EALTH & HUMAN SERVICES - USA AVINIMENT OF HEALTH HUMAIN
Published in final edited form as: Surgery. 2026 January ; 189: 109702. doi:10.1016/j.surg.2025.109702.
A novel therapeutic approach to adrenocortical carcinoma repurposing fingolimod to target sphingolipid metabolism in metastatic disease
Chitra Subramanian, PhD, MBAª, Kelli McNamara, MSª, Daniel Hess, BSa, Seth Wyatt Croslow, PhDb, Yanqi Tan, PhDb, Katja Kiseljak-Vassiliades, DOC, Margaret E. Wierman, MDC, Jonathan V. Sweedler, PhDb, Mark S. Cohen, MD, FSSOa,d,*
aDepartment of Biomedical and Translational Science, Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL
bDepartment of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL
“Department of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz School of Medicine, Aurora, CO
dDepartment of Surgery, Carle Illinois College of Medicine, University of Illinois at Urbana- Champaign, Urbana, IL
Abstract
Introduction: Patients with advanced adrenocortical carcinoma have poor survival and show elevated steroid production. Lipid metabolic profiling of adrenocortical carcinoma has revealed that the upregulation of sphingolipid metabolism, which regulates steroid synthesis, is linked to worse overall survival. Therefore, fingolimod, a Food and Drug Administration-approved drug that targets sphingolipid metabolism, represents a promising option for targeting adrenocortical carcinoma and metastatic spread.
Methods: Adrenocortical carcinoma cell lines were cultured in an appropriate medium. Cell-TiterGlo assessed cell viability. Western-blot and RNA-sequencing examined the targeted pathways. Migration and invasion using decellularized extracellular matrix evaluated metastasis.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Corresponding author: Mark S. Cohen, MD, FSSO, University of Illinois, Urbana-Champaign Carle Illinois College of Medicine, meddean@illinois.edu (M.S. Cohen).
Conflict of Interest/Disclosure
The authors have nothing to disclose regarding this article.
CRediT authorship contribution statement
Chitra Subramanian: Writing - original draft, Validation, Software, Project administration, Investigation, Formal analysis, Writing - review & editing, Visualization, Supervision, Resources, Methodology, Funding acquisition, Data curation, Conceptualization. Kelli McNamara: Validation, Investigation, Data curation, Visualization, Methodology, Formal analysis. Daniel Hess: Validation, Investigation, Data curation, Visualization, Methodology, Formal analysis. Seth W. Croslow: Visualization, Software, Investigation, Data curation, Writing - review & editing, Validation, Methodology, Formal analysis. Yanqi Tan: Writing - review &
editing, Validation, Methodology, Data curation, Visualization, Software, Formal analysis. Katja Kiseljak-Vassiliades: Resources. Margaret Wierman: Resources. Jonathan V. Sweedler: Writing - review & editing, Software, Methodology, Supervision, Project administration, Funding acquisition. Mark S. Cohen: Visualization, Supervision, Resources, Funding acquisition, Writing - review & editing, Validation, Software, Project administration, Conceptualization.
Presented at the 45th Annual Meeting of the American Association of Endocrine Surgeons, May 17-19, 2025.
Seahorse measured metabolic flux. Reverse transcription-polymerase chain reaction determined steroidogenic genes. Mass spectrometry analyzed alterations in sphingolipids.
Results: Fingolimod treatment resulted in half-maximal inhibitory concentration (IC50) of 7.044 umol/L, 5.588 umol/L, and 9.992 umol/L for the adrenocortical carcinoma 1, adrenocortical carcinoma 2, and NCI-H295R, respectively. Fingolimod showed synergistic effect with the standard of care treatment mitotane. Adrenocortical carcinoma cells treated with fingolimod showed a dose-dependent cleavage of poly (ADP-ribose) polymerase (PARP), an upregulation of LC3-II and p-ERK, and a downregulation of p-Akt and p-P65 with no appreciable change in total proteins. Furthermore, the downregulation of oxygen consumption rate, steroidogenic, and transforming growth factor-ß pathway genes was noted after fingolimod treatment. Adrenocortical carcinoma cells in the presence of liver and lung extracellular matrix showed a 2-fold increase in migration and invasion over cells without liver/lung extracellular matrix, which was blocked by >90% after fingolimod treatment. Lipid profiling indicated ceramides, ceramide phospho- ethanolamines and ceramide-l-phosphate, sphingomyelins, triglycerides, phosphatidylcholines as altered lipids.
Conclusion: Fingolimod induces apoptosis in adrenocortical carcinoma cells by targeting sphingolipid metabolism and prevents liver and lung metastatic invasion in vitro. Further in vivo validation studies alone or in combination with immune checkpoint inhibitor will support its clinical translation as a novel repurposed therapy in adrenocortical carcinoma.
TOC summary
Food and Drug Administration-approved drug fingolimod induces cell death by inhibiting the growth, migration, and invasion of adrenocortical carcinoma (ACC) cells via targeting sphingolipid metabolism. The combination of fingolimod with immune checkpoint inhibitor therapy could lead to the development of novel combination immune checkpoint inhibitor therapies for patients with ACC.
Introduction
Adrenocortical carcinoma (ACC) is a rare and aggressive tumor originating from the adrenal cortex, with limited treatment options available. Approximately 30%-50% of ACC patients diagnosed present with distant metastasis and have a poor prognosis with an overall survival rate of less than 1 year.1,2 The most common sites of metastasis include the liver, lungs, and lymph nodes, while metastases to the bone and brain are less common.1,3 Treatment for metastatic ACC is challenging due to limited treatment options, and the 5-year overall survival rate remains less than 15% with a recurrence rate of 70%-80%.4,5 Currently, the primary treatment for ACC is surgical resection, followed by adjuvant therapy with the adrenolytic agent mitotane, either alone or in combination with cytotoxic chemotherapy with EDP (etoposide, doxorubicin, and cisplatin). Despite several clinical trials showing significant drug toxicities with no significant improvement in overall survival rates, mitotane with EDP remains the standard of care for metastatic ACC.2,6 Recent advances in genetic and molecular profiling have led to several novel targeted therapy trials in ACC, including tyrosine kinase inhibitors, vascular endothelial growth factor receptor inhibitors, insulin-like growth factor receptor inhibitors, and rapamycin inhibitors. These, however, all still carried
poor response rates.7-10 Recently, clinical trials with immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, and CTLA-4 have been evaluated again with disappointing response rates, ranging from 6% to 23%.11,12 This poor response to date for all therapies highlights the need for new strategies to improve the survival rates of patients with ACC.
