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Dysregulated paired related homeobox 1 impacts on hepatocellular carcinoma phenotypes

Abstract

Background

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death. Paired related homeobox 1 (PRRX1) is a transcription factor that regulates cell growth and differentiation, but its importance in HCC is unclear.

Methods

We examined the expression pattern of PRRX1 in nine microarray datasets of human HCC tumour samples (n > 1100) and analyzed its function in HCC cell lines. In addition, we performed gene set enrichment, Kaplan-Meier overall survival analysis, metabolomics and functional assays.

Results

PRRX1 is frequently upregulated in human HCC. Pathway enrichment analysis predicted a direct correlation between PRRX1 and focal adhesion and epithelial-mesenchymal transition. High expression of PRRX1 and low ZEB1 or high ZEB2 significantly predicted better overall survival in HCC patients. In contrast, metabolic processes correlated inversely and transcriptional analyses revealed that glycolysis, TCA cycle and amino acid metabolism were affected. These findings were confirmed by metabolomics analysis. At the phenotypic level, PRRX1 knockdown accelerated proliferation and clonogenicity in HCC cell lines.

Conclusions

Our results suggest that PRRX1 controls metabolism, has a tumour suppressive role, and may function in cooperation with ZEB1/2. These findings have functional relevance in HCC, including in understanding transcriptional control of distinct cancer hallmarks.

Peer Review reports

Background

Liver cancer is a common and deadly cancer of which the most prevalent type is hepatocellular carcinoma (HCC) [1]. Recent advances in molecular profiling have highlighted the importance of gene deregulation in hepatocellular carcinogenesis [2]. Accordingly, several potential molecular drivers of HCC have been identified, including mutations in TP53, CTNNB1, and TERT promoter [3], and metabolic genes such as ALB, CPS1, and APOB [4]. However, molecular heterogeneity and the main mechanisms promoting HCC are poorly understood.

Transcription factors are known to control key processes in cancer progression such as growth, metabolism, immune evasion, and metastasis [5, 6]. The paired related homeobox 1 (PRRX1) is a transcriptional co-activator that exist in two isoforms. The functions of PRRX1 include the regulation of cell growth and differentiation. Consistently, Prrx1 deletion is lethal in neonatal mice [7]. PRRX1 has been found to be overexpressed in breast, pancreas, head and neck squamous cell carcinoma, liver, and colon cancer [8,9,10,11,12,13,14]. In murine pancreatic cancer and human breast cancer cell lines, the knockdown of the isoforms PRRX1a or PRRX1b reduced migration and invasion, indicating functional similarities [9, 14, 15]. However, overexpression of PRRX1a led to the induction of genes involved in cell migration whereas PRRX1b was more involved in cell cycle regulation [9], supporting that these isoforms could have distinct molecular functions. The knockdown of PRRX1b inhibited proliferation as well as migratory and invasive capabilities of triple negative breast cancer cell lines [14]. In other studies, knockdown of PRRX1 reduced tumour volume of MDA-MB-231 mice xenografts [14], and its overexpression in colon cancer cell lines increased colony formation and anchorage independent growth [10]. In the HCC cell line HUH7, ectopic expression of PRRX1 induced resistance to 5-fluoruracil [12]. These data indicate a tumour-promoting role of PRRX1 in some cancer settings. On the contrary, PRRX1 has also been shown to exert tumour suppressor functions. For example, low expression of PRRX1 enabled metastatic colonization of lung by breast cancer cells [8]. HCC cells were also shown to migrate more upon PRRX1 knockdown [13]. In clinical contexts, PRRX1 has been associated to contradictory prognostic outcomes showing that high PRRX1 expression predicted improved overall survival in colorectal cancer and HCC [10, 12, 13], but was associated with metastasis and poor survival outcome in breast cancer [8].

Although the precise roles of PRRX1 and its isoforms, PRRX1a and PRRX1b, are unclear in human cancer, PRRX1 was often associated with cancer stemness and epithelial-mesenchymal transition (EMT) [8, 9, 15]. For example, the overexpression of PRRX1 in human colon cancer cell lines induced EPH receptor B2 – an intestinal stem cell marker [10], whereas the knockdown of PRRX1 in breast cancer increased stemness features [8]. Regarding EMT, overexpression of PRRX1 caused the upregulation of the EMT gene TWIST1 [8]. In addition, its knockdown reversed invasiveness [15]. Few studies have investigated PRRX1 expression in HCC [12, 13, 16], and its functions are largely unknown. Based on the contradictory reports on PRRX1, including on expression level in HCC, we set out to comprehensively analyze its expression and to predict its functions in human HCC. Making use of several HCC tissue gene expression datasets and applying functional assays in vitro, we find that PRRX1 is frequently upregulated in human HCC. We identified two EMT transcriptional factors, zinc finger E-box-binding homeobox (ZEB1 and ZEB2) as novel PRRX1-related genes, and report that PRRX1 knockdown affects the phenotype of HCC cell lines, including metabolism.

Materials and methods

Collection of liver cancer microarray datasets and analyses

Eight liver cancer microarray datasets were obtained from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO), whereas the TCGA liver cancer data was accessed via cBioPortal platform (http://www.cbioportal.org/) [17, 18] and http://cancergenome.nih.gov. The expression values for PRRX1 probes were compiled for each dataset, and its differential expression in HCC compared to normal or adjacent non-tumour tissues was determined using Student T-test in GraphPad Prism version 6. Genes positively or negatively co-expressed with PRRX1 in liver cancer data were downloaded from cBioPortal platform and used for correlation analyses in TCGA and GSE14520 datasets. For PRRX1-high versus low expressing tumour comparison, TCGA and GSE14520 samples were ranked based on PRRX1 expression level and divided into two groups (based on median). The high (n = 186) vs low (n = 185) samples from TCGA were subsequently compared in R, using limma package. For Kaplan-Meier overall survival analysis of PRRX1 in combination of other genes, we first considered whether the genes have significant direct or inverse correlation with PRRX1 prior to subsetting samples for comparison. For example, for genes that met the criteria of direct correlation with PRRX1, the tumours with high expression of the gene and PRRX1 were compared with tumours with low expression of both. In contrast, for genes inversely correlated with PRRX1, tumours with high PRRX1 (n = 111) and low expression of the genes were selected and compared with tumours with low PRRX1 (n = 110) and high level of the gene. Analysis of cancer hallmark pathways was done with the ‘GSEAPreranked’ option in GSEA (http://software.broadinstitute.org/gsea/index.jsp).

Cell culture

The Hep3B, HLF and HUH7 cell lines were provided by Prof Kern (Pathology, Heidelberg). The SNU398 cells were obtained from Dr. Francois Helle (University of Picardie Jules Verne, France). The cell lines HLF, HUH7, Hep3B and SNU398 were cultured in Dulbecco Modified Eagle’s medium (DMEM, high glucose, Lonza, BE12–709) supplemented with 2 mM glutamine, 10% fetal bovine serum (FBS), penicillin (100 U/ml), and streptomycin (100 μg/ml). The cells were cultured at 37 °C in a humidified atmosphere containing 5% CO2. Hank’s Balanced Salt Solution (HBSS) (Sigma-Aldrich, H6648) was used for cell washing steps. The cells were used for experiments between passage 2-10. All used cell lines were authenticated by short tandem repeat profiling (STR). PCR Mycoplasma Test (PromoCell, Huissen, Netherlands) was performed to confirm that cells are mycoplasma-free. siRNA transfection and RNA isolation were performed as described in the supplement.

