Identification of FEZ2 as a potential oncogene in pancreatic ductal adenocarcinoma

Data mining from public databases

FEZ1 and FEZ2 expression in different cancers was obtained from Oncomine database (https://www.oncomine.org/) (Rhodes et al., 2004). To investigate FEZ2 expression in pancreatic cancer tissues and normal pancreas tissues, we downloaded the expression data from TCGA database (https://www.cancer.gov/tcga) and GTEx database via UCSC Xena website (https://xenabrowser.net/) (Lonsdale et al., 2013). Copy number and methylation of FEZ2 were also obtained from TCGA data via UCSC Xena website. FEZ2 expression in Logsdon Pancreas dataset and Badea Pancreas dataset were obtained from Oncomine database.

Prognostic analysis of FEZ2 expression and methylation

We assessed the prognostic analysis of FEZ2 expression using DrBioRight (Li et al., 2021), a AI-driven analytics platform which could explore cancer omics data online. The patients were divided into two FEZ2 high expression group and FEZ2 low expression based on the median of FEZ2 expression. To examine the prognostic significance of FEZ2, Cox regression and log-rank test were performed. MethSurv (https://biit.cs.ut.ee/methsurv/), an online tool which could perform prognostic analysis using DNA methylation data (Modhukur et al., 2018), was used to investigate the prognostic role of FEZ2 methylation. The methylation landscape of FEZ2 promoter region was also obtained from MethSurv website.

Chemotherapy resistance analysis

The expression of FEZ2 on PDAC cell lines were obtained from CCLE (Cancer Cell Line Encyclopedia) database (https://portals.broadinstitute.org/), the IC50 (half maximal inhibitory concentration) of Gemcitabine and 5-FU in different PDAC cell lines were obtained from GDSC (Genomics of Drug Sensitivity in Cancer) database (https://www.cancerrxgene.org/). Correlation analysis was performed between FEZ2 expression and IC50 of Gemcitabine and 5-FU.

Immune cell infiltration analysis

We used TIMER database (https://cistrome.shinyapps.io/timer/) to predict the association between immune infiltration and FEZ2 expression (Li et al., 2017). The correlation between FEZ2 and six kinds of immune cells including B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil and dendritic cell in PDAC were performed via TIMER. Then, we explored the association of FEZ2 with tumor purity and cancer associated fibroblast. In addition, we investigated the relationships between FEZ2 and immunotherapy targets including PD-1, PD-L1 and CTLA4.

miRNA regulation of FEZ2

Online tools including TargetScan, miRWalk, miRDB and starBase were used to predict FEZ2 targeted miRNAs. Results from different tools were overlapped together. Then, the expression of miRNAs in PDAC tissues were obtained from TCGA data. In addition, the survival analysis of miR-433 was performed via UALCAN tools (http://ualcan.path.uab.edu/) (Chandrashekar et al., 2017).

Functional enrichment analysis

Patients from TCGA database were divided into high FEZ2 group and low FEZ2 group. Differently expressed genes (DEGs) with |logFC| > 1.326 combined with P < 0.05 were identified as significant. To predict the potential functions of these genes, we performed functional enrichment analysis of DEGs to determine significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The functional enrichment analysis was performed using cluster Profiler package on R software (Yu et al., 2012).

Clinical samples

PDAC tissues (n = 50) and adjacent normal tissues (n = 50) were obtained from patients who had undergone surgical resection at Pancreas Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China. PDAC tissues of patients who received 5-FU chemotherapy (n = 80) were also obtained from patients who had undergone surgical resection at Pancreas Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China. Samples were collected after approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (approval number 2018-SR-344) and written informed consent from the patients.

Human Pancreatic cancer cell lines and culture

The human PC cell lines (BXPC-3, CFPAC, MIAPACA and PANC-1) and a normal human pancreatic ductal cell line (HPNE) that we used in this paper were obtained from ATCC. Conditions including Dulbecco’s modified Eagle’s medium (DMEM) (Wisent, Quebec, Canada) contained with 10% fetal bovine serum (Wisent, Quebec, Canada) at 37 °C in a 5% CO2 in humidified air.

