Therapeutic potential of Momordica charantia L. Extracts on melanoma cells revealed by metabolomic profiling

Abstract

This manuscript evaluates the influence of Momordica charantia L. fruit and seed extracts on B16-F10 melanoma cell lines. The study was concerned with the application of these extracts to the cell lines and subsequent, in comparison to a control group, examination of the resultant metabolite profiles. A comprehensive analysis of metabolites was conducted by employing state-of-the-art Q-TOF LC/MS methodology in which the mature fruit and seed methanol extracts of M. charantia were prepared. The extracts were applied to B16-F10 melanoma cell lines for 48 and 72 hours, after which the cell lines obtained were analyzed using a Q-TOF LC/MS using a C18 column (Poroshell HPH-C18,2.1x100mm,2.7 µm) with a gradient elution program. The raw data obtained was examined using various data analysis methods, and the identified metabolites were reviewed to investigate the potential relationship between the phytochemical components of extract and potential anticancer activity. Certain changes (the levels of L-carnitine, arginine, N-methyltryptamine, sphingosine, prostoglandin, tryptophan and urocanic acid) at the metabolome level were related to potential anticancer activity and antioxidant activity. This study demonstrated that the fruit and seed extracts of M. charantia induced metabolic changes, associated with anticancer activity, on melanoma cells through distinct pathways.

Keywords:
Momordica charantia L.; LC-MS; Metabolomics; Melanoma.

INTRODUCTION

Momordica charantia L. (Cucurbitaceae), commonly known as 'bitter gourd' and 'bitter melon'. It is widely grown in many tropical and subtropical regions of the world (Rakholiya et al., 2014). As with other members of the Cucurbitaceae, M. charantia includes phytochemicals such as polysaccharides, flavonoids, triterpenes, saponins and steroids (Samadov, 2022). M. charantia is popular in traditional medicine for various diseases due to its well-known for its diverse medicinal properties, such its use as antidiabetic (Joseph, Jini, 2013), antibacterial (Costa et al., 2010), immunomodulatory (Deng et al., 2014), anti-inflammatory (Shivanagoudra et al., 2019), antioxidant (Li et al., 2010), antitumor (Fang et al., 2019), anthelmintic (Poolperm, Jiraungkoorskul, 2017), antiviral (Grover, Yadav, 2004), antimutagenic (Guevara et al., 1990), hypocholesterolemic (Matsui et al., 2013), antiulcer (Alam et al., 2009), and hypolipidemic (He et al., 2018) activities. Various preliminary in vitro and in vivo studies using M. charantia extracts, or its purified fractions showed its anticancer properties against melanoma and skin tumors (Grover, Yadav, 2004).

According to previous studies, the biological activity of M. charantia are due to cucurbitane-type triterpenoids, triterpene glycosides, phenolic acids, flavonoids, essential oils, fatty acids, amino acids, lectins, sterols and saponin components, as well as certain proteins in the fruits, seeds, roots, leaves, and vines of M. charantia are responsible for its biological activity (Jia et al., 2017; de Oliveira et al., 2018).

M.charantia has triterpenes derived from fivecarbon isoprene units. According to their chemical structures, the M. charantia-derived cucurbitane type triterpenes can be classified into three subtypes: namely 5, 19-hemiacetal-cucurbitane subtype; the normalcucurbitane subtype, and the nor-cucurbitane subtype. Triterpenoid saponins of M. charantia are derived from triterpenes and mainly composed of quadricyclic triterpenes or pentacyclic triterpenes. All of them can be classified into two groups namely cucurbitane type saponins and oleanane type saponins (Sun et al., 2021).

