Lidocaine inhibits hepatocellular carcinoma cell proliferation, migration, and invasion through the downregulation of SLC6A3

Abstract

Lidocaine, a widely used local anesthetic, has been reported to exert anti-cancer activity against hepatocellular carcinoma (HCC). However, its molecular mechanisms remain incompletely understood. This study sought to elucidate the mechanisms underlying lidocaine’s effects on HCC. Potential lidocaine targets in HepG2 cells were identified using network pharmacology and transcriptomic profiling. The prognostic and clinical relevance of candidate genes were assessed through bioinformatics analyses. Key targets were validated by RT–qPCR. The functional role of SLC6A3 in regulating HepG2 cell proliferation, apoptosis, migration, and invasion was examined through in vitro assays. Network pharmacology predicted 433 lidocaine targets, while transcriptomic profiling revealed 442 differentially expressed genes. Nine overlapping targets (SLC6A3, CHRNB2, GRIN1, ADRA2C, LIPE, SLC18A2, KCNQ2, TERT, and ALOX12) were enriched in pathways associated with neuronal signaling, synaptic transmission, and drug addiction. Among these, SLC6A3 and TERT were significantly associated with poor prognosis and increased tumor immune infiltration. Both genes demonstrated predictive value for 1- to 2-year survival, with SLC6A3 showing the stronger prognostic relevance. Molecular docking revealed hydrophobic interactions between lidocaine and SLC6A3 (binding energy: −5.6 kcal/mol). Silencing of SLC6A3 markedly promoted apoptosis and suppressed proliferation, migration, and invasion of HepG2 cells. Collectively, these findings suggest that lidocaine inhibits HCC progression by targeting and downregulating SLC6A3. Lidocaine exerts anti-HCC effects by directly targeting and downregulating SLC6A3, thereby inducing apoptosis and suppressing tumor progression.

Introduction

Liver cancer ranks as the sixth most prevalent malignancy worldwide, with its incidence projected to surpass 1 million cases by 2025¹. The principal histological subtypes are hepatocellular carcinoma (HCC, ~ 80%), intrahepatic cholangiocarcinoma (~ 15%), and less common variants (~ 5%)². In 2002, liver cancer accounted for more than 600,000 deaths globally, of which 75–90% were attributed to HCC³. Alarmingly, approximately 25–70% of HCC cases are diagnosed at an advanced stage⁴. Although recent therapeutic advances have improved outcomes for advanced HCC, malignant progression remains a major clinical challenge⁵. For instance, in a trial involving 1,171 patients, the combination of tremelimumab with durvalumab (STRIDE regimen) yielded superior efficacy compared with durvalumab or sorafenib monotherapy. Nevertheless, overall survival (OS) after STRIDE remained poor, with 74.8% of patients surviving less than 48 months and 69.3% surviving less than 36 months⁶. These sobering statistics underscore the urgent need for novel therapeutic targets to improve HCC management and patient survival.

Lidocaine is a widely used local anesthetic in surgical practice. Although substantial efforts have focused on the development of novel anesthetics and their clinical applications, the broader biological functions of lidocaine remain insufficiently characterized^7–9. Emerging evidence suggests that lidocaine exerts direct effects on cancer cells and contributes to antitumor activity by modulating signaling pathways and the tumor microenvironment, thereby improving patient survival¹⁰. In HCC, lidocaine has been shown to suppress tumor growth by downregulating USP14 and inhibiting the PI3K/Akt signaling pathway¹¹. Moreover, it enhances the cytotoxicity of chemotherapeutic agents, including cisplatin and 5-fluorouracil, against liver cancer cells¹². Collectively, these findings support the potential of lidocaine as both a therapeutic and adjuvant agent in HCC. Nevertheless, its precise molecular targets and mechanisms of action remain incompletely understood.

Bioinformatics is frequently applied in the study of biomarkers, prognosis, phenotypic analysis, and the development of targeted therapeutic strategies for HCC^13–16. In this study, we combined transcriptomic profiling with network pharmacology to identify potential molecular targets of lidocaine in HCC. Functional enrichment and protein–protein interaction (PPI) analyses were conducted to delineate the underlying pathways. Molecular docking was employed to assess the binding affinity between lidocaine and candidate targets, followed by prognostic evaluation and immune infiltration analysis. The role of a key target gene, SLC6A3, in regulating proliferation, apoptosis, invasion, and migration of HepG2 cells was subsequently validated through in vitro experiments. Our findings indicate that lidocaine exerts multi-target and multi-pathway antitumor effects in HCC, primarily mediated by SLC6A3 inhibition. This study provides mechanistic insight into lidocaine’s anticancer activity and offers a theoretical basis for its optimization and clinical application.

