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. 2025 Dec 17;54(1):100221. doi: 10.1016/j.dmd.2025.100221

On the interface of fatty acid metabolism: A crosstalk between fatty acid ω-hydroxylase CYP4F11 and fatty acid desaturase 2 in non–small cell lung cancer

Huiting Jia 1, Simone Brixius-Anderko 1,∗
PMCID: PMC12879313  NIHMSID: NIHMS2135974  PMID: 41500153

Abstract

Lung cancer is the leading cause of cancer deaths worldwide with non–small cell lung cancer (NSCLC) as the predominant subtype. Drug resistance in patients with NSCLC often limits treatment effectiveness, underscoring the need for novel therapeutic targets. We have previously demonstrated that a knockdown of CYP4F11 attenuates the proliferation and migration of NCI-H460 cells. CYP4F11 is a fatty acid ω-hydroxylase and metabolizes arachidonic acid to the important lipid mediator 20-hydroxyeicosatetraenoic acid. However, the underlying mechanism of how CYP4F11 promotes cancer progression is unknown. Here, we first confirmed that a genetic ablation of CYP4F11 reduces cell proliferation and migration in an additional NSCLC cell line. Conversely, CYP4F11 overexpression markedly enhanced proliferation and migration in both cell models, underlining the relevance of CYP4F11 as a putative drug target. To further examine the impact of CYP4F11, transcriptomic profiling was conducted comparing CYP4F11 knockdown and control cells. Most intriguingly, fatty acid desaturase 2 (FADS2), a key enzyme in arachidonic acid biosynthesis, was one of the most significantly downregulated genes. Further validation confirmed a significant downregulation of FADS2 at both mRNA and protein levels in CYP4F11 knockdown cells, while a CYP4F11 overexpression triggered its expression. This suggests a regulatory mechanism between CYP4F11 and FADS2 through the joint metabolite arachidonic acid. Collectively, our studies identify CYP4F11 as a promoter of NSCLC cell proliferation and migration and establish a crosstalk between CYP4F11 and FADS2. This work provides new mechanistic insights into lipid metabolism–driven oncogenesis and highlights CYP4F11 as a promising therapeutic target for NSCLC.

Significance Statement

CYP4F11 promotes non–small cell lung cancer progression by driving cell proliferation and migration, as evidenced by both loss-of-function and gain-of-function assays. Importantly, we for the first time identified a positive association between CYP4F11 and fatty acid desaturase 2, uncovering a previously unrecognized tumorigenic mechanism at the cancer-lipid metabolism interface that provides new opportunities for targeted intervention.

Key words: Cytochrome P450, CYP4F11, FADS2, Fatty acid metabolism, Arachidonic acid, Non–small cell lung cancer

1. Introduction

Lung cancer remains the leading cause of cancer-related mortality worldwide.1 In the United States alone, approximately 136,000 deaths were reported in 2020, accounting for nearly 350 cases per day (National Center for Health Statistics and the Centers for Disease Control and Prevention).2 Non–small cell lung cancer (NSCLC) constitutes the predominant subtype, representing 85% of all lung cancer cases.3 This subcategory includes large cell carcinoma, lung squamous cell carcinoma, and lung adenocarcinoma.

Cigarette smoking has been identified as the primary etiological factor for lung cancer and is responsible for approximately 85%–90% of all lung cancer cases, with the majority being NSCLC.4 Exposure to toxicants from tobacco causes accumulation of somatic mutations in oncogenes (eg, TP53, NFE2L2, and EGFR).5 In addition, reactive oxygen species and electrophiles produced by smoking tobacco activate redox responses, leading to dysregulated antioxidant defenses and metabolic reprogramming, which further promote tumor initiation and progression.6

Despite advances in early detection and therapy, treatment outcomes for NSCLC remain suboptimal. Platinum-based chemotherapies, a cornerstone of first-line therapy, act by disrupting DNA replication and transcription.7 However, those agents are associated with significant off-target toxicities, such as nephrotoxicity and myelosuppression.8 In addition, resistance remains a major challenge. Some patients exhibit innate resistance due to genetic mutation, whereas others develop adaptive resistance over time through mechanisms including secondary mutations (eg, EGFR T90M) or the activation of bypass pathways.9,10 Immune checkpoint inhibitors have emerged as powerful strategies, yet their efficacy is often limited by immune-related adverse events such as autoimmune-like attacks on normal tissue and low response rates in subsets of patients.7,11 These challenges underscore the urgent need for novel, more effective, and better-tolerated targeted therapeutics for NSCLC.

Emerging evidence implicates a dysregulation of lipid metabolism as a critical driver of tumorigenesis. Lipids not only provide structural components for cellular membranes but also serve as energy storage and signaling molecules.12 Cancer cells frequently reprogram lipid metabolism to sustain rapid proliferation and survival under metabolic stress.13 Among lipid mediators, 20-hydroxyeicosatetraenoic acid (20-HETE), a metabolite of arachidonic acid, has gained attention for its regulatory roles in vascular tone, inflammation, and cell growth.14,15 Although physiologically important, 20-HETE is increasingly recognized as a contributor to pathological conditions, including hypertension, coronary artery disease, and cancer.16, 17, 18

20-HETE is synthesized by cytochrome P450 (P450) ω-hydroxylases of the CYP4A/F families by catalyzing the terminal ω-hydroxylation of arachidonic acid (Fig. 1).19 Among the CYP4A/F isoforms, CYP4F11 is uniquely overexpressed in lung tumors and significantly in squamous NSCLC compared with healthy tissues (according to The Cancer Genome Atlas [TCGA] and genotype-tissue expression datasets). In healthy individuals, CYP4F11 is usually predominantly expressed in the liver and kidney.20 Recent studies found that CYP4F11 expression is upregulated by the redox master regulator and transcription factor NRF2 (nuclear factor erythroid 2–related factor 2) in lung cancer.21 Our prior work demonstrated that a CYP4F11 knockdown attenuates NSCLC cell proliferation and migration, suggesting a functional role in tumor progression.22 However, the mechanistic basis of how CYP4F11 promotes NSCLC progression has not been elucidated yet.