Cucurbitane-type triterpenoids are the most common chemical components (Sur, Ray, 2020). Plants of the genus Momordica contain a particular group of cucurbitacins called momordicosides (Kole, Matsumura, Behera, 2020). M. charantia contains abundant amounts of cucurbitacins, mainly cucurbitacin B and cucurbitacin E (Izawa et al., 2010). It was been observed that the amount of cucurbitacin E in the lyophilized extract of M. charantia was 0.0523 (w/w) (Chanda et al., 2020). In addition to their other biological activities, cucurbitacins are known for their anticancer activity (Izawa et al., 2010). Cucurbitacin B inhibited cell proliferation and migration potential in malignant melanoma (Garg, Kaul, Wadhwa, 2018). Cucurbitacin E is able to inhibit ERK and Akt pathways by reducing hydroxysteroid dehydrogenase-like (HSDL) 2 expression, and thus enabling cucurbitacin E to inhibit melanoma growth in vitro and in vivo (Liu et al., 2022). In addition to these specific studies, there is extensive published research testifying the therapeutical effects of momordicosides against cancer (Raina, Kumar, Agarwal, 2016; Du et al., 2019).

Melanoma is the third and deadliest form of skin cancer among all racial groups with basal cell cancer and squamous cell cancer being two types of non-melanoma skin cancer. Melanoma arises from melanocytes in the stratum basale and appears clinically as dark, rapidly spreading spots (Bradford, 2019; Nataren, Yamada, Prow, 2023). The incidence, morbidity and mortality rates of skin cancers are increasing. Therefore, it constitutes to be an important public health problem (Narayanan, Saladi, Fox, 2010).

Advances in molecular technologies have enabled the identification and validation of diagnostic, prognostic and predictive biomarkers for melanoma, such biomarkers playing an important role in optimizing personalized treatments (Donnelly III, Aung, Jour, 2019). At the diagnosis stage, molecular skin cancer biomarkers can help identify precancerous lesions and accurately classify atypical lesions. After diagnosis, molecular markers can be used to determine tumor boundaries for complete resection and this may facilitate sensitive delivery of targeted therapy (Nataren, Yamada, Prow, 2023; Wu, Qu, 2015; Liu, 2019; Mimeault, Batra, 2012).

After reviewing existing studies, the efficacy of M. charantia against skin cancer and melanoma was observed. Cucurbitacins were found to play a prominent role in this biological activity. Motivated by these findings, a metabolomic approach was initiated by extracting seeds and fruits from M. charantia and applying them to melanoma cell lines (ATCC CRL- 6475) for 48 and 72 hours. The Quadrupole Time-of- Flight Liquid Chromatography Mass Spectrometry (Q-TOF LC/MS) based metabolomics approach was applied and the metabolites in the cell-free lysate of the treated groups were subsequently compared with those of the control group to determine the metabolomic profile. These preclinical findings were evaluated to better understand the anticancer at the molecular level.

MATERIAL AND METHODS

Sample collection

The plant material was collected from the Nilüfer district of Bursa, Turkey in September 2022 and checked by the authorities at the Hacettepe University Faculty of Pharmacy. The color of the fruits was orange and the seeds were red when collected (Figure 1). The voucher specimen was kept in the herbarium of Hacettepe University Faculty of Pharmacy (HUEF23008).

FIGURE 1
Image of the plant material M. charantia.

Preparation of the M. charantia seed and fruit extracts.

The seeds were separated from the fruits and dried at room temperature in the shade. The seeds were then ground coarsely in the grinder and the fruits were cut into small pieces before extraction. The amounts of fruit and seeds were weighed and found to be 79.68 g and 67.33 g, respectively. The materials were individually applied to continuous extraction with 500 mL 70% methanol at 40 °C for 8 hours, with the process being repeated three times using fresh solvent each time. The extracts were then combined and concentrated to dryness in a vacuum at 40 °C. The concentrated extracts of fruits and seeds were lyophilized separately with 5.14 g of fruit and 4.27 g of seed extracts being obtained. The yield was determined as 6.5% and 6.3% for fruit and seed, respectively (Eneş et al., 2024).

