CircRNA Profiling Identifies hsa_circRNA_003201 Associated with a Predicted NLRX1-Related Regulatory Network in an Acne-Associated Inflammatory Sebocyte Model

Abstract

Background

Circular RNAs (circRNAs) are a class of non-coding RNAs that regulate gene expression and have been implicated in inflammatory skin diseases. Sebocytes play a central role in acne pathogenesis by regulating sebum production and inflammatory responses. However, the roles of circRNAs in sebocytes during acne remain poorly understood. This study aimed to characterize circRNA expression profiles and explore their potential functions in acne-associated inflammation.

Methods

Human SZ95 sebocytes were stimulated with formalin-killed Cutibacterium acnes (f-C. acnes) to establish an acne-like inflammatory model. CircRNA expression was profiled using microarray analysis with three biological replicates per group. Differentially expressed circRNAs were identified, and a circRNA-miRNA-mRNA regulatory network was constructed. Predicted target genes were analyzed by GO and KEGG enrichment analyses. Selected circRNAs, miRNAs, and mRNAs were further validated by RT-qPCR using four independent biological replicates. NLRX1 protein expression in acne and control tissues was examined by immunohistochemistry.

Results

Sixteen differentially expressed circRNAs were identified in f-C. acnes-treated sebocytes, including 12 upregulated and 4 downregulated circRNAs (fold change ≥1.5, P<0.05). RT-qPCR confirmed five circRNAs, including hsa_circRNA_003201, hsa_circRNA_100840, hsa_circRNA_102619, hsa_circRNA_051123, and hsa_circRNA_005161. Functional enrichment analyses suggested that predicted target genes were associated with lipid metabolism, epidermal differentiation, inflammatory responses, and NOD-like receptor signaling. The predicted hsa_circRNA_003201/miR-519c-5p/NLRX1 axis was selected for further investigation. NLRX1 protein expression was increased in acne lesions compared with control tissues and showed a consistent pattern with hsa_circRNA_003201 in the acne-like inflammatory model.

Conclusion

This study provides a circRNA expression profile in an acne-like inflammatory sebocyte model and identifies hsa_circRNA_003201 as a candidate circRNA associated with acne-associated inflammation. The findings suggest a potential circRNA-miRNA-mRNA regulatory network involving NLRX1 and provide new insights into acne inflammatory mechanisms. Further functional studies are required to validate the proposed regulatory relationships.

Introduction

Acne vulgaris is one of the most common chronic inflammatory skin disorders worldwide, primarily affecting the pilosebaceous unit.¹ Epidemiological studies indicate that over 85% of adolescents are affected, and many continue to experience symptoms into adulthood.² Acne lesions frequently result in post-inflammatory hyperpigmentation (PIH) and scarring, which can substantially impair patients’ quality of life and impose significant socioeconomic burdens.³ The pathogenesis of acne is multifactorial and involves excessive sebum production, abnormal keratinization, colonization by Cutibacterium acnes (C. acnes), and the activation of inflammatory pathways.⁴ However, the detailed molecular mechanisms underlying acne pathogenesis remain incompletely understood. Increasing evidence indicates that dysregulated innate immune signaling is a central event in acne pathogenesis. Upon stimulation by C. acnes, sebocytes initiate innate immune responses through pattern-recognition receptors, particularly Toll-like receptors (TLRs), resulting in activation of NF-κB and MAPK signaling pathways and subsequent production of pro-inflammatory cytokines and chemokines.^5,6 NOD-like receptors (NLRs) have also been implicated in regulating inflammatory responses in sebocytes.⁷ NLRX1 is an atypical mitochondrial-associated NLR that has been shown to regulate inflammatory homeostasis by modulating NF-κB signaling, mitochondrial function, oxidative stress, and innate immune responses.⁸ However, its role in acne-associated inflammation, particularly in sebocytes, remains unknown.

