Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms
Open accessAbstract
Objectives: Alopecia areata (AA) is an autoimmune-related non-cicatricial alopecia, with complete alopecia (AT) or generalized alopecia (AU) as severe forms of AA. However, there are limitations in early identification of AA, and intervention of AA patients who may progress to severe AA will help to improve the incidence rate and prognosis of severe AA. Methods: We obtained two AA-related datasets from the gene expression omnibus database, identified the differentially expressed genes (DEGs), and identified the module genes most related to severe AA through weighted gene co-expression network analysis. Functional enrichment analysis, construction of a protein–protein interaction network and competing endogenous RNA network, and immune cell infiltration analysis were performed to clarify the underlying biological mechanisms of severe AA. Subsequently, pivotal immune monitoring genes (IMGs) were screened through multiple machine-learning algorithms, and the diagnostic effectiveness of the pivotal IMGs was validated by receiver operating characteristic. Results: A total of 150 severe AA-related DEGs were identified; the upregulated DEGs were mainly enriched in immune response, while the downregulated DEGs were mainly enriched in pathways related to hair cycle and skin development. Four IMGs (LGR5, SHISA2, HOXC13, and S100A3) with good diagnostic efficiency were obtained. As an important gene of hair follicle stem cells stemness, we verified in vivo that LGR5 downregulation may be an important link leading to severe AA. Conclusion: Our findings provide a comprehensive understanding of the pathogenesis and underlying biological processes in patients with AA, and identification of four potential IMGs, which is helpful for the early diagnosis of severe AA.
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Transcriptomic analysis identified 4 immune-monitoring genes with diagnostic effectiveness for severe alopecia areata (LGR5, SHISA2, HOXC13, and S100A3).
“Xiong et al (2023) analyzed gene expression data from transcriptomic AA datasets to identify diagnostic markers for severe AA, identifying 4 immune-monitoring genes with diagnostic effectiveness (LGR5, SHISA2, HOXC13, and S100A3).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Xiong et al, 2023
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Cites
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
GENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585 · see citing works
MicroRNA targets in Drosophila
Enright, A. J., John, B., Gaul, U., Tuschl, T., Sander, C., & Marks, D. S. (2003) · Genome Biology · doi:10.1186/gb-2003-5-1-r1 · Cited by 3,597 · see citing works
STEM: a tool for the analysis of short time series gene expression data
Ernst, J., & Bar-Joseph, Z. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-191 · Cited by 1,703 · see citing works
miRWalk2.0: a comprehensive atlas of microRNA-target interactions
Dweep, H., & Gretz, N. (2015) · Nature Methods · doi:10.1038/nmeth.3485 · Cited by 1,343 · see citing works
Lgr5 marks cycling, yet long-lived, hair follicle stem cells
Jaks, V., Barker, N., Kasper, M., van Es, J. H., Snippert, H. J., Clevers, H., & Toftgård, R. (2008) · Nature Genetics · doi:10.1038/ng.239 · Cited by 966 · see citing works
Pratt, C. H., King, L. E., Messenger, A. G., Christiano, A. M., & Sundberg, J. P. (2017) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2017.11 · Cited by 791 · see citing works
Alopecia areata: a review of disease pathogenesis
Rajabi, F., Drake, L. A., Senna, M. M., & Rezaei, N. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16808 · Cited by 313 · see citing works
Alopecia areata: A multifactorial autoimmune condition
Simakou, T., Butcher, J. P., Reid, S., & Henriquez, F. L. (2018) · Journal of Autoimmunity · doi:10.1016/j.jaut.2018.12.001 · Cited by 311 · see citing works
Betz, R. C., Petukhova, L., Ripke, S., Huang, H., Menelaou, A., Redler, S., Becker, T., Heilmann‐Heimbach, S., Yamany, T., Duvic, M., et al. (2015) · Nature Communications · doi:10.1038/ncomms6966 · Cited by 309 · see citing works
Shulman, Z., Shinder, V., Klein, E., Grabovsky, V., Yeger, O., Geron, E., Montresor, A., Bolomini-Vittori, M., Feigelson, S. W., Kirchhausen, T., et al. (2009) · Immunity · doi:10.1016/j.immuni.2008.12.020 · Cited by 268 · see citing works
Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model
Alderden, J., Pepper, G. A., Wilson, A., Whitney, J. D., Richardson, S., Butcher, R., Jo, Y., & Cummins, M. R. (2018) · American Journal of Critical Care · doi:10.4037/ajcc2018525 · Cited by 262 · see citing works
Epithelial-Mesenchymal Micro-niches Govern Stem Cell Lineage Choices
Yang, H., Adam, R. C., Ge, Y., Hua, Z. L., & Fuchs, E. V. (2017) · Cell · doi:10.1016/j.cell.2017.03.038 · Cited by 249 · see citing works
Histopathologic Features of Alopecia Areata