Cell culture and viability studies

B16-F10 mouse melanoma cell line (ATCC CRL- 6475) was used in the cell culture studies. Dulbecco's Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS), penicillin (100 U/mL), and streptomycin (100 µg/mL) was used as the medium for the cells. Cells were incubated at 37 oC and 5% CO2. To determine the IC50 (Half maximal inhibitory concentration) values of fruit and seed extracts, cells were seeded in 96-well cell culture plates at 1x104 cells in 100 µL DMEM in each well and incubated for 24 hours for adhesion. After 24 hours of incubation, the medium on the cells was removed. 5 mg of each ofthe fruit and seed extracts were weighed and placedin a volumetric flask, and 1 mL of dimethyl sulfoxide (DMSO) was added, mixed in a vortex, and then dissolved in an ultrasonic bath for 20 minutes. 1000 µg/mL solutions were prepared by adding 4 mL of DMEM. Using these prepared stock solutions, cells were incubated with fruit or seed solution at different concentrations (1000.0, 500.0, 250.0, 125.0, 62.5, 31.25 and 15.625 µg/mL) diluted with DMEM for 48 hours and 72 hours (n = 6). Cell viability was then determined by the Water-soluble tetrazolium salt (WST-1) test, with the IC50 value was being calculated using the GraphPad Prism version 6.0 software. For this purpose, at the end of the incubation period, the medium containing the sample on the cells was removed and 100 µL of WST- 1 (0.1 mg/mL) in DMEM was added to each well and incubated for 4 hours. After incubation, absorbance values at 450 nm were determined with a multiplate reader. A control group of cells with 100% cell viability was created by incubating the cells in the medium. The cell viability percentage was determined using the following equation:

$$\text{cell viability}\mathrm{\%}=\frac{\text{Optical density}(OD)\text{of treated wells}}{\text{Optical density}(OD)}\text{of}\text{untreated cells}\text{×}100$$

Preparing the control (C) and test (T) groups

In cell culture studies, cells incubated only in DMEM were used as a control group (C group). The test group (T group) was prepared from incubation of fruit and seed extracts into cell lines in at a constant dose (50 µg/mL) for 48 hours and 72 hours. Triplicate samples were prepared for all the groups, and they were sampled twice for the injections.

Sampling for cell culture metabolomics

In the laminar flow cabin

The medium in the flask was discarded. The flask then washed with phosphate buffered saline (PBS) and 1 mL of cold methanol was added.

Immediately out of cell culture

Liquid nitrogen was added into the washed lidded foam box. The flask taken from the laminar flow cabin was first placed on an ice battery and acclimated to the cold environment for approximately 30-60 seconds. The flasks were gently immersed in nitrogen. To ensure that this step is completed successfully, it was observed that the color of the cells inside the flask turned white. Flasks taken from liquid nitrogen were stored at -80°C until sample preparation.

Sample preparation

With a clean gel scraper, all cells were separated from the surface and collected in the solution in the flask. The entire cell suspension in the flask was withdrawn with an automatic pipette and transferred to microcentrifuge tube. 750 µL of cold methanol was then added to the flask to wash the walls. The remaining cells were collected again with the gel scraper. The cell suspension in the flask was taken with an automatic pipette and added to the same microcentrifuge tube which was vortexed for 1 minute and centrifuged in a refrigerated centrifuge (-4°C, 15000 rpm). For metabolomics studies, 500 µL of the supernatant was taken into the microcentrifuge tube. Methanol was evaporated in a vacuum centrifuge at +4°C. 500 µL of mobile phase (50:50 ACN: Water) was added onto the residue. The microcentrifuge tube was vortexed for 1 minute and centrifuged in a centrifuge device (10,000 rpm, 10 min). 150 µL of the supernatant was taken and placed into the vial containing the insert. The vial lid was closed and vortexed for 5 seconds.

Chromatographic conditions

C18 column (Poroshell HPH-C18, 2.1x100mm, 2.7 µm) was used as the chromatography column. The gradient elution mobile phase composition was acetonitrile and water including 0.1% formic acid. Flow (0.35 mL/min) started with 10% of acetonitrile and increased linearly to 35% acetonitrile till the 3th min. The acetonitrile rate was linearly increased to 90% by the 12th minute and decreased to 10% by the 14th minute, being kept constant at 10% until the 25th minute. All the samples (n=3) were injected into the system in three replications in random order.