RNA extraction and RT-PCR

Total tissues or cells RNAs were extracted by using TRIzol reagent (Thermo Fisher Scientific). Prime Script RT Master Mix (Takara) was used for RNA reverse-transcription according to the manufacturer’s instructions. Quantitative PCR (Thunderbird SYBR qPCR mix, TOYOBO) were carried out in triplicate for the target genes. The levels of miRNAs were measured by qRT-PCR using miDETECT A Track™ miRNA qRT-PCR Kit (RiboBio, Guangzhou, China) The primers for miR-433-3p and U6 small nuclear RNA were purchased from RiboBio Company (Guangzhou, China). The sequences are covered by a patent. Target genes expression levels were determined using the method of delta-delta Ct (cycle threshold) and normalized with GAPDH or U6. Primer sets used for PCR: FEZ2-F ACGAGATTTGGAATGCCCTG and FEZ2-R CAAGGTCCTAGTATGCGATGAC; GAPDH-F GGAGCGAGATCCCTCCAAAAT and GAPDH-R GGCTGTTGTCATACTTCTCATGG; 18s rRNA-F AGCCACCCGAGATTGAGCA and 18s rRNA-R TAGTAGCGACGGGCGGTGTG. hsa-miR-433-3p mimics (Gene Pharma, Shanghai, China) 5′ to 3′ AUCAUGAUGGGCUCCUCGGU ACCGAGGAGCCCAUCAUGAUUU Negative control sense 5′-UUCUCCGAACGUGUCACGUTT-3′ antisense 5′-ACGUGACACGUUCGGAGAATT-3′.

RNA interference

To silence FEZ2, two types of cancer cell lines CFPAC and PANC-1 were transfected with 50 nM siRNA (GenePharma, China) by using Lipofectamine 3000 (Thermo Fisher Scientific). After transfection 48 h, total cell RNA was extracted to validated FEZ2 expression by RT-PCR. Sequence of siRNA include: si-FEZ2#1 sense GAGCUUGUUAACUGAUUAUTT and antisense AUAAUCAGUUAAGCUCTT; si-FEZ2#2 sense GGACCCAGAAGAUGAUGAATT and antisense UUCAUCAUCUUCUGGGUCCTT.

Western blot

Lysates of whole cell were extracted from PANC-1 and CFPAC cells. Fifty micrograms protein of each samples was separated by 10% SDS/PAGE gels, then transferred to nitrocellulose membranes by wet transfer. After blocked with 5% BSA in TBST (10 mM Tris, pH 8.0, 150 mM NaCl, 0.5% Tween 20) for 20 min, the membranes were washed twice with TBST and incubated with primary antibodies at 4 °C for 14 h. Membranes were washed three times with TBST for 10 min and incubated with secondary antibodies for 1 h at room temperature. After that, membrane were washed three times with TBST for 10 min. Finally, membrane was developed with the ECL (Amersham Biosciences) according to the manufacturer’s protocols. The primary antibodies we used for: anti-FEZ2 (sc-390111, 1:1,000, santa cruz), anti-GAPDH (A10868, 1:1,000, Abclonal), anti-β-actin (66009-1-1g, 1:1,000, proteintech), ani-β-catenin(SP328, 1:10,000, abcam).

Drug sensitivity assay

PANC-1 and CFPAC cells were seeded into 96 well-plate at 1 × 10⁴ cells per well, and treated with 5-FU for 48 h. Cell viability was analyzed by CCK-8 assay (Beyotime Biotechnology). The assay was performed triplicate. Drug sensitivity was determined by IC50 (half maximal inhibitory concentration).

Statistical analysis

Quantitative data were presented as the mean values ± S.D. with more than three independent experiments. Statistical differences between groups were calculated using Student’s t-test, one-way ANOVA, two-way ANOVA and Wilcoxon test on R software. The association between different factors was performed by Spearman’s correlation analysis. Chi-squared (χ²) test was used to investigate the association between FEZ2 expression and clinicopathological parameters. Kaplan–Meier analysis and log-rank test were used to examine the relationships between the survival rates and the expression of FEZ2 in PDAC patients. Cox proportional hazard regression model was used for univariate survival analysis. All statistical tests were two-tailed and P < 0.05 was considered as significant. Statistical significance was showed as *P < 0.05, **P < 0.01, ***P < 0.001.

