Introduction

Colorectal cancer (CRC) is a major threat to human health, ranking fourth in incidence and second in mortality among all cancers1. In recent years, numerous studies have shown that metabolic reprogramming plays a pivotal role in the growth and spread of cancer cells2,3,4,5. Metabolic abnormalities in CRC enable cancer cells to adapt to the tumor microenvironment, fostering rapid growth and metastasis6,7,8. These metabolic changes not only characterize CRC but also offer new targets for cancer diagnosis and treatment. Lipid metabolism dysregulation is a critical aspect of metabolic reprogramming. Lipids not only alter the energy supply of cancer cells but also provide essential molecules for constructing intracellular membrane systems, signal transduction9, and antioxidation10, making them potential therapeutic targets. For instance, inhibitors targeting fatty acid synthase (FASN)11, carnitine palmitoyltransferase 1 (CPT1)12, and ATP citrate lyase (ACLY)13 have shown promising anticancer effects in preclinical studies.

Carnitine palmitoyltransferase 2 (CPT2) is an essential enzyme located in the mitochondrial inner membrane and is a key player in the mitochondrial fatty acid oxidation (FAO) pathway14. Its primary function is to transport long-chain fatty acids (LCFAs) into the mitochondrial membrane for β-oxidation15. Dysfunction in CPT2 is associated with various metabolic disorders16, and growing evidence suggests a crucial role for CPT2 in cancer14, particularly in colon cancer17,18. In this study, we discovered abnormal lipid accumulation in CRC and, through analysis of lipid metabolism-related pathways, found that inhibiting CPT2 significantly promoted CRC proliferation. Mechanistically, the downregulation of CPT2 leads to the accumulation of glycerophospholipids, specifically phosphatidylcholine (PC) and phosphatidylethanolamine (PE), which subsequently activate selective autophagy in CRC cells and promote their proliferation.

Results

Dysregulated fatty acid metabolism in colorectal cancer

Metabolic abnormalities are often observed in colorectal tissues. We performed Oil Red O staining on surgically excised CRC and adjacent normal mucosal tissues to observe the lipid distribution. Compared with normal mucosal tissues, CRC tissues presented significant lipid accumulation (Fig. 1A, B, Supplementary data 1). This phenomenon also occurs in colorectal cancer cell lines. CRC cell lines (HCT116, HT29, SW480, and RKO) and normal epithelial cell lines (HIEC and NCM460) was examined, which confirmed consistent results (Fig. 1C, D, Supplementary data 2). To explore the reasons behind the lipid metabolic changes in CRC and their impact on cancer progression, we analyzed lipid metabolism-related pathways in The Cancer Genome Atlas database. The analysis revealed that multiple lipid metabolism pathways were disrupted, with fatty acid metabolism being particularly significant (Fig. 1E, F, Supplementary data 3), suggesting the existence of fatty acid metabolism abnormalities in CRC.

Fig. 1: Fatty acid metabolism is significantly downregulated in colorectal cancer.
figure 1

A Oil Red O staining showing lipid distribution in colorectal cancer (CRC) tissues and adjacent normal mucosa; lipid-rich areas appear red. B Quantification of Oil Red O-positive areas. C Lipid accumulation in colorectal cancer cell lines compared with that in colorectal epithelial cell lines. D Statistical comparison of lipid accumulation between colorectal epithelial and cancer cell lines. E GSEA results of significantly upregulated and downregulated lipid metabolism pathways in colorectal cancer. F Downregulation of fatty acid metabolism is the most prominent alteration in colorectal cancer, as shown by GSEA. The results from at least three independent experiments (n = 3) are presented as the means ± standard deviations (SDs). Statistical significance is considered at p < 0.05, where ns denotes no significance, * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 0.001, and **** indicates p < 0.0001.

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CPT2 is downregulated in colorectal cancer and Correlates with prognosis

We performed differential expression analysis and survival analysis of all genes involved in the fatty acid metabolism pathway. Fifty-one genes were differentially expressed in colorectal cancer tissues (Fig. 2A, Supplementary data 4), and 13 were significantly associated with overall survival in CRC patients (Fig. 2B, Supplementary data 5), while CPT2 (p = 0.0008), HADH (p = 0.0062), CD36 (p = 0.0077), ACOX1 (p = 0.0108), and SUCLG2 (p = 0.0138) showing two types of correlations simultaneously (Fig. 2C). Among them, CPT2 was the most significantly downregulated gene in CRC tissues, which was also linked to patient survival and prognosis. Public database analysis confirmed significant CPT2 downregulation at both mRNA and protein levels (Figure S1A, S1B), and receiver operating characteristic (ROC) analysis showed a diagnostic accuracy of up to 98% (Figure S1C). Low CPT2 expression predicted shorter overall survival (Figure S1D, Supplementary data 6), with the difference widening over time (Figure S1E). Moreover, CPT2 expression varied significantly across different TNM stages (Figure S1F–I), suggesting its role in modulating CRC cell behavior.

