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. 2025 Aug 29;22(5):698–715. doi: 10.21873/cgp.20530

Analysis of Peroxisomal ABCD3 Transporter as a Prognostic Factor in Clear Cell Renal Cell Carcinoma

SIMONE HEYLIGER 1, TAMIEL N TURLEY 2, TRACOYIA ROACH 3, MARILYN D SAULSBURY 1, EQUAR TAKA 3, JORDAN P REYNOLDS 4, JOHN A COPLAND III 2, ADAM M KASE 5,#, R RENEE REAMS 3,#
PMCID: PMC12402713  PMID: 40883030

Abstract

Background/Aim

Clear cell renal cell carcinoma (ccRCC) is among the 10 most common cancers diagnosed in the United States. Despite its severity and aggressive nature, biomarkers that can serve as prognostic and predictive factors for ccRCC are lacking. ABCD3, a peroxisomal long-chain fatty acid transporter, has been shown to serve as a prognostic factor in both prostate and colon cancers. Thus, this study aimed to ascertain if ABCD3, which is highly expressed in kidney tissues, may also serve as a biomarker for renal cancer.

Materials and Methods

Bioinformatics and immunohistochemical staining were employed to systematically investigate the relationship between the ABCD3 gene and protein expression, the immune microenvironment, and survival outcomes in ccRCC. We extensively harnessed data from publicly available databases, including The Cancer Genome Atlas (TGCA), UALCAN, Gene Set Cancer Analysis, UCSC Xena, and various other databases.

Results

In silico analyses of the TCGA database revealed that ABCD3 transcripts and protein levels were significantly reduced (p<0.001) across all tumors and stages. Moreover, decreased ABCD3 expression was associated with poorer patient survival [hazard ratio=0.45 (0.33-0.61), p<0.001]. Immunohistochemical and CPTAC database analyses revealed that ABCD3 was significantly downregulated in ccRCC patients (p<0.05). Multivariate Cox regression analysis revealed that ABCD3 expression is an independent factor for overall survival [HR=0.4534 (0.2859-0.7189), p<0.001].

Conclusion

Our findings suggest that ABCD3 is a novel biomarker for ccRCC and that the downregulation of ABCD3 expression, and other members of the peroxisomal VLCFA oxidation pathway may represent a unique molecular feature of ccRCC.

Keywords: ABCD3, peroxisome, clear cell renal carcinoma, lipid transport, tumor suppression

Introduction

Renal cell carcinoma (RCC) is among the top 10 cancers diagnosed in the United States, with an estimated 81,610 new cases in 2024 and 14,390 patients who died as a result of their disease (1). RCC is a heterogeneous disease that consists of three major histological subtypes: clear cell, papillary, and chromophobe renal carcinomas. Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive histologic subtype, accounting for at least 70% of all kidney cancers (2,3). ccRCC originates from renal stem cells, typically in the proximal nephron and tubular epithelium, with metastasis to the lungs, bones, and liver (3). The majority of patients with RCC are asymptomatic, often discovered incidentally during radiologic imaging for unrelated conditions (4). Notwithstanding, 20-30% of symptomatic patients present at more advanced stages of their disease with a high probability for regional or metastatic spread (4). When stratified by the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) prognostic risk model, patients with intermediate/poor risk metastatic ccRCC now have a median survival of 47 months with ipilimumab plus nivolumab versus 27 months with sunitinib (5).

In 2015, the introduction of immune checkpoint inhibitors revolutionized the management of many metastatic cancers, including ccRCC. Over the last decade, tumor immunotherapy has continued to advance, combining an immune checkpoint inhibitor with tyrosine kinase inhibition. While immunotherapy has significantly improved patient outcomes, challenges, including varying patient response and tumor microenvironment obstacles in aggressive ccRCC, remain. Resistance to immune checkpoint inhibitors can develop through the loss of major histocompatibility complex antigens diminishing the T-cells ability to recognize the cancer and through the release of immunosuppressive cytokines via mitogen-activated kinases (MAPK) and the PI3K/AKT pathways (6). While adding a tyrosine kinase inhibitor to immunotherapy may suppress these immunosuppressive effects; resistance can still develop, highlighting the need for identifying potential biomarkers with prognosis and treatment implications.

