
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00239-0
10.1016/j.tranon.2024.102112
102112
Original Research
HADH suppresses clear cell renal cell carcinoma progression through reduced NRF2-dependent glutathione synthesis
Chu Changbin ab1
Liu Shangjing c1
He Zhiting c
Wu Mingjun a
xia Jing d
Zeng Hongxiang a
Xie Wenhua a
Cheng Rui a
Zhao Xueya a
Li Xi lixi@cqmu.edu.cn
a⁎
a Institute of Life Sciences, Chongqing Medical University, Chongqing, 400016, China
b Department of Urology, Chongqing Red Cross Hospital (People's Hospital of Jiangbei District), Chongqing, 400020, China
c School of Basic Medicine, Chongqing Medical University, Chongqing, 400016, China
d Medical Center of Hematology, Xinqiao Hospital, Army Medical University, Chongqing, China
⁎ Corresponding author at: Institute of Life Sciences, Chongqing Medical University, Chongqing, 400016, China. lixi@cqmu.edu.cn
1 First author, contributed equally.

02 9 2024
11 2024
02 9 2024
49 10211210 3 2024
17 8 2024
25 8 2024
© 2024 The Authors. Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• The low expression of HADH was significantly negatively correlated with the survival prognosis and malignancy of ccRCC.

• HADH overexpression could inhibit NRF2 nuclear translocation in ccRCC cells and suppress the transcription of GLC and GSS, leading to a decrease in intracellular GSH content and inhibition of tumor growth.

• By overexpression of NRF2 in ccRCC cells, we found that NRF2 increased GSH synthesis, decreased intracellular ROS, and restored tumor cell growth in ccRCC cells.

Background

Clear cell renal cell carcinoma (ccRCC) is a serious threat to human life. It is very important to clarify the pathogenesis of ccRCC. In this study we evaluated the clinical value of HADH and explored its role and mechanism in the malignant progression of ccRCC.

Methods

HADH expression and its relationship with prognosis were analyzed using bioinformatics database. RT-PCR, Western blot and immunohistochemistry were used to examine the expression of HADH in ccRCC tissues and tissue microarrays. To examine the cell proliferation, apoptosis, migration and invasion ability, ccRCC cells with HADH overexpressed were constructed. Xenograft experiments were performed to determine the role of HADH. Non-target metabolomics was applied to explore the potential metabolic pathway by which HADH inhibited ccRCC progression. Plasmid pcDNA3.1-NRF2 was used to confirm whether HADH inhibited the process of ccRCC cells through NRF2-related glutathione (GSH) synthesis.

Results

Bioinformatics database analysis showed that HADH expression was significantly decreased in ccRCC tissues, and its low expression predicted a poor prognosis. Both ccRCC tissues and tissue microarrays exhibited a significantly decreased HADH level compared with adjacent normal renal tissues. HADH overexpression inhibited the malignant behaviors of ccRCC cells. Furthermore, HADH overexpression attenuated GSH synthesis and induced oxidative stress damage. Exogenously increased NRF2 effectively attenuated the inhibitive effect of HADH overexpression on ccRCC cells.

Conclusion

Our data revealed that HADH suppressed the malignant behaviors of ccRCC cells by attenuating GSH synthesis through inhibition of NRF2 nuclear translocation, and HADH might be a novel therapeutic target for ccRCC treatment.

Keywords

Clear cell renal cell carcinoma
HADH
Non-target metabolomics
Glutathione synthesis
NRF2
==== Body
pmcIntroduction

Renal cell carcinoma (RCC) is the most common renal carcinoma derived from renal tubular epithelial cells, clear cell RCC (ccRCC) is the most important pathological subtype with high invasive ability and recurrence risk, accounting for approximately 70%-75% of all renal cell carcinoma cases [1]. Localized tumors can be treated by surgical resection. However, 30% of patients with ccRCC are initially diagnosed after metastasis, and have a poor prognosis due to the limited efficacy of conventional radiotherapy and chemotherapy [2]. In recent years, although more innovative treatments such as targeted drug therapy and immunotherapy have been applied in the treatment of metastatic RCC and exhibited good therapeutic effects, drug resistance and serious side effects are still inevitable. Therefore, it is needed to explore the biological characteristics and the molecular mechanism of ccRCC for screening new therapeutic targets.

Metabolic reprogramming refers to changes in metabolic pathways which control tumor energy and biosynthesis [3]. For a long time, metabolic reprogramming has been considered as a hallmark of many cancers, as it can meet the basic needs of tumor cells and improve the level of cell building blocks such as DNA, nucleotides, membrane components, and tumor energy molecules [1].In the 1920s Otto Warburg first recognized aerobic glycolysis, and pointed out that increased glucose uptake and high-speed glycolysis in cancer cells led to an increase in lactic acid production to meet the needs of rapid and unlimited cell proliferation [4]. In addition to the Warburg effect, the anabolism/catabolism of fatty acids and cholesterol in tumor cells also supply central carbon atoms in tumor cells, especially the generation of mitochondrial function, which provides the energy for cell proliferation. In many kinds of cancers, the change of metabolic enzymes involved in lipid metabolism always causes obvious alterations in the intensity of oxidative stress [5]. SCD1 and FADS2 are aberrantly upregulated in ovarian cancer, which accelerates lipid metabolic activities and tumor aggressiveness, while the inhibition of SCD1/FADS2 directly down-regulates GPX4 and GSH/GSSG ratio, resulting in cellular redox imbalance, iron mediated lipid peroxidation and mitochondrial dysfunction in ascites-derived ovarian cancer cells [6]. Compared with nonmalignant prostate, ELOVL5 is the mainly expressed and upregulated ELOVL, which catalyzes fatty acid elongation in prostate cancer [7]. ELOVL5 depletion significantly alters the morphology and function of mitochondria, leading to excessive generation of reactive oxygen species and inhibiting the malignant behavior of prostate cancer cells. Medium-chain acyl-CoA dehydrogenase (ACADM), an enzyme that catalyzes the first step of mitochondrial fatty acid oxidation, is significantly downregulated in hepatocellular carcinoma (HCC). Suppression of ACADM promotes HCC cell motility with elevated triglyceride, phospholipid, and cellular lipid droplet levels, indicating the inhibitory ability of ACADM in HCC [8]. These studies indicate that lipid metabolic reprogramming is a potential therapeutic target of tumors with abnormal lipid metabolism. ccRCC cells contain many lipid droplets (LD), mainly consisting of triglycerides and cholesterol-esters. ccRCC has been considered to be a metabolic disease, in which abnormal lipid accumulation plays an important role in the occurrence and progression of cancer [9,10]. Melatonin promotes tumor slimming and eliminates lipid accumulation in ccRCC through PGC1A/UCP1 mediated autophagy and lipid browning, thereby inhibiting tumor progression [11]. ECHS1 participates in the second step of mitochondrial fatty acids β-oxidation, catalyzing the hydration of short chain enoyl-CoA esters to short chain 3-hydroxy CoA esters. ECHS1 is significantly downregulated in ccRCC tissues and can discriminate ccRCC tissues from adjacent normal renal tissues, and overexpression of ECHS1 powerfully suppresses ccRCC cell proliferation and migration through inhibiting mTOR pathway activation [12]. HADHA catalyzes the third step of long chain fatty acids β-oxidation in mitochondria, and overexpression of HADHA significantly inhibits ccRCC cell growth, induced cell apoptosis, and reduced formation of cytoplasmic lipid droplets (LD) [13]. As a key enzyme for fatty acid β oxidation in mitochondria, which converts short and medium chain fatty acids into ketones, HADH provides energy for the liver, heart, muscles and pancreas during prolonged fasting [14]. The downregulation of HADH has been reported to promote the progression of gastric cancer by activating Akt signaling pathway [15]. We analyzed the database and found that HADH was tightly associated with the progress of ccRCC. However, the precise expression and function of HADH in ccRCC and its molecular mechanism remain poorly understood.