Quantitative PCR

Quantitative polymerase chain reaction (qPCR) was performed using EvaGreen qPCR Mix Plus (Solis BioDyne, Tartu, Estonia) on the AB StepOnePlus. The experiments were performed in triplicates using peptidylprolyl isomerase A (PPIA) as control. The primers (Table 1) were ordered from Eurofins Genomics (Ebersberg, Germany). PRRX1 expression was evaluated in liver tissue (n ≥ 4) from MDR2 knockout mice on a 129 background (3, 6, 9, and 15 months) and age matched controls.

Table 1 Primer list

MTT proliferation assay

Proliferation assays were performed with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Briefly, 7.5–10 × 103 cells per well were seeded in quadruplicates in 48-well plates. The cells were incubated o/n and thereafter transfected with the respective siRNA oligos. After the indicated time, 25 μl of 5 mg/ml MTT reagent (Sigma Aldrich, USA) was added to the wells and incubated for 3–4 h at 37 °C. The media was subsequently aspirated and 250 μl of MTT solubilizing reagent was added to dissolve the formed formazan crystals. The plate was then incubated o/n at 37 °C. Absorbance was read at 560 nm with background correction at 670 nm using Infinite 200 Spectrophotometer (Tecan, Austria) and the data were normalized to the control wells.

Clonogenic assay

The HCC cells were initially seeded into 12 well plates and allowed to attach overnight. Culture medium was then replaced with medium containing siRNA/transfection reagent. After 24 h, the cells were trypsinized and seeded in triplicates in 6-well plates (2500 per well). Medium was changed at day 4 and the experiment terminated at day 8. Cells were fixed for 5 min with methanol (Carl Roth, Karlsruhe, Germany) and stained for 15 min in 0.5% crystal violet (Alfa Aesar, Karlsruhe, Germany). The wells were then washed with running tap water, allowed to dry and photographed.

In vitro scratch assay

5 × 105 cells were seeded in triplicate into 12 well plates (Greiner Bio-One, 665,180), and incubated o/n, followed by siRNA transfection. Next day the wells were scratched with 200 μl pipette tips and reference points made using a needle. Images were taken from the wells at time points 0 and 24 h later using an inverted microscope (Leica, Wetzlar, Germany). ImageJ was used to measure the distance/gap between the two edges of the scratch. Migration was calculated as difference between gap distance at time 0 and 24 h divided by the distance at time 0.

Measurement of glucose consumption and lactate output

Cells (1.5 × 105) were seeded in triplicate into 12 well plates and allowed to attach o/n. Then, siRNA transfection was performed. 48 h after transfection, cell culture medium was collected and analyzed for glucose and lactate using the Roche Cobas C311 Chemistry Analyzer. Cells were lysed with RIPA buffer and data was normalized to protein concentration.

Metabolite analyses

One million of HUH7 and HLF cells were seeded in triplicate in 14.5 cm petri dishes and allowed to attach o/n. The next day, siRNA transfection was performed. After 24 h, medium was changed to growth medium for another 48 h. Thereafter, medium was removed, cells were washed with pre-warmed pure water and immediately snap frozen by adding liquid nitrogen to the petri dishes. Extraction of the samples was performed as follows: frozen HUH7 and HLF cells were extracted directly on petri dishes by adding pre-cooled 1 ml 50% methanol and 10 μl Ribitol (0.2 mg/ml; internal standard for the polar phase). All liquid containing cell debris was transferred to 2 ml reaction tubes on ice. To each tube, 0.5 ml 100% chloroform containing 0.1 mg/ml heptadecanoic acid (internal standard for organic phase) was added and samples were vortexed for 10 s. To separate polar and organic phases, as described previously [19, 20], samples were centrifuged for 10 min at 11,000x g. For derivatization, 0.9 ml of the upper polar phase were transferred to a fresh tube and speed-vac dried without heating. Pellets of the aqueous phase after extraction were dissolved in 20 μl methoximation reagent containing 20 mg/ml methoxyamine hydrochloride in pyridine and incubated for 2 h at 37 °C under vigorous shaking. For silylation, 35 μl N-methyl-N-(trimethylsilyl) trifluoroacetamide were added to each sample. After incubation for 45 min at 50 °C, samples were analyzed by Gas Chromatography/Mass Spectrometry (GC/MS). A GC/MS-QP2010 Plus (Shimadzu, Germany) fitted with a Zebron ZB 5MS column (Phenomenex; 30 m × 0.25 mm × 0.25 μm) was used for GC/MS analysis. The GC was operating with an injection temperature of 250 °C and 1 μl sample was injected with split mode (diluted 1:5). The GC temperature program for polar compounds started with 1 min hold at 40 °C followed by a 6 °C/min ramp to 210 °C, a 20 °C/min ramp to 330 °C and a bake-out for 5 min at 330 °C. Helium was used as carrier gas with consistent linear velocity. The MS was operated with ion source and interface temperatures at 250 °C, a solvent cut time of 5 min and a scan range (m/z) of 40–700 with an event time of 0.1 s. Raw data were processed using the „GCMS solution software” (Shimadzu) and normalized to the internal standard Ribitol as well as to the cell number.

Statistics

Results are presented as mean ± SD unless indicated otherwise. Comparison of sample groups was performed using GraphPad Prism version 6 Software or R statistical software. Where applicable, t-test was applied for unpaired sample comparison, while one-way ANOVA was used for multiple comparisons. Statistical significance was defined with P < 0.05. Quantification of migration was done using ImageJ 1.5 (http:imagej.nih.gov/ij). Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001.

Results

PRRX1 is frequently upregulated in HCC

We analyzed PRRX1 expression in nine human HCC gene expression datasets (Table S1). Out of these datasets, PRRX1 level were upregulated in seven (P < 0.05), and not significantly changed in two datasets (Fig. 1a). In addition, we compared the expression of PRRX1 across liver cancer cohorts in Oncomine – an online repository of manually curated cancer datasets (http://oncomine.org). The Oncomine platform contains the cancer genome atlas (TCGA) liver cancer data and GSE14520 dataset used in our analysis, thus was also advantageous for cross-validating our observation. Indeed, in Oncomine platform PRRX1 was upregulated in three datasets, but not significantly changed in five other datasets (Fig. S1A). Hence, we concluded that when significantly altered, PRRX1 is upregulated in HCC.