Fig. 2: Low expression of CPT2 in colorectal cancer and its impact on prognosis.
figure 2

A Differential expression of fatty acid metabolism-related genes between cancerous and adjacent tissues in TCGA. B Correlation analysis of fatty acid metabolism-related genes with overall survival in TCGA database. C Five genes were both differentially expressed and significantly correlated with survival. D Immunohistochemistry analysis of CPT2 in postoperative CRC tissues and adjacent mucosa (red arrows indicate CPT2 staining). E Quantification of CPT2 protein expression in CRC vs. normal mucosa. F ROC curve analysis of the diagnostic ability of CPT2 protein expression in colorectal cancer from postoperative patients. G Differences in overall survival between colorectal cancer patients with high and low CPT2 expression, with shaded areas indicating the 95% confidence intervals. H ROC curves evaluating the ability of CPT2 protein expression to predict 1-year, 3-year, and 5-year overall survival. I Disease-free survival difference between patients with high and low CPT2 expression, with shaded areas indicating the 95% confidence intervals. J ROC curves evaluating the ability of CPT2 protein expression to predict 1-year, 3-year, and 5-year disease-free survival. Statistical annotations as in Fig. 1.

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To validate these findings in database, we collected 71 postoperative CRC tissue samples along with corresponding clinical information. Immunohistochemistry (IHC) analysis (Supplementary data 7) revealed that CPT2 protein expression in CRC tissues was significantly lower than that in adjacent mucosal tissues (Fig. 2D, E). The diagnostic accuracy of CPT2 protein expression for CRC was 89% (Fig. 2F). CRC patients with low CPT2 expression had shorter survival times than those with high CPT2 expression did (Fig. 2G, I), and this survival difference became more pronounced over the follow-up period (Fig. 2H, J). These findings prompted us to further investigate the role of CPT2 in CRC pathobiology.

CPT2 inhibition promotes CRC proliferation

CPT2 is a key protein in transporting long-chain fatty acids (LCFAs) into the mitochondria for β-oxidation15. Alterations in its function directly affect various biological processes, such as β-oxidation, phospholipid synthesis19, and humoral immunity20. Based on previous experimental results, we sought to analyze the effect of CPT2 on the biological behavior of tumors. Baseline CPT2 expression levels across six colorectal cancer (CRC) cell lines were determined using qRT-PCR and Western blotting. Among these, HCT116 and HT29 exhibited relatively high CPT2 mRNA expression (Fig. 3A, Supplementary data 8), consistent with their protein expression patterns (Fig. 3B, C, Supplementary data 9). Following CPT2 knockdown, proliferation assays demonstrated that over time, the proliferative advantage of CPT2-knockdown CRC cells expanded compared to control cells (Fig. 3D, Supplementary data 10). CPT2 had the most pronounced effect on HCT116 and HT29, which were selected for further study. After CPT2 was downregulated, the results of the EdU assay revealed an increase in the number of EdU-positive CRC cells, indicating that the proportion of proliferating CRC cells significantly increased (Fig. 3E, F, Supplementary data 11), which enhanced their colony-forming ability (Fig. 3G, H, Supplementary data 12). Additionally, in vivo studies using xenograft models in immunodeficient nude mice (Fig. 3I) revealed that CPT2-knockdown CRC cells also exhibited significantly faster growth (Fig. 3J-L, Supplementary data 13, 14). Given the role of CPT2 in promoting CRC proliferation, we aimed to further investigate its underlying mechanisms.

Fig. 3: Low CPT2 expression promotes colorectal cancer cell proliferation.
figure 3

A Baseline CPT2 mRNA levels in CRC and normal epithelial cell lines measured by qRT-PCR. B Baseline CPT2 protein levels in CRC and normal epithelial cell lines measured by Western Blot. C Statistical comparison of CPT2 expression in Western Blot assay. D CCK8 proliferation assays showing the accumulation of proliferative differences over time after CPT2 knockdown. E EdU assay showing changes in the proliferation capacity of colorectal cancer cells following CPT2 knockdown. EdU+ cells, indicating proliferation, are marked in green. F Statistical comparison of EdU+ colorectal cancer cells. G Colony formation assay to assess single-cell colony formation ability before and after CPT2 knockdown in colorectal cancer cells. Each purple dot represents a colony. H Quantification of colony numbers. I Schematic of subcutaneous xenograft model using cells with different CPT2 expression levels. J Promotion of cell proliferation in nude mice after CPT2 knockdown. K Tumor volume growth curve over time in nude mice. L Tumor weights after resection. Data are shown as mean ± SD. Statistical significance as described above.

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CPT2 downregulation enhances glycerophospholipid biosynthesis

To explore the mechanisms underlying CPT2-mediated metabolic changes, we examined lipid droplet (LD) accumulation following CPT2 knockdown. Unexpectedly, the increase in intracellular lipid droplets was not as pronounced as expected (Fig. 4A, B, Supplementary data 15). CPT2 is critical for LCFA metabolism, and its dysfunction results in a reduction in LCFAs entering the mitochondria for β-oxidation15. As LCFAs are basic precursors for intracellular biochemical reactions, their reduced oxidation leads to an accumulation of substrates for other metabolic pathways. Thus, we employed high-throughput mass spectrometry to analyze intracellular metabolite changes following CPT2 knockdown. Metabolomic profiling revealed that, among the 3367 metabolites, glycerophospholipids (GPs) were markedly accumulated in CPT2-knockdown cells (Fig. 4C). GPs are key components of cellular membranes and include phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylinositol (PI), and phosphatidylglycerol (PG)21. ELISAs revealed that PC, PE, and PG levels increased, whereas PI levels decreased in CPT2-knockdown cells (Fig. 4D, E, Supplementary data 16).

