---
title: "Frontiers in Immunology article page — article content not available"
id: "frontiers-in-immunology-10-integrative-transcriptomics-metabolomics-reveals-sphingolipid-metabolic"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-10-integrative-transcriptomics-metabolomics-reveals-sphingolipid-metabolic"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1892145"
published_at: "2026-09-24T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Frontiers in Immunology article page — article content not available
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- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1892145)
- **Published At:** 2026-09-24T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The captured page is from **Frontiers in Immunology** but contains only site navigation and journal section listings; the full article text was not present in the provided source. - The page repeatedly lists journal sections (Autoimmune Disorders, Inflammation, Systems Immunology, Viral Immunology, etc.) and site navigation items such as About, For authors, and Services. - Links on the page include a URL path that contains the article identifier 10.3389/fimmu.2026.1892145, suggesting a DOI-style reference was present, but the manuscript content, abstract, methods, results, and conclusions were not included in the provided source. - No study data, authorship, methods, sample characteristics, analytical approaches (transcriptomics, metabolomics), specific findings (sphingolipid pathways), or clinical implications were available in the captured content. - Because the source lacks the article body, specifics such as experimental design, cohorts, statistical results, and reported limitations are not available and cannot be inferred. - To access full study details, the original article page or publisher record must be retrieved; the provided source does not contain sufficient information to summarize or interpret the research findings. - The absence of article text in the source prevents generation of clinically actionable summaries, evidence grading, or recommendations based on the study.
## Clinical Analysis & Structured Key Points
Frontiers | Integrative transcriptomics–metabolomics reveals sphingolipid metabolic reprogramming in traditional Chinese medicine syndrome differentiation of rheumatoid arthritis ORIGINAL RESEARCH article Front. Immunol. , 24 September 2026 Sec. Autoimmune Disorders Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1892145 Published in Frontiers in Immunology Autoimmune Disorders 7 impact factor 11.3 citescore Part of a Research Topic Integrative Multi-Omics and Computational Modeling for Biomarker Discovery in Complex Human Diseases Submission open 13k views 12 articles Editor & Reviewers Edited by R K Rahul Kaushik Reviewed by S L Shanjie Luan F M Fanyan Meng Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Figure 7 View in article Figure 8 View in article Table 1 Baseline characteristics and sphingolipid metabolites of ra patients stratified by TCM syndrome. View in article ORIGINAL RESEARCH article Front. Immunol. , 24 September 2026 Sec. Autoimmune Disorders Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1892145 Integrative transcriptomics–metabolomics reveals sphingolipid metabolic reprogramming in traditional Chinese medicine syndrome differentiation of rheumatoid arthritis Y L Yao Li 1,2,3,4 Y W Ying Wu 1,2 J Z JinXiang Zeng 1,2 X G Xueyan Guo 1,2,3 W L Weifeng Lv 1,2,4 J L JunWei Lin 1,2,3 K L Kun Lin 1,2 Q G Qihong Guo 2,5 * R S Rong Su 1,2,3 * 1. Department of Laboratory Medicine, Foshan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Foshan, Guangdong, China 2. The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine, Foshan, Guangdong, China 3. Foshan Key Laboratory of Chinese Medicine, Foshan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Foshan, Guangdong, China 4. Guangdong Provincial Clinical Research Center for Laboratory Medicine, Guangzhou, Guangdong, China 5. Nephrology and Rheumatology Department, Foshan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Foshan, China See more Article metrics View details Abstract Objective: To investigate sphingolipid metabolism for