The mRNA expression levels of HGF and MET were analyzed in human tissue samples from normal brain, brain cancer, glioma, and GBM using the R2 Genomics Analysis and Visualization Platform (https://hgserver1.amc.nl/cgi-bin/r2/main.cgi). The results were presented as median and interquartile range. Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/index.html), an online web server based on The Cancer Genome Atlas (TCGA) and GTEx data, was also used to analyze the mRNA expression levels of HGF and MET in GBM tissues and normal tissues. The GBM dataset was selected for analysis, with |Log₂FC| cutoff set to 1 and the P-value cutoff set to 0.01. Whisker plots were generated to show the relative RNA expression levels of HGF and MET in GBM tissues (n=163) and normal tissues (n=207). In addition, we utilized The Cancer Proteome Atlas (TCPA) database (http://tcpaportal.org/tcpa/) to examine the protein expression levels of MET pY1235 and MET in GBM samples. To determine the potential relationship between the expression of HGF/MET and patient survival, an analysis of the R2 database was conducted to evaluate the association between their expression levels and clinical prognosis in brain tumors and gliomas. The Kaplan-Meier curve was generated using the following datasets: Tumor Brain-Madhavan-550-MAS5.0-u133p2 (HGF, 210998_s_at; MET, 203510_at); Tumor Glioma-French-284-AS5.0-u133p2 (HGF, 209960_at; MET, 203510_at); Tumor Brain Lower Grade Glioma (2022-v2)-tcga-532-tpm-gencode36 (HGF, ENSG00000019991.18; MET, ENSG00000105976.16); and Tumor Glioblastoma-TCGA-540-MAS5.0-u133a (HGF, 209960_at; MET, 203510_at). In the Glioma database (French, n=284), patient survival information is available for only 273 samples. In addition, the Kaplan-Meier survival plots were generated using the TCGA database to analyze the relationship between the protein/phosphoprotein expressions of MET and patient survival.

Lidocaine hydrochloride, obtained from Aspen Pharma Trading Ltd., was stored at 4°C until use. Additional chemicals, including TMZ, DMSO, MTT, and other reagents, were purchased from MilliporeSigma. The primary antibodies employed in the present study included HGF (cat. no. ab83760), MET (cat. no. ab216574), phosphorylated (p)-MET (cat. no. ab5662), and cleaved PARP (cat. no. ab32064), all of which were purchased from Abcam. The antibody for α-tubulin (cat. no. 05-829) was acquired from EMD Millipore. The secondary antibody HRP-conjugated goat anti-rabbit IgG was obtained from Leadgene Biomedical, Inc. (cat. no. 20202), while the secondary antibody HRP-conjugated goat anti-mouse IgG was procured from Jackson ImmunoResearch Laboratories, Inc. (cat. no. 115-035-003).

The U87MG (glioblastoma of unknown origin) and DBTRG-05MG cell lines were obtained from the American Type Culture Collection (ATCC) and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 100 U/ml penicillin/streptomycin at 37°C in a humidified 5% CO₂ atmosphere. All cell culture reagents were purchased from Gibco; Thermo Fisher Scientific, Inc. RNA sequencing was conducted to validate the U87MG cell line, as evidenced by the data provided in the supplementary material. To investigate the mechanism of TMZ resistance in GBM, the cells were gradually exposed to increasing doses of TMZ (ranging from 15.625 to 250 µM) over a period of 6 months. Once the cells were able to survive in the drug, they were considered TMZ-resistant and designated as U87MGR cells. To analyze secreted proteins, 2×10⁶ cells were seeded into a 10-cm dish and cultured until they reached ~80% confluency. The cells were then washed twice with 1X PBS to completely remove FBS. Subsequently, the cells were cultured in serum-free DMEM for 48 h, and the supernatant was collected for further analysis. To further determine the relationship between HGF and drug resistance, U87MG cells were pre-treated with conditioned media (CM) from both U87MG and U87MGR cells. Additionally, a portion of CM from U87MGR cells was pre-treated with HGF-neutralizing antibody (1:500) for 1 h before administering to U87MG cells. Then, the cells were treated with TMZ and lidocaine together for 48 h. All cell observation and imaging in the study were conducted using the Hamlet microscope.

