FFAR4 negatively regulates colorectal cancer growth via blocking oxidative phosphorylation

Abstract

Background

Colorectal cancer (CRC) is closely associated with dietary factors and genetic alterations. As lipid-sensing receptors, FFARs may transduce dietary fatty acid cues into tumor-related signaling pathways. However, the role of FFAR4 in CRC remains unclear.

Methods

Integrative multi-omics approaches were used to identify FFAR4 as a key candidate within the FFAR family in CRC. FFAR4 expression and clinical relevance were evaluated using transcriptomic datasets, ROC analysis, and immunofluorescence in clinical CRC tissues. FFAR4 function was assessed by pharmacological activation with TUG891 in CRC cell lines and an MC38 syngeneic tumor model, with proliferation, cell-cycle, and metabolic assessments.

Results

FFAR4 expression was reduced in CRC tissues, and higher FFAR4 levels were associated with improved overall survival. Single-cell RNA sequencing analysis showed that FFAR4 was predominantly expressed in early differentiation states, and ROC analysis yielded an AUC > 0.8 for CRC diagnosis. TUG891-mediated FFAR4 activation inhibited CRC cell proliferation and induced cell-cycle arrest in vitro, and reduced tumor volume and tumor weight in vivo without affecting body weight. Metabolic profiling and extracellular flux analyses showed decreased mitochondrial respiration (OCR), accompanied by reduced NAD⁺ levels, a lower NAD⁺/NADH ratio, and decreased ATP/ADP, indicating altered NADH redox balance and cellular energy deficit. Consistently, aspartate was downregulated, whereas GOT1 and MDH1 were upregulated, suggesting alterations in the malate–aspartate shuttle. Meanwhile, glycolytic activity increased, as reflected by elevated ECAR and lactate levels; however, inhibition of glycolysis with 2-deoxyglucose (2-DG) did not reverse the TUG891-induced anti-proliferative effect or cell-cycle arrest, suggesting that enhanced glycolysis may represent a compensatory response.

Conclusions

FFAR4 suppresses CRC growth by modulating mitochondrial function and cellular metabolism, supporting its potential as a diagnostic biomarker and therapeutic strategy for CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-026-07942-4.

Introduction

Colorectal cancer (CRC) remains one of the most prevalent malignancies worldwide and is the fourth leading cause of cancer-related mortality, accounting for approximately 900,000 deaths annually [1]. Despite substantial advances in biotechnology and therapeutic strategies, including combination systemic therapies and immunotherapy, the overall prognosis of CRC patients remains unsatisfactory. This highlights an urgent need to identify reliable prognostic biomarkers and develop novel targeted therapies with improved efficacy and specificity.

Multiple factors contribute to the development of CRC, including genetic alterations influenced by lifestyle, environmental exposures, and dietary habits. The involvement of free fatty acids—key dietary constituents—has been widely investigated in relation to CRC [2–4]. Research indicates that free fatty acid receptors (FFARs), including FFAR1, FFAR2, FFAR3, and FFAR4, may be associated with these genetic and environmental factors in cancer [5–8]. Specifically, FFAR2 and FFAR3 are activated by short-chain fatty acids, while FFAR1 and FFAR4 are activated by medium- and long-chain fatty acids (LCFA), which are primary components of the human diet [9]. However, the expression patterns, functional roles, and clinical relevance of FFARs in CRC remain incompletely understood.

To systematically characterize the four FFAR genes in CRC, we performed comprehensive bioinformatic and statistical analyses using bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets. Through this integrative approach, FFAR4 emerged as a candidate of particular interest. FFAR4 is predominantly expressed in the intestinal tract and lungs (Fig. S1) and functions primarily as a receptor for LCFAs. It is also the most extensively studied member of the FFAR family across various cancer types [10–12]. In the context of CRC, epithelial FFAR4 has been implicated in maintaining mucosal barrier integrity and limiting tumor development [13]. Both FFAR1 and FFAR4 are implicated in regulating cellular functions during tumor progression in colon cancer DLD1 cells [14], and both FFAR1 and FFAR4 have been reported to regulate cellular functions during tumor progression in colon cancer models. Nevertheless, the therapeutic potential of FFAR4 and the metabolic mechanisms through which it may influence CRC progression remain largely undefined.

Therefore, the present study aimed to investigate the clinical relevance and therapeutic potential of FFAR4 in CRC and to elucidate FFAR4-associated metabolic alterations. Using a combination of multi-omics analyses and functional experiments, we demonstrate that FFAR4 activation is associated with pronounced changes in cellular energy metabolism, including altered redox balance, modulation of the malate–aspartate shuttle, and suppression of mitochondrial oxidative metabolism accompanied by compensatory glycolytic activation. Together, these findings suggest that FFAR4 may function as a tumor-suppressive factor in CRC and highlight FFAR4 activation as a potential metabolic intervention strategy for CRC treatment.

