Expression of YEATS2 in HCC by the databaseThe obtained results of this study demonstrated that HCC tissues had an elevated level of YEATS2 expression compared with healthy tissues (Figs. 1, and 2A).Figure 1YEATS2 expression levels in different tumor types. YEATS2 expression levels in different tumor types from TCGA database were determined by TIMER (*P < 0.05, **P < 0.01, ***P < 0.001).Figure 2YEATS2 expression and clinicopathological features of Liver hepatocellular carcinoma. (A) sample types, (B) cancer stages, (C) tumor grades, (D) lymph node status (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001).In HCC, the expression level of YEATS2 exhibits notable variations across different cancer stages. In comparison to healthy tissues, the expression of YEATS2 increases progressively throughout the first three stages, culminating in its peak expression level in the third stage (Fig. 2B). YEATS2 expression was consistently upregulated across all three grades, surpassing the levels found in healthy tissues. (Fig. 2C). Additionally, the stages of lymph node involvement displayed significantly elevated levels of YEATS2 expression compared to healthy tissues (Fig. 2D).The characteristics of patients were shown in Table 1, in which 374 HCC with both clinical and gene expression data were collected from TCGA database. According to the mean value of relative YEATS2 expression, the patients with hepatocellular carcinoma were divided into low (n = 187) and high (n = 187) expression groups. The association between the expression level of YEATS2 and the clinicopathological characteristics of hepatocellular carcinoma patients was evaluated. Chi-square test revealed that YEATS2 expression was associated with T stage (P = 0.03), Histological grade (P < 0.001), Pathologic stage (P = 0.019), tumor status (P = 0.036) and age (P = 0.008). No significant correlation was found between YEATS2 expression and other clinicopathological factors, including N stage (P = 0.623), M stage (P = 0.622) and adjacent hepatic tissue inflammation (P = 0.737). The ROC curve was employed to assess the efficacy of YEATS2 mRNA expression level AUC in discriminating HCC tissues from non-tumor tissues. Notably, the AUC of YEATS2 attained a value of 0.927(Fig. 3), indicating its strong potential as a biomarker for distinguishing HCC from non-tumor tissue.Table 1 Correlation between YEATS2 expression and clinicopathological characteristics of HCC patients.Figure 3ROC curve established efficiency of YEATS2 mRNA expression level on distinguishing HCC tumor from non-tumor tissue. X-axis represents false positive rate, and Y-axis represents true positive rate.Expression of YEATS2 in HCC by RT-qPCR and western blottingTo comprehend the function of YEATS2 expression in HCC patients, 16 pairs of HCC tumor tissues and surrounding healthy tissues were examined by RT-qPCR. The expression of YEATS2 mRNA in tumor tissues was observed to be higher than in the adjacent healthy tissues (P < 0.0001) (Fig. 4B). Subsequently, YEATS2 protein expression was evaluated using western blotting (WB) in three random pairs of cancerous and healthy tissues. As presented in Fig. 4A and Figure S1 in supplementary file, YEATS2 protein expression was higher in YEATS2 tissues than in the surrounding healthy tissues.Figure 4Expression of YEATS2 in HCC tissues. (A) The expression of YEATS2 in 3 paired HCC detected by Western blot, data was normalized by β-actin; (B) The expression of YEATS2 mRNA in 16 paired HCC detected by Real-time PCR.Survival of patients with HCC based on YEATS2 expressionSubsequently, survival analysis based on YEATS2 expression was performed using the OncoLnc database. According to the obtained results, patients having an elevated expression of YEATS2 displayed poor overall survival. Notably, YEATS2 significantly correlates with clinical outcome of HCC patients, including overall survival (OS), relapse-free survival (RFS), Progression Free Survival (PFS) and Disease specific Survival (DSS) (Fig. 5A–D, OS: HR (95% CI) 2.23 (1.56–3.21), P = 7.6e−06; RFS: HR (95% CI) 1.72 (1.21–2.44), P = 0.0024.; PFS: HR (95% CI) 1.76 (1.29–2.39), P = 0.00026; DSS: HR (95% CI): 2.54(1.59–4.06), P = 6e−05 respectively). Therefore, it is conceivable that high YEATS2 expression might be a risk factor for a poor prognosis in HCC patients.Figure 5Survival analysis for YEATS2 in HCC. (A) The OS curves of HCC patients with high and low expression of YEATS2; (B) The DFS curves of HCC patients with high and low expression of YEATS2; (C) The RFS curves of HCC patients with high and low expression of YEATS2; (D) The DSS curves