Abstract Kidney renal clear cell carcinoma (KIRC) is the most common histological subtype of renal cell carcinoma (RCC), characterized by high metastatic potential and mortality, accounting for over 75% of RCC patients worldwide. However, KIRC patients often exhibit poor prognosis due to the absence of effective and sensitive biomarkers for early detection. As a key xenobiotic and endobiotic receptor in humans, the pregnane X receptor (PXR, NR1I2) exerts regulatory functions on various biological signaling pathways—most of which are associated with tumorigenesis. In this study, we aimed to elucidate the role of PXR in KIRC and investigate its underlying mechanisms. Using The Cancer Genome Atlas (TCGA) database and our clinical KIRC samples, we analyzed the expression profile and prognostic value of PXR. Results showed that PXR was significantly upregulated in KIRC tumor tissues, and high PXR levels were associated with poor overall survival in KIRC patients. Further in vitro experiments demonstrated that modulating PXR markedly influenced KIRC cell proliferation, invasion, migration, and metastasis. RNA sequencing revealed that PXR knockdown in KIRC cells perturbed multiple cell signaling pathways. Mechanistic investigations showed that PXR down-regulation promoted the adenosine monophosphate-activated protein kinase (AMPK) signaling pathway and affected the proliferation, apoptosis and invasion of KIRC cells. Our findings indicate that PXR is consistently upregulated in KIRC tissues and correlates with poor patient prognosis, suggesting it could serve as a potential biomarker for the diagnosis and prognosis of KIRC. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-19812-y. Keywords: Kidney renal clear cell carcinoma (KIRC), Nuclear receptor (NR), Pregnane X receptor (PXR), Adenosine monophosphate-activated protein kinase (AMPK) Subject terms: Renal cancer, Molecular medicine Introduction Renal cell carcinoma (RCC) is the most prevalent malignancy in the urinary system, accounting for 2–3% of the global cancer burden. Based on the morphological characteristics, RCC is mainly classified into three subtypes: kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and kidney chromophobe cell carcinoma (KICH). Among them, KIRC is the most common subtype. Clinically, once the primary tumor size of KIRC exceeds 7 cm or metastasis occurs, the 5-year survival rate of patients drops below 10%^[46]1–[47]3. Although surgery can improve progression-free survival in RCC patients, 30% of them develop tumor cell metastasis postoperatively^[48]4. Due to the lack of obvious early symptoms, KIRC is often difficult to diagnose in a timely manner, leading to poor prognosis for patients^[49]5,[50]6. Nearly 30% of KIRC patients eventually develop metastasis, indicating limited therapeutic efficacy^[51]7. Currently, molecular targeted therapy and immunotherapy are the mainstay for advanced KIRC, but both approaches have poor long-term efficacy and significant adverse reactions. Metastatic KIRC carries an extremely poor prognosis: historical cohort studies show a median overall survival (OS) of 13 months and a 5-year survival rate of ≤ 10%^[52]8. To date, the pathogenesis of KIRC remains incompletely understood. Therefore, in-depth exploration of the pathological mechanisms of KIRC, identification of targeted molecular markers, and development of novel diagnostic and therapeutic strategies are of essential significance. Nuclear receptors (NRs) are a class of transcription factors activated by ligands^[53]9,[54]10. Research indicates that NRs play key regulatory roles in various physiological and pathological processes, including cell proliferation/apoptosis, cellular metabolic activities, chronic inflammatory responses, fibrotic processes, and carcinogenesis^[55]11–[56]13.Pregnane X receptor (PXR, NR1I2), a member of the nuclear receptor superfamily, has been recognized since its discovery in 1998 as a key transcriptional regulator of genes induced by xenobiotics and drugs^[57]14–[58]18.Recent studies have increasingly focused on the role of PXR in various diseases, particularly in cancer development^[59]19,[60]20. It has been reported that PXR activation promotes the transcription of the multidrug resistance gene MDR1, leading to upregulation of P-glycoprotein (P-gp) expression, which enhances tumor cell drug resistance and facilitates cancer progression^[61]21. Furthermore, several polymorphic loci of the PXR gene have been found to be significantly associated with susceptibility to various cancers, suggesting their potential utility as genetic biomarkers for predisposition^[62]22.Therefore, PXR plays a significant role in tumorigenesis and cancer progression.Although PXR expression levels are low in normal kidney tissue, they are significantly upregulated under acute or chronic renal stress conditions^[63]23–[64]26, indicating its critical regulatory role in renal pathological processes.Currently, the regulatory mechanism between PXR and KIRC remains insufficiently studied. Our preliminary bioinformatic analysis suggests that PXR likely plays a critical role in the pathogenesis and progression of KIRC, and targeted intervention of PXR may represent a novel therapeutic strategy for KIRC. AMP-activated protein kinase (AMPK) serves as a crucial cellular energy sensor that becomes activated under various stimuli and subsequently regulates multiple signaling pathways and metabolic processes^[65]27. AMPK activation generally exerts beneficial effects on organismal metabolism, making it an important therapeutic target for conditions such as metabolic syndrome and cancer^[66]28,[67]29.However, the role of AMPK in cancer is dual-faced^[68]30: it can suppress tumorigenesis and progression by modulating inflammatory and metabolic pathways^[69]31, yet it may also promote tumor metastasis through phosphorylation-mediated activation of the Pyruvate Dehydrogenase Complex (PDC), thereby sustaining the Tricarboxylic Acid Cycle(TCA) cycle^[70]32.In this study, based on RNA-Seq analysis, AMPK was demonstrated to play a crucial role in KIRC and exhibited significant regulatory association with PXR expression.Previous studies have indicated a negative regulatory relationship between PXR and AMPK^[71]33, yet the underlying mechanism remains to be elucidated. Therefore, we hypothesize that the PXR-AMPK signaling axis may contribute to the pathogenesis and progression of KIRC, and subsequent functional experiments will be conducted to validate this mechanistic hypothesis. Materials and methods Patients and specimens Tumor specimens and corresponding adjacent