In this study, we investigated the expression pattern of HADH in ccRCC and analyzed the relationship between the level of HADH and the prognosis of ccRCC. Furthermore, the effect of HADH on the proliferation, apoptosis, invasion and metastasis of ccRCC cells and the potential molecular mechanism were also elucidated.

Experimental details

Cell culture

Four human ccRCC cell lines (ACHN, Caki-1, A498, 786-O) and human renal tubular epithelial cell line (HK-2) were purchased from the American Type Culture Collection (ATCC). ACHN and HK-2 were cultured in DMEM (Gibco, United States) containing 10% fetal bovine serum (BI, Israel) supplemented with 1% penicillin/streptomycin (P/S) (Beyotime, China). A498 and 786-O were cultured in RPMI 1640 (Gibco, United States) supplemented with 10% FBS and 1% P/S. Caki-1 was cultured in McCoy's 5A (icell, China) supplemented with 10% FBS and 1% P/S. All the cells above were maintained at 37 °C in a humidified atmosphere of 5% CO2. All cell lines were routinely tested to confirm absence of mycoplasma contamination.

Human samples

Twenty ccRCC tissues with matched adjacent normal renal tissues, which were collected from patients diagnosed with ccRCC in the Department of Urology of the First Affiliated Hospital of Chongqing Medical University, were stored at -80 °C for experiments such as RT-PCR and Western blot. Before our study, prior patients’ consents and ethical approval from the local hospital ethic committees were obtained. ccRCC tissue microarray (TMA) for immunohistochemistry staining was purchased from Shanxi Avilabio Com. (dc-kid11054) .

Bioinformatics analysis

The gene expression profiles related to ccRCC were collected from TCGA database (TCGA KIRC, https://portal.gdc.cancer.gov/) and GEO database (GSE53757, https://www.ncbi.nlm.nih.gov/geo/), and integrated with the lipid metabolism‐related gene sets in GSEA to identify the differential expressed genes (DEGs) using limma package (|logFC|>1.5 and p<0.05). The clinical data in TCGA-KIRC database were downloaded to determine the overall survival (OS) outcomes of DEGs by using Cox regression analyses. GSE40435 was used to validate the expression of HADH. Pan-cancer analysis of HADH was performed through online tool (https://ualcan.path.uab.edu/).

Lentiviral transfection

For HADH overexpression experiments, the ccRCC cell lines (ACHN and Caki-1) were transfected with lentivirus (Lv-HADH and Lv-Vector), which were constructed by Sangon Biotech (Shanghai, China). Briefly, ccRCC cells were seeded in 6-well plates for approximately 12 h, then, cultured with the corresponding lentivirus in the presence of polybrene (5 μg/ml). After 24 h, the medium containing lentivirus was replaced with fresh complete medium supplemented with 3 μg/ml puromycin (Beyotime, China) to kill uninfected cells. Overexpression efficiency was confirmed by RT-PCR and Western blot.

Plasmid transfection

Plasmid pcDNA3.1-NRF2 was used to increase the NRF2 expression and empty vector plasmid pcDNA3.1 was used as a control. ACHN cell in the exponential phase of growth were plated in 6-well plates at 2 × 105 cells/plate and cultured for 24 h. After that, transfection of pcDNA3.1 or pcDNA3.1-NRF2 (presented by Dr. Chen Feilong, Chongqing University) in ACHN cell was performed by using PEI 25,000 (Polysciences, United States) according to the manufacturer's instruction.

RNA isolation and quantitative real-time PCR (RT-PCR) analysis

Total RNA was extracted from ccRCC cell lines or tissues with TRIzol™ reagent (Invitrogen, United States). cDNA was synthesized through Revert Aid First Strand cDNA Synthesis kit (Thermo scientific, United States) according to the manufacturer's protocol. RT-PCR was performed by PowerUp™ SYBR™Green Master Mix (Thermo scientific, United States) in Quantstudio3/5 real-time PCR instrument (Thermo Scientific, United States). The level of β-actin was used as an internal control. The mRNA expression levels of target genes were analyzed by the 2-△△CT methods and expressed as relative mRNA levels compared with the internal control. The following primers were used in this study and purchased from Sangon Biotech (Shanghai, China): HADH (Forward: ACCAGGCAGTTCATGCGTT, Reverse: ACGTGCTTGACGATTATCTTCTT), GCLC (Forward: CCGCTGAGCTGGGAGGAAAC, Reverse: TTGACGGCGTGGTAGATGTGC), GCLM (Forward: GACGGGGAACCTGCTGAACTG, Reverse: TCATGAAGCTCCTCGCTGTGC), GSS (Forward: GGGAGCCTCTTGCAGGATAAA, Reverse: GAATGGGGCATAGCTCACCAC), β-actin (Forward: CATGTACGTTGCTATCCAGGC, Reverse: CTCCTTAATGTCACGCACGAT).

Immunohistochemistry (IHC) staining

HADH and NRF2 proteins were detected by an IHC kit (PV-6001, Zhongshan Goldenbridge Biotechnology Inc., China). Briefly, the paraffin-embedded specimens were cut into slides with a thickness of 4 µm. The tissue microarray and slides were deparaffinized with xylenes and rehydrated, incubated in citric acid buffer at 100 °C for 15 min to restore the antigen. After being treated with 3% H2O2 for 10 min at room temperature to quench the endogenous peroxidase activity, the sections were then incubated with fetal bovine serum for 1 hour (h) to block the nonspecific binding. Next, the sections were incubated with primary antibodies overnight at 4 °C. The primary antibodies include HADH (1:200, ab154088, Abcam, USA). After being washed with PBS containing 0.05% Tween-20, the sections were incubated with secondary antibodies for 1 h at room temperature, followed by DAB staining and hematoxylin counterstain. Images were obtained using an upright light microscope (Olympus, Japan). Positively stained areas (brown) were calculated as the percentage of all examined areas using ImageJ software based on unit area.

