NAT2 : A Novel Target in Intrahepatic Cholangiocarcinoma and its Role in Modulating Tumor Behavior

Abstract

Background:

Intrahepatic cholangiocarcinoma (ICC) is a primary liver cancer typically diagnosed at advanced stages, limiting treatment options and reducing survival rates. Targeted therapy presents a promising strategy to improve outcomes. This study aims to identify novel molecular biomarkers influencing ICC development and explore their roles in tumor progression.

Methods:

Data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed using weighted gene co-expression network analysis (WGCNA) to identify genes related to tumor metastasis and recurrence. Survival analysis and gene set enrichment analysis (GSEA) assessed the relationship between gene expression and survival, as well as associated signaling pathways. Cellular experiments, including small interfering RNA (siRNA) knockdown, cell viability assays, Transwell migration assays, and flow cytometry, were performed.

Results:

The Kaplan-Meier analysis showed that ICC patients with high N-acetyltransferase 2 (NAT2) expression had significantly shorter survival times than those with low expression (P < .001). Gene set enrichment analysis revealed enrichment of MYC and MTORC1 pathways, linked to tumor proliferation, and E2F and G2M pathways, which regulate the cell cycle, in high NAT2 expression samples (P < .01). The NAT2 knockdown reduced RBE cell proliferation (P < .001) and increased late apoptosis (P < .001). Immunofluorescence analysis showed increased Bax and Caspase-3 expression and decreased BCL-2 expression (P < .05), supporting NAT2’s role in regulating ICC cell apoptosis.

Conclusion:

NAT2, a novel therapeutic target, holds significant potential to improve the prognosis of ICC patients.

Introduction

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor arising from the bile duct epithelial cells of the liver, accounting for 15% of primary liver cancer cases. It is the second most common form of primary liver cancer.^1,2 The main treatment options for ICC are currently surgical resection, chemotherapy, and radiotherapy. Due to subtle or nonspecific symptoms, ICC is often diagnosed at advanced stages, by which time the tumor may have already undergone local invasion or distant metastasis. This significantly limits treatment options and diminishes therapeutic efficacy.^3,4 Studies ⁵ have shown that the 5-year survival rate for patients with advanced ICC ranges from 7% to 20%. Only 20% to 30% of these patients are eligible for surgical resection. ⁶ For most patients undergoing surgery, the late-stage diagnosis and complex conditions often result in an expected survival of less than 1 year.^7-9 Even among those who undergo successful surgical resection, long-term survival remains challenging.^10,11 Targeted therapy and immunotherapy have the potential to improve long-term survival in advanced ICC patients, but their current efficacy is limited. This underscores the urgent need for a deeper molecular understanding of ICC to identify novel therapeutic targets.

The pathogenesis of ICC involves complex molecular changes, including disruptions in cell signaling, cell cycle regulation, and apoptosis pathways.^12-14 Key molecular alterations include fusion mutations in FGFR2, as well as mutations in KRAS and TP53, which affect critical signaling pathways involved in cell proliferation and survival, such as the RAS/MAPK and PI3K/AKT pathways. These disruptions result in uncontrolled cell proliferation and the inhibition of apoptosis.^15,16 These disturbances in signaling pathways provide a molecular basis for tumor invasiveness and drug resistance while revealing several potential therapeutic targets. For example, fusion mutations in FGFR2 with genes such as BICC1, AHCYL1, and KIAA1217 promote cell proliferation by activating related signaling pathways. The FGFR inhibitors, such as pemigatinib, have shown promising therapeutic effects in clinical trials. ¹⁷ The KRAS mutations promote tumor cell growth and metastasis by activating the RAS/MAPK pathway while also increasing resistance to conventional treatments. This makes the MEK inhibitor trametinib an effective therapeutic option. ¹⁸

Despite the identification of several therapeutic targets and their clinical effectiveness, therapeutic improvement for ICC remains suboptimal. This is especially evident in advanced ICC patients, who often exhibit high resistance to existing therapies and rapid disease progression. Our understanding of tumor cell proliferation, metastasis, and apoptosis regulation remains incomplete, limiting treatment outcome improvements for advanced ICC patients. There is an urgent need to identify and validate new molecular biomarkers and therapeutic strategies.

