SUPT16H Overexpression Alleviates the Progression of Endometriosis and Systemic Lupus Erythematosus by Regulating Oxidative Stress
other
OA: closed
public-domain-us
AI-generated summary
SUPT16H overexpression alleviates endometriosis and systemic lupus erythematosus progression by regulating oxidative stress and inflammation.
One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works
AI-generated deep summary
The paper studied immune-related biomarkers shared by endometriosis (EM) and systemic lupus erythematosus (SLE) by performing differential expression analysis, immune infiltration analysis, weighted gene co-expression network analysis, and intersecting EM and SLE immune-related gene sets, followed by LASSO, random forest, and SVM-RFE to identify three co-susceptibility diagnostic genes (C1QC, SOCS3, and SUPT16H) with ROC AUCs >0.7 in training and verification datasets. It further assessed relationships between diagnostic genes and immune cells and transcription factors, conducted GSEA, and predicted potential drug associations. In vitro experiments were used to test SUPT16H function and reported that SUPT16H overexpression alleviated EM and SLE progression signals by regulating inflammation and oxidative stress, though the abstract does not specify sample sizes, cohorts’ origin, or the biological model system details as a key limitation. This paper is centrally about endometriosis—SUPT16H as a shared immune/oxidative-stress regulator is proposed to alleviate endometriosis progression while also addressing SLE.
Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works
Abstract
OBJECTIVE: To screen immune-related biomarkers in diagnosing patients with both endometriosis (EM) and systemic lupus erythematosus (SLE).
METHODS: After performing differential expression analysis, immune infiltration analysis, WGCNA, the immune-related genes in EM and SLE were screened. Then the diagnostic genes were identified by three machine learning algorithms, followed by evaluation of the predictive performance of the diagnostic genes by nomogram and ROC curve. Then the correlation between diagnostic genes and immune cells, TFs, GSEA, and potential drugs prediction analyses were performed. Lastly, experiments in vitro were applied to explore the function of SUPT16H in EM and SLE.
RESULTS: Total 20 immune-related genes in EM and SLE were identified by intersecting DEGs and module genes. Using "LASSO", "RF", and "SVM-RFE" algorithms, and total three common diagnostic genes were obtained, namely, C1QC, SOCS3, and SUPT16H. ROC curve shown that AUCs of diagnostic genes were all above 0.7 in training and verification datasets. The targeted drugs for the three diagnostic genes were predicted, containing Pingyangmycin CTD 00001211, VANADIUM PENTOXIDE CTD 00002655, CTD 00001728, and so forth. Also, SUPT16H exerted significant function in occurrence of EM and SLE via regulating inflammation and oxidative stress in cell experiments in vitro.
CONCLUSION: SUPT16H overexpression alleviates the progression of EM and SLE by inhibiting inflammation and oxidative stress. The three co- susceptibility genes (C1QC, SOCS3, and SUPT16H) that strongly related to immunity in EM and SLE could be the promising candidate biomarker for the diagnosis and treatment for EM and SLE patients.
Full text
15,747 characters
· extracted from
oa-doi-fallback
· 5 sections
· click to expand
Objective
To screen immune-related biomarkers in diagnosing patients with both endometriosis (EM) and systemic lupus erythematosus (SLE).
Methods
After performing differential expression analysis, immune infiltration analysis, WGCNA, the immune-related genes in EM and SLE were screened. Then the diagnostic genes were identified by three machine learning algorithms, followed by evaluation of the predictive performance of the diagnostic genes by nomogram and ROC curve. Then the correlation between diagnostic genes and immune cells, TFs, GSEA, and potential drugs prediction analyses were performed. Lastly, experiments in vitro were applied to explore the function of SUPT16H in EM and SLE.
Results
Total 20 immune-related genes in EM and SLE were identified by intersecting DEGs and module genes. Using “LASSO”, “RF”, and “SVM-RFE” algorithms, and total three common diagnostic genes were obtained, namely, C1QC, SOCS3, and SUPT16H. ROC curve shown that AUCs of diagnostic genes were all above 0.7 in training and verification datasets. The targeted drugs for the three diagnostic genes were predicted, containing Pingyangmycin CTD 00001211, VANADIUM PENTOXIDE CTD 00002655, CTD 00001728, and so forth. Also, SUPT16H exerted significant function in occurrence of EM and SLE via regulating inflammation and oxidative stress in cell experiments in vitro.