normal tissues of 6 patients with KIRC who underwent primary surgery at the Second Affiliated Hospital of Dalian Medical University between May 2023 and December 2023 were collected. All samples were snap-frozen in liquid nitrogen immediately after lesion resection and subsequently stored at −80 °C for later RNA and protein extraction. Paraffin-embedded tissues were prepared by the hospital’s pathology department and histopathologically examined by at least two experienced pathologists to confirm tumor malignancy. This study was approved by the Institutional Ethics Committee of the Second Affiliated Hospital of Dalian Medical University (Ethical approval number: 2022-136) and conducted in accordance with the principles of the Declaration of Helsinki. All patients were fully informed about the study’s purpose and procedures prior to sample collection, and written informed consent was obtained from each participant. Experimental groups NC-siRNA group (Negative Control siRNA Group): Cells transfected with non-targeting control siRNA; PXR siRNA1 group: Cells transfected with PXR-specific siRNA (sense 5’-3’: GCUGGAACCAUGCUGACUUTT; anti-sense 5’-3’: AAGUCAGCAUGGUUCCAGCTT); PXR siRNA2 group: Cells transfected with a distinct PXR-specific siRNA (sense 5’-3’: GGAAAGAUCUGUGCUCUUUTT; anti-sense 5’-3’: AAAGAGCACAGAUCUUUCCTT). NC group (negative control); siRNA group (PXR-silenced); siRNA + AMPKi (SBI-0206965)(CAS:1884220-36-3,MedChemExpress,USA) group (PXR-silenced + AMPK inhibitor). Cell culture The human renal clear cell carcinoma cell line 786-O was obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) at 37 °C in a humidified incubator with 5% CO[2]. siRNA transfection Transient transfection of 786-O cells with 50 nM siRNA was conducted following the Lipofectamine 3000 Transfection Kit (Thermo Fisher, USA) protocol. Six hours post-transfection, the original medium was replaced, and cells were cultured in RPMI 1640 medium supplemented with 10% FBS for subsequent experiments. Immunohistochemical staining The samples were dehydrated, embedded in paraffin, and sectioned at a thickness of 4 μm. After deparaffinization and rehydration, sections were immersed in citrate buffer, microwaved at high power for 3 min and then at low power for 10 min. They were subsequently washed three times with phosphate-buffered saline (PBS; pH 7.4) for 5 min each. Endogenous peroxidase activity was quenched with 3% H[2]O[2], followed by three additional 5-min PBS washes. Sections were blocked with 5% bovine serum albumin (BSA; BioFroxx, 4240, Germany) for 1 h. After overnight incubation at 4 ℃ with the primary antibody (Rabbit Anti-PXR, 1:200 dilution; ABclonal, A1229, Wuhan, China), sections were incubated with horseradish peroxidase (HRP)-conjugated secondary antibody (Zhongshan Jinqiao, Beijing, China) for 1 h at room temperature. Finally, antigen–antibody complexes were visualized using diaminobenzidine (DAB), and nuclei were counterstained with hematoxylin. Immunostaining intensity was quantified using ImageJ software (v1.45; NIH, Bethesda, MD, USA; [72]https://imagej.nih.gov/ij/). RNA extraction and real-time quantitative PCR Total mRNA was isolated from renal tissues and 786-O cells using Trizol reagent (Vazyme, China). RNA concentration and purity were detected using a semi-automated nucleic acid/protein analyzer (Thermo, USA). The extracted RNA was reverse transcribed into cDNA and subsequently amplified with a real-time quantitative PCR system (Thermo, USA), using β-actin as the internal reference gene with three replicates per sample. Primer sequences are listed in Table [73]S1. Western blot analysis Tissue and cell samples were homogenized in RIPA lysis buffer on ice, followed by centrifugation at 12,000 rpm and 4 °C to collect the supernatant. Protein concentration was quantified using a BCA assay kit (Thermo, USA), and aliquots were stored at −20 °C. Equal amounts of total protein (30–60 μg) were separated by 10–12% SDS-PAGE electrophoresis and transferred to PVDF membranes (Millipore, USA). After blocking with 5% non-fat milk, the membranes were incubated overnight at 4 °C with corresponding primary antibodies. The next day, the membranes were washed three times with Tris-buffered saline containing Tween 20 (TBST), followed by incubation with appropriate secondary antibodies (diluted 1:5000) at room temperature for 1–2 h. After three additional TBST washes, protein bands were detected using ECL reagent and imaged with a Tanon-5200 system (Tanon, Shanghai, China). The primary antibodies used included: PXR (67912-1-Ig, Proteintech, Wuhan, China), p-AMPK (AP1441, Abclonal, Wuhan, China), AMPK (A1229, Abclonal, Wuhan, China), P53 (PAb240, Cambridge, UK), P65 (E379, Abcam, Cambridge, UK), NF-κB (ab281081, Abcam, Cambridge, UK), p-NF-κB (ab279874, Abcam, Cambridge, UK), PCNA (10205-2-AP, Proteintech, Wuhan, China), Cyclin D1 (ab182858, Abcam, Cambridge, UK), PI3K (60225-1-Ig, Proteintech, Wuhan, China), p-PI3K (80455-1-RR, Proteintech, Wuhan, China), AKT (10176-2-AP, Proteintech, Wuhan, China), p-AKT (28731-1-AP, Proteintech, Wuhan, China), Bax (A19684, Abclonal, Wuhan, China) and Bcl-2 (A19693, Abclonal, Wuhan, China). Cell viability assay Cells were seeded in 96-well plates at a density of approximately 1 × 10^4 cells per well, subjected to the indicated treatments, and cultured until the predetermined time. After incubation, CCK-8 solution (MeilunBio, China) was added at a 1:10 ratio and incubated for 1–2 h. The optical density (OD) at 450 nm was then measured using a multimode microplate reader (Thermo, USA). Cell viability was calculated using the following formula: graphic file with name d33e413.gif Colony formation assay 786-O cells were seeded in 6-well plates at a density of 1,500 cells per well and cultured in RPMI 1640 medium at 37 °C for 10 days. The resulting colonies were washed three times with PBS, fixed with 4% paraformaldehyde (PFA) for 20 min, and stained with 0.1% crystal violet. Stained colonies were imaged using a bright-field microscope (Leica, Wetzlar, Germany). The number of colonies was quantified using ImageJ software. All experiments were performed in triplicate. Wound healing assay 786-O cells were seeded in 12-well plates in RPMI 1640 medium. When cell confluence reached 80–90%, a scratch wound was created in the cell monolayer using a 200-μL micropipette tip. Immediately after scratching, non-adherent cells were removed by washing with PBS. Wound closure was imaged at specified time points using a digital inverted microscope, and the scratch area was quantified with ImageJ software. All experiments were