Cells were seeded at a density of 1.5×10⁵ cells/well in 6-well plates and cultured overnight to allow adherence. After treatment with TMZ (0–1,000 µM) for 48 h, the cells were harvested, washed with cold PBS, and stained using the Annexin V FITC/PI Apoptosis Detection Kit (cat. no. 556547; BD Biosciences) in the dark at 25°C for 15 min. The apoptotic rate was then analyzed using flow cytometry. Flow cytometric analysis was conducted on a BD FACSCanto™ II flow cytometer (BD Biosciences), and data acquisition was carried out using FACSDiva software version 6.1 (BD Biosciences).

Cell lysates were prepared using RIPA buffer (25 mM Tris-HCl pH 7.6, 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate and 0.1% SDS) with a protease inhibitor cocktail. The concentration of total protein was determined using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, Inc.). Equal amounts (40 µg) of protein were separated on SDS-PAGE gels and transferred onto PVDF membranes (MilliporeSigma). The percentage of polyacrylamide used for the separation varied for different protein targets: HGF in conditioned medium: 12%, HGF in cytosol: 8%, pMET/MET: 8%, and cleaved PARP: 12%. The membranes were blocked with 5% skim milk dissolved in Tris-buffered saline (TBS) for 1 h at room temperature. Subsequently, the membranes were hybridized with specific primary antibodies overnight at 4°C. The primary antibodies used in western blotting were diluted as follows: α-tubulin at a ratio of 1:10,000, and HGF, p-MET, MET and cleaved PARP at a ratio of 1:1,000. Following several washes in TBS supplemented with 0.05% Tween-20 (TBST), the membranes were incubated with suitable HRP-conjugated secondary antibodies at room temperature for 1 h. The secondary antibodies used in western blotting were all diluted at a ratio of 1:5,000. After washing the membranes with TBST, the protein bands were visualized using ECL reagents (cat. no. 34580; Thermo Fisher Scientific, Inc.). α-tubulin was used as the loading control for normalization in western blotting. To analyze secreted proteins, cells were cultured in serum-free DMEM for 48 h, and the culture supernatants were then concentrated using a 3-kDa cut-off Amicon Ultra filter (Merck KGaA). Subsequently, the concentrated supernatants were subjected to protein quantification, and equal amounts of total protein were analyzed using western blotting. Due to the absence of specific markers for secretory proteins, equal amounts of total protein were loaded and confirmed using Ponceau staining for uniformity after protein quantification. The protein loading quantity was confirmed to be consistent through Ponceau S staining.

To determine the effect of lidocaine and gene knockdown on TMZ sensitivity in GBM cells, the MTT assay was employed. In brief, 5×10³ cells were seeded in 100 µl of culture medium per well on a 96-well plate and incubated overnight. The cells were then treated with lidocaine either alone or in combination with TMZ for indicated time periods. A total of 4 h prior to the completion of the experimental reaction, MTT reagent (5 mg/ml in PBS) was added to each well, and the cells were incubated for 4 h. The supernatant was subsequently removed, and 100 µl of DMSO was added to each well. The viability of cells was determined by measuring absorbance at 550 nm using a SpectraMax ABS Plus plate reader (Molecular Devices, LLC), and the background reading at 750 nm was subtracted. The results were expressed as a percentage, relative to untreated cells.

A total of 2×10⁵ cells were seeded in 2 ml of culture medium per well on a 6-well plate and incubated overnight. Following treatment with lidocaine, TMZ, or a combination of both for 24 h, the cells were harvested and suspended in culture medium. Subsequently, the cells were seeded on a 6-well plate at a density of 5×10³ cells per well and incubated in an incubator at 37°C with 5% CO₂ for ~2 weeks until colonies formed (consisting of at least 50 cells). After washing with PBS, the cells were fixed with 4% paraformaldehyde for 20 min at room temperature. Subsequently, they were stained with 0.1% crystal violet for 30 min, and the number of colonies was assessed using a light microscope and ImageJ software (version 2.1.0/1.5.3v; National Institutes of Health).

The cell proliferation was evaluated using the MTT assay. Briefly, 5,000 cells were seeded per well on 96-well plates and incubated for 0, 24, 48 and 72 h. MTT reagent (5 mg/ml in PBS) was added to each well, and the cells were incubated for 4 h. After incubation, the plates were centrifuged at 300 × g for 5 min. The supernatant was removed, and 100 µl of DMSO was added to each well. The absorbance was measured at 550 and 750 nm to subtract the background signal (750 nm). Cell proliferation was quantified by measuring the optical density.