Mass spectrometry analysis was performed on an Agilent 6530 Q-TOF LC/MS instrument (Agilent Technologies, 184 Santa Clara, CA). The total analysis time was 25 minutes and the scanning range for the MS device was between 100 and 1700 m/z. The column temperature was 35 °C, the drying gas temperature to 300 °C, and the capillary voltage was 4000 V. The MS device was operated in positive ion mode. Quality control (QC) samples were prepared by pooling the individual samples of the groups.

Data processing

Optimized parameters for experimental conditions were used for the data analysis using MSDIAL (Smith, Zhang, 2023). A normalization process (Misra, 2020; Wu, Li, 2016) using total peak areas was applied for the detected peaks and a table for normalized peaks was created to compare Group C and T. Only reliable peaks were evaluated. Peak shapes were manually checked and a correlation between peak areas and dilution factor was considered as reported in our previous studies (Kaplan, Çelebier, 2020). A statistical test ['t statistical test' (p<0.05 confidence interval)] was performed, and changes in the normalized peak areas changingof more than 1.5 times [fold change (FC) > 1.5] were observed. PCA (Principal Component Analysis) graphs, Volcano plots and head map graphs were created using MetaboAnalyst 5.0 (Pang et al., 2021) software. The m/z and MS/MS values for these peaks were uploaded to KEGG database (Kanehisa, 2002), assisted with MetaboAnalyst 5.0, and metabolites were putatively identified (Kodra et al., 2021).

RESULTS AND DISCUSSION

Cell viability studies

Various methods are used to determine cytotoxicity, cell viability and cell proliferation. Colorimetric methods are cheap, highly reliable, highly reproducible, and fast. In many cell culture studies, analysis is performed using IC50 values (Erkekoğlu, Baydar, 2021). Graphs showing cell viability (%) versus concentration for 24 hours (a), 48 hours (b) and 72 hours (c) for seed and fruit extracts are given in Figure 2. The IC50 values for the fruit and seeds were 106.7 µg/ml and 71.2 µg/ml, respectively, at 24 hours, 44.71 µg/ml and 73.85 µg/ml at 48 hours, and 54.84 µg/ml and 51.01 µg/ml at 72 hours.

FIGURE 2
Cell viability (%) changes against concentration at 24 hours (a), 48 hours (b) and 72 hours (c).

Q-TOF LC/MS analysis and data processing results

C18 and HILIC columns are mainly used in metabolomics studies (Patti, 2011). In this study, we used a C18 column (Poroshell HPH-C18, 2.1x100mm, 2.7 µm) in a gradient elution mode as described in experimental section. The base peak chromatograms of control, fruit and seed for Q-TOF LC/MS injections are given in Figure 3 for 48 hours and 72 hours.

FIGURE 3
Representative base peak chromatograms under optimum conditions for control (A) fruit (B) and seed (C) for 48 hours; control (D) fruit (E) and seed (F) for 72 hours.

Initial optimization was carried out using QC samples, and our final gradient elution program successfully separated both polar and non-polar metabolites within 25 minutes. The column length, particle size, and gradient elution program enabled us to detect polar compounds within the first 10 minutes and non-polar compounds between 10 and 17 minutes. While it is well-known that a single chromatographic method cannot profile the entire metabolome, the subtle yet distinct differences observed in our base peak chromatograms (Figure 3) suggest that we can identify meaningful differences within the groups. Although these differences may not capture the entire metabolome, they provide representative information about a significant portion, offering insights into the molecular effects of the extracts on cancer cells.

While an initial review of the chromatograms can offer preliminary insights into the metabolite profile changes due to extract treatment, multivariate analysis is the most effective way to visualize these differences. PCA was therefore applied to assess the changes in metabolite profiles following treatment with the extracts, as shown in Figures 4A and 4B for 48 and 72 hours, respectively.

FIGURE 4
PCA plot showing the statistical difference of control, fruit and seed at the metabolome level for 48 hours (A) and 72 hours (B).

The PCA graphs showed that the metabolite profiles of the control group and seed extract-treated cells were similar within the first 48 hours, while the fruit extract induced a distinct metabolite profile (Figure 4A). At 72 hours, the profiles of the control and seed extracttreated cells remained similar, but a slightly greater difference was observed between the control and fruit extract-treated cells (Figure 4B).