One of the most widely studied and notable genetic abnormalities associated with ccRCC is the Von-Hippel Lindau (VHL) tumor suppressor gene. Over 200 VHL mutations are associated with an acquired somatic loss of VHL function and implicated in the development of sporadic ccRCC (7-9). Inactivation of VHL results in the upregulation of hypoxia-inducible factors (HIF), a group of transcription factors responsible for regulating the expression of over 1,000 targeted genes, including vascular endothelial growth factor (VEGF), driving the expression of genes involved in cancer progression and metastasis (10). The advancements in scientific knowledge of this biological framework have led to multiple therapies targeting these pathways that have been approved for the management of ccRCC. This includes the mammalian target of rapamycin (mTOR), vascular endothelial growth factor receptor (VEGFR) tyrosine kinase, and hypoxia-inducible factor-2 alpha (HIF-2⍺) inhibitors (11). These advancements highlight the importance and impact of identifying key genomic aberrations that may underlie disease progression and susceptibility to aid in the development of therapeutic strategies to improve patient outcomes. This study examines the expression patterns of ABCD3 in ccRCC and its association with survival and tumor characteristics by analyzing gene and protein expression data from publicly available databases. Here, we report that reduced expression of ABCD3 is evident in ccRCC and may serve as a prognostic biomarker.

Materials and Methods

Evaluation of mRNA and protein expression in ccRCC. The University of Alabama at Birmingham Cancer database (UALCAN; http://ualcan.path.uab.edu/analysis.html; accessed February 25, 2022) is an online open-access platform for facilitating tumor gene and protein expression and survival analyses (12,13). UALCAN includes gene and protein expression (CPTAC dataset), promoter methylation, miRNA expression, and clinicopathological raw data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium [CPTAC (13)]. The association between clinicopathological variables of ccRCC and ABCD3 mRNA expression was examined based on tumor grade and metastatic status in UALCAN. The CPTAC dataset within the UALCAN tool was used to evaluate the protein expression levels of ABCD3 between ccRCC tumor stages, grades, and normal tissue.

Gene expression and cancer data analysis. Gene Set Cancer Analysis (GSCA; http://bioinfo.life.hust.edu.cn/GSCA accessed February 25, 2022) is a comprehensive database for genomic and immunogenomic analyses integrating over 10,000 multi-dimensional genomic data across 33 cancer types from TCGA and over 750 small molecule drugs from Genomics of Drug Sensitivity in Cancer and cancer therapeutics response portal (14). GSCA was used to assess the differential expression of ABCD3 in Renal Chromophobe Carcinoma (KICH), Clear Cell Renal Cell Carcinoma (KIRC), and Renal Papillary Cell Carcinoma (KIRP) datasets derived from the TCGA database. Expression analysis was based on paired normal and tumor samples (n=25 paired KICH samples, n=72 paired KIRC samples, and n=32 paired KICH samples). The screening conditions set in this study were first for single gene expression analysis with the selection of “Input gene set: ABCD3”, “Cancer types: KICH, KIRC and KIRP”, and “Single gene level: Differential expression, Expression & Survival and Expression & Stage”. Gene Set Variation Analysis (GSVA) was performed by selecting “Differential GSVA and GSVA & Pathway activity”.

Gene expression validation. To validate gene expression results obtained from UALCAN-TCGA KIRC clinical information and RNA sequencing dataset (N=537) were downloaded from the University of California Santa Cruz (UCSC) XENA portal (https://xena.ucsc.edu/public/ accessed February 25, 2022), a multi-omics and clinical phenotype database (15). The screening conditions set were the selection of KIRC-TCGA study, genomic data for ABCD3, phenotypic data for the sample type, age at initial pathologic diagnosis, sex, pathologic N, pathologic stage, neoplastic histological grade, and genomic information for ID TCGA-HiSeqV2 data. Primary tumors were selected as a filter to remove duplications in the data, leaving 537 samples. Kaplan-Meier (KM) analysis was selected to ascertain patient survival outcomes. The raw data was utilized to generate a KM plot to evaluate the impact of gene expression on patient survival and to perform receiver operating characteristics/area under the curve (ROC/AUC) analyses. Additionally, the raw data was utilized for univariate and multivariate analyses to determine whether ABCD3 expression was a predictive biomarker for ccRCC.

Gene association and co-expression patterns across cancer types. Tumor Immune Estimation Resource (TIMER2.0; http://timer.cistrome.org/) provides modules for investigating the associations between immune infiltration, clinical correlations, and gene expression across a broad range of TCGA cancer types (16,17). TIMER2.0 was utilized to assess gene associations and co-expression patterns of genes across TCGA cancer types.

Gene set enrichment analysis. LinkedOmics database contains multi-omics and clinical data for 32 cancer types comprising 11,158 patients from the TCGA project (18). The genes co-expressed with ABCD3 were identified using the gene ontology (GO) enrichment analysis in the LinkedOmics database (http://www.linkedomics.org accessed on February 25, 2022). The “LinkFinder” module was used to visualize genes that are differentially expressed within the CPTAC ccRCC cohort. The “LinkInterpreter” module was utilized to systematically identify enriched pathways associated with genes that are co-expressed (upregulated or downregulated) with ABCD3 (GO Biological Process, KEGG, and Wiki-pathways).