performed in triplicate. Transwell invasion assay A transwell chamber assay was performed using a 24-well plate with an 8-μm pore polycarbonate membrane (Corning Inc., USA), following the manufacturer’s protocol. Matrigel was maintained in a liquid state on ice and diluted with ice-cold serum-free RPMI 1640 medium. A 100-μL aliquot of the diluted Matrigel was evenly applied to the upper surface of the polycarbonate membrane. The insert was then placed into a 24-well plate and incubated at 37 °C for 3 h to allow gel solidification. For cell seeding, 1 × 10^5 cells resuspended in 100 μL of serum-free RPMI 1640 medium were added to the upper chamber, while 600μL of complete medium containing 10% FBS was added to the lower chamber as a chemoattractant. After 24-h incubation at 37 °C, non-migrated cells on the upper surface of the membrane were gently removed with a cotton swab. The migrated cells on the lower surface were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet.Images of the cells that traversed the membrane were captured using an inverted microscope (Olympus CKX53, Tokyo, Japan). Flow cytometry 786-O cells in the logarithmic growth phase were harvested, digested with trypsin, resuspended, and seeded in 6-well plates. Plates were incubated overnight at 37 °C until cells reached 60% confluence. The medium was discarded, and cells were washed twice with sterile PBS. Trypsinization was performed, and digestion was terminated by adding complete medium. Cells were then processed using the Annexin V-FITC Apoptosis Detection Kit (Cywin Innovation (Beijing) Biotechnology Co., Ltd., Beijing, China) according to the manufacturer’s protocol. Briefly, cells were resuspended in Annexin V-FITC binding buffer, and 5 µL of Annexin V-FITC and 5µL of PI were added to each sample. Following a 30-min incubation at room temperature in the dark, samples were analyzed using a BD Aria III flow cytometer (New Jersey, USA). Tunel staining Apoptotic cells were detected using a Terminal Deoxynucleotidyl Transferase-mediated dUTP Nick End Labeling (TUNEL) Kit (Vazyme Biotech Co.,Ltd., Nanjing, China). Cells were incubated with proteinase K at 37 °C for 15 min. The coverslips were then covered with TUNEL reaction mixture and incubated at room temperature for 1 h . Nuclei were stained with DAPI (Beyotime Biotechnology Co., Ltd., Shanghai, China) at room temperature for 20 min. After mounting, images were acquired using a Leica SP8 confocal microscope. Cellular immunofluorescence Treated cells were fixed with 4% PFA at room temperature for 20 min and permeabilized with 0.2% Triton X-100 in PBS for 10 min. Nonspecific binding was blocked by incubating with 5% BSA in PBS for 1 h at room temperature. Cells were then incubated overnight at 4 °C with anti-PXR (1:200 dilution; A1229, ABclonal, Wuhan, China) and anti-p-AMPK (1:200 dilution; AP1441, ABclonal, Wuhan, China). The following day, cells were washed and incubated with Alexa Fluor 488-conjugated anti-rabbit IgG (1:500 dilution; Servicebio, [74]P11047, Wuhan, China) and Alexa Fluor 594-conjugated anti-mouse IgG (1:500 dilution; Servicebio, GB25303, Wuhan, China) for 1 h at room temperature protected from light. After three washes with PBS, nuclei were stained with DAPI (Beyotime Biotechnology Co., Ltd., Shanghai, China) for 10 min. Following three additional PBS washes, slides were sealed with an anti-fluorescence quenching mounting medium. Finally, fluorescence images were acquired using a Leica SP8 confocal microscope. Public data access Bioinformatics analysis of data downloaded from TCGA database ([75]https://portal.gdc.cancer.gov/) was performed using R Software (v.3.5.2., R Foundation for Statistical Computing., Vienna, Austria; [76]https://www.r-project.org/) and IBM SPSS Statistics 17.0 (SPSS Inc., Chicago., Illinois, USA; [77]https://www.ibm.com/products/spss-statistics). The Wilcoxon signed-rank test and logistic regression were used to analyze the association between PXR expression and clinicopathological features of KIRC (Table [78]1). Univariate and multivariate analyses were conducted using the Cox proportional hazards model to evaluate the impact of PXR on patient across different clinicopathological characteristics (stage, grade, tumor size, lymph node status and distant metastasis status). Hazard ratios (HRs) were calculated with 95% confidence interval (CI). Overall survival (OS) of different PXR expression groups was assessed using survival analysis, where the Kaplan–Meier method was employed to construct survival curves, and the log-rank test was used to compare the differences between the two curves. Table 1. Patient information in the TCGA cohort. Clinical characteristics Status Number % Age  < 61 years 266 49.5 ≧61 years 271 50.5 Gender Male 346 64.4 Female 191 35.6 Survival Alive 170 31.7 Death 367 68.3 Histological grade G1 14 2.6 G2 230 42.8 G3 207 38.5 G4 78 14.5 GX 5 0.9 Stage I 269 50.1 II 57 10.6 III 125 23.3 IV 83 15.5 T classification T1 275 69.9 T2 69 12.8 T3 182 33.9 T4 11 2.0 M classification M0 426 79.3 M1 79 14.7 MX 30 5.6 N classification N0 240 44.7 N1 17 3.2 NX 280 52.1 [79]Open in a new tab Statistical analysis Statistical analysis was performed using GraphPad Prism 9.0 software (San Diego, CA;[80]https://www.graphpad.com/), and results were presented as mean ± standard error (SEM). Comparisons between two groups were made using an unpaired Student’s t-test. To address potential type I errors arising from multiple statistical comparisons across experimental groups and outcome measures, appropriate correction methods were applied based on the nature of the analyses. For single-factor multiple-group comparisons (e.g., NC-siRNA vs. PXR siRNA1 vs. PXR siRNA2 groups in CCK-8, colony formation, wound healing, and Transwell assays; NC vs. siRNA vs. siRNA + AMPKi groups in mechanistic experiments), one-way analysis of variance (ANOVA) was followed by the Tukey’s Honest Significant Difference (HSD) test for post-hoc pairwise comparisons. For correlative analyses involving PXR expression and multiple clinicopathological features (e.g., stage, grade, T/N/M classification) or tumor microenvironment scores (stromal score, immune score, stemness scores) in the TCGA cohort, the Benjamini-Hochberg (BH) false discovery rate (FDR) correction was implemented. The FDR was set to 0.05, meaning the expected proportion of false-positive results among all significant findings was ≤ 5%. For Western blot and immunofluorescence quantitative analyses, where multiple protein targets (e.g., PXR, p-AMPK, AMPK, p53, Bax, Bcl-2, PCNA, Cyclin D1) were measured across the same