Reprogramming of lipid metabolic profiling is one of the hallmarks of cancer cells.13 Lipid metabolism affects various cellular processes, including migration, invasion, angiogenesis, proliferation, inflammation, and the restructuring of tumor microenvironment.14 Because ACC cells have high avidity for cholesterol, we previously conducted an integrated lipidomic analysis of the ACC database to identify lipid metabolic genes and pathways that may play an essential role in the pathogenesis of ACC. Our pathway analysis of differentially regulated genes revealed that sphingolipid metabolism upregulation is significantly associated with poor prognosis in ACC.15 Sphingolipids are a group of bioactive lipids that include ceramide, glucosylceramide, ceramide-1-phosphate (CIP), sphingosine, and sphingosine-1-phosphate (SIP).16 These lipids are involved in various cellular processes, such as cell survival, signal transduction, migration, and cell death, thus regulating tumor progression and metastasis.17 Research indicates that ceramides are linked to apoptosis, while SIP is known to regulate cellular proliferation, metastasis, angiogenesis, drug resistance, and the tumor immune microenvironment.18 Molecular and genetic studies have demonstrated the role of sphingolipids in tumor development and response to chemotherapy, radiotherapy, and immunotherapy in various cancers.19 However, the exact mechanism of sphingolipid metabolism in ACC pathogenesis and metastasis is not well known. Therefore, this study aims to evaluate the effectiveness of fingolimod, a Food and Drug Administration (FDA)-approved drug for multiple sclerosis targeting SIP receptors, as a potential novel anticancer treatment for ACC.
Methods
ACC data analysis of sphingolipid metabolism genes
To evaluate the hazard ratios (HRs) for overall and disease-free survival related to sphingolipid metabolic genes, we used the interactive web browser Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn). This tool allowed us to compare RNA sequencing data from The Cancer Genome Atlas (TCGA) dataset for ACC with data from the normal adrenal cortex sourced from The Genotype-Tissue Expression (GTEx) project. In addition, we used the R2 Genomic Analysis and Visualization database (https://hgserver1.amc.nl/) to assess overall survival for a subset of patients exhibiting necrosis and mitotic rate greater than 5-50.
Viability assay of ACC cells
Genotypically fingerprinted ACC cell lines, ACC1, ACC2, and NCI-H295R, were cultured in their respective growth media in a 2-dimensional format. Approximately 1,000 cells per well were plated in 96-well plates and allowed to attach. After plating, the cells were treated with varying concentrations of fingolimod, starting at 20 umol/L. After 72 hours of treatment, cell viability was assessed by measuring luminescence using the CellTiter-Glo luminescent reagent (Promega, Fitchburg, WI), which quantifies adenosine
triphosphate present in metabolically active cells, using a BioTek Synergy plate reader (BioTek, Winooski, VT). For the combination therapy, a CellTiter Glo viability assay was conducted using various concentrations of mitotane alone (starting from 200 µmol/L) or in combination with differing concentrations of fingolimod (starting from 20 umol/L). The percentage of viable cells was calculated relative to the control group treated with solvent. The experiments were performed in triplicate, and the results are presented as mean ± standard deviation. The IC50 values for fingolimod were determined using GraphPad Prism software. The synergistic effect between mitotane and fingolimod was calculated using the Bliss model in SynergyFinder.
Western blot analysis after treatment of ACC cells with fingolimod
ACC cell lines ACC1, ACC2, and NCI-H295R were treated with various concentrations of fingolimod (1.25-10 umol/L) or a dimethylsulfoxide solvent control for 24 hours. After the drug treatment, the cells were collected and lysed using radio-immunoprecipitation assay buffer, and the proteins were quantified with a bicinchoninic acid assay reagent (Thermo Fisher Scientific, Waltham, MA). Approximately 20 µg of protein samples were loaded onto a sodium dodecyl sulfate-polyacrylamide gel electrophoresis gel. The separated proteins were then transferred to a nitrocellulose membrane (GE Healthcare Life Sciences, Piscataway, NJ), blocked, and subsequently incubated with the appropriate primary and secondary antibodies. Protein visualization was achieved using a chemiluminescent reagent (Thermo Fisher Scientific), with actin serving as a loading control.
Analysis of steroidogenic and TGFf pathway genes using real-time PCR
ACC cells were treated with IC50 values of fingolimod for 24 hours, and RNA was isolated using the Qiagen RNA extraction kit (Qiagen, Germantown, MD). Approximately 1 mg of the RNA was reverse-transcribed using a high-capacity cDNA reverse transcription kit (Thermo Fisher Scientific,). SYBR Green quantitative real-time polymerase chain reaction (PCR) was performed using gene-specific primers, and relative gene levels were calculated using the 44Ct method.
Measurement of bioenergetics using a Seahorse Extracellular Flux Analyzer
To assess cellular metabolism, we used the Seahorse XFe96 analyzer (Agilent Technologies, Santa Clara, CA) to measure the oxygen consumption rate (OCR) and the extracellular acidification rate following the manufacturer’s protocols. Approximately 20,000 ACC cells (NCI-H295R, ACC1, and ACC2) were plated in an XF96 cell culture plate. After allowing the cells to attach, they were treated with half the IC50 concentration of fingolimod for 24 hours, after incubating the cells in a non-CO2 environment to ensure optimal equilibration in the XF assay medium. After calibration, OCR was analyzed using the mitochondrial stress assay kit, according to the manufacturer’s guidelines (Agilent Technologies), and normalized for cell viability using a proliferation assay kit (Promega).