Immunohistochemical (IHC) analysis. Tissue microarrays (TMA) were prepared from archival formalin-fixed paraffin-embedded tissue samples, with duplicate cores procured from each de-identified ccRCC patient and matched normal tissue under Mayo Clinic Institutional Review Board approval (IRB#14-004094). Immunostaining was performed according to the manufacturer’s protocol. TMA paraffin blocks were cut into 5 μm thick sections, mounted on slides, deparaffinized, blocked with Diluent (DakoCytomation, Glostrup, Denmark) for 30 min, and probed for ABCD3 (1:500 dilution; Sigma-Aldrich, St. Louis, MO, USA, AMAB90995). The slides were scanned at 20X magnification with ScanScope XT (Aperio Technologies Inc, Vista, CA, USA) and were analyzed with the ImageScope software (Aperio Technologies Inc). Scoring analysis based on signal intensity (0-300) was conducted using algorithm-based macros in ImageScope by a trained researcher (H scores). H scores were calculated by the sum of the percentage of stained cells multiplied by an ordinal value that corresponded to signal intensity (0=none, 1=weak, 2=moderate, and 3=strong). Samples with insufficient tumor tissue for H score analysis were excluded from the study.

Statistical analysis. Gene expression across tumor stages and grades was compared using a one-way analysis of variance (ANOVA) followed by a Tukey-Kramer post hoc analysis (GraphPad Prism, La Jolla, CA, USA). Adjusted p-Values were calculated to correct for multiple comparisons using the false discovery rate (FDR) procedure (19). The gene set enrichment analysis results were analyzed for significance using Pearson’s correlation coefficient test with the p-value, and FDR set at 0.05. The ROC analysis and KM survival outcome plots, Cox risk hazard ratio (HR), 95% confidence intervals (CI), and log-rank p-values were generated using the MedCalc statistical software (Ostend, Belgium) and the TCGA-KIRC dataset downloaded via the UCSC Xena webtool. Multicollinearity among covariates was assessed for Cox proportional hazards regression analyses using Pearson correlation coefficients and variance inflation factors (VIF). All variables, except Stage and Tumor (VIF 7.5 and 6.2, respectively), demonstrated acceptable VIFs (<2). Elevated VIFs for Stage and Tumor are consistent with a known overlap between stage and tumor classifications and are included to maintain consistency with univariate models. An AUC higher than 0.6 in ROC analysis indicates that the gene has potential as a prognostic factor. H-score statistical analysis was performed using a 2-tailed paired Student t-test (GraphPad Prism), with p<0.05 considered statistically significant. An asterisk denotes statistically significant results.

Institutional Review Board statement. This study was conducted under protocols described in Hampton University (HU) IRB application #20230630 which was designated as “exempt”. Additionally, Mayo Clinic IRB Committee reviewed and approved the use of de-identified patient samples used for immunohistochemistry in this study (IRB#14-004094).

Data availability statement. ccRCC data was downloaded from the UCSC XENA portal (https://xena.ucsc.edu/public/) to create Kaplan-Meier curves and to perform ROC and multivariate analysis of clinicopathological features relative to ABCD3 expression. Downloaded data was filtered to only include ccRCC primary tumors (n=537) and solid tumor normal (n=72) from the KIRC TCGA database. Demographic information such as sex and age of initial diagnosis as well as gene expression, survival times, stage, and grade were also downloaded. Fifteen (15) samples were excluded due to inefficient information on histological grade, and five (5) were excluded because of insufficient information on pathological stages.

Results

ABCD3 expression in renal cancers. ABCD3 transcripts in the three main renal cell carcinoma subtypes, clear cell (KIRC), papillary (KIRP), and chromophobe (KICH), were significantly downregulated (p<0.001) when compared across several other cancer types and to normal tissue (Figure 1). Gene expression and cancer data analysis were conducted to evaluate changes in expression levels of ABCD3 across KIRC, KIRP, and KICH across cancer stages (Figure 2A and B). ABCD3 transcripts were significantly decreased (p<0.001) in KICH [log2 fold change (FC)=0.47, p=2.22E-9; FDR=5.09E-8], KIRP (Log2 FC=0.61, p=1.08E-9; FDR=4.08E-8) and KIRC (Log2 FC=0.54, p=2.85E-24; FDR=1.20E-22). The most significant reduction was observed in KIRC (Figure 2A). Moreover, across all pathological stages, ABCD3 was significantly differentially expressed (p=9.95E-3, FDR=0.03) in KIRC compared to KICH and KIRP (Figure 2B). Analysis of survival outcomes across renal carcinoma subtypes revealed that decreased ABCD3 expression was associated with changes in survival outcomes and progression-free survival in KIRC (log-rank p=3.62E-5 and p=1.25E-5, respectively; Figure 3) and not in KICH or KIRP (Figure 3).

Figure 1.