experimental groups, a two-step correction was applied: first, one-way ANOVA was used to test for overall group differences per protein; second, the Bonferroni correction was applied to pairwise comparisons (e.g., NC vs. siRNA, siRNA vs. siRNA + AMPKi) for each protein, adjusting the α level by the number of protein targets. For RNA-seq differential gene expression (DEG) analysis, to control false positives when identifying DEGs between PXR-siRNA and NC-siRNA groups, we applied the Benjamini-Hochberg (BH) false discovery rate (FDR) correction (FDR < 0.05) to raw p-values generated by DESeq2. This method was chosen for RNA-seq due to its ability to balance sensitivity and specificity when testing thousands of genes simultaneously—ensuring that the expected proportion of false-positive DEGs among all significant hits is ≤ 5%. A total of 1,720 DEGs (802 upregulated, 918 downregulated) were identified after FDR correction (|log₂FoldChange|> 0, FDR < 0.05), as visualized in the volcano plot and used for subsequent KEGG enrichment analysis. For in vitro functional assays (786-O cells), pre-hoc power calculations were performed to justify sample sizes for key experiments by using GPower 3.1 software ([81]http://www.gpower.hhu.de/), and power calculations were based on a pilot experiment showing a ~ 40% reduction in cell proliferation after PXR knockdown. Assuming α = 0.05, power (1-β) = 0.8, and effect size f = 0.4, we determined a minimum of 3 biological replicates per group was sufficient to detect significant differences—consistent with our experimental design (n = 3 per group for all in vitro assays). For RNA-seq, given the high sensitivity of RNA-seq for detecting gene expression changes, it’s generally showing that n = 3 biological replicates per group (as in our PXR-siRNA and NC-siRNA groups) provides > 80% power to detect DEGs with |log2FoldChange|> 0.5 and FDR < 0.05—validating our sample size choice. For our independent clinical cohort, due to the small sample size (n = 6), power calculations confirmed insufficient power (< 50%) for survival or confounder analyses, which we explicitly acknowledge as a limitation of our study; therefore, consistent with our focus on using this cohort, we solely validated PXR expression (via qPCR, Western blot, IHC) in this cohort. Results The expression of PXR is up-regulated in KIRC To investigate the role of PXR in KIRC, we downloaded RNA-seq datasets from the TCGA database. After normalizing the RNA-seq data, we first analyzed the expression differences of PXR in various tumor tissues and corresponding normal tissues. We found significant differential expression of PXR between tumor and normal tissues in KIRC patients (Supplementary Fig. [82]1, Fig. [83]1A). Additionally, for samples with matched adjacent tissues in the TCGA database, a paired t-test was performed to compare PXR mRNA between tumor tissues and their adjacent non-tumor tissues (Fig. [84]1B). The results showed that PXR mRNA expression was significantly higher in tumor tissues than in non-tumor tissues (Fig. [85]1A, B). Using PXR expression data from the TCGA database, receiver operating characteristic (ROC) curve analysis was applied to evaluate the diagnostic value of PXR in KIRC. The area under the ROC curve (AUC) was 0.727 (Fig. [86]1C), indicating a moderate diagnostic accuracy in differentiating KIRC from normal tissues, supporting PXR’s potential as a supplementary biomarker for KIRC. Total RNA and protein were extracted from tumor tissues and adjacent tissues of an independent cohort of KIRC patients, and PXR expression in KIRC was analyzed by real-time quantitative PCR and Western blot (Fig. [87]1D-F). In parallel, IHC staining was performed to examine PXR protein levels in the validated cohort, which showed significantly higher density and intensity of PXR expression in KIRC tissues compared with adjacent non-tumor tissues (Fig. [88]1G). Collectively, data from TCGA and an independent cohort demonstrate that PXR is upregulated in KIRC with moderate diagnostic accuracy, supporting its potential as a supplementary biomarker for this cancer. Fig. 1. [89]Fig. 1 [90]Open in a new tab Expression of PXR in KIRC patients. (A) T-test comparing PXR mRNA levels between KIRC cases and normal samples from the TCGA database; (B) Paired t-test comparing PXR mRNA levels between tumor tissues and matched adjacent non-tumor tissues from the TCGA database; (C) Receiver operating characteristic (ROC) curve analysis of PXR expression data from the TCGA database; (D) Real-time quantitative PCR (qPCR) validation of PXR mRNA expression differences between tumor and matched non-tumor tissues in an independent clinical cohort; (E) Western blot analysis of PXR expression in tumor and matched non-tumor tissues from the validation cohort; (F) Densitiometric quantification by Western blot results shown in panel E; (G) Immunohistochemistry (IHC) staining confirming higher PXR protein expression in tumor tissues compared to adjacent non-tumor tissues in the validation cohort (Total Magnification: 100X/200X). Data are presented as mean ± SEM. *p < 0.05, ***p < 0.001. PXR is associated with advanced tumor stage, poor prognosis and reduced survival time of KIRC patients To investigate whether elevated PXR expression in KIRC correlates with tumor aggressiveness, we analyzed the relationship between PXR expression and tumor TNM stage, clinical composite stage, and tumor atypia in 537 KIRC patients from the TCGA (Fig. [91]2A-E). PXR expression was significantly correlated with clinical stage (Fig. [92]2A) and primary tumor size (Fig. [93]2B). The observed decrease in PXR expression at T4 stage may be associated with increased patient mortality (Fig. [94]2B), but no significant association was found with tumor atypia, lymph node metastasis, or vascular invasion (Fig. [95]2C-E). Survival analysis using Kaplan–Meier curves with log-rank tests revealed that patients with low PXR expression exhibited significantly longer overall survival (OS) than those with high PXR expression (Fig. [96]2F). Using TCGA KIRC data, we further analyzed the correlations between PXR expression and tumor microenvironment characteristics (Fig. [97]2G). PXR expression showed significant associations with stromal score (p = 0.0052) and immune score (p = 0.002), whereas RNA stemness score (RNAss) (p = 0.43), DNA stemness score (DNAss) (p = 0.25), and composite score (p = 0.48) were not statistically correlated. Univariate and multivariate Cox regression analyses confirmed that PXR expression is an independent prognostic factor for OS in KIRC patients (Table [98]2). Collectively, in