differentiating heat-dampness syndrome from kidney-liver deficiency syndrome in Rheumatoid Arthritis (RA), identify metabolic biomarkers, and integrate transcriptomic data to elucidate molecular mechanisms. Methods: 40 RA patients (kidney-liver deficiency syndrome, n=20; heat-dampness syndrome, n=20) were enrolled and serum untargeted metabolomics profiling was performed using ultra-performance liquid chromatography-tandem mass spectrometry. A least absolute shrinkage and selection operator logistic regression diagnostic model was constructed, and its performance was assessed by receiver operating characteristic curve and decision curve analyses. Three independent transcriptomic datasets were downloaded from the Gene Expression Omnibus database (GSE55235: 10 RA synovium, 10 osteoarthritis synovium, and 10 normal synovium; GSE93272: RA whole blood and healthy control whole blood; GSE17755: RA peripheral blood mononuclear cells and healthy control peripheral blood mononuclear cells), and differential expression and tissue-specific regulation of sphingolipid-metabolism-related genes were analyzed. Immune cell infiltration was estimated using marker gene set mean expression scoring, and Spearman correlation analysis was used to construct metabolite-clinical indicator networks. Results: Sphingosine kinase 1 activity (sphingosine-1-phosphate/C17-sphinganine ratio) was significantly higher in the heat-dampness syndrome group than in the kidney-liver deficiency syndrome group (P = 3.45×10 -6 ). C17-sphinganine was significantly negatively correlated with sphingosine-1-phosphate (r = -0.555). C17-sphinganine alone distinguished kidney-liver deficiency syndrome from heat-dampness syndrome with an area under the curve of 0.945 and an accuracy of 95%. Sphingosine-1-phosphate lyase 1 was markedly upregulated in rheumatoid arthritis synovium compared with normal synovium (log2 fold change = 1.124, P = 2.17×10 -4 ) and also significantly upregulated in peripheral blood albeit with a smaller effect size (log 2 fold change = 0.134, P = 8.44×10 -4 ), demonstrating tissue-specific magnitude-difference regulation. Sphingosine kinase 1 showed a negative correlation trend with neutrophil infiltration. C8-ceramide-1-phosphate was negatively correlated with C-reactive protein (r = -0.47) and positively correlated with mean corpuscular hemoglobin (r = 0.51). Conclusions: C17-sphinganine is a core metabolic biomarker for distinguishing kidney-liver deficiency syndrome from heat-dampness syndrome, enabling precise syndrome classification with a single measurement. The tissue-specific magnitude-difference regulation of sphingosine-1-phosphate lyase 1 and the sphingosine kinase 1-neutrophil correlative trend provide novel insights into how sphingolipid metabolism participates in traditional Chinese medicine syndrome differentiation in RA. 1 Introduction Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic synovial inflammation and progressive joint destruction, with a global prevalence of approximately 0.5%–1% ( 1 – 3 ). Although disease-modifying antirheumatic drugs and biologic agents have markedly improved the prognosis of RA patients, 30%–40% still respond inadequately to current therapies ( 4 , 5 ), suggesting substantial molecular heterogeneity and an urgent need for more precise individualized treatment strategies ( 6 , 7 ). Traditional Chinese Medicine (TCM) has a long history in treating RA, with ‘syndrome differentiation’ serving as the cornerstone of individualized therapy ( 8 – 10 ). Heat-dampness syndrome and kidney-liver deficiency syndrome are the two most common TCM syndromes in clinical RA practice ( 11 , 12 ). Heat-dampness syndrome typically manifests in the active stage of the disease, characterized by joint redness, swelling, heat, pain and elevated inflammatory markers; kidney-liver deficiency syndrome predominantly occurs in the later stages, with dull joint pain, soreness and weakness of the lower back and knees, and anemia tendency ( 13 , 14 ). Our previous untargeted metabolomics study identified sphingolipid metabolism as a significantly perturbed pathway between these two syndromes ( 15 ). Sphingolipid metabolism is an essential network for maintaining