Molecular-level insights into the therapeutic potential of lidocaine: effects on proliferation, autophagy, and cellular impedance in human oral cells

Abstract

Background

Local anesthesia is widely used in dental treatments such as extractions, implants, gum grafts, and jaw surgery. Recent studies have examined the effects of anesthetics, particularly lidocaine, on postoperative outcomes in patients with cancer; however, its specific role in cancer progression in the perioperative context remains to be explored.

Methods

In this study, we aimed to explore the effects of lidocaine on oral epidermoid carcinoma meng-1 (OECM-1) and gingival epithelial Smulow–Glickman (SG) cells using cell viability, flow cytometry, real-time polymerase chain reaction, western blotting, Electric Cell-Substrate Impedance Sensing, and RNA-sequencing analyses.

Results

Our findings revealed contrasting effects of lidocaine on these cell types. In OECM-1 cells, lidocaine suppressed metabolic activity (> 4 mM), cellular proliferation, S-phase population, and late apoptosis (< 4 mM). It also altered mitochondrial membrane potential, induced hypoxia and autophagy, and affected the expression of specific proteins and mRNAs. In contrast, SG cells exhibited different responses, with lidocaine influencing cell cycle profiles; increasing the number of apoptotic cells, cytosolic reactive oxygen species (ROS), and JC-1 aggregates; and altering mRNA expression. Additionally, the combination index analysis showed that lidocaine worked synergistically with cisplatin and 5-fluorouracil in both OECM-1 and SG cells. The cellular impedance patterns indicated similarities between the effects of lidocaine and those of lidocaine and cisplatin combination therapy.

Conclusion

In this study, we identified significant differences between OECM-1 and SG cells in terms of the effects of lidocaine on cell cycle profiles, cytosolic ROS, and mitochondrial potential. These results suggest that local anesthetics, such as lidocaine, may play crucial roles in combination therapies for oral cancers and in wound healing in post-gum grafts beyond their conventional use in pain management.

Introduction

Local anesthetics effectively relieve pain by blocking voltage-gated sodium channels and inhibiting nerve cell depolarization [1]. These agents can be differentiated based on several chemical characteristics, including lipid solubility, protein binding, and acid dissociation constants. These factors significantly impact their potency, duration of action, and onset time [2, 3]. There are two types of local anesthetics based on their characteristic chemical structures: amides and esters [4]. Amide-type local anesthetics are commonly used because of their superior pharmacokinetic properties and lower incidence of adverse effects. Local anesthesia is used to numb specific areas of the body and is commonly employed during biopsies and growth removal. It is routinely used by dentists when performing oral and maxillofacial surgical procedures, such as dental extractions, dental implants, gingival grafts, or jawbone interventions [5]. The treatment of oral cancer is more complex because it requires not only surgical procedures but also follow-up drug therapies after surgery. Oral cancer ranks as the sixth leading cause of cancer-related deaths globally [6, 7]. Oral squamous cell carcinoma (OSCC) can develop in various areas of the oral cavity, including the tongue, upper and lower gingiva, and buccal mucosa [8–10]. Major risk factors for OSCC include excessive alcohol consumption, betel nut chewing, human papillomavirus infection, and smoking [11]. Despite extensive research, the overall 5-year survival rate for OSCC remains at 50% [12]. Surgery is the first-line of treatment for oral cancer. Other treatments include chemotherapy, radiotherapy, and targeted therapies. Platinum-based drugs are the standard treatment for OSCC. However, they are associated with severe side effects and resistance [13]. Therefore, there is an urgent need to develop novel therapeutic approaches.

The level of local anesthetic toxicity is influenced by several factors, including the type of drug, its concentration, the use of adjunctive medications (such as steroids or epinephrine), and the condition of the underlying tissue [14, 15]. In clinical settings, local anesthetic toxicity is most frequently observed during continuous infusions via pain pumps in human chondrocytes and tenocytes [16, 17]. Bupivacaine is particularly favored for intra-articular analgesia because of its prolonged duration of action [18]. Recent studies have shown that bupivacaine and levobupivacaine exert significant cytotoxic effects on human chondrocytes [19]; therefore, the risks associated with dental surgery warrant further investigation. Various perioperative factors, such as surgical stress, blood transfusions, hypothermia, hyperglycemia, and postoperative pain, can significantly affect cancer progression. Although some studies have reported that local anesthesia is associated with longer recurrence-free periods for patients with breast cancer following surgical resection, others have not provided sufficient evidence to support claims that local anesthesia reduces cancer recurrence or improves cancer-related survival [20–22]. To this end, a clearer understanding of the specific role of lidocaine in cancer progression in the perioperative context is warranted.

Lidocaine is a local anesthetic which has been widely used for surgical procedures since 1943 [23, 24]. It prolongs the inactivation of fast voltage-gated sodium channels in the neuronal cell membranes, thereby altering neuronal signal transmission and inhibiting action potential propagation. Its potential impact on postoperative outcomes in patients with cancer has been reported. These studies focused on the efficacy of systemic lidocaine in managing acute and chronic pain as well as the use of perioperative intravenous lidocaine infusion to enhance postoperative analgesia [25, 26]. The benefits of lidocaine, including pain management, stress alleviation, and reduced opioid dependency, do not directly affect cancer biology. Recently, drug repurposing has emerged as a highly effective strategy for identifying and developing novel anticancer agents [27, 28]. The repurposing of lidocaine primarily capitalizes on its multifunctional roles in demonstrating anticancer properties, including the suppression of cell proliferation, invasion, and migration, as well as the induction of apoptosis and autophagy through various potential molecular mechanisms [29–35]. Furthermore, lidocaine is currently undergoing clinical trials for the treatment of pancreatic, breast, and other types of cancers [36].

The risks associated with daily dental surgery and the potential benefits of lidocaine in the context of OSCC represent important clinical challenges that need to be addressed. Our objective was to investigate the cytotoxic effects of lidocaine in daily oral surgery and OSCC and to explore the underlying molecular mechanisms. Hence, we investigated the effects of lidocaine on oral epidermoid carcinoma meng-1 (OECM-1) [37] and Smulow–Glickman (SG) gingival epithelial [38] cells to elucidate the molecular mechanisms underlying its cytotoxicity and potential antitumor effects, including mitochondrial membrane potential (MMP), reactive oxygen species (ROS), hypoxia, and autophagy. Furthermore, we assessed the combination index (CI) of lidocaine with cisplatin to support its potential role in oral cancer chemotherapy. The findings presented in this paper demonstrate that local anesthetics such as lidocaine may play a significant role in combination therapy for oral cancers, in addition to their established on-target use for pain management in oral surgery, thus highlighting the multifaceted potential of lidocaine beyond its traditional use in anesthesia.

