RNA sequencing data and clinical features of hepatocellular carcinoma (HCC) patients were obtained from TCGA database. The R software was utilized to translate the RNA matrix data. A search was conducted on the GEO database utilizing the keywords “hepatocellular carcinoma” and “hepatitis B virus” to identify relevant datasets. The search results were filtered based on the following criteria: (1) human HCC tissues and non-tumor tissues, (2) transcription expression data, and (3) availability of patients’ survival data. The GSE19665, GSE135631, and GSE14520 datasets were selected for further analysis using R packages.

The differential expression of KIF20A at the mRNA level between tumor tissues (including pan-cancer and HCC) and corresponding non-tumor tissues was assessed using data from TCGA and GEO datasets. Additionally, KIF20A protein expression in HCC tissues was examined via immunohistochemistry (IHC) in the Human Protein Atlas (HPA, https://www.proteinatlas.org/). Furthermore, the association between KIF20A mRNA levels and clinical characteristics was explored through analyzing the TCGA-LIHC data. The correlation between KIF20A mRNA expression and survival outcomes in HCC, including OS, DS, and PFI, was evaluated using Cox regression survival analysis with the R package “survival”.

KIF20A-related genes were identified using the LinkedOmics database by applying a filter of |Pearson’s correlation| >= 0.5 and p < 0.05. Similarly, the cBioPortal database was used to screen for KIF20A-associated genes with |Spearman’s correlation| >= 0.5 and p < 0.05. The common KIF20A-related genes identified by LinkedOmics and cBioPortal databases were then subjected to construct a PPI network via STRING. Gene Ontology (GO) and KEGG pathway enrichment analyses were conducted using the DAVID database. To explore the potential biological roles and key functions of signature genes in HCC, GSEA was carried out with a threshold of |NES|> 1, q < 0.25, and p < 0.05.

HepG2 cells were obtained from the American Type Culture Collection (ATCC, USA). Huh-2 and HepG2.2.15 cells were sourced from the National Collection of Authenticated Cell Cultures (China). All cell lines were maintained in base medium supplemented with 10% fetal bovine serum (FBS, Catalog Number: A5670701, Gibco, USA) and cultured in a humidified atmosphere at 37 °C and 5% CO₂. To ensure the absence of mycoplasma contamination, cells were tested every 3 months using the Mycoplasma PCR Detection Kit (Catalog Number: ab289834, Abcam, UK).

The plasmids encoding HBx or GATA2 were constructed by integrating the synthetically produced cDNA into the pcDNA3.1 vectors (Catalog Number: V79020, Invitrogen, USA) via the EcoRI/XbaI MCS. To generate silencing RNAs to knockdown GATA2 or KIF20A, the following sequences are synthesized: si-GATA2 (forward 5’-AGA AAA AGG AUG UAU UUA CAG-3’, reverse 5’-GUA AAU ACA UCC UUU UUC UGC-3’), si-KIF20A (forward 5’-UUC UAA UAG GUC AUA AAG CAG-3’, reverse 5’-GCU UUA UGA CCU AUU AGA ACC-3’). siRNAs or vectors were transfected into HepG2.2.15 cells using Lipofectamine 3000 Reagent (Catalog Number: L3000150, Invitrogen, USA).

Total RNA was isolated from HCC cells utilizing TRIzol reagent (Catalog Number: 15596018CN, Invitrogen, USA) and then transcript into cDNA with the Reverse Transcription Kit (Catalog Number:4368813, Applied Biosystems, USA). cDNA was subsequently used for quantitative RT-PCR analysis with gene-specific primers and SYBR Green Mix (Catalog Number: 4309155, Applied Biosystems, USA). The primers for qPCR: human GATA2 (forward 5’-CCG CCA CAT CCA TCC TAG C-3’, reverse 5’-CTG CGA GTC GAG GTG ATT GA-3’), human KIF20A (forward 5’-CAA GGG ATC CTT TCT CCG CC-3’, reverse 5’-GAA CCT GCT GCT TGT CCT CT-3’), GAPDH (forward 5’-TTT TGC GTC GCC AGC C-3’, reverse 5’-ATG GAA TTT GCC ATG GGT GGA-3’). Relative mRNA expression levels were determined by normalizing the Ct-values of the target genes to GAPDH and applying the ΔΔCt method.

