NKG2D ligand MICA regulates macrophage phenotype through PPAR/EHHADH pathway altering fatty acid oxidation (FAO) in hepatocellular carcinoma (HCC)

doi:10.21203/rs.3.rs-3414659/v1

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NKG2D ligand MICA regulates macrophage phenotype through PPAR/EHHADH pathway altering fatty acid oxidation (FAO) in hepatocellular carcinoma (HCC)

https://doi.org/10.21203/rs.3.rs-3414659/v1

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Background Tumor-associated macrophages (TAMs) play a crucial role in the tumor microenvironment (TME), and the metabolic activities of both tumor cells and TAMs have an impact on the TME. Moreover, the expression of MICA in tumor cells is closely associated with immune cells in hepatocellular carcinoma (HCC). However, it remains unclear whether MICA expression correlates with TAMs and influences the switch in macrophage phenotype by mediating metabolic alterations.

Methods Various biostatistical tools, qPCR, and IHC staining experiments were utilized to analyze data from The Cancer Genome Atlas (TCGA) and collected HCC tumor tissues. Single-cell RNA sequencing (scRNA-seq) analyses and a co-culture model of HCC cells with macrophages were performed to validate the findings from the biostatistical analyses.

Results Through the intersection of differentially expressed genes (DEGs), metabolism-related genes (MRGs), and co-expression genes (CEGs) with MICA in HCC, the EHHADH gene was identified. Gene set enrichment analyses were conducted to further confirm the role of EHHADH. EHHADH expression is decreased in HCC tumors and can serve as a prognostic biomarker for HCC. Expressions of MICA and EHHADH exhibited significant correlations with various phenotypic macrophages and exerted opposing effects on M1-like and M2-like macrophages infiltrating HCC. The underlying metabolic and molecular mechanisms revealed that MICA in tumor cells induced M2-like polarization through the PPAR/EHHADH pathway, which regulates fatty acid oxidation (FAO) in both tumor cells and macrophages.

Conclusions The metabolic gene EHHADH, which is associated with MICA, led to alterations in M2-like macrophages by promoting heightened fatty acid uptake and augmenting levels of FAO within macrophages.

MICA

macrophage

phenotype

EHHADH

fatty acid oxidation

hepatocellular carcinoma

The investigation of the tumor microenvironment (TME) in hepatocellular carcinoma (HCC) has garnered significant interest in recent times. The TME assumes a pivotal role in the initiation and progression of HCC, establishing a conducive environment that facilitates cancer cell proliferation, metastasis, and dampens anti-tumor immune responses [1, 2]. Within the HCC milieu, distinct immune cell populations exhibit diverse functions. Concurrently, cancer cells secrete a repertoire of cytokines that orchestrate tumor advancement by modulating immune cell behavior within the TME, ultimately promoting a tumor-supportive phenotype [3].

Macrophages, as innate immune cells, are widely distributed within the TME and hold significant importance in both autoimmunity and cancer [4]. These macrophages possess a high degree of plasticity and respond to changes in their surroundings by modulating cellular metabolism and immunophenotype. Typically, they can be categorized into two main subtypes: the classically activated M1-like macrophages and the alternatively activated M2-like macrophages [5]. M1-like macrophages, induced by lipopolysaccharide (LPS), exhibit a pro-inflammatory phenotype, crucial for tumor cell elimination and the secretion of pro-inflammatory cytokines. Conversely, M2-like macrophages, induced by interleukin (IL)-4 and IL-13, display an anti-inflammatory phenotype, secreting anti-inflammatory cytokines and promoting tissue remodeling and progression [6, 7]. Within the TME, tumor associated macrophages (TAMs) often adopt an M2-like phenotype, characterized by the secretion of anti-inflammatory cytokines, promotion of tumor cell proliferation, and suppression of anti-tumor immune responses [8].

Numerous investigations have been conducted to explore the intricate relationship between immune cell metabolism and function. In the context of malignancies, the metabolic profile of immune cells undergoes reprogramming, thereby influencing their biological behavior [9]. In the case of M1-like macrophages, their metabolic phenotype is characterized by heightened glycolysis, augmented flux through the pentose phosphate pathway (PPP), increased fatty acid synthesis (FAS), and inhibition of the tricarboxylic acid (TCA) cycle. Notably, M1-like macrophages primarily rely on glycolysis as their energy source. Conversely, M2-like macrophages exhibit reduced glycolysis and enhanced fatty acid oxidation (FAO). The energy requirements of M2-like macrophages predominantly rely on FAO-derived oxidative phosphorylation (OXPHOS) [10, 11]. Consequently, modulating the metabolic pathways of macrophages holds the potential to induce phenotypic switching in these cells.

Major histocompatibility complex (MHC) class I polypeptide-related sequence A (MICA) serves as the ligand for natural killer group 2 member D (NKG2D), a protein encoded by the killer cell lectin like receptor K1 (KLRK1). NKG2D is expressed on a wide range of immune cells, including NK cells, CD8 + T cells, NKT cells, and activated macrophages. Upon binding of MICA to NKG2D, NKG2D engages with the adaptor dimer DAP10, initiating signaling activation that promotes cell-mediated cytotoxicity, cytokine production, and clearance of tumor cells [12, 13].