Since inflammatory signaling in sebocytes may be fine-tuned by post-transcriptional regulators, non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as important regulators of acne-associated inflammation. Previous studies have demonstrated that ncRNAs participate in multiple processes implicated in acne pathogenesis, including C. acnes dysbiosis, abnormal immune function, chronic inflammation, and abnormal keratinocyte differentiation and proliferation.⁴ Among ncRNAs, circRNAs have attracted increasing attention due to their covalently closed loop structures, which confer high stability and resistance to exonuclease-mediated degradation.⁹ CircRNAs regulate gene expression through multiple mechanisms, including miRNA sponging, transcriptional regulation, and modulation of protein interactions or epigenetic processes.^10,11 Increasing evidence indicates that circRNAs participate in inflammatory skin diseases, including psoriasis, lupus erythematosus, atopic dermatitis, lichen planus, and severe acne.¹²

Although miRNAs, such as miR-146a, have been shown to regulate TLR1/2- and TLR4-mediated inflammatory responses in human SZ95 sebocytes, thereby linking innate immune activation to sebocyte proliferation and lipid production,¹³ the roles of circRNAs in sebocyte inflammatory regulation remain largely unexplored. Liang et al identified differentially expressed circRNAs in paired lesional and non-lesional skin from patients with severe acne, suggesting that circRNAs may contribute to acne pathogenesis.¹⁴ Nevertheless, tissue-based profiling studies have not clarified the cell-specific regulatory functions of circRNAs in acne-associated inflammation. As sebocytes are key effector cells involved in sebum production and inflammatory responses during acne development, an in vitro sebocyte model provides an opportunity to identify sebocyte-specific circRNA alterations under acne-associated inflammatory stimulation and explore their potential regulatory mechanisms.

Given the emerging roles of circRNAs in inflammatory regulation, we hypothesized that circRNA-mediated regulatory mechanisms may contribute to sebocyte responses during acne-associated inflammation. Therefore, the present study established a formalin-killed C. acnes-stimulated sebocyte inflammatory model to characterize early circRNA alterations associated with acne-related inflammatory responses.¹⁵ Through integrated bioinformatics analysis and experimental validation, we identified a potential hsa_circRNA_003201/miR-519c-5p/NLRX1 regulatory axis, providing new insights into the post-transcriptional regulation of acne-associated inflammation.

Materials and Methods

Cell Culture and Acne-Like Inflammatory Model Construction

This study was conducted at Huashan Hospital, Fudan University, from June 2024 to December 2025. The immortalized human sebocyte cell line SZ95, kindly provided by Professor Christos C. Zouboulis,¹⁶ was cultured in Sebomed^® Basal Medium (Merck) supplemented with 5 ng/mL human epidermal growth factor, 10% fetal calf serum (FCS), 1% penicillin/streptomycin (Thermo Fisher), and 1 mM CaCl₂ (Sigma-Aldrich). The cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂, and subcultures between passages 30 and 40 were used for the experiments. Cutibacterium acnes (ATCC 6919) was cultured under anaerobic conditions at 37°C until reaching the logarithmic growth phase. Bacteria were collected by centrifugation at 4000×g for 15 min at 4°C, washed three times with phosphate-buffered saline (PBS), and then inactivated with formalin at room temperature for 15 min. After fixation, bacteria were washed thoroughly with PBS to remove residual formalin, aliquoted, and stored at −20°C. To investigate early inflammatory responses induced by C. acnes, SZ95 sebocytes were treated with formalin-killed C. acnes at a multiplicity of infection (MOI) of 100 for 24 h. For circRNA microarray analysis, three independent biological replicates were included in each group. Given the exploratory nature of genome-wide circRNA profiling, the sample size was selected based on commonly used experimental designs for transcriptomic screening studies^17,18 followed by independent validation using RT-qPCR.

CircRNA Microarray Analysis

RNA Extraction and circRNA Enrichment

Total RNA was extracted from f-C. acnes-treated SZ95 sebocytes and untreated control SZ95 sebocytes, using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. RNA concentration and purity were assessed using a NanoDrop ND-1000 spectrophotometer. RNA integrity was evaluated by agarose gel electrophoresis. To enrich circular RNAs, total RNA was treated with RNase R (Epicentre), which selectively digests linear RNAs while preserving circular RNAs.

RNA Amplification and Labeling

CircRNA microarray analysis was performed using the Arraystar Human circRNA Array V2 (8 × 15K; Arraystar, USA), according to the manufacturer’s protocol. Fluorescently labeled cRNA was generated using random priming and subsequently purified using the RNeasy Mini Kit (Qiagen).

Microarray Hybridization and Scanning

Labeled cRNA (1 µg per sample) was fragmented by adding 10× Blocking Agent (5 µL) and 25× Fragmentation Buffer (1 µL), followed by incubation at 60°C for 30 minutes. After dilution with 2× Hybridization Buffer, 50 µL hybridization solution was added to the gasket slide and hybridized with the circRNA expression microarray slide. Slides were incubated at 65°C for 17 h in an Agilent Hybridization Oven, washed, fixed, and scanned using an Agilent Scanner G2505C.