Figure 1

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Expression profiles of ABCD3 in human cancers. TIMER2.0 plot displays ABCD3 expression in TCGA tumors and corresponding normal tissues. ABCD3 expression levels were significantly downregulated in cancer subsets, including renal chromophobe carcinoma (KICH; p<0.001), clear cell renal cell carcinoma (KIRC; p<0.001), and renal papillary carcinoma (KIRP; p<0.001) relative to normal tissues. Asterisks denote statistical significance relative to normal tissue. ACC: Adrenocortical carcinoma; BLCA: bladder urothelial carcinoma; BRCA: breast invasive carcinoma; CESC: cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL: cholangiocarcinoma; COAD: colon adenocarcinoma; DLBC: diffuse large B-cell lymphoma; ESCA: esophageal carcinoma; GBM: glioblastoma multiforme; HNSC: head and neck squamous cell carcinoma; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; LAML: acute myeloid leukemia; LGG: low-grade glioma; LIHC: liver hepatocellular carcinoma; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma; MESO: mesothelioma; OV: ovarian serous cystadenocarcinoma; PAAD: pancreatic adenocarcinoma; PCPG: pheochromocytoma and paraganglioma; PRAD: prostate adenocarcinoma; READ: rectum adenocarcinoma; SARC: sarcoma; SKCM: skin cutaneous melanoma; STAD: stomach adenocarcinoma; TGCT: testicular germ cell tumors; THCA: thyroid carcinoma; THYM: thymoma; UCEC: uterine corpus endometrial carcinoma; UCS: uterine carcinoma; UVM: uveal melanoma.

Figure 2.

Figure 2

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ABCD3 mRNA expression in renal carcinomas histologic subtypes (KICH, KIRC, and KIRP) from the GSCA–TCGA RNA-seq datasets. (A) ABCD3 transcripts were significantly decreased in KICH (Log2 fold change=0.47, p=2.22E-9; FDR=5.09E-8), KIRP (Log2 fold change= 0.61, p=1.08E-9; FDR=4.08E-8) and KIRC (Log2 fold change=0.54, p=2.85E-24; FDR=1.20E-22). (B) ABCD3 expression was differentially expressed across all pathological stages of KIRC. The size of each bubble positively correlates with the FDR significance. Statistical significance is p<0.001. FC: Fold-change; FDR: False Discovery Rate; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma.

Figure 3.

Figure 3

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Bubble plots of overall survival (OS) or progression-free survival (PFS) associated with ABCD3 expression in renal carcinoma subtypes. ABCD3 expression had no significant impact on OS or PFS in KICH and KIRP, whereas patients with KIRC and lower expression of ABCD3 had lower OS and PFS (p<0.001). KICH: Kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma.

ABCD3 gene expression and clinicopathological features. The relationship between ABCD3 expression, clinico-pathological features, and patient survival in ccRCC was evaluated using the complete TCGA-KIRC database. ABCD3 expression was found to be lower (p<0.05) in tumor tissue relative to normal tissue (Figure 4A). Moreover, reduced expression levels were associated with poorer survival outcomes in patients with ccRCC [HR=0.45 (0.33-0.61); p=1.4E-7; Figure 4B]. In a multivariate analysis of ccRCC demographics, clinicopathological features, and ABCD3 expression, the predictive power of ABCD3 expression was an independent factor for overall patient survival [HR=0.4534 (0.2859-0.7189); p<0.001; Table I]. ROC analysis was conducted to determine if ABCD3 expression was a discriminator for survival outcomes. Statistically significant associations with survival were identified at stage [StageAUC=0.702 (95% CI=0.662-0.742; p<0.0001)], grade [GradeAUC=0.636 (95% CI=0.594-0.678; p<0.0001)], age [AgeAUC=0.595 (95% CI=0.552-0.638, p<0.001)], and ABCD3 expression [ABCD3AUC=0.613 (95% CI=0.5684-0.6663; p<0.0001)], with non-significant associations with sex [SexAUC=0.510 (95% CI=0.467-0.553; p=0.65) Figure 4C]. ABCD3 expression levels associated with clinico-pathological features in KIRC relative to normal tissues were found to be significantly lower across histological grades 1-4 [F(4,591)=197.7; p<0.001] and pathological stages I-IV [F(4,518)=193.3; p<0.001; Figure 5A and B]. Moreover, ABCD3 expression decreased as tumor grades progressed from lower grades (Grades 1 & 2) and stages (Stages I & II) to more advanced grades and stages (Grades 3 & 4, p<0.01; Stages III & IV, p<0.0001; Figure 5C and D).

Figure 4.

