Introduction

Oral squamous cell carcinoma (OSCC) is a prevalent form of head and neck squamous cell carcinoma (HNSCC). Approximately 300,000 new cases are diagnosed annually,1 moreover, its five-year survival rate has remained unchanged between 50% and 60% for a long time. Despite the continuous improvement in traditional treatment methods such as surgery, radiotherapy, and chemotherapy in OSCC, as well as breakthroughs in immunotherapy and molecular targeted therapy, the molecular heterogeneity, treatment resistance, and lymph node metastasis of tumours remain the core bottlenecks leading to treatment failure and poor prognosis. Therefore, the search for new biomarkers remains a pressing concern for OSCC treatment.

Lipid metabolism is among the prevalent metabolic abnormalities observed in cancer.2 The lipid metabolism-related gene elongase of very long-chain fatty acids 6 (ELOVL6) serves as a crucial rate-limiting enzyme for the elongation and synthesis of long-chain fatty acids in animals; its primary function is to catalyse the elongation of saturated and monounsaturated fatty acids (ranging from C12 to C18), specifically the elongation of long-chain fatty acids within the endoplasmic reticulum. Since C16 and C18 saturated and monounsaturated fatty acids are the primary components of total fatty acids in cells, ELOVL6 is thought to be an important gene for cellular fatty acid metabolism.3–5 Studies have demonstrated that ELOVL6 played a significant role in various diseases, including type 2 diabetes, atherosclerosis, acute myeloid leukaemia, nonalcoholic steatohepatitis, glioblastoma multiforme, and various tumours through oxidative stress, endoplasmic reticulum stress, and other mechanisms.6–19 Furthermore, a seminal analysis revealed that compared with high ELOVL6 expression, patients with HPV+ HNSCC with low ELOVL6 expression had a higher five-year survival rate.20 A recent study on HNSCC revealed that ELOVL6 facilitated the progression of HNSCC through the activation of the WNT/β-catenin pathway.21 However, few studies have investigated the role of ELOVL6 in OSCC and its relationship with the endoplasmic reticulum (ER) or oxidative stress.

Reactive oxygen species (ROS) are a class of chemically active oxygen-containing molecules and are key indicators of oxidative stress. The literature further indicated that the accumulation of lipids and fatty acids resulted in an increase in ROS.22 Although ROS are generally detrimental to cell survival, oncogenic perturbations that elevate ROS levels can be critical for tumorigenesis, underscoring the pro-tumorigenic role of ROS.23

Therefore, on the basis of the above background, this study investigated whether ELOVL6 promotes OSCC proliferation and migration through fatty acid reprogramming, which regulates ROS levels, or endoplasmic reticulum stress levels in vitro.

Materials and MethodsMaterials

Four OSCC cell lines, HN6, HSC3, HN4 and CAL27, were obtained from the American Type Culture Collection (ATCC). FaDu cells were purchased from the cell bank of the Chinese Academy of Sciences in Shanghai. HOK cells from ScienCell Research Laboratories (Catalogue #2610, Carlsbad, CA, USA) were donated by the Key Laboratory of Jiangsu Provincial Stomatological Hospital. The ELOVL6-homo-291 pGWLV33-new interference lentivirus (ACCGGTGTTGACTTTACAAGAATCTCGAGATTCTTGTAAAGTCAACACTTTT), ELOVL6-homo pGWLV01-new over-expression lentivirus (GCTAGCGCCACCATGAACATGAGCGTGCTGACCCTGCAAGAGTACGAGTTCGAGAAGCAGTTCAACGAGAACGAGGCCATTCAGTGGATGCAAGAGAACTGGAAGAAGAGCTTCCTGTTCAGCGCCCTGTACGCCGCCTTCATCTTCGGCGGCAGACACCTGATGAACAAGAGAGCCAAGTTCGAGCTGAGAAAGCCCCTGGTGCTGTGGAGCCTGACCCTGGCCGTGTTCAGCATCTTCGGCGCCCTGAGAACCGGCGCCTACATGGTGTACATCCTGATGACCAAGGGCCTGAAGCAGAGCGTGTGCGACCAAGGCTTCTACAACGGCCCCGTGAGCAAGTTCTGGGCCTACGCCTTCGTGCTGAGCAAGGCCCCCGAGCTGGGCGACACCATCTTCATCATCCTGAGAAAGCAGAAGCTGATCTTCCTGCACTGGTACCACCACATCACCGTGCTGCTGTACAGCTGGTACAGCTACAAGGACATGGTGGCCGGCGGGGGCTGGTTCATGACCATGAACTACGGCGTGCACGCCGTGATGTACAGCTACTACGCCCTGAGAGCCGCCGGCTTCAGAGTGAGCAGAAAGTTCGCCATGTTCATCACCCTGAGCCAAATCACACAGATGCTGATGGGCTGCGTGGTGAACTACCTGGTGTTCTGCTGGATGCAGCACGATCAGTGCCACAGCCACTTTCAGAACATCTTCTGGAGCAGCCTGATGTACCTGAGCTACCTGGTGCTGTTCTGCCACTTCTTTTTCGAGGCCTACATCGGCAAGATGAGAAAGACCACCAAGGCCGAGTAATCTAGA), and GFP reference lentivirus were acquired from Suzhou Jinweizhi Biotechnology Co., Ltd. The cells were cultured in DMEM supplemented with 10% FBS at 37°C in a 5% CO2 incubator.