Fig. 1.

Fig. 1

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CYP4F11 is a fatty acid ω-hydroxylase and converts arachidonic acid into the potent lipid mediator 20-hydroxyeicosatetraenoic acid (20-HETE).

This study aimed to further examine the role of CYP4F11 in cancer progression and reveal the underlying mechanism. Our long-term goal is to validate robust therapeutic targets and establish a foundation for rational targeted therapeutic strategies for NSCLC by elucidating novel metabolic and signaling networks. To our knowledge, our work presented here is the first to confirm the tumorigenic role of CYP4F11 in NSCLC by enhancing cell proliferation and migration using overexpression models in multiple cell lines. We further reveal a previously unrecognized association between CYP4F11 and another crucial key player, fatty acid desaturase 2 (FADS2), in fatty acid metabolism, which for the first time reveals FADS2 as a downstream target of CYP4F11.

2. Materials and methods

2.1. Materials

The NSCLC cell lines NCI-H460 and NCI-H1437 were purchased from ATCC. HEK293T cells were kindly provided by Dr Da Yang (University of Pittsburgh). NSCLC cells were maintained in complete RPMI 1640 Medium (Gibco) supplemented with 10% (v/v) fetal bovine serum (Gibco) and 1% (v/v) penicillin-streptomycin (Pen-Strep; Gibco). HEK293T cells were cultured in complete Dulbecco’s modified Eagle’s medium (DMEM) medium (Gibco) supplemented with 10% (v/v) fetal bovine serum and 1% (v/v) Pen-Strep. All cells were maintained at 37 °C in a humidified Heracell VIOS 160i CO2 incubator (Thermo Scientific) in the presence of 5% CO2 at 37 °C. Primers are included in Supplemental Fig. 1. All other materials were purchased from Millipore Sigma or a comparable source.

2.2. Transient CYP4F11 knockdown by siRNA transfection

Cells at approximately 70% confluence were transfected with a commercial small interfering RNA (siRNA) targeting CYP4F11 (siCYP4F11_1, Sigma) and subsequently seeded at 5000 cells per well in 96-well plates for proliferation analysis. The transfection was conducted as described before for a CYP4F11 knockdown in NCI-H460 cells, and the siRNA sequence has been reported before.22

2.3. Stable CYP4F11 overexpression using lentivirus infection

NCI-H460, NCI-H1437, and HEK293T cells were thawed, cultured, and maintained under standard conditions until reaching exponential growth. Lentivirus was generated following an optimized protocol adapted from Addgene’s guidelines for stable cell line generation.23

HEK293T cells were seeded in 10-cm dishes at around 70% confluence in the complete DMEM medium. For transfection, the medium was replaced with complete DMEM lacking Pen-Strep, and cells were transfected using FuGENE 6 Transfection Reagent (Promega) with the pLenti plasmid expressing GFP-CYP4F11 (OriGene). The vector pLenti CMV GFP Puro (658-5) (Addgene; Plasmid #17448) served as a control vector. Lentiviral packaging plasmids psPAX2 (Addgene plasmid #12260) and pMD2.G (Addgene plasmid #12259) were used for virus production. At 24 hours after transfection, the medium was replaced with fresh complete RPMI medium without Pen-Strep. Virus supernatants were harvested at 48 hours after transfection, filtered through 0.45-μm Whatman Uniflo sterile PVDF syringe filters, aliquoted into 1 mL portions, and stored at –80 °C for up to 6 months.

For infection, NSCLC cells were seeded at around 50% confluence and treated with 2 mL of virus supernatant supplemented with 8 μg/mL polybrene (Sigma-Aldrich) for 24 hours. After infection, the medium was replaced with complete RPMI 1640 medium without Pen-Strep. Puromycin (Sigma-Aldrich) selection at 5 μg/mL was initiated 48 hours after infection and continued until all control cells died. Surviving cells were maintained in the complete RPMI 1640 medium for the subsequent experiment. Successful lentiviral integration and CYP4F11 overexpression were confirmed by GFP fluorescence using a BZ-X800 microscope (Keyence) and Western blot.

2.4. Western blot analysis

Total protein in cells was extracted using RIPA Lysis and Extraction Buffer (Thermo Scientific) supplemented with 1× Halt Protease Inhibitor Cocktail (Thermo Scientific). Whole cell protein concentration was determined using the Pierce BCA Protein Assay kit (Thermo Scientific). Cell lysates were denatured in 4× SDS loading buffer (200 mM Tris-HCl, pH 6.8, 8% (w/v) SDS, 40% (v/v) glycerol, 60 mM EDTA, 0.02% (w/v) bromophenol blue, 20% (v/v) β-mecaptoethanol) at 100 °C for 5 minutes. Proteins (20 μg per sample) were separated via SDS-PAGE on a 4%–20% Mini-PROTEAN TGX Precast Protein Gel (Bio-Rad) at 80 V for 20 minutes, followed by 120 V for 1 hour. Proteins were transferred onto an Immobilon -FL PVDF Membrane (Millipore Sigma) at 100 V for 100 minutes.