Figure 4

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ABCD3 expression, survival, and ROC analysis in KIRC. (A) mRNA expression of ABCD3 in KIRC compared to normal tissue. (B) Kaplan-Meier (KM) survival plot for ABCD3 high (n=269) and low (n=264) expression groups generated using KM plotter. (C) Receiver operating characteristic (ROC) curve of overall survival times of ABCD3 high and low expression groups. ROC analysis was performed to validate KM survival observations and ascertain if ABCD3 expression could discriminate between high- and low-expression groups in relation to survival. AgeAUC=0.595, 95% confidence interval (CI)=0.552-0.638, p<0.001; SexAUC=0.510, CI=0.467-0.553, p=0.65; GradeAUC=0.636, CI=0.594-0.678, p<0.0001; StageAUC=0.702, CI=0.662-0.742, p<0.0001; ABCD3AUC=0.613, CI=0.5684 - 0.6663, p<0.0001. The asterisks denote statistical significance of p<0.05. AUC: Area under the curve; HR: hazard ratio; KIRC: kidney renal clear cell carcinoma; TPM: transcripts per million.

Table I. Univariate and multivariate Cox regression analysis was performed between ABCD3 expression and clinicopathological features in ccRCC within the TCGA-KIRC database.

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CI: Confidence interval; HR: hazard ratio; M: metastasis; N: node; T: tumor. Bold font indicates statistical significance at p<0.05.

Figure 5.

Figure 5

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ABCD3 expression in KIRC across tumor stages and grades. (A) ABCD3 transcript levels were significantly decreased relative to normal tissues across all grades [F(4, 591)=197.7, p<0.001]. Tukey multiple comparison analysis showed statistically significant differences between Normal vs. Grade 1, Grade 2, Grade 3, and Grade 4 (adjusted p<0.0001); Grade 2 vs. Grade 4 (adjusted p<0.001); Grade 3 vs. Grade 4 (adjusted p<0.0041). ***denotes adjusted p<0.0001. (B) ABCD3 mRNA expression was reduced across all tumor stages relative to normal tissue [F(4, 518)=193.3, p<0.001]. Tukey multiple comparisons showed statistically significant differences between Normal vs. Stage 1, Stage 2, Stage 3, and Stage 4 (adjusted p<0.0001), Stage 1 vs. Stage 4 (adjusted p<0.01), Stage 2 vs. Stage 4 (adjusted p<0.05). ***denotes adjusted p<0.0001. (C) Differential expression of ABCD3 across lower and higher histological grades in ccRCC. ** denotes p<0.01. (D) Differential expression of ABCD3 across lower and higher pathological stages in ccRCC. ***denotes p<0.0001.

ABCD3 protein expression. ABCD3 protein expression was evaluated by the CPTAC proteomic dataset assessed through UALCAN and in commercially available kidney TMAs from the Human Protein Atlas (HPA; Table II) to determine if changes in the transcript levels corresponded to changes in ABCD3 protein expression. ABCD3 protein levels of ccRCC patients were found to be significantly lower (p<0.001) in both clinical stages (I-IV) and histological grades (1-4; Figure 6A and B). Immunostaining images from HPA were used to compare ABCD3 protein expression in normal, adjacent normal, benign, and sarcomatoid carcinoma, transitional cell carcinoma (TCC; cancer of the renal pelvis and ureters), and KIRC tissues (Figure 6C). Quantitative analysis of the intensity of IHC ABCD3 staining revealed ABCD3 protein levels were significantly lower (214.1±14.78) in ccRCC compared to the mean intensity staining in normal tissues (325±19.36). Conversely, ABCD3 expression was elevated in TCC (Figure 6D).

Table II. Clinical features and histopathological characteristics from commercially available kidney tissue microarrays from the Human Protein Atlas.

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CAdj/CI: Cancer adjacent/chronic inflammatory; ccRCC: clear cell renal cell carcinoma; SC: sarcomatoid carcinoma; LNM; lymph node metastasis; Norm Adj: normal adjacent tissue; TCC: transitional cell carcinoma; TNM: Tumor Node Metastasis. Tissue microarray analysis was obtained from the Human Protein Atlas (Cat # HN8O3a).

Figure 6.

Figure 6

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ABCD3 protein expression in ccRCC across tumor stages and grades. ABCD3 protein expression levels were analyzed with UALCAN–CPTAC proteomic datasets. (A) ABCD3 protein levels were significantly lower across all tumor stages relative to normal tissue (p<0.001). (B) ABCD3 protein levels were also significantly reduced across all tumor grades (p<0.001). (C) Select histopathological IHC images of ABCD3 in normal and kidney cancer tissues 1. normal; 2. adjacent normal; 3. benign; 4. sarcomatoid carcinoma (malignant), 5. TCC (malignant), and 6. KIRC (malignant). (D) The mean value for ABCD3 staining intensity in normal tissue (1) was 325±19.36 compared to the mean value for ABCD3 staining intensity in TCC (5) 420±18.93 (p<0.01), and the mean value for KIRC (6) was 214.1±14.78 (p<0.001). Representative IHC images of ABCD3 protein expression in normal and renal carcinoma (obtained from the Human Protein Atlas). **denotes p<0.01, ***denotes p<0.0001. ccRCC: Clear cell renal cell carcinoma; KIRC: kidney renal clear cell carcinoma; TCC: transitional cell carcinoma.