Data Processing and Differential Expression Analysis

Raw image files were processed using Agilent Feature Extraction software (version 11.0.1.1). CircRNA expression profiles were quantile-normalized, and statistical analyses were performed using the limma package in R. The normalized circRNA expression matrix generated from f-C. acnes-treated and control SZ95 sebocytes was used as the input dataset for differential circRNA expression analysis. Low-intensity probes were filtered, and circRNAs detected in at least three of six samples with “present” (P) or “marginal” (M) flags were retained for further analysis. Given the exploratory nature of circRNA profiling, candidate differentially expressed circRNAs were identified using a nominal P value < 0.05 and fold change ≥ 1.5. FDR-adjusted P values were calculated using the Benjamini-Hochberg method and are provided in Supplementary Table 1. Selected candidate circRNAs were further validated by RT-qPCR.

miRNA Response Element Prediction and Network Construction

Candidate circRNAs screened from the circRNA microarray dataset were used as the input circRNA set for miRNA response element (MRE) prediction. Putative MREs for candidate circRNAs were predicted using Arraystar’s miRNA target prediction software based on TargetScan and miRanda algorithms. Predicted circRNA-miRNA pairs and miRNA-mRNA pairs were integrated to construct a candidate circRNA-miRNA-mRNA regulatory network. The output network consisted of candidate circRNAs, predicted miRNAs, and predicted mRNA targets, and was visualized using Cytoscape software. Selected miRNAs and mRNAs from this predicted network were further examined by RT-qPCR.

Functional Enrichment Analysis

Functional enrichment analysis was performed using predicted mRNA targets from the candidate circRNA-miRNA-mRNA network. Gene Ontology (GO) annotation was performed across three categories: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF), using publicly available resources. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using the same gene set. GO and KEGG databases served as the reference annotation resources. Both analyses were conducted using the clusterProfiler package in Bioconductor. Enriched GO terms and KEGG pathways were identified using Fisher’s exact test, and terms with nominal P < 0.05 were considered statistically significant.

RNA Extraction and cDNA Synthesis

Total RNA was extracted from SZ95 sebocytes using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. The RNA samples were then purified using the miRNeasy Mini Kit (QIAGEN). RNA concentration and purity were assessed using a NanoDrop spectrophotometer, and RNA integrity was confirmed by agarose gel electrophoresis. To enrich circRNAs for downstream analysis, total RNA was treated with RNase R (Epicentre) to remove linear RNAs. For cDNA synthesis, miRNAs were reverse transcribed using the miRNA cDNA First Strand Synthesis Kit (Accurate Biology) with stem-loop primers, whereas circRNAs and mRNAs were reverse transcribed using the PrimeScript™ RT Reagent Kit (Takara) according to the manufacturer’s instructions. The resulting cDNA was used for subsequent RT-qPCR analysis.

Real-Time Quantitative PCR (RT-qPCR)

RT-qPCR was performed to validate the expression levels of selected circRNAs, miRNAs, and mRNAs using the QuantStudio™ 5 Real-Time PCR System (Applied Biosystems). CircRNA and mRNA expression levels were measured using 2× PCR Master Mix (Arraystar), whereas miRNA expression was quantified using the appropriate miRNA-specific qPCR system according to the manufacturer’s protocol. Melt curve analysis was conducted to verify amplification specificity. Relative expression levels were calculated using the 2^−ΔΔCt method. GAPDH was used as the internal control for circRNA and mRNA quantification, and U6 was used as the internal control for miRNA quantification. The primer sequences used for RT-qPCR are listed in Supplementary Table 2. All RT-qPCR experiments were performed with four independent biological replicates, and the candidate circRNAs, miRNAs, and mRNAs identified by microarray analysis were validated using independently prepared samples rather than the same samples used for microarray profiling. Detailed statistical information, including exact P values and 95% confidence intervals, is provided in Supplementary Table 3.