The heatmap and volcano plots presented in Figures 5A and 5B for the seed and fruit extracts are consistent with the PCA graphs. The similar colors in the heatmap indicate the resemblance of the profiles, while the number of dots in the upper left and right sections of the volcano plots represent peaks that are seen to be statistically and quantitatively different to the control group.

FIGURE 5
Volcano plots and heatmaps of control, fruit and seed test groups at the metabolome level for 48 hours (A) and 72 hours (B). Upper volcano plot indicates fruit while lower volcano plot indicates seed.

Although the statistical and quantitative data confirm the effects of the seed and fruit extracts and provide insights into the strength of these effects atthe metabolome level, identifying the metabolites corresponding to these peaks is essential to fully understand the biochemical impact of the treatments.

Statistical Evaluation of the Metabolomic Data

The statistical evaluation of the metabolomic data reveals the metabolic effects of M. charantia fruit and seed extracts on melanoma cells. The base peak chromatograms (Figure 3) display noticeable differences in peak intensities and retention times across the control, seed extract, and fruit extracttreated groups. This indicates that both extracts had an influence on the cell metabolome. The PCA plots (Figure 4) further illustrate these changes, showing a clear separation between the fruit extract-treated group and the control at both 48 and 72 hours. The seed extract-treated group, while exhibiting some metabolic changes, is positioned closer to the control group, suggesting that the metabolic impact of the fruit extract is more pronounced within the time points analyzed. Both extracts show evidence of metabolic modulation, though with varying degrees of separation from the control. Volcano plots support these observations by highlighting metabolites that show significant fold changes in response to both treatments (Figure 5). The heat maps (Figure 5) provide a clear visualization of how the treatments affected the metabolome. Each row represents a metabolite, with color intensity indicating changes in concentration-red for upregulation and blue for downregulation. The fruit extract, in particular, resulted in more metabolites with significant changes at 48 hours, while the seed extract showed fewer alterations. The heat maps visually confirm the effect of M. charantia, showing distinct patterns of upregulated and downregulated metabolites across the treatment groups. Both extracts led to metabolic changes, but the extent and nature of these changes varied between the fruit and seed extracts. This statistical evaluation suggests that both extracts influenced the metabolic profile of the melanoma cells, with differing levels of impact observed over time.

Identification of the metabolites

The identification of detected metabolites in untargeted metabolomic approaches is a challenging process. While PCA data, volcano plots, and heatmap graphs can provide clues into the possible presence of a difference between groups, accurate identification of peaks is critical, which can only be achieved by carefully selecting reliable peaks for evaluation. In our experimental conditions, peaks that were found to be statistically significant (p<0.05) and quantitatively different (FC>1.5) were identified, and these peaks were manually checked to prevent false positives.

First, the shape of the peaks was considered byas software-assisted data processing can sometimes indicate ‘ghost peaks’ being real peaks. The second strategy involved using QC samples that were intentionally diluted, and the correlation between the normalized peak areas and the dilution factor was calculated. Peaks with a correlation coefficient greater than R>0.900 were considered real. Peaks that passed this validation process were uploaded to databases, and m/z values, MS/MS spectra, and, when available, previously established retention time indices were used for identification. Some of the results that were found to be significant and consistent with the literature are presented in Table I.

TABLE I
The list of some critical metabolites and their fold changes for the groups

Metabolite name	FC	+/- (Fruit/Control) 48 hours	FC	+/- (Fruit/Control) 72 hours
L-Carnitine	1.8	-	1.4	-
Dopamine	2.6	+	2.1	-
L-Arginine/D-Arginine	1.8	+	1.9	-
N-Methyltryptamine	4.9	+	1.1	+
Sphingosine/3-ketosphinganine	1.0	+	1.9	+
Lipoxin/ Prostoglandin	3.2	+	1.6	+
Metabolite name	FC	+/-* (Seed/Control) 48 hours	FC	+/-* (Seed/Control) 72 hours
L-Tryptophan/ D-Tryptophan	1.6	+	1.5	-
Urocanic acid	2.0	+	2.6	-
SM(d18:1/18:0)	1.3	+	1.8	-

