Home Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
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Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features

  • Yingxi Mo , Jun Zhao , Ran Zhao , Yiying Huang , Ziyuan Liang , Xiaoying Zhou , Jiemei Chu , Xinli Pan , Siyu Duan , Shiman Chen , Liufang Mo , Bizhou Huang , Zhaozhang Huang , Jiale Wei , Qian Zheng EMAIL logo and Wenqi Luo EMAIL logo
Published/Copyright: September 8, 2023
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Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

Clear cell renal cell carcinoma (ccRCC) is a major pathological type of kidney cancer with a poor prognosis due to a lack of biomarkers for early diagnosis and prognosis prediction of ccRCC. In this study, we investigated the aberrant expression of Acyl-coenzyme A oxidase 1 (ACOX1) in ccRCC and evaluated its potential in diagnosis and prognosis. ACOX1 is the first rate-limiting enzyme in the peroxidation β-oxidation pathway and is involved in the regulation of fatty acid oxidative catabolism. The mRNA and protein levels of ACOX1 were significantly downregulated in ccRCC, and its downregulation was closely associated with the tumor-node-metastasis stage of patients. The ROC curves showed that ACOX1 possesses a high diagnostic value for ccRCC. The OS analysis suggested that lower expression of ACOX1 was closely related to the worse outcome of patients. In addition, gene set enrichment analysis suggested that expression of ACOX1 was positively correlated with CDH1, CDH2, CDKL2, and EPCAM, while negatively correlated with MMP9 and VIM, which strongly indicated that ACOX1 may inhibit the invasion and migration of ccRCC by reversing epithelial-mesenchymal transition. Furthermore, we screened out that miR-16-5p is upregulated at the mRNA transcript level in ccRCC and negatively correlated with ACOX1. In conclusion, our results showed that ACOX1 is abnormally low expressed in ccRCC, suggesting that it could serve as a diagnostic and prognostic biomarker for ccRCC. Overexpression of miR-16-5p may be responsible for the inactivation of ACOX1.

Keywords: clear cell renal cell carcinoma; ACOX1; miR-16-5P; bioinformatics

1 Introduction

Approximately 430,000 cases of renal cell carcinoma (RCC) are diagnosed each year, with clear cell renal cell carcinoma (ccRCC) being the main pathological type of RCC. [1]. Despite recent advances in diagnostic methods, the incidence of RCC increases every year, and a significant proportion of patients are diagnosed at an advanced stage, with 17% of patients having distant metastases [2]. Although surgical resection improves the 5-year survival rate in ccRCC, the overall prognosis remains poor [3,4]. To date, although surgery remains the gold standard of treatment for ccRCC, approximately one-third of patients will still have a recurrence after surgery. Therefore, there is an urgent need to find new ways to treat the disease [5,6]. As research into genes and mRNAs deepens, microRNAs help contribute to improve the understanding of cancer development and targeted therapies [7,8]. Therefore, the identification of novel biomarkers for diagnosis and personalized treatment of ccRCC is urgently needed.

A common feature of cancer cells is the ability to rewire their metabolism to sustain the production of ATP and macromolecules needed for cell survival and proliferation [9]. In recent years, lipid metabolism in tumorigenesis has been extensively studied. As lipids provide essential materials for cellular membrane formation and realize other functions for proliferating tumor growth, such as phosphatidylcholine (PC) and phosphatidylethanolamine (PE). Cancer cells use de novo synthesis of fatty acids (FAs) for signal transduction, building the cellular membrane, or energy supply [10,11]. For example, FAs stored within lipid droplets provide adenosine triphosphate (ATP) molecules through β-oxidation, mainly serving as an energy source for cell proliferation [9,12]. β-Oxidation takes place in the mitochondria and peroxisomes and is generally considered to be part of cancer metabolism [13]. In addition to ATP synthesis, β-oxidation is also involved in synthesizing NAPDH in the cytoplasm, thereby facilitating the de novo synthesis of fatty acids by cancer cells. Numerous studies have shown that abnormalities in β-oxidation are closely linked to tumorigenesis and cancer cells rely on their reprogrammed metabolism for proliferation, survival, and metastasis [14].

Acyl-coenzyme A oxidase 1 (ACOX1), the first rate-limiting enzyme for fatty acid β-oxidation (FAO), is a highly conserved enzyme and is mainly expressed in the liver, followed by the kidney, brain, and adipose tissue [15]. FAO takes place in mitochondria and peroxisomes [13], and its abnormalities are associated with tumorigenesis [14]. Peroxisomes are ubiquitous organelles in eukaryotic cells involved in several metabolic pathways, such as FAO, bile acid synthesis, and purine degradation, etc. [16]. As performing the first step of peroxisomal β-oxidation, which is the major enzymatic step [17], an aberrant expression of ACOX1 has been implicated in a variety of cancers. It has been demonstrated that the upregulation of ACOX1 in lymphoma promotes cancer cell proliferation, while its downregulation inhibits proliferation and induces apoptosis [18]. Knocking down ACOX1 promotes lipid hydrolysis by inducing autophagy in the liver, thereby preventing hepatic steatosis. ACOX1 might serve as a new therapeutic option for treating nonalcoholic fatty liver disease (NAFLD) [19].