Cell Culture and Transfection

Frozen OSCC cell lines were retrieved from a liquid nitrogen tank and rapidly thawed, after which the cell suspension was transferred to culture medium for culture and passaging. The quantity of target virus to add was calculated on the basis of the titre of the target virus and the optimal multiplicity of infection (MOI) value of 10 determined through experimentation. Once the cells were at the necessary confluence, the virus was introduced. Following a 24-hour infection period, the medium was replaced with complete medium, and the infection was subsequently monitored for 72 hours.

Quantitative Real-Time Polymerase Chain Reaction (qRT‐PCR)

Total RNA was extracted from OSCC cells using TRIzol reagent (Invitrogen, USA). Reverse transcription was performed with a PrimeScript RT kit (Takara, Japan) using 1 µg of RNA. qRT-PCR was performed using TB Green Premix Ex Taq II (Takara) on a StepOnePlus system (Applied Biosystems). The cycling conditions were 95 °C for 30s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 34s. GAPDH served as the internal control. Relative gene expression was calculated using the 2−ΔΔCt method. All reactions were performed in triplicate.

ELOVL6 forward primer (F): 5′‐CAAAGCACCCGAACTAGGAGA‐3′. ELOVL6 Reverse Primer (R): 5′‐GGAGCACAGTGATGTGGTGA‐3′. ACC (F): 5′‐TGAGGGCTAGGTCTTTTTGGAAG‐3′. ACC (R): 5′‐GCCAGACATGCTGGACCTTA‐3′. ACS (F): 5′‐TGAAACCCGGTTCTGCTACT‐3′. ACS (R): 5′‐TCTCAAAGCGTTCGTGGTTC‐3′. SCD1 (F): 5′‐AAACAGTGTGTTCGTTGCCA‐3′. SCD1 (R): 5′‐GGAGTGGTGGTAGTTGTGGA ‐3′. FAS (F): 5′‐GCAAGCTGAAGGACCTGTCT‐3′. FAS (R): 5′‐AATCTGGGTTGATGCCTCCG‐3′. PERK (F): 5′‐TCTCCTCCAAGACCAACCAC‐3′. PERK (R): 5′‐CTGCAGGAAGATGTGCAGAC‐3′. GAPDH (F): 5′‐ CGGAGTCAACGGATTTGGTC‐3′. GAPDH (R): 5′‐ CTTCCCGTTCTCAGCCTTGA‐3′.