Materials and methods

Identification of lidocaine targets through network pharmacology

Potential targets of lidocaine were predicted using SwissTargetPrediction (http://www.swisstargetprediction.ch, v2019), PharmMapper (http://www.lilab-ecust.cn/pharmmapper, v2017), and the PubChem SEA database (http://sea.bkslab.org), accessed in September 2024. The three datasets were merged, and duplicate entries were removed to obtain the final set of candidate targets. Non-human genes were excluded, and all remaining targets were standardized according to official gene nomenclature. Functional enrichment analysis of the identified targets was performed for Gene Ontology (GO)¹⁷ and Kyoto Encyclopedia of Genes and Genomes (KEGG)¹⁸ pathways using the geometric distribution algorithm, and the results were visualized in R (v3.2.0). A protein–protein interaction (PPI) network was subsequently constructed using the STRING database (v12.0) and visualized in Cytoscape (v3.10.0).

Identification of lidocaine-responsive targets in HCC cells through transcriptomic sequencing

HepG2 cells were purchased from Beina Bio (BNCC338070, China). The cells were thawed and cultured according to the supplier’s instructions. Cells were maintained in high-glucose DMEM supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin (15140-122, Gibco). For lidocaine treatment, the culture medium was replaced with complete medium containing 2 mM lidocaine¹⁹. Transcriptomic sequencing was performed on control HepG2 cells (NC) and lidocaine-treated cells (case) (n = 3 per group).

Total RNA was extracted using TRIzol reagent, and RNA purity and concentration were assessed with a spectrophotometer (NanoDrop 2000, Thermo, USA). Sequencing libraries were prepared and run on the Illumina NovaSeq 6000 platform to generate paired-end reads. Low-quality reads were filtered using fastp²⁰, and clean reads were aligned to the reference genome using HISAT2²¹. Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM)²². Principal component analysis (PCA) was conducted and visualized in R (v3.2.0). Differentially expressed genes (DEGs) were identified using DESeq2 18 with thresholds of q < 0.05 and fold change > 2 or < 0.5. DEGs were subsequently subjected to GO¹⁷ and KEGG¹⁸ enrichment analyses using the geometric distribution algorithm, with visualization performed in R (v3.2.0). PPI networks were constructed using STRING (v12.0) and visualized in Cytoscape (v3.10.0).

Functional enrichment, prognostic, and immune infiltration analyses of shared targets

Functional enrichment analysis of the shared targets was conducted using GO¹⁷ and KEGG¹⁸, and results were visualized in R (v3.2.0). PPI networks were generated using STRING (v12.0) and visualized in Cytoscape (v3.10.0). Prognostic analyses were performed using clinical and transcriptomic data from the TCGA-LIHC dataset. Survival outcomes associated with the key target genes SLC6A3 and TERT were evaluated by univariate and multivariate Cox regression analyses, and survival curves were generated using the Log-rank test with the survival package²³. A nomogram model was constructed and internally validated through calibration analysis with the rms package²⁴. The relationship between shared target genes and immune cell infiltration was further assessed using CIBERSORT²⁵, single-sample gene set enrichment analysis (ssGSEA)²⁶, and ESTIMATE²⁷ algorithms.

Molecular docking of shared targets

Shared targets were identified by intersecting lidocaine-related targets from network pharmacology with DEGs from transcriptomic sequencing, and visualized using Venn diagrams. The overlapping targets included SLC6A3, CHRNB2, GRIN1, ADRA2C, LIPE, SLC18A2, KCNQ2, TERT, and ALOX12. Structural data for lidocaine and the target proteins were retrieved from the PubChem database (http://www.ncbi.nlm.nih.gov) and UniProt (https://www.uniprot.org). Structural optimization was performed using ChemBio3D (vUltra 23.1) and PyMOL (v3.0.3). Molecular docking was conducted with AutoDock Vina (v1.2.3). The docking results were analyzed and visualized using PyMOL (v3.0.3).