24-well plates were plated with HEK-293T cells. Until the cells grow reaching 70% confluence, cells were transfected with 0.75 μg of pGL3-basic/pGL3-basic-KIF20A luciferase reporter plasmid, along with 0.25 μg of overexpression vectors. After 48 hours post-transfection, luciferase activity was measured using Pierce Firefly Luciferase Glow Assay Kit (Catalog Number: 16176, Thermo Fisher, USA). More precisely, the cells were rinsed once with PBS buffer and resuspended in cell lysis buffer. Then the cell plate was shaken on a shaker platform at moderate speed for 15 minutes. Each well was added with 30 µl of cell lysate and 50 µl of working solution. After 10 minutes, light output was detected using luminometer. Firefly luciferase activity was normalized to the activity of the pRL-TK reporter gene.

CCK-8 assay was performed to assessing cell proliferation after 48 hours of transfection. HepG2.2.15 cells were harvested, resuspended with PBS buffer, and plated in a 96-well plate at a density of 5 × 10^{^3} cells per well. The cells were incubated for 24, 48, 72, and 96 hours, followed by adding 10 μL of CCK-8 reagent (Catalog Number: CA1210, Solarbio, China) to each well. After a 2-hour incubation, the absorbance of 450 nm was measured.

Quantification of apoptotic percentage was performed by flow cytometry utilizing an Annexin V-APC/PI staining kit (Catalog Number: V13242, Invitrogen, USA). After transfection, HCC cells were trypsinized and washed with cold PBS. The resuspend cells in 100 μL of Annexin V binding buffer was stained with Annexin V-APC (concentration: 1 μg/mL), and then incubated for 15 minutes at room temperature in the dark. After adding 5 μL of propidium iodide (PI) (concentration: 50 μg/mL), the cell suspension was incubated for an additional 5 minutes. BD FACSCalibur was utilized to perform flow cytometry analysis. FlowJo software (version 10) was employed to analyze apoptosis data.

All experiments were carried out in a minimum of three biological replicates. Statistical analysis was performed using the two-tailed Student’s t-test in GraphPad Prism 8 (San Diego, CA), with a p-value of less than 0.05 considered statistically significant.

To elucidate the involvement of KIF20A in the development of human cancers, we performed a pan-cancer analysis of KIF20A expression using the TCGA dataset, which provides a potential resource for exploring the molecular characteristics of diverse cancer types. In the TCGA cohorts, we noticed an extensive enrichment of KIF20A in pan-cancer ( Figure 1A ). As depicted in Figures 1B, C , KIF20A expression was low in normal samples, but KIF20A mRNA and protein was significantly up-regulated in LIHC tissues. In addition, HCC patients with high histologic grade (p < 0.001) or advanced pathologic T stage (p = 0.002) had further upregulated KIF20A expression levels ( Table 1 ). RNA-sequencing analysis in human HCC tissues showed that while KIF20A mRNA is barely expressed in normal liver samples, HCC patients with stage I/II/III have ∼6.08-fold, ∼7.50-fold and ∼8.02-fold higher expression levels of KIF20A (p < 0.0001) ( Figure 1D ).

Next, we investigated the relationship between KIF20A levels and survival in HCC patients. Our results indicated that high KIF20A expression was obviously associated with worse OS (log-rank p = 0.002; HR = 1.73, 95% CI = 1.22-2.45), DSS (log-rank p = 0.001; HR = 2.13, 95% CI = 1.35-3.37), and PFI (log-rank p < 0.001; HR = 1.72, 95% CI = 1.28-2.31) in the TCGA-LIHC cohort ( Figures 2A–C ). We also evaluated the predictive value of KIF20A expression utilizing ROC curve analysis, which demonstrated an AUC of 0.971 (95% CI: 0.957-0.986) ( Figure 2D ). To evaluate the potential of KIF20A expression as an independent biomarker, we conducted a multivariate analysis of KIF20A levels and clinical characteristics in the TCGA-LIHC cohort. Notably, Cox regression analysis revealed that KIF20A level was a significant independent predictor of OS (HR = 1.727, 95% CI = 1.217-2.451, p = 0.002) ( Figure 2E ), DSS (HR = 2.135, 95% CI = 1.352-3.371, p = 0.001), and PFI (HR = 1.720, 95% CI = 1.282-2.30, p < 0.001) ( Supplementary Figure S1 ).