Materials and methods

Cell culture and chemicals

OECM-1 and SG cells were kindly provided by Professor Meng Ching-Ling at the National Defense Medical Center in Taiwan, Republic of China [37, 38]. The cells were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium (Corning, USA) containing 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin (Thermo Fisher Scientific, USA), as previously described [39]. Acridine orange (AO), cisplatin, 2′,7-dichlorofluorescein diacetate (DCFH-DA), 5-fluorouracil (5-FU), lidocaine, propidium iodide (PI), and thiazolyl blue tetrazolium bromide (MTT) were obtained from Sigma-Aldrich (MO, USA).

Cell metabolic activity analysis

OECM-1 (2.5 × 10⁴) and SG (2.2 × 10⁴) cells were cultured in 96-well plates and treated with 1 mM (0.027%), 2 mM (0.054%), 3 mM (0.081%), 4 mM (0.108%), 5 mM (0.135%), 8 mM (0.216%), and 10 mM (0. 27%) lidocaine or vehicle (double-distilled H₂O; ddH₂O) for 24 h. Following treatment, the cells were exposed to MTT solution for 1 h at 37 °C. Subsequently, 200 µL of dimethyl sulfoxide (DMSO) was added, and absorbances at 570 and 650 nm were measured using an ELISA plate reader (Multiskan EX, Thermo, USA), as previously described [39]. The control group, comprising cells cultured in medium only, was used as a reference for 100% cell survival. For CI analysis, OECM-1 (2.5 × 10⁴) and SG (2.2 × 10⁴) cells were cultured in 96-well plates and treated with lidocaine at varying doses (0, 0.1875, 0.375, 0.75, 1.5, 3, 6, and 12 mM) combined with cisplatin at doses of 0, 0.3125, 0.625, 1.25, 2.5, 5, 10, 20, 40, and 80 µM for 24 h. The CI was calculated using CalcuSyn (Biosoft, UK) to construct isobolograms. Typically, a CI value of < 1 indicates a synergistic effect, whereas a CI value of > 1 indicates an antagonistic effect [40].

Fluorescence-Activated Cell Sorting (FACS) for flow cytometry analyses of cellular proliferation, cell cycle profiles, apoptosis, ROS, MMP, autophagy, and hypoxia

Cell proliferation was evaluated through immunofluorescent staining with incorporated bromodeoxyuridine (BrdU) using the BD Pharmingen BrdU Flow Kit and flow cytometry. Initially, OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Subsequently, the cells were stained with BrdU, harvested, washed with PBS, fixed, and permeabilized before staining with BrdU fluorescent antibodies. The stained cells were resuspended in staining buffer, and FITC-BrdU fluorescence analysis was conducted using a FACSCalibur flow cytometer and Cell Quest Pro software (BD Biosciences, USA).

To establish cell cycle profiles, OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Cell cycle profiles were determined based on cellular DNA content using FACS. Initially, the cells were fixed in 70% ice-cold ethanol, stored at − 30 °C overnight, and subjected to two washes with ice-cold PBS supplemented with 1% FBS. Subsequently, the cells were stained with PI solution (5 µg/mL PI in PBS, 0.5% Triton X-100, and 0.5 µg/mL RNase A) for 30 min at 37 °C in the dark. The stained cells were analyzed using a FACSCalibur flow cytometer and Cell Quest Pro software (BD Biosciences), following established protocols [41].

To establish cell death properties, OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. The early and late stages of apoptosis were assessed using a fluorescein Phycoerythrin (PE)-Annexin V apoptosis detection kit (BD Biosciences). The cells were stained with PE-Annexin V and 7-AAD to evaluate the effects of the indicated concentrations of lidocaine on early (PE-Annexin V positive and 7-AAD negative) and late (PE-Annexin V positive and 7-AAD positive) apoptosis, as previously described [42].

Cytotoxic ROS levels were assessed using the fluorescent marker, DCFH-DA. OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h, followed by staining with DCFH-DA (20 µM) for 40 min at 37 °C, and then harvested. Subsequently, the cells were washed once with PBS, and the fluorescence intensity of DCFH-DA was analyzed on the FL-1 channel of the FACSCalibur flow cytometer using the Cell Quest Pro software (BD Biosciences). The median fluorescence intensity of the vehicle was used as the reference point for M1 gating as previously described [43, 44].

The MitoSOX Red mitochondrial superoxide indicator (M36008; Invitrogen, Thermo Fisher Scientific) is a fluorogenic dye designed for the highly selective detection of superoxide in mitochondria of live cells. In our experimental procedure, OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. After the incubation period, the cells were harvested and stained with 5 mM MitoSOX Red at 37 °C for 20 min, followed by a single wash with PBS and resuspension in PBS. Subsequently, the MitoSOX Red fluorescence was analyzed using flow cytometry (FACSCalibur, BD Biosciences), with the fluorescence intensity assessed on the FL-2 channel of the FACSCalibur flow cytometer using the Cell Quest Pro software (BD Biosciences, USA). The fluorescence intensity of the vehicle was used as a reference point for M1 gating.

Mitochondrial depolarization was assessed by monitoring the decrease in red/green fluorescence intensity ratio. OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Both dead and viable cells were collected, washed with PBS, and then incubated with 1× binding buffer containing the MMP-sensitive fluorescent dye JC-1 for 30 min at 37 °C in the dark. After a single wash with PBS, JC-1 fluorescence was analyzed on FL-1 and FL-2 channels of the FACSCalibur flow cytometer using the Cell Quest Pro software (BD Biosciences) to identify the cytosolic monomer (green fluorescence) and mitochondrial aggregate (red fluorescence) forms of the dye, respectively.

Acidic compartments within the cells were visualized using AO (Sigma, Cat. No. A8097) staining and quantified using flow cytometry for autophagy. In our experimental protocol, OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h, followed by staining with AO (1 µg/ml) for 20 min at 37 °C, and subsequent trypsinization for cell harvesting. The harvested cells were then washed once with PBS, resuspended in 400 µL of PBS, and subjected to analysis via flow cytometry (FACSCalibur, BD Biosciences). The excitation wavelength was set at 488 nm, and fluorescence was detected at 510–530 nm (green fluorescence, FL-1) and 650 nm (red fluorescence, FL-3) in accordance with previous descriptions [45].

To assess hypoxia in live cells, all cell samples were stained using a ROS-ID Hypoxia/Oxidative Stress Detection Kit (ENZ-51042-K500; Enzo Life Sciences, Farmingdale, NY, USA), following the manufacturer’s instructions. Briefly, OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were seeded in 6-well culture plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h, harvested, and washed. The cells were subsequently resuspended in 200 µL of Detection Mix and incubated for 30 min at 37 °C in the dark. Following a single wash with PBS, the fluorescence signals were analyzed on channel FL-3 (hypoxia dye) of the FACSCalibur flow cytometer using the Cell Quest Pro software (BD Biosciences, CA, USA). The fluorescence intensity of the vehicle was used as a reference point for gating, as previously described [46].