MICA plays a crucial role in antitumor immune responses. Our previous investigation demonstrated a positive correlation between MICA expression and the infiltration of NK cells and CD8 + T cells in HCC [14]. However, the association between MICA expression and TAMs in HCC remains unclear. Additionally, cellular metabolism has been shown to influence macrophage function. Previous studies have suggested a connection between MICA and cellular metabolism [15, 16]. Nevertheless, the precise mechanism by which MICA modulates macrophage phenotype through mediating metabolic alterations in HCC remains elusive. Therefore, there is an urgent need to investigate the molecular mechanisms underlying the impact of tumor cell-derived MICA on the metabolic and phenotypic alterations of macrophages in HCC.

In this study, we conducted an analysis utilizing data from The Cancer Genome Atlas (TCGA) and employed various tools, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and receiver operating characteristic (ROC), to identify and select the target gene: Enoyl-CoA Hydratase And 3-Hydroxyacyl CoA Dehydrogenase (EHHADH), a gene associated with metabolism that exhibits co-expression with MICA in HCC. Subsequently, we utilized Gene Expression Profiling Interaction Analysis (GEPIA), cBio Cancer Genomics Portal (cBioportal), and Tumor Immune Estimation Resource (TIMER) to assess the expression levels and prognostic significance of EHHADH. We further explored the association between EHHADH, MICA, and phenotypic markers of macrophages through biostatistical analyses and experimental validation using clinical tumor tissue obtained from HCC patients. Additionally, we investigated the expression of EHHADH and its upstream gene, peroxisome proliferator-activated receptor (PPAR), on various cells within the TME using single-cell RNA sequencing (scRNA-seq) analyses. To confirm the role of MICA in tumor cells in inducing M2-like polarization through the PPAR/EHHADH pathway, we employed a co-culture model consisting of MICA + HCC cells and macrophages. This model allowed us to examine the regulation of FAO in both tumor cells and macrophages.

Data Collection

UCSC Xena (https://xenabrowser.net/) is an online site that was utilized to access secondary developed TCGA data [17]. Specifically, FPKM-format transcriptome data and clinical information files pertaining to HCC in TCGA were obtained from UCSC Xena. This dataset encompassed 374 HCC tumor tissue samples and 50 adjacent normal tissue samples.

To identify metabolism-related genes (MRGs), GeneCards (https://www.genecards.org/), an integrative database [18], was employed. A search using the keyword "metabolism" was conducted, and genes with a correlation score greater than 5 were considered as MRGs.

Furthermore, the cBioPortal (http://www.cbioportal.org/), a freely accessible database that consolidates research data from various sources, including TCGA [19], was utilized. The list of co-expression genes (CEGs) associated with MICA in HCC was downloaded from the cBioPortal. Genes with an absolute correlation value of 0.2 or higher were deemed as CEGs.

Screening for Differentially Expressed Genes (DEGs)

To identify DEGs, all transcriptome data from the HCC cohort in The TCGA were subjected to analysis using the LIMMA R package [20]. DEGs were defined as genes exhibiting a false discovery rate (FDR) of less than 0.05 and an absolute log fold change (|logFC|) greater than 1.

Screening of Target Genes and Functional Enrichment Analysis

The intersection of MRGs, CEGs, and DEGs was selected as the pool of genes co-expressed with MICA in HCC for subsequent analysis. To gain insights into the functional implications of these genes, GO analysis and KEGG pathway analysis were conducted using the clusterProfiler R package [21]. Statistical significance was determined by a P-value threshold of less than 0.05.

Expression and Prognostic Analysis of EHHADH

To explore the expression and prognostic significance of EHHADH in HCC, we utilized the GEPIA online tool (http://gepia.cancer-pku.cn/index.html), which enables bioinformatic analyses of data from TCGA and Genotype-Tissue Expression (GTEx) databases [22]. Firstly, the correlation between EHHADH expression and clinicopathological information in HCC was assessed using GEPIA. Additionally, the differential expression of EHHADH between HCC tumor tissue and normal tissue was determined. Moreover, the levels of EHHADH expression across different pathological stages were compared. Furthermore, the association between EHHADH expression and prognosis in HCC was evaluated.

To complement our analysis, we utilized the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/), which provides information on protein expression in normal tissues, tumor tissues, and cells [23]. The protein expression of EHHADH was explored using the HPA database. To assess the predictive accuracy of EHHADH expression for prognosis in HCC, we constructed receiver operating characteristic (ROC) curves using the pROC R package.

Immune Cell Infiltration Analysis

TIMER (https://cistrome.shinyapps.io/timer/) and TIMER2.0 (http://timer.comp-genomics.org/) databases were used to assess immune cell infiltration in various cancer types [24, 25]. Specifically, we obtained information on the levels of EHHADH mRNA in pan-cancer (33 cancer types) and investigated the relationship between EHHADH expression in HCC and tumor purity as well as immune infiltrating abundance using TIMER. Furthermore, the association between immune infiltration and copy number variation (CNV) of the EHHADH gene was analyzed using TIMER. Additionally, we obtained information on the association between EHHADH expression and tumor purity, as well as the immune-infiltrating abundance of M1-like or M2-like macrophages, through the utilization of TIMER2.0.d via TIMER2.0.

Cancer genomics analysis of the cBioPortal database

Co-expression relationships among genes in patients with HCC were accessed by cBioPortal database.

The acquisition of scRNA-seq data

To obtain scRNA-seq data for our study, we downloaded the data from the GEO database (https://www.ncbi.nlm.nih.gov/geo) for five samples (GSM4505944, GSM4505945, GSM4505953, GSM4505961, and GSM4505964) of GSE149614. In total, we obtained data from 17,392 cells and 25,712 genes across the five patients. Among these patients, two were diagnosed with AJCC stage I, one with stage IIIA, and three with stage IV, all of whom had a background of hepatitis [26].