GBM cells were seeded on a 6-well plate until they reached 100% confluence. The cell monolayer was manually scratched using a 200 µl-sterile pipette tip to create a straight line. The wells were gently washed once with PBS to clean the field of view and images were captured as the controls. The cells were treated with serum-free medium containing lidocaine at 37°C for 24 h. At least five continuous fields per well were recorded before and 24 h after migration using a light microscope (magnification, ×200). The migration rate was calculated as follows: % of wound closure=100× (Scratching area at 0 h-Wound area at 24 h)/Scratching area at 0 h.

A total of 1×10⁵ cells were resuspended in 200 µl of serum-free culture medium and seeded onto the Transwell insert (8-µm pore size; Costar; Corning, Inc.). The lower chamber was filled with 300 µl of culture medium containing 10% FBS. The Transwell inserts were then placed in a 37°C incubator with 5% CO₂ for 24 h. Afterwards, the Transwell inserts were fixed with 3.7% formaldehyde for 15 min and stained with 0.05% crystal violet for 30 min. The cells were washed with PBS, and any remaining stain on the upper side of the Transwell insert was removed with a cotton swab. The number of migratory cells on the lower side of the Transwell was observed under a light microscope at randomly selected different fields of view, images were captured, and cells were counted.

The third-generation lentiviral vectors utilized in the present study were sourced from the RNAi Core Facility at Academia Sinica located in Taipei, Taiwan. Using a transfection reagent (PolyJet; cat. no. SL100688; SignaGen Laboratories), a combination of 2 µg of specific short hairpin (sh)RNA, 2 µg of pCMVΔR8.91, and 0.2 µg of pMD plasmids was introduced into 293T cells (cat. no. CRL-3216; ATCC) to generate the recombinant lentiviruses. Transfection was carried out at 37°C overnight. Following this, a fresh medium replacement was performed, and the cells were further cultured for 24 to 72 h to collect lentiviral particles. GBM cells were infected with the lentiviral supernatant in the presence of polybrene (8 µg/ml). The lentiviral particles were then used to infect cells overnight at a multiplicity of infection (MOI) of 5. After transduction, puromycin (5 µg/ml) was added to the cells for 48 h to select stable cell lines. The lentiviral plasmids targeting HGF were TRCN0000047135 (shHGF#1, 5′-CCG-GCA-GAC-CAA-TGT-GCT-AAT-AGA-TCT-CGA-GAT-CTA-TTA-GCA-CAT-TGG-TC-TGT-TTT-T-3′) and TRCN0000047137 (shHGF#2, 5′-CCG-GGC-AAA-GAC-TAC-CCT-AAT-CAA-ACT-CGA-GTT-TGA-TTA-GGG-TAG-TCT-TTG-CTT-TTT-3′). The lentiviral plasmids targeting MET were TRCN0000040047 (shMET#1, 5′-CCG-GGC-CAG-CCT-GAA-TGA-TGA-CAT-TCT-CGA-GAA-TGT-CAT-CAT-TCA-GGC-TGG-CTT-TTT-G-3′) and TRCN0000009850 (shMET#2, 5′-CCG-GCA-GAA-TGT-CAT-TCT-ACA-TGA-GCT-CGA-GCT-CAT-GTA-GAA-TGA-CAT-TCT-GTT-TTT-G-3′). The control shRNA sequence used was TRC1. Scramble: 5′-CCG-GCC-TAA-GGT-TAA-GTC-GCC-CTC-GCT-CGA-GCG-AGG-GCG-ACT-TAA-CCT-TAG-GTT-TTT-3′. The sequence design was informed by the reference website (https://rnai.genmed.sinica.edu.tw/searchDatabase).