Immunohistochemistry

Clinical skin samples were collected from acne patients and control subjects at Huashan Hospital, Fudan University, during the same study period. Acne samples were obtained from facial biopsy specimens of patients with acne vulgaris (n=6), and control samples were obtained from clinically normal-appearing facial skin adjacent to excised pigmented nevi (n=6). Given that immunohistochemistry was performed to validate the expression pattern observed before, the sample size was determined based on the availability of clinical specimens and previous similar tissue-based studies. All acne lesions were diagnosed according to established clinical criteria and graded using the Pillsbury acne grading system. Control samples were matched to acne specimens according to age, sex, and anatomical site of collection. The skin samples were fixed in 4% formaldehyde at 4°C for at least 24 hours, then dissected, dehydrated, and embedded in paraffin. Thin sections (4 μm) were mounted onto glass slides. Paraffin-embedded tissue sections were deparaffinized and subjected to antigen retrieval. The sections were then incubated overnight at 4°C with primary antibodies: anti-NLRX1 (1:200, Proteintech). After washing, sections were incubated with polyperoxidase-conjugated anti-mouse/rabbit IgG secondary antibodies (1:250, Beyotime). Color development was carried out using 3,3’-diaminobenzidine (DAB; Beyotime, China) for 1–3 minutes. Three random fields were selected from each slide for examination under 200× and 400× magnifications using a Leica microscope equipped with imaging software (Leica Application Suite, Switzerland). Integrated optical density (IOD) and positive staining area were measured using Fiji software, and average optical density (AOD) was calculated as IOD/positive area. The mean AOD value of the three fields was used for statistical analysis.

Statistical Analysis

All statistical analyses were performed using SPSS 15.0 software. Normality was assessed using the Shapiro–Wilk test. Homogeneity of variances was assessed using the F-test. For normally distributed data, comparisons between two independent groups were performed using an unpaired Student’s t-test when variances were equal and Welch’s t-test when variances were unequal. Data are presented as individual values with bars indicating the mean ± SD. Exact P values are indicated in the figures. The detailed results of the normality and variance tests, exact P values, and confidence intervals are provided in Supplementary Table 3 and the corresponding figure legends. A two-sided P value < 0.05 was considered statistically significant.

Results

Differential circRNA Expression Profiles

To investigate early inflammatory responses induced by C. acnes, SZ95 sebocytes were treated with formalin-killed C. acnes at a multiplicity of infection (MOI) of 100 for 24 h. CircRNA microarray analysis was then performed to compare circRNA expression profiles between the model and control groups using three biological replicates per group (n=3). A box plot was generated to assess the distribution and variability of the hybridization data. As shown in Figure 1a, after log₂ normalization, all six samples exhibited comparable distributions without apparent outliers. The overall variation between the model and control groups was displayed in a scatter plot of the circRNA expression profile (Figure 1b). We then applied fold change and nominal P value thresholds as exploratory screening criteria to identify circRNAs showing differential expression trends (Figure 1c). Based on fold change≥1.5 and nominal P<0.05, 16 candidate circRNAs were screened, including 12 upregulated and 4 downregulated circRNAs in the model group. Hierarchical clustering revealed distinct expression patterns of these candidate circRNAs between the model and control groups (Figure 1d). Exact P values, FDR-adjusted P values, and fold changes are provided in Supplementary Table 1.

Figure 1.

CircRNA expression profiling in the f-C. acnes-stimulated inflammatory acne model and control groups. (a) Box plots showing the distribution of normalized circRNA expression values across samples (n = 3 per group); (b) Scatter plot comparing circRNA expression levels between the model and control groups. Green lines indicate the 1.5-fold change threshold; (c) Volcano plot showing circRNAs screened by fold change and nominal P value. Red points indicate candidate circRNAs meeting the exploratory screening criteria of fold change≥1.5 and nominal P < 0.05; (d) Heat map showing the expression patterns of candidate circRNAs between the two groups. Red indicates upregulation, and green indicates downregulation.

Validation of Identified circRNAs by Quantitative RT-qPCR

To validate the circRNA microarray results, the f-C. acnes-stimulated inflammatory acne model was independently repeated using the same procedure, with four biological replicates per group (n=4). The expression levels of ten randomly selected candidate circRNAs were further validated by RT-qPCR (Table 1). Among these, four circRNAs were significantly upregulated, and one circRNA was significantly downregulated in the model group compared with the control group (Figure 2), consistent with the microarray findings. The remaining five circRNAs showed no consistent or statistically significant changes in expression. These results experimentally validated a subset of the candidate circRNAs and supported their prioritization for subsequent analysis.

Table 1.