cell membrane structure and signal transduction ( 16 – 18 ). Sphingosine-1-phosphate (S1P) is its core signaling molecule, participating in immune cell migration and inflammatory cytokine release through five S1P receptor subtypes ( 19 , 20 ). S1P is synthesized by sphingosine kinase 1/2 (SPHK1/2) and irreversibly degraded by S1P lyase (SGPL1) ( 21 , 22 ). Recent studies have confirmed that the S1P signaling pathway plays a critical role in RA synovial inflammation ( 23 – 25 ), and SPHK1 can promote the release of inflammatory cytokines from fibroblast-like synoviocytes ( 26 , 27 ). With the widespread adoption of high-throughput technologies, public databases such as the Gene Expression Omnibus provide resources for cross-dataset validation ( 28 ), and immune infiltration analysis tools enable assessment of tissue immune microenvironments from transcriptomic data ( 29 ). Oxidative stress plays an important role in RA pathogenesis ( 30 , 31 ); glutathione depletion can lead to GPX4 inactivation and thereby trigger ferroptosis ( 32 – 34 ). However, the role of crosstalk between oxidative stress and sphingolipid metabolism in RA syndrome differentiation remains unclear. This study integrates serum metabolomics with multi-dataset transcriptomics to systematically evaluate the diagnostic utility of sphingolipid metabolites for RA syndrome classification and to characterize the tissue-specific regulation of sphingolipid metabolism genes, aiming to provide a molecular basis for the objective diagnosis of TCM syndromes in RA. The study design flowchart is shown in Figure 1 . Figure 1 Flow chart of the study design. 2 Materials and methods 2.1 Study subjects The metabolomics data were obtained from our previous study ( 15 ). Forty RA patients were enrolled and classified into GS (n=20) and HD (n=20) groups according to the Guiding Principles for Clinical Research on New Drugs of Traditional Chinese Medicine. Demographic and clinical data, including age, sex, BMI, disease duration, medication use (methotrexate, glucocorticoids, leflunomide, hydroxychloroquine), and laboratory parameters (CRP, ESR, RF, ACCP, complement C3/C4, liver and renal function tests, and hematological indices), were systematically collected from electronic medical records. Inclusion criteria: fulfillment of the 1987 American College of Rheumatology or the 2010 ACR/EULAR classification criteria for RA. Exclusion criteria: concurrent other autoimmune diseases, severe hepatic or renal insufficiency, or malignant tumors. This study was approved by the Ethics Committee of Foshan Hospital of Traditional Chinese Medicine, and written informed consent was obtained from all subjects. 2.2 Metabolomics profiling Fasting venous blood (5 mL) was collected and centrifuged to obtain serum. Untargeted metabolomics analysis was performed using an Agilent 1290 UPLC system coupled with an Agilent 6545 QTOF mass spectrometer, with data acquired in both positive and negative ion modes. Peak alignment and metabolite identification were performed using MSDIAL (v4.24) with reference to Metlin, MassBank, MoNA, and HMDB databases ( 35 ). Differential metabolites were screened using variable importance in projection ≥ 1 in PLS-DA and Student’s t-test P 0.5 and P 0.05). The proportions of patients receiving methotrexate, glucocorticoids, leflunomide, and hydroxychloroquine were also comparable between the two groups (all P > 0.05), excluding medication use as a major confounder. However, significant inter-group differences were observed for cholinesterase (ChE), uric acid, and monocyte percentage (P < 0.05). These findings establish that the two TCM syndrome groups are broadly comparable in demographic and clinical parameters, supporting the validity of subsequent metabolomic comparisons. Table 1 Variable HD (n=20) GS (n=20) P value Demographics Age, years, mean ± SD 62.50 ± 1.69 63.16 ± 1.66 0.860 BMI, kg/m², mean ± SD 24.01 ± 3.01 23.09 ± 2.98 0.970 Disease duration, years, median (IQR) 3.0 (1.0–6.5) 3.0 (2.0–7.8) 0.586 Sex, Female, n (%) 6 (30%) 4 (20%) 0.715 Inflammatory markers CRP, mg/L, mean ± SD 54.00 ± 14.27 56.13 ± 14.94 0.850 ESR, mm/h, mean ± SD 56.26 ± 7.06 57.17 ± 7.54 0.870 RF, IU/mL, mean ± SD 