• ^*
  the amount of metabolites in the fruit and seed extract treated group compared to the control ("+" an increase and "-" a decrease indicate)

Biological activity Identification of the metabolites

Tryptophan is predominantly obtained through dietary means as an essential amino acid. The metabolism of tryptophan is associated with various biological processes, including neural transmission, inflammation, and immune response. In mammalian cells, it is metabolized by kynurenine, serotonin and indole pathways. Tryptophan also serves as the precursor for serotonin (5-HT), which plays a role in the physiological regulation of various behavioral and neuroendocrine functions (Hubková et al., 2022), it have noted lower serum levels of tryptophan in melanoma patients compared to healthy individuals (Weinlich et al., 2006). In melanoma cell lines treated with M. charantia seed extract for 48 hours, an increase in tryptophan level was observed compared to the control. This increase in the level of tryptophan was not stable and a decrease in the amount occurred in 72 hours. This might be a simple argument to prove that the effect of the seed extract was acute within 48 hours and it activated the immune response but this response was not constant as expected. An alternative view is that this situation can be involved in indoleamine dioxygenase (IDO) activity. IDO converts tryptophan into immunosuppressive metabolites (Balachandran et al., 2011). It is speculated that M. charantia seed extract may reduce the utilization of tryptophan by melanoma cells by inhibiting the activity of IDO enzyme.

L-Carnitine (3-hydroxy-4-N-trimethylammonium butyrate) is a naturally occurring compound found in all mammalian species. The primary function of endogenous L-carnitine is to facilitate the transport of fatty acids across the inner mitochondrial membrane, making them available for mitochondrial β-oxidation (Evans, Fornasini, 2003). It has been demonstrated that lipolysis is upregulated in malignant cells, and fatty acid beta-oxidation serves as the predominant energy source for certain cancer types (Zhang, Du, 2012). In a study conducted with patients suffering from advanced melanoma cancer, significant increases in serum concentrations of carnitine and acylcarnitines were observed (Bayci et al., 2018). In this study, treatment of melanoma cell lines with M. charantia fruit extract for 48 and 72 hours resulted in a decrease in carnitine concentration, when compared to the control. This result was in a correlation with the reported results. By that way, creatinine level might be followed up with a targeted metabolomics study to summarize the effect. However, some further studies must be performed to prove this hypothesis, possibly including the association of cell viability results with carnitine levels.

The involvement and activity of components in the dopamine pathway in cancer are dynamic and variable. Melanogenesis is an enzymatic process dependent on L-DOPA, which is commonly irregular in melanocytes of a melanoma tumor. It has been suggested that pharmacological levels of L-DOPA, dopamine, and their analogs may inhibit macromolecular synthesis in certain tissue culture melanoma cell lines (Krummel, Neifeld, Taub, 1982). Dopamine levels was observed to increase in cell lines treated with M. charantia fruit extract, when compared to the control group. As indicated in the relevant study, this increase could potentially exert a negative impact on cell proliferation. In melanoma cell lines treated with M. charantia seed extract for72 hours, an increase in dopamine concentration was observed compared to the control.

The endogenous production of arginine occurs through the urea cycle, catalyzed by argininosuccinate synthase and argininosuccinate lyase from citrulline. Argininosuccinate synthase 1 is highly expressed in healthy tissues. A study revealed the complete absence of argininosuccinate synthase in malignant melanoma cells, while in a group treated with M. charantia fruit extract for 48 hours, the levels of arginine increased compared to the control group. This increase may be associated with a decrease in the utilization of arginine by cancer cells (Yoon et al., 2012).

For rapidly proliferating cells, such as tumor cells requiring excessive amounts of arginine to support their growth and survival, endogenous arginine is insufficient. The absence of argininosuccinate synthase in melanoma cells makes them entirely dependent on exogenous arginine. This dependence, known as auxotrophy, can be utilized as an anti-cancer treatment by depleting external support. The potential clinical applications of arginine deprivation for the treatment of malignant melanoma have been investigated and show promise and effectiveness (Qiu, Huang, Sui, 2015). It is for that reason that there must be wide ranging discussions about the changing arginine levels upon treatment with consideration of certain competitive biochemical processes.