Recent research ¹⁹ has drawn attention to N-acetyltransferase 2 (NAT2), a phase II metabolic enzyme traditionally known for its role in the acetylation of aromatic and heterocyclic amines. The NAT2 activity varies across individuals due to genetic polymorphisms, which influence drug metabolism and cancer susceptibility. ²⁰ Emerging evidence has suggested that NAT2 may also play a role in modulating tumor cell behavior, including proliferation, apoptosis, and migration, particularly in breast and colorectal cancers.^21,22 However, its role in ICC remains largely uncharacterized. This study aims to explore the potential of NAT2 as a novel molecular biomarker and therapeutic target in ICC, thereby addressing an important gap in current cancer biology and treatment research.

Methods

Data sources and gene screening

Gene expression data were obtained from public databases: The Cancer Genome Atlas (TCGA-CHOL; https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO) data sets GSE107943 and GSE76297 (https://www.ncbi.nlm.nih.gov/geo/). Differentially expressed genes (DEGs) were identified using the R package weighted gene co-expression network analysis (WGCNA) (version 1.70-3), applying a |log2 fold change| > 1.5 and adjusted P-value < .05 as screening thresholds. The R package Venn Diagram (version 1.6.20) was used to perform intersection analysis across data sets to identify candidate genes simultaneously associated with tumor metastasis, recurrence, and tumor-normal tissue differences.

Differential genes associated with intrahepatic cholangiocarcinoma survival

The TCGA survival data set and GSE107943 were standardized using the Z-score method and merged to eliminate potential biases from platform and batch effects (n = 66). Survival analysis was conducted using the “survival” R package (version 3.2-13; https://cran.r-project.org/web/packages/survival) to evaluate the correlation between the identified differential genes and ICC patient survival. The Kaplan-Meier survival curves were generated to compare survival times between groups with high and low gene expression, examining the impact of gene expression on survival prognosis. Log-rank test was applied, and P < .05 was considered statistically significant.

Gene set enrichment analysis

The gene with the most significant prognostic association (NAT2) was selected for pathway enrichment analysis using gene set enrichment analysis (GSEA) software (version 4.1.0; https://www.gsea-msigdb.org/gsea/). Predefined hallmark gene sets were obtained from the Molecular Signatures Database (MSigDB v7.4). The following parameters were used: Number of permutations = 1000; Permutation type = “gene_set”; Enrichment statistic = “weighted”; Significance thresholds: false discovery rate (FDR) < 0.25 and nominal P-value < .05.

Cell culture

The human ICC cell line RBE was purchased from Wuhan Procell Life Science & Technology Co., Ltd. (Procell; Wuhan, China, Cat# CL-0191; RRID: CVCL_4896) and authenticated by short tandem repeat (STR) profiling. The ICC cell lines were cultured in DMEM/RPMI-1640 medium (Hyclone, USA) supplemented with 10% fetal bovine serum (FBS, GIBCO, USA) and 1% antibiotics (penicillin and streptomycin, Solarbio, USA). Cells were cultured in an incubator at 37°C with 5% CO₂ and 95% O₂.

Small interfering RNA knockdown experiment

The RBE cells were seeded into 6-well plates at a density of 3 × 105 cells per well. After approximately 24 hours, when cells reached 60% to 70% confluence, transfection was performed using Lipofectamine 2000 (Invitrogen, USA, Cat#11668019). The transfection procedure was as follows: 50 nM small interfering RNA (siRNA) targeting NAT2 (siNAT2) or negative control siRNA (siNC) was diluted in 100 μL of serum-free Opti-MEM medium. Separately, 5 μL of Lipofectamine 2000 reagent was diluted in another 100 μL of serum-free Opti-MEM and incubated at room temperature for 5 minutes. The 2 dilutions were then mixed and incubated for 20 minutes at room temperature to form siRNA-lipid complexes. Before transfection, cells were washed once with serum-free medium, and 800 μL of serum-free Opti-MEM plus 200 μL of the siRNA-lipid complex was added to each well. After 6 hours of incubation, the transfection medium was replaced with complete medium containing 10% FBS, and cells were cultured for another 42 hours. The efficiency of NAT2 knockdown was evaluated by quantitative real-time polymerase chain reaction (qRT-PCR) 48 hours after transfection. The siRNAs were synthesized by Invitrogen (20 μM stock concentration). Knockdown efficiency was assessed by qRT-PCR 48 hours post-transfection. The specific siRNA sequences are listed in Table 1.