KIRC patients, PXR expression is associated with tumor progression metrics (e.g., T stage, clinical stage, which serve as surrogates for tumor volume), reduced survival, and tumor microenvironment characteristics (stromal and immune scores), and is confirmed as an independent prognostic factor for overall survival. Fig. 2. [99]Fig. 2 [100]Open in a new tab Clinical relevance of PXR in KIRC. Analysis of KIRC data from the TCGA database revealed that PXR expression was significantly correlated with clinical stage (A) and primary tumor size (T stage) (B). However, no significant associations were observed between PXR expression and tumor grade G (C), lymph node metastasis (N) (D), or hematogenous metastasis (M) (E). Kaplan–Meier survival analysis with log-rank tests showed that patients with low PXR expression had significantly longer overall survival (OS) than those with high PXR expression (F). PXR expression was significantly associated with tumor microenvironment characteristics, specifically stromal score and immune score, but not with DNA stemness score (DNAss), RNA stemness score (RNAss) and composite score (G). Table 2. Univariate and multivariate COX regression analyses of the impact of PXR expression on OS in KIRC patients. Parameter Univariate Analysis Multivariate Analysis HR 95% CI p value HR 95% CI p value Age 1.031 1.017–1.045 6.25 × 10^–06 1.034 1.019–1.050 1.19 × 10^–05 Gender 0.949 0.689–1.306 0.746 1.021 0.735–1.418 0.902 Grade 2.365 1.916–2.919 1.12 × 10^–15 1.586 1.251–2.011 1.41 × 10^–04 Stage 1.926 1.6833–2.20 1.35 × 10^–21 1.958 1.396–2.746 9.91 × 10^–05 T 1.984 1.678–2.346 1.05 × 10^–15 0.796 0.558–1.134 0.206 M 2.145 1.695–2.714 2.04 × 10^–10 1.006 0.646–1.568 0.978 N 0.867 0.742–1.013 0.073 0.890 0.757–1.045 0.154 PXR 1.739 1.206–2.507 0.003 1.545 1.022–2.337 0.039 [101]Open in a new tab PXR knockdown suppresses KIRC cell colony formation, migration, and invasion phenotypes To clarify the role of PXR in KIRC cell proliferation, we employed siRNA technology to knock down PXR expression in the 786-O cell model. The efficiency of PXR silencing was confirmed at both mRNA and protein levels (Fig. [102]3A-C). Subsequent CCK-8 proliferation assays demonstrated that both PXR-siRNA1 and PXR-siRNA2 transfection significantly reduced the cellular growth rate compared to the negative control (NC-siRNA) group. Notably, the PXR-siRNA1 group exhibited the most pronounced inhibitory effect, with proliferative capacity declining to approximately one-fourth of that observed in the control group (Fig. [103]3D, E). Fig. 3. [104]Fig. 3 [105]Open in a new tab Knockdown of PXR with siRNAs in 786-O cells. (A) qPCR was used to detect the mRNA expression levels of PXR in the three groups of cells (NC-siRNA group, PXR-siRNA1 group, and PXR-siRNA2 group); (B) Western blot analysis was utilized to examine the protein expression of PXR; (C) Quantitative analysis of PXR protein levels based on the western blot results in panel B; (D) Representative images showing the distinct proliferation states of cells in the NC-siRNA group, PXR-siRNA1 group, and PXR-siRNA2 group (Total Magnification: 40X); (E) CCK-8 proliferation assay was conducted to assess the cell growth ability. Data are presented as mean ± SEM. *p < 0.05, **p < 0.01. To further systematically evaluate the role of PXR in the malignant phenotype of KIRC, we conducted multiple functional assays following gene silencing. Colony formation assays demonstrated that NC-siRNA cells formed dense and large-sized colonies, whereas PXR-silenced groups (PXR-siRNA1 and PXR-siRNA2) showed markedly reduced colony numbers and sizes (Fig. [106]4A). Microscopic counting and quantitative analysis (Fig. [107]4B) indicated that the average number of colonies in the NC-siRNA group was significantly higher than that in the two knockdown groups, with the PXR-siRNA1 group showing the most pronounced reduction, followed by a significant decrease in the PXR-siRNA2 group. Fig. 4. [108]Fig. 4 [109]Open in a new tab Effect of PXR on the proliferative, migratory and invasive abilities of KIRC cells. (A, B) Colony formation assay was performed to examine the effect of PXR knockdown on the proliferative capacity and clonogenic potential of 786-O cells. Representative colony images (A) and quantitative analysis of colony numbers (Total Magnification: 1X) (B) are shown; (C, D) Wound healing assay was utilized to assess the influence of PXR knockdown on the migratory ability of 786-O cells. Representative images of scratch closure at 0 h and 24 h (Total Magnification: 40X); (C) and quantitative analysis of migration rate (D) are presented; (E, F) Transwell invasion assay was conducted to determine the effect of PXR knockdown on the invasive potential of 786-O cells. Representative images of invaded cells (Total Magnification: 40X);(E) and quantitative analysis of invaded cell numbers per field (F) are displayed. Experiments were repeated three times with multiple biological replicates. Data are presented as mean ± SEM. *p < 0.05, **p < 0.01 vs. control group (NC-siRNA). In terms of cell migration and invasion capabilities, wound healing assays indicated that under initially consistent scratch widths, the NC-siRNA group displayed strong migratory ability with significant scratch closure, while PXR- siRNA1 and PXR-siRNA2 groups showed markedly restricted cell migration and delayed wound closure. Quantitative analysis of migration rates (Fig. [110]4D) further confirmed that the NC-siRNA group had the highest migration rate, whereas both knockdown groups exhibited significantly impaired migration capacity. These results collectively demonstrate that PXR promotes multiple malignant behaviors of KIRC cells, including colony formation, migration, and invasion, while inhibition of PXR expression effectively reverses these aggressive phenotypes. Effect of PXR on apoptotic capacity of KIRC cells To investigate the role of PXR in apoptosis of KIRC cells, this study employed TUNEL staining and flow cytometry for dual verification.TUNEL staining results (Fig. [111]5A, B) showed that apoptotic cells exhibited green fluorescence signals. The NC-siRNA group displayed only sporadic and weak fluorescence, indicating rare apoptotic cells. In contrast, the PXR-siRNA1 and PXR-siRNA2 treatment groups showed significantly enhanced green fluorescence intensity, with the PXR-siRNA1 group exhibiting the most prominent signals. Since PXR expression was lowest in the PXR-siRNA1 group, these results suggest a dose-dependent inhibitory effect of PXR on apoptosis.Furthermore, quantitative flow cytometry analysis (Fig. [112]5C, D) confirmed