohort. (O) The relationship between immune cell infiltration and ATIC expression in the ICGC cohort. (P) The relationship between immune function and ATIC expression in the ICGC cohort. (Q) Candidate small-molecule compounds with therapeutic potential. (R) Drugs for which high-risk patients may show high resistance. ns, not significant.

Figure 4

Verification of the differential expression of risk-related genes in tissues and cells. (A) Multivariate Cox regression analysis of genes in the LASSO regression model. (B) Expression of risk-related genes in liver cancer tissues and adjacent normal tissues (HPA database). (C) ATIC immunohistochemistry and quantitative results for patients from Zhongnan Hospital.

Figure 5

ATIC is expressed at high levels in liver cancer cells and tumor tissues and is associated with a poor prognosis. (A) Survival curve of patients with high and low ATIC expression (TCGA database). (B) ATIC expression in different cell lines. (C) ATIC mRNA levels in tumor tissues and adjacent normal tissues (patients treated at Zhongnan Hospital). (D) ATIC mRNA expression in cells transfected with the siRNA. (E) ATIC protein expression level in tumor tissues and adjacent normal tissues. (F) ATIC protein expression in different cell lines. (G) Protein expression level after ATIC knockdown. NC, negative control. ns, not significant.

Figure 6

ATIC knockdown inhibits the malignant behavior of tumor cells. (A) Cell viability detected using CCK-8 assays. (B) Representative micrographs and colony numbers from the colony formation assays. (C) Representative micrographs and cell numbers from the Transwell assays. (D) Wound healing assay results showing the differences in migratory capacities (left panel); statistical analysis of the wound healing assay results (right panel). (E) Cell cycle assays conducted using flow cytometry. (F) Apoptosis assays conducted using flow cytometry. (G) ATIC protein expression after transfection of pcDNA/ATIC. (H) Apoptosis and autophagy-related protein expression after transfection of the siRNA. FL caspase, full-length caspase; CL caspase, cleaved caspase. *, P<0.05; **, P<0.01; and ***, P<0.001. ns, not significant.

Figure 7

ATIC inhibits autophagy and affects the malignant behavior of tumors in vivo and in vitro. (A) Autophagosomes observed using electron microscopy. (B) Statistical analysis of tumor volumes in the two groups. (C) Representative images of lung metastases observed in the two groups of nude mice. (D) H&E staining of xenograft tumors. (E and F) ATIC overexpression promoted tumor proliferation. (G) ATIC overexpression promoted tumor migration, as assessed using wound healing assays. *, P<0.05; **, P<0.01; and ***, P<0.001.

Figure 8

ATIC inhibits autophagy through the AKT/FOXO3 pathway. (A) Western blot analysis of the AKT/FOXO3 pathway after ATIC knockdown. (B) Western blot analysis of the AKT/FOXO3 pathway after ATIC overexpression. (C) Cell viability assays after exposure to different treatments. (D) Colony formation assays with the indicated cells. (E) Transwell results. (F) Electron microscopy images of cells treated with siRNA 3 and SC79. (G) Apoptosis assay conducted using flow cytometry. (H) Western blot analysis of the AKT/FOXO3 pathway and LC3 levels in different treated cells. ns, not significant.