Verification of shared target gene expression

The mRNA expression levels of shared target genes (SLC6A3, CHRNB2, GRIN1, ADRA2C, LIPE, SLC18A2, KCNQ2, TERT, and ALOX12) were assessed in control (NC) and lidocaine-treated (case) HepG2 cells (n = 3 per group). Total RNA was extracted using TRIzol reagent, and RNA purity and concentration were measured with a spectrophotometer (NanoDrop 2000, Thermo, USA). The first-strand cDNA was synthesized according to the manufacturer’s protocol using the FastKing RT Kit (With gDNase, KR116, Tiangen Biochemical Technology, China). Quantitative PCR (qPCR) amplification was performed using the Taq Pro Universal SYBR qPCR Master Mix (Q712-02, Vazyme, China) in a 7500 Real-Time PCR System (Applied Biosystems, USA). Data were analyzed and quantified using the 2^−ΔΔCt method. Primer sequences are provided in Table 1.

Table 1.

List of primer sequences.

Gene	Forward (5’-3’)	Reverse (5’-3’)
GAPDH	TTGCCCTCAACGACCACTTT	TGGTCCAGGGGTCTTACTCC
SLC6A3	TGGTATGGAGTCAGTGAT	GATGAAGAGCGTGAAGAG
CHRNB2	TGTCCTTCTATTCCAATG	GTGCTTTACTTCAATCTT
GRIN1	CCTGTCCATCCTCAAGTC	ACATTCCTGATACCGAACC
ADRA2C	TCAAGTTCTTCTTCTGGAT	AGGATGTGCTTAAAGGAT
LIPE	CTCAAGGCTCATCCACAA	CGAGAAGAAGGCTATGTTG
SLC18A2	GAGAAGAATGCTACAGAA	GAGTTATCATAATAGGAGAAG
KCNQ2	TTCATCTACCACGCCTAC	GCTCTTCTCATACTCCTTG
TERT	ATCAGCAAGTTTGGAAGAA	CAGGATGGAGTAGCAGAG
ALOX12	CTAAACCAATTCCGAACA	GGCTTCAGATATTCATAGG

Construction of HCC cell models with SLC6A3 overexpression and knockdown

For SLC6A3 overexpression, the pcDNA3.1(+) plasmid was employed, while pGMLV-SC5 RNAi was used for knockdown. The short hairpin RNA (shRNA) sequences targeting SLC6A3 were as follows: KD-SLC6A3 #1: 5′-GTCTTGTGTTGCTGTAATAA-3′; KD-SLC6A3 #2: 5′-CCATTTACTTTGCCCATATTA-3′; KD-SLC6A3 #3: 5′-CCCAGCAGAGGCCGTAAATT-3′.

Transfection procedure: HepG2 cells were seeded in complete medium without penicillin/streptomycin. For each transfection, 10 µL of complete medium and 20 µL of Lipofectamine 2000 (11668-019, Invitrogen, USA) were mixed with 10 µg of the corresponding plasmid per group. Cells were incubated under standard culture conditions for 48 h before phenotypic assays. The resulting groups were designated as OE-NC, OE-SLC6A3, KD-NC, KD-SLC6A3#1, KD-SLC6A3#2, and KD-SLC6A3#3.

HCC cell viability assay

Cell viability was assessed using the CCK-8 kit (CK04, Dongren Chem). HepG2 cells were seeded in 96-well plates at 1 × 10⁴ cells per well. After removing the culture medium, 100 µL of basal medium and 10 µL of CCK-8 solution were added to each well. Blank control wells contained 100 µL of basal medium and 10 µL of CCK-8 solution without cells. Plates were incubated at 37 °C for 2 h under light protection. Absorbance at 450 nm was measured using a microplate reader (EL800, BIO-TEK) to calculate cell viability.

HCC cell apoptosis assay

Apoptosis was evaluated using the Double Staining Apoptosis Detection Kit (A005-4, Seven Seas Bio). HepG2 cells seeded in 6-well plates at 1 × 10⁵ cells per well were digested with 0.25% trypsin (25200-056, Gibco) for 1–2 min, and digestion was terminated with 0.5 mL of complete medium. Cells were collected by centrifugation at 1000 rpm for 3 min (twice), and the pellet was resuspended in 400 µL of binding buffer. Three tubes were prepared as blank control, FITC single positive control, and PI single positive control. FITC (5 µL) was added to each tube except the blank and PI controls and incubated for 15 min protected from light. PI (10 µL) was added to each tube except the blank and FITC controls and incubated for 5 min protected from light. Apoptotic cells were analyzed by flow cytometry (Novocyte Advanteon VBR, Agilent, USA).