Table 1.

siRNA sequences used for NAT2 knockdown experiment.

Primer name	Sequence (5’ to 3’)
NAT2 (human) siRNA-265	GGUGGUGUCUCCAGGUCAAUC
	UUGACCUGGAGACACCACCCA
NAT2 (human) siRNA-978	CCAACUCACUAAUUAUCAACU
	UUGAUAAUUAGUGAGUUGGGU
NAT2 (human) siRNA-1254	GGUAAAUGAAUAAAGAAUAUU
	UAUUCUUUAUUCAUUUACCAG
NAT2 (human) siRNA-NC	UUCUCCGAACGUGUCACGUTT
	ACGUGACACGUUCGGAGAATT

Cell viability assay

After 48 hours of siRNA transfection, 2000 cells per well were seeded into 96-well plates. The cells were cultured under standard conditions. At designated time points (0 and 24 hours), the supernatant was gently aspirated, and 110 μL of RPMI-1640 medium containing 10 μL of CCK-8 reagent (7Sea Biotech; Cat# 20140419) was added to each well. The plates were incubated at 37°C with 5% CO₂ for 2 hours to allow the CCK-8 reagent to react with live cells and produce a color change. Absorbance (OD) was measured at 450 nm using a multi-mode microplate reader (Synergy HTX, BioTek Instruments, USA) to assess cell viability and proliferative capacity.

Transwell assay

After 48 hours of siRNA transfection, RBE cells were harvested by trypsinization, washed once with serum-free medium, and resuspended at a density of 1 × 105 cells per well in 200 μL of serum-free RPMI-1640 medium. Cells were seeded into the upper chambers of 24-well Transwell inserts with an 8-μm pore membrane (Corning, New York). The lower chambers were filled with 600 μL of RPMI-1640 medium containing 10% FBS as a chemoattractant. After 24 hours of incubation at 37 °C in a humidified incubator with 5% CO2, non-migrated cells on the upper surface of the membrane were carefully removed using a cotton swab. Migrated cells on the underside of the membrane were fixed with 4% paraformaldehyde for 15 minutes and stained with 0.1% crystal violet for 20 minutes. After 3 gentle washes with phosphate-buffered saline (PBS), cells were imaged under an optical microscope (XDS-500C; Shanghai Caikon Optical Instrument Co, Ltd, China). Cell migration was quantified using MiVnt microscopic image analysis software, counting migrated cells in 5 randomly selected fields per membrane.

Flow cytometry analysis of apoptosis

Forty-eight hours post-transfection, RBE cells were harvested using trypsin, washed once with PBS, and resuspended at a concentration of 1 × 106 cells/mL in 1× binding buffer. Apoptosis was analyzed using an Annexin V-FITC/Propidium Iodide (PI) Apoptosis Detection Kit (BD Biosciences, USA; Cat# 556547) following the manufacturer’s protocol. Briefly, 100 μL of cell suspension was incubated with 5 μL of Annexin V-FITC and 5 μL of PI for 30 minutes in the dark at room temperature. Samples were analyzed immediately without additional washing using a flow cytometer (FACSCalibur; BD Biosciences, USA). Fluorescence signals were detected in the FL1 (FITC, green) and FL3 (PI, red) channels to distinguish early apoptotic (Annexin V+/PI−), late apoptotic (Annexin V+/PI+), and necrotic cells. FlowJo software (version 10.8.1) was used to calculate the percentage of cells in each apoptotic stage.