Fig. 1
figure1

PRRX1 is upregulated in human HCC and shows a variable pattern in vitro. A PRRX1 expression in human HCC cohorts. NT - non-tumour, HCC - hepatocellular carcinoma. Data was analysed with Student’s t-test. Information about sample size per cohort is contained in Table S1. B PRRX1 expression in tumours showing its gene amplification. Data was analysed with Student’s t-test. Amp: amplification C PRRX1 expression in tumours with TP53 and CTNNB1 mutation in TCGA liver cancer cohort. Data was analysed with Student’s t-test. D Kaplan-Meier overall survival analysis (logrank test) based on PRRX1 expression in GSE14520 dataset. High PRRX1 n = 110; low PRRX1 n = 110. E PRRX1 expression in human HCC cell lines after 48 h culture. Normalized to the basal expression in HLF (lowest PRRX1 level). Bars indicate mean ± SD, n = 3 per group. F Ratio of PRRX1a to PRRX1b isoform expression in HCC cell lines after 48 h culture. Bars indicate mean ± SD, n = 3 per group

We then compared the alteration frequency of PRRX1 in TCGA cancer datasets via cBioPortal (http://cbioportal.org). PRRX1 alterations in liver cancer (i.e. HCC and cholangiocarcinoma) along with its alterations in lung, bladder and breast cancer cohorts, ranked very high (within top 10% of all TCGA cohorts) (Fig. S1B). Of note, PRRX1 was amplified in ~ 10% (n = 36) of TCGA HCC tumour samples, but this amplification did not strikingly overlap with known mutated genes in HCC, i.e. TP53 or CTNNB1 (Fig. S1C). As expected, HCC tumours with high amplification showed higher mRNA expression (P = 0.0007) compared to the tumours with no amplification (Fig. 1b). Further, we compared PRRX1 expression based on two frequently mutated genes in HCC (i.e. TP53 and CTNNB1). Accordingly, tumours with CTNNB1 mutation showed low PRRX1 level (P < 0.0001), whereas no expression change was observed with TP53 mutations (Fig. 1c), suggesting that the different oncogenic background influences PRRX1 level.

A prior study using the GSE14520 dataset showed an improved OS with high PRRX1 expression [12]. To gain insight on association of PRRX1 with clinicopathological variables, we analyzed clinical data from one of the two platforms used in GSE14520 (GPL3921, n = 225 HCC samples). Upon Kaplan-Meier overall survival (OS) analysis, we found a tendency towards improved outcome for patients with high PRRX1-expressing tumours, but this was not statistically significant (Fig. 1d). Also, PRRX1 was not associated with other clinical variables analyzed, e.g. tumour size, stage, alanine transaminase (ALT) and alpha-fetoprotein (AFP) level (Fig. S2A).

We further analyzed PRRX1 expression patterns in experimental HCC models. In multi-drug resistance (Mdr2) knockout mice – a liver fibrosis model that progresses to HCC in about 12 months [21, 22] – we observed higher Prrx1 expression in Mdr2 knockout mice than in age - matched controls and a lower expression of Prrx1 in older mice with HCC compared to the younger Mdr mice (Figure S2B).

In HCC cell lines, PRRX1 expression was variable. Specifically, it was highest in SNU398, a cell line with epithelial-like appearance albeit reported to be poorly differentiated [23]. Other cell lines with epithelial features, HUH7 and Hep3B, showed higher PRRX1 level when compared to HLF cells that display more mesenchymal properties (Fig. 1e). In these four cell lines, we additionally analyzed expression of PRRX1 isoforms, a and b. With the exception of SNU398, the HCC cell lines (Fig. 1f) expressed less of the truncated isoform (i.e. PRRX1a) than the longer isoform PRRX1b. These analyses support that PRRX1 is frequently upregulated in liver tumours, while data from experimental models suggest that PRRX1 may be more expressed in well differentiated cells.

PRRX1 correlates directly with cancer pathways and inversely with metabolic pathways

To predict the functions or pathways associated with the expression of PRRX1, we first identified PRRX1 co-expressed genes in TCGA HCC data from the cBioPortal platform (Table S2). When ranked based on Pearson score, the topmost PRRX1-co-expressed genes included several known candidates in HCC, e.g. TGFB3, HNF1A, IL10 [3] as well as novel targets such as PLXDC1, EMX2OS, and GLT8D2 (Fig. 2a). Functional annotation analyses using the top positively correlated genes (n = 1022, ~ 5% of the gene list), indicated an overrepresentation of cancer processes, i.e., extracellular matrix (ECM) and signal transduction activities (Fig. 2b). Specific processes that emerged include ECM-receptor interaction (n = 15 genes, e.g., COL6A1/A2/A3, ITGA8/10, LAMA2/4), focal adhesion (n = 29 genes, e.g.. CAV1, PDGFRA, PDGFRB, TNC, COL1A1), and PI3K-AKT signaling (n = 35 genes, e.g. PDGFRA/RB, FGF1/9, FGFR1, JAK2, LPAR1/4/5/6, GNG2, PIK3R3/R5) (Tables S3 and S4). Consistently, gene ontology (GO) analyses for biological processes and cellular components supported a positive correlation with cancer processes, notably ECM organization, cell adhesion and signal transduction, as mentioned earlier (Fig. 2c, Fig. S3, and Tables S5 and S6). Unsurprisingly, gene set enrichment analysis (GSEA) of PRRX1 co-expressed genes also identified EMT (Fig. 2d). Further, the GO class ‘cellular component’ contained 293 genes clustered to plasma membrane, and those included MMP2, CAV1, FGFR1, PLPPR4, TGFB3, MSR1, and transporters such as SLC1A5, SLC6A6, SCN3A and SLC7A3. Similarly, > 300 genes were assigned as integral membrane components (Fig. S3), altogether implicating PRRX1 in membrane dynamics, cell plasticity and molecule transport. Other cancer types that showed high PRRX1 alteration frequency in the TCGA cohorts (e.g., cholangiocarcinoma and lung cancer), also showed similar pathway annotation patterns as observed in HCC (i.e. focal adhesion and ECM organization, Fig. S4).

Fig. 2
figure2

Functional enrichment analyses of PRRX1 positively and negatively co-expressed genes. A PRRX1 positively and negatively correlated genes in TCGA liver cancer data. PRRX1-correlated genes were obtained from cBioPortal platform (http://www.cbioportal.org/). B KEGG pathway annotation of genes positively and negatively correlated with PRRX1. C Gene ontology of ‘biological process’ of PRRX1-correlated genes. D GSEA plot showing enrichment of epithelial–mesenchymal transition in the genes positively correlated with PRRX1. NES – normalized enrichment score