16 Upregulated and Downregulated (Fold-Change≥1.5 and P<0.05) circRNAs Sorted by Fold Change in the Model Group versus the Control Group

circRNA	P-value	FC (abs)	Regulation	Chrom	Validated by RT-qPCR
hsa_circRNA_003201	0.016	2.127	Up	chr4	Yes
hsa_circRNA_061206	0.026	1.912	Up	chr20	–
hsa_circRNA_100840	0.045	1.812	Up	chr11	Yes
hsa_circRNA_405629	0.047	1.792	Up	chr17	–
hsa_circRNA_051123	0.049	1.715	Up	chr19	Yes
hsa_circRNA_101940	0.024	1.704	Up	chr17	–
hsa_circRNA_406350	0.044	1.666	Up	chr3	No
hsa_circRNA_006111	0.036	1.625	Up	chr2	No
hsa_circRNA_023948	0.040	1.594	Up	chr11	–
hsa_circRNA_405327	0.033	1.581	Up	chr15	–
hsa_circRNA_100534	0.015	1.516	Up	chr10	No
hsa_circRNA_005161	0.035	1.501	Up	chr13	Yes
hsa_circRNA_000367	0.037	2.427	Down	chr11	No
hsa_circRNA_102619	0.034	1.864	Down	chr2	Yes
hsa_circRNA_406768	0.021	1.782	Down	chr6	–
hsa_circRNA_100072	0.017	1.543	Down	chr1	No

Figure 2.

Validation of identified circRNAs by quantitative RT-PCR. Relative expression levels of selected candidate circRNAs in the model and control groups were measured by RT-qPCR and calculated using the 2^−ΔΔCt method. Data are presented as individual values with bars indicating the mean±SD. Each dot represents one independent biological replicate; n=4 per group. Statistical significance was determined using a two-tailed unpaired Student’s t-test according to the results of variance homogeneity testing.

Prediction of circRNA–miRNA–mRNA Network

According to the ceRNA hypothesis, circRNAs may function as miRNA sponges through shared miRNA response elements (MREs), thereby influencing the expression of downstream target genes. To explore the potential regulatory network involving the validated candidate circRNAs, Arraystar miRNA target prediction software was used to predict circRNA-miRNA interactions, and the resulting circRNA-miRNA-mRNA network was visualized in Cytoscape (Figure 3a).

Figure 3.

Prediction of the circRNA-miRNA-mRNA network and functional enrichment analysis. (a) Predicted circRNA-miRNA-mRNA interaction network visualized using Cytoscape. Yellow, red, and blue nodes represent candidate circRNAs, predicted miRNAs, and predicted mRNAs, respectively; (b–d) Top enriched GO terms for circRNA host genes, including biological process, molecular function, and cellular component categories. (e) Top enriched KEGG pathways for circRNA host genes.

To further investigate the potential functions of these candidate circRNAs, GO and KEGG enrichment analyses were performed based on the predicted mRNA targets. GO analysis included biological processes (BP), molecular functions (MF), and cellular components (CC). In the BP category, a notable enrichment was observed in the Adenosine receptor signaling pathway (GO:0001973) (Figure 3b), suggesting a potential association with inflammatory signaling. For the MF category, enriched terms included Calcium-dependent protein binding (GO:0048306), Palmitoyl hydrolase activity (GO:0098599), Phosphatidylinositol phosphate 4-phosphatase activity (GO:0034596), Palmitoyl-(protein) hydrolase activity (GO:0008474), Transferase activity, transferring acyl groups other than amino-acyl groups (GO:0016747), Notch binding (GO:0005112), and Caspase binding (GO:0089720) (Figure 3c). These terms are mainly associated with calcium-dependent signaling, lipid metabolism, protein modification, and apoptosis-related inflammatory regulation. The CC category enriched terms included the Cornified envelope (GO:0001533), Beta-catenin destruction complex (GO:0030877), and Endoplasmic reticulum exit site (GO:0070971) (Figure 3d), indicating potential involvement in epidermal differentiation, Wnt/β-catenin-related regulation, and intracellular protein trafficking. KEGG pathway analysis showed that the predicted target genes were enriched in several pathways, among which the NOD-like receptor signaling pathway was of particular interest (Figure 3e). Given the established role of NOD-like receptor signaling in innate immune activation and inflammatory responses, this pathway may be relevant to acne-associated inflammation. Together, these bioinformatic analyses suggested that the candidate circRNAs may be associated with inflammatory signaling, lipid metabolism, epidermal differentiation, and innate immune regulation, providing a basis for subsequent prioritization of key circRNA-miRNA-mRNA axes.