Immunofluorescence staining

Forty-eight hours post-transfection, siNAT2-transfected cells were fixed with 4% formaldehyde for 15 minutes and washed 3 times with PBS (5 minutes each). Cells were permeabilized with PBS containing 0.1% Triton X-100 for 10 minutes and then blocked with PBS containing 1% bovine serum albumin (BSA) for 1 hour at room temperature. Cells were incubated overnight at 4°C with the following primary antibodies: anti-Bax (Abcam, Cat# ab32503, rabbit monoclonal, Lot# GR3235279-11, 1:200 dilution), anti-Bcl-2 (Abcam, Cat# ab692, mouse monoclonal, Lot# GR3251502-6, 1:200 dilution), and anti-Caspase-3 (Abcam, Cat# ab32351, rabbit monoclonal, Lot# GR325607-1, 1:200 dilution). The next day, cells were washed 3 times and incubated for 1 hour at room temperature with FITC-conjugated secondary antibody (Abcam, Cat# ab150077, goat anti-rabbit IgG, 1:500 dilution). After incubation, cells were washed with PBS and counterstained with 0.1% Hoechst (Merck, Germany, Cat# 14533) at 37°C for 10 minutes to visualize nuclei. Following additional PBS washes, coverslips were mounted using anti-fade mounting medium. Images were acquired using a fluorescence microscope (Olympus BX53, Japan) equipped with a DP74 digital camera. Image acquisition and analysis were performed using cellSens Dimension software (Olympus, version 1.18).

Real-time quantitative polymerase chain reaction

Total RNA was extracted from siNC (control siRNA), si-RBE (RBE cells transfected with NAT2 siRNA), and untreated RBE cells using TRIzol reagent (Invitrogen, USA). Reverse transcription and PCR amplification were performed using the TaKaRa PCR Kit (AMV) Ver. 3.0 (TaKaRa Bio, Japan). The qRT-PCR was carried out using SYBR Green PCR Master Mix (Applied Biosystems, USA) on a StepOnePlus Real-Time PCR system (Applied Biosystems, USA). The expression levels of NAT2 and GAPDH were measured, with GAPDH serving as the internal control to normalize expression and account for inter-sample variability. Knockdown efficiency was evaluated based on relative NAT2 expression using the 2^−ΔΔCt method. Primer sequences are listed in Table 2.

Table 2.

Primer sequences used in the experiment.

Gene name		Sequence (5’ to 3’)
NAT2	Forward	GGTGTCTCCAGGTCAATCAACTTCT
	Reverse	GGTGAACCATGCCAGTGCTGTATT
GAPDH	Forward	AGATCATCAGCAATGCCTCCT
	Reverse	TGAGTCCTTCCACGATACCAA

Statistical analysis

Statistical analysis was performed using Prism 10.1.2 (GraphPad, San Diego, California) with a 2-sided Student’s t-test to compare differences between 2 groups. Results are expressed as the mean ± standard deviation (SD) from at least 3 independent experiments. Prognostic analysis was performed to identify the optimal cutoff value, and the Kaplan-Meier survival curves were generated. P-values were calculated using the log-rank test, with P < .05 considered statistically significant.

Results

Differentially expressed genes

The WGCNA revealed that the turquoise module was significantly positively correlated with tumor metastasis (r = 0.27, P = .01, Figure 1A). Furthermore, the pink, brown, and yellow modules were significantly positively correlated with tumor recurrence (r values of 0.11, 0.25, and 0.15; P-values of .04, .02, and .04, respectively, Figure 1B). The gray module showed a significant positive correlation in the differential expression analysis between tumor and adjacent normal tissues (r = 0.29, P < .001, Figure 1C). Venn diagram analysis identified 20 genes that were differentially expressed in all 3 conditions—tumor metastasis, recurrence, and tumor-normal tissue differences (Figure 1D).

Figure 1.

WGCNA and differential gene identification. (A) WGCNA results related to tumor metastasis. (B) WGCNA results related to tumor recurrence. (C) WGCNA between tumor and adjacent normal tissues. (D) Venn diagram showing differential genes associated with tumor metastasis, recurrence, and tumor-normal tissue differences.

Genes associated with survival

The Kaplan-Meier survival curves showed that patients with high NAT2 expression had significantly lower survival rates than those with low NAT2 expression (P < .001, Figure 2). This suggests that NAT2 may serve as a poor prognostic marker.

Figure 2.

Kaplan-Meier survival curves for ICC survival.