Materials and methods

Data collection and processing

Gene expression data and clinical data for TCGA-COAD patients were retrieved from the GDC data portal (https://portal.gdc.cancer.gov). Patients with overall survival (OS) of fewer than three months or missing survival information were excluded from subsequent analysis. The gene expression data, initially presented as fragments per kilobase of transcript per million mapped reads (FPKM), were converted to transcripts per million (TPM) to enable unbiased comparisons. Additionally, we acquired data from four microarray datasets (GSE100179 [15], GSE44076 [16], GSE41328 [17], GSE156355 [18], GSE178341 [19]) containing transcriptome profiling data of normal and tumor tissues via the Gene Expression Omnibus (GEO) platform (https://www.ncbi.nlm.nih.gov/geo/). We also obtained a scRNA-seq dataset, GSE178341, from the GEO platform, which includes transcriptome profiling data from 62 patients. All microarray and RNA-seq data included in this study were normalized and log2 transformed as previously described [20].

Single-cell RNA sequencing data analysis

The scRNA-seq dataset GSE178341, comprising samples from 62 patients (62 tumor tissues and 36 adjacent normal tissues), was retrieved from the Gene Expression Omnibus (GEO). Cells were excluded if they met any of the following criteria: (1) fewer than 200 genes; (2) fewer than 500 unique molecular identifiers (UMIs); (3) more than 50% of UMIs mapping to the mitochondrial genome. After excluding low-quality cells, a total of 370,114 cells were considered for subsequent analysis. The scRNA-seq profiles were normalized using the NormalizeData function to scale the data, and variable features were selected using the FindVariableFeatures function to identify the most informative genes for downstream analysis. Cell integration was performed using the fastMNN algorithm. A k-nearest neighbors (KNN) graph was constructed using the FindNeighbors function, and cell clustering was conducted with the FindClusters function in Seurat. Uniform Manifold Approximation and Projection (UMAP) was employed to visualize cells in a two-dimensional space. Seven major cell types were identified based on previously published markers [19]. A similar procedure was applied for epithelial cell subclustering analysis to further resolve the heterogeneity within the epithelial cell population.

To explore the evolutionary trajectories of different cell types within epithelial cells, we utilized the R package Monocle2 [21], which is designed for inferring pseudotime and reconstructing cell lineage relationships. Default parameters were used for this analysis. This method arranged cells into a developmental trajectory segmented into distinct branches, simulating cell evolution or differentiation. These branches represent different evolutionary paths and stages of cellular development, providing insights into the dynamic processes within epithelial cells.

Human tissue samples and immunofluorescence staining

Tissue microarrays (TMAs) constructed from surgically resected colorectal cancer (CRC) tumor tissues, along with corresponding clinical information (array ID: HColA030PG08-1), were obtained from Shanghai Outdo Biotech Co., Ltd. (Shanghai, China). The TMA consisted of 15 cores, each with a diameter of 2.0 mm, derived from formalin-fixed, paraffin-embedded tumor specimens. Patients included in the analysis ranged in age from 40 to 79 years, and samples with poor image quality were excluded from subsequent analyses.

TMA sections were deparaffinized, rehydrated, and subjected to antigen retrieval, followed by incubation with a primary antibody against FFAR4 and a fluorescence-conjugated secondary antibody. Nuclei were counterstained with DAPI. Slides were scanned using a Pannoramic 250 Flash III scanner (3DHISTECH Ltd.), and images were acquired with Pannoramic Viewer software.

Cell culture and treatments

Human colorectal cancer cell lines HCT116 (CCL-247EMT; ATCC) and HT29 (HTB-38; ATCC) were obtained from the Shanghai Institute of Cell Biology, Chinese Academy of Sciences, Shanghai, China. HCT116 and HT29 cells were maintained in RPMI 1640 supplemented with 5% (v/v) fetal bovine serum (FBS, BC-SE-FBS01; Bio-Channel). The cells were incubated at 37 °C in a 5% CO₂ atmosphere. Cells were treated with varying concentrations of TUG891 prepared in DMSO, whereas control groups were exposed to an equal volume of DMSO alone.

Cell proliferation assay

To assess cell proliferation, a CCK-8 assay (100–106; Goonie) was performed. Briefly, 3000 cells were seeded in each well of 96-well plates and subsequently exposed to TUG891, followed by an incubation period of 72 h. Then, 10 µL of CCK-8 solution was added to each well and incubated for an additional hour. The absorbance was measured at an optical density of 450 nm using a microplate reader (TECAN, Switzerland).

Cell cycle assay

After treatment, trypsinized cells were fixed in cold 70% ethanol. Following ethanol removal by centrifugation, the cells were stained with PI/RNase Staining Buffer (BD Biosciences, USA) for 20 min in a light-restricted environment, following the manufacturer’s guidelines. DNA content was quantified using a Beckman Coulter flow cytometer.

Cell apoptosis assay

Following the indicated treatments, cell suspensions containing 1 × 10⁵ cells in 100 µL of assay buffer were incubated with 5 µL propidium iodide (PI) solution and 5 µL FITC-labeled Annexin V. The staining reaction proceeded for 15 min at 37 °C in the dark. Subsequently, 400 µL of Binding Buffer was added to each sample. The stained cells were then kept on ice until flow cytometric analysis was performed.