that PXR knockdown promoted cell apoptosis: the apoptosis rate was low in the NC-siRNA group, while it increased significantly in the PXR-silenced groups, particularly in the PXR-siRNA1 group. Both experiments consistently indicate that PXR acts as a potential regulatory factor promoting cell survival in KIRC. Fig. 5. [113]Fig. 5 [114]Open in a new tab Effect of PXR on apoptosis in 786-O cells. (A) TUNEL staining was performed to assess cell apoptosis, where green fluorescence intensity reflects the level of apoptosis (Total Magnification: 200X);Scale bar: 20 μm; (B) Quantitative analysis of cell apoptotic cells in panel A (n = 3); (C) Flow cytometry was used to detect the level of cell apoptosis; (D) Quantitative analysis of apoptotic cells in panel C (n = 3). Experiments were repeated three times with multiple biological replicates. Data are presented as mean ± SEM. **p < 0.01, ***p < 0.001 vs. control group (NC). RNA sequencing reveals PXR knockdown-induced activation of AMPK signaling pathway in KIRC cells To elucidate the molecular mechanisms underlying the pro-apoptotic and anti-proliferative effects of PXR knockdown in KIRC, we performed transcriptome sequencing (RNA sequencing) on 786-O cells with PXR silenced (PXR-siRNA1 group) and control cells (NC siRNA group). To verify the reliability of RNA-seq data and the rationality of sample selection, we performed correlation analysis of gene expression profiles across all samples (3 biological replicates in the NC-siRNA group and 3 biological replicates in the PXR-siRNA1 group). As shown in Supplementary Fig. [115]2, the pairwise correlation coefficient (Pearson’s r) between samples within the same group was > 0.97, indicating high consistency and reproducibility of gene expression patterns among biological replicates. This high intra-group correlation confirms that our sample selection was rational, with minimal technical variation, and supports the reliability of subsequent differential expression and pathway enrichment analyses. Cluster heatmap analysis (Fig. [116]6A) revealed distinct distribution patterns of differentially expressed genes (DEGs) between the PXR-siRNA1 group and the NC-siRNA group, indicating significant transcriptional changes upon PXR knockdown. The volcano plot (Fig. [117]6B) further visualized DEGs between the two groups, with 802 genes up-regulated and 918 genes down-regulated in the PXR-siRNA1 group compared to the NC-siRNA group. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (Fig. [118]6C) showed significant enrichment of pathways related to human papillomavirus infection, thyroid hormone signaling, and notably, the AMPK signaling pathway. Additionally, extraction and analysis of genes involved in material and energy metabolism from the RNA-seq data revealed that PXR expression impacts the transcription of rate-limiting enzymes in 786-O cells, as well as genes associated with development, aging, and diabetes (Supplementary Fig. [119]3). Collectively, these findings suggest that PXR exerts its regulatory effects on KIRC cell biology partially through modulating the AMPK signaling pathway and key metabolic genes. Fig. 6. [120]Fig. 6 [121]Open in a new tab High-throughput transcriptome sequencing Analyses after PXR knockdown in 786-O cells. (A) Hierarchical clustering analysis was performed to compare gene expression changes across different groups, with red and green representing up-regulated and down-regulated genes, respectively; (B) Volcano plot illustrating the global distribution of differentially expressed genes between the PXR-siRNA1 group and NC-siRNA group; (C) KEGG enrichment analysis of PXR-siRNA1 group vs. NC-siRNA group. PXR regulates KIRC cell proliferation via the AMPK signaling pathway To clarify the regulatory relationship between PXR and the AMPK signaling pathway, this study employed PXR knockdown combined with the AMPK inhibitor SBI-0206965 for mechanistic investigation. CCK-8 assay results (Fig. [122]7A, B) showed that NC group cells maintained stable proliferation over 36 h; after PXR knockdown (siRNA group), cell proliferation was significantly inhibited, confirming the previously observed pro-proliferative role of PXR. However, in the combined treatment group with AMPK inhibitor (siRNA + AMPKi), the proliferation inhibition caused by PXR knockdown was markedly reversed, indicating that PXR likely regulates KIRC cell proliferation partly through the AMPK pathway—that is, PXR deficiency inhibits growth by activating AMPK, while AMPK inhibition counteracts this effect. Fig. 7. [123]Fig. 7 [124]Open in a new tab PXR regulates KIRC cell proliferation via the AMPK pathway. (A) Representative bright-field images showing distinct proliferation states of 786-O cells in the NC group, siRNA group, and siRNA + AMPKi group (Total Magnification: 40X); (B) CCK-8 proliferation assay was performed to evaluate cell growth capacity over a 36-h time course; (C) Western blot analysis depicting the expression profiles of key proteins (PXR, p-AMPK, AMPK, PI3K, p-PI3K, AKT, p-AKT, p53, Bax, Bcl-2, PCNA, Cyclin D1) in the NC, siRNA, and siRNA + AMPKi groups; β-actin served as the loading control; (D) Quantitative analysis of protein expression levels from panel C, normalized to β-actin; (E) Immunofluorescence staining illustrating the subcellular localization and expression of p-AMPK (green) and PXR (red) proteins in the three groups; nuclei were counterstained with DAPI (blue) (Total Magnification: 200X); scale bar: 20 μm. Data are presented as mean ± SEM. *p < 0.05, ** p < 0.01 vs. NC group, #p < 0.05, ##p < 0.01 vs. siRNA group. At the protein level, Western blot analysis (Fig. [125]7C, D) further elucidated the regulatory role of the PXR-AMPK axis in cell proliferation. In the PXR-knockdown group (siRNA group), the expression level of p-AMPK was approximately twice that of the NC group, while the p-PI3K/PI3K and p-AKT/AKT ratios showed a decreasing trend compared to the NC group. The expression of the tumor suppressor protein p53 increased about threefold relative to the control, and the Bax/Bcl-2 ratio was also elevated by approximately twofold. In addition, the expression levels of the proliferation marker PCNA and the cell cycle protein Cyclin D1 were reduced to nearly half of those in the control group. However, in the group treated with the AMPK inhibitor in combination (siRNA + AMPKi group), all the aforementioned molecular alterations induced by PXR knockdown were markedly reversed, demonstrating that the AMPK signaling pathway plays a central role in mediating PXR’s regulation of key proteins involved in