Herein, we aimed to demonstrate the differential expression of ACOX1 in ccRCC and evaluate its diagnostic and prognostic value in ccRCC. In addition, we tried to reveal the molecular mechanism for the aberrant expression of ACOX1 in ccRCC, from the aspect of miRNAs.

2 Materials and methods

2.1 Cell lines and tissue samples

The human renal epithelial cell line 293T and ccRCC cell lines 786-O and Caki-2 were procured from Procell Life Science & Technology Co., Ltd. (Wuhan, China). The 293T human renal epithelial cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Gibco, USA) supplemented with 10% fetal bovine serum (FBS; Invitrogen) and penicillin (100 U/ml). The human ccRCC cell lines 786-O (CL-0010, China) were maintained in 1640 medium (Invitrogen, Carlsbad, CA, USA) supplemented with 10% FBS and penicillin (100 U/ml). The Caki-2 (CL-0326, CHINA) was cultured in McCoy’s 5a Medium Modified supplemented with 10% FBS and penicillin (100 U/ml). All cell lines used in this study were cultured under standard culture conditions (5% CO2, 37°C).

The complementary deoxyribonucleic acid (cDNA) tissue array (Cat no: me cDNA-HKidE030CS01, Shanghai OUTDO Biotech Co., Ltd.) used in this study included 15 ccRCC samples and 15 matched precancerous tissue samples. All 15 patients were enrolled in this study based on pathological examination, and all patients provided written informed consent.

Sixty-four formalin-fixed, paraffin-embedded ccRCC tissues and para-carcinoma tissues were obtained from patients at Guangxi Medical University Cancer Hospital (Nanning, Guangxi, China). Complete clinicopathological and follow-up data are available for 15 ccRCC patients (nine males and six females).

  1. Informed consent: Informed consent has been obtained from all individuals included in this study.

  2. Ethical approval: The research related to human use has been complied with all the relevant national regulations, institutional policies, and in accordance with the tenets of the Helsinki Declaration, and has been approved by the Ethics Committees of Guangxi Medical University Cancer Hospital (Nanning, China [LW2020043]).

2.2 TCGA database

The mRNA-seq expression data were downloaded from the University of California, Santa Cruz Xena browser (https://xenabrowser.net/), including 534 ccRCC tissues and 72 normal tissue samples. On the other hand, corresponding clinicopathological parameters include age, sex, tumor-node-metastasis stage, pathological stage, and tumor diameter.

2.3 Gene expression omnibus (GEO) database

We searched ccRCC-relevant microarray data sets of ACOX1 from the National Center of Biotechnology Information Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/) to examine the role of ACOX1 in ccRCC. By using the following terms: (renal OR kidney) AND (cancer OR carcinoma* OR tumor OR tumor OR tumors OR tumors OR neoplasm* OR malignance* OR oncology* OR KIRC OR ccRCC OR RCC). The inclusion criteria were as follows: (1) human tissue samples, (2) no less than four cancer and non-cancer samples, and (3) data sets containing ACOX1 mRNA expression data. The meta-analysis was performed using StataSE-64 software.

2.4 The University of ALabama CANcer (UALCAN) database

UALCAN (http://ualcan.path.uab.edu/) is a public online available database for analyzing the cancer omics data (TCGA, MET500, Clinical Proteomic Tumour Analysis Consortium (CPTAC), and Children’s Brain Tumour Tissue Consortium (CBTTC). We used the database to explore transcription and expression levels of ACOX1 in ccRCC and normal tissues.

2.5 RNA isolation and Quantitative Real-time reverse transcription polymerase chain reaction (qRT-PCR)

Total RNA was prepared with TRIZol reagent (Invitrogen, Carlsbad, CA, USA) and reverse transcribed into cDNA with PrimeScript RT-PCR kit (Takara Bio, code No. 15596-026, USA). RR036A, Japan). Quantitative RT-PCR was then performed on a QuantStudio 5 Flex Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA) using the SYBR Green quantitative PCR kit (#A25742, Thermo Fisher Scientific, Waltham, MA, USA). The Ct (2−ΔΔCt) method was used to calculate the ACOX1 mRNA transcript level. The reference gene for quantification assays was β-actin. The sequences of the qRT-PCR primers are as follows:

  • ACOX1 Forward: 5′-CCCATAAGCCTTTGCCAGGA-3′;

  • ACOX1 Reverse: 5′-GGCTTCACCTGGGCATACTT-3′;

  • β-actin-Forward: 5′-GCTCAGACACCATGGGGAAG-3′;

  • β-actin-Reverse: 5′-TGTAGTTGAGGTCAATGAAGGGG-3′.