RNA Isolation and Quantitative PCR (qPCR)

OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were cultured in 6-well plates and exposed to 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. OECM-1 and SG cells were lysed using the TRIzol reagent (Invitrogen) to extract total RNAs. Subsequently, reverse transcription for first strand cDNA synthesis was performed using M-MuLV reverse transcriptase (Protech Technology, Nankang, Taipei, Taiwan) with 1 µg of total RNA for 60 min at 42 °C, as previously described [39]. Real-time PCR was performed using the CFX96 touch Real-time system (Bio-Rad Laboratories) and SYBR Green 2× qPCR master mix (Bioman Scientific, New Taipei, Taiwan). The qPCR primers used are detailed (Table 1), and all reactions were performed in triplicate. Relative mRNA quantification was determined using the comparative Ct method (2^−ΔΔCt), with GAPDH serving as the internal control.

Table 1.

qPCR primers used in this study

Primer Name	Accession number	Sequence (5′→3′)
GAPDH	NM_002046.7	Forward: 5′-ACCCAGAAGACTGTGGATGG-3′ Reverse: 5′-TCTAGACGGCAGGTCAGGTC-3′
ATF4	NM_001675.4	Forward: 5′-TTCTCCAGCGACAAGGCTAAGG-3′ Reverse: 5′-CTCCAACATCCAATCTGTCCCG-3′
CHOP	NM_001195053.1	Forward: 5′-GGTATGAGGACCTGCAAGAGGT-3′ Reverse: 5′-CTTGTGACCTCTGCTGGTTCTG-3′
HIF-1α	NM_001243084.1	Forward: 5′-GAACGTCGAAAAGAAAAGTCTCG-3′ Reverse: 5′-CCTTATCAAGATGCGAACTCACA-3′
p53	NM_000546.5	Forward: 5’-CCTCAGCATCTTATCCGAGTGG-3’ Reverse: 5’-TGGATGGTGGTACAGTCAGAGC-3’
p21	NM_000389.4	Forward: 5’-AGGTGGACCTGGAGACTCTCAG-3’ Reverse: 5’-TCCTCTTGGAGAAGATCAGCCG-3’

Impedance measurement using Electric Cell-Substrate Impedance Sensing (ECIS)

The ECIS Z system, electrode arrays, and assay software were obtained from Applied BioPhysics (Troy, NY, USA). In this study, 8W1E arrays, featuring 8 wells with one 250 μm diameter active gold sensing electrode, were utilized. The instrument captured impedance data at frequencies ranging from 4 to 64 kHz. Cells were seeded into electrode cultureware at densities of 1.2 × 10⁵ (OECM-1) and 1.5 × 10⁵ (SG) cells/cm². Following cell confluency, 20 h after cell seeding, the cells were treated with 5 or 10 mM lidocaine, 40 µM cisplatin, 40 µM cisplatin plus 5 mM lidocaine, 250 µM 5-FU, and 250 µM 5-FU plus 5 mM lidocaine. Subsequent changes in impedance at 16 kHz were monitored for 50 h. To quantify the cell impedance of each cell-covered electrode, measurements were performed at eight frequencies. The impedances of cell-free electrodes (medium control) and electrodes covered with SG or OECM-1 cells were recorded. For data analysis, zero time was used as a reference to normalize duplicates using the toolbar n/n₀.

RNA-sequencing, differential expression, and enrichment analyses

For RNA-sequencing, 1 µg of total RNA from OECM-1 and SG cells treated with 0, 3, or 5 mM lidocaine were used for library preparation. Poly(A)+ mRNA was isolated using Oligo(dT) beads and fragmented using divalent cations at a high temperature. First- and second-strand cDNA syntheses were performed using random primers. The resulting double-stranded cDNA was purified, end-repaired, and A-tailed in a single reaction, followed by adaptor T–A ligation. The adaptor-ligated fragments were size-selected using DNA Clean Beads. Libraries were then amplified by PCR using primers P5 and P7, validated, and assigned unique multiplexing indices. Sequencing was performed on Illumina HiSeq, NovaSeq, or MiSeq 2000 platform (2 × 150 bp paired-end) according to the manufacturer’s protocol [47].

Transcript sequences were generated in FASTA format from the reference GFF annotation file and indexed. Using this reference, gene and isoform expression levels were quantified from clean paired-end reads using HTSeq (v0.6.1). Differential expression analysis was performed using the DESeq2 Bioconductor package (v1.6.3), which applies data-driven prior distributions to estimate dispersion and log₂ fold changes. Genes with an adjusted P-value (Padj) ≤ 0.05 were considered significantly differentially expressed. To further validate the results, EdgeR (v3.4.6) was applied using the thresholds of |log₂ fold change| > 1 (fold change > 2) and false discovery rate (FDR; Q-value or Padj) < 0.05.

Pathway enrichment analysis of differentially expressed genes was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. In-house scripts were used to map genes to KEGG pathways, and enrichment was evaluated using the RichFactor, Q-value, and gene count per pathway. The RichFactor represents the ratio of differentially expressed genes to the total number of genes in each pathway, where higher values indicate greater enrichment. The Q-value, a Padj accounting for multiple testing, ranges from 0 to 1, with smaller values indicating greater significance.

Gene Set Enrichment Analysis (GSEA) was performed to explore the potential molecular mechanisms by which lidocaine regulates tumor progression. The “h.all.v2024.1.Hs.symbols.gmt” hallmark gene set was used to identify significantly enriched pathways between groups.

Statistical analysis

Values are presented as the mean ± SD of a minimum of three independent experiments. A two-tailed Student’s t-test was used for two-group comparisons, whereas one-way analysis of variance (ANOVA) with Tukey’s multiple comparison tests were employed for multi-group analyses using SPSS 20.0 for Windows (SPSS, Chicago, IL). Statistical significance was set at P < 0.05.