HCC cell invasion assay

The invasive capacity of HCC cells was assessed using Transwell chambers coated with Matrigel (356234, CORNING). Matrigel (50 µL) and all experimental equipment were pre-cooled at 4 °C. The Matrigel was evenly applied to the Transwell membrane and allowed to polymerize at 37 °C for 30 min. Cell suspensions (100 µL, 1 × 10⁵ cells/mL) were seeded into the upper chambers, with three replicates per group, and incubated for 48 h. Cells were subsequently fixed with 4% paraformaldehyde (200 µL in the upper chamber; 1 mL in the lower chamber) at room temperature for 30 min, followed by staining with 500 µL of crystal violet for 15 min. After washing with PBS, invaded cells were imaged under a microscope (IX73, Olympus, Japan) and quantified using ImageJ.

HCC cell migration assay

Cell migration was evaluated using the wound healing assay. HepG2 cells were seeded in 12-well plates at 5 × 10⁴ cells per well. Three horizontal reference lines were drawn on the back of each well. A 10 µL pipette tip was used to create a scratch perpendicular to the reference lines. Detached cells were removed by washing with PBS, and serum-free medium was added to maintain consistent volume. Images were captured at 0 h and 48 h using a microscope (IX73, Olympus, Japan), and the wound area was quantified using ImageJ.

Statistical analysis

All data were analyzed using GraphPad Prism 9.5. Comparisons between two groups were performed with the student’s t-test, while multiple-group comparisons were conducted using one-way ANOVA followed by Tukey’s post-hoc test. Data are presented as mean ± standard deviation (SD) from at least three independent experiments. Statistical significance was defined as p < 0.05.

Results

Summary and analysis of the molecular targets of lidocaine

To systematically identify potential molecular targets of lidocaine, we integrated data from SwissTargetPrediction, PharmMapper, and the SEA databases. This approach yielded 433 putative therapeutic targets (Fig. 1A). Functional enrichment analyses using GO and KEGG indicated that these targets are predominantly associated with neuronal signaling and synaptic modulation, G-protein–coupled receptor (GPCR) pathways, tumor microenvironment regulation, and inflammatory and immune responses (Fig. 1B–C). PPI network analysis revealed extensive interconnections among the predicted targets (Fig. 1D). MCODE clustering identified the top eight functional modules, with scores of 5.6, 4.0, 3.4, 3.33, 3.0, 3.0, 3.0, and 3.0, respectively (Fig. 1E). These clusters likely represent key functional modules through which lidocaine exerts its pharmacological effects against HCC.

Fig. 1.

Network pharmacology analysis of lidocaine action targets. A Venn diagram summarizing lidocaine targets identified from the SwissTargetPrediction, PharmMapper, and SEA databases. B Bubble plot representing the GO functional enrichment analysis of lidocaine action targets. C Bubble plot illustrating the KEGG functional enrichment analysis of lidocaine action targets. D-E Protein–protein interaction (PPI) network of lidocaine action targets (D), subdivided into eight functional clusters (E). p < 0.05 was considered statistically significant. Data were obtained from independent experiments with at least three biological replicates. KEGG Analysis has been licensed by Kanehisa laboratories

Transcriptomic profiling of lidocaine-treated HepG2 cells

Transcriptome sequencing was performed on lidocaine-treated HepG2 cells (case, n = 3) and untreated controls (NC, n = 3). Sequencing output per sample ranged from 6.45 to 7.03 Gb, with Q30 base percentages of 94.58–94.77% and an average GC content of 50.63%. Mapping rates to the reference genome were high, between 97.9% and 98.04% (Fig. 2A–B). PCA revealed clear separation between case and NC groups along principal component 1 (PC1), which accounted for 80.66% of the variance (Fig. 2C), indicating substantial transcriptomic alterations induced by lidocaine. Differential expression analysis identified 442 DEGs, comprising 290 upregulated and 152 downregulated genes (Fig. 2D). Functional enrichment analysis demonstrated that these DEGs were predominantly involved in calcium signaling, cellular responses, signal transduction, receptor activity, metabolism, enzymatic function, hormone biosynthesis, and neurodevelopment (Fig. 2E–F). PPI network construction of the top 20 genes by interaction score revealed eight significant interaction pairs (Fig. 2G). These results indicated that lidocaine affects HCC cells by regulating the expression of multiple genes.

Fig. 2.