2.6 Immunohistochemistry (IHC) staining

Paraffin-embedded ccRCC tissues were cut into 4-μm thick sections. An SP-9000 detection kit (ZSGB-Bio, Beijing, China) was used to perform the IHC staining. The slides were subsequently baked at 60°C for 1 h, deparaffinized, and rehydrated in xylene and decreasing concentrations of ethanol, followed by antigen retrieval in 10 mM Tris–citrate buffer (pH of 7.0) in a pressure cooker. Endogenous peroxidase activity of the sections was blocked by a streptavidin-peroxidase staining kit (No. SP-9000; Zsbio, China), and each section was then incubated with 5% normal goat serum for 30 min at room temperature to block nonspecific bindings. Subsequently, the slides were incubated with a primary ACOX1 antibody (Cat No. 10957-1-AP, Wuhan Sanying [diluted 1:200]) at 4°C overnight. After washing the primary antibody with phosphate-buffered saline, the slides were then incubated with a secondary antibody (SP-9000, ZSGB-BIO, Beijing, China), at room temperature for 10 min. Finally, the slides were developed with 3,3′-diaminobenzidine (DAB, ZLI9018ZSGB-BIO, Beijing, China), and hematoxylin counterstaining, dehydrating, clearing, and mounting were performed.

2.7 Evaluation of IHC staining

IHC staining results were confirmed by at least two experienced pathologists in a double-blind manner. The final IHC scores were the product of the “positive staining rate score” and “staining intensity score.” The positive staining rate score was recorded based on the percentage of positive staining cells as follows: <10% is 0, 10–50% is 1, 51–75% is 2, and ≥75% is 3. The intensity of cell staining was defined as follows: 0, negative; 1, light yellow; 2, brown or yellow; and 3, brown. The percentage and intensity scores of the five random fields were multiplied. Finally, the staining score was graded as 2–3, weakly positive (+); 4–6, moderately positive (++); 7–9, strongly positive (+++); and ≥9 (+), all positive expressions. Images were obtained using an Olympus microscope (Olympus BX53, Japan).

2.8 Gene expression profiling interactive analysis (GEPIA)

GEPIA (http://gepia2.cancer-pku.cn/#index) online database contains RNA sequencing data from TCGA and GTEX, including 9,736 tumor tissues and 8,587 normal tissue samples. We used GEPIA to generate a overall survival curve of ACOX1 to explore the effect of ACOX1 expression on the prognosis of patients with ccRCC.

2.9 Gene set enrichment analysis (GSEA)

GSEA was performed on the dataset (534 ccRCC samples and 72 matched precancerous samples) from TCGA. The data set (h.all. v6.1.symbols) was categorized into high- and low-expression groups based on the median ACOX1 gene expression, and the signaling mechanism involved was analyzed in the ACOX1 overexpression group by using GSEA 4.2.3. Statistical significance was considered when the false discovery rate (FDR) was <0.25, and the P-value of the normalized enrichment score (NES) was <0.05.

2.10 Prediction of miRNAs potentially targeting the ACOX1mRNA

Targeted prediction of potential miRNAs of ACOX1 was performed using four public databases: miRDB (http://mirdb.org/), Tarbase (https://dianalab.e-ce.uth.gr/html/diana/web/index.php? r = tarbasev8%2Findex), ENCORI (https://starbase.sysu.edu.cn/), and TargetScan (https://www.targetscan.org/vert_72/).

2.11 Statistical analysis

In this study, IBM SPSS 23.0 and GraphPad Prism 8.0 were used for data analysis. The data were presented as the mean ± standard deviation (x ± s) in datasets. A t-test was used to analyze the expression of ACOX1 in the ccRCC and paracancerous samples from the TCGA and GEO databases. The meta-analysis was performed using Stata-SE-64 12.0 statistical software and the diagnostic value of the ACOX1 gene expression in ccRCC was evaluated based on the ROC curve. Survival analysis was performed using GEPIA. Kaplan–Meier survival analysis and univariate and multivariate Cox regression analyses were used to analyze the relationship between ACOX1 expression and poor prognosis in patients with ccRCC. Statistical significance was set at P < 0.05.

3 Results

3.1 Expression of ACOX1 significantly downregulated in ccRCC

To discover the changes in ACOX1 in ccRCC, based on the TCGA database, a significant down-regulation of ACOX1 was observed in patients with ccRCC (n = 533) compared with the normal control tissues (n = 72 [10.52 ± 0.41 vs 11.43 ± 0.63, P < 0.01]; Figure 1a).