Validation of ceRNA Network Regulation in f-C. acnes-Treated SZ95 Sebocytes by RT-qPCR

To further evaluate the candidate ceRNA network, the expression levels of selected predicted mRNAs and miRNAs were examined by RT-qPCR. Among the predicted mRNAs, NLRX1 was significantly upregulated in the model group compared with the control group. This expression pattern was consistent with the upregulation of hsa_circRNA_003201 observed in the validation experiment, supporting the potential involvement of the hsa_circRNA_003201-associated axis in the response to f-C. acnes stimulation. CCL2 was also upregulated in the model group; however, its associated circRNA, hsa_circRNA_102619, was downregulated, which was inconsistent with the expression pattern expected for a typical ceRNA network. The remaining predicted mRNAs did not show statistically significant changes under the same conditions (Figure 4a).

Figure 4.

Validation of predicted mRNAs and miRNAs in f-C. acnes-treated SZ95 sebocytes. (a) Relative expression levels of selected predicted target genes in control and f-C. acnes-treated SZ95 sebocytes; (b) Relative expression levels of selected miRNAs in control and f-C. acnes-treated SZ95 sebocytes. RNA expression was measured by RT-qPCR and calculated using the 2^−ΔΔCt method. Data are presented as individual values with bars indicating the mean±SD. Each dot represents one independent biological replicate; n = 4 per group. Statistical significance was determined using a two-tailed unpaired Student’s t-test or Welch’s t-test according to the results of variance homogeneity testing.

To further assess the potential miRNA components of the predicted ceRNA axes, selected miRNAs were also examined by RT-qPCR (Figure 4b). Among them, miR-519c-5p showed an expression pattern generally opposite to that of hsa_circRNA_003201 and NLRX1, consistent with the predicted inverse relationship between miRNAs and their circRNA/mRNA targets in a ceRNA network. Together, these results prioritized the hsa_circRNA_003201/miR-519c-5p/NLRX1 axis as a candidate regulatory pathway for further analysis.

Immunohistochemical Analysis of NLRX1 in Acne Lesions

To further validate NLRX1 protein expression in clinical samples, IHC staining was performed on acne lesions and control skin tissues (n = 6 per group). Control tissues were matched with acne specimens according to age, sex, and anatomical site of sample collection, and detailed sample information is provided in Table 2. As shown in Figure 5a, NLRX1 staining was stronger in acne lesions than in control skin. In the epidermis, acne lesions showed more intense and widespread positive staining, whereas control skin exhibited weak or limited staining. Similarly, in sebaceous glands, acne lesions displayed increased staining intensity and a higher proportion of NLRX1-positive cells compared with controls. Semi-quantitative analysis based on average optical density (AOD) further showed that NLRX1 expression was significantly increased in both the epidermis and sebaceous glands of acne lesions (Figure 5b). These findings indicate that NLRX1 protein expression is upregulated in acne lesions, suggesting its potential involvement in acne-associated inflammation.

Table 2.

Clinical Characteristics of Acne Patients and Control Subjects

Characteristic	Control (n=6)	Acne (n=6)
Age, years	27.83±5.49	27.50±7.18
Sex (Female/Male)	2:4	3:3
Sampling location	Face	Face
Acne severity (Pillsbury grade)	N/A	Grade III

Figure 5.

Increased NLRX1 expression in acne lesions detected by immunohistochemistry. (a) Representative immunohistochemical staining of NLRX1 in the epidermis and sebaceous glands of control skin and acne lesions (n=6). Scale bar=200μm. (b) Semi-quantitative analysis of NLRX1 expression based on mean optical density (AOD) in the epidermis and sebaceous glands. Data are presented as the mean±SD. Both groups passed the normality test and showed homogeneity of variance; therefore, an unpaired Student’s t-test was used for between-group comparisons. In acne lesions, the 95% confidence interval (CI) for the AOD value of NLRX1 was 30.78 to 69.53 in the epidermis and 25.20 to 78.66 in the sebaceous glands. Exact P values are indicated in the figure.