Fig. 4: CPT2 knockdown induces glycerophospholipid accumulation.
figure 4

A Lipid droplet levels in cells before and after CPT2 knockdown. B Quantitative comparison of lipid droplet content. C Metabolomic profiling showing altered metabolites in HCT116 cells after CPT2 silencing. D, E ELISA analysis of five glycerophospholipid (GP) subtypes (PC, PE, PS, PI, PG) in HCT116 and HT29 cells post–CPT2 knockdown. Data represent mean ± SD of at least three independent experiments. Significance indicators as in prior figures.

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LCFAs are crucial precursors for GP synthesis. Along with glycerol-3-phosphate (G3P), LCFAs form diacylglycerol (DAG) under the catalysis of enzymes such as glycerol-3-phosphate acyltransferases (GPATs), which are then modified to form various GP subtypes22. We hypothesized that the accumulation of GPs was driven by enhanced biosynthesis from excess cytoplasmic LCFAs. To verify this, we silenced members of the GPAT enzyme family to determine whether they could reverse CPT2-knockdown-induced GP accumulation. The GPAT family consists of four members: GPAT1, GPAT2, GPAT3, and GPAT4. GPAT1 and GPAT2 are expressed in the mitochondria, whereas GPAT3 and GPAT4 are expressed in the endoplasmic reticulum membrane22. Immunohistochemical analysis of clinical CRC samples revealed that GPAT3 and GPAT4 were predominantly expressed, whereas GPAT1 and GPAT2 were expressed at low levels (Fig. 5A). Upon silencing individual GPAT isoforms, ELISA analyses demonstrated that only GPAT4 knockdown significantly reduced total GP levels and its subclasses (Fig. 5B, C, Supplementary data 17), while knockdown of other isoforms had no comparable effect (Figure S2A–F, Supplementary data 18–20), suggesting that GPAT4 plays a dominant role in GP biosynthesis in CRC. In rescue experiments (Supplementary data 21), GPAT4 knockdown successfully reversed CPT2-knockdown-induced GP accumulation (Fig. 5D, E). The levels of the major GP subtype—PC, PE, and PG—also showed a consistent decline (Fig. 5F–O), confirming that the GP accumulation resulting from CPT2 downregulation is driven by increased biosynthesis from LCFAs.

Fig. 5: GPAT4-mediated glycerophospholipid biosynthesis drives lipid accumulation in CRC.
figure 5

A Expression levels of four GPAT isoforms in clinical CRC tissues (red arrows indicate positive IHC staining). B, C Silencing GPAT4 significantly reduced total glycerophospholipid levels and subtypes. D, E ELISA showing that GPAT4 knockdown reversed CPT2 knockdown–induced GP accumulation. F–O ELISA results showing reversal of specific GP subtype accumulation (PC, PE, PG) by GPAT4 silencing after CPT2 knockdown. These data confirm that increased GP biosynthesis is responsible for lipid accumulation under CPT2 deficiency. Statistical significance as described above.

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Glycerophospholipid accumulation promote autophagy

GPs are fundamental components of the intracellular membrane system, but the mechanisms by which their abundance promotes CRC progression remain unclear. Thus, we conducted transcriptomic sequencing of CPT2-knockdown HCT116 CRC cells and gene ontology analysis of the differentially expressed genes. Consistent with the metabolomic results, transcriptomic analysis revealed significant disruptions in membrane transportation-related biological processes, such as vesicles and autophagy, following CPT2 knockdown (Fig. 6A). Based on the transcriptomic and metabolomic findings, we assessed autophagy levels by analyzing the protein ratio of LC3-II to LC3-I, a widely used marker of autophagosome formation. Western blotting showed that CPT2 knockdown significantly increased the LC3-II/LC3-I ratio, indicating enhanced autophagy (Fig. 6B–D, Supplementary data 22). To determine whether this increase reflected a true elevation in autophagic flux, we treated the cells with Bafilomycin A1 (Baf), which inhibits autophagosome–lysosome fusion and leads to autophagosome accumulation. After treatment with 0.1 μM Baf, CPT2-deficient CRC cells showed a marked increase in autophagosome accumulation compared to controls (Fig. 6E–G, Supplementary data 23), confirming elevated autophagic flux upon CPT2 downregulation.

Fig. 6: Glycerophospholipid accumulation enhances selective autophagy in CRC cells.
figure 6

A Gene ontology enrichment of RNA-seq data showing biological processes affected by CPT2 knockdown. B Western blot analysis of autophagy markers after CPT2 knockdown and GPAT4 rescue. The LC3-II to LC3-I ratio serves as a marker of autophagy levels. C, D Statistical comparison of autophagy-related protein expression after CPT2 knockdown and GPAT4 rescue. E Bafilomycin A1 (Baf) treatment used to assess autophagic flux post–CPT2 knockdown. F, G Statistical comparison of LC3-II accumulation with and without Baf treatment. H, I CCK-8 assays showing PC and PE effects on CRC cell proliferation. J Western blot analysis showing PC-induced elevation of autophagic flux. K, L Quantification of LC3-I and LC3-II band intensities after PC treatment. M Western blot analysis showing PE-induced elevation of autophagic flux. N, O Quantification of LC3-I and LC3-II band intensities after PE treatment. Statistical significance as described above.