Western blot

Tissue samples were lysed in RIPA buffer supplemented with protease and phosphatase inhibitors. Protein concentrations were determined using the Pierce BCA Protein Assay Kit (Beyotime, China). Equal amounts of protein were separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto polyvinylidene fluoride (PVDF) membranes (IPVH07850; Millipore, Darmstadt, Germany). Membranes were blocked with 5% non-fat milk in Tris-buffered saline containing 1% Tween-20 (TBST) for 1 h at room temperature, followed by overnight incubation at 4 °C with the appropriate primary antibodies. The primary antibodies included Cyclin D1 (Proteintech, China), GOT1 (ABclonal, China), GOT2 (ABclonal, China), MDH1 (ABclonal, China), MDH2 (ABclonal, China), OXPHOS Cocktail (Proteintech, China), and β-actin (Cell Signaling Technology, USA), all diluted 1:1000 in TBST containing 3% bovine serum albumin (BSA). Membranes were then incubated with HRP-conjugated goat anti-mouse or anti-rabbit IgG secondary antibodies for 1.5 h at room temperature. Protein bands were detected using a Universal Hood III imaging system (Bio-Rad, California, USA), and images were presented with minimal processing.

Nontargeted metabolites analysis

Metabolites were extracted from cultured cells using a methanol/acetonitrile/deionized water protocol. Briefly, 5 × 10⁶ cells in 40 µL RIPA and resuspended in 300 µL of methanol containing internal standards (IS). The homogenate was vortexed for 30 s, then 1000 µL MTBE was added, followed by another 30 s of vertexing. Subsequently, 250 µL of H₂O was added, and the mixture was incubated on a shaker at 1,000 g for 10 min at room temperature. After incubation, 100 µL of the lower layer was taken and dried under a stream of nitrogen. The extracted metabolites were reconstituted in 30 µL methoxyamine hydrochloride (20 mg/mL) in pyridine. The mixture was incubated in a metal bath for 90 min at 37 °C, followed by the addition of 30 µL MSTFA and a further 60 min of incubation at 37 °C for GC-MS analysis. Raw data were processed using Compound Discoverer 3.2 software (Thermo Fisher Scientific, Waltham, MA), which facilitated metabolite identification, quantification, and relative abundance profiling. Principal component analysis (PCA) was applied to assess metabolic differences between control and FFAR4-activated groups. Identified metabolites were subjected to metabolic pathway enrichment analysis using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/) to explore the involved biochemical pathways.

Metabolic flux analysis

Cellular metabolic flux was evaluated using the Seahorse XF Glycolysis Stress Test Kit and the XF Cell Mito Stress Test Kit (Seahorse, 102353). The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were continuously monitored using the Seahorse XF96 Analyzer. Prior to measurement, cells were seeded at a density of 1.5 × 10⁴ cells per well in Seahorse XF96 microplates and incubated under standard conditions to allow attachment. Assays were performed according to the manufacturer’s instructions, and data were analyzed using Seahorse Wave software. OCR and ECAR values were normalized to cell number.

Intracellular NAD⁺/NADH ratio analysis

To assess intracellular redox status, NAD⁺ and NADH levels were measured using a commercial NAD⁺/NADH Assay Kit (WST-8; Beyotime Biotechnology). HCT116 and HT29 cells were plated in six-well plates (1.0 × 10⁶ cells/well) and allowed to adhere overnight. The following day, the culture medium was refreshed with solutions containing FIGs, FIGs-L, or FIGs-LC at a concentration of 0.3 mg/mL. After 4 h of exposure, cells were gently rinsed and lysed in 200 µL of extraction buffer. Absorbance values corresponding to NAD⁺ and NADH were subsequently recorded using a microplate reader, and the ratio was calculated according to the kit instructions.

Intracellular ATP/ADP ratio analysis

The intracellular ATP/ADP ratio was measured using an ATP/ADP Ratio Assay Kit (Beyotime, China) according to the manufacturer’s instructions. Briefly, cells were lysed and ATP-derived luminescence was measured, followed by enzymatic conversion of ADP to ATP and a second luminescence reading. The ATP/ADP ratio was calculated from the two measurements after background subtraction and normalization as indicated. Experiments were performed in triplicate.

In vivo tumor model and treatment

MC38 colorectal cancer cells were cultured under standard conditions and subcutaneously implanted into 8-week-old male C57BL/6 mice to establish a syngeneic tumor model. When tumors reached a measurable size, mice were randomly assigned to receive either PBS or TUG891 treatment. Mice in the control group were administered an equal volume of vehicle, while the treatment group received TUG891 according to the predefined dosing regimen.

Throughout the experiment, body weight and tumor volume were monitored at regular intervals. Tumor volume was calculated using a standard formula. At the experimental endpoint, mice were euthanized, tumors were excised, and tumor weights were measured for subsequent analyses.

Statistical analysis

Statistical analyses were conducted using R version 4.1. Data visualization was performed using the ggplot2 package in R, known for its versatility and capability to create elegant and complex plots. An unpaired t-test was used to evaluate the differential expression of FFARs between tumor and normal tissues, while a paired t-test was applied where appropriate. Kaplan–Meier analysis, conducted with data from the TCGA database, investigated the potential association between FFAR expression and overall survival (OS) in patients. Optimal cutoffs for FFAR expression were determined using the surv_cutpoint function in the R package survminer, facilitating the identification of thresholds that maximize the statistical significance of survival differences.

All experiments were performed at least in triplicate, and representative data are presented. Results are shown as mean ± standard deviation (SD) for each group. Data were analyzed using Prism 8.0 (Graphpad software, San Diego, USA). Statistical significance was determined using an unpaired two-tailed t-test for single-variable experiments. P < 0.05 was considered statistically significant. Exact P values are provided for statistically significant comparisons, while non-significant differences are indicated as “ns” (P ≥ 0.05).