Western Blotting (WB)

Total protein was extracted from OSCC cells using RIPA lysis buffer (Beyotime, China) supplemented with protease and phosphatase inhibitors. The protein concentration was determined using a BCA kit (Thermo Fisher Scientific, USA). Equal amounts of protein (20 µg per lane) were separated by 10% SDS-PAGE and transferred onto PVDF membranes (Millipore, USA). The membranes were blocked with 5% non-fat milk in TBST for 1 h at room temperature, and then incubated overnight at 4 °C with primary antibodies against anti-PERK (1:1000; Abcam; ab229912), ATF4 (1:1000; 10835-1-AP; Proteintech), and phospho-PERK (1:1000; 29546-1-AP; Proteintech), and GAPDH (1:5000; Cell Signaling Technology). After being washing with TBST, the membranes were incubated with an HRP-conjugated secondary antibody (1:5000) for 1 h at room temperature. The protein bands were visualized using an enhanced chemiluminescence (ECL) substrate (Bio-Rad, USA) and imaged with a ChemiDoc system (Bio-Rad). The above experiment was independently repeated three times. The band intensities were quantified using ImageJ software and normalized to those of GAPDH.

CCK8 Assay

OSCC cells (3000–5000 cells/well) were seeded in 96-well plates. After treatment, 10 μL of CCK-8 solution was added to each well and incubated at 37 °C for 1–4 h. The absorbance was read at 450 nm using a microplate reader. Blank wells contained medium and CCK-8 without cells. All the assays were performed in triplicate.

Transwell Assay

OSCC cells (2 × 104) in serum-free medium were added to the upper Transwell chamber (8-μm pore size). The lower chamber contained 10% FBS as a chemoattractant. After 24 h, non-migrated cells were removed, and migrated cells were fixed, stained with crystal violet, and counted using ImageJ in five randomly selected fields per well. Assays were performed in triplicate.

Cell Scratch Assay

OSCC cells were seeded into 6‑well plates and cultured until they reached 90–100% confluence. A straight scratch was created across the monolayer using a sterile 200‑ μL pipette tip. The detached cells were gently removed by washing with PBS, and fresh serum‑free medium was added. The scratch area was photographed under an inverted microscope at 0 h and 24 h (or at the indicated time points) at 100× magnification. The width or area of the wounds was measured using ImageJ software. The percentage of wound closure was calculated as follows: (area at 0 h - area at 24 h)/area at 0 h × 100%. For each well, three random fields along the scratch were analysed. All experiments were performed in triplicate.

Absolute Quantification of Long-Chain Fatty Acids by Gas Chromatography-Tandem Mass Spectrometry (GC-MS/MS)

Cells from four independent biological replicates per condition were collected. Prior to extraction, 10 μL of internal standard (nonadecanoic acid methyl ester, C19:0, 1 mg/mL in chloroform) was added to each cell pellet. Lipids were extracted by adding 1 mL of ice-cold chloroform/methanol (2:1, v/v), followed by vortexing for 1 min and incubation at 4 °C for 30 min. After centrifugation at 3000 rpm for 10 min at 4 °C, the organic phase was transferred to a fresh tube and dried under a nitrogen stream. For derivatization, 2 mL of 1% sulfuric acid in methanol (v/v) was added to the dried lipid residue, vortexed for 1 min, and heated at 80 °C for 30 min in a constant-temperature water bath. After cooling, 1 mL of n-hexane was added and vortexed, followed by addition of 5 mL of ice-cold distilled water (4 °C). The mixture was vortexed, allowed to stand for 5 min, and centrifuged at 12,000 rpm for 10 min at 4 °C. A 700-μL aliquot of the upper organic phase was transferred into a tube containing ~100 mg of anhydrous sodium sulfate to remove residual water. After vortexing and centrifugation, 300 μL of the supernatant was diluted 100-fold.

GC–MS/MS analysis was performed on a Thermo Trace 1300 gas chromatograph coupled with a Thermo TSQ 9000 mass spectrometer (Thermo Fisher Scientific, USA) using an HP‑88 fused‑silica capillary column (100 m × 0.25 mm × 0.20 μm). Helium was used as the carrier gas at a constant flow of 1.0 mL/min, with an injection volume of 1 μL in split mode (split ratio 10:1). The oven temperature program was as follows: initial 50 °C for 1 min, ramped at 4 °C/min to 155 °C, then at 6 °C/min to 210 °C, and finally at 15 °C/min to 240 °C, held for 2 min. The injector temperature was 250 °C and the transfer line temperature was 260 °C. The mass spectrometer was operated in electron ionization mode (70 eV) with data acquisition in selected reaction monitoring (SRM) mode. A mixed standard of long‑chain fatty acid methyl esters was used for identification.