Preprocessing and Analysis of scRNA-seq Data

The scRNA-seq data obtained from the previous section were subjected to preprocessing and analysis using the R software "Seurat" package (v 4.2.0). To ensure the retention of high-quality data, raw matrices underwent filtering to remove cells with less than 200 transcripts per cell and more than 15% mitochondrial genes, as well as genes with less than 3 cells expressing them. The "NormalizeData" function of the "Seurat" package was then applied to the data, with the normalization method set as "LogNormalize".

Principal component analysis (PCA) was performed using the Seurat package in R software (v 4.2.1), along with the JackStraw and ElbowPlot functions, to select the top 20 principal components (PCs). Cell clustering analysis was conducted using the "FindNeighbors" and "FindClusters" functions in the Seurat package. Subsequently, cell clustering and visual analysis were performed using the "RunTSNE" and "RunUMAP" functions.

To compare differences in gene expression between clusters, the "FindAllMarkers" function was utilized. Marker genes for each cluster were identified based on an adjusted P-value < 0.05 and |log2 (fold change)| > 1. The annotation of cell clusters was based on marker genes from the CellMarker databases and genes reported in the literature [27].

Patient Sample

Clinical HCC tumor tissue and adjacent liver samples were obtained from 60 HCC patients who underwent hepatectomy at the Second Affiliated Hospital of Guangxi Medical University. All human tissues were obtained according to protocols (No. 2022-KY-0208) approved by the Ethics Committee of Second Affiliated Hospital of Guangxi Medical University. Prior informed consent was obtained from the patients.

Quantitative real-time PCR (qRT-PCR)

Total RNA was extracted using TRIzol reagent (New Cell & Molecular Biotech). RNA was reverse transcribed into cDNA using cDNA synthesis kit (A230, GenStar). Subsequent qRT-PCR was performed using a PCR kit (A303, GenStar) and relative expression was calculated by the 2-ΔΔCt method [28]. Primer sequences used in this study were provided in Supplementary Table 1.

Immunohistochemistry (IHC)

Formalin-fixed and paraffin-embedded sections were subjected to de-paraffinization using xylene followed by rehydration. Heat-mediated antigen retrieval was performed using citrate buffer (pH = 6). Subsequently, the sections were incubated overnight at 4°C with the following primary antibodies: anti-EHHADH (1:400, Proteintech Cat# 26570-1-AP), anti-CD86 (1:200, Affinity Biosciences Cat# DF6332), anti-CD206 (1:1000, Proteintech Cat# 60143-1-Ig), and anti-MICA (1:500, Proteintech Cat# 66384-1-Ig). Afterward, the sections were incubated with a secondary antibody (PV9000, ZSGB-BIO) for 20 minutes. Staining was visualized using 3,3'-diaminobenzidine solution (DAB) (ZLI-9017, ZLI-9018, ZLI-9019, ZSGB-BIO), and counterstained with hematoxylin (G1080, Solarbio). The sections were then subjected to hydration, dehydration, and sealing. Finally, the staining intensity was quantified by calculating the Average Optical Density (AOD) values using Fiji software [29, 30].

Cell Culture

The mouse Hepa 1–6 and human Huh-7 and HepG2 hepatic cancer cells and the THP-1 macrophage cells were purchased from the Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences. HCC cell lines were cultured in Dulbecco's Modified Eagle Medium (C11995500BT, Thermo Fisher), while THP-1 cells were cultured in RPMI 1640 Medium (C11875500BT, Thermo Fisher). The medium was supplemented with 10% heat-inactivated fetal bovine serum (FBS) and 100 U/mL penicillin, and 100 mg/mL streptomycin (C125C5, New Cell & Molecular Biotech). All cell lines were cultured at 37°C in a humidified atmosphere of 5% CO₂.

Lentivirus infection

A lentiviral vector carrying human MICA cDNA or empty vector was constructed by Genechem (Shanghai, China). According to the standardized protocols, HCC cells were seeded and the following day infected with lentivirus for 48 hours according to the experimental MOI. Next, the culture medium was changed, and the cells stably expressing MICA gene were sorted out by flow cytometry and expanded. Clones were subsequently screen by qRT-PCR and Western blotting for MICA mRNA and protein expression.

Western blotting

Cells were treated with RIPA cell lysate supplemented with 1% PMSF. Subsequent centrifugation was performed to collect the supernatant to obtain proteins. The proteins were then mixed with 5× sampling buffer and subjected to boiling. Next, 30 µg of protein solution was loaded onto a sodium dodecyl sulfate-polyacrylamide gel for electrophoresis. Following electrophoresis, the proteins were transferred onto a 0.45 µm polyvinylidene difluoride membrane. The membrane was then blocked with a 5% BSA blocking solution for 1 hour. Incubation with primary antibodies was carried out overnight at a dilution of 1:1000. The primary antibodies used were anti-EHHADH (1:1500, Proteintech Cat# 26570-1-AP), anti-MICA (1:2000, Proteintech Cat# 66384-1-Ig), and anti-GAPDH (1:5000, Solarbio Cat# K200057M). After washing the membrane three times with Tris-buffered saline and Tween-20 (TBST), it was incubated with fluorescent secondary antibodies diluted 1:20,000 for 1 hour. The membrane was then scanned using an Odyssey imaging system (Odyssey CLX, LI-COR, Lincoln, NC, USA) [31].