proliferation and apoptosis. To further visually verify the effect of PXR on AMPK activity, we used cellular immunofluorescence to detect the expression and localization of p-AMPK (the activated form of AMPK) (Fig. [126]7E). The results showed that the fluorescence signal of p-AMPK was weak in the NC group, consistent with baseline AMPK activity; after PXR knockdown (siRNA group), the p-AMPK fluorescence intensity significantly increased, suggesting that PXR inhibition activates AMPK. In the combined inhibitor group (siRNA + AMPKi), AMPK phosphorylation was blocked, and the fluorescence signal markedly weakened. This result directly demonstrates that PXR can regulate the activation status of AMPK in KIRC cells. Discussion The role of PXR in cancer pathogenesis represents an emerging research field. On one hand, accumulating evidence indicates its association with malignancies such as breast cancer, liver cancer, prostate cancer, and esophageal cancer^[127]34–[128]37. In human physiology, PXR exhibits tissue-specific expression, with prominent expression in the liver, intestine, and kidney. Within the renal system, its relatively high abundance in proximal renal tubular epithelial cells has been previously associated with nephrotoxicity, diabetic nephropathy, and chronic kidney disease (CKD)^[129]23,[130]38,[131]39. On the other hand, PXR demonstrates significant potential as a clinical biomarker^[132]40. Studies indicate that PXR gene polymorphisms can markedly influence therapeutic response and safety in patients with various malignant tumors^[133]41. Genetic variations in PXR may disrupt the metabolism of commonly used chemotherapeutic agents, leading to altered drug clearance rates and increased risk of severe adverse events such as hepatotoxicity or myelosuppression^[134]22. Therefore, PXR genetic testing holds considerable clinical value for guiding individualized treatment strategies and evaluating efficacy versus toxicity. However, although the role of PXR in various cancers has been gradually elucidated^[135]42,[136]43, its function in the pathogenesis of KIRC remains insufficiently explored. Our study addresses this gap by demonstrating that PXR expression exerts a negative regulatory effect on KIRC progression. Experiments involving graded knockdown of PXR revealed that as PXR expression decreased with the PXR siRNA1 group showing the lowest expression, the apoptosis rate increased in a concentration-dependent manner, indicating an inverse correlation between PXR expression levels and the extent of apoptosis. Furthermore, RNA sequencing analysis suggested that the AMP-activated protein kinase (AMPK) signaling pathway, which is closely associated with cancer development, may act as a potential mediator through which PXR influences KIRC. Subsequent mechanistic experiments confirmed that PXR affects KIRC progression by suppressing the AMPK signaling pathway, thereby establishing a novel regulatory axis in KIRC biology. Validation of datasets from The Cancer Gene Atlas (TCGA) database, combined with our analysis of independent patient samples, revealed a significant upregulation of PXR in KIRC tumor tissues compared to non-tumor tissues. Further analysis indicated that PXR expression levels are significantly correlated with immune and stromal scores in the tumor microenvironment. Previous studies indicate that PXR may suppress the production of pro-inflammatory cytokines such as IL-6 and TNF-α by inhibiting NF-κB and activator protein-1 (AP-1) signaling pathways.This mechanism reduces the infiltration of immune cells within tumors or local tissues, thereby decreasing the immune score^[137]44. On the other hand, PXR activation can also inhibit vascular endothelial cell proliferation and extracellular matrix remodeling^[138]45, which leads to decreased stromal scores and collectively alters the tumor immune microenvironment.These mechanisms collectively suggest that high PXR expression is closely associated with poor patient prognosis^[139]46. This finding is consistent with the broader role of nuclear receptors in cancer biology, wherein their dysregulation often promotes disease progression^[140]47.Previous studies have emphasized the multifaceted roles of nuclear receptors in KIRC, including the regulation of cell migration, invasion, and epithelial-mesenchymal transition (EMT)^[141]48–[142]50.Building on these insights, we hypothesized that PXR might similarly impact KIRC cell proliferation and migration. Our functional assays confirmed this hypothesis: PXR knockdown in KIRC cells significantly enhanced apoptosis while reducing proliferation, invasion, and migration capabilities. Importantly, the magnitude of these phenotypic changes positively correlated with the degree of PXR depletion.These results align with prior reports implicating PXR in apoptosis, oxidative stress, cell cycle regulation, and tumor invasiveness across various cancer types^[143]46,[144]51–[145]55, further supporting PXR as a potential prognostic marker and therapeutic target in KIRC. RNA-sequencing technology, a powerful tool for analyzing the expression status of transcription factors and their regulatory genes, enabled us to dissect the transcriptional activity within KIRC cells^[146]56. Through this approach, we identified the AMPK signaling pathway as a key player in KIRC biology. AMPK, a central regulator of energy homeostasis and cellular stress responses^[147]57, is known to govern multiple cellular processes in tumor cells, including apoptosis, cell growth, differentiation, polarity, autophagy, immune function, and inflammation^[148]58–[149]60, beyond its classic role as an energy sensor. While AMPK may exert tumor-suppressive effects under specific conditions, accumulating studies also indicate that AMPK can contribute to cancer progression and exhibit oncogenic activity in certain pathological contexts^[150]30.For example, in colorectal cancer research, non-muscle myosin IIA (NMIIA)-mediated activation of the AMPK signaling pathway has been shown to promote the expression of the mammalian target of rapamycin (mTOR), thereby enhancing cell growth and invasion ^[151]61. In our study, we observed that PXR knockdown led to increased expression of p-AMPK protein. Furthermore, when PXR-knockdown KIRC cells were treated with an AMPK inhibitor, they exhibited restored proliferative capacity, increased expression of cell cycle-related proteins (Cyclin D1), and decreased expression of proteins associated with cellular apoptosis (Bax). Furthermore, PCNA, a potential molecular marker for cancer prognosis evaluation^[152]62, exhibited significantly