Results

Effects of lidocaine on metabolic activity, cell proliferation, cell cycle profile, and apoptosis in OECM-1 and SG cells

In the present study, we sought to elucidate the molecular mechanisms underlying the cytotoxicity of lidocaine to support its potential antitumor function. First, OECM-1 and SG cell lines were treated with lidocaine to assess its impact on metabolic activity (Fig. 1A and B). Lidocaine generally suppressed metabolic activity, with IC₅₀ values of approximately 6.2 mM in OECM-1 and SG cells (P(ANOVA) = 8.0 × 10^− 10 and 5.9 × 10^− 8, respectively). Interestingly, lidocaine at concentrations lower than 3.5 mM was found to stimulate metabolic activity in OECM-1 cells. Next, we investigated the mechanisms underlying the effect of lidocaine on metabolic activity to determine whether it suppresses cellular proliferation or induces apoptosis. Using a BrdU proliferation kit, we evaluated the influence of lidocaine on the proliferation of OECM-1 and SG cells (Fig. 1C and D); lidocaine significantly suppressed cellular proliferation in both cell lines (P(ANOVA) = 8.6 × 10^− 13 and 1.5 × 10^− 17, respectively). Furthermore, cell cycle profile analysis revealed a decrease in the S-phase population (P(ANOVA) = 1.5 × 10^− 9 and 1.3 × 10^− 12, respectively) and an increase in the subG1phase population (P(ANOVA) = 1.1 × 10^− 13 and 6.7 × 10^− 8, respectively) (Fig. 1E and F). Specifically, lidocaine increased the populations of the G2/M and G1 phases in OECM-1 cells (P(ANOVA) = 7.8 × 10^− 5 and 9.2 × 10^− 7, respectively) but decreased the population of the G2/M phase in SG cells (P(ANOVA) = 4.4 × 10^− 10).

Fig. 1.

Effect of lidocaine on cell viability, cell proliferation, and the cell cycle profile in OECM-1 and SG cells. Panel A depicts OECM-1 (2.5 × 10⁴) cells, whereas panel B shows SG (2.2 × 10⁴) cells cultured in 96-well plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Cell viability was assessed using the MTT assay. Panel C depicts OECM-1 (3.5 × 10⁵) cells, whereas panel D represents SG (3.5 × 10⁵) cells cultured in 6-well plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Subsequently, the cells underwent BrdU proliferation analysis. Panels E and F show OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells, respectively, cultured in 6-well plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Subsequently, the cells underwent cell cycle profile analysis. The cell cycle profiles were assessed using flow cytometry analysis with PI staining. The bars (A–F) depict the mean ± SD of three independent experiments. Statistical significance was determined using Student’s t-tests and compared with the vehicle; * P < 0.05, ** P < 0.01, and *** P < 0.001

Considering the elevated subG1 population, we further assessed the effects of lidocaine on early and late apoptosis using an Annexin V apoptotic kit (Fig. 2). In OECM-1 cells, late and total apoptosis were suppressed at concentrations below 5 mM lidocaine (P(ANOVA) = 1.3 × 10^− 7 and 5.2 × 10^− 7, respectively) (Fig. 2A–C), whereas in SG cells, lidocaine generally induced early, late, and total apoptosis (P(ANOVA) = 3.8 × 10^− 7, 7.4 × 10^− 5, and 1.2 × 10^− 6, respectively) (Fig. 2D–F).

Fig. 2.

Effect of lidocaine on apoptosis in OECM-1 and SG cells. Panels A–C and D–F depict OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells, respectively, cultured in 6-well plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. A and D Cellular apoptosis was assessed using Annexin V apoptosis analysis with 7-AAD staining. B and E Early apoptotic cells are PE-Annexin V-positive and 7-AAD negative, whereas late apoptotic cells are both PE-Annexin V-positive and 7-AAD positive. C and F Total apoptotic cells of both cells were calculated and plotted. Data from three independent experiments is depicted through symbols and bars representing the mean ± SD. Statistical significance was determined using Student’s t-tests, with * P < 0.05, ** P < 0.01, and *** P < 0.001

Effects of lidocaine on ROS generation, mitochondrial depolarization, and autophagy in OECM-1 and SG cells

We investigated the effect of lidocaine on mitochondrial and cytosolic ROS levels using MitoSOX and DCFH-DA, respectively (Fig. 3). Lidocaine significantly increased mitochondrial ROS levels in a dose-dependent manner in both cell types (P(ANOVA) = 5.0 × 10^{− 17} and 2.0 × 10^{− 13}, respectively) (Fig. 3A and B), whereas decreased the cytosolic ROS levels specifically in SG cells (P(ANOVA) = 9.4 × 10^{− 1} and 1.2 × 10^{− 2}, respectively) (Fig. 3C and D). Interestingly, lidocaine had no effect on cytosolic ROS generation in OECM-1 cells (Fig. 3D). It is well established that mitochondria serve as the primary source of ROS generation through the electron transport chain [48, 49].

Fig. 3.

Effect of lidocaine on the levels of mitochondrial and cytosolic ROS in OECM-1 and SG cells. Panels A and C represent OECM-1 (3.5 × 10⁵) cells, whereas panels B and D depict SG (3.5 × 10⁵) cells cultured in 6-well plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Subsequently, the cells were analyzed to measure MitoSOX (A and B for mitochondrial ROS) and DCFH-DA (C and D for cytosolic ROS) intensities. The fluorescence intensities of MitoSOX Red and DCFH-DA were reapectively analyzed on the FL-2 and FL-1 channel of the FACSCalibur flow cytometer. The bars depict the mean ± SD of three independent experiments. Statistical significance was determined using Student’s t-tests compared with the vehicle, with * P < 0.05 and *** P < 0.001

ROS produced by impaired mitochondria can trigger mitophagy, which is associated with the loss of MMP [50]. Therefore, we assessed MMP using JC-1 dye in OECM-1 and SG cells (Fig. 4). JC-1 forms dimers in the mitochondria, producing red fluorescence, and monomers in the cytoplasm, resulting in green fluorescence, which can be used to measure mitochondrial depolarization. Our JC-1 data indicated higher levels of red JC-1 aggregates in SG cells than in OECM-1 cells (Fig. 4A and C). Interestingly, lidocaine increased red JC-1 aggregates and decreased green JC-1 monomers in OECM-1 cells (Fig. 4A), whereas it had the opposite effect in SG cells (Fig. 4C). Consequently, lidocaine elevated the red/green fluorescence intensity ratio in OECM-1 cells (P(ANOVA) = 2.3 × 10^{− 6}) and decreased the red/green fluorescence intensity ratio in SG cells in a dose-dependent manner (P(ANOVA) = 3.3 × 10^{− 13}) (Fig. 4B and D).

Fig. 4.