For quantification, the peak area of each fatty acid was divided by the peak area of the internal standard (C19:0), and the resulting ratio was interpolated into a ten‑point calibration curve (1–2000 μg /mL) to obtain the concentration. No total ion current or total peak area normalization was applied. The fatty acid content was then calculated as (C × Vextract × dilution factor) / cell count, yielding values in μg /106 cells. The method was validated by assessing correlation coefficients, limits of detection and quantification, etc.

All experimental data were processed and visualized using R (V3.6.3). Principal component analysis (PCA) and hierarchical clustering analysis were performed on the normalized fatty acid concentrations (μg /106 cells) from all four biological replicates per condition. A P-value < 0.05 was considered statistically significant.

ROS Detection by Immunofluorescence

ROS levels were detected using a commercial ROS assay kit (Servicebio; China; Cat. No. G1706-100T). OSCC cells were seeded into confocal dishes and cultured to 50–70% confluence. The cells were incubated with DCFH-DA (diluted 1:1000 in serum--free DMEM) at 37 °C for 30 min in the dark. After the cells were washed three times with PBS, fluorescence was visualized under a fluorescence microscope (ex/em = 488/525 nm). As positive controls, cells were treated with a ROS inducer for 45 min before imaging. The fluorescence intensity of at least three random fields per sample was quantified using ImageJ. All experiments were performed in triplicate.

Statistical Analysis

The data are presented as the mean ± standard deviation (x ± s) of three independent biological replicates (n = 3 per group). Two-group comparisons were analysed by unpaired two-tailed Student’s t-test, while multi-group comparisons were assessed by one-way ANOVA. The result was determined to be statistically significant when P <0.05. GraphPad Prism 8.0 was used for all the analyses.

ResultsELOVL6 Expression in Various OSCC Cell Lines

qRT‒PCR was used to assess the expression of ELOVL6 in various cell lines, including HN4, HN6, Cal27, FaDu, HSC3, and HOK cells. The results indicated that the mRNA expression levels of ELOVL6 in the Cal27 and HOK cell lines were comparable. However, ELOVL6 expression was significantly greater in HSC3, HN4, FaDu, and HN6 cells than in HOK cells (P < 0.05). Notably, among all the cell lines tested, HN6 cells presented the highest expression of ELOVL6 (Figure 1A).

Figure 1 The biological behavior of ELOVL6. (A) Expression of ELOVL6 in various cell lines. (B) Following lentiviral transfection, the expression level of ELOVL6 mRNA was assessed via qRT‒PCR. (C) CCK8 assay at 36 hours revealed that, compared with that of cells in the control group, the proliferation rate of cells in the OE-ELOVL6 group was considerably elevated at 36 hours, and that of cells in the KD-ELOVL6 group decreased at 36 hours. (D) Scratch assays were used to assess the migration ability of CAL27 and HN6 cells with varying ELOVL6 expression levels. (E) Transwell migration assays revealed the impact of varying ELOVL6 expression levels on migratory capacity. (F) In the Transwell invasion assay, HN6 cells demonstrated increased invasion in the OE-ELOVL6 group and decreased invasion in the KD-ELOVL6 group. Similarly, CAL27 cells exhibited enhanced invasion in the OE-ELOVL6 group. **P <0.01; ***P <0.001; ****P <0.0001.

Transfected of Cells with ELOVL6 Interference or Over-Expression Lentivirus

On the basis of the expression profile, the CAL27 cell line was selected for high expression of ELOVL6, whereas the HN6 cell line was selected for both knockdown and over-expression experiments. We did not perform knockdown in CAL27 cells because the endogenous ELOVL6 level was too low to allow a meaningful dynamic range. These target cell lines were transfected with the ELOVL6-homo-291 pGWLV33-new interference lentivirus (KD-ELOVL6) and the homo-ELOVL6 pGWLV01-new over-expression lentivirus (OE-ELOVL6). A GFP reference lentivirus served as the control. Following transfection, the cells were visualized under a fluorescence microscope. Additionally, qRT‒PCR was used to assess the expression levels of the genes after lentivirus transfection. The results revealed a significant reduction in the mRNA expression level of ELOVL6 in cells in the KD-ELOVL6 group compared with that in cells in the GFP group; conversely, cells in the OE-ELOVL6 group presented notably higher ELOVL6 mRNA levels (Figure 1B).