Transcriptomic profiling to identify the targets of lidocaine action in HepG2 cells. A Bar graph depicting the number of genes detected through transcriptome sequencing. B Box plot showing the FPKM values of the detected genes. C: Principal component analysis (PCA) plot of the samples. D Volcano plot showing the distribution of differentially expressed genes (DEGs); red represents significantly upregulated genes, blue indicates downregulated genes, and light pink and blue denote genes with non-significant changes. E Circle plot of GO functional enrichment analysis for the DEGs. F Bubble plot of KEGG functional enrichment analysis of DEGs. G Protein–protein interaction network of the top 20 DEGs. p < 0.05 was considered statistically significant. Data were obtained from independent experiments with at least three biological replicates. KEGG Analysis has been licensed by Kanehisa laboratories.

Functional, prognostic, and immune correlation analysis of shared targets

Intersection of the 433 lidocaine-related targets with the 442 DEGs yielded nine shared key targets (Fig. 3A). These targets exhibited six types of interactions and were enriched in biological processes related to cocaine addiction, neuronal signaling, chemical synaptic transmission, cell–cell signaling, and amphetamine addiction (Fig. 3B). Prognostic analyses using HCC clinical data indicated that SLC6A3 and TERT were significantly associated with overall survival, with higher expression predicting poorer outcomes (Table 2; Fig. 3C). The prognostic impact of SLC6A3 was more pronounced than that of TERT. Nomogram analysis further confirmed SLC6A3 and TERT as robust prognostic indicators, improving prediction of 1–2-year survival probabilities (Fig. 3D–E). Immune infiltration analysis revealed significant associations between all nine shared targets and immune cell infiltration, with SLC6A3 exhibiting the strongest correlation (Fig. 3F). These results suggest that SLC6A3 may serve as the primary target mediating lidocaine’s anti-HCC effects.

Fig. 3.

Functional, prognostic, and immune infiltration analysis of shared targets. A Venn diagram of the nine shared key genes obtained by intersecting lidocaine targets and DEGs. B PPI network and functional enrichment analysis of the nine key genes. C Survival plot illustrating the correlation between SLC6A3 and TERT expression and overall survival in HCC patients. D-E Prognostic nomogram (E) and corresponding calibration curves (F) for survival prediction based on SLC6A3 and TERT expression. F Heatmap of the correlation between shared targets and immune infiltration, as analyzed by CIBERSORT, ssGSEA, and ESTIMATE. p < 0.05 was considered statistically significant. Data were obtained from independent experiments with at least three biological replicates

Table 2.

Cox regression analysis of the nine shared genes with clinical data from the TCGA-LIHC dataset

Characteristics	Total(N)	Univariate analysis		Multivariate analysis
		Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Pathologic stage	349
Stage I	173	Reference		Reference
Stage II&Stage III&Stage IV	176	2.090 (1.429–3.055)	< 0.001	2.183 (1.283–16.833)	0.045
Pathologic T stage	370
T1	183	Reference		Reference
T2&T3&T4	187	2.126 (1.481–3.052)	< 0.001	1.909 (1.136–7.508)	0.034
Pathologic N stage	258
N0	254	Reference
N1	4	2.029 (0.497–8.281)	0.324
Pathologic M stage	272
M0	268	Reference		Reference
M1	4	4.077 (1.281–12.973)	0.017	1.599 (0.370–6.919)	0.530
BMI	336
<= 25	177	Reference
> 25	159	0.798 (0.550–1.158)	0.235
Tumor status	354
Tumor free	202	Reference		Reference
With tumor	152	2.317 (1.590–3.376)	< 0.001	2.033 (1.274–3.244)	0.003
SLC6A3	373
Low	185	Reference		Reference
High	188	1.575 (1.111–2.232)	0.011	1.548 (1.283–2.436)	0.029
CHRNB2	373
Low	187	Reference
High	186	0.924 (0.654–1.306)	0.653
GRIN1	373
Low	186	Reference
High	187	1.215 (0.860–1.718)	0.270
ADRA2C	373
Low	187	Reference
High	186	1.055 (0.747–1.490)	0.762
LIPE	373
Low	186	Reference
High	187	0.944 (0.669–1.334)	0.745
SLC18A2	373
Low	186	Reference
High	187	1.072 (0.760–1.512)	0.693
KCNQ2	373
Low	184	Reference
High	189	0.951 (0.674–1.341)	0.773
TERT	373
Low	186	Reference		Reference
High	187	1.355 (1.158–1.917)	0.018	1.325 (1.243–2.083)	0.022
ALOX12	373
Low	187	Reference
High	186	0.960 (0.677–1.363)	0.821

Molecular docking of lidocaine with shared targets

The 2D chemical structure of lidocaine (Fig. 4A) was obtained from the PubChem database for molecular docking with shared targets. Docking analysis revealed that SLC6A3, CHRNB2, ADRA2C, SLC18A2, KCNQ2, and TERT formed stable complexes with lidocaine primarily via hydrophobic interactions. In contrast, GRIN1, LIPE, and ALOX12 exhibited both hydrophobic interactions and hydrogen bonding (Table 3).