Given the results described above, we sought then to elucidate the involvement of KIF20A in HBV-related HCC tissues. To achieve it, we first screened out GSE dataset that contains HBV-related HCC samples and patients’ clinical information. In GSE19665 dataset, KIF20A mRNA expression was obviously higher in HBV-infected HCC cases (n = 5) than in surrounding liver tissues (n = 5) (p = 0.0316, Figure 3A ). Additionally, KIF20A levels were upregulated ∼8.3-fold in HBV-related HCC tissues (n = 15) in comparation with non-tumor tissues (n = 15) in GSE135631 dataset (p < 0.0001, Figure 3B ). To examine how transcription of KIF20A oncogene behaved under HBV infection, we acquired the GSE14520 dataset to analyze more HBV-related HCC samples. As depicted in Figure 3C , the HBV-HCC tissues (n = 212) showed a higher KIF20A mRNA level than non-tumor cases (n = 221) in GSE14520 dataset (p < 0.0001). GSE14520 dataset revealed that a higher KIF20A expression level associates to worse OS in HBV-related HCC patients (p = 0.008, Figure 3D ). ROC curve was generated to determine the diagnostic ability of KIF20A. An AUC of 0.579 (CI = 0.502-0.657) was calculated, indicating the medium specific ability of KIF20A expression to predict the patients’ prognosis in 212 HBV-related HCC tissues ( Figure 3E ).

To characterize possible genetic or epigenetic alterations of KIF20A in tumors, we first studied in silico a set of pan-cancer via cBioportal database. The cBioportal genomics analysis revealed KIF20A amplification or high mRNA in 8% (348 cases) of HCC ( Figure 4A ). Up to a third of the KIF20A copy number gain (111 cases, 31.9%) or amplification (2 cases, 0.5%) occurred with gene alteration in HCC ( Figure 4B ). To validate structural variation in HCC, samples from patient with HCC was subjected to GSCA copy number variation (CNV) analysis. The pie plot exhibits us a global profile and the constitute of the heterozygous/homozygous CNV of KIF20A gene in HCC. As shown in Figures 4C, D , HCC tumor has 31.35% heterozygous amplification, 10.81% heterozygous deletion and 1.08% homozygous amplification in KIF20A gene. However, data-mining of the available profile of HCC geneset showed that KIF20A methylation was not associated with KIF20A mRNA upregulation (Spearman correlation = -0.11) ( Figure 4E ).

To understand how KIF20A impacts essential gene expression to contribute to HCC tumor progression and clinical prognosis, we characterized changes to downstream mRNA levels in HCC. We performed the LinkedOmics analysis and showed that a total of 417 genes were correlated with KIF20A expression levels in HCC tissues, including 388 positively related genes and 29 negative related genes (|Pearson’s correlation|  >= 0.5, p  <  0.05) ( Figure 5A ). Heat map in Figures 5B, C shows the top 50 positively and negatively correlated genes. In addition, Spearman correlation analysis via cBioportal is also indicative of 463 genes that were related to KIF20A levels (|Spearman’s correlation|  >= 0.5, p  <  0.05). To screen out the more relevant genes in HCC samples, we applied a condition of higher correlation (>= 0.75) and overlapped common genes between cBioPortal and LinkedOmics ( Figure 5D ). We excavated the STRING database and conducted PPI analysis using the overlapped 104 KIF20A-coexpressed genes. Interestingly, we observed potential interactions between KIF20A and co-expressed genes. The minimum required interaction score in the PPI network is highest confidence (0.9). The PPI network shows the potential co-expressed proteins with KIF20A (p-value < 1.0e-16, Figure 5E ). Moreover, GO and KEGG enrichment analysis indicated that KIF20A affected several biological process and pathways related to cell response, such as cell division and cell cycle ( Table 2 ). As shown in Figures 5F–J , the mRNA levels of the top five co-expressed genes exhibited a significant positive correlation with KIF20A expression, suggesting a potential functional relationship between these genes in HCC (Pearson correlation >= 0.8, p < 0.001). Importantly, single gene expression and GSEA analysis provided evidence that KIF20A may be implicated in the PLK1 pathway and cell cycle in HCC, implying a potential role for KIF20A in the regulation of cellular processes and its possible involvement in the pathogenesis of HCC ( Figures 5K, L ).