After isolating total RNAs using REzol reagent (Protech Technology Enterprise CO., Ltd.) and reverse transcribing the mRNA to cDNA using a PrimeScript RT Reagent kit (Takara Bio, Inc.), the levels of HGF mRNA were detected using KAPA SYBR FAST qPCR Master Mix, according to the manufacturer's instructions. The MyGo PCR Detection System (IT-IS Life Science Ltd.) was used for detection. The following primers were used in the experiment: HGF forward, 5′-GAA-TGC-ATG-ACC-TGC-AAC-GG-3′ and reverse, 5′-TGT-CGG-GAT-ATC-TTT-CCG-GC-3′; and GAPDH forward, 5′-CAC-CCA-TGG-CAA-ATT-CCA-TGG-CA-3′ and reverse, 5′-TCT-AGA-CGG-CAG-GTC-AGG-TCC-ACC-3′. The RT-qPCR thermocycling conditions included an initial denaturation at 95°C for 180 sec, followed by 40 cycles with denaturation at 95°C for 10 sec, annealing at 60°C for 20 sec, and extension at 72°C for 30 sec. The relative level of gene expression was determined using the comparative C_q (2^−ΔΔCq) method (36), with GAPDH used as the normalization reference.

The log-rank test was employed for Kaplan-Meier survival data analysis. The results are presented as the mean ± SEM, obtained from at a minimum of three separate experiments. The statistical analyses performed out using the Graph Pad Prism 6.0 software (Dotmatics). A comparison between two groups was performed using unpaired Student's t-test. When examining multiple variables, one-way analysis of variance with Bonferroni's post-hoc test was used. P<0.05 was considered to indicate a statistically significant difference.

To understand the clinical significance of HGF and its receptor MET, publicly available online databases (R2 database) were used to compare the expression levels of these biomolecules in healthy brain tissues and tumor brain tissues. The results indicated that HGF expression in brain tumors (Madhavan), gliomas (French), and GBM (Pfister) was upregulated compared with normal brain tissues (Berchtold) (Fig. 1A). However, there was no significant difference in the expression levels of MET among the groups. In addition, GEPIA database was used to analyze the expression levels of HGF and MET in GBM. The mRNA expression levels of HGF in GBM were elevated compared with normal tissues, but there was not a significant difference in MET expression (Fig. 1B). As the mRNA levels of MET did not appear to be clinically related to the level of deterioration, TCPA database was utilized to analyze the protein expression levels of p-MET and MET in GBM and low-grade gliomas (LGG). Protein levels of p-MET were significantly higher in GBM compared with LGG (Fig. 1C). Next, the R2 database was utilized to obtain gene expression data in brain tumors and gliomas, allowing to examine the potential impact of HGF and MET expression on the overall survival of patients. The present analysis revealed that patients with tumors exhibiting elevated expression levels of HGF and MET experienced poorer survival outcomes (Fig. 1D). Information on the data obtained from the R2 database, including tumor type, patient age, sex distribution and sample size, are presented in Tables I and II. The results suggested that increased HGF and MET expression in brain tumors and gliomas may worsen malignancy, impacting patient survival. This emphasizes the imperative for an in-depth investigation into the roles of HGF and MET in GBM. Finally, the TCPA database was used to analyze the relationship between the levels of p-MET (Y1235)/MET and the survival probability of GBM patients. The results revealed that a higher phosphorylation level of MET was significantly associated with a poorer survival rate in GBM patients, while MET expression itself was not significantly associated to survival rate (Fig. 1E). Fig. 1A-C and 1E are all derived from the GBM database. However, it is important to clarify that the analysis in Fig. 1D actually comes from the brain tumor and glioma databases, not the GBM database. Interestingly, the results in Fig. 1D suggested that high expression of HGF and MET in brain tumors and gliomas may exacerbate their malignancy, thereby influencing patient survival rates. These findings indicated a crucial role for HGF/MET in brain cancer, yet in GBM, the actual activation of MET appears to be more significant than its expression level in influencing patient prognosis. Therefore, inhibiting abnormal HGF/MET axis activation may be a potential treatment strategy for GBM.