Whiting, D. A. (2003) · Archives of Dermatology · doi:10.1001/archderm.139.12.1555 · Cited by 220 · see citing works
Wang, X.-S., Chen, H.-Y., Tian, R.-Y., Zhang, Y., Drutskaya, M., Wang, C., Ge, J.-F., Fan, Z., Kong, D., Wang, X.-X., et al. (2017) · Nature Communications · doi:10.1038/ncomms14091 · Cited by 208 · see citing works
Heid, H., Werner, E., & Franke, W. W. (1986) · Differentiation · doi:10.1111/j.1432-0436.1986.tb00562.x · Cited by 198 · see citing works
Machine learning approaches for predicting high cost high need patient expenditures in health care
Yang, C., Delcher, C., Shenkman, E. A., & Ranka, S. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0568-3 · Cited by 198 · see citing works
Joost, S., Jacob, T., Sun, X., Annusver, K., Manno, G. L., Sur, I., & Kasper, M. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.09.059 · Cited by 196 · see citing works
Bertolini, M., Zilio, F., Rossi, A., Kleditzsch, P., Emelianov, V. U., Gilhar, A., Keren, A., Meyer, K. C., Wang, E., Funk, W., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094260 · Cited by 162 · see citing works
McElwee, K. J., Freyschmidt‐Paul, P., Hoffmann, R., Kissling, S., Hummel, S., Vitacolonna, M., & Zöller, M. (2005) · Journal of Investigative Dermatology · doi:10.1111/j.0022-202x.2005.23692.x · Cited by 159 · see citing works
Frontiers in alopecia areata pathobiology research
Gilhar, A., Britva, R. L., Keren, A., & Paus, R. (2019) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.08.035 · Cited by 99 · see citing works
Potter, C. S., Pruett, N. D., Kern, M. J., Baybo, M. A., Godwin, A. R. F., Potter, K. A., Peterson, R. L., Sundberg, J. P., & Awgulewitsch, A. (2010) · Journal of Investigative Dermatology · doi:10.1038/jid.2010.391 · Cited by 81 · see citing works
Zhou, Y., Shi, W., Zhao, D., Xiao, S., Wang, K., & Wang, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.937886 · Cited by 76 · see citing works
Endogenous Retinoids in the Pathogenesis of Alopecia Areata
Duncan, F. J., Silva, K. A., Johnson, C. J., King, B. L., Szatkiewicz, J. P., Kamdar, S. P., Ong, D. E., Napoli, J. L., Wang, J., King, L. E., et al. (2012) · Journal of Investigative Dermatology · doi:10.1038/jid.2012.344 · Cited by 66 · see citing works
Żeberkiewicz, M., Rudnicka, L., & Malejczyk, J. (2020) · Central European Journal of Immunology · doi:10.5114/ceji.2020.101264 · Cited by 66 · see citing works
Romain, G., Strati, P., Rezvan, A., Fathi, M., Bandey, I. N., Adolacion, J. R. T., Heeke, D., Liadi, I., Marques-Piubelli, M. L., Solis, L. M., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci159402 · Cited by 61 · see citing works
Differential expression of CXCL 9, CXCL 10, and IFN ‐γ in vitiligo and alopecia areata patients
Maouia, A., Sormani, L., Youssef, M., Helal, A. N., Kassab, A., & Passeron, T. (2016) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.12559 · Cited by 52 · see citing works
Resident human dermal γδT-cells operate as stress-sentinels: Lessons from the hair follicle
Uchida, Y., Gherardini, J., Pappelbaum, K., Chéret, J., Schulte‐Mecklenbeck, A., Groß, C. C., Štrbo, N., Gilhar, A., Rossi, A., Funk, W., et al. (2021) · Journal of Autoimmunity · doi:10.1016/j.jaut.2021.102711 · Cited by 41 · see citing works
Polkoff, K. M., Gupta, N. K., Green, A. J., Murphy, Y., Chung, J., Gleason, K. L., Simpson, S. G., Walker, D. M., Collins, B., & Piedrahita, J. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-13056-w · Cited by 39 · see citing works
Serum cytokine and chemokine profiles in patients with alopecia areata
Bilgiç, Ö., Sivrikaya, A., Ünlü, A., & Altinyazar, H. C. (2015) · Journal of Dermatological Treatment · doi:10.3109/09546634.2015.1093591 · Cited by 36 · see citing works
İslamoğlu, Z. G. K., & Demirbaş, A. (2019) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.13131 · Cited by 35 · see citing works
Díaz‐Basabe, A., Burrello, C., Lattanzi, G., Botti, F., Carrara, A., Cassinotti, E., Caprioli, F., & Facciotti, F. (2021) · Molecular Oncology · doi:10.1002/1878-0261.13104 · Cited by 30 · see citing works
Hoxc13 is a crucial regulator of murine hair cycle
Qiu, W., Lei, M., Tang, H., Yan, H., Wen, X., Zhang, W., Tan, R., Wang, D., & Wu, J. (2015) · Cell and Tissue Research · doi:10.1007/s00441-015-2312-7 · Cited by 29 · see citing works
Impact of alopecia areata on psychiatric disorders: A retrospective cohort study
Kim, J. C., Lee, E., & Choi, J. W. (2019) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2019.06.1304 · Cited by 24 · see citing works
Zhang, T., & Nie, Y. (2022) · Dermatology · doi:10.1159/000515764 · Cited by 23 · see citing works
The CD8α–PILRα interaction maintains CD8 + T cell quiescence
Zheng, L., Han, X. N., Yao, S., Zhu, Y., Klement, J. D., Wu, S. Z., Ji, L., Zhu, G., Cheng, X., Tobiásová, Z., et al. (2022) · Science · doi:10.1126/science.aaz8658 · Cited by 22 · see citing works
Differential expression of CXCL1, CXCL9, CXCL10 and CXCL12 chemokines in alopecia areata.