Results

Landscape of FFAR family expression in colorectal cancer

To investigate the characteristics of the four FFAR genes in CRC, a comprehensive analysis was conducted using bulk RNA-seq from TCGA-COAD. The prognostic relevance of FFARs was initially investigated by analyzing their relationship with overall survival. The results revealed that FFAR1 and FFAR2 do not exhibit a significant association with OS, whereas FFAR3 and FFAR4 show a significant correlation with OS (Fig. 1A-D). Subsequently, the expression levels of FFARs in normal and tumor tissues were compared using TCGA-COAD data. FFAR3 did not show a significant difference in expression between tumor and normal tissues, whereas FFAR4 was significantly downregulated in CRC tissues (Fig. 1E-H). Based on these expression patterns and survival analyses, FFAR4 was selected for detailed mechanistic investigation. Further analysis of FFAR4 expression in normal and tumor tissues was performed using data from the GEO datasets. FFAR4 expression was significantly downregulated in tumor tissues, confirming the findings from the TCGA-COAD data (Fig. 1I-L). In addition, data from the Human Protein Atlas (HPA) revealed that FFAR4 is highly expressed in normal colon tissue compared with other human tissues (Fig. S1). In summary, FFAR4 emerges as the most prominently altered FFAR gene in CRC, exhibiting significant downregulation in tumor tissues and a strong positive correlation with improved overall survival outcomes.

Fig. 1.

Decreased FFAR4 expression in CRC. (A-D) Kaplan-Meier survival curves comparing patients in high and low expression levels of FFAR1–4 in colorectal cancer patients. The optimal cutoffs for FFARs were determined using the surv_cutpoint function in the R package survminer. (E-H) Expression levels of FFARs in tumor and normal tissue samples from TCGA-COAD. (I-L) Expression comparisons of FFAR4 between normal and paired tumor tissues from the same individuals across four datasets. Comparisons between E-H were performed using unpaired t-test, whereas comparisons between I–L were performed using a paired t-test

Correlation between FFAR4 expression and epithelial cell transformation in colorectal cancer

We analyzed the expression profiles of the four FFARs at the single-cell level using the dataset GSE178341, which includes transcriptome data from 370,114 cells derived from both tumor and adjacent normal tissues. UMAP dimensionality reduction identified 40 cell subsets from seven distinct cell lineages: epithelial cells (Epi), T and NK-like cells (TNKILC), stromal cells (Strom), B cells, myeloid cells, plasma cells, and mast cells (Fig. 2A-C). Visualization of FFARs expression revealed that FFAR1-3 are barely expressed across the cell subsets, whereas FFAR4 is predominantly expressed in epithelial cells (Fig. 2D-G).

Fig. 2.

FFAR4 association with epithelial cell malignant progression in CRC. (A) UMAP dimensionality reduction categorized cells into 30 distinct subclusters. (B) Dot plot depicting the average and percent expression of key marker genes across different cell subclusters. (C) UMAP plot visualizing the annotation of key cell subtypes. Epithelial cells (Epi), TNKILC (T cells, NK cells, and innate lymphoid cells), Stromal cells (Stroma), Plasma cells (Plasma), Myeloid cells (Myeloid), and Mast cells (Mast). (D-G) Feature plots showing the expression patterns of FFAR1–4 across cell types. (H) Epithelial cells clustered into 20 distinct subclusters using UMAP dimensionality reduction. (I) Bubble plot depicting the average and percent expression of biomarkers in different cell subtype of epithelial cells. (J) UMAP plot highlighting specific epithelial cell subtypes: adenoma-specific cells (ASC), serrated-specific cells (SSC), absorptive cells (ABS), crypt top colonocytes (CT), goblet cells (GOB), stem cells (STM), tuft cells (TUF). (K) UMAP plot depicting the distribution of normal and tumor epithelial cells. (L) UMAP plot showing FFAR4 expression across different epithelial subtypes. (M) Violin plot illustrating FFAR4 expression across different epithelial subtypes. (N) Pseudotime trajectory analysis of epithelial cells colored by pseudotime, suggesting a progression from normal to malignant states. (O) Dynamic expression profile of FFAR4 along the epithelial cell pseudotime trajectory

Next, we clustered the epithelial cells into seven subclusters, which included two cancer cell types (ASC and SSC) and five normal cell types (CT, GOB, STM, TAC, and TUF) (Fig. 2H-K) [22]. The expression of FFAR4 in each subcluster was visualized (Fig. 2L). Violin plots indicated that FFAR4 is minimally expressed in TUF and STM, but is specifically expressed in GOB and SSC cells (Fig. 2M). To further investigate the developmental trajectories of epithelial cells, we conducted pseudotime trajectory analysis, with arrows indicating the proposed direction of differentiation. CT (normal epithelial cells) served as the origin of the trajectory, which branched into two major routes—one toward GOB (top-left), the other toward STM (bottom-left) (Fig. 2N). FFAR4 expression patterns across pseudotime and subclusters were also visualized, showing that FFAR4 is predominantly expressed at the early stages of epithelial differentiation. This suggests that FFAR4 may play a significant role in the early stages of cellular development (Fig. 2O). Taken together, these results imply that FFAR4 may be a critical factor in the malignant evolution of epithelial cells, particularly during the early stages of differentiation and development in colorectal cancer.