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PC exchange between vesicles and autophagosomes is critical for the elongation and maturation of autophagosomes23, a core process in selective autophagy. The autophagy protein LC3-I binds to PE, converting it into its active form, LC3-II, which regulates autophagosome elongation and fusion24. GPAT4 knockdown partially reversed the CPT2 knockdown–induced increase in LC3-II (Fig. 6B–D), suggesting that the enhanced autophagy is mediated by GP accumulation. To further validate this, we supplemented the culture medium with exogenous PC (1 μM) or PE (1 μM) and evaluated their effects on autophagy and proliferation. PC significantly promoted CRC cell proliferation, while the effect of PE was relatively limited (Fig. 6H, I). Notably, both PC and PE supplementation significantly increased autophagy levels, as indicated by LC3-II expression (Fig. 6J–O, Supplementary data 24, 25). These results support the conclusion that CPT2 downregulation regulates autophagy in CRC cells by promoting GP accumulation, particularly PC.

CRC cells employ lipophagy to regulate lipid homeostasis

Following CPT2 knockdown, immunofluorescence (IF) staining revealed a significant increase in LC3-positive autophagosomes (green fluorescence), indicating elevated autophagic flux (Fig. 7A, B, Supplementary data 26). Subsequent transmission electron microscopy (TEM) analysis of CPT2-knockdown HCT116 cells showed an increased number of membrane-bound structures (Fig. 7C). Upon closer examination, we observed structures resembling lipid droplets either enclosed within or in close proximity to autophagosomes (Fig. 7C, red arrows). To further clarify this observation, we used LipidTOX staining (red fluorescence) to label lipid droplets and LC3 immunofluorescence (green fluorescence) to visualize autophagosomes. Co-localization analysis revealed that, following CPT2 knockdown, a portion of autophagosomes co-localized with lipid droplets within the cytoplasm (Fig. 7D, red arrows). The significant increase in co-localization events (Fig. 7E, Supplementary data 27) suggests that CPT2 downregulation may activate lipophagy, a form of selective autophagy that targets lipids, to maintain intracellular lipid homeostasis.

Fig. 7: CRC cells activate lipophagy to regulate intracellular lipid homeostasis.
figure 7

A Immunofluorescence analysis of autophagosome number and distribution after CPT2 knockdown, with LC3 localized to autophagosome membranes. B Statistical comparison of autophagosome numbers via immunofluorescence analysis. C Transmission electron microscopy showing increased membrane-bound structures in CPT2-deficient cells (red arrows indicate lipid droplet–like structures). D Co-localization of lipid droplets (LipidTOX, red) and autophagosomes (LC3, green); merged images show yellow signal. E Statistical results of Pearson’s R values for co-localization events across 50 cells. F IF staining showing that ATG5 knockdown inhibits lipid droplet clearance. G Quantification of intracellular lipid levels under different genetic conditions (CPT2 or ATG5 silencing). The experiments were performed using the HCT116 cell line. Statistical significance as described above.

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Lipophagy is a subtype of selective autophagy in which cells specifically target and degrade lipid droplets for recycling. To validate the involvement of selective autophagy in this process, we knocked down ATG5, a key gene required for autophagosome formation, and monitored the effect on lipid accumulation. ATG5 silencing inhibited selective autophagy and significantly increased intracellular lipid content after CPT2 silencing (Fig. 7F, G, Supplementary data 28). These results suggest that colorectal cancer cells engage lipophagy in response to CPT2 downregulation as a compensatory mechanism to clear excess lipids and restore metabolic balance.

CPT2 downregulation promotes CRC proliferation by regulating autophagy

Our previous experiments demonstrated that CPT2 downregulation enhances CRC cell proliferation. Since selective autophagy enhances a cell’s capacity for self-renewal and promotes proliferation, we hypothesized that the tumor-promoting effect of CPT2 knockdown is mediated by enhanced autophagy. To investigate this, we used a subcutaneous xenograft model in nude mice. CPT2-knockdown (CPT2-KD), ATG5-knockdown (ATG5-KD), and CPT2/ATG5 double-knockdown (Combo) HCT116 cells were implanted into mice. Compared with that in the CPT2-knockdown group, tumor growth was significantly slower in the doube-knockdown group (Fig. 8A–C, Supplementary data 29, 30), indicating that inhibition of autophagy partially reverses the growth advantage conferred by CPT2 deficiency. This conclusion was further supported by in vitro experiments. ATG5 knockdown partially reversed the increase in the number of EdU-positive CRC cells caused by CPT2 knockdown (Fig. 8D–G, Supplementary data 31) and diminished the enhanced colony-forming ability of these cells (Fig. 8H–J, Supplementary data 32). Finally, in CCK8 proliferation assays, the proliferative advantage induced by CPT2 knockdown was significantly reduced with ATG5 knockdown (Fig. 8K, L, Supplementary data 33). Taken together, these results indicate that the mechanism by which CPT2 downregulation promotes CRC proliferation is mediated through enhanced selective autophagy driven by the accumulation of glycerophospholipids.