Preparation and validation of decellularized liver and lung
Rat liver and lungs were harvested from adult female Wistar rats and initially washed with sterile water for 72 hours, with the water being replaced every 24 hours while agitating on
a shaker at 4℃. After this, the organs were further washed with a solution containing 1% Triton-X100 and 0.1% ammonia hydroxide for another 72 hours, with the wash solution changed every 24 hours. Then, the organs were washed in 0.5% sodium dodecyl sulfate for 24 hours at room temperature and washed again with penicillin/streptomycin for another 24 hours on a shaker before being lyophilized. The decellularization of the extracellular matrix (ECM) component was confirmed by analyzing the DNA content according to standard protocols. In brief, DNA was isolated using a Monarch DNA isolation kit following the manufacturer’s instructions (New England Biolabs GmbH, Ipswich, MA). The concentration of the isolated DNA was then measured using a BioTek Synergy plate reader (BioTek). To ensure complete removal of cellular material, cryosectioned liver and lung tissues (14 um thickness) were stained with the nuclear stain Hoechst 33342 in 150 mmol/L ammonium acetate solution for 1 minute. The tissues were then washed with the same ammonium acetate solution and allowed to dry before imaging. Fluorescence images were captured using an Axio Imager M2 (Zeiss, Jena, Germany) equipped with an AxioCam ICC5, using a 0.63× camera adaptor, a transmitted light VIS-LED lamp, and an X-cite Series 120 Q mercury lamp (Lumen Dynamics, Mississauga, Canada). For imaging the stained tissue, a 4’,6-diamidino-2-phenylindole (excitation 350-370 nm; emission 450-490 nm) dichroic filter cube was used, and the images were acquired using a 20× objective lens.
Migration and invasion of ACC cells in the presence of decellularized liver and lung ECM
The ECM material was dissolved using the Freytes protocol.20 Approximately 1 mg of tissue was dissolved in a milliliter of pepsin in 0.01 mol/LHCl on a stir plate for 48 hours at room temperature and then neutralized with 0.1 mol/L sodium hydroxide. This ECM solution was combined with hydrogel to assess migration and invasion using a Boyden chamber in a 24-well plate. For the migration assays, the plates were coated with either Matrigel alone or Matrigel containing liver or lung ECM material, while for the invasion assays, the inserts were coated. An equal number of cells (approximately 50,000), with varying concentrations of fingolimod and solvent control, were placed in the upper well of either the 8-um standard polycarbonate membrane (Corning Inc, Corning, NY) or the coated membranes. The lower well contained 10% fetal bovine serum, which served as a chemoattractant. After 24 hours of incubation, the membranes were fixed in 2% paraformaldehyde, stained with 1% crystal violet in 20% methanol for 20 minutes, and washed with distilled water. Any nonmigratory cells inside were removed using cotton swabs before imaging. Migration and invasion were quantified by counting the number of cells per field using light microscopy.
Mass spectrometry-based lipid metabolic profiling
Bulk LC-MS/MS measurements: Lipid extraction was performed using the Bligh and Dyer method on cultured cell pellets. The cell pellets were spiked with the internal standard SPALSH LIPIDOMIX II as an internal standard and subjected to biphasic separation. The bottom layer was dried using a SpeedVac concentrator before redissolving in a mixture of isopropanol/acetonitrile/water (4:3:1, vol/vol/vol). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was conducted using a Waters Acquity UHPLC system coupled to a Synapt G2-Si Time-of-Flight Mass Spectrometer (Waters Corporation, Milford, MA) equipped with an electrospray ionization (ESI) source as previously reported.20
Chromatographic separation was achieved with the following mobile phases: mobile phase
A consisted of acetonitrile/water (60:40, vol/vol) with 10 mmol/L ammonium formate and 0.1% (vol/vol) formic acid, while mobile phase B consisted of isopropanol/acetonitrile (90:10, vol/vol) with 10 mmol/L ammonium formate and 0.1% (vol/vol) formic acid. Gradient programming included 60% mobile phase B at 0 minutes, 57% B at 2 minutes, 50% B at 2.1 minutes, 46% B at 12 minutes, 30% B at 12.1 minutes, 1% B at 18 minutes, returning to 60% B at 18.1 minutes, and held until 20 minutes. The flow rate was maintained at 400 µL/min throughout the run. The mass spectrometer was operated in both positive and negative ionization modes, with the following parameters: capillary voltage of 2.5 kV, cone voltage of 40 V (positive) or 30 V (negative), source temperature at 120℃, and desolvation temperature at 500℃. The desolvation gas flow was set to 1,000 L/h, and the cone gas flow was 50 L/h. Data were acquired in resolution mode with a scan rate of 0.2 scans/s. Leucine enkephalin (2 µg/mL) was used as the lock mass reference, infused at a flow rate of 15 µL/min. Full MS data were acquired, followed by data-dependent acquisition for MS/MS fragmentation. Peak detection, alignment, and lipid identification were performed using MS-DIAL(version 5.5),21,22 leveraging the in silico LC-MS/MS lipidomics database. Lipids were identified based on MS2 spectral matching.
Single-cell MALDI FT-ICR measurements: Cultured ACC cells (control and 24-hour IC50 concentration of fingolimod treated) were prepared and analyzed as previously reported.23 Briefly, cells were deposited onto indium tin oxide-coated microscope slides and cleaned using 150 mmol/L ammonium acetate, and then a matrix solution containing 45 mg/mL 2,5-dihydroxybenzoic acid dissolved in 70% methanol +0.1% trifluoroacetic acid was deposited using an HTX M5-Sprayer (HTX Technologies). Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) analysis was performed with a SolariX XR 7T FT-ICR mass spectrometer (mass spectrometer equipped with an APOLLO II dual MALDI/ESI source; Bruker). 2M data points were acquired in positive mode with a mass range of m/z 100-1,600, yielding a transient length of 0.9787 seconds. MicroMS was used as previously described for targeted, image-guided acquisitions.24 Custom python scripts using DATSIGMA software25 analyzed the data, binning it with a 3-ppm (parts per million) continuous bin width across the m/z 400-1,000 lipid region. Features present in <5% of samples were removed, and the remaining features were mass-matched to the LIPID MAPS Structure Database with a ±0.005 Da tolerance. Cells with <5 lipid matches were excluded from the dataset. For any m/z features with isobaric lipid species, the lowest ppm mass error assignment was chosen.