Figure 1 The transcriptional level of ACOX1 in ccRCC. (a) ACOX1 mRNA expression in 533 ccRCC tissues and 72 normal tissues in the TCGA database. (b) Forest plot of mRNA transcript levels of ACOX1 in ccRCC. (c) Sensitivity analysis of the ACOX1 in ccRCC. (d) Begg funnel plot analysis of ACOX1 expression in ccRCC. (e) ACOX1 transcript levels in ccRCC cell lines and immortalized cell 293 T. (f) mRNA transcript levels in 15 ccRCC primary tumor tissues and matched paraneoplastic tissues (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 1

The transcriptional level of ACOX1 in ccRCC. (a) ACOX1 mRNA expression in 533 ccRCC tissues and 72 normal tissues in the TCGA database. (b) Forest plot of mRNA transcript levels of ACOX1 in ccRCC. (c) Sensitivity analysis of the ACOX1 in ccRCC. (d) Begg funnel plot analysis of ACOX1 expression in ccRCC. (e) ACOX1 transcript levels in ccRCC cell lines and immortalized cell 293 T. (f) mRNA transcript levels in 15 ccRCC primary tumor tissues and matched paraneoplastic tissues (*P < 0.05, **P < 0.01, ***P < 0.001).

Next, we performed a meta-analysis of the microarrays containing ACOX1 data in the GEO database. The analysis revealed that ACOX1 transcriptional levels were downregulated in 507 ccRCC tissues and 325 normal tissues (P = 0.000, I2 = 92.8%). There was significant heterogeneity (Figure 1b), although sensitive sexuality analysis verified that the expression of ACOX1 was downregulated in ccRCC (95% CI: −1.38 to −0.26, P = 0.000 [Figure 1c]). Simultaneously, Beggar’s funnel plot was used to evaluate publication bias, and the results revealed no publication bias (P = 0.428 > 0.05 [Figure 1d]).

Figure 2 The protein expression level of ACOX1 in ccRCC. (a) Protein expression levels of ACOX1 in 110 ccRCC tissues and 84 normal tissues. (b) Representative HE staining and IHC staining of ACOX1 in ccRCC and normal tissues. (c) Statistical analysis data of IHC staining score.
Figure 2

The protein expression level of ACOX1 in ccRCC. (a) Protein expression levels of ACOX1 in 110 ccRCC tissues and 84 normal tissues. (b) Representative HE staining and IHC staining of ACOX1 in ccRCC and normal tissues. (c) Statistical analysis data of IHC staining score.

Further, to evaluate whether its transcript expression levels were altered, we verified the transcript levels of ACOX1 in ccRCC cell lines and tissues by real-time PCR. A downregulation of mRNA transcription was found in Caki-2 and 786-O renal cancer cell lines compared to normal kidney 293T cell lines (Figure 1e). Also, downregulation of ACOX1 was found in tissue microarrays of 15 paired ccRCCs (Figure 1f).

In addition, using the CPTAC database, we observed that ACOX1 protein expression levels were significantly reduced in ccRCC tissues (n = 110) compared to normal tissues (n = 84; Figure 2a). IHC was used to evaluate ACOX1 protein levels in ccRCC tissues (n = 59) and normal tissues (n = 64; Figure 2b). Our results revealed that ACOX1 expression is significantly higher in normal tissues than in ccRCC tissues (Figure 2c). A lower ACOX1-positive rate was observed in 35 of 64 ccRCC tissues, while positive expression of ACOX1 was 47.5% (28 of 59) in normal tissue, suggesting ACOX1 downregulation in ccRCC (χ 2 = 0.057, P = 0.858). ccRCC patients with N (n = 4) and M stages (n = 6) exhibited prominently lower levels of ACOX1 expression (P < 0.05; Table 2).

3.2 ACOX1 is an effective biomaker for the diagnosis and prognosis of ccRCC patients

The ROC curve was used to assess the diagnostic value of ACOX1 in ccRCC. We found that the area under the curve (AUC) was 0.916 (P < 0.001) based on the TCGA database (Figure 3a). In ccRCC cDNA, its AUC was 0.898 (P > 0.001; Figure 3b). These findings suggested that ACOX1 could serve as a clinical diagnostic molecular marker for ccRCC. Figure 3c and d shows that the overall survival and disease-free survival analysis (DFS) of ACOX1 expression were performed using the GEPIA database. The plots indicated that ccRCC patients with a decreased ACOX1 expression had poor OS (log-ranch test, P = 3.4 × 10−7; Figure 3c). The OS rate (log-ranch test, P = 0.0007) and disease-free survival (DFS: HR = 9.5 × 10−7) are shown in Figure 3d. Thus, our data suggest that ACOX1 could serve as a prognostic molecular marker for ccRCC.