Fig. 8: Downregulation of CPT2 promotes colorectal cancer cell proliferation via autophagy.
figure 8

A subcutaneous xenograft model in nude mice revealed the reversal effect of the autophagy inhibition (ATG5-KD) on CPT2 knockdown-induced proliferation. B Tumor volume growth curve over time in nude mice. C Final tumor weights at resection. D, E EdU assay showing that ATG5 knockdown partially reverses CPT2-induced cell proliferation (green = EdU + ). F, G Statistical comparison of EdU+ colorectal cancer cells. H Colony formation assay showing reduced clonogenicity with ATG5 silencing in CPT2-deficient cells. Each purple dot represents a colony. I, J Quantification of colony formation. K, L CCK-8 assay showing reduced proliferation in CPT2-knockdown cells after ATG5 inhibition. “Combo” refers to simultaneous knockdown of both CPT2 and ATG5. Data shown as mean ± SD. Statistical notation as before.

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Discussion

Metabolic reprogramming enables cancer cells to adapt to the changing microenvironment during tumor development and is a key hallmark of cancer25. Previous studies have indicated that lipid metabolism abnormalities are present in colorectal cancer26. Our study corroborated these findings through Oil Red O staining of CRC tissues and cell lines, revealing significant lipid accumulation. Lipids play diverse and critical roles, including serving as fundamental components of the cellular structure and contributing to energy metabolism, signal transduction, protective functions, and biosynthesis27,28. When these functions were analyzed, we highlighted that CPT2, a key gene in fatty acid metabolism, regulates CRC development. CPT2 facilitates the transport of long-chain fatty acids (LCFAs) into the mitochondrial matrix for β-oxidation, so its abnormal expression directly disrupts LCFA metabolism15. Since LCFAs are essential raw materials for metabolic processes, fluctuations in their levels can lead to substantial changes in metabolite distribution and content within cells.

Intuitively, reduced CPT2 expression might decrease the energy derived from LCFAs, potentially inhibiting tumor cell growth. However, our results revealed the opposite effect. These findings suggest that LCFA accumulation in the cytoplasm likely activates previously unidentified mechanisms that promote tumor progression. Metabolomic analysis of CRC cells with CPT2 downregulation revealed a significant increase in the levels of phosphatidylcholine (PC) and phosphatidylethanolamine (PE), both of which are crucial components of cellular membranes. These findings are consistent with our transcriptomic analysis, which indicated that the accumulation of membrane materials enhances membrane-associated biological processes. Specifically, an increase in the amount of membrane material directly increases the level of selective autophagy in CRC cells, thereby improving their ability to adapt to adverse internal and external conditions and accelerating tumor progression.

Although LCFAs are important precursors for glycerophospholipid (GP) synthesis29, other potential mechanisms for GP accumulation, such as enhanced GP synthesis via pathways independent of LCFAs or reduced degradation, could also be involved and warrant further investigation30,31. Therefore, we designed rescue experiments to exclude other pathways associated with GP accumulation. The GPAT family, which includes GPAT1, GPAT2, GPAT3, and GPAT4, is critical for the synthesis of GPs from LCFAs22. Inhibiting GPAT can block or significantly reduce GP synthesis from LCFAs while minimally affecting other GP synthesis and degradation pathways. Among the GPAT isoforms, GPAT1 and GPAT2 are located in the mitochondria and are expressed predominantly in liver tissue32,33. This characteristic makes them have a minimal impact on LCFA metabolism when LCFAs are impaired in entering the mitochondria. Our immunohistochemistry results also revealed that GPAT1 and GPAT2 are minimally expressed in CRC tissues, whereas GPAT3 and GPAT4, which are abundant, likely play a more dominant role in CRC. We confirmed that GPAT4 is the primary enzyme responsible for GP synthesis in CRC. Previous studies have also suggested that GPAT4 promotes the formation of intracellular lipid droplets34, which is consistent with our findings. Through rescue experiments, we demonstrated that the accumulation of GPs due to CPT2 knockdown is driven by enhanced biosynthesis from LCFAs rather than alternative mechanisms.

LC3-II, which binds to PE during autophagosome formation35, interacts with membrane lipids to induce phagophore elongation and closure36. The extension of the autophagosome membrane relies on fusion with surrounding vesicles37,38 and endoplasmic reticulum structures39,40. Therefore, the abundance of surrounding membrane structures is a crucial factor for autophagosome maturation. Electron microscopy revealed significant accumulation of vesicles around autophagosomes in CPT2-knockdown cells, which aligns with our transcriptomic analysis results. GPAT4 knockdown partially reversed this phenotype, validating that GP accumulation promotes selective autophagy through increased membrane biogenesis. Importantly, we observed that lipid droplets co-localize with LC3-positive autophagosomes, suggesting the activation of lipophagy—a subtype of selective autophagy that targets lipid droplets. When we silenced ATG5, a core autophagy gene, lipid droplet clearance was impaired, and intracellular lipid accumulation increased. These findings reveal a compensatory mechanism in which CRC cells activate lipophagy to maintain lipid homeostasis following CPT2 inhibition.

Autophagy can provide CRC cells with a survival advantage by impacting not only cell proliferation and apoptosis but also responses to surrounding tissue hypoxia, immune infiltration, chemotherapy41, and even the gut microbiota42. The role of CPT2 in vivo may extend beyond what is currently understood, and further research is needed to explore these aspects in detail.