Co-culture

After seeding THP-1 cells into six-well plates, the cells were subjected to treatment with 100 nM phorbol 12-myristate 13-acetate (PMA) (MedChemExpress) for a duration of 24 hours. This treatment led to the differentiation of THP-1 cells into macrophages. Following a change in the culture medium, the macrophages were inoculated into six-well plates and co-cultured with MICA + HCC cells or negative control (NC) HCC cells, which were inoculated in the upper 6-well Transwell device (0.4 µm pore size, 3412, Corning), for either 24 or 72 hours. In order to inhibit PPAR-α activity, the macrophages in the co-culture system were treated with GW6471 (10 µM, HY-15372, MedChemExpress) [32]. Next, both HCC cells and macrophages were washed and collected for subsequent experiments.

Immunofluorescence (IF) staining

Coverslips were positioned within six-well plates. THP-1-derived macrophages, obtained after co-culture, were collected and seeded into six-well plates with coverslips. These cells were then cultured for a duration of 24 hours. Following this, they were fixed using 4% paraformaldehyde, permeabilized with 0.5% Triton X-100 (T8200, Solarbio), and subsequently blocked with 5% goat serum (ZLI-9021, ZSGB-BIO). Subsequently, the macrophages were incubated with anti-EHHADH (1:200, Proteintech Cat# 26570-1-AP), anti-CD86 (1:500, Affinity Biosciences Cat# DF6332), anti-CD206 (1:500, Proteintech Cat# 60143-1-Ig), anti-IL-10 (1:300, Proteintech Cat# 60269-1-Ig), and anti-TNF-α (1:300, Proteintech Cat# 60291-1-Ig) antibodies at 4 ℃ overnight. They were then incubated with secondary antibodies (1:1000, Cell Signaling Technology; 1:1000, Proteintech Cat# SA00013-1). Finally, the cell nuclei were stained using DAPI-containing anti-fluorescence quenching encapsulants (Seven Biotech) [33]. The fluorescence intensity was subsequently quantified using ImageJ software [34].

Oil Red O staining

The extent of lipid accumulation in HCC cells and macrophages following co-culture was evaluated using Oil Red O staining. Cells obtained after co-culture were seeded in a 96-well plate. Subsequently, the cells were treated with 250 µM of free fatty acid oleate (oleic acid: palmitic acid ratio of 2:1) for a duration of 24 hours. After this treatment, the cells were fixed using 4% paraformaldehyde for 30 minutes and then incubated with 50 µl of Oil Red working solution (prepared by mixing Oil Red O stock solution with ddH₂O in a 6:4 ratio) for 15 minutes. Following the incubation, the Oil Red O solution was removed, and the cells were washed with ddH₂O until all excess stains were completely removed. The oil red dye was subsequently washed off using 100 µl of DMSO. The resulting lipid accumulation data were measured using a fluorescence microplate spectrophotometer at a wavelength of 510 nm [35].

BODIPY staining

The level of lipid accumulation in HCC cells and macrophages after co-culture was assessed by BODIPY staining. Cells were fixed with 4% paraformaldehyde for 15 min. Next, cells were stained with 10 µM BODIPY 493/503 (HY-W090090, MedChemExpress) for 20 min. Subsequently, cell nuclei were stained with DAPI-containing anti-fluorescence quenching encapsulants (Seven Biotech) [36]. Finally, fluorescence intensity was quantified using ImageJ software [34].

FAO testing

The ACADVL, ACADM, and HADHA expression in FAO pathway were measured with a commercial colorimetric assay kit (ab118182, Abcam) according to the manufacturer’s protocol [37].

Statistical Analysis

Statistical analyses were conducted utilizing R (version 4.2.1) and SPSS (version 22.0). A significance level of P < 0.05 (two-sided) was employed to determine statistical significance for all analyses. Additionally, the TCGA data analysis online platform Sangerbox (http://vip.sangerbox.com/home.html) was utilized for data analysis and visualization purposes [38].

EHHADH was identified as a candidate gene through the integration of DEGs, MRGs, and CEGs with MICA in HCC.

To identify DEGs, transcriptome data in FPKM format from TCGA HCC patients obtained from the UCSC Xena database were analyzed. A total of 1,555 DEGs were identified and visualized in a volcano plot (Fig. 1a). The top 50 DEGs were further depicted in a heat map (Fig. 1b). Additionally, GeneCards analysis revealed 1,058 MRGs with a correlative score greater than 5. Furthermore, cBioportal analysis identified 1,537 CEGs with an absolute correlation value of 0.2 or higher. By intersecting the DEGs, MRGs, and CEGs, we identified 27 metabolism-related genes that exhibited co-expression with MICA in HCC (Fig. 1c).

Subsequently, functional enrichment analyses were conducted on the identified set of 27 genes using GO and KEGG databases. In terms of biological processes (BP), these genes were primarily associated with small molecule catabolic processes, carboxylic acid catabolic processes, and organic acid catabolic processes (Fig. S1a). Analysis of cellular components (CC) revealed enrichment in the mitochondrial matrix, cytoplasmic vesicle lumen, and secretory granule lumen (Fig. S1b). In relation to molecular function (MF), these genes were predominantly involved in coenzyme binding, organic acid binding, and carboxylic acid binding (Fig. S1c). KEGG pathway enrichment analysis indicated that the intersecting genes were primarily associated with valine, leucine, and isoleucine degradation, biosynthesis of amino acids, and propanoate metabolism (Fig. S1d). Notably, the EHHADH gene was found to be present in the most frequently occurring signaling pathways based on KEGG pathway enrichment analysis (Fig. 1d). Consequently, among the 27 metabolic genes co-expressed with MICA in HCC, the EHHADH gene was selected for further investigation.