Expression of FEZ2 in pancreatic cancer and its correlation with copy number and methylation

To investigate the expression pattern of FEZ family in different cancer types, we searched Oncomine online website and found FEZ1 and FEZ2 were both upregulated in different cancer types (Fig. 1A). However, only FEZ2 was upregulated in pancreatic cancer. To further validate the expression pattern of FEZ2 in pancreatic cancer, we compared 178 PDAC tissues from TCGA database with 165 normal pancreas tissues from GTEx database (Fig. 1B). The results showed that FEZ2 was significantly upregulated in PDAC tissues. Logsdon Pancreas dataset (Fig. 1C), Badea Pancreas dataset (Fig. 1D) and Pei Pancreas dataset (Fig. 1E) showed the similar results. Besides, protein expression analysis by immunohistochemistry from The Human Protein Atlas (THPA) showed FEZ2 was highly expressed in PDAC tumor cells (strong intensity) compared with pancreas cells (moderate intensity) (Figs. 1F and 1G). Taken together, these data demonstrated the upregulation of FEZ2 in pancreatic cancer.

To understand the mechanism of FEZ2 upregulation in PDAC, we analyzed the correlation between FEZ2 mRNA expression with its copy number and DNA methylation. The results showed that FEZ2 expression was positive correlated with its copy number (Fig. 1H), while the methylation level on FEZ2 promoter region was negative correlated with its expression (Fig. 1I). These indicated that increased copy number and reduced methylation level might be the reason of FEZ2 upregulation in PDAC.

FEZ2 methylation and prognosis of PDAC patients

To clarify the clinical value of FEZ2 methylation, we investigated the methylation pattern of different CpG sites on FEZ2 promoter region with clinical parameters including ethnicity, race, age and so on, the results were summarized as heatmap (Fig. 2A). Besides, survival analysis of different methylation sites showed that six independent methylation sites were significantly correlated with PDAC patients’ survival (Figs. 2B–2G). Among them, only one site showed that higher methylation predicted shorter survival time, other five sites suggested lower methylation predicted shorter survival time. Therefore, these methylation sites of FEZ2 might have clinical value for patient’s prognosis prediction. Since FEZ2 methylation was negatively correlated with its expression, High FEZ2 mRNA expression might be a risk factor for PDAC progression.

FEZ2 was a risk factor for disease-free interval of PDAC patients and associated with 5-FU resistance

To understand the role of FEZ2 on PDAC patient’s prognosis, we performed survival analysis based on TCGA database. As the results showed (Fig. 3B), the higher expression of FEZ2 predicted a significant shorter Disease-free interval. Other prognosis indicators including Overall survival (Fig. 3A), Progression-free interval (Fig. 3C) and Disease-specific interval (Fig. 3D) showed no significant correlation with FEZ2 expression. As disease-free interval can be usually used as an indicator to evaluate the effect of chemotherapy. This meant that high FEZ2 expression might promote tumor progression and chemotherapy resistance after surgery. To further determine the potential functions of FEZ2 on chemotherapy resistance, we explored the correlation between FEZ2 expression and IC50 of Gemcitabine and 5-FU in PDAC cell lines (Figs. 3E, 3F). As the results showed, IC50 of 5-FU was positively correlated with FEZ2 expression, but the IC50 of Gemcitabine showed no significant correlation with FEZ2. Collectively, these results indicated FEZ2 was a risk factor for disease-free interval of PDAC patients and might enhanced 5-FU resistance of PDAC cells.

Association between FEZ2 and immune signatures

To investigate the correlation between FEZ2 expression and immune cell infiltration, we used TIMER database to perform this analysis. As the results showed (Fig. 4A), FEZ2 expression was positive corelated with the infiltration of many immune cells including B cell (r = 0.321), CD8+ T cell (r = 0.57), macrophage (r = 0.532), neutrophil (r = 0.397) and dendritic cell (r = 0.486). In addition, we also focused on the correlation of FEZ2 with tumor purity and cancer associated fibroblast infiltration. As the results showed in Fig. 4B, FEZ2 was negatively correlated with tumor purity and positively correlated with cancer associated fibroblast infiltration. Moreover, because of the increasing expectation of immunotherapy, we further examined the correlation between FEZ2 expression and immunotherapy targets including PD-1 (PDCD1) (Fig. 4C), PD-L1 (CD274) (Fig. 4D) and CLTA4 (Fig. 4E). We found that these immunotherapy targets were positively correlated with FEZ2. Taken together, FEZ2 expression was positively correlated with immune cell infiltration, and increasing of cancer associated fibroblast infiltration might be a reason for chemotherapy resistance. Moreover, since FEZ2 was positively related to immunotherapy targets, immunotherapy might be a good choice for the patients with high FEZ2 expression.