To further validate our findings that decreased ABCD3 expression is significantly associated with both clinical stages and histological grades, patient TMAs were prepared in-house from matched normal and ccRCC tissue samples from stages I, II, III, and IV (primary and metastatic) for IHC analysis. At the protein level, staining of ABCD3 confirmed reduced levels in ccRCC patient samples at stages I-III compared to normal-matched tissue (Figure 7A). Further, H-score analysis revealed a statistically significant difference in ABCD3 protein levels between ccRCC patient samples and matched normal samples (Figure 7B).

Figure 7.

Figure 7

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ABCD3 expression profile in normal renal tissue and clear cell renal cell carcinoma (ccRCC). Tissue microarray (TMA) of ccRCC patient samples versus matched normal tissue stained for ABCD3 expression in stages I, II, III, IV (primary and metastatic; normal n=45, 24, 36, 13 and tumor n=52, 29, 38, 35, respectively). (A) Immunohistochemical staining of ABCD3. H-score±standard deviation from the mean is shown. (B) ABCD3 expression was quantified by H-Score (staining intensity, with an index between 0-300 calculated as a percent of the total area). **p<0.01, unpaired t-test. B) Stage I, C) Stage II, D) Stage III, E) Stage IV (primary and metastatic).

Gene Enrichment Pathway Analysis. Co-expressed genes often share pathways and biological processes, suggesting a coordinated role in regulating essential cellular functions. Accordingly, we investigated genes co-expressed with ABCD3 and specific cancer-related pathways. GSVA revealed a significant (p<0.05, FDR<0.05) negative correlation between ABCD3 expression and gene sets associated with apoptosis, cell cycle, DNA damage, and epithelial-mesenchymal transition pathways in ccRCC (Figure 8A and B). Conversely, a significant (p<0.05, FDR <0.5) positive correlation was seen in ccRCC between ABCD3 and gene sets associated with PIK3-AKT and receptor tyrosine kinase pathways (RTK). KIRP was found to have one positive correlation between ABCD3 and gene sets related to the RTK pathway (p<0.05, FDR<0.5). In contrast, KICH had no significant correlations (as defined by p<0.05, FDR<0.05) between ABCD3 and gene pathways in KICH (Figure 8A). Gene Set Enrichment Analysis accessed through Linkedomics was performed to analyze KEGG and GO Biological Processes databases using the CPTAC proteomic datasets (Figure 8C and D). Apart from peroxisomal-transport-related pathways, ABCD3 protein expression was positively correlated with mitochondrial-related functions and, in agreement with GSCA findings, inversely correlated with cell cycle regulation and DNA repair in both KEGG and GO-BP databases (Figure 8E and F).

Figure 8.

Figure 8

Figure 8

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Pathway and ABCD3 co-expression analysis. (A) A heatmap demonstrating the correlation between gene set variation analysis of ABCD3 and protein activity in KIRP, KIRC and KICH (GSCA). Positive and negative associations are indicated by blue and red. (B) A heatmap of pathway activity associated with ABCD3 expression in KIRC. Predicted pathways that may be affected by high or low expression of ABCD3 are indicated. Blue represents an inhibitory effect and red indicates activation. The most highly enriched pathways of genes co-expressed with ABCD3 in the (C) GO biological processes database and the (D) KEGG database (CPTAC-KIRC dataset). (E) Enrichment plots for mitochondrial, cell cycle and DNA damage-related pathways of proteins co-expressed with ABCD3 (GO Biological Process). (F) Enrichment plots for oxidative phosphorylation, base-excision repair and cell cycle-related pathways of proteins co-expressed with ABCD3 (KEGG). Significance was set at p<0.05 and FDR <0.05. *denotes p≤0.05; #FDR ≤0.05. EMT: Epithelial-mesenchymal transition; GO-BP: Gene Ontology-Biological Processes; KICH: kidney chromophobe; KIRC: kidney renal clear cell carcinoma; KIRP: kidney renal papillary cell carcinoma; RTK: receptor tyrosine kinase.

Discussion

The ABC transporters represent the largest family of transmembrane proteins. They are subdivided into seven subfamilies (A-G) and are localized in numerous organelles. These proteins function primarily as transmembrane transporters of structurally unrelated macromolecules required for numerous cell processes. Additionally, they serve protective roles whereby many transporters act as efflux pumps for xenobiotics. Several members, most notably of the A, B, and C subfamilies, have been implicated in cancer chemoresistance. The transporters of the D subfamily of the ABC family, called peroxisomal or ALD transporters, are encoded by four genes which produce the adrenoleukodystrophy protein (ALDP/ABCD1), ALDP-related protein (ALDRP/ABCD2), 70-kDa peroxisomal membrane protein (PMP70/ABCD3), and the PMP70-related protein (P70R/ABCD4). These proteins are mainly responsible for transporting fatty acids across peroxisomal and lysosomal membranes.

