FBXL19 promotes malignant behaviours by activating MAPK signalling and negatively correlates with prognosis in hepatocellular carcinoma

Abstract

FBXL19 is a member of the Skp1-Cullin-F-box family of E3 ubiquitin ligases and is linked to a variety of vital biological processes, such as cell proliferation, migration, and differentiation. Previous studies have identified it as an oncogene in breast cancer and glioma. However, its role in hepatocellular carcinoma (HCC) remains unclear. To comprehensively elucidate its role in tumour biology and its underlying mechanisms, a variety of sophisticated methods, including bioinformatics analysis, RNA-sequencing technique, and in vitro cell biology experiments, were used. Here, we found that FBXL19 was upregulated in patients with HCC and correlated with poor prognosis. In in vitro experiments, the specific targeting of short hairpin RNAs via lentiviruses successfully induced the knockdown of FBXL19, resulting in notable inhibition of the proliferation, migration, and invasion of HCC cells. Furthermore, FBXL19 downregulation resulted in significant induction of G0/G1 phase cell cycle arrest. Importantly, FBXL19 knockdown inhibited tumour malignant behaviour primarily by inactivating extracellular signal-regulated protein kinase 1/2 and p38 mitogen-activated protein kinases. In conclusion, this study revealed that FBXL19 was upregulated in patients with HCC, and that its expression was negatively correlated with prognosis. Thus, FBXL19 displays oncogenic properties in HCC by activating mitogen-activated protein kinase signalling.

Keywords: Cell proliferation; FBXL19; Hepatocellular carcinoma; Migration.

Fig. 1

Workflow of the study design.

Fig. 2

Screening of potential biomarkers among FBXL family members via differential expression, Cox regression, and survival analysis in TCGA-LIHC cohort. The gene expression of 22 FBXLs was screened between normal tissues and HCC tissues. The results (Fig. S1) showed that 20 FBXLs were differentially expressed except for FBXL4 and FBXL14. Thereafter, 20 FBXLs were analysed in the subsequent step. A. Univariate Cox regression analysis was performed in subgroups of important clinicopathological variables and the expression level of 20 FBXLs genes. B. Multivariate Cox regression analysis was further performed, including seven significant factors from univariate Cox regression analysis. C & D. Kaplan–Meier curves of overall survival analysis based on mRNA expression of FBXL5 and FBXL19, respectively.

Fig. 3

FBXL19 overexpression in patients with HCC is associated with poor outcomes. The transcriptome sequencing data and clinical characteristics of HCC were obtained from the TCGA database using the cBioPortal website (http://www.cbioportal.org) to analyse the FBXL19 mRNA expression level. The protein level of FBXL19 was further analysed in 87 clinical samples via immunohistochemical staining. A. The FBXL19 mRNA expression level in HCC tissues was significantly higher than that in normal tissues. B. In the 50 tumour-normal paired cases, most tumour tissues had higher FBXL19 mRNA level than paracancerous normal tissues. C. Compared to normal tissues, FBXL19 expression was confirmed in cancer tissues at different periods according to TNM staging criteria. D & E. the mRNA level of FBXL19 was evaluated in different stages (D) or grades (E) of HCC progression. F & G. The protein level of FBXL19 in HCC was detected using immunohistochemical staining (F) and quantification analysis (G). H. Kaplan–Meier analyses of overall survival in patients with HCC with low FBXL19 in stage 1/2, low FBXL19 in stage 3/4, high FBXL19 in stage 1/2 and high FBXL19 in stage 3/4 (N = 337). I. Kaplan–Meier analyses of overall survival in patients with HCC with low FBXL19 in grade 1/2, low FBXL19 in grade 3/4, high FBXL19 in grade 1/2 and high FBXL19 in grade 3/4 (N = 365). *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 4

FBXL19 knockdown in SNU-449 cells inhibits cell proliferation and cell cycle progression. SNU-449 cells were infected with Ctrl, shFBXL19-1 or shFBXL19-2 lentivirus, and FBXL19 knockdown was identified for subsequent functional assays. A. The mRNA expression of FBXL19 was tested among five HCC cell lines and one human normal liver tissue (HNL) using qRT-PCR. B & C. Confirmation of FBXL19 knockdown using qPCR (B) and western blotting (C). D. The proliferation of SNU-449 cells after FBXL19 knockdown was performed using CCK8 assays. E & F. Clonogenic ability was examined via plate cloning assays. The representative images (E) and statistical analysis (F) of colony formation were shown. G & H. Cell cycle was determined via flow cytometric analysis using PI staining. The representative histograms (G) and statistical analysis of the percentage of cells in each phase (H) are presented. All data are presented as means ± SD of three independent repeated experiments. **p < 0.01, ***p < 0.001.

Fig. 5

FBXL19 knockdown in SNU-449 cells suppressed cell motility, migration, and invasion. A & B. Cell motility was tested via scratch assay. Representative images (A) of scratch assay at 0, 24, 48 h and statistical analysis (B) are shown. C. Cell migration was assessed using transwell assay. D. Cell invasion was examined via transwell assay with matrigel coating inserts. Images on the left are representative images, while those on the right are statistical graphs in the panels C and D. For scratch, migration and invasion assays, data are presented as means ± SD of three independent experiments; p-values are calculated using one-way ANOVA with Bonferroni correction. ***p < 0.001.

Fig. 6

FBXL19 promotes malignant cell properties via activating ERK1/2 and p38 pathways. A. Heatmap of the signal intensities of differential MAPK1/3 activation-related genes in SNU-449 cells by shFBXL19-mediated knockdown. B. GSEA of MAPK1/3 activation from differentially expressed genes between SNU-449-Ctrl and SNU-449-shFBXL19 cells. C. The protein levels of p-ERK1/2, p-p38 and p-JNK in SNU-449 cells transfected with shFBXL19 were observed via western blotting. D-H. The mRNA expression of cell cycle regulators (CDK4, CDK6, and CyclinB) and metastasis markers (Vimentin and Twist1) were detected via qRT-PCR. **p < 0.01, ***p < 0.001.

Fig. S1.1

Gene expression analysis of 22 FBXLs in TCGA-LIHC cohort to identify differentially expressed genes (DEGs). ^nsp >0.05, *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. S1.2

Fig. S2

The X-tile program was utilized to identify the optimal cut-off values for FBXL5 and FBXL19.