ELOVL6 Promotes Cell Proliferation in OSCC

Cell proliferation was assessed at 36 hours in cell lines expressing varying levels of ELOVL6. As shown in Figure 1C, the KD-ELOVL6 group presented a notably lower relative proliferation rate in HN6 cells (P < 0.0001); conversely, the OE-ELOVL6 group presented an elevated relative proliferation rate (P < 0.0001). Additionally, a significant difference in the relative proliferation rate was observed between the CAL27 control group and the CAL27 OE-ELOVL6 group (P < 0.0001).

ELOVL6 Facilitates the Migration and Invasion of OSCC Cells

In the scratch assays, compared with the CAL27 cells in the GFP group, the CAL27 cells in the OE-ELOVL6 group exhibited greater scratch closure ability (P < 0.0001). Similarly, compared with that in the GFP group, the wound closure ability in the KD-ELOVL6 group was weaker in HN6 cells (P < 0.001); conversely, the wound closure ability in the OE-ELOVL6 group was greater (P < 0.001) (Figure 1D).

As shown in Figure 1E, a transwell migration experiment involving HN6 cells revealed that the migration capacity of HN6 cells in the KD-ELOVL6 group was significantly lower than that of HN6 cells in the GFP group (P< 0.0001); conversely, the migration ability of cells in the OE-ELOVL6 group was notably greater (P < 0.0001). Comparable findings were observed in the transwell migration assay conducted with CAL27 cells, indicating that the migration capacity of CAL27 cells in the OE-ELOVL6 group was enhanced (P < 0.0001).

The results of the transwell invasion assay indicated that compared with the cells in the GFP group, the HN6 cells in the KD-ELOVL6 group presented a marked reduction in invasion capacity (P < 0.001). Conversely, the invasion ability of the HN6 cells in the OE-ELOVL6 group increased (P < 0.0001). Additionally, the invasion capacity of CAL27 cells in the OE-ELOVL6 group was enhanced (P < 0.01) (Figure 1F).

ELOVL6 Influences the Expression of Fatty Acids

Long-chain fatty acids involved in targeted metabolism were analysed in four pairs of HN6 cells, which were divided into OE-ELOVL6 and GFP groups. The total ion chromatography (TIC) chromatograms for the blank, mixed standard, and sample groups are shown in Figure 2A.

Figure 2 (A) TIC chromatograms for the blank, mixed standard, and sample groups. TIC chromatogram for the blank group. (B) The relative quantitative values of metabolites across the eight samples were visualized in a hierarchical clustering diagram, where the level of metabolite abundance is denoted by colour variation. In this diagram, columns correspond to individual samples, whereas rows signify specific metabolites. (C) A dendrogram illustrating the similarity among samples using Euclidean clustering. (D) The precise positioning of the eight samples within the model space was shown; the PC1 value of PCA was 0.569, and the PC1 value of PLS-DA was 0.568.

In this experiment, the pheatmap package in the R programming language (version 3.6.3) was used to scale eight datasets, resulting in a hierarchical clustering diagram that represented the relative quantitative values of the metabolites (Figure 2B). Additionally, the European clustering method was applied to compute the distance matrix of samples and associated data, which led to the generation of a comprehensive dendrogram (Figure 2C). Furthermore, PCA revealed a PC1 value of 0.569, and the PC1 value of partial least squares-discriminate analysis (PLS-DA) was 0.568 (Figure 2D).

Compared with those in the GFP group, distinct alterations in various fatty acid levels were observed in the OE-ELOVL6 group. Specifically, following the over-expression of ELOVL6 in HN6 cells, a notable reduction in its direct substrate, palmitic acid (PA), was detected (P < 0.05). Conversely, the levels of several long-chain fatty acids, including oleic acid, trans-apigenic acid, trans-isooleic acid, apigenic acid, isooleic acid, trans linoleic acid, trans-7-nonadecenoic acid, trans-10-nonadecenoic acid, arachidic acid, eicosadienoic acid, docosanoic acid, arachidonic acid, eicosapentaenoic acid, and docosatetraenoic acid (P < 0.05), significantly increased in the OE-ELOVL6 group (Figure 3A–D).

