(M1530-01-01) Untargeted Lipidomics Identifies Distinct Metabolic Alterations in Huh-7 Hepatocellular Carcinoma Cells Receiving Monotherapy or Combination Therapy with Atorvastatin and Doxorubicin

Purpose: Hepatocellular carcinoma (HCC) is an aggressive form of liver cancer often associated with poor clinical outcomes due to late-stage detection, rapid disease progression, and the emergence of resistance to standard therapeutic strategies (1). Lipids play a critical role in supporting rapid cancer cell proliferation, energy production, and membrane biosynthesis (2,3). Previous studies have highlighted the potential of statins, cholesterol-lowering agents, as adjuvants to chemotherapy due to their ability to interfere with tumor metabolism (4,5). Although a recent study examined the effects of atorvastatin and doxorubicin (DOX) combination therapy on the polar metabolome of HCC cells (6), the lipidomic landscape after such a treatment remains largely unexplored. Understanding how this combination affects lipid pathways may reveal metabolic vulnerabilities that could be utilized to enhance treatment efficacy. The purpose of this study was to perform untargeted lipidomics on samples from Huh-7 liver cancer cells treated with atorvastatin (ATOR), doxorubicin, or their combination. Using LC-HRMS, we aimed to identify treatment-induced alterations in lipid metabolism. This work seeks to expand the mechanistic understanding of statin-chemotherapy synergy by focusing on lipidomic changes.

Methods: Huh-7 hepatocellular carcinoma cells were exposed to doxorubicin (DOX), atorvastatin (ATOR), or a combination of both compounds. Control samples were left untreated. Following treatment of cell cultures, lipid extraction was performed using a modified Bligh&Dyer method to capture a broad range of lipid species. Each group was represented by five biological replicates, and a pooled quality control (QC) sample was prepared and analyzed throughout the run to assess analytical stability. Untargeted lipidomic profiling was performed using ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-ESI-Q-TOF MS). Chromatographic separation was carried out using a C18 reversed-phase column. Mass spectrometry data were acquired in both positive and negative electrospray ionization (ESI) modes with a scan range of m/z 100 -1200. QC samples were injected regularly throughout the run to monitor analytical stability. Raw data files were pre-processed using the MetaboAnalystR pipelines for peak detection, alignment, and normalization. Features were filtered based on technical reproducibility in QC samples (CV < 20%). Probabilistic quotient normalization (PQN) was applied using pooled QC samples. Log transformation and Pareto scaling were used to adjust for batch effects and improve data distribution. Multivariate statistical analysis included ANOVA, PCA, PLS-DA, and volcano plot comparisons. Volcano plots were used to identify significantly altered features (p-value < 0.05; fold-change >1.1 or < 0.9) in each treatment group compared to control. Features significantly altered in both volcano plots and ANOVA (FDR, p < 0,05) were selected for annotation, which was performed using accurate mass data against databases from LIPID MAPS and HMDB.

Results: Pre-processing of LC-MS data yielded 4984 features in positive and 4983 features in negative ESI mode. After filtering based on technical reproducibility (CV < 20% in QC samples), 818 features from positive and 1153 features from negative mode were retained for statistical analysis. PCA revealed clear treatment-dependent clustering in both ionization modes. The doxorubicin group partially overlapped with control, while the atorvastatin and combination groups appeared on opposite sides of the control group. Tight clustering of QC samples in PCA confirmed analytical stability throughout the LC-MS run. Volcano plot analysis identified 486 significantly altered features across treatments: 254 from positive and 232 from negative ESI. ANOVA further narrowed this to 51 (positive) and 191 (negative) features for identification. Feature annotation suggested that atorvastatin treatment upregulated 180 features, primarily involving neutral lipids and sterol-related species, while downregulating 24 features across a range of lipid classes. Doxorubicin altered 60 features: 10 ceramides were upregulated, while 26 sterol- and 24 PC/PE-related features were downregulated. In the combination group, 138 features were downregulated, including 54 cholesteryl esters, 33 free fatty acids, and 26 ceramides while only 4 features were upregulated. A total of 32 features were altered between multiple groups, 26 of which showed anti-correlated patterns between atorvastatin and combination treatment, while 3 ceramides remained positively correlated between the doxorubicin and combination groups.

Conclusion: This study revealed distinct, treatment-specific alterations in the lipid profile of Huh-7 hepatocellular carcinoma cells, advancing insight into how statin-chemotherapy combinations affect lipid metabolism. Atorvastatin led to broad upregulation of cholesteryl esters, sterols, free fatty acids, and ceramides, consistent with mevalonate pathway inhibition and compensatory lipid accumulation. Doxorubicin reduced sterol- and phospholipid-related features while increasing ceramides, likely reflecting membrane remodeling and stress signaling. Combination treatment reversed many atorvastatin-driven changes, with broad lipid downregulation, especially cholesteryl esters and fatty acids, suggesting that doxorubicin may suppress statin-induced remodeling through cytotoxic stress or metabolic disruption. Anti-correlated features between treatments suggests possible crosstalk between the mevalonate and sphingolipid pathways. Acquired data support a role for disrupted lipid homeostasis in the effects of statin-chemotherapy co-treatment. These findings underscore the value of untargeted lipidomics in revealing treatment-induced metabolic shifts and identifying lipid-related vulnerabilities that could be exploited to improve HCC treatment outcomes.

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Figure 1. Horizontal bar chart showing the number of upregulated and downregulated lipid features by class for each treatment group. Lipid classes are sorted by total number of altered features. Bars represent counts of features with fold change >1.1 (upregulated) or <0.9 (downregulated) following treatment with atorvastatin, doxorubicin, or their combination.

Figure 2. Principal Component Analysis (PCA) scores plot of lipidomic profiles (negative ion mode) from Huh-7 cells treated with atorvastatin (ATOR), doxorubicin (DOX), or their combination (COMB), alongside untreated controls and pooled quality control (QC) samples.

Figure 3. Principal Component Analysis (PCA) scores plot of lipidomic profiles (positive ion mode) from Huh-7 cells treated with atorvastatin (ATOR), doxorubicin (DOX), or their combination (COMB), alongside untreated controls and pooled quality control (QC) samples.