Figure 3 ACOX1 mRNA expression in ccRCC and correlated with diagnosis, and prognosis. (a) ROC curve of ACOX1 expression in 533 ccRCC tissues and 72 normal tissues based on TCGA database. (b) ROC curve of ACOX1 expression in 15 ccRCC tissues and matched paraneoplastic tissues from cDNA microarrays. (c) Analysis of the relationship between the mRNA expression level of ACOX1 and overall survival based on TCGA data. (d) Analysis of the relationship between the mRNA expression level of ACOX1 and disease-free survival of ccRCC patients based on TCGA data.
Figure 3

ACOX1 mRNA expression in ccRCC and correlated with diagnosis, and prognosis. (a) ROC curve of ACOX1 expression in 533 ccRCC tissues and 72 normal tissues based on TCGA database. (b) ROC curve of ACOX1 expression in 15 ccRCC tissues and matched paraneoplastic tissues from cDNA microarrays. (c) Analysis of the relationship between the mRNA expression level of ACOX1 and overall survival based on TCGA data. (d) Analysis of the relationship between the mRNA expression level of ACOX1 and disease-free survival of ccRCC patients based on TCGA data.

Based on clinical data from ccRCC patients in TCGA, we found that ACOX1 expression was lower in male patients (n = 345) than in female patients (n = 188) (10.49 ± 0.41 vs. 10.57 ± 0.41, P < 0.05). In the stage of patients, mRNA levels of ACOX1 were expressed at a lower level in patients with ccRCC in T3–T4 (n = 191) than in patients with T1–T2 (n = 342 [10.44 ± 0.38 vs. 10.57 ± 0.43]). Meanwhile, in terms of distant metastases and pathological staging, patients with stage M (n = 79) were lower than those with the non-M stage (n = 422 [10.38 ± 0.39 vs. 10.56 ± 0.41, P < 0.05]). And patients with stages III–IV (n = 207) were also lower than those with stages I–II (n = 324 [10.43 ± 0.38 vs. 10.58 ± 0.42, P < 0.05]). However, no significant differences in ACOX1 expression levels were observed between other clinicopathological features such as age and lymphatic metastases (Table 1). Analysis of the clinical information of the patients in the IHC revealed that ACOX1 was only significantly altered in distant metastases, as shown in Table 2 (P = 0.046).

Table 1

Correlation of ACOX1 mRNA expression with clinicopathological parameters of patients with ccRCC based on the TCGA database

Relevant expression of ACOX1
Clinicopathological parameters n Mean ± SD t P-value
Tissue Normal 72 11.43 ± 0.63 −13.955 <0.001*
ccRCC 533 10.52 ± 0.41
Age <60 245 10.53 ± 0.40 0.605 0.545
≥60 288 10.51 ± 0.43
Gender Male 345 10.49 ± 0.41 −2.203 0.028*
Female 188 10.57 ± 0.41
T T1–T2 342 10.57 ± 0.43 3.562 <0.001***
T3–T4 191 10.44 ± 0.38
N No 240 10.55 ± 0.42 1.747 0.082
Yes 16 10.36 ± 0.35
M No 422 10.56 ± 0.41 3.608 <0.001***
Yes 79 10.38 ± 0.39
Pathologic stage I–II 324 10.58 ± 0.42 4.082 <0.001***
III–IV 207 10.43 ± 0.38

SD, standard deviation; ccRCC, clear cell renal clear cell carcinoma; T, tumor; N, lymph node; M, metastasis.

*P < 0.05 was considered statistically significant.

Table 2

Correlation of ACOX1 expression with clinicopathologic characteristics in patients with ccRCC IHC staining

Relevant expression of ACOX1
Clinicopathological parameters Case Low/n (%) High/n (%) X 2 P-value
Tissue ccRCC 64 35 (54.7) 29 (45.3) 0.057 0.858
Adjacent non-cancerous kidney 59 31 (52.5) 28 (47.5)
Age <60 42 27 (64.3) 15 (35.7) 0.166 0.683
≥60 22 13 (59.1) 9 (40.9)
Gender Male 38 25 (65.8) 13 (34.2) 0.432 0.511
Female 26 15 (57.7) 11 (42.3)
T T1–T2 42 27 (64.3) 15 (35.7) 0.188 0.665
T3–T4 22 13 (59.1) 9 (40.9)
N No 60 40 (66.7) 20 (33.3) 4.551 0.033
Yes 4 0 (0.00) 4 (100.0)
M No 58 39 (67.2) 19 (32.8) 3.972 0.046*
Yes 6 1 (16.7) 5 (83.3)
Pathologic stage I–II 42 29 (69.0) 13 (31.0) 2.235 0.135
III–IV 22 11 (50.0) 11 (50.0)
Tumor diameter <5 cm 31 20 (64.5) 11 (35.5) 0.336 0.562
≥5 cm 28 16 (57.1) 12 (42.9)

SD, standard deviation; ccRCC, clear cell renal clear cell carcinoma; T, tumor; N, lymph node; M, metastasis.