Given the functional relevance of EHHADH as an enzyme involved in FAO [39, 40], our initial investigation focused on examining the differential expression of EHHADH mRNA in pan-cancer samples (33 cancer types) compared to normal tissues. Notably, we observed a significant decrease in EHHADH mRNA levels specifically in HCC tumors when compared to normal tissues (Fig. S1e, 1e). Furthermore, we found a pronounced decrease in EHHADH mRNA expression in advanced tumor stages (Fig. 1f). Consistently, our analysis of collected samples confirmed a decreased EHHADH mRNA expression in HCC tumors compared to background liver tissues (Fig. 1g). To complement our findings, we also examined EHHADH protein expression using IHC staining from the Human Protein Atlas (HPA) database. Interestingly, we observed high EHHADH expression in normal liver tissue and moderate expression in HCC tumors (Fig. S1f). Moreover, our analysis of our collected HCC samples further validated the decreased EHHADH protein levels in tumors (Fig. 1h).

Furthermore, we performed an analysis of DEGs in HCC samples with high and low EHHADH expression levels. A total of 456 DEGs were identified, with 285 being up-regulated and 171 being down-regulated. To gain further insights into the functional implications of these DEGs, we performed GO and KEGG pathway analyses. In terms of BP, the enriched categories included small molecule catabolic process, organic acid biosynthetic process, and fatty acid metabolic process. For CC, the significant categories involved blood microparticles, immunoglobulin complexes, and cytoplasmic vesicle lumens. In relation to MF, the identified categories were primarily associated with monooxygenase activity, antigen binding, and oxidoreductase activity, specifically acting on CH-OH group donors. Additionally, the KEGG pathway analysis revealed a significant correlation with metabolism of xenobiotics by cytochrome P450, chemical carcinogenesis, and the PPAR signaling pathway (Fig. S1g).

To assess the potential of the EHHADH gene as a prognostic biomarker in HCC, we conducted Kaplan-Meier survival analysis. Our findings revealed that HCC patients with high EHHADH mRNA levels exhibited a significantly improved overall survival (OS) compared to those with low levels (hazard ratio [HR] = 0.67, p < 0.05; Fig. 1i). Furthermore, we performed receiver operating characteristic (ROC) curve analysis to evaluate the predictive accuracy of EHHADH. The area under the curve (AUC) for EHHADH was determined to be 0.776 (95% confidence interval [CI]: 0.730–0.822; Fig. 1j), indicating that EHHADH holds promise as a prognostic biomarker for HCC.

Exploring the relationship between MICA and EHHADH with macrophage infiltration and its associated phenotype

Building upon our previous study which demonstrated a positive correlation between MICA expression and infiltration of NK cells and CD8 + T cells in HCC [14], we proceeded to investigate the impact of EHHADH on the immune microenvironment of HCC. Initially, we assessed the correlation between EHHADH expression, tumor purity, and infiltration of immune cells in HCC using the TIMER database. Our analysis revealed a significant negative correlation between EHHADH mRNA expression and the infiltration of five immune cell types, namely B cells, CD8 + T cells, CD4 + T cells, neutrophils, and macrophages (Fig. S2a). Furthermore, we observed a link between the copy number variation (CNV) of EHHADH and the infiltration of immune cells within the TME (Fig. S2b).

Subsequently, we sought to investigate the correlation between EHHADH expression and distinct macrophage phenotypes. To accomplish this, we utilized the TIMER 2.0 database to analyze the relationship between EHHADH expression, tumor purity, and macrophage infiltration. Our analysis revealed a significant negative correlation between EHHADH expression and the infiltration of M1-like macrophages, while a positive correlation was observed with M2-like macrophages (Fig. S2c, S2d). Interestingly, we also observed a positive correlation between MICA expression and macrophage infiltration in HCC (Fig. S2e).

Given the co-expression of MICA and EHHADH in HCC (Fig. 1c), as well as their significant correlation with macrophage infiltration in HCC (Fig. S2c-S2e), our subsequent investigation focused on examining the correlation of EHHADH and MICA with classical phenotypic markers of macrophages using the cBioportal database. Initially, we observed a significant negative correlation between EHHADH mRNA level and MICA expression (Fig. S3a). Furthermore, our analysis revealed a significant negative correlation between EHHADH expression and the levels of CD68 and ITGAM (CD11b), which are considered overall markers of macrophages [41] (Fig. S3b, S3c), consistent with previous findings from TIMER. Additionally, EHHADH expression exhibited a clear negative correlation with the levels of CD86 and CD80, markers of M1-like macrophages [42] (Fig. S3d, S3e), indicating a negative association between EHHADH and M1-like macrophage infiltration. Conversely, EHHADH expression displayed a significant positive correlation with the level of CD206 (MRC1), a marker of M2-like macrophages [43] (Fig. S3f). Furthermore, we analyzed the correlation between MICA expression and macrophage markers. As expected, MICA expression positively correlated with CD68 and CD86, but negatively correlated with CD206 (Fig. S3g-S3i).