The top PRRX1-negatively correlated genes (n = 1022) were mostly involved in metabolism, a process that has not yet been associated with PRRX1 in HCC (Fig. 2b and c). We found that prominent inversely correlated metabolic processes include glycine, serine and threonine metabolism (n = 16 genes, e.g., SHMT1, CHDH, GATM), fatty acid degradation (n = 16 genes, e.g., ECI1, ECI2, ACOX1), valine, leucine and isoleucine degradation (n = 16 genes, e.g. ACADSB, EHHADH, ECHS1), and peroxisome activity (n = 42 genes, e.g. ACOX1/2, HACL1, PEX1, 5, 6, 7, 16, and 19) (Fig. 2b). Metabolism also dominated the GO term ‘biological processes’ of the negatively correlated genes (Fig. 2c, Table S7). Further, the GO term ‘cellular component’ implicated peroxisomes and mitochondria (Fig. S3, Table S8) – two organelles involved in antioxidant defenses and cellular respiration, respectively. Given the link to metabolism, we wondered if the PRRX1 inversely correlated genes included consistently altered metabolic genes in human HCC [24]. Indeed, overlap of all PRRX1-correlated genes with metabolic genes from human HCC revealed 135 common elements (Table S9, Fig. S5A). Of these, 124 metabolic genes were downregulated in HCC and several of them belonged to amino acid and fatty acid metabolism, and also small molecule transport (Fig. S5B). Thus, PRRX1 expression correlated with the downregulation of metabolic pathways in HCC. In cholangiocarcinoma and lung cancer, we also observed a negative correlation with metabolic pathways, suggesting that similar functions predicted for PRRX1 in HCC may apply to other cancer types (Fig. S4). Taken together, these data suggest that PRRX1 promotes cancer pathways and contributes to a suppressed metabolic gene program, as previously seen in HCC [4, 24, 25].

Integrative analyses identify EMT genes ZEB1 and ZEB2 as novel transcription factors related to PRRX1

Based on our in silico analyses of several HCC patient gene expression datasets, which linked PRRX1 to metabolism and cancer pathways, we first focused on its influence towards cellular plasticity and EMT. Thus, we stratified TCGA HCC dataset into high versus low PRRX1-expressing tumour samples, and analyzed the differential gene expression. We observed that the PRRX1-high tumours expressed several cancer-associated genes (e.g. MMP2/9, ZEB2, VIM) (Fig. 3a, Table S10). This expression pattern was consistent with that of the co-expressed gene list from cBioPortal (which generally reflected EMT, ECM, cancer and metabolic pathway alterations as earlier noted in Fig. 2). We compiled ~ 150 genes involved in these processes (Table S11), including the topmost PRRX1 co-expressed genes (Fig. 2a), and sought to identify those correlated with PRRX1 in TCGA HCC data and another dataset (GSE14520). Of those genes, 19 significantly correlated with PRRX1 in both datasets (Table 2). The EMT transcription factors ZEB1 and ZEB2 emerged alongside HNF1A, IL10, and 15 metabolic genes, e.g., gamma-glutamyltransferase 5 (GGT5), glucose transporter 2 (SLC2A2), NADPH oxidase 4 (NOX4), alcohol dehydrogenase 6 (ADH6), and hepatic lipase C (LIPC) (Fig. S6A). Kaplan-Meier OS analysis for each of the 19 genes in combination with PRRX1 showed that liver tumours with high PRRX1 and low ZEB1 (P = 0.0369), high ZEB2 (P = 0.0328), high GGT5 (P = 0.0015) or high SLC7A8 (P = 0.0016) showed improved OS outcome (Fig. 3b, Fig. S6B). ZEB1 or ZEB2 did not predict OS when analyzed alone (Fig. S7A) indicating a synergistic impact with PRRX1. Since PRRX1 is associated with EMT [8, 9, 15], but no prior study had linked it with ZEBs in HCC, we performed further analysis on ZEBs.

Fig. 3
figure3

ZEB1 and ZEB2 are novel PRRX1 related genes. A Volcano plot showing genes differentially expressed in PRRX1-high tumours from TCGA dataset. B Kaplan Meier OS analysis (logrank test) of high PRRX1 combined with low ZEB1 or high ZEB2 in GSE14520 dataset, based on its negative correlation with ZEB1 and positive correlation with ZEB2 in this dataset. H = high, L = low. C Pearson correlation of PRRX1 and ZEB1 or ZEB2 in two HCC collectives. D ZEB1 and ZEB2 expression in two representative HCC cohorts. Data was analysed with Student’s t-test. NT – non-tumour. TCGA: NT n = 50, HCC n = 373; GSE14520: NT n = 220, HCC n = 225. (E) Differential expression of ZEB1 and ZEB2 compared to PRRX1 in HCC cell lines. Cells were cultured for 48 h prior to analysis, bars indicate mean ± SD, n = 3 per group

Table 2 Top genes significantly correlated with PRRX1 in human HCC

Analyses of multiple HCC cohorts showed that both ZEB1 and ZEB2 expression positively correlated with PRRX1 in TCGA, GSE25097 and GSE55092 datasets. In addition, ZEB2 correlated positively with PRRX1 in the dataset GSE64041. On the other hand, we found one dataset each (GSE14520 and GSE36376) where ZEB1 and ZEB2, respectively, were negatively correlated with PRRX1 (Fig. 3c, Fig. S7B). Thus, PRRX1 is more often positively correlated with ZEB1 and ZEB2 in patients’ HCC tumour samples. However, besides OS, PRRX1 combined with ZEB1 or ZEB2 expression was not significantly associated with several clinicopathological variables (Tables S12, S13). Next, we sought to determine whether ZEB1 and ZEB2 show a consistent expression pattern across the HCC cohorts. Out of nine datasets, we found that ZEB1 was high in six while ZEB2 was high in two datasets. ZEB2 was significantly low in five datasets (Fig. 3d, Fig. S7C), thus indicating that ZEB1 is generally high in HCC while ZEB2 is often downregulated. We also analyzed ZEB1/2 expression in tumours with CTNNB1 and TP53 mutations, which are frequent in HCC. We found that ZEB1 is downregulated in TP53-mutated tumours and not changed in CTNNB1 mutations, whereas ZEB2 was upregulated in tumours with CTNNB1 mutation and not changed in TP53-mutated tumours (Fig. S7D), further supporting that the ZEBs often have a contrasting expression pattern.

In cell lines, we measured the basal mRNA level of ZEB1/2 relative to PRRX1 and observed a heterogeneous expression pattern. Specifically, poorly differentiated cell line HLF, which expresses PRRX1 at low level (Fig. 1e), showed comparatively higher levels of both ZEB1/2 (Fig. 3e). SNU398 cells, which express high PRRX1, showed low ZEB1 and high ZEB2, whereas HUH7 and Hep3B cells (well-differentiated cells) expressed higher ZEB1 and lower ZEB2 compared to PRRX1 expression (Fig. 3e). Taken together, we identify ZEB1 and ZEB2 as EMT genes correlated with PRRX1 and assumingly acting with it to influence tumour characteristics.