Results
Increased expression of sphingolipid metabolism genes SPHK1, SPHK2, and SGPL1 is associated with a worse prognosis in ACC
Integrated lipidomic analysis revealed sphingolipid metabolism dysregulation as the top metabolic pathway altered in ACC.15 Therefore, we first evaluated the Kaplan-Meier survival analysis of sphingolipid metabolism-related genes SPHK1, SPHK2, SGPL1, CERS2, ELOVL5, and GLAC using ACC patient samples from TCGA and GTEx project datasets. This analysis included 77 ACC tumors and 128 normal adrenal cortices, accessed through the GEPIA website. The survival analysis, using a median cutoff for the ACC
TCGA tumors, revealed that higher expression levels of SPHK1, SPHK2, and SGPL1 were significantly associated with poor overall survival and disease-free survival. In contrast, higher expression levels of CERS2, ELOVL5, and GLAC were not found to be significantly linked to poor survival outcomes (Figure 1, A and B). The log-rank Pvalues for overall and disease-free survival for significant genes were as follows: SPHK1 had Pvalues of .018 and. 024; SPHK2 had Pvalues of .0018 and .00057; and SGPL1 had Pvalues of .00083 and .00043 (Figure 1, A). The HRs derived from the Cox proportional hazards model for overall and disease-free survival indicate that the upregulation of significant genes SPHK1, SPHK2, and SGPL1 is associated with poorer survival outcomes. In contrast, the HRs for the genes CERS2, ELOVL5, and GLAC did not show a significant link to poor survival outcomes (Figure 1, A and B). The HRs for overall and disease-free survival for significant genes are as follows: SPHK1 has HRs (high) = 2.3 and 2.4; SPHK2 has HRs (high) = 3.0 and 4.0; and SGPL1 has HRs (high) = 3.5 and 4.3, all with significant Pvalues. In addition, gene expression analysis by ACC stage in the TCGA dataset shows that SPHK1, SPHK2, and SGPL1 genes are expressed at higher levels in the more aggressive stages III and IV compared with stage I ACC. Furthermore, increased expression of SPHK1, SPHK2, and SGPL1 in the TCGA dataset is significantly correlated with other predictors of poor prognosis, such as necrosis and mitotic rates greater than 5-50, which are associated with worse overall survival (P <. 01).
Fingolimod treatment of ACC cells reduces the viability
Because fingolimod is an FDA-approved drug that targets sphingolipid metabolism genes upregulated in ACC, we first conducted a CellTiter Glo viability assay on ACC cell lines (ACC1, ACC2, and NCI-H295R) after 72 hours of fingolimod treatment. Our results (Figure 1, C) indicated reduced viability of all 3 cell lines in a dose-dependent manner. The IC50 values for ACC1, ACC2, and NCI-H295R cell lines were 7.044 ± 0.841, 5.588 ±0.650, and 9.992 ± 0.798 umol/L, respectively, after 72 hours. Next, we performed combination studies to evaluate whether fingolimod can synergize with the standard-of-care treatment drug mitotane. Results shown in Figure 1, D, demonstrated a strong synergy between fingolimod and mitotane for all 3 ACC cell lines.
Fingolimod treatment induces apoptotic and autophagy-dependent cell death by modulating the Akt/NFKB/ERK pathway
Because we observed that fingolimod treatment led to a reduction in ACC cell viability, we further investigated the mechanism behind this reduction by analyzing for the induction of apoptosis and autophagy using Western blot analysis, which showed a dose-dependent significant increase in both PARP cleavage and LC3-II levels in ACC cells treated with fingolimod (Figure 2). To gain further insight into the mechanisms of fingolimod-induced cell death, we conducted a Western blot analysis of the effect of fingolimod treatment on survival pathway proteins in ACC, specifically Akt, ERK, and NF-KB. These results indicate that fingolimod treatment caused a significant dose-dependent decrease in the levels of phosphorylated Akt (p-Akt) and phosphorylated P65 (p-P65), while there was an increase in phosphorylated ERK (p-ERK) levels. Importantly, the total levels of Akt, ERK, and P65 remained unchanged, suggesting that fingolimod induces ACC cell death in part through the Akt/ERK/NF-KB pathways (Figure 3).
RNAseq analysis identifies the steroid biosynthetic pathway as the top regulated pathway by fingolimod in ACC cells
To further investigate the ACC pathways affected by fingolimod treatment, we performed RNA sequencing analysis of ACC cells after IC50 concentration fingolimod treatment. The volcano plot analysis and KEGG pathway analysis of the differentially expressed genes (Figure 4, A and B) identified the steroid biosynthetic pathway as the highly significantly regulated pathway and transforming growth factor-ß (TGFB) as the less significantly regulated pathway after fingolimod treatment of ACC cells. Therefore, we performed RT-PCR analysis of steroidogenic pathway genes after treating the ACC cells with IC50 concentrations of fingolimod. Our findings, presented in Figure 4, C and D, demonstrate that fingolimod treatment of ACC cells led to the significant downregulation of steroidogenic genes CYP11A1, CYP21A2, CYP11B1, CYP21A2, StAR, and HSD3B2 (P <. 05 for each) for all 3 cell lines. However, only TGFß pathway genes FST, SMAD7, and BMP4 were downregulated for all 3 cell lines, whereas ROCK1 was upregulated in NCI-H295R and downregulated in ACC1 and ACC2 (P <. 05 for each).
Extracellular flex assay using Seahorse
Previous studies have shown that the knockdown of SGPL1 regulates the OCR. Therefore, to assess how treatment with fingolimod that targets SGPL1 affects metabolic flux, we conducted mitochondrial and glycolytic stress assays on NCI-H295R, ACC1, and ACC2 cell lines using fingolimod concentrations <IC50 levels to ensure cell viability. Our findings demonstrated that fingolimod treatment significantly reduces oxidative phosphorylation (Figure 5).