*P < 0.05 was considered statistically significant.

3.3 Enrichment analysis of ACOX1 in the ccRCC-related signaling pathways

GSEA was used to evaluate the functional and signaling pathways associated with ACOX1. About 21 criteria for the most enriched signaling pathways that were included. The main transduction pathways involved in ACOX1 associated with ccRCC were as follows: epithelial–mesenchymal transition (EMT), myelocytomatosis (MYC)-targets V1 and MYC targets V2 related to proliferation, TNF-α/NF-kB signaling pathway, the P53 pathway, and the apoptotic pathway (Table 3). At the same time, the correlation between ACOX1 and EMT-related genes was analyzed, and a good correlation was found (Figure 5). It is assumed that ACOX1 expression is associated with the above-mentioned signaling pathways, i.e., activation of this series of signaling pathways in ccRCC may occur through the ACOX1 downregulation.

Figure 4 miR-16-5P may target the regulation of ACOX1 in ccRCC. (a) Three microRNAs were predicted to target the regulation of ACOX1 using miRDB,Tarbase8.0, ENCORI, and TargetScan. (b–d) Analysis of mRNA levels of three microRNAs in ccRCC based on the TCGA database in 240 ccRCC tissues and 70 normal tissues. (e–g) Correlation of three microRNAs with ACOX1 in ccRCC.
Figure 4

miR-16-5P may target the regulation of ACOX1 in ccRCC. (a) Three microRNAs were predicted to target the regulation of ACOX1 using miRDB,Tarbase8.0, ENCORI, and TargetScan. (b–d) Analysis of mRNA levels of three microRNAs in ccRCC based on the TCGA database in 240 ccRCC tissues and 70 normal tissues. (e–g) Correlation of three microRNAs with ACOX1 in ccRCC.

3.4 MiR-16-5p involved in the horizontal expression of ACOX1 mRNA in ccRCC

As shown in Figure 4a, we predicted three miRNAs that might regulate ACOX1 expression based on miRDB (http://mirdb.org/), Tarbase8.0 (https://dianalab.e-ce.uth.gr/html/diana/web/index.php? r = tarbasev8%2Findex), ENCORI (https://starbase.sysu.edu.cn/), and TargetScan (https://www.targetscan.org/vert_72/) databases.

Figure 5 Correlation of ACOX1 mRNA transcript levels in ccRCC with EMT-related genes. (a–f) Based on the TCGA database, the expression of ACOX1 with CDH1, CDH2, CDKL2, EPCAM, MMP9, and VIM was analyzed.
Figure 5

Correlation of ACOX1 mRNA transcript levels in ccRCC with EMT-related genes. (a–f) Based on the TCGA database, the expression of ACOX1 with CDH1, CDH2, CDKL2, EPCAM, MMP9, and VIM was analyzed.

We further analyzed the expression data of the three miRNAs through TCGA and found that all three miRNAs were highly expressed in ccRCC (Figure 4b–d). Only miR-16-5p was negatively correlated with ccRCC (P < 0.001), while the rest were positively correlated with ccRCC (Figure 4e–g). It is assumed that miR-16-5p might act as an upstream regulator to suppress the expression of the ACOX1 gene in ccRCC.

In addition, the mRNA transcript levels of miR-16-5p were analyzed for comparison with the clinical information obtained from TCGA. MiR-16-5p expression was significantly lower in ccRCC (n = 240) tissues than in normal tissues (n = 70, P < 0.0001; Table 4). T-stage, lymphatic infiltration, and distant metastasis were not significantly associated with the miR-16-5p expression level.

Table 3

Enrichment analysis of signaling pathways involved in the regulation of ACOX1

GS follows link to MSigDB SIZE ES NES NOM P-value FDR q-value
ALLOGRAFT_REJECTION 195 −0.59 −3.01 0.000 0.000
INTERFERON_GAMMA_RESPONSE 196 −0.52 −2.68 0.000 0.000
EPITHELIAL_MESENCHYMAL_TRANSITION 194 −0.51 −2.61 0.000 0.000
E2F_TARGETS 187 −0.49 −2.45 0.000 0.000
INTERFERON_ALPHA_RESPONSE 92 −0.53 −2.41 0.000 0.000
INFLAMMATORY_RESPONSE 197 −0.46 −2.34 0.000 0.000
TNFA_SIGNALING_VIA_NFKB 197 −0.45 −2.29 0.000 0.000
G2M_CHECKPOINT 184 −0.42 −2.16 0.000 0.000
IL6_JAK_STAT3_SIGNALING 87 −0.48 −2.15 0.000 0.000
WNT_BETA_CATENIN_SIGNALING 42 −0.52 −1.99 0.000 0.000
HYPOXIA 190 −0.39 −1.97 0.000 0.000
ANGIOGENESIS 36 −0.51 −1.87 0.002 0.001
APICAL_JUNCTION 193 −0.36 −1.82 0.000 0.001
MYC_TARGETS_V1 188 −0.35 −1.76 0.000 0.001
MYC_TARGETS_V2 58 −0.42 −1.72 0.000 0.002
DNA_REPAIR 139 −0.35 −1.72 0.000 0.002
P53_PATHWAY 190 −0.34 −1.7 0.000 0.002
COMPLEMENT 195 −0.31 −1.6 0.002 0.007
IL2_STAT5_SIGNALING 194 −0.31 −1.59 0.000 0.007
APOPTOSIS 158 −0.31 −1.53 0.000 0.010