Membranes were blocked with 3% (w/v) BSA in 1× Tris-buffered saline with 0.1% (v/v) Tween 20 detergent for 1 hour. Subsequently, membrane was sectioned for probing with primary antibody targeting CYP4F11 (Invitrogen; Cat. No. MA1-12890, 1:500) and FADS2 (Proteintech; Cat. No. 68026-1-Ig, 1:5,000). The secondary antibody used includes goat antimouse lgG HRP for CYP4F11 detection (Promega; Cat. No. W4021) and goat anti-Rabbit IgG (H+L) HRP (Invitrogen; Cat. No. 31460). Membranes were washed 3 times with 1× Tris-buffered saline with 0.1% (v/v) Tween 20 for 10 minutes before and after incubation, followed by detection using the Pierce ECL Western Blotting Substrate (Thermo Scientific) and imaging with an iBright 1500 imaging system (Invitrogen) and an x-ray film processor (Model700LE; Alphatek). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Invitrogen; Cat. No. MA5-31457) was used as an internal control for normalization.

Protein band intensities were quantified using ImageJ (National Institute of Health).24 Original blotting images were converted to 8-bit grayscale, and pixels were used as the measurement unit. Regions of interest corresponding to each band were defined using the lane selection and region of interest manager functions to ensure consistent size across all samples. The integrated density (area × mean gray value) for each band was measured after background subtraction. Relative protein expression was calculated by normalizing the integrated density of the protein of interest to its corresponding GAPDH loading control. For comparison across experiments, values for the control group were set to 1.0, and all other values were calculated relative to this control. Differences in protein expression level between genetically modified cells and control cells were assessed using Student’s t test. Triplicate data are reported as mean ± SD. Significance is indicated as follows: P ≥ .05 (ns), P < .033 (∗), P < .002 (∗∗), P < .001 (∗∗∗).

2.5. Proliferation assay using MTT and cell counting

Cells were seeded at 5000 cells per well in 96-well plates for proliferation analysis. MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assays (CyQUANT MTT Cell Proliferation Assay Kit) were performed at different time points according to the manufacturer’s protocol, and the absorbance at 590 nm was measured using a Synergy H1 microplate reader (BioTek). In parallel, cell numbers were quantified at the same time points using a Countess 3 Automated Cell Counter (Invitrogen). Data were analyzed using GraphPad Prism (GraphPad) and were normalized as proliferation percentage, with 0 hours set to 0%, and the maximum value at the final time point set to 100%. Nontargeting siRNA-transfected cells and the blank GFP vector served as control groups. Differences in proliferation percentage between genetically modified cells and control cells will be compared using a 2-way ANOVA test. The data are reported as mean ± SD. Significance is indicated as follows: P ≥ .05 (ns), P < .033 (∗), P < .002 (∗∗), P < .001 (∗∗∗).

2.6. Wound healing assays

For migration analysis, 6 × 105 cells per well were seeded in a 6-well plate. After 12 hours of serum starvation, a scratch was introduced in each well using a pipette tip. Images of the wound area were captured at 0, 4, 8, 12, and 24 hours using a BZ-X800 microscope (Keyence). The wound gap area was quantified in ImageJ (National Institutes of Health) based on pixel intensity as described before.22 Migration was normalized based on reduced wound area changes and was normalized with the total wound area as 100%. Differences in proliferation percentage between genetically modified cells and control cells will be compared using a 2-way ANOVA test. Triplicate data are reported as mean ± SD. Significance is indicated as follows: P ≥ .05 (ns), P < .033 (∗), P < .002 (∗∗), P < .001 (∗∗∗).

2.7. RNA sequencing analysis

Total RNA was harvested from NCI-H460 cells (siCYP4F11 and control cells, respectively) using the TRIzol reagent (Invitrogen) and purified with the RNA Clean & Concentrator kit (Zymo). A total of 500 ng of total RNA was used for library preparation in collaboration with NovoGene. The strand specific libraries were constructed using the NEBNext ultra II directional RNA Library Prep Kit (New England Biolabs) according to the manufacturer's instructions. Raw sequencing reads in FASTQ format were processed using the fastp software to remove adapter sequences, reads containing adapters, reads containing poly-N, and low-quality reads.25 Clean reads were aligned to the GRCH38 reference genome using HISAT2 (2.2.1), and gene level counts were quantified with featureCounts (version 2.0.6).26,27 Gene expression levels were calculated as fragments per kilobase of transcript per million mapped reads. Read counts were normalized using the edgeR R package (version 4.0.16) to correct for differences in sequencing depth across samples.28 Genes with extremely low baseline expression were filtered by applying a log (transcripts per million + 1) transformation to minimize false positives.29

Differential gene expression data were analyzed as above and visualized using GraphPad Prism (GraphPad). A volcano plot was generated to display the global distribution of differentially expressed genes, and the top significantly upregulated genes were further summarized in a heatmap generated in GraphPad Prism (GraphPad) under the “heatmap” graph type.

2.8. Real-time quantitative PCR

For RNA extraction, 6 × 105 cells per well were seeded in a 6-well plate and grown in a monolayer. Total RNA was isolated using TRIzol reagent (Invitrogen), with 1 mL per well according to the manufacturer’s protocol. RNA concentration was measured by a NanoDrop 2000 spectrophotometer (Thermo Scientific). One microgram of RNA was reverse-transcribed to cDNA as per the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) under the condition described in the manufacturer’s protocol. The RT master mix was prepared without an RNase inhibitor. The reverse transcription was performed using VeritiPro 96-Well Thermal Cycler (Applied Biosystems). Quantitative polymerase chain reaction (qPCR) was performed using the Luna Universal qPCR Master Mix (New England BioLabs) with a 20 μL reaction according to the manufacturer’s protocol on a QuantStudio 6 Pro Real-Time PCR System (Applied Biosystems). The primers used are described in Supplemental Fig. 1. The relative mRNA expression levels of CYP4F11 and FADS2 were normalized to the housekeeping gene GAPDH, and fold changes were calculated using the ΔΔCt method.30 Differences in relative mRNA fold change between genetically modified cells and control cells are assessed using Student’s t test. Triplicate data (n = 3 or 4) are reported as mean ± SD. Significance is indicated as follows: P ≥ .05 (ns), P < .033 (∗), P < .002 (∗∗), P < .001 (∗∗∗).