Association between FEZ2 expression and miRNA regulation

To further investigate the molecular mechanism of FEZ2 upregulation in PDAC, we identified FEZ2-targeted miRNAs through multiple tools prediction including TargetScan, miRWalk, miRDB and starBase (Fig. 5A). Four miRNAs were identified by overlapping prediction results from different tools, including miR-200c, miR-152, miR-381 and miR-433. Boxplots revealed the expression of miRNA based on TCGA database (Figs. 5B–5E). Among them, only miR-433 was significantly low expressed in PDAC tissues. In addition, the patients with low miR-433 expression predicted a poor prognosis (Fig. 6F). The results revealed that FEZ2 and miR-433 was negatively correlated in tumor tissues (n = 45, r = −0.3259, P = 0.03) (Fig. 5G). As expected, the FEZ2 expression was significantly inhibited by miR-433-3p mimics compared with the mimic NC (Fig. 5H). This suggested that miR-433 downregulation might upregulated FEZ2 in PDAC via ceRNA mechanism.

FEZ2 regulated signaling pathways in PDAC

To gain insight into the function of FEZ2 in PDAC at the transcriptome level, we divided the PDAC patients into FEZ2 higher expression group and FEZ2 lower expression group from TCGA database. DEGs were selected by |logFC| > 1.326 combined with P < 0.05. As the volcano plot showed (Fig. 6A), finally we got 162 upregulated genes and 748 downregulated genes. Gene Ontology showed these DEGs were enriched in terms of Biological Process (Fig. 6B), Cellular Component (Fig. 6C) and Molecular Function (Fig. 6D). Specially, as the results showed in Fig. 6B, FEZ2 was involved in biological process including regulation of hormone levels, modulation of chemical synaptic transmission and regulation of trans-synaptic signaling. Further gene pathway enrichment analysis revealed that over-representative suppressed pathways included pancreatic secretion, protein digestion and absorption, indicating that the mis-regulation of FEZ2 may cause losing normal digestive functions of the pancreas cells. In contrast, the most representative active pathway was Wnt signaling pathway (Fig. 6E), which was proved to promote the progression of many cancers. These results suggested that FEZ2 might promote PDAC progression through Wnt signaling.

FEZ2 promoted proliferation and migration in PDAC cell lines

To validated the potential oncogenic functions of FEZ2, we validated the expression and functions of FEZ2 in PDAC cell lines and our clinical samples. First, we examined the expression of FEZ2 mRNA in PDAC tissues (n = 40) and adjacent normal tissues (n = 40), and found FEZ2 was significantly upregulated in our PDAC tissues (Fig. 7A). Then, we compared FEZ2 expression in different PDAC cell lines (Fig. 7B). PANC-1 and CFPAC were chosen for the later experiments. FEZ2 was silenced in PANC-1 and CFPAC cell lines by using two different siRNAs (siFEZ2#1 and siFEZ2#2). Both siRNA #1 and #2 showed > 50% depletion of FEZ2 expression in these two cell lines (Fig. 7C), and the corresponding FEZ2 protein levels examined by Western blot were consistent with the mRNA levels (Fig. 7D). As shown in Fig. 7E, although there was no much differences in 24 h, the depletion of FEZ2 in siRNA#2 and #3 significantly suppressed the proliferation of PANC-1 and CFPAC cell lines after 72 h. Transwell assay (Fig. 7F) and wound-healing assay (Fig. 7G) showed the migration of PANC-1 cells were inhibited by si-FEZ2#1.

FEZ2 promoted 5-FU resistance and Wnt signaling activation in PDAC cell lines

As 5-FU is one of the first-line chemotherapeutic agents [4], to further validate the potential functions of FEZ2 on 5-FU resistance, we performed the drug sensitivity assay by using PDAC cell lines. As the results showed, depletion of FEZ2 sensitized PANC-1 and CFPAC cell lines to 5-FU treatment displaying by decreased cell viabilities (Fig. 8A). In addition, by comparing FEZ2 expression and overall survival rate of PDAC patients who received 5-FU chemotherapy (n = 80), we found that higher FEZ2 expression was correlated with shorter overall survival time (Fig. 8B). To examine the intracellular signaling mechanism between FEZ2 and Wnt/β-catenin signaling pathway followed by western blotting. As shown in (Fig. 8C), the expression of cytoplasmic β-catenin was significantly inhibited by si-FEZ2. Collectively, these results indicated FEZ2 enhanced 5-FU resistance and Wnt signaling activation in PDAC cell lines.