Western blot analysis

Total protein was extracted by using cell lysis buffer with protease and phosphatase inhibitor (Sigma-Aldrich, United States), and protein concentration was measured by using Nanophotometer (Implen, Germany). The protein was separated by gel electrophoresis, and transferred to polyvinylidene difluoride membranes (Millipore, United States). The membranes were blocked in 5% skim milk for 1 h at room temperature, and then incubated with primary antibodies at 4 °C overnight. Subsequently, the membranes were incubated with corresponding HRP-conjugated secondary antibody for 1 h at room temperature. Chemiluminescent substrates solution (Thermo Fisher, United States) and Amersham Imager 600 (General Electric Company, United States) were used to detect the immunoreactive signals of target bands. Antibodies used in this study were as following: HADH (1:1000, ab154088, Abcam, United States), Caspase3 (1:1000, 9662S, CST, United States), NRF2 (1:1000, 16,396–1-AP, Proteintech, China), HSP90 (1:5000, 13,171–1-AP, Proteinteck, China), Lamin B1 (1:1000, 17,416, CST, United States), GCLC (1:2000, 12,601–1-AP, Proteintech, China), GCLM (1:2000, 14,241–1-AP, Proteintech, China), GSS (1:2000, 15,712–1-AP, Proteintech, China), β-actin (1:1000, 4970S, CST, United States), Goat Anti-Mouse IgG (1:10,000, 115–035–003, Jackson ImmunoResearch, United States), Goat Anti-Rabbit IgG (1:10,000, 111–035–003, Jackson ImmunoResearch, United States). Protein band images were quantified by densitometry using ImageJ software.

Cell viability assay

The measurement of cell viability was performed using a CCK8 kit (Dojindo, Japan) according to the manufacturer's protocol. Briefly, ccRCC cells were seeded into 96-well culture plates at a concentration of 2.5 × 103 cells/well. 0, 24, 48, 72 and 96 h after seeding, the supernatants were removed and replaced with 100 μl fresh medium containing 10 μl CCK-8 reagent per well and the cells were incubated for 1–2 h at 37 °C in the dark. OD value was immediately measured by a microplate reader (Thermo scientific, United States) at 450 nm.

Colony formation assay

Cells in logarithmic phase were seeded into each well of a 6-well plate at a density of 1000 cells/well and incubated for two weeks. Complete medium was replaced every three days. Colonies were washed with PBS and fixed with 100% methanol for 30 min. Then, the cells were stained with 0.01% crystal violet solution (C0121, Beyotime, China) at 37 °C for 20 min. The number of visible colonies in each well was counted under a microscope.

Immunofluorescence staining

Cells were cultured in 24-well culture plates with slides to get cell crawling slides, fixed with 4% paraformaldehyde for 15 min, permeabilized with 0.1% Triton X-100 for 30 min, blocked with 5% goat serum for 1 h at room temperature, and then incubated with primary antibodies against Ki67 (1:200, ab15580, Abcam, United States) or NRF2 (1:200, 16,396–1-AP, Proteintech, China) at 4 °C overnight. The next day, the slides were rewarmed for 30 min, washed with PBS, and incubated with fluorescent secondary antibody (Cy3-conjugated Affinipure Goat Anti-Rabbit IgG (H+L), 1:100, SA00009–2, Proteintech, China) at room temperature for 1 h. Cell nuclear was stained with DAPI (Thermo scientific, United States) for 10 min at room temperature. The fluorescence images were captured by a fluorescence microscope (Olympus, Japan). Mean fluorescence intensity was calculated using ImageJ software.

Wound healing assay

The cells were seeded in 6-well plates and cultured to approximately 90% confluence. A straight scratch across each well was made with a 200μl pipette tip, then, the cells were washed three times with PBS slightly and incubated in serum-free medium. The wound closure was photographed with an inverted microscope at 0 h, 12 h or 24 h, respectively. Areas covered by migrated cells were quantified by ImageJ software.

Apoptosis Assay

The cell apoptosis was analyzed with an Annexin V-PE/7-AAD detection kit (CA1030, Solarbio, China). Briefly, cells were seeded into 6-well plates. After being cultured for 24 h, the cells were digested with trypsin without EDTA, resuspended with complete medium and centrifuged by 1000 rpm for 3 min. Cell precipitation was washed with cold PBS three times and 500 μl 1  ×  Binding Buffer was added to resuspend the pellet at a density of 1.0  ×  106 cells per ml. One hundred microliters of the binding buffer was incubated with 5μl Annexin V-PE and 10μl 7-AAD for 15 min in the dark at room temperature. Cell samples were analyzed by flow cytometry (Beckman, United States). Early apoptotic (PE positive, 7-AAD negative), late apoptotic and dead cells (PE positive, 7-AAD positive) can be discriminated on the basis of a double-labeling for Annexin V-PE and 7-AAD.

Transwell assay

The membrane at the bottom of the upper chamber of a transwell plate (8 μm polycarbonate filter, Corning, United States) was coated without or with fifty microlitres of 50 mg/l matrigel matrix (Corning, United States) to evaluate cell migration or invasion potential. 2.0  ×  104 cells in 200 μl serum-free medium were placed into the upper chamber, and the lower chamber was filled with 700 μl medium with 10% FBS as a chemoattractant. After 24 h of incubation at 37 °C, the chambers were washed with PBS, fixed with 4% paraformaldehyde for 15 min, and then stained with 0.01% crystal violet solution for 10 min. 5 fields were randomly selected and photographed under an inverted microscope. The cells were counted with ImageJ software.

Xenograft study

The mice experiments were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals with approval from the Ethics Committee of Chongqing Medical University. In this study, twenty male BAL/c nude mice (four-week-old) were obtained from Beijing HFK Bioscience CO. (Beijing, China). All mice were kept under IVC condition and allowed access to food and water ad libitum. The mice were randomly distributed into the ACHN HADH group and the ACHN Vector group after one week of adaptation to the laboratory animal facility. ACHN HADH and ACHN Vector mice were inoculated subcutaneously in the right axillary with 8 × 106 ACHN cells with HADH overexpressed or ACHN cells with Vector resuspended in 100μl serum-free medium. The weight and tumor status of mice were measured and recorded every 3–5 days by tumor width (W) and length (L) with a caliper. Six weeks after inoculation, the mice were euthanized. Tumors were harvested for IHC and HE staining. Tumor volume (V) was calculated using the formula V = 1/2 × L × W2 and tumor weight was detected by precision balance.