Effect of lidocaine on MMP and autophagy in OECM-1 and SG cells. A and B OECM-1 (3.5 × 10⁵) and (C and D) SG (3.5 × 10⁵) cells were cultured in 6-well plates and exposed to 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. The live cells were then stained with 5 µM JC-1 dye. JC-1 fluorescence was analyzed on FL-1 (for green) and FL-2 (for red) channels of the FACSCalibur flow cytometer. The percentages of red and green fluorescence intensities are plotted in panels A and C, whereas the red/green fluorescence intensity ratios are presented in panels B and D. E and F OECM-1 (3.5 × 10⁵) and SG (3.5 × 10⁵) cells were cultured in 6-well plates and exposed to 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. E Identification of autophagic cells was conducted using AO (1 µg/mL) staining and quantified via flow cytometry in OECM-1 and SG cells. The excitation wavelength was set at 488 nm, and fluorescence was detected at 510–530 nm (green fluorescence, FL-1) and 650 nm (red fluorescence, FL-3). The values represent the percentages of cells with a significant proportion of acidic vesicular organelles. F The bars depict the means ± SDs of three independent experiments, and statistical significance was determined using Student’s t-tests and compared with the vehicle, with * P < 0.05, ** P < 0.01, and *** P < 0.001

AO is known to accumulate at high concentrations within acidic vesicles [51]. In its monomeric form, it emits green fluorescence, whereas the stacking of acidic vesicular organelles leads to red fluorescence emission. In this study, we utilized flow cytometry to quantify acidic vesicular organelles, demonstrating that lidocaine significantly induced the formation of autophagolysosomes in OECM-1 and SG cells in a dose-dependent manner (P(ANOVA) = 9.6 × 10^{− 15} and 6.2 × 10^{− 11}, respectively) (Fig. 4E and F).

Synergistic effects of lidocaine with cisplatin and 5-FU in OECM-1 and SG cells

Typical chemotherapy agents used for oral and oropharyngeal cancers include cisplatin and 5-FU [52]. These drugs can be administered alone or in combination with other agents such as in certain forms of targeted therapy or immunotherapy. To assess the relationship between lidocaine, cisplatin, and 5-FU in OECM-1 and SG cells, we used a CI (Fig. 5A–D). Generally, a CI value of less than 1 indicates synergy, whereas a value greater than 1 suggests an antagonism [40, 53]. In OECM-1 cells, the CI values for lidocaine plus cisplatin were consistently below 1, whereas similar values were observed for the specific combination of lidocaine and cisplatin in SG cells (Fig. 5A and C). Our data revealed that although cisplatin and 5-FU exhibited insensitivity in both cell types, cisplatin demonstrated better sensitivity than 5-FU. Furthermore, the sensitivity to both cisplatin and 5-FU was higher in OECM-1 cells than in SG cells. Moreover, lidocaine decreased the ED₅₀ values of both cisplatin and 5-FU in OECM-1 and SG cells (Fig. 5B and D); therefore, the required drug concentrations decreased as the lidocaine concentrations decreased. Specifically, in OECM-1 cells, lidocaine reduced the ED₅₀ from 183 to 11 µM in the case of cisplatin and from 1269 to 190 µM in the case of 5-FU, as lidocaine concentrations decreased from 3.6 to 1.6 and 1.4 mM, respectively. Similarly, in SG cells, lidocaine decreased the ED₅₀ from 213 to 13.4 µM in the case of cisplatin and from 2309 to 163 µM in the case of 5-FU, as lidocaine concentrations decreased from 2.6 to 2 and 1.2 mM, respectively. Overall, these results support the synergistic action of lidocaine with cisplatin or 5-FU in both cell types.

Fig. 5.

Combination indices (CIs) of cisplatin and 5-FU with lidocaine in OECM-1 and SG cells. OECM-1 (2.5 × 10⁴) and SG (2.2 × 10⁴) cells were cultured in 96-well plates and treated with lidocaine at varying doses (0, 0.1875, 0.375, 0.75, 1.5, 3, 6, and 12 mM) combined with (A and B) cisplatin at doses of 0, 0.3125, 0.625, 1.25, 2.5, 5, 10, 20, 40, and 80 µM and (C and D) 5-FU at doses of 0, 6.25, 12.5, 25, 50, 100, 200, 400, 800, and 1600 µM. The metabolic activity was measured using the MTT assay. Panels A and C depict the CI (the red dashed line represents a value of 1) of lidocaine plus cisplatin and 5-FU in OECM-1 and SG cells, whereas panels B and D show isobolograms (ED50) of cisplatin or 5-FU calculated using CalcuSyn software

Combinatory effects of lidocaine with cisplatin or 5-FU on anchorage-dependent cultured OECM-1 and SG cells

Cellular impedance was first measured using microelectrodes by Giaever and Keese, who used ECIS to study the characteristics of anchorage-dependent cultured cell lines [54, 55]. The ECIS system has been instrumental in the label-free detection of oral cancer cells as well as in studies of drug-induced cellular activities such as cell adhesion, spreading, proliferation, and drug-induced apoptosis. In the present study, we used ECIS to determine the cellular activities of lidocaine in combination with cisplatin or 5-FU (Fig. 6A and B). Cisplatin decreased the impedance index of both cell types, lidocaine decreased the impedance index of OECM-1 cells, and 5-FU increased the impedance index of SG cells. In addition, we conducted real-time impedance measurements of lidocaine combined with cisplatin after 15 and 30 h of treatment (Fig. 6C and D). Our data indicate that the suppressive effects of cisplatin, either alone or in combination with lidocaine, on the impedance index at 30 h (P(ANOVA) = 7.3 × 10^{− 32} and 7.8 × 10^{− 31}, respectively) were more pronounced than those at 15 h (P(ANOVA) = 7.4 × 10^{− 27} and 2.4 × 10^{− 28}, respectively) in both cell types. Moreover, cisplatin exhibited a delayed response time in SG cells compared with that in OECM-1 cells.

Fig. 6.

Impedance measurement of cisplatin or 5-FU with lidocaine in OECM-1 and SG cells. Approximately 20 h after cell seeding, OECM-1 (1.2 × 10⁵ cells/cm²) and SG (1.5 × 10⁵ cells/cm²) cells formed confluent monolayers on the working electrodes. In panel A for OECM-1 and panel B for SG cells, 5 mM (green) or 10 mM (blue) lidocaine, 40 µM cisplatin (olive), 40 µM cisplatin plus 5 mM lidocaine (gray), 250 µM 5-FU (purple), and 250 µM 5-FU plus 5 mM lidocaine (cyan) were added. The subsequent changes in impedance at 16 kHz were monitored for 50 h. For each period (15 and 30 h), at least eight replicate wells were plotted for further analysis of lidocaine, cisplatin, and their combination (n = 8) (C and D). The bars represent the mean ± SD of three independent experiments, and statistical significance was determined using Student’s t-tests, with *** P < 0.001