3. Results

3.1. The impact of a CYP4F11 knockdown on NCI-H1437 cell proliferation and migration

Because the function of CYP4A/F ω-hydroxylases remains poorly characterized in NSCLC cells, we first profiled the CYP4F expression. Only CYP4F3 and CYP4F11 are detectably expressed, whereas other CYP4A/F isoforms exhibited negligible expression (according to the Gepia analysis using the TCGA database). To establish relevant in vitro models for functional studies, we examined CYP4F expression patterns in NSCLC cell lines. In our prior work, we selected NCI-H460 cells, a large lung cell carcinoma cell line, which display high CYP4F11 expression (99.7 number of transcripts per million [nTPM]) but a very low CYP4F3 expression level (according to the Human Protein Atlas database) for our cell-based study.31 We then demonstrated that a genetic knockdown of CYP4F11 significantly attenuates cell proliferation and migration.22 To extend these findings to additional cell lines exhibiting a lower CYP4F11 expression, we chose the NCI-H1437 cells with a relative CYP4F11 expression of 30.9 nTPM. This lung adenocarcinoma cell line exhibits a low CYP4F3 expression (2.2 nTPM) and was therefore suitable to expand our studies on CYP4F11.31

To compare the cancer cell phenotype between those 2 cell lines, we first examined the general cell viability of NCI-H460 and NCI-H1437 cells using MTT and cell counting assay. Here, we observed that NCI-H460 cells exhibited higher baseline viability than NCI-H1437 (Supplemental Fig. 2), which coincides with the relative CYP4F11 expression level. A lower expression of CYP4F11 in NCI-H1437 compared with NCI-H460 cells was confirmed via Western blot analysis.

We next evaluated the functional role of CYP4F11 in NCI-H1437 cells using siRNA. The knockdown was confirmed by Western blot analysis (Fig. 2A).22 The cell proliferation of NCI-H1437 cells was monitored via the MTT assay and revealed a marked reduction in cell viability after CYP4F11 silencing (Fig. 2B). Specifically, the proliferation of knockdown cells decreased from ∼40% to ∼10% compared with control cells on day 1 and from ∼100% to ∼40% on day 2. To validate these findings, an automated cell counting assay was performed as an orthogonal approach. Consistent with the MTT results, knockdown cells exhibited reduced proliferation compared with control cells (∼40% vs ∼10% on day 1 and 100% vs 40% on day 2) (Fig. 2C). Collectively, these results demonstrate that a CYP4F11 knockdown attenuates NCI-H1437 cell proliferation, aligning with our previous findings in NCI-H460 cells.22

Fig. 2.

Fig. 2

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CYP4F11 was knocked down in NCI-H1437 cells using siRNA. The knockdown was confirmed by Western blot analysis and evaluation of a band at ∼61 kDa, which is the molecular weight of CYP4F11 (A). A transient CYP4F11 knockdown attenuated the cell proliferation as confirmed by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay (B) and cell counting assay (C). The migration capability was assessed via wound healing assay (D) and revealed a reduced migration of siCYP4F11 cells compared with siControl cells between 0 and 24 hours (E). All data were normalized and analyzed with a 2-way ANOVA test (n = 3–4; ∗∗P ≤ .01; ∗∗∗P ≤ .001). CYP4F11, cytochrome P450 4F11; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.

The cell migration capability was then evaluated using a wound healing assay. No significant difference was observed at 4 hours between knockdown and control cells. After 8, 12, and 24 hours, CYP4F11 knockdown cells exhibited wider wound gaps compared with control cells, indicating impaired migration (Fig. 2, D and E). These results demonstrate that a CYP4F11 knockdown significantly attenuates the migration of NCI-H1437 cells, which is consistent with our findings in NCI-H460 cells.22 Notably, the migratory capability of NCI-H460 was generally higher.

3.2. The impact of a stable CYP4F11 overexpression on cell proliferation and migration

CYP4F11 was then overexpressed in both cell lines via lentiviral transduction. Transduction efficiency was assessed by GFP fluorescence microscopy and by immunoblotting with an anti-GFP antibody. Robust GFP fluorescence was detected in both control and overexpression groups via microscopy confirming a successful transduction with stronger GFP signals in NCI-H1437 cells than in NCI-H460 cells (Supplemental Fig. 3). This suggests a higher overexpression efficiency in NCI-H1437 cells, which was confirmed by Western blot analysis. Immunoblotting revealed a clear 88 kDa band corresponding to the CYP4F11-GFP fusion protein in overexpression samples but not in control samples with a higher protein abundance in NCI-H1437 cells (Fig. 3, A and D). A strong 27 kDa GFP band was present in control vector samples (Supplemental Fig. 4), which is consistent with our fluorescence results.

Fig. 3.

Fig. 3

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CYP4F11 was overexpressed (OE) in NCI-H460 cells (top panel) and NCI-H1437 cells (bottom panel). The GFP-CYP4F11 overexpression was confirmed via Western blot analysis indicated by a band at 88 kDa (A, D). A CYP4F11 overexpression promotes the proliferation of both cell lines as indicated by the MTT assay (B, E) and cell counting assay (C, F). All data were normalized and analyzed with a 2-way ANOVA test (n = 3–4; ∗∗P ≤ .01; ∗∗∗P ≤ .001). FADS2, fatty acid desaturase 2; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide.