To investigate the relationship between the HGF/MET pathway and TMZ sensitivity in GBM cells, TMZ-resistant cells were established from U87MG cells, which are referred to as U87MGR. Flow cytometric analysis revealed that ~80% of parental cells underwent apoptosis when treated with 500 µM TMZ, whereas resistant cell lines exhibited an apoptotic rate of ~50% even after TMZ treatment at concentrations up to 1,000 µM, indicating that U87MGR cells were highly resistant to TMZ (Fig. 2A). Next, HGF expression was compared between the two cell lines and it was identified that the expression of HGF in U87MGR cells was higher than that in U87MG cells (Fig. 2B). Furthermore, cell culture supernatants were collected and concentrated for subsequent analysis using electrophoresis. Ponceau S staining showed that the total amount of loaded protein was consistent across the samples (Fig. 2C, left panel), and the results of western blotting revealed that HGF secretion by U87MGR cells was higher than that by U87MG cells (Fig. 2C, right panel). As high levels of secreted HGF can activate the MET pathway, the activation levels of MET in these cells was further analyzed. MET was highly activated in U87MGR cells compared with U87MG cells (Fig. 2D). During stressful conditions such as starvation or chemotherapy, cancer cells may activate certain signaling pathways that give them an advantage in survival (37). Therefore, U87MG and U87MGR cells were cultured in serum-free medium for 24 and 48 h and the changes in HGF expression and MET activation upon starvation were assessed. With increasing starvation time, TMZ-resistant cells exhibited elevated HGF levels, and this effect was particularly evident after 48 h of starvation (Fig. 2E and F). Notably, the levels of activation of the MET pathway induced by starvation in U87MGR cells were markedly higher compared with their parental cells (Fig. 2G). Next, U87MG and U87MGR cells were treated with increasing doses of TMZ and the expression of HGF and the activation of MET were assessed through RT-qPCR or western blot analyses. The mRNA levels of HGF in both U87MG and U87MGR cells were increased upon TMZ treatment (Fig. 2H and I). However, western blot results were inconsistent, possibly due to protein secretion into the extracellular space. The expression levels of MET in U87MG cells were reduced markedly in a dose-dependent manner. However, in U87MGR cells, MET expression was maintained, and its phosphorylation level was elevated with increasing doses of TMZ (Fig. 2J). These results revealed increased HGF expression and secretion in TMZ-resistant GBM cells, accompanied by enhanced MET receptor activation. Even under adverse conditions such as starvation or drug treatment, resistant cell lines maintained elevated HGF and MET expression. This strongly implies an association between HGF/MET pathway activation and TMZ resistance.

A recent study demonstrated that lidocaine can reduce the mobility of cancer cells and enhance their sensitivity to drug treatment by inhibiting the MET pathway (35). Given the increased activation of the MET pathway observed in TMZ-resistant GBM cells, it was reasoned that if lidocaine can inhibit the MET pathway, it may potentially suppress the malignant behavior of GBM cells and enhance the therapeutic effect of TMZ. To evaluate the cytotoxic effect of lidocaine, U87MGR cells were treated with lidocaine (0–3 mM) for 24 and 48 h, and cell viability was assessed using the MTT assay. Lidocaine significantly reduced cell viability only at concentrations higher than 1.5 mM (Fig. 3A). Subsequently, it was investigated whether lidocaine would impact the expression levels of HGF. Lidocaine did not have a substantial impact on HGF expression in both U87MG and U87MGR cells (Fig. 3B and C). The effect of lidocaine on the MET pathway was further analyzed using western blotting. Lidocaine effectively reduced the expression and activation of MET in a dose-dependent manner, even at a non-toxic concentration of 0.75 mM (Fig. 3D). Next, U87MGR cells were treated with a non-toxic dose of lidocaine, and then stimulated with TMZ for combined treatment to observe whether TMZ sensitivity could be restored in TMZ-resistant cells. Both the MTT assay (Fig. 3E) and colony formation assay (Fig. 3F) indicated that TMZ combined with 0.75 mM lidocaine could effectively enhance TMZ sensitivity in U87MGR cells. The trend observed in the cleaved PARP (Fig. 3G) was consistent with the results from the cell function assays. Based on the aforementioned experimental findings, lidocaine was found to primarily inhibit the expression of MET in U87MGR cells. The effect of lidocaine on the expression of MET was also compared between U87MG and U87MGR cells. U87MGR cells exhibited higher levels of MET expression compared with U87MG cells (Fig. 3H). However, both cell types showed a dose-dependent decrease in MET expression in response to lidocaine treatment. Thus, lidocaine primarily attenuates the activation intensity of the MET pathway by downregulating MET expression levels, rather than impacting the expression of HGF. Taken together, lidocaine cannot directly reverse the high expression of HGF and dephosphorylate MET in U87MGR cells. However, by reducing the expression of MET, it effectively diminishes the overall phosphorylation levels of MET in U87MGR cells. In addition, the effects of lidocaine on cell growth and migration in U87MG cells were further investigated. Lidocaine did not significantly inhibit the growth of U87MG cells, with only a slight inhibition observed after 72 h of treatment (Fig. 3I). To determine whether lidocaine affects cell motility in U87MG and U87MGR cells, the cells were treated with non-toxic concentrations of lidocaine (0.375–0.75 mM) and cell migration was measured through wound healing (Fig. 3J and L) and Transwell assays (Fig. 3K and M). The results revealed that lidocaine significantly inhibited cell migration in both cell types. These results indicated that the effects of lidocaine on TMZ sensitivity and migration are likely due to a decrease in the activity of the MET pathway in the U87MG cell line.