Differential gene expression regulation by lidocaine in OECM-1 and SG cells

In this study, we identified three key differences between OECM-1 and SG cells regarding the effects of lidocaine, specifically in relation to cell cycle profiles, cytosolic ROS, and MMP. Therefore, we explored the differential regulation of gene expression by lidocaine at concentrations of 3 and 5 mM using RNA-sequencing in both OECM-1 and SG cells. Treatment with 3 mM lidocaine in OECM-1 cells resulted in the downregulated expression of 175 genes and upregulated expression of 443 genes, whereas treatment with 5 mM lidocaine led to the downregulated expression of 456 genes and upregulated expression of 857 genes (Fig. 7A). In SG cells, the 3 mM treatment resulted in downregulated and upregulated expression of 59 and 268 genes, respectively, whereas the 5 mM treatment resulted in downregulated and upregulated expression of 249 and 672 genes. Notably, gene expression increased in a dose-dependent manner. Pathway enrichment analysis of differentially expressed genes was conducted using the KEGG database. These genes were further classified into six pathways: organismal systems, metabolism, human diseases, genetic information processing, environmental information processing, and cellular processes (Fig. 7B–E). Among these pathways, significant interactions were observed, such as cytokine–cytokine receptor interactions and efferocytosis, corresponding to the environmental information processing and cellular processes pathways, respectively, in response to both 3 and 5 mM lidocaine in OECM-1 and SG cells. In addition to efferocytosis, both lidocaine concentrations induced ferroptosis in SG cells. Other pathways were affected by lidocaine in OECM-1 and SG cells. In OECM-1 cells, 3 and 5 mM lidocaine both affected genes associated with parathyroid hormone synthesis, secretion, and action; steroid biosynthesis; the MAPK signaling pathway; cytokine–cytokine receptor interaction; the cAMP signaling pathway; and ECM–receptor interaction. In SG cells, 3 and 5 mM lidocaine both affected genes associated with the insulin signaling pathway, hematopoietic cell lineage, circadian rhythm, fatty acid biosynthesis, fluid shear stress and atherosclerosis, AGE–RAGE signaling pathway in diabetic complications, microRNAs in cancer, insulin resistance, cytokine–cytokine receptor interaction, TNF signaling pathway, and viral protein interaction with cytokine and cytokine receptor.

Fig. 7.

Differential gene expression regulation by lidocaine in OECM-1 and SG cells. A Volcano plot illustrating the differentially expressed genes in OECM-1 and SG cells following treatment with 3 and 5 mM lidocaine compared with those in the vehicle. Genes with higher expression in lidocaine-treated cells are represented as red dots, whereas blue dots denote genes with reduced expression. The Y-axis represents −log₁₀ p-values, and the X-axis displays log₂ fold change values. B–E Bars showing KEGG pathway enrichment statistics in OECM-1 and SG cells following treatment with 3 and 5 mM lidocaine compared with those in the vehicle. Genes of six pathways including organismal systems, metabolism, human diseases, genetic information processing, environmental information processing, and cellular processes are categorized

GSEA was performed to identify enriched pathways within the ranked gene lists. The analysis highlighted several hallmark pathways, including hypoxia, TNFα signaling via NF-κB, inflammatory response, IL2–Stat5 signaling, IL6–Stat3 signaling, epithelial-to-mesenchymal transition (EMT), myogenesis, UV response DN, apoptosis, and the p53 pathway (Fig. 8). In both OECM-1 and SG cells, lidocaine enhanced the hallmark pathways related to hypoxia, TNFα signaling via NF-κB, inflammatory response, and IL2–Stat5 signaling. Lidocaine treatment at 3 and 5 mM also amplified IL6–Stat3 signaling and EMT pathways in OECM-1 cells, with a 5 mM concentration similarly affecting SG cells. Additionally, myogenesis was enhanced in OECM-1 cells at both concentrations, whereas only the 3 mM dose elicited this response in SG cells. In SG cells, lidocaine at both concentrations increased UV response DN and apoptosis hallmarks, with 3 mM enhancing UV response DN and 5 mM increasing apoptosis in OECM-1 cells. The hallmark p53 pathway was elevated by both lidocaine concentrations in OECM-1 cells.

Fig. 8.

GSEA pathway enrichment analysis in OECM-1 and SG cells following lidocaine treatment compared with those in the vehicle. Gene expression changes in OECM-1 and SG cells following treatment with 3 and 5 mM lidocaine compared with those in the vehicle are associated with pathways such as hypoxia, TNFα signaling via NF-κB, inflammatory response, IL2–Stat5 signaling, IL6–Stat3 signaling, EMT, myogenesis, UV response DN, apoptosis, and the p53 pathway

We further verified the findings from the RNA-sequencing analysis. Utilizing a cellular hypoxia analysis kit, we observed that lidocaine created a hypoxic environment in both OECM-1 and SG cells (p(ANOVA) = 1.5 × 10^− 10 and 3.8 × 10^− 13, respectively) (Fig. 9A and B). We conducted real-time PCR analysis to investigate the mRNA levels of ATF4, CCAAT/enhancer-binding protein homologous protein (CHOP), hypoxia-inducible factor-1α (HIF-1α), p21, and p53, which are related to stress, hypoxia, and cell cycle (Fig. 9C and D). We observed consistent trends in ATF4, CHOP, HIF-1α, and p21 expression in both cell lines. However, we observed a decreasing trend in the level of p53 mRNA in OECM-1 cells (Fig. 9C) and an increasing trend in p53 levels in SG cells (Fig. 9D).

Fig. 9.

Effect of lidocaine on hypoxic level and the expressions of genes related to stress, hypoxia, and cell cycle in OECM-1 and SG cells. Panels A and C present OECM-1 (3.5 × 10⁵) cells, whereas panels B and D depict SG (3.5 × 10⁵) cells cultured in 6-well plates and treated with 0, 1, 2, 3, 4, and 5 mM lidocaine for 24 h. Panels A and B represent the measurement of hypoxia intensity. (C and D) Relative mRNA quantification was determined using the comparative Ct method (2^−ΔΔCt), with GAPDH serving as the internal control. The bars depict the mean ± SD of three independent experiments, and statistical significance was determined using Student’s t-tests and compared with the vehicle, with * P < 0.05, ** P < 0.01, and *** P < 0.001

Discussion

In the present study, we used OECM-1 and gingival epithelial SG cells to explore the potential mechanisms of action of lidocaine. Our findings indicated distinct working mechanisms of lidocaine in these two cell types. Specifically, in OECM-1 cells, lidocaine suppressed metabolic activity, cellular proliferation, S-phase population, and late apoptosis. Additionally, it induced various cellular responses that affected subG1 and G2/M populations, early apoptotic cells, mitochondrial ROS, hypoxia, and autophagy, along with the expression of specific mRNAs. Conversely, the effects of lidocaine on SG cells included the suppression of the G2/M population, as well as induction of the G1 population and total apoptosis. Notably, CI analysis revealed that lidocaine acted synergistically with 5-FU and cisplatin in both OECM-1 and SG cells. Furthermore, for both cell types, similar impedance patterns were observed for lidocaine alone and in combination with cisplatin. In summary, our findings demonstrate that local anesthetics such as lidocaine may play a role in combination therapies for oral cancers, in addition to their established on-target usage for pain management in oral surgery and other dental and medical procedures.