In vivo lung metastasis study

Twenty 4-week-old male BAL/c nude mice (Beijing HFK Bioscience CO., China) were kept in this study. ACHN cells with HADH overexpressed or ACHN cells with Vector (2 × 106) were resuspended in 100μl serum-free medium at 4 °C. The cells were inoculated slowly through the tail vein into mice within 60 s to avoid embolism formation. The weight of mice was measured every 3–5 days and the mice were euthanized after six weeks inoculation. Lung of all nude mice were analyzed with HE staining to evaluate lung metastatic nodule.

Non-target metabolomics analysis

ACHN cells with HADH overexpressed and ACHN cells with Vector were cultured in 10 cm dishes and each group contained six repeats. The cells were harvested with trypsin and washed three times with cold PBS. The cell pellets were placed immediately in liquid nitrogen and sent to the Mass Spectrometry Laboratory of Chongqing Medical University for screening differential metabolites according to laboratory procedures. The MS raw data was converted into. mzXML format through ProteoWizard, and then processed by using an XCMS program to acquire Peak alignment, retention time and peak area. A supervised model of partial least squares discrimination analysis (PLS-DA) was applied to assess the metabolic alterations among groups. A permutation test was performed 200 times to assess the risk of overfitting for the PLS-DA model. Single dimensional statistical analysis included Student's t test and multiple of variation analysis, and R software was used to draw volcanic maps.

GSH/GSSG measurement

Intracellular GSH was detected with a GSH and GSSG Assay Kit (S0053, Beyotime, China) according to the manufacturer's instruction [16]. Cells were digested with trypsin, harvested by centrifugation at 1000 rpm for 5 min at room temperature, and resuspended in protein removal reagent M solution. The samples were placed rapidly in liquid nitrogen and thawed at 37 °C for three times. The supernatant was collected by centrifugation at 10,000 rpm for 10 min. GSSG could be reduced to GSH by GSH reductase, and total GSH can react with the substrate DTNB to produce yellow TNB. The OD value of reaction product was measured at 412 nm with a microplate reader. The total GSH and GSSG content of the samples were calculated according to the standard curve, and the GSH content was obtained by the formula: GSH = total GSH - GSSG × 2 (μM). Experiments were done in at least three parallel wells.

Intracellular ROS measurement

Intracellular ROS levels were measured with Dihydroethidium (S0063, Beyotime, China). Briefly, cells (2 × 105 cells/well) were cultured in 6-well plates to 80% confluence, then washed three times with PBS, stained with 5 μM serum-free Dihydroethidium (DHE) in the dark for 30 min at 37 °C. Subsequently, the cells were washed with PBS again to fully remove the DHE which had not entered the cells. The fluorescence images were acquired using a fluorescence microscope (Olympus, Japan) and fluorescence intensity was calculated using ImageJ software, or intracellular ROS levels were directly determined by the fluorescence intensity of DHE with a flow cytometry.

Lipid peroxidation measurement

Cell samples were subjected to lipid peroxidation measurement as described in the MDA Assay Kit (S0131, Beyotime, China). Briefly, cells were lysed with lysis buffer on ice. To form the MDA-TBA adduct, TBA solution was added to each sample and the mixture was incubated at 100 °C for 15 min. Then, each reaction product was put into a 96-well plate and the OD value was measured at 532 nm by a microplate reader (Thermo scientific, United States), which reflected the lipid peroxidation induced by ROS.

Mitochondrial membrane potential measurement

Mitochondrial membrane potential (MMP, Δψm) was measured by Mito-Tracker Red CMXRos staining (C1035, Beyotime, China). Cells were cultured to 80% confluence in 12-well plates and stained with Mito-Tracker Red CMXRos (100 nM) in the dark for 30 min at 37 °C. Images were captured by a fluorescence microscope.

Transmission electron microscopy (TEM)

Cells were centrifuged at 1000 rpm for 10 min, and then fixed with 2.5% glutaraldehyde in the dark at 4 °C overnight. Cell masses were dehydrated in gradient alcohol and embedded in epoxy resin. The ultrastructural changes of cells including mitochondria were detected by atransmission electron microscope (JEOL JEM-1400 Plus, Nippon Electronics Co, Japan).

Statistical analysis

The associations between HADH expression and clinicopathological characteristics of ccRCC patients were analyzed using the Chi-squared test by SPSS version 20 (SPSS Inc., Chicago, IL, USA), Log-rank test was used for overall survival and disease free survival. All the other statistical analyses were performed with GraphPad Prism 7 (GraphPad Software Inc., USA). Data were presented as means ± standard deviation (SD). Comparisons between two groups were analyzed by Student's t-test. One-way analysis of variance (ANOVA) was used to analyze the significance among multiple groups. A supervised model of partial least squares discrimination analysis (PLS-DA) was applied to assess the metabolic alterations among groups. Pathway enrichment analysis was conducted by KEGG database. The statistical significance was defined as p < 0.05 (*, **, ***, and **** stood for p < 0.05, p < 0.01, p < 0.001, and p<0.0001, respectively).

Results

Low expression of HADH was associated with poor prognosis in patients with ccRCC

Metabolic reprogramming was closely related to tumorigenesis [3], especially lipid metabolism was significantly disordered in ccRCC [17,18]. By analyzing the common genes in two independent databases TCGA KIRC (composed of 539 ccRCC cases and 72 paired normal renal cases) and GSE53757 (composed of 72 paired ccRCC and normal renal cases) combined with the lipid metabolism‐related gene sets in GSEA, 86 differentially expressed genes (DRGs) were acquired, 58 genes were downregulated while 28 genes were upregulated in ccRCC compared with normal renal tissues (Supplement Table 1). Subsequently, among these DRGs, 41 genes were associated with overall survival (OS) in TCGA KIRC (Supplement Table 2). Because β-oxidation played a significant role in fatty acid metabolism [19], HADH, which was the key enzyme in the third step of fatty acid β-oxidation in mitochondria, was applied for further analysis.