Modulation of PRRX1 alters HCC cell phenotype

We knocked down PRRX1 in HCC cell lines to experimentally validate its relationship with ZEB1/2 (Fig. 4a). Consistent with the positive correlation with ZEB1/2 seen in patient datasets, the knockdown of PRRX1 caused a significant downregulation of ZEB1 in all three cell lines tested. ZEB2 was less strongly regulated and changed significantly only in two cell lines (Fig. 4b). Furthermore, we tested the ability of the cells to migrate (as key EMT phenotype). Under basal conditions, the cell line HLF migrated more than HUH7, SNU398 and Hep3B cells. However, cell migratory response was variable upon PRRX1 knockdown, being increased in HUH7 and Hep3B cells, reduced in HLF and unchanged in SNU398 cells (Fig. 4c). This variability may be linked to the differentiation status of the cells. Further, HCC cell lines showed an increase in cell proliferation after 48 h (Fig. 4d) and clonogenicity after 8 days upon PRRX1 knockdown (Fig. 4e). Altogether, these findings suggest that modulating PRRX1 alters ZEB1/2 and is accompanied by various cellular phenotypes associated with cancer.

Fig. 4
figure4

PRRX1 acts as tumour suppressor in HCC cell lines and affects ZEB1/2 expression. A PRRX1 knockdown in different HCC cell lines as determined by qPCR analysis. The knockdown was induced by transfection of 25 nM siPRRX1. Samples were taken after 48 h; bars indicate mean ± SD and are representative of 3 experiments, each in triplicate. B ZEB1 and ZEB2 expression as determined by qPCR 48 h after siPRRX1 knockdown. Bars indicate mean ± SD and representative of 3 experiments each in triplicates. C Cell migration scratch assay 24 h after siPRRX1 transfection. Bars indicate mean ± SD of gap distance (n = 18 equidistant measurements) after scratching the monolayer with a 200 μl pipette tip. D MTT proliferation assay as measured 48 h after siPRRX1 transfection. Experiment was repeated at least 3 times. Bars indicate mean ± SD, n = 12 per group (4 from each experiment). E Colony formation assay 8 days after PRRX1 knockdown. The experiment was repeated at least 3 times. CV = crystal violet. Bars show CV quantification mean values ± SD, n = 9 per group from one representative experiment

PRRX1 and HCC metabolism

Our previous work revealed consistent metabolic gene changes in HCC [24]. Thus, we further interrogated metabolic alterations, based on the negative correlation between PRRX1 expression and metabolic genes we observed in the array analyses. To first test a functional link, we measured glucose consumption and lactate output, which are indicative of the Warburg effect (aerobic glycolysis) and are well known to be elevated in cancer. Indeed, the knockdown of PRRX1 led to an increased glucose consumption in HUH7 and HLF, with a similar upward trend in SNU398 cells (Fig. 5a). In accordance, lactate secretion into the supernatant was increased in HUH7 and SNU398 significantly, and by tendency in HLF cells (Fig. 5a). Based on these findings, distinct gene expression patterns of key components of the glycolytic pathway were determined. A strong effect was monitored in HUH7, with hexokinases 1/2 being increased upon PRRX1 knockdown, as were PDHX, LDHA, and SLC1A5 (Fig. 5b). In contrast, lactate dehydrogenases B (LDHB) was reduced. Effects in HLF were less pronounced, but LDHA/B, which catalyze the conversion of pyruvate to lactate under anaerobic conditions, were strongly increased (Fig. 5b). Notable alterations of tricarboxylic acid cycle (TCA) genes were also detected in both cell lines. As shown in Fig. 5c, crucial enzymes of the TCA cycle were significantly increased in both cell lines upon PRRX1 knockdown, e.g. isocitrate dehydrogenases 3A/B (IDH3A/B), oxoglutarate dehydrogenase (OGDH), several succinate dehydrogenase complex flavoprotein subunits (SDH), fumarate hydratase (FH), and others (Fig. 5c, Fig. S8A). A few tested targets were reduced upon PRRX1 knockdown, e.g. phosphoenolpyruvate carboxykinase 1 (PCK1), which is responsible for the regulation of gluconeogenesis, and the succinate-CoA ligase GDP/ADP-forming subunit 1 (SUCLG1) in both cell lines (Fig. S8A). We also accessed amino acid metabolism genes, especially those involved in glutaminolysis and found them to be deregulated upon PRRX1 knockdown although for some the direction was cell type dependent (Fig. 5d, Fig. S8B). Considering glutaminase 1 (GLS1), which catalyzes the hydrolysis of glutamine to glutamate, the upregulation was consistent in both lines whereas glutamate-pyruvate transaminase 1 (GPT1) was strongly repressed (Fig. 5d). Based on the plethora of metabolic gene expression changes upon PRRX1 modulation, we performed mass spectrometry-based metabolomics to determine changes at the metabolite level. Gas chromatography-mass spectrometry revealed alterations in glycolysis, TCA cycle and amino acid metabolism, with the effect being strongest in HUH7 (the well differentiated cell) (Fig. 5e). While major changes in glycolysis were restricted to the increase of glucose-6-phosphate and fructose-6-phosphate (in HUH7), most analyzed metabolites in TCA cycle were increased. In contrast, HLF cells showed less significant metabolite changes and for all altered analytes, a reduction upon PRRX1 knockdown was observed. Consistent with these findings, an increase in several amino acids was found (except for serine that was significantly reduced) in HUH7 (Fig. 5e), whereas in HLF, the effects were minor.

Fig. 5
figure5

PRRX1 and metabolism of HCC cells. A Glucose consumption and lactate output were measured 48 h after siPRRX1 transfection. Bars indicate mean ± SD and a representative picture of 3 experiments is shown, each in triplicate. B Expression of genes involved in glycolysis were determined by qPCR 48 h after siPRRX1 transfection in HUH7 and HLF cells. Bars indicate mean ± SD and representative of 3 experiments each in triplicates. C Expression of genes involved in the TCA cycle were analyzed by qPCR 48 h after siPRRX1 transfection in HUH7 and HLF cells. Bars indicate mean ± SD and representative of 3 experiments, each in triplicates. D Expression of genes involved in amino acid metabolism as determined by qPCR 48 h after siPRRX1 transfection in HUH7 and HLF cells. Bars indicate mean ± SD and representative of 3 experiments each in triplicates. E Alterations of metabolites. Heatmap of changes in metabolite levels of glycolysis, TCA cycle and amino acids in HUH7 and HLF cells 48 h after siPRRX1 transfection. n = 3 per cell line and condition. White numbers indicate a p < 0.05; black numbers are not significant. The experiment was performed in triplicate

These complex changes on metabolite and gene expression levels are illustrated in Fig. 6a (HUH7) and 6B (HLF) in which affected components of glycolysis and TCA cycle are marked. Taken together, these data support that PRRX1 controls metabolism in HCC cells in a highly context dependent manner.