Tryptamine,aneurotransmitterand hormone precursor which is produced as a result of tryptophan metabolism, plays a role in the synthesis of neurotransmitters such as serotonin and melatonin. Melatonin, on the other hand, is a key indole amine involved in regulating biological rhythms. It is synthesized, not only in the pineal gland in mammals, but also in various other parts of the body, including the eyes, bone marrow, gastrointestinal system, skin, and lymphocytes (Srinivasan et al., 2011). Epidemiological studies have suggested that melatonin exhibits significant apoptotic, anti-angiogenic, oncostatic, and anti-proliferative effects on various oncologic cells (Bhattacharya etal., 2019). Melatonin induces substantial changes in different immune cell ratios, enhancing their vitality and improving immune cell metabolism in the tumor microenvironment (Moradkhani et al., 2020). It suggested that melatonin and its metabolites could be used in antineoplastic treatment by increasing the effectiveness of BRAF/MEK inhibitors in patients with advanced melanoma (Kleszczyński, Böhm 2020). In this study, N-methyltryptamine level increased significantly in melanoma cells treated with M. charantia fruit extract, as compared to the control.

Lipid metabolism is important for cancer cell survival to maintain high levels of metabolic activity, membrane integrity, and signal transduction (Chen et al., 2017). In this study, changes in lipid metabolism were observed in melanoma cells applied to M. charantia fruit extract.

Sphingoid bases, especially sphinganine, promote differentiation and ceramide production in keratinocytes. Free sphinganine is a dermatological agent that strengthens the formation and maintenance of an intact epidermal lipid barrier, with beneficial effects in skin and hair care applications (Sigruener et al., 2013). 3-Ketosphinganine triggers the accumulation of dihydrosphingolipids and causes autophagy in cancer cells (Ordóñez et al., 2016). A significant amount of sphingosine content has been found in sphingomyelins of melanoma B16 tumors (Dyatlovitskaya et al., 2001). This shows that high sphingosine-1-phosphate (S-1-P) levels occur in the advanced melanomas which result from increased activity of sphingosine kinase-1 (SPHK1) (Madhunapantula et al., 2012). In melanoma cell lines treated with M. charantia fruit extract for 72 hours, an increase in 3-ketosphinganine and/or sphingosine levels were observed when compared to the control. This result was unexpected and it cannot be determined using obtained data why the level was stable in 48 hours, but increased in 72 hours. Further monitoring of SPHK1 activity is essential to clarify the underlying cause.

Eicosanoids, including prostaglandins and leukotrienes, are biologically active lipids involved in various pathological processes, such as inflammation and cancer (Wang, DuBois, 2010). Human malignant melanoma cells overexpress cyclooxygenase-2 (COX-2), the inducible isoform of COX, and the ratelimiting enzyme in the production of prostanoids. Increased expression of COX-2 is associated with the development and progression of malignant epithelial cancers, raising the possibility that prostaglandins produced by melanocytic cells may have a functional role in skin carcinogenesis (Nicolaou et al., 2004). LipoxinA4, an anti-inflammatory lipid mediator that has been suggested to have antitumor potential, has natural antitumor properties and can reduce cell proliferation, inhibit tumor cell invasion and tumor growth (Du et al., 2021). (15S)-hydroxy-11a, 9a-(epoxy methano) prosta-5Z, 13E-dienoic acid (thromboxane B2 (TXB2) and thromboxane A2-mimicking agent) increases the proliferation of B16 amelanotic melanoma cells in vitro. TXB2 reduces basal cyclic adenosine monophosphate (cAMP) levels and blocks the increase in intracellular and released cAMP by B16a cells in response to PGE1. Prostacyclin and thromboxane synthetase inhibitors reduce tumor cell proliferation and inhibit DNA synthesis (Honn, Meyer, 1981). In melanoma cell lines treated with M. charantia fruit extract for 72 hours, an increase in lipoxin or prostaglandin concentrations were observed, when compared to the control. A targeted approach must be performed to find the type of the metabolite (for instance lipoxin A4, prostaglandin E2) but this results briefly suggest that an acute inflammation occurred in the cancer cells which was reduced within 72 hours.