In conclusion, our study demonstrates that CPT2 downregulation in colorectal cancer leads to the cytoplasmic accumulation of glycerophospholipids, primarily via GPAT4-mediated biosynthesis from long-chain fatty acids. This lipid remodeling enhances selective autophagy, particularly lipophagy, thereby promoting CRC cell survival and proliferation. These findings uncover a novel mechanism by which metabolic reprogramming drives tumor progression and may offer new therapeutic strategies in colorectal cancer.

Methods

Clinical sample collection

Clinical specimens were obtained from colorectal cancer (CRC) patients who underwent surgical resection between January 2018 and September 2020. A total of 71 matched CRC tumor tissues and adjacent normal mucosal tissues were collected, and all samples were histologically confirmed by experienced pathologists. None of the enrolled patients received preoperative chemotherapy or radiotherapy. Patients with serious comorbidities that could affect prognosis were excluded.

Database analysis

For public database analysis, immunohistochemistry data were retrieved from the Human Protein Atlas (HPA, https://www.proteinatlas.org/) and mRNA expression data were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). We performed gene set enrichment analysis (GSEA) on lipid metabolism-related gene sets to compare tumor tissues with normal mucosa. For genes involved in fatty acid metabolism, we conducted both univariate Cox regression analysis and differential expression analysis to identify core regulators. CPT2 was identified as a central gene involved in lipid metabolism dysregulation. Kaplan–Meier survival curves and receiver operating characteristic (ROC) curves were used to evaluate its clinical significance.

Cell lines

Human colorectal cancer cell lines HCT116 and HT29 were purchased from Wuhan Procell Biotechnology Co., LTD. Authentication was validated by STR analysis. All cell lines tested negative for mycoplasma contamination.

siRNA transfection

CRC cells were seeded in 6-well plates and cultured at 37 °C in 5% CO₂ until approximately 70% confluence was reached. For each well, transfection complexes were prepared as follows: Solution A: 7.5 μL Lipofectamine 3000 diluted in 125 μL Opti-MEM®. Solution B: 75 pmol siRNA diluted in 125 μL Opti-MEM®. Solutions A and B were mixed, incubated for 15 minutes at room temperature, and then added dropwise to each well. After 6 hours of incubation, the medium was replaced with 2 mL of fresh complete medium containing serum and antibiotics.

Lentiviral transduction

HCT116 cells were seeded into 6-well plates at a density of 1 × 10⁵ cells/well 24 hours prior to infection. The multiplicity of infection (MOI) was set to 20. Lentiviral particles were diluted in culture medium and added to each well together with Polybrene (final concentration: 5 μg/mL). After 16 hours, the medium was replaced with fresh complete medium. At 72 hours post-infection, infection efficiency was assessed by fluorescence microscopy and Western blotting (Figure S3A–F, Supplementary data 34–37). Stable cell lines were selected using 4 μg/mL puromycin for 48 hours. Surviving cells were considered successfully transduced and were used for downstream xenograft experiments.

Western blot

For every million cells, 100 μL of protein lysis buffer was added. The samples were heated at 95 °C for 10 minutes before centrifugation at 14000 × g for 15 minutes, and the resulting supernatant was used for protein analysis. Electrophoresis was performed at 150 V for 40 minutes. The PVDF membranes were activated in methanol for 15 seconds, equilibrated in transfer buffer for 15 minutes, and then assembled for transfer via filter papers and SDS‒PAGE gels. Membrane transfer was carried out at 200 mA for 60 minutes. After blocking with 5% non-fat milk for 1 hour at room temperature, the membranes were incubated overnight at 4 °C with primary antibodies diluted as follows: anti-GAPDH (1:5000), anti-CPT2 (1:5000), anti-LC3 (1:5000), and anti-ATG5 (1:3000). The membranes were washed three times with TBST (10 minutes each) and incubated with secondary antibodies for 1 hour at room temperature. After washing, the membranes were visualized using chemiluminescence detection in a dark room.

Subcutaneous tumor model in mice

Purchased from Vital River Laboratory Animal Technology [License No.: SCXK(Jing)2021-0006], housed under SPF conditions, no genetic modification. All mice underwent 2 week of acclimatization. Inclusion criteria: Six-week-old specific pathogen–free (SPF) NU/NU female nude mice, weight 18–20 g. Exclusion criteria: No tumor growth 7 days post-inoculation or tumor volume >1500 mm³. Sample size (n = 6) was determined by power analysis. Only the experimental operator was aware of the group assignments. Group allocation was blinded to tumor volume measurers and data analysts. Cells in the CPT2-KD group and ATG5-KD group were stably transduced via lentiviral vectors (VP013-U6-MCS-CMV-ZsGreen-PGK-PURO) targeting CPT2 and ATG5, respectively, using the shRNA sequences shCPT2: CTCCGTTGTTCTGAACTTTAA and shATG5: CCTTTCATTCAGAAGCTGTTT. The transduction efficiency was provided in Supplementary Fig. S3. The Combo group received dual knockdown of both genes. HCT116 cells (10⁷ cells/mL) were suspended in PBS, and 100 μL of cell suspension was injected subcutaneously into the right axilla of each mouse using a 1 mL syringe. Tumor growth was monitored every 3 days. After three weeks, mice were euthanized, and tumors were excised. Tumor volumes and weights were recorded for analysis.