Discussion

Acne vulgaris is a chronic inflammatory disease of the pilosebaceous unit. It is most common in teenagers and adults aged 20–30 years and occurs most prominently at sebaceous gland-rich sites such as the face, chest, and back. Accumulating evidence indicates that acne is driven largely by dysregulated host inflammatory responses rather than by infection alone, underscoring the importance of innate immune activation in its pathogenesis.^19,20 In this context, non-coding RNAs, particularly circRNAs, have emerged as important regulatory molecules of immune response, inflammation, and cellular proliferation in diverse biological systems.^21,22 However, their role in acne pathophysiology remains insufficiently understood.

In the present study, we profiled circRNA expression in an in vitro C. acnes-stimulated SZ95 sebocyte model and identified 16 differentially expressed circRNAs. Among the ten circRNAs selected for RT-qPCR validation, five circRNAs showed expression patterns consistent with the microarray results. We further constructed a circRNA-miRNA-mRNA ceRNA network and performed GO and KEGG enrichment analyses. The predicted target genes were enriched in pathways related to lipid metabolism, epidermal differentiation, inflammatory responses, and NOD-like receptor signaling, suggesting that these circRNAs may be involved in multiple aspects of acne-associated inflammation. These findings support the concept that circRNAs may participate in sebocyte dysfunction and innate immune regulation during acne development.

Notably, we identified a potential hsa_circRNA_003201/miR-519c-5p/NLRX1 regulatory axis. The coordinated expression patterns of these molecules, together with bioinformatic prediction, suggest that hsa_circRNA_003201 may participate in a ceRNA-like network that regulates NLRX1 expression in sebocytes. CircRNAs are increasingly recognized as miRNA sponges that fine-tune gene expression in inflammatory pathways, and our findings add to this concept by implicating a circRNA-related regulatory axis in acne-associated sebocyte responses. Although this axis remains hypothetical at present, it provides a biologically plausible framework for understanding post-transcriptional regulation in acne inflammation.

NLRX1 has been increasingly recognized as a context-dependent regulator of innate immune responses. It was initially identified as a mitochondria-associated NOD-like receptor involved in the regulation of antiviral and inflammatory signaling.^8,23–25 Although several studies have shown that NLRX1 negatively regulates MAVS-dependent antiviral signaling and TLR-induced NF-κB activation,^26,27 it has also been reported to amplify NF-κB and JNK signaling by promoting mitochondrial reactive oxygen species production.²⁴ This context-dependent behavior is particularly relevant to acne, in which oxidative stress, altered lipid metabolism, and inflammatory activation of sebocytes are central pathogenic events.^28,29 Sebocytes are not only lipid-producing cells but also active immune-responsive cells that contribute to acne-associated inflammation through dysregulated lipogenesis and oxidative stress. Bowe et al proposed that lipid peroxidation links sebaceous lipid metabolism to follicular inflammation.³⁰ Previous studies have shown that C. acnes can induce ROS production via the TRAF6-ECSIT-NLRX1 axis, leading to mitochondrial dysfunction and oxidative stress.³¹ Therefore, NLRX1-mediated alterations in mitochondrial function may influence sebaceous lipogenesis, lipid peroxidation, and the inflammatory properties of sebum.³² Taken together, NLRX1 upregulation in acne lesions may reflect either a pro-inflammatory amplifier or a compensatory response to excessive inflammation and oxidative stress. Further studies using NLRX1 knockdown or overexpression in sebocytes are needed to clarify its precise functional role in acne pathogenesis.

In addition to NLRX1, we observed increased CCL2 expression in f-C. acnes-treated sebocytes, whereas its associated circRNA, hsa_circRNA_102619, was downregulated. CCL2, also known as monocyte chemoattractant protein-1 (MCP-1), is a well-characterized chemokine involved in the recruitment and activation of monocytes, macrophages, and T cells, and its elevated expression has been associated with the severity of acne inflammation.^33,34 The discordant expression pattern between CCL2 and hsa_circRNA_102619 suggests that not all predicted circRNA-miRNA-mRNA interactions strictly conform to the classical ceRNA model. Such discrepancies underscore the complexity of post-transcriptional regulation, including transcription factor dynamics, RNA-binding proteins, epigenetic regulation, and RNA modifications, which may collectively influence gene expression in inflammatory skin diseases.³⁵

This study has several limitations. First, the sample size used for the microarray analysis and clinical validation was relatively limited, which may have affected the robustness and generalizability of the findings. Second, the in vitro experiments were conducted using a single sebocyte cell line, which may not fully recapitulate the cellular heterogeneity and inflammatory microenvironment of acne lesions in vivo. Third, although expression associations among circRNAs, miRNAs, and mRNAs were observed, functional experiments were not performed to validate the proposed regulatory mechanisms. Future studies should include circRNA knockdown or overexpression, miRNA mimic or inhibitor assays, and NLRX1 gain- and loss-of-function experiments. Fourth, the functional impact of the proposed hsa_circRNA_003201/miR-519c-5p/NLRX1 axis on sebocyte biology was not directly assessed, including potential effects on lipid metabolism, cytokine secretion, ROS production, mitochondrial function, and downstream inflammatory signaling.