Biological pathways associated with N-acetyltransferase 2 expression

The GSEA identified several pathways significantly associated with NAT2 expression, including MYC, G2M, E2F, MTORC1, KRAS, and APOPTOSIS (Figure 3). These findings suggest that NAT2 may promote tumor proliferation and metastasis and inhibit apoptosis in cholangiocarcinoma.

Figure 3.

GSEA identifies biological pathways associated with NAT2 expression.

Effects of N-acetyltransferase 2 knockdown on intrahepatic cholangiocarcinoma cell function

The REB-Si2 knockdown sequence showed the highest knockdown efficiency (P < .0001, Figure 4A). The CCK-8 assay results indicated that NAT2 knockdown significantly reduced the survival rate of RBE cells (P < .001, Figure 4B). Transwell assay results demonstrated that NAT2 knockdown inhibited cell invasion (Figure 4C and D). Flow cytometry analysis revealed a significant increase in the proportion of late apoptotic cells after NAT2 knockdown (Figure 4E and F).

Figure 4.

Effect of NAT2 knockdown on cell function. All data are presented as mean ± standard deviation (SD) from 3 independent biological replicates (n = 3). (A) Quantitative RT-PCR analysis of NAT2 mRNA expression after siRNA-mediated knockdown. Relative expression: RBE-Control (0.98 ± 0.07), RBE-siNC (1.03 ± 0.10), RBE-si-1 (0.56 ± 0.02), RBE-si-2 (0.15 ± 0.03), and RBE-si-3 (0.48 ± 0.04). (B) CCK-8 assay showing reduced cell viability post-NAT2 knockdown. RBE-siNC: (0.98 ± 0.03) and RBE-si-NAT2: (0.79 ± 0.04). (C) Representative Transwell invasion images after NAT2 knockdown. Cells were stained and imaged at 100× magnification. Scale bar = 100 μm. (D) Quantification of invaded cells: RBE-siNC (119.33 ± 8.08) and RBE-si-NAT2 (50.33 ± 4.04). Inhibition rate: 57.82%. (E) Flow cytometry analysis showing increased late apoptosis 48 hours post-transfection. (F) Late apoptotic cell rates: RBE-Control (5.34 ± 1.16), RBE-siNC (5.66 ± 1.23), and RBE-si-NAT2 (16.47 ± 1.92).

Immunofluorescence analysis results

Immunofluorescence experiments were conducted to assess the expression of apoptosis-related genes (Bax, BCL-2, Caspase-3) in cells with NAT2 knockdown and control cells (Figure 5A to C). The results showed that, following NAT2 knockdown, the expression of Bax and Caspase-3 was significantly elevated (P < .05 for both, Figure 5D and E), while the expression of BCL-2 was significantly reduced (P < .001, Figure 5F).

Figure 5.

Immunofluorescence analysis. All data are presented as SD from 3 independent biological replicates (n = 3). (A-C) Representative images showing Bax, Caspase-3, and BCL-2 staining by confocal microscopy. Magnification: 400×. Scale bar = 50 μm. (D) Bax fluorescence intensity: RBE-Control (1.31 ± 0.19), RBE-siNC (0.99 ± 0.39), and RBE-si-NAT2 (2.55 ± 0.49). (E) Caspase-3 fluorescence intensity: RBE-Control (0.59 ± 0.34), RBE-siNC (1.16 ± 0.22), and RBE-si-NAT2 (1.88 ± 0.45). (F) BCL-2 fluorescence intensity: RBE-Control (1.45 ± 0.14), RBE-siNC (1.80 ± 0.10), and RBE-si-NAT2 (1.17 ± 0.23).

Discussion

This study combined bioinformatics analysis and in vitro experiments to investigate the role of NAT2 in ICC. Our findings revealed that NAT2 is significantly upregulated in ICC tissues and associated with poor patient survival. Functionally, NAT2 knockdown inhibited proliferation and migration, while promoting the apoptosis of ICC cells, suggesting that NAT2 plays a regulatory role in tumor progression.

Traditionally, NAT2 is recognized as a phase II metabolic enzyme involved in the acetylation and detoxification of aromatic amines and hydrazines.^23,24 However, recent evidence suggests that NAT2 may also directly influence tumor cell behavior. For instance, NAT2 overexpression has been linked to enhanced tumor cell proliferation and migration in colorectal cancer ²⁵ and increased inflammatory signaling in other malignancies.^26,27 Our study extends these observations by providing direct evidence of NAT2’s functional role in ICC cell biology.