Fig. 6
figure6

Summary of metabolic changes after knockdown of PRRX1 (A) in HUH7 and (B) in HLF cells. Red indicates upregulation, green indicates downregulation of genes or metabolites, italics: gene expression, bold: dysregulated metabolites. See text for details

Discussion

Although previous studies investigated the abundance of the transcriptional co-activator PRRX1 in HCC [12, 13], conflicting expression patterns were reported and PRRX1 co-regulated genes were not entirely known. In addition, the functions of PRRX1 in HCC remained largely unknown. In this study, we combined bioinformatics analysis of a large cohort averaging over 1000 HCC tumour samples to resolve the expression pattern of PRRX1 in HCC. The analysis was complemented with experiments to gain insight on the functional relevance of PRRX1 in this cancer type. We found that PRRX1 is frequently upregulated in human HCC cohorts, suggesting that its function is unilateral in HCC. But in our analysis, and consistent with the previous report by Hirata et al. [12], PRRX1 level did not correlate with several clinicopathological parameters of HCC patients. Although Hirata et al. found that high PRRX1 expression predicted longer OS, we observed a similar but marginal tendency that was not statistically significant. We attribute this discrepancy to differences in the number of samples used for the OS analysis. The study by Fan et al. [13] also pointed to a favorable outcome with high PRRX1 (determined by immunohistochemistry) as low expression correlated with vascular invasion, intrahepatic and distant metastasis as well as advanced tumour stage. Thus, based on the pattern of association with clinical parameters observed by others and us, PRRX1 can be considered as a tumour suppressor in HCC, at least in the advanced stages of the disease.

PRRX1 is a typical example of an enigmatic gene that has context-specific functions in HCC. Consistent with this view, a tumour suppressor function seems incompatible with our observation of elevated PRRX1 in HCC cohorts. Of note, these cohorts also include TCGA data in which Hirata et al. [12] showed that high PRRX1 expression predicted better OS. Furthermore, the observation that PRRX1 expression is significantly altered in tumours with mutations in CTNNB1, but not TP53 implies that its expression patterns could substantially vary with additional molecular stratification.

HCC cell lines also provide evidence of variable PRRX1 level, with a notable low expression in HLF (a high migrating cell line), although not reflecting PRRX1 correlation to CTNNB1 (there is an extensive heterogeneity in the mutational landscape of the cell lines – making it hard to attribute even their phenotypic differences to the various mutations [26]). Nevertheless, PRRX1 level did not clearly enable discrimination of well from poorly differentiated HCC cell lines unlike we and others have shown for other proteins, e.g. caveolin-1, albumin, alpha fetoprotein, SMADs, and WNT signaling targets [23, 27,28,29].

Evidence from prior studies in colon, breast, and pancreatic cancer support that PRRX1 has context-dependent functions – i.e. it can have a tumour promoter or suppressor function. Our experimental data with cell lines support a tumour suppressor function for PRRX1 in HCC. For example, through in silico analysis we predicted that PRRX1 likely acts by repressing metabolism in HCC. The knockdown of PRRX1 promoted an aerobic glycolytic phenotype (i.e. increased glucose consumption and lactate output). In a recent genome-wide association study linking type II diabetes sensitivity with gene expression patterns, the authors showed that PRRX1 is positively linked with insulin sensitivity, supporting the finding that PRRX1 is related to glucose metabolism [30]. In HUH7 cells, the TCA cycle was also strongly affected at gene and metabolite levels. These observations reflect a tumour suppressive function, even though more studies are required to validate the role of PRRX1 in cell metabolism and to define whether it is critical for HCC progression.

An increased proliferation rate upon PRRX1 knockdown also supports a suppressive function. The observation of these phenotypes, even in the cells with comparatively low basal PRRX1 (i.e. HUH7 and HLF), raises another possibility that certain functions of PRRX1 are conserved in the different HCC cell lines regardless of basal expression.

In agreement with other studies, we also observed that PRRX1 function is ambiguous, which likely depends on undetermined factors. For instance, Fan et al. showed an increased migration upon PRRX1 knockdown in HCC cell lines HEPG2 and SMMC7721 [13]. However, when tested across other cell lines in our work, migration was increased in Hep3B and HUH7 cells, unchanged in SNU398, and reduced in HLF cells. Similarly, with regards to the PRRX1 expression isoforms, most HCC cells expressed more PRRX1b isoform than PRRX1a. The exception was SNU398, which showed no change in proliferation and migration after PRRX1 knockdown. Thus, it could be speculated that PRRX1a, rather than PRRX1b is involved in its phenotypic roles in HCC. Further functional studies will be required to clearly resolve the conditions under which PRRX1 may exert specific functions in HCC and the exact contribution of the isoform ratio.

Our analysis of human HCC datasets indicated a direct correlation between PRRX1 and cancer-related processes, e.g. PI3K-Akt signaling pathway, focal adhesion, extracellular matrix, actin cytoskeleton activities and EMT. The exact role of PRRX1 in controlling the predicted cancer processes needs empirical validation, especially given that the knockdown of PRRX1 induced pro-cancer activities whereas its high expression in tumours had marginal clinicopathological correlation. PRRX1 has been described as EMT gene [8, 9, 15] and prior studies link ZEB1 with unfavorable clinical outcome in HCC [31,32,33]. Interestingly, we identified EMT genes ZEB1/2 as specific candidates that together with PRRX1 impact on clinical OS outcome. We found that PRRX1 correlated with ZEB1/2 in three HCC cohorts analyzed. Whereas none of the three genes independently predicted overall survival in the datasets we investigated, high PRRX1 and either low ZEB1 or high ZEB2 expression predicted a better OS.

Finally, an interesting observation is related to patients with CTNNB1 mutations. In these patients, PRRX1 expression was significantly lower whereas ZEB2 was increased. Also, tumours with TP53 mutations showed significantly reduced ZEB1 expression. It can be assumed that distinct cancer subtypes, defined by mutation status, will be associated with PRRX1/ZEB1/2 patterns and thus determine disease mechanisms and therewith outcome. Further work will have to shed light on this aspect.

Conclusion

We have provided evidence of consistent upregulation of PRRX1 in human HCC. We propose that this gene has anti-tumour functions, at least in advanced tumour stages based on patients’ OS data and our experimental observations. We identify a novel function of PRRX1 in modulating glycolysis and TCA cycle, and that PRRX1 correlated with EMT transcription factors ZEB1/2 (especially with respect to patients’ survival outcome). These findings will enable further in-depth mechanistic studies of the functional relevance of PRRX1 in human liver cancer, and its crosstalk with ZEB1/2.

Availability of data and materials

The publicly available microarray datasets analyzed in this study (GSE25097, GSE64061, GSE55092, GSE36376, GSE39791, GSE62232, GSE57957 and GSE14520) can be accessed at in the NCBI GEO repository (https://www.ncbi.nlm.nih.gov/geo/), while TCGA liver cancer data can be accessed via cBioPortal platform (http://www.cbioportal.org/) and http://cancergenome.nih.gov.