Despite these limitations, our study provides an initial overview of circRNA expression in an acne-like inflammatory cell model and identifies candidate regulatory axes that may be associated with acne-related inflammation. In particular, the hsa_circRNA_003201/miR-519c-5p/NLRX1 axis represents a candidate regulatory network warranting further investigation. Given the stability and tissue-specific expression of circRNAs, these molecules may have potential relevance for future biomarker or therapeutic studies; however, their diagnostic performance and therapeutic utility in acne remain unproven. Future studies incorporating larger clinical cohorts, functional validation, and mechanistic analyses are necessary to further elucidate the roles of circRNAs and NLRX1 in acne development.

Conclusion

In conclusion, this study provides a systematic circRNA expression profile in a f-C. acnes-stimulated inflammatory acne model and identifies hsa_circRNA_003201 as a candidate circRNA associated with acne-related inflammation. Integrated circRNA-miRNA-mRNA analysis suggested a putative regulatory network involving NLRX1, offering a basis for further investigation into the molecular mechanisms underlying sebocyte inflammatory responses. However, the proposed hsa_circRNA_003201/miR-519c-5p/NLRX1 axis remains bioinformatically predicted and expression-associated, and further functional validation is required to confirm direct regulatory interactions and causal effects. Overall, these findings provide preliminary evidence that circRNAs may contribute to acne-related inflammation and may serve as potential candidates for future mechanistic and translational studies.

Funding Statement

We gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 82304053 and 82173444), the Clinical Research Plan of Shanghai Hospital Development Center (Grant No. SHDC22022302), and the National Key Research and Development Program of China (Grant No. 2023YFC2509000).

Data Sharing Statement

The raw circRNA microarray datasets are available from the corresponding author, Xiaoyao Fan (E-mail: xiaoyao_fan@126.com), upon reasonable request. Other data supporting the findings of this study are included in the paper and its Supplementary Materials.

Ethics Approval and Informed Consent

The use of these human tissue samples was approved by the Institutional Review Board of Huashan Hospital, Fudan University (HIRB) under approval number KY2024-027. The ethical approval obtained from HIRB covered the research conducted in the current study, including the collection and use of residual/discarded human skin tissue samples obtained during surgery or biopsy procedures for immunohistochemical analysis. Written informed consent was obtained from all study participants prior to sample collection and before the commencement of the study. The study was conducted in accordance with the principles stated in the Declaration of Helsinki. The SZ95 sebocyte cell line is an established human sebaceous gland cell line. Its use in the current in vitro study was exempted from additional IRB approval and informed consent by the Institutional Review Board of Huashan Hospital, Fudan University (HIRB), as no human participants, newly collected human specimens, or identifiable private information were involved.

Author Contributions

Mengjie Fan and Jin Chen should be regarded as co-first authors. Mengjie Fan: Conceptualization, Writing - Original Draft, Writing - Review & Editing. Jin Chen: Conceptualization, Writing - Review & Editing. Yangfan Lai: Software, Writing - Original Draft, Writing - Review & Editing. Ye Liu: Methodology, Writing - Review & Editing. Yanjun Dan: Validation, Writing - Review & Editing. Nushofarin Ibragimova: Validation, Writing - Review & Editing. Haoyi Du: Validation, Writing - Review & Editing. Ying Ma: Conceptualization, Supervision, Resources, Funding acquisition, Writing - Review & Editing. Xiaoyao Fan: Project administration, Validation, Resources, Funding acquisition, Writing - Review & Editing. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

Xiaoyao Fan and Ying Ma report support for the paper from the National Natural Science Foundation of China, the National Key Research and Development Program of China, the Clinical Research Plan of SHDC. The authors declare no other competing interests. The authors confirm that no artificial intelligence (AI) tools were used in the preparation, writing, or editing of the paper. All content, analyses, and interpretations are entirely of the authors’ own work.