To verify the role of KIF20A in HCC cells, we first tested the KIF20A mRNA expression in HepG2 cell line (HBV negative) and HepG2.2.15 cell line (HBV positive). HBV infection in HepG2.2.15 cells significantly increased KIF20A expression at the mRNA level, indicating translational regulation of KIF20A by HBV (p = 0.0323, Figure 6A ). Next, we transfected plasmids into HepG2 and Huh-7 cells to upregulate KIF20A expression levels (p = 0.018, Figure 6B ) and detected the HBX-KIF20A binding on the KIF20A promoter. As shown in Figure 6C , luciferase reporter gene analysis suggested that HBx binds with the promoter site of KIF20A (p < 0.0001).

Established study reported that HBx is as a non-specific regulatory factor via interacting with other transcriptional factors, instead of directly binding to DNA in HCC (Andrisani and Barnabas, 1999). We focused our investigation to HBx-interacting transcription factors essential for HCC cells. For HCC cells, ChIP-chip assay has identified that HBx was enriched on the promoter of 144 transcript factors, including GATA2 (Sung et al., 2009). Thus, we hypothesized that GATA2 is an intermediate transcript factor regulating KIF20A expression in HBV-related HCC. PROMO database predicted that GATA2 binds to the regulatory sequences in the promoter of KIF20A with 9-bp binding sites ( Figure 6D ). To determine that GATA2 plays a critical role in binding to KIF20A promoter, luciferase reporter constructs containing the wild type or mutant KIF20A promoter sequences were co-transfected with pcDNA3.1/pcDNA-GATA2 into 293T cells ( Figure 6E ). In the GATA2-overexpressed group, the construct containing the wild-type KIF20A promoter sequence showed the highest increase in relative luciferase activity (p = 0.023, Figure 6F ). However, the activity of the mutant type KIF20A reporter construction was reduced by 3.5-folds, compared to the activity of the wild-type group (p = 0.018, Figure 6F ). The result showed that GATA2 activated the KIF20A promoter through the critical promoter region between -76 and -68.

To evaluate the potential role of GATA2 in the pathogenesis of HBV-related HCC, we created siRNA molecules and overexpression plasmids targeting GATA2, and our data demonstrated that these constructs significantly impacted KIF20A expression levels in HepG2.2.15 cells (p < 0.001, Figures 7A, B ). Supplementary Figure S2 showed the silencing efficiency of siRNAs (all p < 0.001). By performing the CCK-8 cell proliferation assay, we found that HepG2.2.15 cell proliferation was remarkably increased by GATA2 overexpression at 72-96 hours (p < 0.001, Figure 7C ), but reduced after GATA2 silencing (p < 0.001, Figure 7D ). Flow cytometry showed that silencing GATA2 promoted HepG2.2.15 cell apoptosis (20.17% vs 6.11%, Figure 7E ). Inversely, the apoptotic rate of GATA2 overexpression group in HepG2.2.15 cells was similar with the untreated group (6.53% vs 6.48%, Figure 7E ). Additionally, Figures 7F–J also demonstrated the importance of KIF20A in promoting HCC cell growth and inhibiting cell apoptosis. Furthermore, KIF20A reversed the effect of GATA2 interference on HepG2.2.15 cell growth (p < 0.05, p < 0.01, p < 0.001, Figures 7K, L ).