Conclusion
SUPT16H overexpression alleviates the progression of EM and SLE by inhibiting inflammation and oxidative stress. The three co- susceptibility genes (C1QC, SOCS3, and SUPT16H) that strongly related to immunity in EM and SLE could be the promising candidate biomarker for the diagnosis and treatment for EM and SLE patients.
Conflicts of Interest
None.
Supporting Information
| Filename | Description |
|---|---|
| aji70230-sup-0001-SuppMat1.xlsx405 KB | Supporting file 1: 3856 DEGs acquired between EM and control samples. |
| aji70230-sup-0002-SuppMat2.xlsx64.4 KB | Supporting file 2: 538 DEGs obtained between SLE and control samples. |
| aji70230-sup-0003-SuppMat3.csv29.7 KB | Supporting file 3: 20 immune-related genes in EM and SLE. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
References
- 1P. T. K. Saunders, L. H. R. Whitaker, and A. W. Horne, “Endometriosis: Improvements and Challenges in Diagnosis and Symptom Management,” Cell Reports Medicine 5, no. 6 (2024): 101596, https://doi.org/10.1016/j.xcrm.2024.101596.
- 2M. J. Griffiths, A. W. Horne, D. A. Gibson, N. Roberts, and P. T. K. Saunders, “Endometriosis: Recent Advances That Could Accelerate Diagnosis and Improve Care,” Trends in Molecular Medicine 30, no. 9 (2024): 875–889, https://doi.org/10.1016/j.molmed.2024.06.008.
- 3J. P. Ferrari-Souza, M. T. Pedrotti, E. E. Moretto, L. P. Farenzena, L. G. Crippa, and J. S. Cunha-Filho, “Endometriosis and Systemic Lupus Erythematosus: Systematic Review and Meta-analysis,” Reproductive Sciences (Thousand Oaks, Calif) 30, no. 4 (2023): 997–1005, https://doi.org/10.1007/s43032-022-01045-3.
- 4R. K. Hamouda, H. Arzoun, and I. Sahib, “The Comorbidity of Endometriosis and Systemic Lupus Erythematosus: A Systematic Review,” Cureus 15, no. 7 (2023): e42362, https://doi.org/10.7759/cureus.42362.
- 5N. Shigesi, M. Kvaskoff, S. Kirtley, et al., “The Association Between Endometriosis and Autoimmune Diseases: A Systematic Review and Meta-Analysis,” Human Reproduction Update 25, no. 4 (2019): 486–503, https://doi.org/10.1093/humupd/dmz014.
- 6Y. H. Fan, P. Y. Leong, J. Y. Chiou, Y. H. Wang, M. H. Ku, and J. C. Wei, “Association Between Endometriosis and Risk of Systemic Lupus Erythematosus,” Scientific Reports 11, no. 1 (2021): 532, https://doi.org/10.1038/s41598-020-79954-z.
- 7C. Zeng, H. Liu, Z. Wang, and J. Li, “Novel Insights Into the Complex Interplay of Immune Dysregulation and Inflammatory Biomarkers in Preeclampsia and Fetal Growth Restriction: A Two-Step Mendelian Randomization Analysis,” Journal of Translational Autoimmunity 8 (2024): 100226, https://doi.org/10.1016/j.jtauto.2023.100226.
- 8E. Yoshii, H. Yamana, S. Ono, H. Matsui, and H. Yasunaga, “Association Between Allergic or Autoimmune Diseases and Incidence of Endometriosis: A Nested Case-Control Study Using a Health Insurance Claims Database,” American Journal of Reproductive Immunology 86, no. 5 (2021): e13486, https://doi.org/10.1111/aji.13486.
- 9L. Pan, M. P. Lu, J. H. Wang, M. Xu, and S. R. Yang, “Immunological Pathogenesis and Treatment of Systemic Lupus Erythematosus,” World Journal of Pediatrics: WJP 16, no. 1 (2020): 19–30, https://doi.org/10.1007/s12519-019-00229-3.
- 10S. Chen, Y. Liu, Z. Zhong, C. Wei, Y. Liu, and X. Zhu, “Peritoneal Immune Microenvironment of Endometriosis: Role and Therapeutic Perspectives,” Frontiers in Immunology 14 (2023): 1134663, https://doi.org/10.3389/fimmu.2023.1134663.