Although considerable overlap exists between members of the D subfamily and substrate specificity, ABCD3 has a distinct substrate specificity. In contrast to ABCD1 and 2, it is not linked to adrenoleukodystrophy. ABCD1 and 2 may preferentially transport very long-chain fatty acids (VLCFA), whereas ABCD3 may preferentially transport branched-chain fatty acids, long-chain fatty acids, bile acid precursors, and dicarboxylic acids (20,21). ABCD3 is highly expressed in the liver and kidneys and is thought to play a key role in peroxisomal fatty acid oxidation and transport. Other fatty acid transporters have been shown to have direct effects on ABCD3. Solute carrier family 27 member 2 (SLC27A2), also known as fatty acid transport protein 2 (FATP2), is a long-chain fatty acid transporter with Acyl-CoA synthetase activity that is over expressed in colorectal cancer and has been shown to regulate ABCD3. This overexpression of SLC27A2 in colorectal cancer leads to increased ABCD3 expression and elevated fatty-acids which impacts cancer proliferation, metastasis, inflammation and immunosuppression (22). Several studies have suggested that ABCD3 may serve as a biomarker for several cancers, given its role in metabolic pathways, immune interactions, and gene expression profiles that correlate with clinical outcomes (23,24). In breast cancer, the expression of ABCD3 has been shown to correlate with tumor progression and response to therapy, with higher levels associated with more aggressive forms of the disease (25). Similarly, in colorectal cancer, ABCD3 is involved in fatty acid β-oxidation and ATP synthesis, indicating its role in the metabolic adaptation of cancer cells to nutrient availability allowing cancer cells to thrive in a nutrient-depleted tumor microenvironment (24). In colorectal cancer, elevated ABCD3 (i.e. PMP70) is also strongly correlated with chemotherapy resistance and depletion of PMP70 significantly reduced the viability of resistant cells to chemotherapy, both in vitro and in vivo (26). Moreover, the expression levels of ABCD3 have been linked to patient prognosis across various cancers. For instance, high ABCD3 expression is associated with poor outcomes in glioma patients, where it correlates with immune infiltration and tumor aggressiveness (27). In prostate cancer, elevated ABCD3 levels have been observed in more aggressive tumors, and its downregulation has been associated with increased chemosensitivity, suggesting that ABCD3 may serve as a prognostic biomarker (28,29). This is echoed in findings from ovarian cancer, where downregulation of ABCD3 was linked to better chemotherapy sensitivity and longer time to progression (30). Interestingly, the expression of ABCD3 is not uniform across all tumor types. In parotid gland tumors, lower levels of ABCD3 were noted in malignant tissues compared to benign tumors, indicating a potential shift in metabolic pathways during malignant transformation (31). This suggests that the role of ABCD3 may vary depending on the cancer type and its stage, highlighting the need for further research to clarify these dynamics.

Here, we demonstrate that ABCD3 is also dysregulated in ccRCC and show that ABCD3 transcripts were reduced across all tumor stages and were found to be decreased in a grade and stage-dependent manner. Moreover, lower expression of ABCD3 is associated with worse overall survival and progression-free survival in patients treated in the pre-immunotherapy era, in contrast to other cancer types, where high ABCD3 expression is associated with poorer outcomes. This highlights the contrasting influence ABCD3 can have on tumor biology and potentially the tumor immune microenvironment, as tumors with high ABCD3 expression are found in non-immunogenic tumor types (i.e., breast, prostate, glioma, colorectal cancer). This may be the result of the immunosuppressive effects from polymorphonuclear-myeloid derived suppressor cells (PMN-MDSC). PMN-MDSC are known to be associated with poor clinical outcomes in cancer and exclusively upregulate FATP2 (SLC27A2) leading to immunosuppressive activity through uptake of arachidonic acid and synthesis of prostaglandin E2 (32). As demonstrated previously, SLC27A2 regulates ABCD3, therefore, provides insights into pro-tumorigenic mechanisms of SLC27A2-ABCD3 axis. ccRCC is a highly immunogenic tumor that contributes to the efficacy of immune checkpoint inhibitors (ICI) in this tumor type. The relationship between ABCD3 and the tumor microenvironment of ccRCC is unknown; however, in gliomas, high ABCD3 expression was associated with increased macrophages, neutrophils, and T-helper-2 (Th2) cells, while dendritic cells, T-helper-17 cells (Th17), and T-regulatory cells (Treg) were decreased in glioblastoma (27). Therefore, further investigation into how the reduced expression of ABCD3 in ccRCC could impact the efficacy of ICIs and whether it could serve as a predictive biomarker of response.