The effects of CYP4F11 overexpression on the cell phenotype were then examined with both cell models. In NCI-H460 cells, no significant difference in proliferation was observed on days 1 and 2 using MTT and cell counting assays. The cell proliferation increased from ∼60% in control cells to ∼90% in overexpression cells on day 3, which was consistent for both assay types (Fig. 3, B and C). CYP4F11 overexpression led to elevated proliferation of NCI-H1437 cells reaching ∼80% compared with ∼60% by day 2 as assessed by the MTT assay and ∼95% compared with ∼40% when using cell counting (Fig. 3, E and F). Thus, CYP4F11 overexpression elicited a more proliferative response in NCI-H1437 cells than in NCI-H460 cells, which correlated with a higher overexpression of CYP4F11.

In addition to proliferation, CYP4F11 overexpression markedly promoted cell migration. The migration of NCI-H460 cells increased from ∼60% in control cells to ∼90% in overexpression cells at 24 hours with significant differences already observed at 4, 8, ad 12 hours (Fig. 4, A and B). A similar trend was detected in NCI-H1437 cells, where CYP4F11 overexpression resulted in an increase of migration from ∼70% to ∼90% compared with control cells at 24 hours (Fig. 4, C and D). However, a significant difference could only be observed at 8 and 24 hours. The promigratory effect of CYP4F11 overexpression was more pronounced in NCI-H460 cells than in NCI-H1437 cells, as indicated by a larger 24-hour wound closure increment and higher migration rate (Fig. 4, B and D).

Fig. 4.

Fig. 4

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A stable CYP4F11 overexpression (OE) promoted the migration of NCI-H460 (top panel) and NCI-1437 (bottom panel) cells as assessed by the wound healing assay (A, C). The wound closing was monitored and quantified between 0 and 24 hours (B, D). All data were normalized and analyzed with a 2-way ANOVA test (n = 3; ∗∗P ≤ .01; ∗∗∗P ≤ .001; ∗∗∗∗P ≤ .0001).

3.3. Transcriptomics studies with NCI-H460 CYP4F11 knockdown cells

To investigate the underlying molecular mechanism for the observed pro-proliferative and promigratory effects of CYP4F11, we performed transcriptomic profiling comparing NCI-H460 siCYP4F11 with siControl cells. Our analysis identified genes that were significantly altered upon CYP4F11 knockdown, with the global distribution visualized in a volcano plot (Fig. 5A). The top genes with significant change were further summarized in a heatmap (Fig. 5B), including protein coding genes HIST1H3B, ZNHIT1, MGAT5, CYP4F11, BOLA2B, FADS2, SHISAL1, VGF, HIST1H2BL, TMSBB15B, CAMK2N2, and HIST1H1B.

Fig. 5.

Fig. 5

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RNA sequencing was conducted to compare the transcription between NCI-H460 siCYP4F11 and siControl cells. Differentially expressed genes were visualized using a volcano plot (A). Top downregulated genes were summarized in a heatmap (B). Our subsequent analysis focused on the fatty acid desaturase 2 (FADS2, red boxed), which catalyzes the first and rate-limiting step in arachidonic acid synthesis from linoleic acid via desaturation at C6 yielding dihomo-γ-linolenic acid. Fatty acid desaturase 1 (FADS1) catalyzes the final desaturation resulting in arachidonic acid (C).

Interestingly, FADS2 was among the most significantly downregulated genes. FADS2 is a key enzyme essential in the formation of arachidonic acid from linoleic acid and catalyzes the first and rate-limiting step (Fig. 5C).32 Because both FADS2 and CYP4F11 are involved in arachidonic acid pathways, their expression and function may be interrelated. Based on this rationale, we next examined the potential association between FADS2 and CYP4F11 in our cell models.

3.4. The impact of CYP4F11 expression on FADS2 mRNA and protein level

To validate the association between FADS2 and CYP4F11, we assessed their relationship at both the mRNA and protein levels. In NCI-H460 cells, a CYP4F11 knockdown resulted in an ∼20% reduction of CYP4F11 mRNA (Fig. 6A). Notably, FADS2 mRNA decreased by ∼60%, indicating that a CYP4F11 knockdown significantly decreases FADS2 expression, which is consistent with our RNA sequencing results. Silencing CYP4F11 expression in NCI-H1437 cells revealed that the CYP4F11 mRNA level was reduced by ∼70% in knockdown cells compared with control cells (Fig. 6D). Importantly, the FADS2 mRNA level decreased by ∼95% in CYP4F11 knockdown cells, further supporting a strong correlation between CYP4F11 and FADS2 transcription.

Fig. 6.

Fig. 6

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The FADS2 mRNA level decreases after knocking down CYP4F11 via siRNA in both NCI-H460 (top panel) and NCI-1437 cells (bottom panel), whereby the mRNA level of CYP4F11 and FADS2 was normalized to GAPDH, respectively (A, D). The FADS2 protein level decreased after CYP4F11 knockdown in both cell lines, whereby the protein level of CYP4F11 and FADS2 was normalized to GAPDH, respectively (B, C, E, F). All data were normalized and analyzed with a Student’s t test (n = 3; ∗∗P ≤ .01; ∗∗∗P ≤ .001; ∗∗∗∗P ≤ .0001). FADS2, fatty acid desaturase 2; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.

Western blot analysis further assessed the impact on the protein level. The CYP4F11 protein expression was reduced by ∼60% in siCYP4F11 NCI-H460 cells compared with control cells, confirming a robust knockdown (Fig. 6, B and C). Similarly, the FADS2 protein content decreased by ∼20% after CYP4F11 knockdown. The CYP4F11 protein amount was reduced by ∼50% in siCYP4F11 NCI-H1437 cells compared with control cells, indicating successful knockdown of CYP4F11 (Fig. 6, E and F). Here, the FADS2 protein level was significantly reduced with an ∼20% decrease.