Boyden-chamber migration and invasion assay
Studies have shown that the lung and liver are the primary sites of metastasis in ACC.26 To further investigate this, we evaluated the migration of ACC cells in the presence of ECM components from rat lung and liver tissues. Decellularized lung and liver tissues were confirmed by measuring DNA content and using Hoechst nuclear staining on the decellularized material. The DNA content of the decellularized lung decreased to 19.89 ng/mg of tissue, and the liver decreased to 2.6 ng/mg of tissue, compared with 817.04 ng/mg and 1853.09 ng/mg for fresh lung and liver, respectively (Pvalue <. 001) (Figure 6, A). This significant DNA reduction as well as complete lack of Hoechst nuclear staining indicates that cellular DNA was effectively removed, as a DNA content of less than 50 ng/mg is considered optimal for decellularized ECM.27 In the presence of lung and liver ECM, the migration and invasion of ACC cells increased by 1.5-2.0 times. However, there was a dose-dependent decrease in migration and invasion observed for control, lung, and liver conditions across all 3 ACC cell lines. Specifically, the NCI-H295R, ACC1, and ACC2 cell lines showed migration percentages of 0.99%-2.33%, 2.2%-19.1%, and 7.6%-14.8%, respectively, while invasion percentages were 6.2%-13.6%, 29%-37%, and 6.6%-13.12% for a 5 umol/L concentration of fingolimod (Figure 6, B and C). These results suggest that fingolimod treatment effectively prevented the migration and invasion of ACC cells.
Lipid metabolic profiling by single-cell mass spectrometry
Because we observed that fingolimod targets SIP and modifies sphingolipid metabolism, we conducted lipid metabolic profiling studies of fingolimod-treated ACC cells using MALDI MS and LC-MS/MS techniques. Mass spectrometry revealed a total of 75 lipids that were differentially regulated (Supplementary Table), with 36 lipids significantly upregulated and 39 downregulated, as illustrated in Figure 7, A. Further analysis indicated specific upregulation of ceramides and CIP (cerPE) that promote apoptosis. In contrast, we observed significant downregulation of phospholipids such as phosphatidylcholine (PC) and phosphatidylethanolamine (PE), as well as sphingomyelins, which are typically associated with increased cell transformation and tumor progression, as shown in Figure 7, B.
Discussion
Current therapeutic strategies for ACC still lack the ability to significantly improve overall disease survival, despite several new trials with targeted therapies and even ICIs in recent years. Although a small portion of patients may experience some early response, most patients still progress, and most early responders will relapse. The effectiveness of immunotherapy is influenced by factors such as the mutational burden of the cancer and the level of immune cell infiltration. Although advanced ACC is associated with a high mutational burden, it also leads to elevated levels of glucocorticoids and cortisol, creating an immunosuppressive environment.28 Therefore, novel less-toxic therapeutic strategies that target tumor-specific vulnerabilities are vitally needed for ACC.
Our integrated bioinformatic analysis of lipid metabolism revealed an upregulation in sphingolipid metabolism, which regulates steroid biosynthesis in ACC.15 Therefore, in the present study, we first analyzed data from TCGA using the GEPIA platform to investigate the expression of genes involved in SIP generation and their association with ACC survival. Our analysis revealed significantly elevated expressions of SIP-regulating genes (SPHK1, SPHK2, and SGPL1) in stages III and IV of ACC, linked to metastasis and advanced disease. Further survival analysis demonstrated that higher expression levels of these genes correlated with an increased hazard ratio and poor survival, including for cases exhibiting necrosis and mitoses between 5 and 50 associated with a poorer prognosis for patients with advanced ACC.26 Because fingolimod is the SIP receptor modulator that regulates several cellular processes involved in cellular proliferation and metastasis, we explored the potential of fingolimod as a novel potential therapy for ACC.
Cell viability assay results indicated that fingolimod treatment significantly reduced the viability of ACC cells, showed synergy with standard of care treatment mitotane, and induced apoptosis and autophagy in the cells, supporting its potential repurposing role as an anticancer drug for ACC. To identify the mechanisms responsible for cell death, we examined the pathways involved in decreased viability. Our findings revealed cleavage of PARP and increased expression of LC3-II, indicating that fingolimod induces apoptosis and autophagy by targeting the Akt/Erk/P65 pathways. These results are like previous studies that show that the knockdown of SGPL1 or SPHK1 reduces the viability of ACC cells.29,30 Furthermore, we conducted RNA sequencing analysis on ACC cells treated with fingolimod, which identified steroid biosynthesis as the top-regulated pathway. Our results showed
that fingolimod treatment downregulated genes associated with this pathway, suggesting that fingolimod may help to overcome steroid-dependent immunosuppression. Consistent with results indicating that SGPL1 knockdown in ACC cells decreases OCR, fingolimod treatment also led to a downregulation of OCR. Furthermore, we also observed decreases in migration and invasion in the presence of liver and lung ECM, suggesting the ability of fingolimod to target the metastatic niche in vitro, which is corroborated by previous in vivo studies showing reduced metastatic potential after knockdown of SPHK1 in a murine model of breast cancer.31 Lastly, our examination of genes related to sphingolipid metabolism revealed downregulation of lipids involved in oncogenesis and metastasis, such as Hex2Cer, SM, PC, and PE, while apoptosis-promoting ceramides in ACC cell lines were upregulated after fingolimod treatment. Limitations of this study include a lack of in vivo translation, which is planned for future translational validation studies with this drug. In addition, combination studies with fingolimod and current therapies in ACC would be important in the future to demonstrate any synergies that could lower cytotoxic drug toxicity or improve immune check inhibition efficacy.
In conclusion, our results support that targeting of sphingolipid metabolism with fingolimod induces ACC cell death and inhibits cell growth, migration, and invasion. Sphingolipids (SIP and ceramide) play an important role in altering the function of immune cells in the tumor microenvironment. Hence, targeting of SIP metabolism has potential to improve the efficacy of immunotherapies and this should be studied further in vitro and in vivo. Recent studies have shown that melanoma patients treated with PD-1 therapy have high amounts of the SIP-generating sphingosine kinase encoded gene SPHK1. Furthermore, knocking down this gene enhanced the efficacy of ICI therapy and reduced the metastatic burden in murine models of melanoma and breast.31 Given the effectiveness of fingolimod in targeting SIP in ACC cells, further in vivo evaluation of SIP inhibition, particularly in combination with ICI therapy, could lead to novel combination ICI therapies for patients with ACC.
Discussion
Dr Priya Dedhia (Ohio State University, Columbus) That was a really cool talk. A lot of great data. I’m curious, how does sphingolipid compare with mitotane? We don’t exactly know how mitotane works, but are there any similarities or differences in terms of the steroid metabolism pathways compared with mitotane?