Figure 3 The abundance patterns of metabolites in HN6 cells over-expressing ELOVL6 and in control cells. (A) Comparison of overall metabolite abundance between the OE-ELOVL6 group and the GFP-NC group. (B) Z score chart: a measure of the content of different metabolites at the same level on the basis of the mean and standard deviation of the GFP-NC group. (C) Correlation heat map analysis of differential metabolites. (D) Differential abundance of metabolites between the OE-ELOVL6 group and the GFP-NC group. *P <0.05; **P <0.01; ***P <0.001.

ELOVL6 Influences the Expression of Genes Linked to Fatty Acid Metabolism

The results indicated that FAS and ACS expression was significantly increased in cells in the OE-ELOVL6 group (P < 0.05), whereas weak expression was detected in cells in the KD-ELOVL6 group (P < 0.05). Similarly, ACC expression was high in cells in the OE-ELOVL6 group (P < 0.01), while in KD-ELOVL6 cells, the ACC level was also upregulation (P < 0.05). In contrast, SCD1 expression was elevated specifically in CAL27 cells in the OE-ELOVL6 group, with comparable expression levels observed in HN6 cells in the OE-ELOVL6 group (P < 0.05). However, no significant difference in SCD1 expression was detected in HN6 cells in the KD-ELOVL6 group (P > 0.05) (Figure 4).

Figure 4 HN6 and CAL27 cells, which presented varying expression levels of ELOVL6, presented distinct expression patterns of fatty acid synthesis-related genes, namely, ACS, FAS, ACC, and SCD1. *P <0.05; **P <0.01; ***P <0.001.

Role of ELOVL6 in the Endoplasmic Reticulum Stress-Related Apoptosis Pathway

qRT‒PCR and WB experiments were conducted to analyse relevant genes and proteins in HNSCC cells. The findings revealed no notable difference in the expression of ELOVL6 among tumour cells with varying expression levels. Furthermore, the expression of the PERK gene, as determined by qRT‒PCR, was similar to the results described above (Figure 5A and B).

Figure 5 WB and qRT‒PCR were used to examine the expression of PERK, p-PERK and ATF4 in CAL27 and HN6 cells that presented varying levels of ELOVL6 expression. (A) The WB results indicated that there were no notable differences in the expression of PERK, p-PERK or ATF4 in CAL27 and HN6 cells after the expression of ELOVL6 was altered. (B) Differential expression of ELOVL6 in CAL27 and HN6 cells did not result in a significant difference in PERK expression, as determined via qRT‒PCR (P > 0.05, ns means no significance).

ELOVL6 Influences ROS Production in OSCC Cells

The immunofluorescence results indicated that the fluorescence intensity of the ROS in the HN6 cells in the GFP group was notably greater than that in HN6 cells in the KD-ELOVL6 group but weaker than that in the HN6 cells in the OE-ELOVL6 group (P< 0.05) (Figure 6).

Figure 6 In HN6 cells, ROS levels were lower in the KD-ELOVL6 group than in the GFP-NC group, whereas ROS levels were greater in the OE-ELOVL6 group, as determined by immunofluorescence. *P <0.05; **P <0.01.

Discussion

This study investigated the activity of ELOVL6 in OSCC and its potential mechanisms. ELOVL6 expression significantly increased the proliferation, migration, and invasion abilities of OSCC cells. Further metabolic and immunofluorescence analyses revealed that ELOVL6 may drive the malignant progression of OSCC by remodelling the long-chain fatty acid profile and upregulating intracellular ROS levels. Our findings regarding the biological activity of ELOVL6 in OSCC cells were consistent with previously reported observations in hepatocellular carcinoma, HNSCC, and pancreatic ductal adenocarcinoma,16,21,24,25 suggesting that ELOVL6, as a key gene involved in lipid metabolism, played an important regulatory role in cancer progression and represented a potential therapeutic target. Notably, in the present study, both knockdown and over-expression experiments were successfully performed in HN6 cells. However, in CAL27 cells, only over-expression was achieved because the baseline expression of ELOVL6 in CAL27 cells was relatively low, which precluded effective knockdown during lentiviral transduction. Therefore, only CAL27 over-expression data were presented in this study. Furthermore, in our previously published clinical study,26 we reported that high ELOVL6 expression was associated with poor prognosis in patients with OSCC, further supporting the functional evidence at the cellular level that ELOVL6 promoted the malignant phenotype of OSCC cells.