To further investigate the association between CYP4F11 and FADS2, we evaluated their expression at both mRNA and protein levels in CYP4F11 overexpressing cells. In NCI-H460 cells, qPCR analysis revealed that the CYP4F11 mRNA level was elevated to approximately 6-fold in overexpression cells compared with control cells (Fig. 7A). Notably, the FADS2 mRNA level showed a 50-fold increase. In NCI-H1437 cells, the CYP4F11 mRNA level was increased 7-fold in overexpression cells compared with control cells, whereas the FADS2 mRNA level increased 8-fold (Fig. 7D).

Fig. 7.

Fig. 7

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The FADS2 mRNA level increases after overexpression (OE) of CYP4F11 in both NCI-H460 (top panel) and NCI-1437 cells (bottom panel) whereby the mRNA level of CYP4F11 and FADS2 was normalized to GAPDH, respectively (A, D). The FADS2 protein level increased after CYP4F11 overexpression in both cell lines, whereby the protein level of CYP4F11 and FADS2 was normalized to GAPDH, respectively (B, C, E, F). All data were normalized and analyzed with a Student’s t test (n = 3; ∗∗P ≤ .01; ∗∗∗P ≤ .001; ∗∗∗∗P ≤ .0001). FADS2, fatty acid desaturase 2; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.

Reflecting the augmentation of mRNA, the CYP4F11 protein level was increased in NCI-H460 cells, confirming successful overexpression on the translational level (Fig. 7, B and C). Consistent with the transcriptional changes, FADS2 protein was markedly elevated, showing a 20% increase relative to the control, which correlated with a CYP4F11 overexpression. We also confirmed a markedly higher CYP4F11 protein expression in NCI-H1437 cells, which resulted in a more than 2-fold increase in FADS2 protein expression (Fig. 7, E and F).

4. Discussion

In our previous study, we demonstrated that a CYP4F11 knockdown attenuates cell proliferation and migration of NCI-H460 cells.22 Here, we expanded these findings by validating the functional role of CYP4F11 in 2 independent NSCLC models, NCI-H460 and NCI-H1437 cells, and by providing the first gain-of-function evidence supporting its protumorigenic role. In addition, we investigated the previously unreported mechanism by which CYP4F11 contributes to oncogenesis.

4.1. CYP4F11 is directly correlated with NSCLC cell viability and migration

Previous work has demonstrated that CYP4F11 plays a unique role in metabolizing both xenobiotic compounds and fatty acids.33 CYP4F11 expression is induced by the tumor-related transcription factor NRF2 in NSCLC.21 NRF2 is a transcription factor that protects cells against oxidative stress by upregulating antioxidant and iron-regulating genes.34 It has been demonstrated that cigarette smoking activates NRF2 by oxidizing its repressor KEAP1 through reactive oxygen species.35 The NRF2 constitutive activation has been implicated in cancer development by reprogramming metabolism, thus promoting cell survival and proliferation.36 As a downstream target of NRF2, CYP4F11 might contribute to the development of NSCLC. CYP4F11 is capable of converting arachidonic acid into the potent lipid mediator 20-HETE. 20-HETE has been a long known key player in cancer development by binding to its receptor GPR75 and activating tumor-related signaling pathways.37,38 Nevertheless, CYP4F11 is a highly versatile enzyme, and its function is not limited to 20-HETE production. Our purpose is to delve deeper into the underlying mechanism of CYP4F11 in NSCLC.

Our prior work demonstrated that a CYP4F11 knockdown attenuates cell proliferation and migration of NCI-H460 cells. In the present study, we confirmed that silencing CYP4F11 inhibits cell proliferation and migration of an additional cell line, NCI-H1437. Specifically, a CYP4F11 knockdown produced a more pronounced suppression of proliferation and migration in NCI-H460 cells than in NCI-H1437 cells, which might indicate a greater dependence of NCI-H460 cells on CYP4F11.22 We have confirmed that NCI-H460 cells have a significantly higher CYP4F11 protein expression level than NCI-H1437 cells (Supplemental Fig. 2), which could explain the differences in cell proliferation and migration and further emphasize the dependence of NCI-H460 cells on CYP4F11.

Compared with NCI-H460 cells, a CYP4F11 overexpression elicited a stronger proliferative response in NCI-H1437 cells (Fig. 3). This could be explained with a higher transduction efficiency and with the fact that the endogenous level of CYP4F11 is much lower in this cell line, so that a CYP4F11 overexpression has a more prominent impact. The cell proliferation of NCI-H460 had to be monitored up to day 3 to observe a significant difference. In contrast, differences in NCI-H1437 cell proliferation could already be observed at day 2 (Fig. 4). The transduction efficiency in NCI-H460 cells was lower, which is likely caused by the high endogenous CYP4F11 level. Interestingly, the effect of a CYP4F11 overexpression on migration is more prominent in NCI-H460 cells, which underlines cell type–dependent differences in cell motility and potentially a different impact of CYP4F11 on different cell types. In general, the baseline migratory capability of NCI-H460 cells is higher, and they might more readily migrate. Our results suggest that CYP4F11-driven proliferation and migration are cell dependent, influenced by transduction efficiency, intrinsic cell motility, and the individual endogenous CYP4F11 level.