decreased expression after PXR knockdown, indicating suppressed proliferative capacity of cancer cells that may be associated with favorable prognosis.However, when co-treated with an AMPK inhibitor, PCNA expression was restored, suggesting that KIRC may employ a regulatory mechanism distinct from that in normal renal cells: enhanced PXR-mediated oncogenic activity inhibits the AMPK signaling pathway, thereby promoting tumor progression. Based on the aforementioned protein expression results, we further investigated the specific molecular regulatory mechanism of the PXR-AMPK signaling axis in KIRC. The PI3K/Akt signaling pathway serves as a critical downstream target of AMPK and plays a central role in regulating cell growth and survival^[153]63. Studies have shown that multiple oncogenes can activate PI3K, and its aberrant overexpression is considered a hallmark of malignant tumors^[154]64. Concurrently, p53, as a key downstream effector of this pathway, can positively regulate Bax expression^[155]65. Multiple studies suggest that the PI3K/Akt/p53 signaling axis is closely associated with tumorigenesis and progression^[156]66,[157]67, making inhibition of this pathway a promising anticancer strategy^[158]68. In addition to indirect regulation of p53 through PI3K/Akt, studies have also found that wild-type p53 can directly bind to PXR and inhibit its transcriptional activity; conversely, PXR activation can also suppress p53 expression^[159]69. In this study, knockdown of PXR significantly reduced the phosphorylation levels of key proteins in the PI3K/Akt pathway, while the addition of an AMPK inhibitor restored the expression of p-PI3K and p-Akt. This observation aligns with previous reports indicating that AMPK activation negatively regulates the PI3K/AKT signaling pathway^[160]70, thereby contributing to the upregulation of p53.As a downstream regulatory target of p53, decreased Bcl-2 levels and increased Bax expression collectively promote p53-mediated apoptosis^[161]71, a finding validated in this study. Notably, Bax expression is also influenced by cross-talk between AMPK and other regulatory networks. For example, AMPK can enhance Bax transcription by activating Forkhead box O (FoxO)^[162]72,and it can also induce apoptosis by mediating the upregulation of p53 upregulated modulator of apoptosis (PUMA) /phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1/NOXA) gene expression via p53^[163]73.This study primarily focused on validating the regulatory mechanism of the AMPK-PI3K/Akt axis on Bax. However, the roles of FoxO and PUMA/NOXA in AMPK-mediated Bax expression and apoptosis require further in-depth investigation. Although this study preliminarily confirmed the role of the PXR-AMPK/PI3K/Akt signaling axis in promoting Bax expression and inducing apoptosis, several issues require further investigation. First, a systematic comparison between PXR and classical markers in KIRC (such as VHL and CAIX)^[164]74 has not been conducted, Although the tissues and cellular models employed in this study are derived from KIRC, incorporating such comparative analyses would significantly enhance the reliability of the experiments and provide more robust evidence for validating the inhibitory effects of PXR knockdown in KIRC. Second, the interaction mechanism between PXR and AMPK in KIRC remains incompletely elucidated. Although existing studies have identified an indirect regulatory relationship between PXR and AMPK^[165]33, and AMPK can be activated by kinases such as liver kinase B1 (LKB1), calcium/calmodulin-dependent protein kinase kinase beta (CaMKKβ), and transforming growth factor β-activated kinase 1(TAK1)^[166]75, the specific upstream kinase(s) that play a dominant role in KIRC still need in-depth exploration. This study also has several limitations. During sample collection, to ensure sample homogeneity (age, gender, tumor stage, etc.), only six patient tumor samples were initially included. Although all adjacent non-cancerous tissues were pathologically confirmed to be free of tumor infiltration and obvious lesions, and sampling locations were distant from tumor margins (> 2 cm), potential interference from molecular-level variations in adjacent non-cancerous tissues on PXR expression analysis cannot be entirely ruled out. Future studies should expand sample size and optimize sampling strategies to enhance the generalizability and reliability of the findings. Regarding experimental models, current findings are primarily based on cell culture studies. Further validation of this pathway’s function within the physiological tumor microenvironment requires in vivo models (e.g., NOD/SCID mouse xenografts of 786-O cells^[167]76). Additionally, cell experiments exclusively utilized the 786-O cell line. While this cell line exhibits typical KIRC characteristics such as VHL deficiency and HIF pathway activation, KIRC cell lines (769-P, Caki-1, ACHN, etc.)^[168]77,[169]78exhibit high genetic and phenotypic heterogeneity.A single cell model cannot fully represent the biological characteristics of KIRC. Future plans require replication across multiple cell lines to enhance the credibility of experimental results. In summary, our study demonstrates that PXR influences the regulation and proliferation of KIRC through the AMPK pathway. This is supported by evidence from bioinformatic analyses, clinical samples, and in vitro validation. This work lays a foundation for further exploration of the PXR-AMPK axis as a potential therapeutic target in KIRC, while also highlighting the need for continued investigation into the complex molecular mechanisms underlying this regulation. From a clinical perspective, the widespread high expression of PXR in KIRC and its close association with poor prognosis make it a promising interventional target. Inhibiting PXR function may alleviate its suppressive effect on AMPK, thereby restoring AMPK-mediated negative regulation of tumor growth and metabolism. Studies have shown that AMPK agonists (such as metformin) can significantly inhibit tumor growth in renal cancer models^[170]79. Given the functional relationship between PXR and AMPK, the combined application of PXR inhibitors and AMPK agonists is expected to yield synergistic antitumor effects. In conclusion, targeting the PXR-AMPK signaling axis shows promising clinical potential for KIRC treatment. Supplementary Information Below is the link to the electronic supplementary material. [171]Supplementary Material 1^ (482.9KB, docx) [172]Supplementary Material 2^ (9.9MB, docx) [173]Supplementary Material 3^ (11.7KB, docx) Acknowledgements