Speaker: Daniel Hess We haven’t actually performed any studies on mitotane effects, specifically on steroid pathways, or any western blot analysis. We have done a fair amount of cell viability testing with mitotane in combination with sphingolipid and have found that it’s very good at synergizing, but we have not done any type of pathway analysis.
Dr Matthew Nehs (Harvard University, Boston) How do you get the drug concentration high enough to have an effect in vivo? For it to be useful as a treatment, it must occur inside the tumor or the local tumor microenvironment. Sometimes that comes with the tradeoff of systemic toxicities. So how would you deliver it and/or mitigate the toxicities?
Speaker: Daniel Hess: We haven’t done any in vivo testing yet, but sphingolipid was initially used as a multiple sclerosis drug, and it’s generally treated in concentrations of 0.5 mg given orally, or 1.25 mg given orally. So it’s usually treated with pretty high concentrations, and it’s given for up to 41/2 years sometimes. So in that sense, there is a history of it being used in that way, but we have not actually performed the experiments to translate it to in vivo work, yet.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
Seahorse assay XFe96 was performed at the University of Illinois Urbana Champaign Cancer Center, TEP facility.
Funding/Support
This research was funded in part by the National Institutes of Health (R01 CA270147 and R01 CA216919; M.S.C. and Brian S.J. Blagg) and by the Carle Illinois College of Medicine, University of Illinois Urbana-Champaign Cancer Center. Tumor engineering and phenotyping was used for the Seahorse experiment.
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6. Lagana M, Grisanti S, Cosentini D, et al. Efficacy of the EDP-M scheme plus adjunctive surgery in the management of patients with advanced adrenocortical carcinoma: the brescia experience. Cancers (Basel). 2020;12:941. [PubMed: 32290298]
7. Kroiss M, Quinkler M, Johanssen S, et al. Sunitinib in refractory adrenocortical carcinoma: a phase II, single-arm, open-label trial. J Clin Endocrinol Metab. 2012;97:3495-3503. [PubMed: 22837187]
8. Naing A, Lorusso P, Fu S, et al. Insulin growth factor receptor (IGF-1R) antibody cixutumumab combined with the mTOR inhibitor temsirolimus in patients with metastatic adrenocortical carcinoma. Br J Cancer. 2013;108:826-830. [PubMed: 23412108]
9. Fassnacht M, Berruti A, Baudin E, et al. Linsitinib (OSI-906) versus placebo for patients with locally advanced or metastatic adrenocortical carcinoma: a double-blind, randomised, phase 3 study. Lancet Oncol. 2015;16:426-435. [PubMed: 25795408]
10. Wortmann S, Quinkler M, Ritter C, et al. Bevacizumab plus capecitabine as a salvage therapy in advanced adrenocortical carcinoma. Eur J Endocrinol. 2010;162:349-356. [PubMed: 19903796]
11. Habra MA, Stephen B, Campbell M, et al. Phase II clinical trial of pembrolizumab efficacy and safety in advanced adrenocortical carcinoma. J Immunother Cancer. 2019;7:253. [PubMed: 31533818]
12. Le Tourneau C, Hoimes C, Zarwan C, et al. Avelumab in patients with previously treated metastatic adrenocortical carcinoma: phase 1b results from the JAVELIN solid tumor trial. J Immunother Cancer. 2018;6:111. [PubMed: 30348224]
13. Butler LM, Perone Y, Dehairs J, et al. Lipids and cancer: emerging roles in pathogenesis, diagnosis and therapeutic intervention. Adv Drug Deliv Rev. 2020;159:245-293. [PubMed: 32711004]
14. Fernandez LP, Gomez de Cedron M, Ramirez de Molina A. Alterations of lipid metabolism in cancer: implications in prognosis and treatment. Front Oncol. 2020;10:577420. [PubMed: 33194695]
15. Subramanian C, Cohen MS. Identification of novel lipid metabolic biomarkers associated with poor adrenocortical carcinoma prognosis using integrated bioinformatics. Surgery. 2022;171:119- 129. [PubMed: 34353633]
16. Yamaji T, Hanada K. Sphingolipid metabolism and interorganellar transport: localization of sphingolipid enzymes and lipid transfer proteins. Traffic. 2015;16:101-122. [PubMed: 25382749]
17. Hannun YA, Obeid LM. Author correction: sphingolipids and their metabolism in physiology and disease. Nat Rev Mol Cell Biol. 2018;19:673.
18. Newton J, Lima S, Maceyka M, Spiegel S. Revisiting the sphingolipid rheostat: evolving concepts in cancer therapy. Exp Cell Res. 2015;333:195-200. [PubMed: 25770011]
19. Ogretmen B Sphingolipid metabolism in cancer signalling and therapy. Nat Rev Cancer. 2018;18:33-50. [PubMed: 29147025]
20. Paglia G, Astarita G. Metabolomics and lipidomics using traveling-wave ion mobility mass spectrometry. Nat Protoc. 2017;12:797-813. [PubMed: 28301461]
21. Tsugawa H, Cajka T, Kind T, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 2015;12:523-526. [PubMed: 25938372]
22. Takeda H, Matsuzawa Y, Takeuchi M, et al. MS-DIAL 5 multimodal mass spectrometry data mining unveils lipidome complexities. Nat Commun. 2024;15:9903. [PubMed: 39609386]
23. Subramanian C, McNamara K, Croslow SW, et al. Novel repurposing of sulfasalazine for the treatment of adrenocortical carcinomas, probably through the SLC7A11/xCT-hsa-miR-92a-3p- OIP5-AS1 network pathway. Surgery. 2025;177:108832. [PubMed: 39424480]
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A
SGPL1
SPHK1
SPHK2
F value = 3.3 Pr(>F) = 0.0253
F value = 2.17
5
Pr(>F) = 0.0992
5
F value = 2.79
Pr(>F) = 0.0465
10
Log2(TPM)+1
+
4
Log2(TPM)+1
Log2(TPM)+1
-
3
M
2
24
-
-
0
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Disease Free Survival
Disease Free Survival
Disease Free Survival
1.0
Low SGPL1 TPM
1.0
High SGPL1 TPM
Low SPHK1 TPM
1.0
High SPHK1 TPM
Low SPHK2 TPM
Logrank p=4e-04 HR(high)=3.5
Logrank p=0.018
High SPHK2 TPM
HR(high)=2.3
Logrank p=0.0018
0.8
p(HR)=0.00083
0.8
p(HR)=0.021
0.8
HR(high)=3
p(HR)=0.0027
Percent survival
n(high)=38 n(low)=38
Percent survival
n(high)=37
n(high)=38
nflow)=38
Percent survival
0.6
0.6
0.6
n(low)=38
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
Overall Survival
Overall Survival
Overall Survival
1.0
Low SGPL1 TPM
1.0
1.0
.High SGPL1.TPM
Low SPHK1 TPM
Low SPHK2 TPM
Logrank p=0.00043
High SPHK1 TPM
Logrank-p=0.024
High SPHK2 TPM
HR(high)=4.3
Logrank p=0.00057
0.8
p(HR)=0.0011
0.8
HR(high)=2.4
p(HR)=0.029
0.8
HR(high)=4.1
p(HR)=0.0014
Percent survival
n(high)=38 n(low)=38
Percent survival
n(high)=37
n(high)=38
0.6
0.6
n(low)=38
Percent survival
0.6
n(low)=38
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
Author Manuscript
B
ELOVL5
GALC
CERK
F value = 0.756 Pr(>F) = 0.523
F value = 0.0285 Pr(>F) = 0.993
6
F value = 0.529
(
Pr(>F) = 0.664
Log2(TPM)+1
Log2(TPM)+1
5
Log2(TPM)+1
5
-
4
.