Surgical removal of the primary tumor is considered the standard treatment for many cancer types. However, recent studies suggested that this approach may inadvertently promote recurrence and metastasis [56, 57]. The likelihood of such outcomes is influenced by several factors, including the metastatic potential of the tumor, host immune response, and inflammatory reaction. During the perioperative period, various factors can affect these parameters, including surgical stress, blood transfusions, hypothermia, hyperglycemia, and postoperative pain. Lidocaine has been shown to have direct effects on both cancer cells and stromal components, such as neurons, blood vessels, and immune cells [25, 58]. Although bupivacaine is commonly used for postoperative analgesia due to its prolonged duration of action [59, 60], we selected lidocaine as our primary model for two key reasons. In OSCC, where hypermethylation often drives malignant progression, the capacity of lidocaine to reverse these epigenetic alterations presents a distinct therapeutic advantage over other amide-linked anesthetics [61]. Additionally, the clinical feasibility of systemic intravenous lidocaine infusion enables plasma concentrations that can influence circulating tumor cells, a benefit not achievable with bupivacaine due to its significant cardiotoxic profile [62]. The repurposing of lidocaine capitalizes on its diverse functional role as a chemosensitizer, facilitating the suppression of cellular proliferation, invasion, and migration. Our current findings suggest that lidocaine may act as a chemosensitizer by suppressing proliferation and inducing apoptosis and autophagy. Additionally, lidocaine induces mitochondrial ROS generation and reduces the impedance index in OECM-1 and SG cells. Based on our RNA-sequencing analysis, these two cell types appear to respond to lidocaine exposure through a combination of shared and distinct pathways. However, the challenge lies in identifying specific target molecules, such as proteins and microRNAs, to elucidate the detailed mechanisms by which lidocaine acts on OECM-1 and SG cells.

Considering the role of lidocaine in suppressing cellular proliferation and inducing apoptosis, we observed an increase in the subG1 population and a decrease in the S-population in both OECM-1 and SG cells. In SG cells, an increase in the G1 phase population corresponds to a reduction in the G2/M phase population. Herein, higher concentrations of lidocaine decreased the G1 phase population and increased the G2/M phase population in OECM-1 cells. These variations in the G1 and G2/M phases in both cell types may be mediated by the differential expression of proteins related to the cell cycle, such as p21 and cyclin D1, which are influenced by lidocaine, depending on the specific cellular context. Autophagy is the process of delivering intracellular components and dysfunctional organelles to the lysosomes for degradation and recycling. Based on the ANOVA results, lidocaine appeared to induce greater autophagy in OECM-1 cells than in SG cells. However, the distinct genetic backgrounds of OECM-1 and SG cell lines may have contributed to the observed differences. Our RNA-sequencing data support this notion, indicating that the cellular response to lidocaine exposure involves both overlapping and unique signaling pathways. The baseline levels of autophagy also differ between the two cell types, with functional roles of autophagy possibly varying as well.

Oxidative stress occurs when there is an imbalance between ROS production and the body’s capacity to neutralize ROS using antioxidants [63]. Compared with normal cells, cancer cells are known to be more sensitive to acute increases in ROS levels [64]. Our findings indicated that lidocaine elevated the levels of mitochondrial ROS in both OECM-1 and SG cells. Because the cytosolic redox capacity is greater than that of the mitochondrial capacity, any H₂O₂ diffusing into the cytosol is typically eliminated by cytosolic antioxidant systems. However, in this study, when the ROS levels generated by the mitochondria exceeded the cytosolic redox defense, lidocaine suppressed the level of cytosolic ROS in SG cells but not in OECM-1 cells, implying that SG cells possess higher levels of cytosolic antioxidant systems than OECM-1 cells do. Our current data on SG cells show that the suppression of cytosolic ROS by lidocaine aligns with its observed ROS-scavenging activity during dental surgery [65]. The balance between ROS production and antioxidant capacity remains a significant concern for both OECM-1 and SG cells. Additionally, our findings revealed a distinct pattern of MMP disruption induced by lidocaine in OECM-1 and SG cells, suggesting divergent mitochondrial functions between the two cell types. Lidocaine induced hypoxia and partly stabilized the HIF-1α protein. Notably, although a mild elevation of mitochondrial superoxide levels can enhance specific functions such as lifespan, high levels of superoxide can be toxic [66]. This underscores the importance of considering the location and quantity of superoxide radicals in relation to their effects on specific functions.

EMT is closely associated with tumor progression, metastasis, and resistance to conventional therapies and small-molecule targeted inhibitors [67]. Researchers have shown that the overexpression of EMT-inducing genes is associated with resistance to cisplatin and 5-FU [68]. In the present study, we employed ECIS to determine the changes in impedance as cells adhered to and spread on small electrodes [54, 55]. The insulating plasma membranes of the cells constrained the electrical current, resulting in significant changes in the measured impedance. Additionally, small fluctuations in impedance were observed as live cells continuously altered their morphology, providing insights into the motility, migration, and invasion of metastatic cancer cells. Real-time impedance analysis indicated that 10 mM lidocaine exhibited the fastest response, whereas the combination of 5 mM lidocaine and cisplatin had the most pronounced suppressive effect on OECM-1 and SG cells. Cisplatin displayed a delayed response and 5-FU demonstrated a higher impedance than those observed in the control medium in SG cells. Interestingly, the synergistic effects of lidocaine combined with cisplatin or 5-FU in both cell types were not directly associated with impedance status. Notably, OECM-1 cells used in this study were derived from a primary culture of a patient with oral cancer, whereas SG gingival epithelial cells were originally derived from human-attached gingiva. In addition to its role in tumor metastasis, EMT is a critical stage in the wound healing process [69]. Similarly, GSEA showed that exposure to 5 mM lidocaine enhanced hallmarks of EMT in both OECM-1 and SG cells. In contrast, the effects of 3 mM lidocaine were limited to OECM-1 cells. This suggests that lower concentrations have a more selective effect on SG cells. In light of these findings, it is worth considering whether the efficacy of chemotherapy for oral cancer cells or wound healing after gum grafting is influenced by lidocaine, depending on whether the cell type is epithelial or mesenchymal.