One of the most significant findings of this study is the pro-apoptotic effect observed after NAT2 silencing. The NAT2 knockdown resulted in increased expression of Bax and Caspase-3 and decreased BCL-2 expression, suggesting the activation of the intrinsic mitochondrial apoptotic pathway. Bax promotes cytochrome c release from mitochondria, which activates the caspase cascade, while caspase-3 serves as a key executor of apoptosi.^28,29 The reduction in BCL-2—a key anti-apoptotic protein—further facilitates this process by removing its inhibitory effect on Bax. ³⁰ These findings provide mechanistic support for NAT2’s involvement in apoptosis regulation in ICC cells, complementing prior studies that emphasized NAT2’s role in metabolic adaptation but did not explore its signaling functions.

The GSEA further revealed that high NAT2 expression was associated with the enrichment of key oncogenic pathways, including MYC, MTORC1, E2F, G2M checkpoint, and KRAS signaling. These pathways are central to tumor cell proliferation, metabolism, and survival. The MYC, a key regulatory factor, is closely linked to the progression of various cancers due to its overexpression. The MYC accelerates the cell cycle by promoting key proteins involved in the G1/S transition and E2F target genes, driving cell growth and metabolism. ³¹ In ICC samples with high NAT2 expression, the enrichment of the MYC signaling pathway suggests that NAT2 may promote tumor cell proliferation and metabolism by activating MYC. The E2F activation enhances the expression of genes necessary for DNA replication, supplying essential proteins for the DNA synthesis phase. ³² This suggests that NAT2 may influence the E2F pathway to prepare cells for replication. The G2M pathway regulates the transition from G2 to mitotic M phase, ensuring correct DNA replication and damage repair before cell division, thus ensuring orderly mitosis. ³³ The MTORC1 activation is closely related to enhanced energy metabolism and survival in tumor cells, ³⁴ suggesting that NAT2 may influence tumor cell energy metabolism and survival by modulating this pathway. Similarly, KRAS, a commonly mutated oncogene, promotes cell proliferation and survival via its signaling pathway, ³⁵ further supporting the idea that NAT2 regulates tumor cell behavior through KRAS signaling. In addition, the enrichment of apoptosis pathways in high NAT2 expression samples may reflect the cell’s regulatory response to cell death stress. The convergence of NAT2 with these pathways suggests that it may serve as a regulatory node in multiple pro-tumorigenic networks.

Furthermore, our analysis highlighted a potential clinical implication of NAT2 in drug metabolism. The NAT2 polymorphisms result in rapid or slow acetylator phenotypes, which can influence individual responses to chemotherapy. ³⁶ Individuals with a rapid acetylation phenotype tend to clear the drug more quickly from the body, potentially requiring higher or more frequent doses to maintain therapeutic efficacy. In contrast, individuals with a slow acetylation phenotype metabolize drugs more slowly, leading to prolonged retention and an increased risk of toxicity, particularly with agents like cyclophosphamide, which can cause severe bone marrow suppression. ³⁷ Although our study did not explore NAT2 genotype-phenotype correlations in ICC patients, this represents an important area for future research, particularly in the context of personalized medicine.

While this study offers new insights into the role of NAT2 in ICC, its limitations must be acknowledged. First, this research primarily relies on in vitro experiments and publicly available databases; future studies should validate these findings using animal models and prospective clinical trials. Second, although NAT2 knockdown was confirmed at the mRNA level by qRT-PCR, the absence of protein-level validation via Western blotting remains a limitation. Future research should incorporate both transcript and protein analyses to provide a more comprehensive mechanistic understanding. In addition, this study did not investigate the direct relationship between NAT2 gene polymorphisms and chemotherapy responses in patients, an important area for future research.

Conclusion

In conclusion, this study elucidates the complex role of NAT2 in ICC, including its regulation of tumor cell proliferation, migration, and apoptosis, as well as its potential involvement in key signaling pathways. These findings provide a scientific foundation for developing novel therapeutic strategies targeting NAT2 and offer new perspectives for improving the prognosis of ICC patients.