0
”
(
5
2
*
2
-
3
1
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Disease Free Survival
Disease Free Survival
Disease Free Survival
1.0
Low ELOVL5 TPM
High
1.0
Low
0
Low CERK TPM
Logrank p=0.16
High GALC TPM
HR(high)=1.6
Logrank p=0.19
High TPM
HR(high)=1.6
Logrank p=0.27
0.8
p(HR)=0.16
0.8
p(HR)=0.19
00
HR(high)=1.4
n(high)=38
p(HR)-0,27
Percent survival
n(high)=38
0.6
n(low)=38
Percent survival
0.6
n(low)=38
Percent survival
n(high) 38
0.6
n(low)=38
0.4
0.4
0.4
0.2
0.2
3
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
Overall Survival
Overall Survival
Overall Survival
1.0
Low ELOVL5 TPM
1.0
High ELOVL5 TPM
Low TPM
a
High GALC TPM
Low CERK TPM
Logrank p=0.66
Logrank p=0.84
High CERK TPM
Logrank p=0.26
0.8
HB(high)=1,2
p(HR)=0.66
0.8
HR(high)=1.1
p(HR)=0.84
0.8
HR(high)=1:5
Percent survival
n(high)=38
p(HR)=0.27
n(low)=38
Percent survival
n(high)=38
n(high)=38
0.6
0.6
n(low)=38
Percent survival
0.6
n(low)=38
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
C
- NCI-H295R
D
NCI-H295R
ACC1
ACC2
120
+ ACC1
20
20-
20-
+ ACC2
100
7
10
10
10
80
千
1
% Viability
60
5
Synergy Score
5
Synergy Score
5
Fingolimod (UM)
Fingolimod (UM)
40
20
2.5
20
2.5
Fingolimod (UM)
Synergy Score
2.5
20
20
0
0
0
1.25
1.25
1.25
0
-7
-6
-5
-4
20
-20
Fingolimod (AM)
0.625
0.625
0.625
20
ACC1 (IC50uM)= 7.044±0.841 ACC2 (IC50uM) =5.588±0.650
0.3125
0.3125
0.3125
NCI-H295R (IC50uM)=9.992±0.798
0-
0-
3.125 6.25 12.5
0
0
25
50
100
200
0
3.125 6.25 12.5
25
50
100
200
0
3.125 6.25 12.5 25
50
100
200
Mitotane (UM)
Mitotane (UM)
Mitotane (UM)
SyneryFInder (D). The experiments were conducted twice in triplicate, and the mean value ± standard deviation was plotted in GraphPad Prism to determine the IC50 values (P <. 05). ACC, adrenocortical carcinoma; GTEx, Genotype-Tissue Expression; IC50, half-maximal inhibitory concentration; TCGA, The Cancer Genome Atlas; TPM, transcripts per million.
NCI-H295R Fingo (u.M)
ACC1
ACC2 Fingo (u.M)
Fingo (LM)
0
1.25
2.5
5.0
10.0
0
1.25
2.5
5.0
10.0
0
1.25
2.5
5.0
10.0
LC3-I
LC3-I
LC3-I
LC3-II
LC3-II
LC3-II
0.0
0.03
0.08
0.12
1.31
0.76
1.86
1.99
2.62
3.18
0.37
0.96
1.49
1.67
1.94
PARP
PARP
PARP
Cleaved
Cleaved
Cleaved PARP
PARP
PARP
0.0
0.21
0.34
0.30
0.99
0.0
0.04
0.06
0.08
1.18
0.0
0.03
0.07
0.08
0.98
Actin
Actin
Actin
NCI-H295R
ACC1
ACC2
Fingo (LM)
Fingo (AM)
Fingo (LM)
0
1.25
2.5
5.0
10.0
0
1.25
2.5
5.0
10.0
0
1.25
2.5
5.0
10.0
P-P65
P-P65
P-P65
0.31
0.07
0.04
0.05
0.01
1.31
0.20
0.06
0.06
0.02
0.83
0.0
0.0
0.0
0.0
P65
P65
P65
P-ERK
P-ERK
P-ERK
0.39
0.59
0.62
0.69
0.70
0.03
0.05
0.11
0.44
0.89
0.67
0.89
1.57
2.07.