Fig. 4.

Molecular docking analysis of shared targets and lidocaine. A 2D structure of lidocaine obtained from the PubChem database. B-J Molecular docking interactions between lidocaine and the proteins SLC6A3 (B), CHRNB2 (C), GRIN1 (D), ADRA2C (E), LIPE (F), KCNQ2 (G), SLC18A2 (H), TERT (I), and ALOX12 (J). The interaction diagrams show the hydrophobic interactions (green) and hydrogen bonds (yellow dashed lines) between lidocaine (green) and the target proteins (blue). Molecular docking was conducted with AutoDock Vina (v1.2.3, https://vina.scripps.edu/download). The docking results were analyzed and visualized using PyMOL (v3.0.3, https://www.pymol.org)

Table 3.

Molecular docking results of shared targets and lidocaine.

Target	Binding energies (kcal/mol)	Binding interactions	Amino acid residue	Figure
SLC6A3	-5.6	Hydrophobic interaction	LEU459, PHE154, TYR151, ILE564, LEU474, LEU475, PHE462, LEU233, LEU560, ILE230, and TRP556	Figure 4B
CHRNB2	-4.7	Hydrophobic interaction	PHE462, PHE310, LEU307, VAL306, PHE303, VAL469, PHE466, and ILE465	Figure 4C
GRIN1	-5.6	Hydrophobic interaction	TRP470, LEU466, PHE467, ILE468, PHE124, TRP128, VAL127, LEU132, LEU131, LEU351, PRO352, ILE354, LEU488, ILE484, and PHE477
		Hydrogen bond	SER149 (3 Å) and TYR367 (3.1 Å)	Figure 4D
ADRA2C	-6.4	Hydrophobic interaction	TYR405, VAL414, PRO415, PHE419, CYS202, LEU204, TYR402	Figure 4E
LIPE	-5.4	Hydrophobic interaction	PRO480, PRO476, ALA477, CYS1033, LEU1032, ALA219, and TRP222	Figure 4F
		Hydrogen bond	SER226(3.1 Å)
SLC18A2	-4.7	Hydrophobic interaction	TRP11, LEU21, LEU23, ILE223, PHE27, and PHE24	Figure 4G
KCNQ2	-5.8	Hydrophobic interaction	MET565, TYR562, PRO561, PHE555, and PRO335	Figure 4H
TERT	-5.6	Hydrophobic interaction	TYR122, LEU123, PHE99, TYR176, and PHE645	Figure 4I
ALOX12	-6.5	Hydrophobic interaction	MET164, TYR139, MET164, TYR139, MET376, ILE390, PHE386, LEU382, TYR614, PRO109, PRO95, CYS96, LEU108, and TYR97	Figure 4J
		Hydrogen bond	CYS96 (3 Å) and ARG135 (3 Å)

Expression validation of shared genes

Quantitative PCR analysis demonstrated that lidocaine treatment significantly downregulated SLC6A3, CHRNB2, LIPE, KCNQ2, and TERT (Fig. 5A–B, E–F, H), whereas GRIN1, ADRA2C, and ALOX12 were upregulated (Fig. 5C–D, I). Expression of SLC18A2 was modestly increased, but this change did not reach statistical significance (Fig. 5G).

Fig. 5.

Validation of the mRNA expression of shared targets in lidocaine-treated HepG2 cells. A-I: RT-qPCR validation of the mRNA expression levels of the shared target genes SLC6A3 (A), CHRNB2 (B), GRIN1 (C), ADRA2C (D), LIPE (E), KCNQ2 (F), SLC18A2 (G), TERT (H), and ALOX12 (I) in HepG2 cells (NC) and lidocaine-treated HepG2 cells (Case). ns indicates p  > 0.05, *indicates p  < 0.05, **indicates p  < 0.01, and ****indicates p  < 0.0001. Data were obtained from independent experiments with at least three biological replicates

SLC6A3 mediates lidocaine effects on HCC cell phenotype

HepG2 cells were transfected with SLC6A3 overexpression or knockdown plasmids, with KD-SLC6A3#1 showing the highest knockdown efficiency and selected for subsequent experiments (designated KD-SLC6A3, Fig. 6A). Knockdown of SLC6A3 significantly reduced cell viability and promoted apoptosis (Fig. 6B–C). In wound-healing assays, KD-SLC6A3 cells exhibited a larger cell-free area compared with KD-NC, indicating impaired migratory capacity (Fig. 6D–E). Similarly, Transwell assays demonstrated markedly decreased invasive ability following SLC6A3 knockdown (Fig. 6F). Conversely, SLC6A3 overexpression enhanced proliferation, migration, and invasion while reducing apoptosis. These findings establish SLC6A3 as a critical regulator of HCC cell behavior and support the hypothesis that lidocaine exerts anti-HCC effects primarily through downregulation of SLC6A3.