To validate the findings from our biostatistical correlative analyses, we conducted qPCR and IHC staining experiments using HCC tissue samples that we collected. Our results demonstrated a positive correlation between MICA mRNA and protein expression with the levels of CD68 and CD86, while a negative correlation was observed with CD206 (Fig. S4a-S4c, 2a-2d). Conversely, EHHADH mRNA and protein expression exhibited a negative correlation with CD68 and CD86 levels, but a positive correlation with CD206 (Fig. S4d-S4f, 2e-2g). Notably, the mRNA and protein expression of EHHADH showed a negative correlation with MICA (Fig. S4g, 2h). These experimental findings were consistent with the analyses conducted using the cBioportal database. Overall, our results indicate that MICA and EHHADH have opposing effects on the infiltration of M1-like and M2-like macrophages in HCC.

The scRNA-seq data analysis revealed the expression of the EHHADH gene in various cell types within early- and late-stage HCC tumors.

Since our previous research demonstrated the predominant expression of MICA in HCC tumor cells [14], and the confirmation of the significant correlation between MICA and EHHADH expression with macrophage infiltration in HCC (Fig. S2-S4, Fig. 2), as well as the negative correlation between EHHADH and MICA (Fig. S3a, Fig. 2h), we proceeded to investigate the cell clusters in which EHHADH was expressed. By utilizing scRNA-seq data from the GEO database, we identified distinct cell types in early- and late-stage HCC (Fig. 3a). Interestingly, EHHADH expression was predominantly localized to HCC cells (Fig. 3b).

Given that EHHADH was found to be enriched in the PPAR signaling pathway (Fig. S1g), we further examined the expression of PPAR on HCC cells. Consistently, PPAR-α and PPAR-γ, two subtypes of PPAR, were primarily expressed on HCC cells, mirroring the expression pattern of EHHADH (Fig. 3b). Importantly, the mRNA expression levels of PPAR-α, PPAR-γ, and EHHADH were found to be decreased in late-stage HCC cells compared to early-stage cells (Fig. 3c-3e).

Co-culture of HCC cells with macrophages to validate the correlation between MICA and EHHADH expression with macrophage polarization

To investigate the potential correlation between MICA and EHHADH expression and the polarization of macrophages in HCC, we performed a co-culture experiment involving HCC cells and macrophages. Initially, we established HCC cells with stable expression of MICA (Fig. 4a). Subsequently, we confirmed a decrease in EHHADH and PPAR-α mRNA levels in MICA + HCC cells (Fig. 4b). Also, we confirmed the decreased EHHADH protein expression in MICA + HCC cells by IF staining (Fig. 4c). Furthermore, we observed an increase in CD86 mRNA levels and a decrease in CD206 levels in macrophages co-cultured with MICA + HCC cells for 24 hours. However, the expression of CD86 and CD206 mRNA reversed after 72 hours of co-culture (Fig. 4d, 4e). Additionally, we noted an increase in the mRNA level of the pro-inflammatory cytokine TNF-α and a decrease in the mRNA level of the anti-inflammatory cytokine IL-10 in macrophages co-cultured for 24 hours. Conversely, this alteration was reversed in macrophages co-cultured for 72 hours (Fig. 4f, 4g). Furthermore, we validated these findings through IF staining for the corresponding markers (Fig. 4h, 4i).

Increased FAO level induced phenotypic alteration in macrophages

Given the known metabolic crosstalk between tumor cells and macrophages mediated by free fatty acids (FFAs), which enhances FAO and induces M2-like polarization [44], we aimed to investigate whether the alteration of macrophages was induced by MICA + HCC cells through an increase in FAO levels. Initially, we confirmed an increase in lipid accumulation in macrophages co-cultured with MICA + HCC cells for 72 hours compared to 24 hours (Fig. 5a, 5b). Additionally, we observed an increase in lipid accumulation in co-cultured MICA + HCC cells for 72 hours (Fig. 5a, 5c). Furthermore, we also confirmed these findings by BODIPY staining (Fig. 5d, 5e).

Subsequently, we examined the alteration of FAO in macrophages using the co-culture model. Since ACADVL, ACADM, and HADHA are key enzymes involved in the FAO pathway, assessing their expression would provide insights into FAO levels. We observed a decrease in FAO in macrophages co-cultured with MICA + HCC cells for 24 hours (Fig. 5f). Conversely, FAO levels significantly increased in macrophages co-cultured with MICA + HCC cells for 72 hours (Fig. 5g). Taken together, our findings suggest that MICA + HCC cells induce alterations in macrophages through increased lipid accumulation and FAO levels.

The alteration of macrophages through FAO pathway is regulated by the PPAR-α/EHHADH signaling pathway.

Our previous scRNA-seq data analysis revealed that EHHADH and PPAR-α are predominantly expressed in HCC cells (Fig. 3b). Additionally, an increase in EHHADH levels in TAMs with elevated FAO levels was observed [40]. Therefore, we aimed to investigate whether the FAO of macrophages is also regulated by the PPAR-α/EHHADH pathway. As anticipated, the mRNA expression of EHHADH and PPAR-α was higher in macrophages co-cultured for 72 hours compared to 24 hours (Fig. 6a, 6b). Furthermore, both WB and IF staining assays confirmed a significant increase in EHHADH protein expression in macrophages co-cultured for 72 hours (Fig. 6c-6e). Importantly, blocking the PPAR-α/EHHADH pathway with the PPAR-α inhibitor GW6471 resulted in a switch in the phenotype and function of macrophages (Fig. 6f).