Squalene is structurally a triterpene compound that is one of the major components (about 13%) of skin surface lipids. Experimental studies have shown that squalene can effectively inhibit chemically induced skin, colon and lung tumorigenesis (Huang, Lin, Fang, 2009). In melanoma cell lines treated with M. charantia fruit extract for 72 hours, an increase in squalene concentration was observed, when compared to the control.

Urocanic acid is an endogenous compound found in mammalian skin. Trans-urocanic acid, which is enzymatically synthesized from histidine and found in the upper epidermis, is photoisomerized to cis-urocanic acid by exposure to UV radiation. Cis-urocanic acid (cis-UCA), an endogenous compound of the skin, can acidify the cytosol by transporting protons into cells. cis-UCA has been shown to dose-dependently reduce the number of viable human melanoma, cervical carcinoma, and fibrosarcoma cells at weakly acidic extracellular pH (Laihia et al., 2010). In melanoma cell lines treated with M. charantia seed extract for 72 hours, an decrease in urocanic acid concentration was observed compared to the control.

Sphingolipid(SL)metabolismalterations have been frequently reported in cancer, including melanoma, a skin cancerwithpoorprognosis.In normal cells, de novo synthesized ceramide is converted to sphingomyelin (SM), the most abundant SL, by sphingomyelin synthase 1 (SMS1) and, to a lesser extent, SMS2, encoded by the SGMS1 and SGMS2 genes, respectively. SMS1 is expressed at low levels in the majority of human melanoma biopsies. Furthermore, low SMS1 expression has been associated with a worse prognosis in metastatic melanoma patients (Bilal et al., 2019). In melanoma cell lines treated with M. charantia seed extract for 72 hours, an decrease in SM(D18:1/18:0) concentration was observed, when compared to the control.

Limitations of the study

While untargeted metabolomics studies are powerful, they have limitations, particularly in data interpretation. While vast amounts of data is generated by these approaches, often leading to the detection of numerous metabolite features, accurately identifying and annotating these metabolites can be challenging. This means that a high rate of false positives can occur, and it is often difficult to determine the biological relevance of the detected features. Our experiments addressed this issue by discarding false-positive peaks and employing optimized strategies. Nevertheless, false-positive metabolite matches remain challenging, despite using as much available data as possible (m/z, MS/MS, retention time index) to confirm the results.

Weconductedanextensiveliteraturesearch focused on melanoma and its biomarkers to mitigate this issue. We applied a strategy to individually evaluate all metabolites, searching keyword combinations such as cancer - individual metabolite, melanoma - individual metabolite, and melanoma - the pathway involving the individual metabolite. Although the PCA graphs, volcano plots, heat maps, and identified metabolites suggest that the effects of the extracts were targeted rather than random, a more focused strategy incorporating both metabolomics and proteomics is necessary in this cell culture study to better understand the underlying mechanisms and identify potential prognostic markers.

CONCLUSION

In this study, we designed an in vitro cell culture experiment utilizing a Q-TOF LC/MS-based untargeted metabolomics approach. The results obtained in this study support previously published findings on the phytochemical effects of M. charantia, its extracts, and specific cucurbitacins on cancer cells. Seed and fruit extracts were found to influence metabolome levels related to ‘anti-cancer’ activity through various pathways. This is the firstknownstudy to comprehensively demonstrate the effects of M. charantia seed and fruit extracts on the melanoma cell line at the metabolome level. The preliminary findings from this study should be further validated through proteomic and targeted metabolomic analyses.

ACKNOWLEDGEMENTS

This study is a part of DE MSc thesis.

• Funding
  This study was performed using the Hacettepe University Department of Analytical Chemistry’s own sources.

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Edited by

• Associated Editor:
  Camila Manoel Crnkovic

Publication Dates

• Publication in this collection
  15 Dec 2025
• Date of issue
  2025

History

• Received
  16 Aug 2024
• Accepted
  06 Nov 2024