Fig. 6.

SLC6A3 expression affects HepG2 cell phenotype. A RT-qPCR validation of SLC6A3 overexpression and knockdown efficiency. B Assessment of SLC6A3’s effect on HepG2 cell viability by CCK-8 assay. C Flow cytometric analysis of the apoptotic rate in HepG2 cells following SLC6A3 overexpression or knockdown. D-E Wound healing assay evaluating the migration ability of HepG2 cells at 0 h (D) and 48 h (E) after SLC6A3 modulation. F Transwell assay assessing the invasive ability of HepG2 cells following SLC6A3 overexpression or knockdown. OE corresponds to over expression and KD means knock down. ns indicates p  > 0.05, *indicates p  < 0.05, **indicates p  < 0.01, and ****indicates p  < 0.0001. Data were obtained from independent experiments with at least three biological replicates

Discussion

HCC is the most common form of primary liver cancer and is associated with high mortality, with a 5-year survival rate of only 19.6%²⁸. Emerging evidence indicates that lidocaine, beyond its established analgesic effects, may exert therapeutic benefits in HCC patients^29–31. Clarifying the molecular mechanisms underlying lidocaine action could inform targeted therapies and broaden its clinical utility in HCC management.

In this study, prognostic analyses revealed that elevated expression of SLC6A3 or TERT was associated with poorer patient survival, with SLC6A3 emerging as the stronger predictor for 1–2-year survival. SLC6A3 (solute carrier family 6 member 3) encodes the dopamine transporter (DAT or DAT1) and belongs to the solute carrier superfamily of sodium–chloride–dependent neurotransmitter transporters, located on chromosome 5p15.3. SLC6A3’s primary function is the reuptake of dopamine from the synaptic cleft into presynaptic neurons, thereby regulating dopamine homeostasis³². Additionally, studies have revealed that dopamine’s protein activity and transmission are regulated by interactions between the DAT and dopamine D2 autoreceptors. SLC6A3 protein expression may be associated with the high recycling process of DAT³³. Previous studies have implicated SLC6A3 in neurological and psychiatric disorders—including Parkinson’s disease, ADHD, PTSD, and depression—as well as in gastric cancer^34–38. In HCC, prior work demonstrated that carvacrol suppresses cell viability via downregulation of SLC6A3³⁹. Consistently, our data show that SLC6A3 knockdown significantly reduces HepG2 cell survival, induces apoptosis, and impairs invasion and migration. Previous studies have revealed that lidocaine inhibits cytoskeletal remodeling and cancer cell migration by blocking calcium release and actin polymerization⁴⁰. When dopamine binds externally or when the DAT is phosphorylated internally, multiple pathways and responses participate in altering cytoskeletal behavior⁴¹. These may be associated with the mechanism underlying impaired cell migration. Furthermore, we demonstrate that lidocaine binds to SLC6A3 through hydrophobic interactions and downregulates its expression, indicating that SLC6A3 is a key mediator of lidocaine’s anti-HCC effects. In the experiment shown in Fig. 6, this study employed a plasmid approach rather than lidocaine treatment to investigate the role of SLC6A3 in hepatocellular carcinoma. The results indicate that downregulation of SLC6A3 promotes apoptosis and suppresses the malignant phenotype of HCC cells. Pathway analysis revealed that the shared target genes were significantly enriched in dopamine-related pathways. SLC6A3 encodes the dopamine transporter protein, which regulates dopamine concentration via reuptake³². Dysregulation of the dopamine axis has been implicated in HCC progression: increased dopamine secretion, upregulation of DRD1⁴² and DRD4⁴³, and downregulation of DRD3⁴⁴ are all associated with malignancy. Low-dose dopamine negatively affects HCC survival⁴⁵, while upregulation of tyrosine hydroxylase (TH), the rate-limiting enzyme in dopamine biosynthesis, correlates with patient outcomes⁴⁶. We also observed a strong association between SLC6A3 expression and immune cell infiltration. Notably, SLC6A3 has been identified as a potential antigen for prostate cancer vaccines⁴⁷ and exhibits a strong negative correlation with neutrophil infiltration⁴⁸. Additionally, SLC6A3 may participate in mitochondrial oxidative stress processes in HCC⁴⁹. Given that members of the SLC family have been implicated in ferroptosis susceptibility^50–53 and immune regulation⁵¹ through pathways including C/EBPβ–PAK6 signaling, FSP1 acetylation, and metabolic reprogramming, the potential involvement of SLC6A3 in ferroptosis warrants further investigation. Collectively, our findings suggest that lidocaine exerts anti-HCC effects by downregulating SLC6A3, thereby restoring dopamine homeostasis, promoting ferroptosis, and modulating the tumor immune microenvironment. These results provide mechanistic insights into the therapeutic potential of lidocaine in HCC and highlight SLC6A3 as a promising molecular target.