In summary, the expression of MICA in tumor cells leads to the infiltration of M1-like macrophages in early-stage HCC. However, in the late stage, a decrease in EHHADH expression is observed in MICA + HCC cells, resulting in reduced FAO and increased FFAs. The increased FFAs are transported into macrophages, enhancing FAO levels and inducing M2-like macrophage polarization through the PPAR-α/EHHADH pathway (Fig. 7a, 7b).

In this study, we have identified the functional regulation of FAO in HCC tumor cells and macrophages by the MICA-associated metabolic enzyme EHHADH and its upstream molecule PPAR. Our findings are consistent with previous studies that have demonstrated the ability of EHHADH to promote FAO [39, 40, 45]. FAO is a crucial process in lipid metabolism that is closely linked to the biological functions of both tumor cells and macrophages. Suppressed FAO has been shown to promote the aggressiveness of HCC cells [46]. FAO also provides energy for anti-inflammatory M2-like macrophages [40, 47, 48], indicating that FAO has diverse effects on HCC cells and macrophages.

Furthermore, we have discovered that EHHADH expression is decreased in HCC tumors and can serve as a biomarker for predicting the prognosis of patients with HCC, as supported by our biostatistical analyses. This finding is consistent with a previous study that identified EHHADH as a tumor suppressor gene in HCC tissues, with higher expression levels being associated with better prognosis [47]. We have also confirmed the decreased expression of EHHADH in our collected tumor tissues.

Subsequently, we have conducted an analysis on the TIMER database and found a significant association between EHHADH expression and the infiltration of five types of immune cells in tumors, namely B cells, CD8 + T cells, CD4 + T cells, neutrophils, and macrophages. Among these, TAMs have been shown to possess suppressive effects on antitumor immunity and promote tumor progression [49–51]. Additionally, our analyses using the cBioPortal platform have revealed a negative correlation between EHHADH expression and the levels of M1-like macrophage markers CD80 and CD86, while a positive correlation was observed with the M2-like macrophage marker CD206. These findings suggest a potential relationship between EHHADH expression and macrophage polarization. Furthermore, our qPCR and IHC experiments have provided consistent results with the biostatistical analyses, supporting the notion that increased EHHADH expression may lead to macrophage polarization towards an M2-like phenotype through the promotion of FAO.

In contrast, we have observed a positive correlation between MICA expression and the levels of CD80 and CD86, while a negative correlation was found with CD206. These findings suggest a potential association between MICA and macrophage phenotype, indicating that MICA and EHHADH may have opposing effects on macrophage polarization. Moreover, our co-culture experiments involving MICA + HCC cells and macrophages have confirmed that MICA influences macrophage alteration through EHHADH.

Fatty acid metabolism plays a crucial role in various biological processes, including energy production, carbohydrate synthesis, and intracellular signaling regulation [52]. M2-like macrophages primarily rely on FAO for energy, whereas M1-like macrophages depend on glycolysis [53, 54]. The different modes of energy supply have a significant impact on macrophage polarization and associated biological functions. Our study has demonstrated a negative correlation between MICA levels and EHHADH in HCC cells. Additionally, we have observed that the decreased EHHADH in HCC cells leads to increased FFA absorption by macrophages, thereby promoting FAO levels and enhancing M2-like macrophage polarization.

The previous study has demonstrated that increased FAO in the TAMs leads to the conversion of a M2-like phenotype. Furthermore, inhibition of FAO has been shown to reverse the immunosuppressive activity of macrophages and inhibit HCC tumorigenesis [48]. Conversely, induction of glycolysis in macrophages promotes a reliance on glucose metabolism and subsequent conversion to a M1-like phenotype. The enhanced glucose utilization and increased glycolysis in macrophages have been associated with M1-like macrophage polarization, an increased M1/M2 ratio, remodeling of tumor immunity, and exerting anti-tumor effects in HCC [55]. Our findings are consistent with these previous studies. However, further investigations are required to explore the specific blockade of EHHADH and FAO in macrophages, as this will determine the phenotypic alterations and functional remodeling of macrophages, ultimately improving the effectiveness of anti-tumor immunity.

In conclusion, this study provides evidence that the MICA-related metabolic gene EHHADH has potential as a prognostic biomarker for HCC. It was observed that MICA induces macrophages to polarize towards a M1-like phenotype, resulting in pro-inflammatory effects during the early stages of HCC. However, in late-staged HCC cells, MICA induces a decrease in EHHADH expression, leading to macrophage conversion to the anti-inflammatory M2 phenotype. This conversion is facilitated by increased fatty acid utilization by macrophages, promoting FAO for energy production. Consequently, EHHADH emerges as a promising therapeutic target for HCC. Combining the induction of MICA (pro-inflammatory effect) in cancer cells with the specific inhibition of EHHADH and FAO (anti-inflammatory effect) in macrophages may offer a beneficial therapeutic approach for HCC.