- 11J. Crump, A. Suker, and L. White, “Endometriosis: A Review of Recent Evidence and Guidelines,” Australian Journal of General Practice 53, no. 2 (2024): 11–18, https://doi.org/10.31128/ajgp/04-23-6805.
- 12M. Aringer, “Inflammatory Markers in Systemic Lupus erythematosus,” Journal of Autoimmunity 110 (2020): 102374, https://doi.org/10.1016/j.jaut.2019.102374.
- 13M. Ritchie, B. Phipson, D. Wu, et al., “limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies,” Nucleic Acids Research 43, no. 7 (2015): e47.
- 14G. Yu, L. G. Wang, Y. Han, and Q. Y. He, “clusterProfiler: An R Package for Comparing Biological Themes Among Gene Clusters,” Omics: A Journal of Integrative Biology 16, no. 5 (2012): 284–287, https://doi.org/10.1089/omi.2011.0118.
- 15P. Langfelder and S. Horvath, “WGCNA: An R Package for Weighted Correlation Network Analysis,” BMC Bioinformatics 9, no. 1 (2008): 559, https://doi.org/10.1186/1471-2105-9-559, 2008/12/29.
- 16B. Jiang, P. Sun, J. Tang, and B. Luo, “ GLMNet: Graph Learning-Matching Networks for Feature Matching,” (2019).
- 17A. Liaw and M. Wiener, “Classification and Regression by randomForest,” R News 23, no. 23 (2002): 18–22.
- 18X. Robin, N. Turck, A. Hainard, et al., “pROC: An Open-Source Package for R and S+ to Analyze and Compare ROC Curves,” BMC Bioinformatics 12, no. 1 (2011): 77, https://doi.org/10.1186/1471-2105-12-77.
- 19R. Tibshirani, “The LASSO Method for Variable Selection in the Cox Model,” Statistics in Medicine 16, no. 4 (1997): 385–395.
10.1002/(SICI)1097-0258(19970228)16:43.0.CO;2-3CAS PubMed Web of Science® Google Scholar
- 20J. Reimand, R. Isserlin, V. Voisin, et al., “Pathway Enrichment Analysis and Visualization of Omics Data Using G:Profiler, GSEA, Cytoscape and EnrichmentMap,” Nature protocols 14, no. 2 (2019): 482–517, https://doi.org/10.1038/s41596-018-0103-9.
- 21M. Yoo, J. Shin, J. Kim, et al., “DSigDB: Drug Signatures Database for Gene Set Analysis,” Bioinformatics 31, no. 18 (2015): 3069–3071, https://doi.org/10.1093/bioinformatics/btv313.
- 22J. Shen, M. Chen, D. Lee, et al., “Histone Chaperone FACT Complex Mediates Oxidative Stress Response to Promote Liver Cancer Progression,” Gut 69, no. 2 (2020): 329–342, https://doi.org/10.1136/gutjnl-2019-318668.
- 23L. Clower, T. Fleshman, W. J. Geldenhuys, and N. Santanam, “Targeting Oxidative Stress Involved in Endometriosis and Its Pain,” Biomolecules 12, no. 8 (2022): 1055, https://doi.org/10.3390/biom12081055.
- 24A. Lupu, G. Stoleriu, A. H. Nedelcu, et al., “Overview of Oxidative Stress in Systemic Lupus Erythematosus,” Antioxidants (Basel, Switzerland) 14, no. 3 (2025): 303, https://doi.org/10.3390/antiox14030303.
- 25J. E. Miller, S. H. Ahn, R. M. Marks, et al., “IL-17A Modulates Peritoneal Macrophage Recruitment and M2 Polarization in Endometriosis,” Frontiers in Immunology 11 (2020): 108, https://doi.org/10.3389/fimmu.2020.00108.
- 26J. Wu, H. Xie, S. Yao, and Y. Liang, “Macrophage and Nerve Interaction in Endometriosis,” Journal of Neuroinflammation 14, no. 1 (2017): 53, https://doi.org/10.1186/s12974-017-0828-3.
- 27H. Lv, B. Liu, and Y. Dai, “TET3-Overexpressing Macrophages Promote endometriosis,” Journal of Clinical Investigation 134, no. 21 (2024): 17, https://doi.org/10.1172/jci181839.
