Low lncRNA ZNF385D‑AS2 expression and its prognostic significance in liver cancer

Figure 1.. Boxplots showing differences in ZNF385D-AS2 expression according to tissue type (A), patient age (≤55 vs. >55 years) (B), sex (C) and survival status (D) as well as clinical stage (E), TNM stage (F-H), pathological status (I), race (J) and survival time (>3 vs. ≤ years) (K). lncRNA ZNF385D-AS2, long non-coding RNA zinc finger protein 385D antisense RNA 2.

Figure 2.. lncRNA ZNF385D-AS2 expression in several common digestive system tumors based on TCGA database. The expression level of ZNF385D-AS2 in tumors was found to be lower than that in the normal tissues. The result data is logarithmically processed and its absolute value is calculated. lncRNA ZNF385D-AS2, long non-coding RNA zinc finger protein 385D antisense RNA 2.

Figure 3.. Kaplan-Meier curves for survival of HCC patients according to ZNF385D-AS2 expression in HCC tissues. Patients were divided into high and low ZNF385D-AS2 expression groups using the median value of ZNF385D-AS2 expression as the cut-off point. (A) Kaplan-Meier curves of overall survival (OS), and log-rank tests showed that ZNF385D-AS2 low-expression was associated with poor OS. Survival analysis and subgroup analysis according to clinical stage (B and C) and male sex (D) were performed based on Kaplan-Meier curves. HCC, hepatocellular carcinoma; lncRNA ZNF385D-AS2, long non-coding RNA zinc finger protein 385D antisense RNA 2. Patients were divided into high and low ZNF385D-AS2 expression groups using the median value of ZNF385D-AS2 expression as the cut-off point. Survival analysis and subgroup analysis according to female sex (E) and TNM stage (F-H) were performed based on Kaplan-Meier curves. lncRNA ZNF385D-AS2, long non-coding RNA zinc finger protein 385D antisense RNA 2. Patients were divided into high and low ZNF385D-AS2 expression groups using the median value of ZNF385D-AS2 expression as the cut-off point. Survival analysis and subgroup analysis according to TNM stage…

Figure 4.. ROC curve to identify the optimal cut-off value for dividing patients into high and low ZNF385D-AS2 expression groups. AUC, area under the curve; ROC, receiver-operating characteristic curve. HR, hazard ratio; lncRNA ZNF385D-AS2, long non-coding RNA zinc finger protein 385D antisense RNA 2.

Figure 5.. Possible ceRNA regulatory and co-expression networks. (A) After comparing several lncRNA databases, the possible ceRNA patterns in HCC were predicted and plotted in Cytoscape. (B) Using the ‘WGCNA’ package in R, a possible co-expression regulatory network was mapped. The results highly suggest that there is a regulatory signaling axis between ZNF385D-AS2 and miR-96 and miR-182. ceRNA, competing endogenous RNA; HCC, hepatocellular carcinoma; lncRNA ZNF385D-AS2, long non-coding RNA zinc finger protein 385D antisense RNA 2.

Figure 6.. Significant GO terms identified by DAVID. (A) Cytoscape was used to conduct an analysis map of genes enriched in the GO terms and to construct an interaction network for the related genes. (B and C) mRNAs most highly enriched in the GO terms. GO, Gene Ontology. DAVID, Database for Annotation, Visualization and Integrated Discovery.

Figure 7.. Significant KEGG pathways identified by KOBAS. Using the genes predicted in Fig. 5, 29 different KEGG pathways were found to be enriched. These signaling pathways were roughly divided into four broad categories, of which 13 genes were most correlated. KOBAS, KO-Based Annotation System. KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 8.. Circos plot derived from possible co-expression and ceRNA regulatory networks. Linked lines in the middle of the Circos plots show the relationship between each pair of co-expressed genes. The next track shows the labels of these genes. The outermost track shows the chromosome localization information of these genes. Plots were generated using the Circos visualization tool by R. ceRNA, competing endogenous RNA.