Consistent phenotype changes after both gain- and loss-of-function modification of CYP4F11 support its potential as a therapeutic target. Among the CYP4F isoforms, only CYP4F11 and CYP4F3 are detectably expressed in NSCLC. Most interestingly, their expression levels are positively correlated in lung squamous cell carcinoma (as analyzed with Gepia using the TCGA database) (Supplemental Fig. 5).31,39 CYP4F11 and CYP4F3 share common enzymatic properties and can both produce 20-HETE, whereby the catalytic ability of CYP4F3B is 4-fold higher than that of CYP4F11.33,40 The observed correlation of CYP4F11 and CYP4F3 expression suggests a possible shared regulatory mechanism and could reflect a compensatory or synergistic role in lipid and eicosanoid metabolism. Future studies are warranted to elucidate the function of CYP4F3 because CYP4F3 might represent an additional key player in tumor-associated lipid signaling.

CYP4F11 is an emerging promising drug target for the treatment of NSCLC. However, developing inhibitors that selectively target CYP4F11 while sparing other essential CYP4F isoforms is mandatory to minimize off-target toxicity. The only known and commonly used inhibitor to date is the small molecule HET0016. Although HET0016 is a commonly used CYP4A/F pan inhibitor, it lacks isoform specificity and suppresses all 20-HETE–producing P450 enzymes.22,41 Thus, the rational design of CYP4F11-specific compounds, potentially guided by the HET0016 scaffold, represents a promising direction for future drug development.

4.2. CYP4F11 is intertwined with the expression of FADS2

Beyond its pro-proliferative and promigratory role, we were invested in unraveling the underlying mechanism of CYP4F11 action in NSCLC. Thus, we used transcriptomics to evaluate differentially expressed genes in NCI-H460 siCYP4F11 and siControl cells. The expression of several genes was significantly reduced on CYP4F11 knockdown (Fig. 5, A and B). Four of the identified genes are intertwined with the chromatin organization. HIST1H3B encodes the replication-dependent histone H3.1, a key structural component of the nucleosome.42,43 Elevated HIST1H3B expression has been reported in invasive breast carcinoma44 and was recently identified as a redox sensor that can promote multidrug resistance and metastasis.45 HIST1H2BL encodes a replication-dependent histone H2B type 1-L, another nucleosomal core component.46 Abnormal succinylation of HIST1H2BL has been identified in patients with lung adenocarcinoma, and its knockdown attenuates lung cancer cell viability and migration.47 HIST1H1B encodes a replication-dependent histone that stabilizes higher order chromatin structure.43 HIST1H1B is overexpressed in basal-like breast cancer promoting cancer cell proliferation and migration.48 ZNHIT1 (Zinc Finger HIT-Type Containing 1) is a key component of a complex that catalyzes histone variant exchange from H2A to H2B histones.49 A knockdown of components of this complex attenuates lung tumor progression by suppressing the transcription of downstream oncogenes.50 Hence, our RNA sequencing results indicate that a CYP4F11 knockdown might reduce the expression of distinct histone isoforms and have an indirect impact on histone structure and transcription. Based on these findings, the impact of the knockdown on chromatin structure and organization could attenuate DNA replication and thus inhibit cell proliferation. However, further experimental validation is required to confirm this observation.

Another intriguing downregulated gene, BOLA2B, encodes a member of the BolA-like protein family.51 Proteins in this family interact with glutaredoxin 3 to form a cluster binding complex involved in iron trafficking, cellular redox buffering, and reactive oxygen species modulation.51,52 BOLA2B is markedly overexpressed in lung squamous carcinoma.53 Functionally, BOLA2B knockdown suppresses breast cancer cell proliferation, likely through cell cycle–related mechanisms, although the precise molecular pathways remain unclear.53 Our RNA sequencing results reveal a potential functional association between BOLA2B and CYP4F11. Given that BOLA2B participates in iron-sulfur cluster trafficking and CYP4F11 is a heme-dependent monooxygenase, their interplay may reflect coordinated regulation of cellular iron metabolism and redox homeostasis. They could also be connected by the redox master regulator NRF2. However, the underlying mechanism link between CYP4F11 and BOLA2B remains to be further investigated.

Among all significantly downregulated genes, FADS2 emerged as particularly intriguing because it is directly linked to CYP4F11 through its involvement in the arachidonic acid pathway. FADS2 is a 46–52 kDa membrane protein in the endoplasmic reticulum. FADS2 desaturates omega-3 and omega-6 polyunsaturated fatty acids at the delta-6 and occasionally at the delta-8 position.54 Most notably, FADS2 serves as a key enzyme in arachidonic acid biosynthesis from linoleic acid, catalyzing the initial and rate-limiting step (Fig. 5C).55 FADS2 is abundantly expressed in multiple organs such as the liver, lung, brain, adrenal gland, and kidney. Prior studies demonstrated that an FADS2 knockdown significantly suppresses the proliferation of lung cancer cells.32,56,57 The correlating expression pattern, the joint metabolite arachidonic acid, the joint localization in the cell, and similar impact on cancer cell proliferation suggest that CYP4F11 and FADS2 may be functionally intertwined in NSCLC.

To validate the association between CYP4F11 and FADS2 in NSCLC, we evaluated their expression in CYP4F11 knockdown and overexpression cells at both the mRNA and protein levels. We confirmed the RNA sequencing results and found that FADS2 is positively associated with CYP4F11 expression transcriptionally and translationally in both NCI-H460 and NCI-H1437 cells. Here, a CYP4F11 knockdown decreases FADS2 mRNA and protein levels, whereas a CYP4F11 overexpression leads to an increase. Our results implicate a potential role of CYP4F11 in controlling FADS2 regulation (Fig. 8A).

Fig. 8.