Colony formation assay

After transfection, cells in the logarithmic growth phase were digested and resuspended. Cells were counted and seeded into 6-well plates at a density of 1000 cells per well. The plates were placed in an incubator for further cultivation, with appropriate treatments applied. After two weeks, the culture medium was discarded, and the cells were washed with PBS. Cells were then fixed with 1 mL of 4% paraformaldehyde for 15 minutes. After washing, 1 mL of 0.5% crystal violet was added for 30 minutes. The cells were then washed repeatedly with PBS until the dye was clear, air-dried, and photographed.

CCK-8 proliferation assay

After transfection, cells in the logarithmic growth phase were digested and resuspended. The target cells were seeded in 96-well plates at 3000 cells per well and treated according to the experimental groups. Due to solubility constraints, both PC and PE were added at a final concentration of 1 μM. At 0, 24, 48, 72, and 96 hours, 10 μL of CCK-8 solution was added to each well and mixed thoroughly. The 96-well plates were incubated for 2 hours, and the absorbance at 450 nm was measured via a microplate reader. The absorbance values are proportional to cell viability and were used to calculate cell proliferation.

EdU proliferation assay

After transfection, cells in the logarithmic growth phase were seeded in 96-well plates at 3000 cells per well and treated according to the experimental setup. After 48 hours, the EdU medium was prepared by diluting the EdU in complete medium at a 1:1000 ratio (final concentration: 50 μM). The original medium was discarded, and 100 μL of EdU medium was added to each well for a 2-hour incubation. The cells were then fixed with 4% paraformaldehyde for 30 minutes, neutralized with 2 mg/mL glycine, and permeabilized with 0.5% Triton X-100 for 10 minutes. After washing with PBS, 100 μL of Apollo® staining solution was added to each well and incubated in the dark at room temperature for 30 minutes. The cells were washed with 0.5% Triton X-100 and counterstained with Hoechst. Fluorescence images were captured using a fluorescence microscope.

Quantitative real-time PCR (qRT‒PCR)

The cells were digested, collected, washed with PBS three times, and lysed with 1 mL of TRIzol on ice for 5 minutes. Then, 200 μL of chloroform was added, and the mixture was vortexed for 15 seconds. The samples were subsequently centrifuged at 12,000 × g for 15 minutes at 4 °C, after which the upper aqueous phase (approximately 500 μL) was transferred to a new tube. Isopropanol (equal volume) was added, mixed by inversion, and allowed to sit for 10 minutes to precipitate the RNA. The samples were subsequently centrifuged at 12,000 × g for 10 minutes at 4 °C. The supernatant was discarded, and the pellet was washed with 1 mL of 75% ethanol. After centrifugation at 12,000 × g for 5 minutes at 4 °C, the pellet was air-dried and resuspended in DEPC water. The RNA concentration and purity were determined via a Nanodrop2000 spectrophotometer. Using a reverse transcription kit, 20 μL reactions were prepared. Quantitative PCR was performed in 10 μL reaction volumes, and the results were normalized to reference gene GAPDH. Primer sequences are shown in Supplementary Data 38.

ELISA

Five million cells from each of the CPT2-knockdown and control groups were collected. The cells were mixed with 1 mL of 80% methanol and subjected to repeated freeze‒thaw cycles in liquid nitrogen. The samples were then centrifuged at 12,000 r/min for 10 minutes at 4 °C, and the supernatant was collected for analysis. In the standard wells, 50 μL of standard solution at various concentrations was added to each precoated well. In the sample wells, 10 μL of test sample and 40 μL of sample dilution were added. Horseradish peroxidase (HRP)-labeled detection antibodies (100 μL) were added to both the standard and sample wells, and the wells were sealed and incubated at 37 °C for 60 minutes. After the liquid was discarded, the wells were washed five times with washing buffer. Substrate solutions A and B (50 μL each) were added to each well and incubated in the dark at 37 °C for 15 minutes. The reaction was terminated with 50 μL of stop solution. The optical density (OD) was measured at 450 nm within 15 min, and the concentration of each sample was calculated on the basis of a standard curve.

Immunofluorescence (IF) staining

Cells in the logarithmic growth phase were seeded into 96-well plates at a density of 3000 cells per well and subjected to the different experimental treatments. After 48 hours of incubation, the culture was halted, and the cells were fixed with ice-cold methanol for 20 minutes. After being washed with PBS, the cells were permeabilized with 0.5% Triton X-100 at room temperature for 20 minutes. Following another PBS wash, the cells were blocked with 3% BSA for 1 hour at room temperature to prevent nonspecific antibody binding. After removing the blocking solution, the membranes were incubated overnight at 4 °C with primary antibodies diluted as follows: anti-LC3 (1:500). After being washed with PBS, the cells were incubated with the fluorescently labeled secondary antibody in the dark for 1 hour. Nuclear staining was performed via the use of Hoechst dye, and the cells were incubated in the dark for 10 minutes. After a final wash with PBS, fluorescence microscopy was used to observe and capture images of the stained cells.