of HCC patients with high and low expression of YEATS2.Relationships between YEATS2 promoter methylation and clinicopathological characteristicsUsing UALCAN database, we explored if promoter methylation of YEATS2 was related to clinicopathological characteristics of HCC patients. YEATS2 promoter methylation level was significantly lower in primary tumor than in normal tissue (P < 0.001, Fig. 6A). Based on clinical stages, stage 1, stage 2 and stage 3 had higher levels of YEATS2 promoter methylation than stage 4 (Fig. 6B). However, there were no significant differences in the levels of promoter methylation of YEATS2 between N0 and N1 stages, which was consistent across various tumor grades (Fig. 6C,D). This suggests that aberrant DNA methylation may play a role in the development and progression of HCC.Figure 6UALCAN analysis of YEATS2 promoter methylation in HCC. The level of YEATS2 promoter methylation in HCC was compared based on different sample types (A), individual cancer stages (B), Tumor grade (C), nodal metastasis status (D).Identification of DEGs in HCCAs shown in Venn (Fig. 7), 500 genes were obtained from cBioPortal, 522 from UALCAN, and 92 co-expressed genes from Coexpedia, respectively, and the overlapping genes were DVL3, ACTL6A, PLXNA1, ABCC5, and ILF3. A total of 83 co-expression of DEGs between HCC-normal and HCC-tumor were detected by GEPIA analysis, including 28 downregulated DEGs and 55 upregulated DEGs.Figure 7Venn of YEATS2 co-expressed genes.GO function and KEGG pathway analysis of DEGsTo analyze the biological classification of DEGs, functional and pathway enrichment analyses were performed using DAVID. The most enriched terms of downregulated and upregulated DEGs were selected in Table 2 (Fig. 8), according to the P-values. The DEGs were mainly enriched in BP, including carboxylic acid metabolic process, oxoacid metabolic process, cofactor metabolic process, organic acid metabolic process, and single-organism catabolic process for downregulated DEGs, and for upregulated DEGs including cell cycle, cell cycle process, nuclear division, mitotic cell cycle and organelle fission. In CC, the downregulated DEGs were particularly enriched in blood microparticle, extracellular space, endocytic vesicle lumen, high-density lipoprotein particle, and lipoprotein particle, and upregulated DEGs were mainly enriched in chromosome, chromosomal part, spindle, chromosomal region, and chromosome, centromeric region. In addition, the MF analysis also displayed that the downregulated DEGs were significantly enriched in organic acid, sodium symporter activity, lipid transporter activity, solute: sodium symporter activity, alcohol binding, and glycine N-acyltransferase activity, and the upregulated DEGs including ATP binding, pyrophosphatase activity, hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides, hydrolase activity, acting on acid anhydrides and adenyl ribonucleotide binding. KEGG pathway analysis revealed that the downregulated DEGs were mainly enriched in tyrosine metabolism, PPAR signaling pathway, metabolic pathways and primary bile acid biosynthesis, while the upregulated DEGs were mainly enriched in mismatch repair and cell cycle (Table 3).Table 2 GO analysis of DEGs in HCC samples.Figure 8GO function analysis of DEGs.Table 3 KEGG analysis of DEGs in HCC samples.PPI network construction and hub gene selectionBased on the information in the STRING protein relationship, we made the PPI network of the co-expressed DEGs (Fig. 9A). The most module of DEGs was shown by using the MCODE plug-in (Fig. 9B). Top 13 nodes ranked by degree were identified as hub genes and shown by using the cytohubba plugin molecular (Fig. 9C).Figure 9PPI network of the YEATS2-correlated genes. (A) PPI network of the co-expression DEGs. Upregulated genes are marked in light pink; downregulated genes are marked in light green. (B) The most module of DEGs. (C) Top 13 nodes ranked of degree represented by different degrees of color (from red to yellow).Expression and correlation of hub genes with YEATS2Hierarchical clustering effectively distinguished live carcinoma samples from non-cancerous specimens through the identification of hub genes (Fig. 10). Notably, these 13 hub genes exhibited a strong positive correlation with the expression of the YEATS2 gene, which was notably overexpressed in cancer tissues. Furthermore, the Kaplan–Meier analysis of these 13 hub genes revealed that those with higher expression levels were associated with poorer prognoses (Fig. 11).Figure 10Hierarchical clustering of YEATS2 and hub genes.Figure 11Kaplan–Meier curve of hub genes.