Increasing evidence indicates that the aberrant activation of lipid metabolism is critically involved in tumour development and progression across various malignancies. For instance, in colorectal cancer, FGFR4 had been identified as a key driver of lipid metabolic reprogramming through the upregulation of FASN, thereby promoting tumour proliferation and invasion.27 Similarly, in triple-negative breast cancer, FGFR4 had been reported to regulate fatty acid metabolism by activating the AKT/RYR2 axis, and inhibition of FGFR4 led to suppressed tumour growth and invasion and altered lipid accumulation.28 These findings collectively suggested that lipid metabolism-related genes were frequently co-activated in cancers through upstream signalling pathways. On the basis of the role of lipid metabolism in tumours mentioned above, this study included a metabolomics analysis. The results revealed that ELOVL6 over-expression led to decreased PA levels and increased long-chain fatty acid levels in HN6 cells, which directly reflected the metabolic alterations mediated by ELOVL6. Numerous studies have established a close link between PA, ER stress, and ROS.29,30 In the present study, we also observed that the expression of fatty acid synthesis-related genes, including ACC, FASN, SCD1, and ACS, which were upregulated in OE-ELOVL6 OSCC cells. Among these, ACC serves as the first rate-limiting enzyme in de novo fatty acid synthesis and is frequently over-expressed in various cancers.31FASN, one of the most extensively studied lipogenic oncogenes, is significantly upregulated in multiple tumour types, including breast, prostate, ovarian, and hepatocellular carcinoma, and is closely associated with poor prognosis.32 Similarly, SCD1 is highly expressed in most malignancies and is involved in tumour proliferation, metastasis, stemness maintenance, drug resistance, ferroptosis resistance, and tumour plasticity.33ACS family members are also known to regulate cancer proliferation, metastasis, and therapeutic resistance.34 These findings were consistent with those of previous reports demonstrating the cotranscriptional regulation of these lipogenic genes. Notably, ELOVL6, together with ACC and PA, constitutes a complete fatty acid carbon chain elongation-desaturation axis: ELOVL6 elongates PA to stearic acid, which is subsequently desaturated by SCD1 to oleic acid. Moreover, the upregulation of the rate-limiting enzyme ACC at the start of the same metabolic pathway and the terminal synthase FASN at its end is a typical hallmark of the activation of the entire de novo lipid synthesis program in tumour cells. The coordinated upregulation of ACC, FASN, SCD1, and ACS in OE-ELOVL6 OSCC cells further supported the activation of the entire de novo lipid synthesis program. Collectively, these results suggested that ELOVL6 might synergistically regulate fatty acid synthesis and elongation together with the above genes, possibly through common transcription factors.

Interestingly, the results of the study revealed that ACC levels were elevated in both OE-ELOVL6 and KD-ELOVL6 cells. This counterintuitive observation may arise from complex feedback regulation and compensatory mechanisms intrinsic to cellular lipid metabolism. Both excessive activation and inhibition of ELOVL6 may disrupt the normal homeostasis of downstream fatty acid products, triggering two major pathways that converge on ACC expression. First, the aberrant metabolic flux may activate the upstream transcription factor SREBP-1c, resulting in a compensatory upregulation of genes involved in de novo lipogenesis, including ACC.24 Second, lipotoxic stress—such as the accumulation of palmitate or increased oxidative stress elicited by dysfunctional ELOVL6—may induce cellular stress signalling pathways (eg, AMPK activation), which in turn may increase ACC expression in an attempt to restore metabolic balance.35,36 Therefore, in future experiments, it will be necessary to verify the expression of ACC and ELOVL6, distinguish the total protein content of ACC from its phosphorylation activity status, and detect the activation levels of SREBP-1c and AMPK to clarify the dominant mechanism involved.