Fig. 8

Open in a new tab

CYP4F11 might modulate FADS2 expression through the joint metabolite arachidonic acid. CYP4F11 metabolizes arachidonic acid to 20-HETE, which has proliferative and migratory effects on lung cancer cells. Arachidonic acid is synthesized from linoleic acid, whereby FADS2 catalyzes the rate-limiting first step (A). Both CYP4F11 and FADS2 are located in the ER membrane with the catalytic moiety exposed to the cytosol. The cytochrome b5-like domain of FADS2 might act as an allosteric regulator of the CYP4F11-mediated catalysis of arachidonic acid (B). The FADS2 model was generated with AlphaFill.56 The CYP4F11 model was reported in the paper by Jia et al22 20-HETE, 20-hydroxyeicosatetraenoic acid; ER, endoplasmic reticulum; FADS2, fatty acid desaturase 2.

4.3. The potential implication of a coregulation of CYP4F11 and FADS2 in NSCLC

Given that both enzymes act within the arachidonic acid pathway, CYP4F11 might modulate FADS2 expression via the shared metabolite, thereby reinforcing substrate supply for 20-HETE synthesis. Using complementary loss- and gain-of-function studies integrated with transcriptomics and protein analysis, we revealed a CYP4F11-dependent regulation of FADS2, extending the mechanism from a metabolic function of CYP4F11 to a potentially regulatory one. An FADS2 knockdown has been previously reported to attenuate lung cancer cell proliferation and is, thus, also a potential therapeutic target.32 The impact of CYP4F11 on cell proliferation and migration might derive from a secondary, indirect inhibition of FADS2 through the observed downregulation of expression. To date, we have only a sparse understanding of how CYP4F11 regulates FADS2. To start with, the expression of both proteins is directly or indirectly tied to the regulation via the transcription factor PPARα (peroxisome proliferator–activated receptor α), which is a key player in lipid metabolism. FADS2 expression is induced by PPARα.57 Although a direct regulation of CYP4F11 via PPARα has not been reported yet, CYP4F11 expression can be induced by retinoid X receptor α.58 Both transcription factors often form heterodimers to bind DNA and activate target genes, which is almost exclusively the case.59 Hence, PPARα might represent a joint regulatory key player in both CYP4F11 and FADS2 expression. In addition, one possibility is a potential accumulation of arachidonic acid when CYP4F11 is knocked down and cannot metabolize arachidonic acid to 20-HETE. This could lead to a feedback regulation of FADS2 via product inhibition because FADS2 catalyzes the rate-limiting step in arachidonic acid synthesis. This assumption will be confirmed with lipidomic studies in the future. In addition, future studies need to further elucidate the role of FADS2 in lung cancer, the exact underlying regulatory mechanism between CYP4F11 and FADS2 expression, and the role of arachidonic acid and other fatty acids in this complex metabolic pathway.

A most intriguing FADS2 feature is the presence of a cytochrome b5 (CYB5)–like domain that is a heme containing moiety and responsible for the electron supply needed for desaturase function.60 The CYB5-like domain of FADS2 structurally resembles CYB5, a hemoprotein known to modulate the catalytic activity of cytochrome P450 enzymes.61 CYB5 can stimulate or inhibit P450 catalysis, acting as an allosteric modulator in an isoform- and reaction-specific manner through direct protein-protein interaction.60,62 It is possible that FADS2 regulates CYP4F11 activity via allosteric interaction through the CYB5-like domain (Fig. 8B). Both proteins are membrane proteins and are localized in the endoplasmic reticulum, which increases the likelihood of protein-protein interactions. Future cell-based and biochemical studies are needed to prove this hypothesis and to evaluate a coregulation on a metabolic level.

In summary, we have further elucidated the role of CYP4F11 in NSCLC cell models and confirmed its potential as therapeutic target. In addition, we identified that FADS2, a key player in arachidonic acid biosynthesis, is closely intertwined with CYP4F11 expression, which represents a potential novel regulatory role of CYP4F11 in fatty acid metabolic pathways.

Conflict of interest

Simone Brixius-Anderko is an editorial fellow with DMD under the mentorship of Wayne Backes, who is an editor of DMD. The other author declare no conflicts of interest.

Acknowledgments

The authors thank Zehua Wang and Meishu Xu from University of Pittsburgh Center for Pharmacogenetics for their assistance with cell-based experiments. The authors also thank Sihan Li, University of Pittsburgh Center for Pharmacogenetics, for assistance with RNA-seq data analysis and Xiaofei Wang for assistance with RNA-seq data submission. They would also like to thank the UPMC Hillman Cancer Center.

Disclaimer

Given his role as an editor of DMD, Dr Wayne Backes had no involvement in the peer review of this article and had no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to another journal editor.

Financial support

This work was supported by the National Institutes of Health [Grant R35GM155319] (to S.B.-A.) and a New Investigator Award of the American Association of Colleges of Pharmacy (to S.B-A.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data availability

The authors declare that all the data supporting the findings of this study are available within the paper and its Supplementary Material. RNA sequencing results have been deposited in the NCBI GEO database under the accession number GSE310024.

CRediT authorship contribution statement

Huiting Jia: Writing – Review and Editing, Writing – Original draft, Investigation, Methodology, Formal Analysis, Visualization, Conceptualization. Simone Brixius-Anderko: Supervision, Project administration, Funding acquisition, Writing – Review and Editing, Writing – Original draft, Visualization, Formal analysis, Conceptualization.

Footnotes

This article has supplemental material available at dmd.aspetjournals.org.

Supplemental material

Supplementary Material
mmc1.docx (4.5MB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material
mmc1.docx (4.5MB, docx)

Data Availability Statement

The authors declare that all the data supporting the findings of this study are available within the paper and its Supplementary Material. RNA sequencing results have been deposited in the NCBI GEO database under the accession number GSE310024.

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