HADH was obviously downregulated in many types of cancers according to the online resource Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) (Fig. 1A and B), such as breast cancer, colon cancer, and ccRCC. In order to study the expression of HADH in ccRCC, we analyzed the HADH mRNA levels in ccRCC tissues and adjacent normal renal tissues in TCGA KIRC, the result showed that the mRNA level of HADH was significantly lower in ccRCC tissues compared with normal tissues (Fig. 1C). Then, we analyzed the relationship between the expression of HADH and the prognosis in patients with ccRCC, and the results indicated that high HADH expression was correlated with good overall survival (OS) (Fig. 1D) and disease free survival (DFS) (Fig. 1E). According to further analysis of the TCGA KIRC, we found that the expression of HADH in ccRCC had a significantly negative trend with the increase of pathological grade, clinical stage, T classification and metastasis in ccRCC, however, there were no statistical differences in age, gender and N classification (Fig. 1F and G, Table 1). Additionally, the downregulation of HADH in ccRCC was further confirmed based on GSE40435 (composed of 101 paired ccRCC and normal renal cases) (Fig. 1H). Taken together, the bioinformatics database analysis revealed that HADH was downregulated in ccRCC tissues and was a “good” prognostic factor.Fig. 1 Low expression of HADH was associated with poor prognosis in patients with ccRCC. (A) The protein expression level of HADH in different types of malignant tumors between adjacent normal tissues and cancer tissues in CPTAC database. (B) The mRNA expression level of HADH in different types of malignant tumors between adjacent normal tissues and cancer tissues in TCGA database. (C) The mRNA expression level of HADH between ccRCC tissues (n=538) and adjacent normal tissues (n=72) in the TCGA KIRC. (D) Overall survival and (E) disease free survival curve of ccRCC patients with low and high HADH expression in TCGA KIRC. Relative expression levels of HADH at different stages (F) and grades (G) in TCGA KIRC. (H) The mRNA expression level of HADH between ccRCC tissues and paired adjacent normal tissues in the GSE40435 dataset (n=101). * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Fig 1

Table 1 Correlation between HADH expression and clinicopathological characteristics of ccRCC.

Table 1Characteristics	HADH	P value	
	High expression cases	Low expression cases		
Age(y)		
≥60	140	149	0.436000	
<60	129	120	
Gender		
Male	167	185	0.103000	
Female	102	84	
Grade		
G1	9	5	0.000006	
G2	137	98	
G3	96	110	
G4	20	55	
Gx	7	1	
Clinical stage		
Ⅰ	164	107	<0.000001	
Ⅱ	30	29	
Ⅲ	53	70	
Ⅳ	20	62	
unkown	2	1	
T classification		
T1	167	110	0.000004	
T2	33	38	
T3	67	112	
T4	2	9	
N classification		
N0	124	117	0.293000	
N1	5	11	
Nx	140	141	
Metastasis		
M0	231	197	0.000005	
M1	19	59	
Mx	19	13	

HADH was downregulated in ccRCC clinical specimens and cell lines

To confirm the results from public database, RT-PCR, Western blot and immunohistochemistry (IHC) staining were performed on ccRCC tissues and paired normal renal tissues to detect the expression of HADH at the mRNA and protein levels. As shown in (Fig. 2A, B and C), HADH mRNA and protein levels were significantly down-regulated in ccRCC tissues compared with those in paired adjacent normal renal tissues. Moreover, the IHC results of ccRCC tissue microarray showed the same tendency (Fig. 2D, E and F). Furthermore, ccRCC cell lines (ACHN, Caki-1, 786-O and A498) and normal renal tubular epithelial cells (HK-2) were also cultured to validate the protein level of HADH. Consistent with ccRCC tissue, the expression of HADH in the four ccRCC cell lines showed a notable decrease compared with that in the control cell line HK-2 (Fig. 2G and H). Therefore, ACHN and Caki-1 cell lines were selected for cell transfection with lentivirus in our subsequent study because of their low HADH level. Collectively, these data confirmed that HADH was downregulated in both ccRCC tissues and ccRCC cell lines, indicating that HADH might be a tumor suppressor gene in ccRCC.Fig. 2 HADH was downregulated in ccRCC clinical specimens and cell lines. (A) Relative HADH mRNA expression in the frozen ccRCC tissues compared with paired normal renal tissues was detected by RT-PCR. β-actin was used as an internal control. (B and C) Western blot of HADH expression in the frozen ccRCC tissues compared with paired normal renal tissues. HSP90 was used as a loading control. (D) Gross tissue microarray images showed IHC staining of HADH in 16 pairs of ccRCC and adjacent normal tissues. (E and F) Representative images of IHC staining from the cohort shown in Fig. 2D, positive staining (DAB) was denoted by the brown color. (G and H) The protein expression level of HADH in four ccRCC cell lines (ACHN, Caki-1, 786–0 and A498) and normal renal tubular epithelial cell line (HK-2). *** p < 0.001, **** p < 0.0001.

Fig 2

Overexpression of HADH inhibited the malignant behaviors of ccRCC cells in vitro

Since HADH was significantly downregulated in ccRCC, we proposed a hypothesis that HADH might regulate the malignant behaviors of ccRCC. To investigate the effect of HADH on ccRCC, ACHN and Caki-1 cell lines were chosen to construct HADH overexpressed cells or Vector cells by transfecting with lentivirus for subsequent experiments (Fig. 3A and B). CCK8 assays and the cell proliferation marker Ki67 showed that overexpression of HADH significantly attenuated ACHN and Caki-1 cell proliferation (Fig. 3C and D). Besides, the colony formation assay demonstrated that colony numbers were significantly decreased when HADH was overexpressed (Fig. 3E). Hence, these data indicated that overexpression of HADH suppressed the proliferation of ccRCC cells. The wound healing assay showed that HADH overexpression significantly impaired the motility of ACHN and Caki-1 cells (Fig. 3F). The transwell assay demonstrated that HADH overexpression remarkably decreased the migration and invasion of ACHN and Caki-1 cells (Fig. 3G). Western blot results also indicated that HADH overexpression significantly increased the expression of apoptotic proteins cleaved Caspase3 (Fig. 3H). We also observed a marked increase in the apoptotic cell population in the HADH overexpressed ccRCC cells compared with the Vector ccRCC cells, as detected by a flow cytometry (Fig. 3I). Overall, these data above evinced that HADH overexpression can inhibit the malignant behaviors of ccRCC cell lines in vitro.Fig. 3 Overexpression of HADH inhibited the malignant behaviors of ccRCC cells in vitro. (A) The mRNA level of HADH in ACHN and Caki-1 cells infected with Vector Lentivirus or HADH overexpressed Lentivirus was detected by RT-PCR. β-actin was used as an internal control. (B) The protein level of HADH in ACHN and Caki-1 cells infected with Vector Lentivirus and HADH overexpressed Lentivirus was detected by Western blot. HSP90 was used as a loading control. (C) Cell proliferation ability was detected by CCK8 assay in ACHN and Caki-1 cells. (D) Expression of Ki67 was detected by immunofluorescence staining. (E) Cell proliferation ability was detected by clone formation assay. (F) Tumor cell migration ability was detected by the wounding healing assay in ACHN and Caki-1 cells. (G) Cell migration and invasion ability was detected by the transwell assay in ACHN and Caki-1 cells. (H) The expression level of apoptosis-related protein cleaved Caspase3 was detected by Western blot in ACHN and Caki-1 cells. HSP90 was used as a loading control. (I) Cell apoptosis was detected by a flow cytometry in ACHN and Caki-1 cells. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Fig 3