In the multivariate Cox regression analysis model, ABCD3 retained its predictive status. The reduction in ABCD3 transcripts also correlated with a decrease in protein expression in both the CPTAC proteomic data and tissue microarrays. Our predictive bioinformatic and immunohistochemical analyses indicate that ABCD3 may be a prognostic factor with probable tumor suppression activity. Further, our gene enrichment studies indicate that ABCD3 protein expression is associated with DNA repair, apoptosis, and cell cycle-related pathways. Moreover, growth factor-related kinase pathways (RTK and PIK3-AKT) are known drivers of cancers (33-35). RTKs are key components of several growth factor-mediated pathways, including the epidermal growth factor receptor (EGFR), VEGFR, and brain-derived growth factor (BDNF). Hence, RTKs are critical for regulating biological processes (angiogenesis, cellular proliferation, cell survival) necessary for tumorigenesis (36-39). In contrast, the PIK3-AKT pathway may regulate EMT and cancer stemness in renal carcinomas (33,35).

It should be noted that ABCD3 is one of several genes (CPT1A, LPL, CPT2, EHHADH) in the peroxisomal fatty acid oxidation pathway that is downregulated (40). Xu et al. demonstrated that downregulation of SLC27A2 predicted poor prognosis and upregulation of SLC27A2 could inhibit proliferation, migration and invasion of RCC cell lines (41). As demonstrated by Pinto et al., there is a direct relationship between SLC27A2 and ABCD3 where high SLC27A2 leads to increased ABCD3 expression (6). HIFs, which are upregulated in ccRCC, have recently been shown to suppress lipid metabolism and cause excessive accumulation of lipids (42). Studies have shown that when the actions of HIF were reversed, lipid accumulation and tumorigenesis in ccRCC were significantly diminished (42). Furthermore, when phospholipase C-like 1 (PLCL1), a protein that regulates lipid browning and fatty acid metabolism, is upregulated, it reduces lipid accumulation, tumor formation, and tumor volume in ccRCC (43,44). This suggests that accumulation of the lipid substrates from this pathway may be critical for renal tumor progression and survival. Thus, loss of ABCD3 function may shift lipid metabolism and lead to the accumulation of pro-tumorigenic lipids, indicating that ABCD3 may exert anti-tumor effects by preventing excessive lipid accumulation. As ABCD3 is downregulated, very long chain fatty acids will accumulate which can serve as energy reserves for cancer cells in a hypoxic environment. Intracellular lipid droplet formation is a hallmark of ccRCC and as lipids accumulate, lipid peroxidation and reactive oxygen species also increase; this can activate HIF pathways leading to angiogenesis, metabolic adaptation, and immune regulation (45). Therefore, targeting lipid metabolism in ccRCC patients with low ABCD3 expression may provide a mechanism for overcoming the accumulation of pro-tumorigenic lipids.

Limitations to this study include a small sample size and limited samples available for normal and stage IV (primary and metastatic) tissue. Notwithstanding, the results from our immunohistochemical analysis confirmed our bioinformatic findings, demonstrating that ABCD3 is downregulated in ccRCC relative to normal tissues. Moreover, the TCGA and CPTAC datasets had small numbers of samples from racial minorities, thereby limiting our ability to ascertain if the ABCD3 dysregulation is also applicable to ccRCC of different racial groups. The survival data from the TCGA KIRC has limitations as this dataset was created in the pre-immunotherapy era.

Conclusion

Biomarkers to predict prognosis and response to therapy and guide novel therapeutic strategies in ccRCC remain a priority. This study demonstrates ABCD3 as a promising biomarker that predicts poor survival outcomes when downregulated. Given the role of ABCD3 in transporting branched long-chain fatty acid for oxidation, it is presumable that ABCD3 is integral for the removal of tumorigenic lipids in renal cells. Therefore, the downregulation of ABCD3 expression and other members of the peroxisomal fatty acid oxidation pathway may represent a unique molecular risk signature for clear cell renal cell carcinoma. Further investigation into the reduced expression of ABCD3 and the therapeutic vulnerabilities of ccRCC is warranted to develop novel therapeutic strategies and improve or predict response to current immune-based therapies.

Conflicts of Interest

The Authors declare no conflicts of interest in relation to this study.

Authors’ Contributions

The Authors contributed to the preparation in the following ways: Conceptualization, RRR, SOH, and MDS; methodology, SOH, TR, ET, TNT, JAC; writing - original draft preparation, SOH, TR, RRR, TNT, AMK; writing - review and editing, RRR, MDS, TNT, AMK, JAC, JPR; and funding acquisition, RRR. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

We thank Zaneta Belay, Michael Dumas and Ugonna Ononuju for their assistance with the scoring of representative IHC images of ABCD3 from the HPA.

Funding

This research was supported by the National Cancer Institute of the National Institutes of Health through Grants P20CA264075 (SOH, MDS) and 5U54CA233396 (RRR) as well as The John Q. Ranney Research Fund for Renal Cell Carcinoma (JAC).

Artificial Intelligence (AI) Disclosure

No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.

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