AOD	Average Optical Density
BP	Biological process
cBioportal	cBio Cancer Genomics Portal
CC	Cellular component
CEG	Co-expression genes
CNV	Copy number variation
DAB	3,3′-diaminobenzidine
DAP10	DNAX-Activation Protein 10
DEG	Differentially Expressed Gene
EHHADH	Enoyl-CoA Hydratase And 3-Hydroxyacyl CoA Dehydrogenase
FAO	Fatty acid oxidation
FAS	Fatty acid synthesis
FBS	Fetal bovine serum
FFA	Free fatty acid
GEPIA	Gene Expression Profiling Interaction Analysis
GO	Gene Ontology
HCC	Hepatocellular carcinoma
HPA	The Human Protein Atlas
IHC	Immunohistochemistry
IL	Interleukin
KEGG	Kyoto Encyclopedia of Genes and Genomes
LPS	Lipopolysaccharide
MF	Molecular function
MHC	Major histocompatibility complex
MICA	Major histocompatibility complex class I polypeptide-related sequence A
MRGs	Metabolism-related genes
NC	Negative control
NKG2D	Natural killer group 2, member D
PCA	Principal component analysis
PCs	Principal components
PMA	Phorbol 12-myristate 13-acetate
PPP	pentose phosphate pathway
qRT-PCR	Quantitative real-time PCR
ROC	Receiver operating characteristic
scRNA-seq	Single cell RNA sequence
TAM	Tumor associated macrophages
TCA	Tricarboxylic acid cycle
TCGA	The Cancer Genome Atlas
TIMER	Tumor IMmune Estimation Resource
TME	Tumor microenvironment

Authors' Contributions

Y.Y. and D.A.G. designed the research. J.H., Q.W., X.L., Y.Y., P.Y., M.Y., J.L., X.C., Q.D., and Y.Y. performed research and analyzed the data. Y.Y. and D.A.G. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (82360526, Y.Y.), the Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation (2023GXNSFDA026011, Y. Y.), the Scientific Research Project of Guangxi Health Commission (S2021114, Y. Y.).

Availability of data and materials

The supporting data and materials in this research are available upon reasonable request to the corresponding author.

Declarations

Ethics approval and consent to participate

This study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University (No. 2022-KY-0208). Written informed consent was obtained from all patients or their families.

Consent for publication

Written informed consent for publication was obtained from all participants.

Competing interests

No potential conflicts of interest were disclosed.

Author details

1. Department of General Surgery, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530007, China. 2. Thomas E. Starzl Transplantation Institute, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15260, USA.

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Table 1 Primer sequences

FigureS1.tif
Fig.S1 Enrichment analysis of target gene sets, EHHADH expression and associated DEG function enrichment analysis in HCC. A-C GO functional enrichment analysis of gene sets shows BP, CC and MF. DKEGG shows pathway enrichment analysis. The X-axis represents proportion of genes, and Y-axis represents different enrichments. Different colors represent different properties (p-values), and different sizes represent the number of genes. E EHHADH mRNA expressions are showed in pan-cancer. F IHC staining for EHHADH from HPA. GFunctional enrichment of DEGs from high and low EHHADH expression in HCC is showed. P-value Significant Codes: * P< 0.05, ** P < 0.01, *** P < 0.001.
FigureS2.tif
Fig.S2 Correlation of EHHADHexpression with different immune cell infiltration level. A Correlation of EHHADHwith tumor purity and infiltration level of B cells, CD8+ T cells, CD4+ T cells, macrophages and neutrophils. BRelationship between CNV of EHHADH gene and different immune cells infiltration level. C Correlation of EHHADH mRNA expression with M1 macrophage infiltration. D Correlation of EHHADHwith M2 macrophage infiltration. E Correlation of MICA with macrophage infiltration. P-value Significant Codes: * P < 0.05, ** P < 0.01, *** P < 0.001.
FigureS3.tif
Fig.S3 Correlation of EHHADH, MICA and macrophage phenotype signature genes. A-F Correlation of EHHADH mRNA expression with MICA, CD68, ITGAM, CD86, CD80, and MRC1 level. G-I Correlation of MICA mRNA expression with CD68, CD86, and MRC1 level.
FigureS4.tif
Fig.S4 The qPCR to verify correlation of MICA& EHHADH and macrophage infiltration in HCC. A-C Correlation of MICA mRNA expression with CD68, CD86, CD206 mRNA level is showed in tumor and background liver tissues from HCC patients (n=27). D-F Correlation of EHHADHmRNA expression with CD68, CD86, and CD206 mRNA level is showed in tumor and background liver tissues. G Correlation of EHHADH mRNA expression with MICA mRNA level is showed in tumor and background liver tissues.

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NKG2D ligand MICA regulates macrophage phenotype through PPAR/EHHADH pathway altering fatty acid oxidation (FAO) in hepatocellular carcinoma (HCC)

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Abstract

Figures

Introduction

Materials and methods

Data Collection

Screening for Differentially Expressed Genes (DEGs)

Screening of Target Genes and Functional Enrichment Analysis

Expression and Prognostic Analysis of EHHADH

Immune Cell Infiltration Analysis

Cancer genomics analysis of the cBioPortal database

The acquisition of scRNA-seq data

Preprocessing and Analysis of scRNA-seq Data

Patient Sample

Quantitative real-time PCR (qRT-PCR)

Immunohistochemistry (IHC)

Cell Culture

Lentivirus infection

Western blotting

Co-culture

Immunofluorescence (IF) staining

Oil Red O staining

BODIPY staining

FAO testing

Statistical Analysis

Results

Exploring the relationship between MICA and EHHADH with macrophage infiltration and its associated phenotype

Increased FAO level induced phenotypic alteration in macrophages

Discussion

Abbreviations

Declarations

References

Tables

Supplementary Files

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