Overexpression of HADH suppressed tumor growth and lung metastasis in vivo

To elucidate the role of HADH in the modulation of ccRCC progression in vivo, we performed subcutaneous xenograft models in nude mice. After six weeks of inoculation with ACHN cells with HADH overexpressed or ACHN cells with Vector, subcutaneous transplantation tumors in the HADH overexpressed group showed a significant regression of tumor growth compared with those in the Vector group (Fig. 4A, B, C and D). To confirm the impact of HADH on ccRCC metastasis in vivo, ACHN cells with HADH overexpressed or ACHN cells with Vector were inoculated slowly through the tail vein into nude mice, and HADH overexpression remarkably decreased the number of lung metastasis nodules (Fig. 4E, F and G). Taken together, these results suggested that HADH repressed ccRCC progression in vivo.Fig. 4 Overexpression of HADH suppressed tumor growth and lung metastasis in vivo. (A) Body weight curve of nude mice with subcutaneous xenograft tumors injected with HADH overexpressed cells or Vector cells (n=10 in each group). (B) Growth curve of subcutaneous xenograft tumors from mice injected with HADH overexpressed cells or Vector cells. (C) Photo of subcutaneous xenograft tumors from mice injected with HADH overexpressed cells or Vector cells. (D) Weight of subcutaneous xenograft tumors from mice injected with HADH overexpressed cells or Vector cells. (E) Body weight curve of nude mice injected with HADH overexpressed cells or Vector cells through tail vein (n=10 in each group). (F) Photo of lungs with metastatic nodules in nude mice with tail vein metastasis injected with HADH overexpressed cells or Vector cells. (G) After HE staining, the number of metastatic nodules in the lungs of nude mice was identified under a microscope. ** p < 0.01, **** p < 0.0001.

Fig 4

HADH overexpression reduced glutathione and increased ROS in ccRCC cells

To further elucidate the comprehensive mechanism how HADH suppressed ccRCC progression, non-target metabolomics was performed in ACHN cells with HADH overexpressed and ACHN cells with Vector. In the principal component analysis score plots (PLS-DA) for both negative electrospray ionisation (ESI−) and positive electrospray ionisation (ESI+) models, the quality control (QC) samples clustered tightly together, which confirmed data reliability (Supplement Figure 1). The levels of the differential metabolites in ACHN cells with HADH overexpressed were significantly different from those in ACHN cells with Vector in ESI− and ESI+ models (Fig. 5A).A total of 165 differential annotated metabolites were detected in all samples, including 86 (ESI−) and 79 (ESI+) metabolites (Supplement Table 3 and Supplement Table 4). In order to investigate the functional characteristics and classification of these differential metabolites, we carried out pathway enrichment analysis and identified the significant enriched pathways in ESI− and ESI+ models. We found that GSH metabolism was the top five metabolic enrichment pathways shared by both ESI− and ESI+ models (Fig. 5B). By extracting the non-target metabolomics data, we found that the five differential metabolites involved in the GSH metabolism pathway included Cysteinylglycine, L-Glutamic acid, Pyroglutamic acid, γ-Glutamyl cysteine and γ-Glutamylalanine, whose relative abundance were significantly reduced in the HADH overexpressed cells (Fig. 5C). Subsequently, we detected the total GSH content, GSH (reduced form), and GSH/GSSG ratio in ACHN and Caki-1 cells with HADH overexpressed or Vector. The data showed that HADH overexpression decreased the GSH content and GSH/GSSG ratio (Fig. 5D).Fig. 5 HADH overexpression reduced GSH and increased ROS in ccRCC cells. (A) The volcano plot showed the abundance difference of metabolites between ACHN cells with HADH overexpressed and ACHN cells with Vector. Metabolites with significant changes were presented in red (upregulated) or blue (downregulated). (B) Metabolic pathway enrichment analysis of differential metabolites between ACHN cells with HADH overexpressed and ACHN cells with Vector according to the KEGG pathway in both ESI- and ESI+ models. (C) The non-target metabolomics data showed the relative abundance of five differential metabolites in glutathione metabolism pathway between ACHN cells with HADH overexpressed and ACHN cells with Vector. (D) Total GSH, reduced GSH and reduced GSH/GSSG ratio were detected by a microplate reader with the GSH and GSSG Assay Kit in ccRCC cells with HADH overexpressed and ccRCC cells with Vector. (E) ROS accumulation in ACHN cells was observed by a fluorescence microscope with DHE staining. (F) Lipid peroxidation marker MDA was detected by a microplate reader with the MDA Assay Kit in ccRCC cells with HADH overexpressed and ccRCC cells with Vector. (G) Mitochondrial structural changes in ACHN cells were observed by a transmission electron microscope. (H) The changes of mitochondrial membrane potential in ACHN cells were observed by a fluorescence microscope with Mito-Tracker Red CMXRos staining. * p < 0.05, *** p < 0.001, **** p < 0.0001.

Fig 5

GSH was the most important non-enzymatic intracellular ROS scavenger, which existed in a reduced form (GSH) and an oxidized disulfide form (GSSG) [20]. GSH metabolism was a key metabolic alteration involved in ccRCC progression, GSH and GSH-related metabolites, including cysteine and γ-glutamyl cysteine, were significantly increased in advanced ccRCC and were associated with worse survival outcomes in patients with ccRCC [21], so we proposed a hypothesis that the inhibitive effect of HADH on ccRCC growth might relate to GSH depletion and oxidative stress injury. ROS was byproducts of oxidative metabolism which was primarily produced in mitochondria [22]. We detected ROS production and mitochondrial in ccRCC cells with HADH overexpressed or Vector. DHE staining showed that ROS production was significantly increased in HADH overexpressed cells compared with Vector cells (Fig. 5E). As a biomarker of oxidative stress, malondialdehyde (MDA) existed in both tissue and blood, and its concentration was directly proportional to the cell damage caused by free radicals [23]. Compared with that in Vector cells, MDA level were significantly increased in HADH overexpressed cells (Fig. 5F). Then transmission electron microscopy (TEM) was used to detect mitochondrial morphology. We found that HADH overexpression caused the presence of structural aberrations in mitochondria, including mitochondrial swelling, disorganized and fragmented cristae (Fig. 5G). The mitochondrial membrane potential (ΔΨm) was a valuable indicator of mitochondrial function, loss of ΔΨm reflected mitochondrial dysfunction, and the feeble ΔΨm implied a primary change of cell apoptosis. We found that HADH overexpression induced ΔΨm changes in ccRCC cells, which demonstrated by Mito-Tracker Red CMXRos staining (Figs. 5H). Altogether, these data showed that HADH overexpression induced oxidative stress injury and mitochondria damage in ccRCC cells.