Abbreviations

CTNNB1:

Catenin beta 1

ECM:

Extracellular matrix

EMT:

Epithelial-to-mesenchymal transition

FH:

Fumarate hydratase

GO:

Gene Ontology

GOT1/2:

Glutamic-oxaloacetic transaminases 1/2

GLS1:

Glutaminase 1

GPT1/2:

Glutamate-pyruvate transaminases 1/2

GSEA:

Gene Set Enrichment Analysis

HCC:

Hepatocellular carcinoma

HK1/2:

Hexokinase 1/2

IDH3A/B:

Isocitrate dehydrogenases 3A/B

LDHA/B:

Lactate dehydrogenase A/B

KEGG:

Kyoto Encyclopedia of Genes and Genomes

MDH1:

Malate dehydrogenase 1

OGDH:

Oxoglutarate dehydrogenase

OS:

Overall survival

PCK1:

Phosphoenolpyruvate carboxykinase 1

PDHX:

Pyruvate dehydrogenase complex

PPIA:

Peptidylprolyl isomerase

PRRX1:

Paired related homeobox 1

qPCR:

quantitative polymerase chain reaction

SDHA/B/C/D:

Succinate dehydrogenase complex flavoprotein subunits A/B/C/D

SLC1A5:

Transmembrane sodium-dependent transporter of amino acid

SUCLG1/2:

Succinate-CoA ligase GDP/ADP-forming subunit 1/2

siRNA:

small interfering RNA

TCA:

Tricarboxylic acid cycle

TCGA:

The Cancer Genome Atlas

TP53:

Tumor protein P53

VIM:

Vimentin

ZEB1/2:

Zinc finger E-box-binding homeobox ½

References

  1. 1.

    Sia D, Villanueva A, Friedman SL, Llovet JM. Liver Cancer cell of origin, molecular class, and effects on patient prognosis. Gastroenterology. 2017;152(4):745–61. https://doi.org/10.1053/j.gastro.2016.11.048.

    CAS  Article  Google Scholar 

  2. 2.

    Llovet JM, Villanueva A, Lachenmayer A, Finn RS. Advances in targeted therapies for hepatocellular carcinoma in the genomic era. Nat Rev Clin Oncol. 2015;12(7):408–24. https://doi.org/10.1038/nrclinonc.2015.103.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Zucman-Rossi J, Villanueva A, Nault JC, Llovet JM. Genetic landscape and biomarkers of hepatocellular carcinoma. Gastroenterology. 2015;149(5):1226–1239 e1224. https://doi.org/10.1053/j.gastro.2015.05.061.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Cancer Genome Atlas Research Network. Electronic address wbe, Cancer genome atlas research N: comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell. 2017;169(7):1327–1341 e1323.

    Article  Google Scholar 

  5. 5.

    Bradner JE, Hnisz D, Young RA. Transcriptional addiction in Cancer. Cell. 2017;168(4):629–43. https://doi.org/10.1016/j.cell.2016.12.013.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Ell B, Kang Y. Transcriptional control of cancer metastasis. Trends Cell Biol. 2013;23(12):603–11. https://doi.org/10.1016/j.tcb.2013.06.001.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Martin JF, Olson EN. Identification of a prx1 limb enhancer. Genesis. 2000;26(4):225–9. https://doi.org/10.1002/(SICI)1526-968X(200004)26:4<225::AID-GENE10>3.0.CO;2-F.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Ocana OH, Corcoles R, Fabra A, Moreno-Bueno G, Acloque H, Vega S, et al. Metastatic colonization requires the repression of the epithelial-mesenchymal transition inducer Prrx1. Cancer Cell. 2012;22(6):709–24. https://doi.org/10.1016/j.ccr.2012.10.012.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Reichert M, Takano S, von Burstin J, Kim SB, Lee JS, Ihida-Stansbury K, et al. The Prrx1 homeodomain transcription factor plays a central role in pancreatic regeneration and carcinogenesis. Genes Dev. 2013;27(3):288–300. https://doi.org/10.1101/gad.204453.112.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Takahashi Y, Sawada G, Kurashige J, Uchi R, Matsumura T, Ueo H, et al. Paired related homoeobox 1, a new EMT inducer, is involved in metastasis and poor prognosis in colorectal cancer. Br J Cancer. 2013;109(2):307–11. https://doi.org/10.1038/bjc.2013.339.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Jiang J, Zheng M, Zhang M, Yang X, Li L, Wang SS, et al. PRRX1 regulates cellular phenotype plasticity and dormancy of head and neck squamous cell carcinoma through miR-642b-3p. Neoplasia. 2019;21(2):216–29. https://doi.org/10.1016/j.neo.2018.12.001.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Hirata H, Sugimachi K, Takahashi Y, Ueda M, Sakimura S, Uchi R, et al. Downregulation of PRRX1 confers Cancer stem cell-like properties and predicts poor prognosis in hepatocellular carcinoma. Ann Surg Oncol. 2015;22(Suppl 3):S1402–9.

    Article  Google Scholar 

  13. 13.

    Fan M, Shen J, Liu H, Wen Z, Yang J, Yang P, et al. Downregulation of PRRX1 via the p53-dependent signaling pathway predicts poor prognosis in hepatocellular carcinoma. Oncol Rep. 2017;38(2):1083–90. https://doi.org/10.3892/or.2017.5785.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Lv ZD, Yang ZC, Liu XP, Jin LY, Dong Q, Qu HL, et al. Silencing of Prrx1b suppresses cellular proliferation, migration, invasion and epithelial-mesenchymal transition in triple-negative breast cancer. J Cell Mol Med. 2016;20(9):1640–50. https://doi.org/10.1111/jcmm.12856.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Takano S, Reichert M, Bakir B, Das KK, Nishida T, Miyazaki M, et al. Prrx1 isoform switching regulates pancreatic cancer invasion and metastatic colonization. Genes Dev. 2016;30(2):233–47. https://doi.org/10.1101/gad.263327.115.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Yi K, Kim H, Chung Y, Ahn H, Sim J, Wi YC, et al. Clinicopathologic correlations of E-cadherin and Prrx-1 expression loss in hepatocellular carcinoma. J Pathol Transl Med. 2016;50(5):327–36. https://doi.org/10.4132/jptm.2016.06.22.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4. https://doi.org/10.1158/2159-8290.CD-12-0095.

    Article  Google Scholar 

  18. 18.

    Huang Q, Tan Y, Yin P, Ye G, Gao P, Lu X, et al. Metabolic characterization of hepatocellular carcinoma using nontargeted tissue metabolomics. Cancer Res. 2013;73(16):4992–5002. https://doi.org/10.1158/0008-5472.CAN-13-0308.

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Abu El Maaty MA, Alborzinia H, Khan SJ, Buttner M, Wolfl S. 1,25(OH)2D3 disrupts glucose metabolism in prostate cancer cells leading to a truncation of the TCA cycle and inhibition of TXNIP expression. Biochim Biophys Acta Mol Cell Res. 2017;1864(10):1618–30. https://doi.org/10.1016/j.bbamcr.2017.06.019.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Lodd E, Wiggenhauser LM, Morgenstern J, Fleming TH, Poschet G, Buttner M, et al. The combination of loss of glyoxalase1 and obesity results in hyperglycemia. JCI Insight. 2019;4(12):1–17 e126154. https://doi.org/10.1172/jci.insight.126154.

  21. 21.