The clinical diagnostic potential of FFAR4 in colorectal cancer

Due to the high mortality and poor survival rates of CRC, identifying specific biomarkers for its diagnosis remains critically important. The diagnostic relevance of FFAR4 expression in CRC was evaluated using ROC curve analysis. As shown in Fig. 3A, FFAR4 expression demonstrated robust discrimination between CRC and normal tissues, with an area under the curve (AUC) of 0.823. Further stage-specific analyses revealed that FFAR4 maintained diagnostic potential across different CRC stages, with AUC values of 0.806 for stage I, 0.818 for stage II, 0.815 for stage III, and 0.864 for stage IV (Fig. 3B-E). Additionally, immunofluorescence staining confirmed the differential expression of FFAR4 at the protein level. As shown in Fig. 3F, FFAR4 expression was downregulated in tumor tissues compared with matched adjacent normal tissues, indicating reduced FFAR4 protein levels in colorectal cancer. Collectively, these results support the diagnostic relevance of FFAR4 in CRC.

Fig. 3.

Diagnostic performance and immunofluorescence validation of FFAR4 in tumor tissues. (A) ROC curves demonstrate that FFAR4 expression effectively differentiates tumor tissues from normal counterparts. The X-axis denotes the true positive rate (sensitivity), while the Y-axis reflects the false positive rate (1–specificity). (B-E) ROC curve analyses stratified by CRC stage (I–IV), further validating FFAR4’s diagnostic value across different tumor stages. (F) Immunofluorescence staining showing FFAR4 expression in CRC tissues and matched adjacent normal tissues. Nuclei were counterstained with DAPI. Scale bar = 100 μm

FFAR4 activation retards cell growth in colorectal cancer cells

To investigate the functional role of FFAR4 in CRC, in vitro experiments were performed using HCT116 and HT29 cell lines. Cells were treated with increasing concentrations of the FFAR4 agonist TUG891 (0–80 µM) for 48 h. As shown in Fig. 4A-B, CCK-8 assays demonstrated that TUG891 inhibited CRC cell proliferation in a dose-dependent manner, and 40 µM was identified as the optimal concentration (EC50) for subsequent experiments. Flow cytometric analysis revealed that treatment with 0, 40, and 80 µM TUG891 resulted in significant G0/G1 phase accumulation, accompanied by a reduction in S and G2/M phase populations in both CRC cell lines (Fig. 4C). Consistently, western blot analysis showed that Cyclin D1 expression was markedly decreased following treatment with 40 µM TUG891 (Fig. 4D). In contrast, apoptosis analysis indicated that TUG891 treatment did not significantly induce apoptosis in CRC cells (Fig. 4E). To investigate whether the anti-proliferative effect of TUG891 depends on FFAR4, FFAR4 was transiently knocked down in colorectal cancer (CRC) cells. As shown in Fig. 4F, the knockdown efficiency of FFAR4 was confirmed at both the mRNA and protein levels. Compared with TUG891 treatment alone, FFAR4 knockdown significantly reduced the inhibitory effect of TUG891 on cell proliferation (Fig. 4G). In addition, FFAR4 knockdown markedly attenuated TUG891-induced G0/G1 phase cell cycle arrest (Fig. 4H). Collectively, these findings indicate that FFAR4 activation suppresses colorectal cancer cell growth primarily through the induction of cell cycle arrest, and this effect is dependent on FFAR4 expression.

Fig. 4.

FFAR4 activator TUG891 reduces cell growth. (A-B) CCK-8 assays showing the effects of increasing concentrations of TUG891 (0–80 µM) on the proliferation of HCT116 and HT29 cells after 48 h. (C) Flow cytometric analysis of cell cycle distribution in HCT116 (upper panel) and HT29 (lower panel) cells following treatment with increasing concentrations of TUG891. (D) Western blot analysis of Cyclin D1 expression in CRC cells treated with 40 µM TUG891 in HCT116 (upper panel) and HT29 (lower panel) cells. (E) Flow cytometric analysis of apoptosis in CRC cells following TUG891 treatment, as assessed by Annexin V/PI staining. (F) Validation of FFAR4 knockdown efficiency at both the mRNA and protein levels. (G) CCK-8 assays assessing cell proliferation in control, FFAR4 knockdown, TUG891-treated, and combined FFAR4 knockdown plus TUG891 groups. (H) Cell cycle analysis showing the effects of FFAR4 knockdown on TUG891-induced G0/G1 phase arrest. Data are presented as the mean ± standard deviation (SD). Statistical significance was determined using one-way ANOVA followed by Tukey’s multiple comparisons tests. Exact P values are shown for significant differences. ns, not significant (P ≥ 0.05)

FFAR4 activation promotes glycolysis and is associated with suppressed of oxidative phosphorylation