10.1172/jci181839Google Scholar
- 28M. M. Ahamada, Y. Jia, and X. Wu, “Macrophage Polarization and Plasticity in Systemic Lupus Erythematosus,” Frontiers in Immunology 12 (2021): 734008, https://doi.org/10.3389/fimmu.2021.734008.
- 29E. J. Yoo, K. H. Oh, H. Piao, et al., “Macrophage Transcription Factor TonEBP Promotes Systemic Lupus Erythematosus and Kidney Injury via Damage-Induced Signaling Pathways,” Kidney International 104, no. 1 (2023): 163–180, https://doi.org/10.1016/j.kint.2023.03.030.
- 30Y. Liu and M. J. Kaplan, “Neutrophil Dysregulation in the Pathogenesis of Systemic Lupus Erythematosus,” Rheumatic Diseases Clinics of North America 47, no. 3 (2021): 317–333, https://doi.org/10.1016/j.rdc.2021.04.002.
- 31L. K. Symons, J. E. Miller, K. Tyryshkin, et al., “Neutrophil Recruitment and Function in Endometriosis Patients and a Syngeneic Murine Model,” FASEB Journal 34, no. 1 (2020): 1558–1575, https://doi.org/10.1096/fj.201902272R.
- 32W. Zhang, Y. Chen, and H. Pei, “C1q and Central Nervous System Disorders,” Frontiers in Immunology 14 (2023): 1145649, https://doi.org/10.3389/fimmu.2023.1145649.
- 33K. Schulz and M. Trendelenburg, “C1q as a Target Molecule to Treat Human Disease: What Do Mouse Studies Teach Us?,” Frontiers in Immunology 13 (2022): 958273, https://doi.org/10.3389/fimmu.2022.958273.
- 34R. A. van Schaarenburg, C. Magro-Checa, J. A. Bakker, et al., “C1q Deficiency and Neuropsychiatric Systemic Lupus Erythematosus,” Frontiers in Immunology 7 (2016): 647, https://doi.org/10.3389/fimmu.2016.00647.
- 35D. Song, W. Y. Guo, F. M. Wang, et al., “Complement Alternative Pathway׳s Activation in Patients With Lupus Nephritis,” American Journal of the Medical Sciences 353, no. 3 (2017): 247–257, https://doi.org/10.1016/j.amjms.2017.01.005.
- 36C. Huang, J. Lin, L. Chen, W. Sun, J. Xia, and M. Wu, “Upregulation of C1QC as a Mediator of Blood-Brain Barrier Damage in Type 2 Diabetes Mellitus,” Molecular Neurobiology 62, no. 4 (2025): 5234–5251, https://doi.org/10.1007/s12035-024-04615-5.
- 37A. Kimura, I. Kinjyo, Y. Matsumura, et al., “SOCS3 is a Physiological Negative Regulator for Granulopoiesis and Granulocyte Colony-Stimulating Factor Receptor Signaling,” Journal of biological chemistry 279, no. 8 (2004): 6905–6910, https://doi.org/10.1074/jbc.C300496200.
- 38J. A. B. Pedroso, A. M. Ramos-Lobo, and J. Donato, Jr, “SOCS3 as a Future Target to Treat Metabolic Disorders,” Hormones (Athens, Greece) 18, no. 2 (2019): 127–136, https://doi.org/10.1007/s42000-018-0078-5.
- 39S. Y. Chen, M. F. Liu, P. Y. Kuo, and C. R. Wang, “Upregulated Expression of STAT3/IL-17 in Patients With Systemic Lupus Erythematosus,” Clinical Rheumatology 38, no. 5 (2019): 1361–1366, https://doi.org/10.1007/s10067-019-04467-8.
- 40M. Ma, X. Zhang, Y. Zheng, et al., “The Fly Homolog of SUPT16H, a Gene Associated With Neurodevelopmental Disorders, is Required in a Cell-Autonomous Fashion for Cell Survival,” Human Molecular Genetics 32, no. 6 (2023): 984–997, https://doi.org/10.1093/hmg/ddac259.
- 41R. Bina, D. Matalon, B. Fregeau, et al., “De Novo Variants in SUPT16H Cause Neurodevelopmental Disorders Associated With Corpus Callosum Abnormalities,” Journal of Medical Genetics 57, no. 7 (2020): 461–465, https://doi.org/10.1136/jmedgenet-2019-106193.