Previous studies have reported that lidocaine suppresses HCC progression via the circ_ITCH/miR-421/CPEB3 axis⁵⁴, downregulation of USP14³⁰, and upregulation of CPEB3¹⁹. In contrast, our study identified nine novel shared key targets of lidocaine in HCC: SLC6A3, CHRNB2, GRIN1, ADRA2C, LIPE, SLC18A2, KCNQ2, TERT, and ALOX12, all distinct from previously reported targets. Beyond SLC6A3, TERT and ALOX12 have been implicated in liver disease. TERT (telomerase reverse transcriptase), located on chromosome 5p15.33, maintains telomere length and is re-expressed in approximately 90% of cancers, facilitating limitless tumor cell proliferation⁵⁵. Therapeutic strategies targeting TERT promoter mutations have demonstrated potential in liver tumors⁵⁶. In our study, lidocaine treatment significantly reduced TERT expression in HCC cells, suggesting that inhibition of TERT contributes to the suppression of tumor proliferation. ALOX12 (arachidonate 12-lipoxygenase), located on chromosome 17p13.1, is a member of the lipoxygenase family with critical roles in inflammatory responses⁵⁷. It has also been identified as a key mediator of p53-dependent tumor suppression via ferroptosis⁵⁸. ALOX12 activation has been associated with recurrence in fatty liver–associated HCC⁵⁹. Consistent with these findings, lidocaine treatment significantly upregulated ALOX12 expression in HCC cells, suggesting that enhancement of ferroptosis contributes to HCC cell apoptosis. Together, these results underscore the central role of ferroptosis in mediating lidocaine’s anti-HCC effects.

This study highlights several novel molecular targets and pathways for lidocaine action, providing a theoretical framework for drug development. However, the contributions of other identified targets to HCC progression remain to be elucidated. Furthermore, validation of these findings in additional biological replicates, additional HCC cell lines, clinical and in vivo samples is necessary to enhance their reliability. The precise molecular mechanisms linking SLC6A3, dopamine regulation, ferroptosis, and immune modulation warrant further investigation. Ultimately, confirmation of these results in animal models and clinical studies will be essential for advancing lidocaine-based therapeutic strategies in HCC. Further investigation is needed to clarify the role of DAT, encoded by the SLC6A3 gene, in the mechanism by which lidocaine exerts its therapeutic effects on HCC through SLC6A3 regulation. Incorporating in vitro co-culture assays to validate how SLC6A3 modulates immune cell infiltration in HCC would significantly enrich our findings.

Conclusion

This study identifies SLC6A3 downregulation as a central mechanism mediating the anti-HCC effects of lidocaine. These effects are likely linked to modulation of dopamine homeostasis, induction of ferroptosis, and remodeling of the tumor immune microenvironment. Our findings provide mechanistic insights and a theoretical basis for the potential therapeutic application of lidocaine in HCC.

Author contributions

Peiyang Li: Conceptualization, Formal analysis, Investigation, Writing - original draft. Wulan Tong: Data curation, Validation. Hong He: Methodology, Formal analysis. Hao Liu: Software, Visualization. Xi Yang: Funding acquisition, Project administration, Writing-review & editing.

Funding

This study was supported by Sichuan Science and Technology Program (Grant No. 2023YFS0142).

Data availability

The data will be provided by the corresponding author upon request. The datasets generated and/or analyzed during the current study are available in the GEO repository, accession number: GSE325921.

Declarations

Competing interests

The authors declare no competing interests.