Although activation of FFAR4 markedly suppresses the growth of colorectal cancer (CRC) cells, the underlying mechanism remains unclear. Treatment with the FFAR4 agonist TUG891 resulted in an obvious color change in the culture medium (Fig. 5A and C), accompanied by a significant increase in extracellular lactate levels (Fig. 5B and D), suggesting altered cellular metabolism. Neither buffering the medium with sodium bicarbonate nor replacing it with fresh medium reversed the growth inhibition induced by TUG891 (Fig. 5E), indicating that lactate-associated acidification is unlikely to account for the observed growth suppression. To further investigate whether lactate signaling contributes to the anti-proliferative effect of TUG891, knockdown of the lactate receptor GPR81 was performed. Efficient suppression of GPR81 expression was confirmed at the mRNA level (Fig. S2A). Notably, GPR81 knockdown did not rescue the growth inhibition induced by TUG891 (Fig. S2B), further supporting that lactate-mediated signaling is unlikely to mediate the observed suppression of CRC cell growth. Further analysis of lactate metabolism showed that FFAR4 activation was accompanied by lactate accumulation, consistent with enhanced glycolytic activity (Fig. 5F). However, glucose supplementation failed to restore TUG891-inhibited cell proliferation (Fig. 5G). Moreover, inhibition of glycolysis with 2-deoxy-D-glucose (2-DG) further enhanced TUG891-induced growth suppression (Fig. S2C), indicating that glycolysis is compensatory upregulated to support cell survival upon FFAR4 activation. Consistently, extracellular flux analysis revealed that TUG891 treatment significantly increased basal glycolysis, as reflected by elevated extracellular acidification rate (ECAR) (Fig. 5H), while markedly reducing both basal and maximal oxidative respiration, as measured by oxygen consumption rate (OCR), in HCT116 and HT29 cells (Fig. 5I). Collectively, these results indicate that FFAR4 activation suppresses mitochondrial OXPHOS while inducing a compensatory increase in glycolytic activity, together contribute to CRC cell growth inhibition.

Fig. 5.

FFAR4 activation promotes glycolysis and is associated with inhibition of oxidative phosphorylation. (A) Medium color shift observed in HCT116 cultures treated with increasing concentrations of TUG891. (B) TUG891-induced modulation of lactate secretion in HCT116 medium at 40 µM concentration. (C) Color shift in HT29 culture medium following treatment with increasing TUG891 concentrations. (D) Modulation of medium lactate content in HT29 cells following treatment with 40 µM TUG891. (E) Evaluation of TUG891-mediated antitumor effects in HCT116 (upper) and HT29 (lower) cells under pH-stabilized conditions achieved through NaHCO₃ addition or regular medium renewal. (F) Lactate metabolic pathway. (G) Effects of glucose supplementation on TUG891’s antitumor activity in HCT116 (left) and HT29 (right) cells. (H) Effect of 40 µM TUG891 on extracellular acidification rate (ECAR) in HCT116 (left) and HT29 (right) cells. (I) Impact of 40 µM TUG891 on oxygen consumption rate (OCR) in HCT116 (left) and HT29 (right) cells. Data are presented as mean ± standard deviation (SD). Statistical significance was determined using one-way ANOVA and Tukey’s multiple comparisons tests. ns, not significant (P ≥ 0.05)

TUG891 (FFAR4 activation) is associated with altered NAD⁺/NADH homeostasis and mitochondrial dysfunction

To further characterize the metabolic alterations induced by FFAR4 activation, we performed untargeted metabolomic profiling comparing FFAR4 agonist–treated cells with control counterparts (Fig. 6C). In both HCT116 and HT29 cells, TUG891 treatment resulted in a clear separation of metabolic profiles from controls (Fig. 6A and B), indicating broad metabolic alterations following FFAR4 activation. KEGG pathway enrichment analysis identified the malate–aspartate shuttle as one of the most significantly altered metabolic pathways (Fig. 6D and E). Consistent with the metabolomics results, Western blot analysis revealed increased expression of the cytosolic malate–aspartate shuttle enzymes GOT1 and MDH1, whereas the mitochondrial counterparts GOT2 and MDH2 showed no obvious changes (Fig. 6F), suggesting selective alterations in cytosolic redox shuttling. Notably, FFAR4 activation was also associated with reduced expression of the mitochondrial OXPHOS proteins ATP5A1 (Complex V) and UQCRC1 (Complex III), providing a molecular basis for the impaired mitochondrial respiration observed above (Fig. 6F). Given the suppression of mitochondrial OXPHOS, we next examined downstream consequences on mitochondrial function and cellular redox homeostasis. Electron transport chain activity was markedly impaired in TUG891-treated cells (Fig. 6G and J), accompanied by a significant reduction in the ATP/ADP ratio in both HCT116 and HT29 cells (Fig. 6K and L), indicating compromised cellular energy status. Under these conditions, the observed decrease in the mitochondrial NAD⁺/NADH ratio is more likely attributable to impaired NADH oxidation and disrupted redox homeostasis, rather than increased NADH production. To further assess the functional role of the malate–aspartate shuttle, cells were treated with the GOT inhibitor aminoxyacetate (AOA). As shown in Fig. S2D, pharmacological inhibition of GOT further enhanced TUG891-induced growth suppression, as evidenced by reduced cell viability in CCK-8 assays, supporting a compensatory role of the malate–aspartate shuttle in maintaining cell survival upon FFAR4 activation. Together, these findings indicate that FFAR4 activation suppresses mitochondrial OXPHOS and triggers a compensatory upregulation of the malate–aspartate shuttle, contributing to metabolic rewiring and altered redox homeostasis in CRC cells.