Oil red O staining

Tissue Staining

Fresh tissue samples were sectioned into 5 μm thick frozen slices and air-dried at room temperature. The sections were immersed in 100% isopropanol for 5 minutes, followed by 60% isopropanol for 2 minutes, and then stained with Oil Red O for 10 minutes. After washing with PBS to remove excess stain, the nuclei were counterstained with hematoxylin for 1 minute. The sections were rinsed again with PBS and mounted with neutral resin. The stained tissue was observed under a microscope.

Cell staining

For cell staining, the culture medium was aspirated from the cells in a 6-well plate, followed by washing with PBS. The cells were then fixed with 4% paraformaldehyde for 30 minutes at room temperature. After the fixative was removed with PBS, 2 mL of Oil Red O staining solution was added to each well, and the cells were incubated at room temperature for 10 minutes. The staining solution was discarded, and the wells were thoroughly washed with PBS before observing lipid droplets under a microscope. Lipids stained with Oil Red O appear red.

LipidTOX staining

The culture medium was discarded, and cells were gently rinsed once with PBS. Cells were then fixed with 4% paraformaldehyde at room temperature for 15 minutes. After fixation, cells were washed three times with PBS (5 minutes each). Cells were permeabilized with 0.1% Triton X-100 for 10 minutes at room temperature, followed by blocking with 1% bovine serum albumin (BSA) for 30 minutes. LipidTOX dye was diluted at 1:200, added to the cells, and incubated in the dark for 30 minutes at room temperature. Cells were then washed three times with PBS (5 minutes each). Nuclei were counterstained with Hoechst for 5 minutes, followed by two PBS washes. Slides were mounted with anti-fade reagent, dried in the dark, and stored at 4 °C.

Immunohistochemistry (IHC)

Tissue samples were embedded in paraffin and cut into 3 μm sections. The slides were incubated at 60 °C for 4 hours before deparaffinization. Antigen retrieval was performed by heating the slides in citrate buffer for 10 minutes using a pressure cooker, followed by natural cooling to room temperature. After washing with PBS, the tissue samples were circled with a wax pen to prevent liquid diffusion. Hydrogen peroxide (3%) was applied for 10 minutes at 37 °C to block endogenous peroxidase activity. The tissue samples were blocked with goat serum for 30 minutes at 37 °C. Following blocking, the tissue sections were incubated with primary antibodies diluted in PBS overnight at 4 °C: CPT2 (1:500), GPAT1 (1:300), GPAT2 (1:200), GPAT3 (1:200), and GPAT4 (1:100). After equilibration to room temperature, the samples were washed three times with PBS. Secondary antibodies were applied, and the samples were incubated at 37 °C for 30 minutes. Followed by washing with PBS, DAB was used as a chromogen, and the reaction was stopped with tap water once brown staining appeared. Hematoxylin was used for counterstaining for 5 minutes, followed by differentiation with acid alcohol for 15 seconds and rinsing with running water for 30 minutes. The slides were dehydrated, cleared with xylene, and mounted with neutral resin. Observations and photographs were taken under a microscope. The intensity of staining was scored as follows: no staining (negative) was scored as 0, pale yellow (weakly positive) was scored as 1, brownish-yellow (positive) was scored as 2, and dark brown (strongly positive) was scored as 3. The percentage of positively stained cells was scored as follows: ≤25% = 1, 26–50% = 2, 51–75% = 3, and >75% = 4. The final score is calculated by multiplying the two individual scores.

Transcriptome sequencing

HCT116 cells in the logarithmic growth phase were treated with either negative control siRNA (NC) or siCPT2 for 48 hours. The cells were collected, washed three times with PBS, and lysed with 1 mL of TRIzol for 5 minutes on ice. The samples were preserved on dry ice and sent to Biomarker Technologies for high-throughput sequencing.

Metabolite analysis

HCT116 cells in the logarithmic growth phase were treated with NC or siCPT2 for 48 hours. The cells were collected, washed three times with PBS, and preserved on dry ice. The samples were sent to the PTM BIO company for high-throughput metabolomic analysis.

Transmission electron microscopy

HCT116 cells in the logarithmic growth phase were treated with NC or siCPT2 for 48 hours. The cells were digested with 0.25% trypsin and centrifuged at 1500 rpm for 5 minutes. After being washed with PBS, the cells were transferred to a 1.5 mL centrifuge tube and fixed with 1 mL of 2.5% glutaraldehyde at room temperature for 1 day. The cells were then washed with 0.1 M phosphate buffer three times, followed by fixation with 1% osmium tetroxide for 2 hours. The samples were dehydrated at 4 °C in a series of ethanol concentrations (50%, 70%, 90%, and 100%) and pure acetone, each for 20 minutes. After resin embedding and polymerization, 50–60 nm sections were prepared and stained with uranyl acetate and lead citrate. The sections were observed under a transmission electron microscope.

Note: Autophagosomes are characterized as vesicles (typically 0.5–1.5 μm in diameter) that contain undegraded organelles or cytoplasmic debris. After fusion with lysosomes, contents appear as dense and heterogeneous materials.

Statistics and reproducibility

Data were obtained from at least three independent experiments. All the data were analyzed via SPSS28.0 software. Independent t-tests or one-way ANOVA were used to compare differences between groups. All p-values are two-sided. A p-value of less than 0.05 was considered statistically significant. Nonsignificant results are labeled “ns”, and p-values are annotated as follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), and p < 0.0001 (****).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.