Since lidocaine can reduce the expression and activation of MET, which affects cellular functions, a gene knockdown experiment was conducted for verification. First, the expression of HGF in U87MGR cells was knocked down using a lentiviral system, and its inhibitory effect was examined through RT-qPCR and western blotting. The expression of HGF in the cells was significantly suppressed (Fig. 4A and B). Western blot analysis of cleaved PARP levels revealed that reducing HGF expression in U87MGR cells substantially elevated TMZ sensitivity (Fig. 4C). Due to the higher expression and secretion of HGF in U87MGR cells compared with U87MG cells, their respective CM were collected. Then, U87MG cells were pre-treated with the two types of concentrated CM and their effect on the combined treatment efficacy of lidocaine and TMZ was analyzed. The CM from U87MGR cells significantly increased the resistance of U87MG cells to TMZ. However, when HGF antibodies were used to react with HGF in the CM from U87MGR cells first, the sensitivity of U87MG cells to TMZ was effectively restored (Fig. 4D). These results indicated that activation of the HGF/MET pathway may induce TMZ resistance, and lidocaine can improve TMZ sensitivity by inhibiting this pathway. Subsequently, it was investigated whether the migration capability of U87MGR cells was affected by the downregulation of HGF gene expression. Suppressed HGF expression significantly inhibited cell migration, as assessed by the wound healing assay (Fig. 4E) and Transwell assay (Fig. 4F). MET expression was also knocked down, which resulted in a strong inhibition of MET expression (Fig. 4G). MTT and western blot analysis demonstrated that downregulation of MET expression significantly enhanced TMZ sensitivity in U87MGR cells (Fig. 4H and I). Next, the impact of MET knockdown on cell mobility was evaluated through wound healing and Transwell assays. Reduced MET expression significantly impaired cell migration (Fig. 4J and K). These results suggested that inhibiting the HGF/MET pathway can effectively enhance TMZ sensitivity and reduce mobility in U87MGR cells.

To verify the present findings, another GBM cell line, DBTRG-05MG cells, were selected to investigate the effect of lidocaine on the MET pathway and further analyze its relationship with TMZ sensitivity and cell migration. This choice was based on a previous study by the authors, which revealed that DBTRG-05MG cells exhibit a high level of resistance to TMZ (37). The DBTRG-05MG cells were cultured in a serum-free medium for 24 and 48 h, and the impact of starvation on MET activation was evaluated using western blotting. The MET pathway was significantly activated in cells after 48 h of starvation (Fig. 5A). Furthermore, TMZ was able to significantly inhibit MET activation in a dose-dependent manner (Fig. 5B). The cytotoxicity of lidocaine was evaluated by exposing DBTRG-05MG cells to various concentrations of lidocaine (0–3 mM) for 24 and 48 h, and cell viability was assessed using the MTT assay. Lidocaine significantly decreased cell viability only at concentrations higher than 1.5 mM (Fig. 5C). Furthermore, lidocaine effectively reduced the expression and activation of MET in a dose-dependent manner (Fig. 5D). After co-treatment with 0.75 mM lidocaine and TMZ (125–250 µM) for 48 h, DBTRG-05MG cells exhibited an increase in TMZ sensitivity compared with treatment with TMZ alone, as determined using the MTT assay (Fig. 5E), colony formation (Fig. 5F), and analysis of cleaved PARP levels (Fig. 5G). The effect of lidocaine on the growth and migration of DBTRG-05MG cells was also analyzed. A slight inhibition was observed only after treating cells with lidocaine for 72 h (Fig. 5H). In addition, lidocaine was found to have a significant ability to reduce cell migration (Fig. 5I). These results suggested that lidocaine can enhance TMZ sensitivity and inhibit cell migration by suppressing the activation of the HGF/MET signaling pathway also in DBTRG-05MG cells.