111.7 ± 32.78 115.7 ± 21.29 0.080 ACCP, RU/mL, mean ± SD 63.44 ± 7.92 66.06 ± 7.43 0.640 Sphingolipid metabolites C17-Sphinganine, median (IQR) 78,997 (23,751–144,624) 230,303 (222,379–238,844) <0.001 Sphingosine-1-Phosphate (S1P), median (IQR) 207,893 (27,606–259,534) 24,374 (22,501–27,301) <0.001 SPHK1-related metabolic ratio (S1P/C17-Sphinganine), median (IQR) 4.66 (0.18–9.29) 0.11 (0.10–0.11) <0.001 Medications, n (%) Leflunomide use 11 (55%) 11 (55%) 1.000 Methotrexate (MTX) use 11 (55%) 12 (60%) 1.000 Glucocorticoids (GC) use 19 (95%) 17 (85%) 0.598 Hydroxychloroquine (HCQ) use 12 (60%) 9 (45%) 0.527 Baseline characteristics and sphingolipid metabolites of ra patients stratified by TCM syndrome. Data are presented as mean ± SD, median (IQR), or n (%). P values were calculated using Student’s t-test, Wilcoxon rank-sum test, or Chi-square/Fisher’s exact test as appropriate. Bold P < 0.05 was considered statistically significant. HD, heat-dampness syndrome; GS, kidney-liver deficiency syndrome; BMI, body mass index; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; RF, rheumatoid factor; ACCP, anti-cyclic citrullinated peptide antibody; MTX, methotrexate; GC, glucocorticoids; HCQ, hydroxychloroquine; IQR, interquartile range; SD, standard deviation. We further investigated whether the discriminative power of these sphingolipid metabolites was driven by their correlation with conventional clinical parameters. Spearman correlation analysis between sphingolipid metabolites and 40 clinical indicators revealed that, after stringent FDR correction (q < 0.05), no metabolite-clinical indicator pair reached statistical significance ( Supplementary Table S2 ). The absence of significant correlations with inflammatory markers (CRP, ESR), liver function, renal function, and hematological indices indicates that the sphingolipid metabolic signature is largely independent of routine clinical parameters. This independence reinforces the specificity of C17-Sphinganine as a TCM syndrome-related biomarker, rather than a proxy for systemic inflammation or organ dysfunction, further supporting its potential value for objective syndrome classification. 3.2 Diagnostic performance of sphingolipid metabolites for TCM syndrome classification We first evaluated the potential of three sphingolipid metabolic indices to discriminate between the two TCM syndromes. As shown in Figure 3A , C17-Sphinganine was markedly elevated in the GS group compared with the HD group (P = 2.32×10 -4 ), whereas S1P(d18:1) showed the opposite pattern, being significantly higher in HD patients (P = 4.19×10 -4 ). Consequently, the SPHK1-related metabolic ratio (estimated as the S1P/C17-Sphinganine peak-area ratio, not direct enzymatic activity measurement), was profoundly higher in the HD group than in the GS group (median: 4.66 vs. 0.11, P = 3.45×10 -6 ). These reciprocal alterations indicate that GS is characterized by C17-Sphinganine accumulation with suppressed SPHK1 activity, whereas HD is associated with enhanced S1P production and elevated SPHK1 activity. Figure 3 Evaluation of the diagnostic performance of sphingolipid metabolites in distinguishing RA HD from GS. (A) Boxplot comparison of C17-Sphinganine, S1P(d18:1), and SPHK1 activity between the GS (n=20) and HD (n=20) groups. Boxes indicate median and interquartile range; whiskers represent data range. Statistical significance was determined by Wilcoxon rank-sum test (***P < 0.001). (B) Scatter plot of the correlation between C17-Sphinganine and S1P(d18:1). Green dots represent the GS group; orange dots represent the HD group. (C) ROC curves of C17-Sphinganine, S1P(d18:1), and the C17/S1P ratio for distinguishing HD from GS. (D) Confusion matrix of the combined logistic regression model. HD, heat-dampness syndrome; GS, kidney-liver deficiency syndrome; S1P, sphingosine-1-phosphate; SPHK1, sphingosine kinase 1; AUC, area under the curve; ROC, receiver operating characteristic. Spearman correlation analysis revealed a strong inverse relationship between C17-sphinganine and S1P (d18:1) (r = −0.555, P < 0.001, Figure 3B ). Unlike the high-dimensional metabolite-clinical indicator a
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