Zainodini, N., Hassanshahi, G., Arababadi, M. K., Khorramdelazad, H., & Mirzaei, A. (2013) · PubMed · doi:10.22034/iji.2013.16802 · Cited by 20 · see citing works
Granzyme A potentiates chemokine production in IL ‐17‐stimulated keratinocytes
Cheuk, S., Martini, E., Bergh, K., Chang, D., Réthi, B., Ståhle, M., & Eidsmo, L. (2017) · Experimental Dermatology · doi:10.1111/exd.13284 · Cited by 19 · see citing works
Emerging drugs for the treatment of alopecia areata
Ramírez-Marín, H. A., & Tosti, A. (2022) · Expert Opinion on Emerging Drugs · doi:10.1080/14728214.2022.2149735 · Cited by 18 · see citing works
Xiong, J., Wu, B., Hou, Q., Huang, X., Jia, L., Li, Y., & Jiang, H. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.782750 · Cited by 17 · see citing works
Self-citationXiong, J., Ji, B., Wang, L., Yan, Y., Liu, Z., Fang, S., Wu, M., Wang, Y., & Jian-xing, S. (2019) · Stem Cells International · doi:10.1155/2019/7135974 · Cited by 17 · see citing works
Self-citationAlternaria scalp infection in a patient with alopecia areata. Coexistence or causative relationship?
Rudnicka, L., & Łukomska, M. (2012) · Journal of Dermatological Case Reports · doi:10.3315/jdcr.2012.1120 · Cited by 15 · see citing works
Investigation of the relationship between alopecia areata and inflammatory blood parameters
Dere, G., & Gündoğdu, M. (2021) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.14048 · Cited by 14 · see citing works
Induction of alopecia areata in C3H/HeJ mice using cryopreserved lymphocytes
Hashimoto, K., Yamada, Y., Sekiguchi, K., Matsuda, S., Mori, S., & Matsumoto, T. (2021) · Journal of Dermatological Science · doi:10.1016/j.jdermsci.2021.04.009 · Cited by 13 · see citing works
Identification and verification of EOMEs regulated network in Alopecia areata
Yuan, X. Y., Tang, Y., Zhao, Z., Liu, F., Shi, W., Zhang, Y., & Li, J. (2020) · International Immunopharmacology · doi:10.1016/j.intimp.2020.106544 · Cited by 12 · see citing works
Wu, M., Xu, C., Jiang, J., Xu, S., Xiong, J., Fan, X., Ji, K., Zhao, Y., Ni, H., Wang, Y., et al. (2022) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbac020 · Cited by 12 · see citing works
Self-citationKim, J. E., Lee, Y. J., Lee, K. J., Park, S.-H., & Kang, H. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113228 · Cited by 11 · see citing works
Stem cell markers (cytokeratin 17 and cytokeratin 19) in scarring and nonscarring alopecia
Abdou, A. G., Elsakka, D. M., Gaber, M. W., Wahed, M. A., Saleh, A. A., & Shehata, W. (2016) · Journal of Cutaneous and Aesthetic Surgery · doi:10.4103/0974-2077.191650 · Cited by 9 · see citing works
Blockade of S100A3 activity inhibits murine hair growth
Guan, W., Deng, Q., Yu, X., Yuan, Y., Gao, J., Li, J., Zhou, L., Xia, P., Han, G., Han, W., et al. (2015) · Genetics and Molecular Research · doi:10.4238/2015.october.28.14 · Cited by 8 · see citing works
Wu, Q., Wang, C., Jing, Q., Lu, J., Liang, B., Liu, L., Wu, J., Yu, Q., Deng, X., & Zhang, Z. (2022) · Revista do Instituto de Medicina Tropical de São Paulo · doi:10.1590/s1678-9946202264031 · Cited by 4 · see citing works
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Xiong, J., Liu, Z., Jia, L., Sun, Y., Guo, R., Xi, T., Li, Z., Wu, M., Jiang, H., & Li, Y. (2024) · Bioactive Materials · doi:10.1016/j.bioactmat.2024.02.023 · Cited by 24 · see citing works
Self-citationGay-Mimbrera, J., Gómez‐Arias, P. J., Álvarez-Heredia, P., Batista-Duharte, A., Rivera‐Ruiz, I., Aguilar-Luque, M., Juan-Cencerrado, M., Mochón‐Jiménez, C., Cebrián-García, Á., Andújar, E., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1565241 · Cited by 11 · see citing works
Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
Delivery Strategies of siRNA Therapeutics for Hair Loss Therapy
Jin, S., & Sung, J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25147612 · Cited by 6 · see citing works
miR-181b-5p/SOCS2/JAK2/STAT5 axis facilitates the metastasis of hepatoblastoma
Lv, Y., Xie, X., Zou, G., Kong, M., Yang, J., Chen, J., & Xiang, B. (2023) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbad027 · Cited by 6 · see citing works
Hair loss – A growing problem among medical students
Meher, A., Safi, A., Momo, N. R., Zaheer, A., Korrapati, N. H., Ajesh, N., & Nanduri, K. (2023) · Cosmoderma · doi:10.25259/csdm_118_2023 · Cited by 4 · see citing works
Al-Samarraay, M. S., Magableh, A. A. A. R., Mahmood, R. I., Joudar, S. S., Zahid, I. A., Al‐Obaidi, J. R., Al‐Saffar, A. Z., Albahri, A. S., Albahri, A. S., Alamoodi, A. H., et al. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00887-4 · Cited by 2 · see citing works
Wenjie, Y., Yue, W., Mengjiao, W., Yumei, Z., & Huang, X. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1695618 · Cited by 2 · see citing works
Chen, A., Shang, L., Ju, Y., & Zhuo, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-33927-2 · Cited by 2 · see citing works
Artificial Intelligence in Hair Disorders: A Narrative Review
Lecumberri, A., Vaño-Galvan, S., Asz‐Sigall, D., & Saceda‐Corralo, D. (2026) · Dermatology and Therapy · doi:10.1007/s13555-026-01677-2 · Cited by 2 · see citing works
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Tang, A., Wei, M. L., Haemel, A. K., La, C., Sirota, M., & Lee, E. Y. (2025) · PubMed · doi:10.48550/arxiv.2505.09527 · Cited by 5 · see citing works
Kapoor, R., Bellani, D., Patil, R., Bose, D., Pola, M., Singh, P. A., Mishra, M., & Shome, D. (2025) · ADMET & DMPK · doi:10.5599/admet.3030 · Cited by 1 · see citing works
Gao, Y., Luo, R., Zhao, C., Wu, X., Xi, T., Jia, L., & Xiong, J. (2025) · Archives of Dermatological Research · doi:10.1007/s00403-025-04354-0
Self-citationZhang, M., Guo, Y., Sun, Y., & Gao, N. (2026) · Archives of Dermatological Research · doi:10.1007/s00403-026-04524-8
Jia, Y., Chen, W., Zhao, Y., 龙永香, & Tian, Y. (2026) · Archives of Dermatological Research · doi:10.1007/s00403-026-04865-4
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Qu, Y., Jin, M., Ma, J., Liu, Z., Li, Y., Wang, Y., Su, X., Song, H., Zong, B., Lv, Y., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10962153/v1
Self-citation
- Authors
- Xiong, J., Chen, G., Liu, Z., Wu, X., Xu, S., Xiong, J., Ji, S., & Wu, M.
- Affiliations
- Tongji University (CN)
- Second Military Medical University (CN)
- Corresponding author
- Ji, Shizhao
- Wu, Minjuan
- Venue
- Precision Clinical Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-05-22
- Volume
- 6
- Issue
- 2
- Pages
- pbad009-pbad009
- ISSN
- 2096-5303 (ISSN-L)
- 2516-1571
- PMC
- PMC10268596
- OpenAlex
- W4378086723
- Cited by
- 16
- References
- 51
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 2 · 2023: 2
- FWCI
- 5.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Precision Clinical Medicine (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (32071186)
- Topics
- Health Sciences › Medicine › Urology › Hair Growth and Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- alopecia areata, severe alopecia areata, disease progression, gene regulatory networks, weighted gene co-expression network, competing endogenous RNA network, immune cell infiltration, Lgr5, HOXC13, S100A3, hair follicle stem cells, early diagnosis, differentially expressed genes, autoimmune alopecia
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