Fig. 6.

TUG891 reduces the NAD⁺/NADH ratio and disrupts mitochondrial redox homeostasis. (A-B) Principal-component analysis (PCA) of gene expression data, showing the clustering of gene profiles for control and FFAR4 activation groups in HCT116 cells (A) and HT29 cells (B). (C) Changes in metabolite levels with and without TUG891 treatment in HCT116 cells (upper panel) and HT29 cells (lower panel). Log2(FC) indicates log2-transformed fold change of the TUG891-treated group relative to the control group. (D-E) Over-Representation Analysis identified significantly enriched KEGG pathways based on different metabolites in HCT116 cells (D) and HT29 cells (E). (F) Western blot analysis of malate–aspartate shuttle–related enzymes (GOT1, GOT2, MDH1, and MDH2) and OXPHOS proteins in control and TUG891-treated cells. (G-H) Measurement of NAD⁺ levels and the NAD⁺/NADH ratio in HCT116 cells comparing vehicle and FFAR4 activator groups. (I-J) Measurement of NAD⁺ levels and the NAD⁺/NADH ratio in HT29 cells comparing vehicle and FFAR4 activator groups. (K, L) Measurement of the cellular ATP/ADP ratio in HCT116 (K) and HT29 (L) cells comparing vehicle and FFAR4 activator–treated groups. The data were expressed as the mean ± standard deviation (SD)

TUG891 suppresses colorectal tumor growth in vivo

To evaluate the anti-tumor effects of TUG891 in vivo, MC38 colorectal cancer cells were subcutaneously implanted into mice, which were subsequently treated with either PBS or TUG891. No significant difference in body weight was observed between the two groups throughout the treatment period (Fig. 7A), indicating that TUG891 was well tolerated under the experimental conditions. Compared with the PBS-treated group, TUG891 treatment markedly suppressed tumor growth, as evidenced by a significant reduction in tumor volume and a pronounced growth arrest over time (Fig. 7B). Representative images of excised tumors further illustrated the inhibitory effect of TUG891 on tumor growth (Fig. 7C). Consistently, tumor weight at the experimental endpoint was significantly lower in the TUG891-treated group than in the PBS group (Fig. 7D). Immunofluorescence analysis revealed increased FFAR4 expression in tumor tissues from TUG891-treated mice (Fig. 7E). Western blot analysis further showed upregulation of GOT1 and MDH1. By contrast, ATP5A1 and UQCRC1 levels appeared slightly reduced under in vivo conditions (Fig. 7F). Collectively, these in vivo results demonstrate that TUG891 effectively suppresses colorectal tumor growth with good tolerability and is associated with increased FFAR4 expression and selective alterations in metabolic-related proteins.

Fig. 7.

TUG891 suppresses tumor growth in an MC38 syngeneic mouse model. (A) Body weight of mice treated with PBS or TUG891 during the experimental period (n = 7). The data were expressed as the mean ± standard deviation (SD). (B) Tumor growth curves showing tumor volume over time (n = 7). The data were expressed as the mean ± Error of the Mean (SEM). (C) Representative images of excised tumors at the experimental endpoint (n = 7). (D) Tumor weights measured at sacrifice (n = 7). The data were expressed as the mean ± standard deviation (SD). (E) Immunofluorescence staining of FFAR4 in tumor tissues from PBS- and TUG891-treated mice. (F) Western blot analysis of GOT1, MDH1, and oxidative phosphorylation–related proteins (ATP5A1 and UQCRC1) in tumor tissues. ns, not significant (P ≥ 0.05)

Discussion

Colorectal cancer (CRC) is increasingly recognized as a disease driven by genetic alterations and metabolic dysregulation [23, 24]. In this context, our integrated bulk and single-cell transcriptomic analyses identify FFAR4 as a consistently downregulated gene in CRC, with higher FFAR4 expression significantly associated with improved patient survival, highlighting a potential tumor-suppressive role for FFAR4.

Previous studies have suggested that FFAR4 exerts important biological functions in the gastrointestinal system [13, 25, 26]. Extending these observations, we demonstrate that pharmacological activation of FFAR4 markedly suppresses CRC cell proliferation and induces cell-cycle arrest, accompanied by metabolic alterations centered on impaired mitochondrial oxidative metabolism. This metabolic state is characterized by reduced mitochondrial respiratory capacity and disrupted cellular energy homeostasis. In contrast, the concomitant increase in glycolytic activity is more likely to represent a compensatory adaptation to mitochondrial dysfunction rather than a direct driver of growth inhibition. Further metabolomic and molecular analyses revealed selective modulation of the malate–aspartate shuttle following FFAR4 activation, suggesting that under conditions of limited NADH re-oxidation, cells remodel redox shuttling pathways to maintain metabolic balance. Consistent with this interpretation, the observed decrease in the NAD⁺/NADH ratio and impairment of electron transport chain (ETC) activity are more likely to reflect secondary redox imbalance resulting from mitochondrial dysfunction (Fig. 8).

Fig. 8.