HADH negatively regulated the expression of glutathione synthesis genes and nuclear translocation of NRF2 in ccRCC cells

The major determinant of GSH synthesis was the activity of the rate-limiting enzyme, glutamate cysteine ligase (GCL), which was composed of a catalytic subunit (GCLC) and a modifier subunit (GCLM). The second enzyme of GSH synthesis, GSH synthase (GSS), was also regulated in a coordinated manner as GCL subunits and its upregulation could further enhance the capacity of the cell to synthesize GSH [24,25]. In order to validate whether the decrease of GSH content induced by HADH overexpression was dependent on the reduction of GSH synthase, we examined the mRNA and protein levels of GCLC, GCLM, and GSS by RT-PCR and Westren blot. The results showed that HADH overexpression decreased the expression of GCLC, GCLM, and GSS (Fig. 6A and B). NRF2 was a master antioxidant transcription factor that served a critical role in protecting cancer cell from oxidative stress injury through promoting the expression of several enzymes responsible for GSH synthesis, including GCLM, GCLC and GSS [26,27]. The bioinformatics analysis about the correlation between NRF2 gene (NFE2L2) and HADH indicated that NFE2L2 was significantly negatively correlated with HADH in ccRCC tumor cells (Supplement Figure 2). And then we confirmed that the nuclear NRF2 was significantly decreased in ACHN cells with HADH overexpressed using immunofluorescence staining (Fig. 6C). Additionally, Western blot also showed a remarkably reduced nuclear NRF2 in ccRCC cells with HADH overexpressed compared with ccRCC cells with Vector (Fig. 6D). These data suggested that HADH overexpression suppressed the nuclear translocation of NRF2 and might decrease the expression of its downstream target genes, resulting in reduced GSH production in ccRCC cells. Given that HADH overexpression inhibited the nuclear translocation of NRF2 and reduced the expression of its target genes GLC and GSS, we postulated that NRF2 was required for HADH-mediated tumor growth suppression in ccRCC. To validate our hypothesis, we overexpressed NRF2 with plasmid pcDNA3.1-NRF2 in ACHN cells, and confirmed by immunofluorescence staining (Fig. 6E).Fig. 6 HADH negatively regulated the expression of glutathione synthesis genes and nuclear translocation of NRF2 in ccRCC cells. (A) The mRNA expression of GCLC, GCLM and GSS was detected by RT-PCR in ccRCC cells with HADH overexpressed and ccRCC cells with Vector. (B) The protein level of GCLC, GCLM and GSS was detected by Western blot in ccRCC cells with HADH overexpressed and ccRCC cells with Vector. (C) The expression of NRF2 in ACHN cells with HADH overexpressed or Vector was observed by immunofluorescence staining. (D) Protein levels of NRF2 in cytoplasm and nucleus of ACHN and Caki-1 cells with HADH overexpressed or Vector were detected by Western blot. (E) The expression of NRF2 was observed by immunofluorescence staining in ACHN cells infected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2. * p < 0.05, ** p < 0.01, *** p < 0.001.

Fig 6

Subsequently, CCK-8 assays, apoptosis assays and transwell assays were performed to estimate whether NRF2 overexpression affected cell proliferation, apoptosis and migration in ACHN cells. We found that in ACHN cells with HADH overexpressed, NRF2 overexpression promoted cell proliferation, migration and invision, however, inhibited cell apoptosis (Fig. 7A, B and C), which indicated that NRF2 partially rescued the HADH-mediated tumor cell suppression. Meanwhile, exogenous NRF2 increased the expression of GCLC and GCLM both at mRNA and protein levels, while GSS mRNA and protein levels were not affected (Fig. 7D and E). In addition, NRF2 overexpression increased GSH content, accompanied by decreased accumulation of ROS and MDA in ACHN cells with HADH overexpressed (Fig. 7F, G and H). Taken together, these data indicated that HADH overexpression suppressed tumor growth through NRF2-dependent glutathione synthesis.Fig. 7 HADH overexpression suppressed tumor growth through NRF2-dependent glutathione synthesis. (A) The proliferation ability of ACHN cells transfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2 was detected by CCK8. (B) Cell apoptosis was analyzed using a flow cytometry in ACHN cells twransfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2. (C) Cell migration and invasion ability was detected by the transwell assay in ACHN cells transfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2. (D) The mRNA expression of GCLC, GCLM and GSS in ACHN cells transfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2 was detected by RT-PCR. (E) The protein level of GCLC, GCLM and GSS in ACHN cells transfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2 was detected by Western blot. (F) Total GSH, reduced GSH and reduced GSH/GSSG ratio in ACHN cells transfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2 were detected by a microplate reader with the GSH and GSSG Assay Kit. (G) Fluorescence intensity of DHE in ACHN cells transfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2 was detected by a flow cytometry with DHE staining. (H) Lipid peroxidation in ACHN cells transfected with plasmid pcDNA3.1 or pcDNA3.1‑NRF2 was detected by a microplate reader with the MDA Assay Kit. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Fig 7

Discussion

Increasing evidence indicates that HADH is differentially expressed in various types of cancers and closely linked to tumor development and progression. Reduced HADH level can disrupt fatty acid β-oxidation and stimulate fatty acid accumulation, which leads to fatty acid metabolism reprogramming and promotes tumor development [9]. The expression of HADH was decreased in gastric cancer tissues, and down-regulation of HADH could inhibit the expression of PTEN and promote the phosphorylation of AKT, further stimulates the proliferation, migration, and invasion of gastric cancer cells [15]. Nevertheless, unlike gastric cancer, HADH was highly expressed in colon cancer cells, high HADH levels promoted cell proliferation and were significantly associated with poor clinical outcomes [28]. Consistent with our study, the expression of HADH was markedly downregulated in ccRCC tissues compared with that in adjacent normal tissues, and HADH expression was associated with tumor-infiltrating immune cells (TIICs) especially regulatory T cells [29,30]. Because the latter study revealed the potential role of HADH in ccRCC was related to immune infiltration mainly using several online databases and CIBERSORT, which might lead to a bias, it was necessary to conduct more rigorous wet laboratory experiments to explore the exact role of HADH in the progression of ccRCC. In our study, we also found that HADH expression was lower in ccRCC samples compared with their paired normal renal samples both at mRNA and protein level, and was highly negatively correlated with clinical outcomes in patients with ccRCC. Furthermore, HADH overexpression not only inhibited ccRCC proliferation, migration and invasion, and aggravated cell apoptosis in vitro, but also suppressed the ccRCC tumor growth and lung metastasis in vivo.