In this study, the IC₅₀ of lidocaine for OECM-1 cells was determined to be 2.6 mM. Although lidocaine typically exhibits antitumor effects at millimolar concentrations across various cell lines, this finding is particularly noteworthy in comparison with existing literature. Previous studies have reported IC₅₀ values for lidocaine in breast cancer cell lines (MCF-7, MDA-MB-231, and BT-474), liver cancer (HepG2 and Hep3B), and neuroblastoma (SH-SY5Y) ranging from approximately 3.1 to 9 mM [31, 58, 70, 71]. The relatively lower IC₅₀ observed in OECM-1 cells in this study suggests that OSCC cell lines may have a unique susceptibility to lidocaine, potentially related to the specific expression profiles of voltage-gated sodium channels or differences in metabolic pathways. The distinction between malignant and nonmalignant cell responses is fundamental for therapeutic viability. Local anesthetic toxicity is most frequently encountered with continuous infusions via pain pumps in human chondrocytes and tenocytes [16, 17]. In our study, the IC₅₀ of lidocaine in SG cells was found to be 3.6 mM, whereas in human TC28a chondrocytes, it was 8 mM. These different mechanisms highlight a promising therapeutic window for lidocaine in both on- and off-target applications. Notably, the concentrations of lidocaine used in these in vitro cell–based experiments were significantly lower than those typically required to achieve local anesthesia in plastic surgery, dermatology (1%), or dental practice (2%).

The primary objective of combination therapy is to achieve synergistic effects that enable dosage reduction and subsequent reduction in toxicity. In OECM-1 cells, the CI values for the combination of lidocaine and cisplatin were consistently below 1, whereas similar values were observed for specific combinations of lidocaine and cisplatin in SG cells. In comparison with the ECIS data, we analyzed drug-induced cellular activities, including cell adhesion, spreading, proliferation, and drug-induced apoptosis, in both OECM-1 and SG cells. Our current findings indicate that the synergistic effects of lidocaine with cisplatin in both cell types are not directly associated with changes in cellular impedance. As previously noted, the OECM-1 cells were derived from a primary culture of a patient with oral cancer, whereas SG gingival epithelial cells were originally derived from human-attached gingiva [37]. In addition to its role in tumor metastasis, EMT is a critical stage in the wound healing process [69]. To this end, it is worth considering whether the efficacy of chemotherapy for oral cancer cells or wound healing after gum grafting is influenced by lidocaine, depending on whether the cell type is epithelial or mesenchymal.

The use of OECM-1 and SG cells in our study presents a limitation, as one represents OSCC, whereas the other consists of relatively normal gingival epithelial cells. This distinction may have resulted in different genetic responses to lidocaine. Furthermore, various types of OSCC exhibit different degrees of responsiveness to lidocaine treatment. Further investigations involving a range of OSCC types are necessary to validate and clarify the mechanisms underlying the chemosensitizing effects of lidocaine. Additionally, without employing specific inhibitors targeting apoptosis, autophagy, or ROS to corroborate our findings regarding lidocaine, we cannot definitively determine whether these pathways are primary drivers or mere consequences. Furthermore, comparing lidocaine with levobupivacaine, bupivacaine, and ropivacaine strengthens the evidence for the functional role of amide-type local anesthetics in both OECM-1 and SG cells.

Conclusions

Our findings indicated distinct working mechanisms of lidocaine in these two cell types. Specifically, in OECM-1 cells, lidocaine suppressed metabolic activity, cellular proliferation, S-phase population, and late apoptosis. Additionally, it induced various cellular responses that affected subG1 and G2/M populations, early apoptotic cells, mitochondrial ROS, hypoxia, and autophagy, along with the expression of specific mRNAs. Conversely, the effects of lidocaine on SG cells included the suppression of the G2/M population, as well as induction of the G1 population and total apoptosis. Notably, CI analysis revealed that lidocaine acted synergistically with 5-FU and cisplatin in both OECM-1 and SG cells. Furthermore, for both cell types, similar impedance patterns were observed for lidocaine alone and in combination with cisplatin. These differences highlight the multifaceted functions of lidocaine, in addition to its established on-target use for pain management in oral surgery, suggesting that local anesthetics may play a crucial role in combination therapies for oral cancers and wound healing in post-gum grafts beyond their conventional use in pain management.

Abbreviations

OSCC

Oral squamous cell carcinoma

OECM-1

Oral epidermoid carcinoma meng-1

SG

Smulow–Glickman

5-FU

5-fluorouracil

RPMI

Roswell Park Memorial Institute

FBS

Fetal bovine serum

DCFH-DA

2’,7-dichlorofluorescein diacetate

PI

Propidium iodide

MTT

Thiazolyl blue tetrazolium bromide

CI

Combination index

FACS

Fluorescence-Activated Cell Sorting

MMP

Mitochondrial-Membrane Potential

BrdU

Bromodeoxyuridine

PE

Phycoerythrin

ACTN

α‑actinin

qPCR

Quantitative PCR

ECIS

Electric Cell-Substrate Impedance Sensing

KEGG

Kyoto Encyclopedia of Genes and Genomes

GSEA

Gene Set Enrichment Analysis

AO

Acridine orange

CHOP

CCAAT/enhancer-binding protein homologous protei

HIF-1α

Hypoxia-inducible factor-1α

EMT

Epithelial-to-mesenchymal transition

Authors’ contributions

Conceptualization, Wei-Zhi Huang, Gunng-Shinng Chen, Shiao-Pieng Lee, and Jia-Lin Chen; data curation, Wei-Zhi Huang, Gunng-Shinng Chen, and Shu-Ting Liu; formal analysis, Wei-Zhi Huang and Shu-Ting Liu; funding acquisition, Wei-Zhi Huang, Jia-Lin Chen, Shiao-Pieng Lee, and Shih-Ming Huang; investigation, Shu-Ting Liu and Shih-Ming Huang; methodology, Gunng-Shinng Chen, Shiao-Pieng Lee, and Shih-Ming Huang; project administration, Shih-Ming Huang; resources, Gunng-Shinng Chen, Shiao-Pieng Lee, and Shih-Ming Huang; supervision, Shih-Ming Huang; validation, Wei-Zhi Huang and Shu-Ting Liu; writing—original draft, Wei-Zhi Huang and Jia-Lin Chen; writing—review and editing, Jia-Lin Chen. All authors read and approved of the final manuscript.

Funding

This work was supported by grants from the National Science and Technology Council [NSTC 112-2635-B016-002 to Jia-Lin Chen], the Tri-Service General Hospital (TSGH-E-113217 to Wei-Zhi Huang and TSGH-D-115132 to Jia-Lin Chen), the Ministry of National Defense-Medical Affairs Bureau (MND-MAB-D-113115 to Shiao-Pieng Lee), and the Teh-Tzer Study Group for Human Medical Research Foundation [B1141022 to Shih-Ming Huang], Taiwan, Republic of China.

Data availability

The datasets generated and/or analyzed during this study are available from the corresponding author upon reasonable request. Additionally, our RNA-seq data has been deposited in the NCBI Gene Expression Omnibus under accession number GSE316363.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.