    Katzenellenbogen M, Pappo O, Barash H, Klopstock N, Mizrahi L, Olam D, et al. Multiple adaptive mechanisms to chronic liver disease revealed at early stages of liver carcinogenesis in the Mdr2-knockout mice. Cancer Res. 2006;66(8):4001–10. https://doi.org/10.1158/0008-5472.CAN-05-2937.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Liu Y, Meyer C, Xu C, Weng H, Hellerbrand C, ten Dijke P, et al. Animal models of chronic liver diseases. Am J Physiol Gastrointest Liver Physiol. 2013;304(5):G449–68. https://doi.org/10.1152/ajpgi.00199.2012.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Yuzugullu H, Benhaj K, Ozturk N, Senturk S, Celik E, Toylu A, et al. Canonical Wnt signaling is antagonized by noncanonical Wnt5a in hepatocellular carcinoma cells. Mol Cancer. 2009;8(1):90. https://doi.org/10.1186/1476-4598-8-90.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Nwosu ZC, Megger DA, Hammad S, Sitek B, Roessler S, Ebert MP, et al. Identification of the consistently altered metabolic targets in human hepatocellular carcinoma. Cell Mol Gastroenterol Hepatol. 2017;4(2):303–323 e301. https://doi.org/10.1016/j.jcmgh.2017.05.004.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Allain C, Angenard G, Clement B, Coulouarn C. Integrative genomic analysis identifies the Core transcriptional hallmarks of human hepatocellular carcinoma. Cancer Res. 2016;76(21):6374–81. https://doi.org/10.1158/0008-5472.CAN-16-1559.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Qiu Z, Li H, Zhang Z, Zhu Z, He S, Wang X, et al. A Pharmacogenomic landscape in human liver cancers. Cancer Cell. 2019;36(2):179–193 e111. https://doi.org/10.1016/j.ccell.2019.07.001.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Cokakli M, Erdal E, Nart D, Yilmaz F, Sagol O, Kilic M, et al. Differential expression of Caveolin-1 in hepatocellular carcinoma: correlation with differentiation state, motility and invasion. BMC Cancer. 2009;9(1):65. https://doi.org/10.1186/1471-2407-9-65.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Dzieran J, Fabian J, Feng T, Coulouarn C, Ilkavets I, Kyselova A, et al. Comparative analysis of TGF-beta/Smad signaling dependent cytostasis in human hepatocellular carcinoma cell lines. PLoS One. 2013;8(8):e72252. https://doi.org/10.1371/journal.pone.0072252.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Meyer C, Dzieran J, Liu Y, Schindler F, Munker S, Muller A, et al. Distinct dedifferentiation processes affect caveolin-1 expression in hepatocytes. Cell Commun Signal. 2013;11(1):6. https://doi.org/10.1186/1478-811X-11-6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Timmons JA, Atherton PJ, Larsson O, Sood S, Blokhin IO, Brogan RJ, et al. A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease. Nucleic Acids Res. 2018;46(15):7772–92. https://doi.org/10.1093/nar/gky570.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Hashiguchi M, Ueno S, Sakoda M, Iino S, Hiwatashi K, Minami K, et al. Clinical implication of ZEB-1 and E-cadherin expression in hepatocellular carcinoma (HCC). BMC Cancer. 2013;13(1):572. https://doi.org/10.1186/1471-2407-13-572.

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Wan T, Zhang T, Si X, Zhou Y. Overexpression of EMT-inducing transcription factors as a potential poor prognostic factor for hepatocellular carcinoma in Asian populations: a meta-analysis. Oncotarget. 2017;8(35):59500–8. https://doi.org/10.18632/oncotarget.18352.

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Yamada S, Okumura N, Wei L, Fuchs BC, Fujii T, Sugimoto H, et al. Epithelial to mesenchymal transition is associated with shorter disease-free survival in hepatocellular carcinoma. Ann Surg Oncol. 2014;21(12):3882–90. https://doi.org/10.1245/s10434-014-3779-2.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank Dr. Francois Helle (University of Picardie Jules Verne, France) for the SNU398 cell line, and the staff of the Zentrum für Medizinische Forschung, Mannheim, for their support with glucose and lactate assays. We thank the Metabolomics Core Technology Platform of the University of Heidelberg for support with GC/MS-based metabolite analyses. We thank Dr. Nina Steele (University of Michigan, Ann Arbor, USA) for proofreading the manuscript.

Funding

The PRRX1 project was funded by Wilhelm Sander-Stiftung (2016.006.2) grant to CM. SD is supported by funds from the DFG (Do373/13–1), the BMBF program LiSyM (Grant PTJ-FKZ: 031 L0043). The funding bodies did not influence the content of this article. Open Access funding enabled and organized by Projekt DEAL.

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Contributions

WP and ZN planned experiments, performed dataset analyses, and wrote manuscript. WP and MH performed experimental work. MB performed GC/MS-based metabolite analyses. CM conceived the study, discussed data presentation and supervised the study. CM, SD and ME critically revised the paper. All authors read and approved the final version.

Corresponding author

Correspondence to Christoph Meyer.

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Supplementary Information

Additional file 1: Figure S1.

PRRX1 expression in HCC. Figure S2. Analysis of PRRX1 expression with respect to clinicopathological variables, and in experimental models. Figure S3. Cellular components in which are involved genes positively and negatively correlated with PRRX1 in TCGA liver cancer data. Figure S4. KEGG pathway annotation of PRRX1 correlated genes in various cancers where its alteration frequency is high. Figure S5. Annotation of PRRX1 inversely correlated genes. Figure S6. Correlation and survival analyses of PRRX1 and its co-expressed genes. Figure S7. Survival analysis and expression of ZEB1/2 also with respect to TP53 and CTNNB1 mutation. Figure 8S. PRRX1 and metabolic targets. Table S1. Human HCC microarrays. Table S3. KEGG pathway annotation of genes co-expressed (positively) with PRRX1. Table S4. KEGG pathway annotation of genes inversely correlated with PRRX1. Table S5. GO Biological processes for PRRX1 positively co-expressed genes. Table S6. GO Cellular components for PRRX1 positively co-expressed genes. Table S7. GO Biological processes for PRRX1 inversely correlated genes. Table S8. GO Cellular components for PRRX1 inversely correlated genes. Table S9. Metabolic targets in the PRRX1 co-expressed gene list. Table S11. 148 genes as potential candidates that likely cooperate or are coregulated with PRRX1. Table S12. ZEB1 expression in combination with PRRX1 with respect to the clinicopathological variables (n= number of patients). Table S13. ZEB2 expression in combination with PRRX1 with respect to the clinicopathological variables (n= number of patients).

Additional file 2: Table S2.

PRRX1 positively and negatively correlated genes in TCGA liver cancer data.

Additional file 3: Table S10.

Genes differentially expressed in PRRX1-high tumours relative to PRRX1-low tumours.

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Piorońska, W., Nwosu, Z.C., Han, M. et al. Dysregulated paired related homeobox 1 impacts on hepatocellular carcinoma phenotypes. BMC Cancer 21, 1006 (2021). https://doi.org/10.1186/s12885-021-08637-3

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Keywords

  • EMT
  • Epithelial mesenchymal transition
  • Liver cancer
  • Metabolism
  • PRRX1