- 42T. Smol, C. Thuillier, E. Boudry-Labis, et al., “Neurodevelopmental Phenotype Associated With CHD8-SUPT16H Duplication,” Neurogenetics 21, no. 1 (2020): 67–72, https://doi.org/10.1007/s10048-019-00599-w.
- 43Y. Lan, X. Li, Y. Liu, et al., “Pingyangmycin Inhibits Glycosaminoglycan Sulphation in Both Cancer Cells and Tumour Tissues,” Journal of Cellular and Molecular Medicine 24, no. 6 (2020): 3419–3430, https://doi.org/10.1111/jcmm.15017.
- 44C. K. Shan, Y. B. Du, X. T. Zhai, et al., “Pingyangmycin Enhances the Antitumor Efficacy of Anti-PD-1 Therapy Associated With Tumor-Infiltrating CD8(+) T Cell Augmentation,” Cancer Chemotherapy and Pharmacology 87, no. 3 (2021): 425–436, https://doi.org/10.1007/s00280-020-04209-7.
- 45S. Bansal, A. Tomer, and P. Jain, “Natural Product-Inspired Vanadium Pentoxide Nanoparticles Unlock Diabetic Therapeutic Potential: In Vitro and In Silico Evaluation,” ACS Applied Bio Materials 8, no. 3 (2025): 2027–2051, https://doi.org/10.1021/acsabm.4c01534.
- 46S. L. Hwang and H. W. Chang, “Natural Vanadium-Containing Jeju Ground Water Stimulates Glucose Uptake Through the Activation of AMP-Activated Protein Kinase in L6 Myotubes,” Molecular and Cellular Biochemistry 360, no. 2 (2012): 401–409, https://doi.org/10.1007/s11010-011-1062-4.
- 47S. Nivetha, T. Srivalli, P. M. Sathya, et al., “Nickel-Doped Vanadium Pentoxide (Ni@V(2)O(5)) Nanocomposite Induces Apoptosis Targeting PI3K/AKT/mTOR Signaling Pathway in Skin Cancer: An in Vitro and In Vivo Study,” Colloids and Surfaces B, Biointerfaces 234 (2024): 113763, https://doi.org/10.1016/j.colsurfb.2024.113763.
- 48S. Das, A. Roy, A. K. Barui, et al., “Anti-Angiogenic Vanadium Pentoxide Nanoparticles for the Treatment of Melanoma and Their in Vivo Toxicity Study,” Nanoscale 12, no. 14 (2020): 7604–7621, https://doi.org/10.1039/d0nr00631a.
Article Metrics
Total unique accesses to an article’s full text in HTML or PDF/ePDF format.More metric information
Scite metrics
Explore this article's citation statements on scite.ai
Share QR Code
Generating QR code
QR code copied to clipboard!
Something went wrong while generating your QR code. Please try again in a moment. If the issue persists, refresh the page or contact support.
Export citation
Unable to load citation data. Please try again in a moment.
How to cite
Elkins, L. J., & Spiegelman, M. (2021). pyUserCalc: A revised Jupyter notebook calculator for uranium-series disequilibria in basalts. Earth and Space Science, 8, e2020EA001619. https://doi.org/10.1029/2020EA001619
Download Citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click on download.
This feature enables you to download the bibliographic information (also called citation data, header data, or metadata) for the articles on our site.
Citation manager file format
Use the dropdown list to choose how to format the bibliographic data you're harvesting. Several citation manager formats are available, including EndNote and BibTex. You can then copy the formatted citation (as displayed) or download it as file, to your device. If the RefWorks format is chosen, the 'Download' button will be replaced with an option to directly export to RefWorks
Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.
My notes (saved in your browser only)
Ask this paper
Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works
Condition tags
MeSH descriptors
Citation neighborhood (no data yet)
We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.
Source provenance
- europepmc
- last seen: 2026-06-16T06:07:01.518242+00:00
- pubmed
- last seen: 2026-06-16T06:02:31.273061+00:00
- unpaywall
- last seen: 2026-05-11T08:34:28.763810+00:00
License: public-domain-us
· commercial use OK
· attribution required
Courtesy of the U.S. National Library of Medicine
Courtesy of the U.S. National Library of Medicine