Proposed mechanism by which the FFAR4 agonist TUG891 blocking oxidative phosphorylation. TUG891 activates membrane-associated FFAR4, leading to suppression of mitochondrial oxidative phosphorylation (OXPHOS) and electron transport chain activity, resulting in impaired mitochondrial respiration, a reduced NAD⁺/NADH ratio, and disrupted energy homeostasis. As a compensatory response, glycolytic activity is increased, accompanied by selective remodeling of the malate–aspartate shuttle to buffer redox imbalance. These metabolic alterations induce cell-cycle arrest and inhibit tumor cell proliferation, indicating that FFAR4 activation elicits an antitumor metabolic phenotype functionally resembling OXPHOS-targeting strategies, with potential to complement glycolysis-inhibitory approaches

Although CRC cells are typically characterized by enhanced glycolysis, accumulating evidence indicates that mitochondrial oxidative metabolism remains an essential support for tumor growth and represents a therapeutically exploitable metabolic vulnerability [27, 28]. Accordingly, targeting the oxidative phosphorylation (OXPHOS) pathway has shown antitumor efficacy in multiple cancer models [29–31]. Several clinically used agents, such as metformin [32] and atovaquone [33], have been reported to impair mitochondrial respiration, highlighting the translational potential of OXPHOS inhibition. Among these, metformin—a classical inhibitor of mitochondrial complex I—has been extensively investigated for its antitumor activity in CRC and other malignancies [34, 35]. In this context, the metabolic phenotype induced by FFAR4 activation—characterized by suppressed OXPHOS accompanied by compensatory glycolytic engagement—shares functional features with established OXPHOS-targeting strategies. Notably, unlike agents that directly inhibit components of the respiratory chain, FFAR4 activation modulates tumor cell metabolism through receptor-mediated signaling pathways, potentially representing a more adaptive and regulatory mode of metabolic intervention rather than direct respiratory blockade. This distinction provides a rationale to explore whether FFAR4 activation could complement existing metabolic therapies, including strategies that simultaneously target mitochondrial respiration and glycolysis, although direct combination studies will be required to substantiate this possibility.

From a translational perspective, FFAR4 agonists have shown promising potential in the context of metabolic diseases [36–39]. In the mouse models used in this study, TUG891 did not induce significant body weight loss within the tested dose range, suggesting acceptable short-term tolerability. Nevertheless, its long-term safety, pharmacokinetic properties, and intratumoral exposure–response relationships require systematic evaluation.

In summary, this study reveals a tumor-suppressive role for FFAR4 in CRC from clinical, functional, and metabolic perspectives, and supports FFAR4 activation as a potential therapeutic strategy to inhibit CRC progression through modulation of mitochondrial metabolism and redox homeostasis.

Conclusions

This study demonstrates that FFAR4 functions as a tumor-suppressive regulator in colorectal cancer by disrupting mitochondrial oxidative metabolism and inducing metabolic stress. FFAR4 activation leads to impaired OXPHOS, altered redox homeostasis, and compensatory metabolic responses that fail to sustain tumor growth. These findings support FFAR4 as a potential biomarker and therapeutic target and provide a metabolic rationale for targeting mitochondrial vulnerabilities in CRC.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CRC

Colorectal cancer

ROC

Receiver operating characteristic

FFAR

Free fatty acid receptors

LCFA

Long-chain fatty acids

RNA-seq

RNA sequencing

scRNA-seq

single-cell RNA sequencing

OS

Overall survival

FPKM

Fragments Per Kilobase of exon model per Million mapped fragments

TPM

Transcripts per million

GEO

Gene Expression Omnibus

UMIs

Unique molecular identifiers

KNN

K-nearest neighbors

UMAP

Uniform Manifold Approximation and Projection

PI

Propidium iodide

IS

Internal standards

PCA

Principal component analysis

OCR

Oxygen consumption rate

ECAR

Extracellular acidification rate

SD

Standard deviation

Epi

Epithelial cells

TNKILC

T and NK-like cells

Strom

Stromal cells

OXPHOS

Oxidative phosphorylation

GPCRs

G protein-couple receptors

Author contributions

Conceptualization and Roles/Writing – original draft: LYW and PPL; Funding acquisition: LYW, CFL and PPL; Data curation: BFH and DSL; Formal analysis and Methodology: LYW, PPL, WW, and CFL; Supervision: LYW and PPL; Resources: QW, Visualization: BFH, FF. All authors reviewed and approved the final manuscript.

Funding

The Natural Science Major Research Project of Anhui Educational Committee (2022AH040105), The Natural Science Key Research Project of Anhui Educational Committee (2022AH050695, 2022AH050778), The Anhui Provincial Natural Science Foundation (2508085QH296), and The Basic and Clinical Cooperation Research Funding of the Third Affiliated Hospital of Anhui Medical University (2023sfy017).

Data availability

The metabolomic data generated in this study have been deposited in Zenodo and are publicly available at https://zenodo.org/records/18232510.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki. For immunofluorescence analysis, a colorectal cancer tissue microarray comprising four samples was obtained from Outdo Biotech (Shanghai, China), and the use of these samples was approved by the Ethics Committee of Shanghai Outdo Biotech Company (Approval No. SHYJS-CP-2210015). All animal experiments were carried out in strict compliance with institutional guidelines and were approved by the Animal Welfare Committee of Anhui Medical University (Approval No. LLSC20242092).

Consent for publication

Not applicable.

Consent to participate

Not applicable.

Competing interests

The authors declare that they have no competing interests.