GS, signaling pathway name; SIZE, total number of genes under this pathway; ES, enrichment score; NES, normalized enrichment score; NOM P-value, represents the confidence of the enrichment result;, and FDR q-value, P-value corrected for multiple hypothesis testing.

4 Discussion

As the most common and malignant histological subtype of kidney cancer, ccRCC has had a continued rise in incidence in recent years. It is widely accepted that a metabolic hallmark of cancer cells is lipidomic remodeling, which broadly encompasses alterations in fatty acid transport de novo lipogenesis, storage as lipid droplets, and β-oxidation to generate ATP [20]. ccRCC is a highly metabolic tumor, which is composed of the appearance of “transparent cells.” The reason for cell forming is the excessive accumulation of a large amount of lipid and glycogen deposits filling the cytoplasm of tumor cells [21,22]. In recent years, research has verified that metabolic adaptation in ccRCC leads to the activation of lipid storage pathways, which is a necessary step in the development of malignant tumors [23]. Therefore, it is necessary to explore more deeply the role of additional molecules in lipid metabolism reprogramming.

ACOX1 is the enzyme required for the first step of peroxisomal β-oxidation, which catalyzes the desaturation of acyl-CoA to 2-trans-enoyl-CoA. It plays a crucial role in lipid metabolism, involved in various aspects of lipid metabolism, including lipid synthesis and catabolism, bile acid synthesis, α-oxidation of branched-chain fatty acids, and β-oxidation of very long fatty acids [24,25]. In humans, ACOX1 deficiency causes impaired peroxisomal β-oxidation, producing a rare neuroinflammatory and neurodegenerative peroxisomal disease, pseudoneonatal adrenoleukodystrophy [26,27]. Some studies have shown that ACOX1 activity increasing due to SIRT5 expression downregulated in primary hepatocellular carcinoma cancer results in poor HCC survival [28]. Abnormal upregulation of ACOX1 by PPAR activation was reported to stimulate hepatic fatty acid oxidation, resulting in excess energy burning in the liver and contributing to the development of liver cancer in rodents [29,30]. In our study, we found that ACOX1 mRNA and protein levels were downregulated in ccRCC through bioinformatic analysis and experimental validation. This leads us to speculate that ACOX1 might act as a tumor suppressor in ccRCC as well.

Because of highly metastasizing, it is necessary to screen out biomarkers with high efficacy for the diagnosis and prognosis of ccRCC and explore the valuable targets for therapy, to improve the life expectancy of patients [31]. To date, a large number of biomarkers have been validated. p53 Overexpression is indicative of a poor prognosis in ccRCC [32]. CD146, one of the cell adhesion molecule family, is overexpressed in ccRCC and associated with poor prognosis [33]. Recently, CTHRC1, NOP2, and P4HB have shown a strong ability to diagnosis and prognosis for ccRCC [34,35,36]. ACOX1 can be combined with MMP1, suppressor of cytokine signaling 3 (SOCS3) to diagnose oral squamous cell carcinoma (OSCC), and downregulation in bladder cancer might be due to PDK1 down-regulation, suggesting that it might serve as a potential biomarker and therapeutic target for bladder [37,38]. In this study, the ROC curves demonstrated that ACOX1 might serve as a diagnostic marker for ccRCC. ACOX1 mRNA expression was correlated with T and M stages and pathological grading by analyzing the TCGA data and the clinicopathological characteristics of patients. OS and DFS illustrate that a lower expression level of ACOX1 is associated with a worse prognosis in patients with ccRCC. In a word, we report that ACOX1 is a potential diagnostic and prognostic molecular biomarker of ccRCC, both at mRNA and at protein levels. However, it is worth further evaluation in larger clinical sample sizes.

Recent studies have shown that acidosis-induced activation of TGF-β2 promotes partial EMT and fatty acid metabolism [39]. Epithelial cells acquired strengthened motility through EMT, which plays a crucial role in initiating ccRCC metastasis [40]. We analyzed the correlation between ACOX1 and the EMT-related genes and found a positive correlation with cadherin 1 (CDH1), CDH2, cyclin-dependent kinase-like 2 (CDK2), and epithelial cell adhesion molecule, and a negative correlation with matrix metalloproteinase MMP9 and vimentin, suggesting that ACOX1 might be regulated by EMT and affect its metastasis in ccRCC.