To investigate the possible molecular mechanism through which HADH overexpression inhibited the ccRCC malignant behavior, we found that glutathione (GSH) metabolism was obviously changed in ccRCC cells. GSH is the most abundant antioxidant in cells that can convert hydrogen peroxide into water, and is required for the protection against oxidative damage and detoxification of endogenous and exogenous molecules. The reduced form GSH can be converted to the oxidized form GSSG through a reaction regulated by glutathione peroxidase. It was shown that increased GSH could potentially alleviated cisplatin-induced toxicity on HK-2 cells with pretreatment of Bismuth Zinc Citrate [31]. Functional VHL reconstruction restored the cellular oxidative metabolism and β-oxidation to increase fatty acid degradation to prevent ferroptosis induced by reduced form GSH synthesis in ccRCC cells. Moreover, inhibiting GSH biosynthesis effectively inhibited tumor growth in a mouse model of renal cancer. This research results suggested that targeting GSH biosynthesis and GPX activity may be a promising strategy for treating ccRCC [25]. Our data also agreed with the previous study that the upregulation of glutathione/oxidized glutathione pathway accompanied with elevated GSH level and inhibited fatty acid β-oxidation were correlated with higher grade, higher stage and metastasis in patients with ccRCC [32].

It is believed that the interaction between cell metabolism and redox condition can affect the proliferation of cancer cells [22,33]. Because mitochondria produces a large amount of ROS in the process of oxidative phosphorylation, and ROS can transform reduced glutathione into oxidized glutathione, oxidative metabolism may affect the redox environment of cells by changing glutathione levels. This precisely regulated antioxidant system is crucial for the survival of cancer cells and the resistance to various harmful pressure, including chemotherapeutic compounds [34]. Therefore, the effectiveness of methods targeting cancer metabolism to induce tumor cell death may depend on the response capacity of glutathione buffering system to the changes in ROS level. Sulforaphene induced the generation of a large amount of ROS and suppressed the viability of colon cancer cells through an obvious decrease in cellular glutathione levels [35]. The fatty acid derivative palmitoylcarnitine abrogated colorectal cancer cell survival, partly due to the inability to prevent oxidative stress through the glutathione redox systemby depleting glutathione, thereby making the cells sensitive to elevated H2O2 [36].In our study, HADH overexpression induced oxidative stress injury and mitochondrial structure damage in ccRCC cells.

Glutathione biosynthesis consists of two enzymatic reactions catalyzed by glutamate-cysteine ligase (GCL) and GSH synthetase (GSS). A previous study described that Bucillamine induced glutathione biosynthesis via activation of the transcription expression of GLC in hepatoma cells [37]. Our study showed that the upregulation of HADH inhibited the expression of GLC, GCLM and GSS mRNA, and then attenuated total GSH level in ccRCC cells. It is reported that GCLC, GCLM, and GSS are transcriptional targets of Nuclear Factor E2-related factor 2 (NRF2) [38,39]. NRF2 is a transcription factor that coordinates the antioxidant capacity in many tissues and cells, and plays a powerful role in protecting cells from endogenous and exogenous oxidative stress damage [40,41]. Some reports show that abnormal activation of NRF2 may promote the progression, metastasis and drug resistance of renal cell carcinoma, resulting in resistance to radiotherapy and chemotherapy [[42], [43], [44], [45]]. Our study showed that NRF2 was negatively regulated by HADH overexpression, and exogenous increase of NRF2 promoted cell proliferation and suppressed cell apoptosis in ccRCC cells, accompanied by an increased GSH level and a decreased ROS level. But how HADH regulates NRF2 needs further exploration.

In summary, low expression of HADH can be a potential marker for diagnosis and poor prognosis for ccRCC. Moreover, HADH overexpression inhibited the malignant progression of ccRCC through downregulation of NRF2-dependent GSH synthesis. Our results may provide a new insight regarding the relationship between HADH and NRF2-dependent glutathione metabolism that should help us understand the tumorigenic mechanism in ccRCC, and serve as a novel potential target for the treatment of ccRCC.

Conclusion

HADH is a tumor suppressor that plays a vital role in preventing ccRCC cell proliferation, migration, and invasion through inhibition of NRF2-dependent glutathione synthesis pathway.

Ethical approval

All study procedures were performed on approval by the Institutional Ethics Committee of the First Hospital of Chongqing Medical University (approval number: 2021–465) and was in concordance with the Helsinki Declaration.

Funding

This study was supported by grants from the 10.13039/501100013290 National Key R&D Program of China (2018YFA0800401 ), the 10.13039/501100001809 National Natural Science Foundation of China (82070899 , 82000744 , and 82011530460 ), the Natural Sciences Foundation of Chongqing (CSTB2022NSCQ-MSX0827 ), the Chongqing Medical University (CQMU) Program for Youth Innovation in Future Medicine (W0046 ), and the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202300444 ).

CRediT authorship contribution statement

Changbin Chu: Writing – review & editing, Writing – original draft, Validation, Methodology, Conceptualization. Shangjing Liu: Writing – review & editing, Writing – original draft, Validation, Data curation. Zhiting He: Writing – review & editing, Methodology, Data curation. Mingjun Wu: Validation, Methodology, Investigation. Jing xia: Validation, Methodology, Investigation, Data curation. Hongxiang Zeng: Writing – review & editing, Investigation. Wenhua Xie: Validation, Formal analysis. Rui Cheng: Writing – original draft, Visualization. Xueya Zhao: Writing – review & editing, Funding acquisition, Formal analysis. Xi Li: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Supplement Fig. 1. PLS- DA score plots for ACHN HADH cell samples and ACHN Vector cell samples in ESI- and ESI+ models.

Image, image 1

Supplement Fig. 2. The correlation analysis indicated a significant negative correlation between NFE2L2 and HADH in ccRCC tumor cells. (A) Correlation analysis of NFE2L2 and GCLC based on TCGA KIRC. (B) Correlation analysis of NFE2L2 and GCLM based on TCGA KIRC. (C) Correlation analysis of NFE2L2 and GSS based on TCGA KIRC. (D) Cluster the single-cell sequencing data of ccRCC in the GEO dataset and label cell types based on existing annotation information (Raw data for the single cell RNA and TCR-seq experiments was deposited at https://www.ncbi.nlm.nih.gov/sra/PRJNA705464). (E) Extract cancer cells from single-cell data for subpopulation clustering, and display different subpopulations of tumor cells through UMAP dimensionality reduction. (F). Heat map shows the expression of HADH in different subgroups of tumor cells. (G) According to the expression pattern of HADH, tumor cells are defined as different subgroups. (H) The violin diagram showed the expression of HADH in different subgroups of tumor cells. (I) Heat map showed the expression of NFE2L2 in different subgroups of tumor cells. (J) The violin diagram showed the expression of NFE2L2 in different subgroups of tumor cells. (K) Correlation analysis of the expression of HADH and NFE2L2 in tumor cells.

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Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgements

The Authors express their thanks to the researchers and study participants for their contributions.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.102112.
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