In further mechanistic exploration, no significant activation of the ER stress‑associated apoptotic pathway (PERK/p‑PERK/ATF4) was observed in this study. However, ROS levels were positively correlated with ELOVL6 expression. These findings suggested that in OSCC cells, the aberrant fatty acid metabolism induced by ELOVL6 -- particularly the remodelling of the long‑chain fatty acid profile—may not reach the threshold required to trigger the classic PERK‑ATF4 ER stress apoptotic pathway. Instead, this process may promote tumour cell proliferation, migration, and invasion through redox signalling, such as sustained ROS accumulation. Notably, considering the known protective role of SCD1 against saturated fatty acid‑induced ER stress, the elevated oleic acid levels accompanying ELOVL6 over-expression in this study may also confer a certain degree of resistance to ER stress. In addition, considering that previous studies have reported that ELOVL6 influenced the biological behaviour of HNSCC via the Wnt/β‑catenin pathway,21ELOVL6 may exert synergistic effects through multiple signalling pathways to regulate tumour behaviour. Collectively, the results of this study not only revealed the pro‑tumourigenic function of ELOVL6 in OSCC but also provided new experimental evidence and a theoretical perspective for understanding how metabolic reprogramming of fatty acids can promote malignant behaviours while supporting cell survival.

Despite the above findings, several limitations should be acknowledged. This result belonged to exploratory discovery and needed to be validated in a larger sample queue. We acknowledged that our study measured ELOVL6 only at the mRNA level, and protein-level validation was not performed. Future investigations should include Western blotting in relevant tissue samples to confirm the translational significance of our findings. We used a single GFP‑only lentivirus as the control for both the knockdown and over-expression experiments rather than dedicated scramble shRNA or empty vector controls. However, because all the vectors shared an identical backbone and the GFP control normalizes for transduction and backbone effects, we believed that the conclusions drawn from the results of these experiments remained valid, though future studies with matched controls were warranted. Moreover, the present study was based mainly on in vitro experiments, and further in vivo validation is still needed. The upstream transcriptional regulation of ELOVL6 and its co‑upregulated lipogenic genes, involving factors such as SREBP1 or ChREBP, remains unclear, and causal relationships need to be established through pathway blockade or rescue experiments. Moreover, the assessment of ER stress was not performed using different concentrations of PA or different time points, and other branches of the ER stress response (IRE1‑XBP1 and ATF6) were not examined. The potential protective effect of elevated oleic acid warrants further investigation using SCD1 inhibitors or oleic acid manipulation. Future studies addressing these aspects will help to establish ELOVL6‑mediated fatty acid metabolism as a valid therapeutic target in OSCC.

Conclusion

In summary, ELOVL6 may promote the proliferation, migration and invasion of OSCC cells by regulating long-chain fatty acid levels and ROS levels, rather than via the classical ER stress-induced apoptotic pathway in vitro. It is important to emphasize that the current findings are correlative, and further mechanistic studies (eg, blocking or rescue experiments) are required to establish causality. Targeting ELOVL6 and its associated fatty acid elongation axis may represent a potential therapeutic strategy for OSCC.

Abbreviations

ELOVL6, elongase of very long-chain fatty acids 6; OSCC, oral squamous cell carcinoma; HNSCC, head and neck squamous cell carcinoma; ATCC, American Type Culture Collection; qRT‐PCR, quantitative real-time polymerase chain reaction; WB, Western blotting; ROS, reactive oxygen species; TIC, total ion chromatography; PCA, principal component analysis; ROC, receiver operating characteristic; PA, palmitate.

Data Sharing Statement

The raw data supporting the conclusions of this article will be made available by the corresponding author, without undue reservation.

Ethics Approval and Consent to Participate

The Declaration of Helsinki guidelines were followed in this study.

Consent for Publication

The study was published with the consent of all participants.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study supported by Jiangsu Province Capability Improvement Project through Science, Technology and Education-Jiangsu Provincial Research Hospital Cultivation Unit (YJXYYJSDW4), Jiangsu Provincial Medical Innovation Center (CXZX202227).

Disclosure

The authors declare no conflicts of interest in this work.

References

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