Epigenetic mechanisms, including DNA methylation, histone modification, and microRNA (miRNA), are currently receiving much attention [41]. miRNA regulates the expression of numerous metabolic genes and can produce heritable phenotypic changes without a shift in DNA sequence. More than 40 miRNAs are associated with urological cancers and several oncogenic pathways, especially in apoptosis, proliferation, EMT, and angiogenic signaling, all of which are regulated by multiple miRNA targets [42]. We observed that miR-16-5p was negatively correlated with ACOX1 expression in ccRCC and that miR-16-5p can target a fixed sequence of the ACOX1 gene, suggesting that it may be one of the mechanisms underlying ACOX1 downregulation in ccRCC. Ding et al. reported that miR-103-3p negatively regulates ACOX1 in NAFLD and suggested a potential therapeutic target [43]. In OSCC miR-31-5p regulates ACOX1 and promotes OSCC migration and invasion by affecting lipid metabolism, thereby triggering intracellular signaling changes [44]. In a study by Li et al., miR-15a could promote adipocyte differentiation by targeting ACOX1, sterol carrier protein 2, and acetyl-coenzyme A acyltransferase 1 in the PPAR signaling pathway [45]. Sun et al. also reported that SIRT in colorectal cancer (CRC) could inhibit miR-15b-5p transcription and then restore ACOX1 expression, through which SIRT1 promotes fatty acid oxidation and inhibits CRC metastasis, suggesting a potential target for metastable CRC therapy [46]. Sequencing studies showing that miRNAs have a central role in the physiological regulation of RCC, with dysregulation, is widespread in RCC [47]. As a conserved endogenous non-coding RNA molecule, miRNA affects various cellular processes, including cell development, differentiation, apoptosis, and proliferation [48]. These findings reveal that miRNAs play a crucial role in regulating tumor development and that miR-16-5p might play a role in ccRCC by regulating ACOX1 expression.

In conclusion, our study demonstrates that ACOX1 is downregulated in ccRCC, revealing its potential significance for the diagnosis and prognosis of ccRCC, and we find that miR-16-5p might act as an upstream regulator to suppress the expression of the ACOX1 gene in ccRCC. In addition, a limitation of this study is the lack of validation of the biological function of ACOX1 in ccRCC, which will provide directions for future studies. Therefore, further efforts are still needed.

Table 4

mRNA Expression of hsa-miR-16-5p and its correlation with clinicopathological parameters of patients with ccRCC

Relevant expression of ACOX1
Clinicopathological parameters n Mean ± SD t P-value
Tissue Normal 70 8.23 ± 0.488 12.075 <0.0001*
ccRCC 240 9.18 ± 0.61
Age <60 114 9.64 ± 0.48 −0.935 0.351
≥60 146 9.70 ± 0.54
Gender Male 97 9.65 ± 0.45 −0.670 0.504
Female 162 9.69 ± 0.55
T T1–T2 156 9.63 ± 0.56 −1.758 0.080
T3–T4 103 9.74 ± 0.44
N No 123 9.67 ± 0.50 0.150 0.881
Yes 9 9.64 ± 0.37
M No 221 9.66 ± 0.55 −0.840 0.402
Yes 38 9.74 ± 0.27

SD, standard deviation; ccRCC, clear cell renal clear cell carcinoma; T, tumor; N, lymph node; M, metastasis. *P < 0.05 was considered statistically significant.

# These three authors contributed equally to this manuscript.

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Acknowledegments

This work was supported by the National Natural Science Fund (32260170) and Youth program of the Scientific Research Foundation of Guangxi Medical University Cancer Hospital (#2021-01).

  1. Funding information: Authors state no funding involved.

  2. Author contributions: Qian Zheng performed most of the bioinformatic analysis, and meta-analysis， and wrote and prepared the original draft. Qian Zheng, Ran Zhao, and Wenqi Luo carried out the IHC experiment; Jun Zhao carried out the qPCR. Yiying Huang, Ziyuan Liang, Xiaoying Zhou, Jiemei Chu, and Xinli Pan analyzed and interpreted the data. Yingxi Mo and Xiaoying Zhou revised the manuscript. Wenqi Luo and Qian Zheng designed and supervised the study. All authors read and approved the final manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

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

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Received: 2023-02-18
Revised: 2023-05-18
Accepted: 2023-07-30
Published Online: 2023-09-08

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0696
Keywords for this article
clear cell renal cell carcinoma; ACOX1; miR-16-5P; bioinformatics
Creative Commons
BY 4.0

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  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
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