4.13
ERK
ERK
ERK
P-Akt
P-Akt
P-Akt
0.46
0.43
0.38
0.24
0.23
0.90
0.04
0.03
0.03
0.0
0.901
0.07
0.02
0.01
0.0
Akt
Akt
Akt
Actin
Actin
Actin
A
up . no . down
C
1.20
90
NCI-H295R
BACC1
-log10 of qval
Fold Change vs Control
1.00
WACC2
60
0.80
30
0.60
0.40
0
-20
-10
log2 of Fold change
0
10
20
0.20
B
Statistics of Pathway Enrichment
0.00
Control
CYP11A1
CYP11B1
CYP21A2
StAR
HSD3B2
Steroid biosynthesis
Aldosterone synthesis and secretion
Cortisol synthesis and secretion
AGE-RAGE signaling pathway in diabetic complications
D
Arrhythmogenic right ventricular cardiomyopathy (ARVC)
1.4
NCI-H295R
ECM-receptor interaction
ACC1
Relaxin signaling pathway
pvalue
Fold Change vs Control
1.2
ACC2
Terpenoid backbone biosynthesis
0.0100
pathway_name
Focal adhesion
0.0075
0.0050
1
Dilated cardiomyopathy (DCM)
0.0025
Hypertrophic cardiomyopathy (HCM)
0.8
Osteoclast differentiation
Gene_number
Phospholipase D signaling pathway
10
0.6
Cushing syndrome
20
30
Parathyroid hormone synthesis, secretion and action
0.4
Glutamatergic synapse
GnRH signaling pathway
0.2
TGF-beta signaling pathway
Sulfur metabolism
0
Amoebiasis
0.2
0.3
0.4
0.5
0.6
0.7
Control
FST
SMAD7
BMP4
ROCK1
Rich factor
NCI-H295R
ACC1
ACC2
200.0
Rot/AA
400.0
Oligomycin
Rot/AA
Oligomycin
Rot/AA
Oligomycin
350.0
FCCP
OCR (pmol/min)
150.0
FCCP
500.0
300.0
FCCP
OCR (pmol/min)
250.0
400.0
OCR (pmol/min)
100.0
200.0
300.0
150.0
200.0
50.0
100.0
Control Fingolimod (2.5M)
50.0
Control Fingolimod (2.5}M)
100.0
Control
Fingolimod (2.5 M)
0.0
0.0
20
40
60
80
0.0
0
20
40
60
80
0
Time (min)
0
20
40
60
80
Time (min)
Time (min)
75.0
125.0
140.0
100,0
120.0
OCR (pmol/min)
50.0
100.0
OCR (pmol/min)
OCR (pmol/min)
75.0
80.0
60.0
25.0
50.0
40.0
25.0
20.0
0.0
0.0
Basal
Spare Respiratory Capacity
0.0
Basal
Spare Respiratory Capacity
Basal
Spare Respiratory Capacity
50.0
80.0
100.0
40.0
80.0
OCR (pmol/min)
60.0
OCR (pmol/min)
OCR (pmol/min)
30.0
60.0
40.0
20.0
40.0
10.0
20.0
20.0
0.0
0.0
0.0
Proton Leak
ATP Production
Proton Leak
ATP Production
Proton Leak
ATP Production
Author Manuscript
A
Fresh Wister rat Liver
Decellularized Liver
DNA Content
900
800
Unstained
700
ng/mg of tissue
600
500
400
300
200
Hoechst stain
100
0
Fresh Liver
Decellularized Liver
Fresh Wister rat Lung
Decellularized Lung
2000
1750
Unstained
ng/mg of tissue
1500
1250
1000
750
500
250
Hoechst stain
0
Fresh Lung
Decellularized Lung
B
ACC1
ACC1
ACC2
NCI-H295R
Control
Liver
Lung
200
200
O Control
200
Fingo (0.0μ.Μ)
Percent Migration
O Control Liver Lung
150
Percent Migration
150
@Liver
Percent Migration
@ Control Liver Lung
Lung
150
Fingo (2.5}M)
100
100
100
Fingo (5.0}M)
50
50
50
0
0
0
1
Fingo (7.5}M)
0
2.5
5
7.5
0
Fingolimod (LM)
2.5
5
7.5
0
2.50
5.00
10.00
FIngolimod (AM)
Fingolimod (LM)
C
Control
Liver
Lung
200
200
Fingo (0.0UM)
Percent Invasion
O Control @ Liver Lung
O Control
250
Percent Invasion
0 Liver
150
150
Lung
Percent Invasion
Control Liver
200
Lung
Fingo (2.5}M)
100
100
150
100
Fingo (5.0}M)
50
50
50
0
0
0
Fingo (7.5UM)
0
2.5
5
7.5
0
2.5
5
7.5
0
2.50
5.00
10.00
Fingolimod (LM)
Fingolimod (LM)
Fingolimod (AM)
Urbana-Champaign. Representative images are provided on the left, and the percentages of migrated and invaded cells were counted and plotted after normalizing for viability at each concentration. Each assay was performed in triplicate, and values were presented as mean ± standard deviation. * P <. 05, ** P <. 01, *** P <. 001. ACC, adrenocortical carcinoma.
A
Lipid gene expression
B
Cer38:1;02
CerPE 38:1;02
Hex 2Cer40:0;02
SM 34:1;02
5
le4
p = 5.456e-14T
25
le4
p = 8.547e-26T
le4
p = 1.078c-05
le5
p =6.190c-04
1
1
4.
5
1
0
Downregulated
Not Significant
Upregulated
4
20
4.
35
w
w
15
3
2.
2.
-Log10 Adjusted P- Value
Mean Intensity
Mean Intensity
Mean Intensity
Mean Intensity
30
N
10-
1
I
5
1
25
1
CerPE 38:1;02
0
0
20
Control
Fingo
Control
Fingo
0
Control
Fingo
0
Control
Fingo
PC 36:1
PC 36:1
PC 36:2
PE 30:4;02
TG 48:4
15 - PE 30:4,02
TG 48:4
Cer 38:1;02
le4
p = 1.022e-18
200
le2
p = 8.003e-07
6
175
35
le3
p = 3.899e-15
5
le4
p = 1.668e-14
10
Mean Intensity
5
Mean Intensity
150
Mean Intensity
30-
Mean Intensity
4
4
125
25-
5
PC 36:20
SM 34:1:02
3
100
20-
3
q=0.05
75-
15-
2
0
2
50-
10-
1
-2
-1
0
1
2
1
25-
5
Log,Fold Change
0
Control
Fingo
0
Control
Fingo
0
Control
Fingo
0
Control
Fingo