S100A12在脓毒症中的多组学证据及临床转化挑战
Multi-Omics Evidence for S100A12 in Sepsis and Challenges in Clinical Translation
DOI: 10.12677/acm.2026.1693033, PDF,    科研立项经费支持
作者: 吕亚男:济宁医学院临床医学院,山东 济宁;石继魁*:济宁市第一人民医院重症医学科,山东 济宁
关键词: S100钙结合蛋白A12;脓毒症;转录组学;循环蛋白;生物标志物;临床转化;S100 Calcium-Binding Protein A12; Sepsis; Transcriptomics; Circulating Protein; Biomarker; Clinical Translation
摘要: S100钙结合蛋白A12 (S100A12)在多项脓毒症全血转录组、单细胞测序及机器学习研究中反复被识别为差异信号或重要特征,部分循环蛋白研究亦提示其与脓毒症识别、短期预后及器官损伤相关。然而,全血转录信号受表达细胞比例和单细胞转录水平影响,循环蛋白浓度还可能受到蛋白释放与清除、肾功能、血液净化、样本类型及检测平台差异等因素影响,因此不能仅凭转录水平升高判断其循环蛋白检测具有稳定的临床价值。本文围绕S100A12的生物学基础、转录组证据、循环蛋白检测及与脓毒症的临床关联进行综述,并分析其临床转化面临的主要问题。总体而言,S100A12可作为脓毒症相关髓系炎症的候选标志物,但其疾病及器官特异性、转录–蛋白对应关系以及相较常规指标的增量价值仍需进一步验证,现阶段尚不宜作为独立的临床决策指标。
Abstract: S100 calcium-binding protein A12 (S100A12) has repeatedly been identified as a differential signal or important feature in multiple whole-blood transcriptomic, single-cell sequencing, and machine-learning studies of sepsis, and several circulating-protein studies have also suggested associations with sepsis recognition, short-term prognosis, and organ injury. However, whole-blood transcriptomic signals are affected by the proportions of S100A12-expressing cells and cell-specific transcriptional activity, whereas circulating protein concentrations may additionally be influenced by protein release and clearance, renal function, blood purification, sample type, and analytical platform. Therefore, increased transcript abundance alone cannot establish that measurement of the circulating protein provides consistent clinical value. This review summarizes the biological basis of S100A12, transcriptomic evidence, evidence from circulating-protein studies, and clinical associations in sepsis, and analyzes major barriers to clinical translation. Overall, S100A12 may serve as a candidate marker of sepsis-related myeloid inflammation, but its disease specificity, organ specificity, correspondence between transcript and protein levels, and incremental value over conventional indicators require further validation. At present, S100A12 should not be used as an independent clinical decision-making tool.
文章引用:吕亚男, 石继魁. S100A12在脓毒症中的多组学证据及临床转化挑战[J]. 临床医学进展, 2026, 16(9): 165-173. https://doi.org/10.12677/acm.2026.1693033

参考文献

[1] Singer, M., Deutschman, C.S., Seymour, C.W., Shankar-Hari, M., Annane, D., Bauer, M., et al. (2016) The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Journal of the American Medical Association, 315, 801-810.
https://doi.org/10.1001/jama.2016.0287
[2] Loi, M.V., Sultana, R., Nguyen, T.M., Tia, S.T., Lee, J.H. and O’Connor, D. (2025) The Diagnostic Utility of Host RNA Biosignatures in Adult Patients with Sepsis: A Systematic Review and Meta-Analysis. Critical Care Explorations, 7, e1212.
https://doi.org/10.1097/cce.0000000000001212
[3] Meijer, B., Gearry, R.B. and Day, A.S. (2012) The Role of S100A12 as a Systemic Marker of Inflammation. International Journal of Inflammation, 2012, 1-6.
https://doi.org/10.1155/2012/907078
[4] Payen, D., Lukaszewicz, A.C., Belikova, I., Faivre, V., Gelin, C., Russwurm, S., et al. (2008) Gene Profiling in Human Blood Leucocytes during Recovery from Septic Shock. Intensive Care Medicine, 34, 1371-1376.
https://doi.org/10.1007/s00134-008-1048-1
[5] 于帆, 韦唯, 楼爽, 等. 基于转录组学分析筛选早期脓毒症患者外周血关键基因及其相关机制研究[J]. 中国急救医学, 2022, 42(12): 1044-1049.
[6] Pietzsch, J. and Hoppmann, S. (2009) Human S100A12: A Novel Key Player in Inflammation? Amino Acids, 36, 381-389.
https://doi.org/10.1007/s00726-008-0097-7
[7] Lira-Junior, R., Holmström, S.B., Clark, R., Zwicker, S., Majster, M., Johannsen, G., et al. (2020) S100A12 Expression Is Modulated during Monocyte Differentiation and Reflects Periodontitis Severity. Frontiers in Immunology, 11, Article 86.
https://doi.org/10.3389/fimmu.2020.00086
[8] Tardif, M.R., Chapeton-Montes, J.A., Posvandzic, A., Pagé, N., Gilbert, C. and Tessier, P.A. (2015) Secretion of S100A8, S100A9, and S100A12 by Neutrophils Involves Reactive Oxygen Species and Potassium Efflux. Journal of Immunology Research, 2015, 1-16.
https://doi.org/10.1155/2015/296149
[9] Hofmann, M.A., Drury, S., Fu, C., Qu, W., Taguchi, A., Lu, Y., et al. (1999) RAGE Mediates a Novel Proinflammatory Axis: A Central Cell Surface Receptor for S100/Calgranulin Polypeptides. Cell, 97, 889-901.
https://doi.org/10.1016/s0092-8674(00)80801-6
[10] Foell, D., Wittkowski, H., Kessel, C., Lüken, A., Weinhage, T., Varga, G., et al. (2013) Proinflammatory S100A12 Can Activate Human Monocytes via Toll-Like Receptor 4. American Journal of Respiratory and Critical Care Medicine, 187, 1324-1334.
https://doi.org/10.1164/rccm.201209-1602oc
[11] Ravasi, T., Hsu, K., Goyette, J., Schroder, K., Yang, Z., Rahimi, F., et al. (2004) Probing the S100 Protein Family through Genomic and Functional Analysis. Genomics, 84, 10-22.
https://doi.org/10.1016/j.ygeno.2004.02.002
[12] Fuellen, G., Nacken, W., Sorg, C. and Kerkhoff, C. (2004) Computational Searches for Missing Orthologs: The Case of S100A12 in Mice. OMICS: A Journal of Integrative Biology, 8, 334-340.
https://doi.org/10.1089/omi.2004.8.334
[13] Bagheri, V. (2022) S100A12 Is Not Expressed in Rodents: Transgenic Mouse Model Is Needed. Molecular Immunology, 152, 35.
https://doi.org/10.1016/j.molimm.2022.10.003
[14] Yang, Y., Zhang, Y., Li, S., Zheng, X., Wong, M., Leung, K., et al. (2022) A Robust and Generalizable Immune-Related Signature for Sepsis Diagnostics. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19, 3246-3254.
https://doi.org/10.1109/tcbb.2021.3107874
[15] Zhang, W. (2024) Critical Roles of S100A12, MMP9, and PRTN3 in Sepsis Diagnosis: Insights from Multiple Microarray Data Analyses. Computers in Biology and Medicine, 171, Article 108222.
https://doi.org/10.1016/j.compbiomed.2024.108222
[16] Lin, S., Yan, J., He, S. and Luo, L. (2025) Identification of Pyroptosis-Related Gene S100A12 as a Potential Diagnostic Biomarker for Sepsis through Bioinformatics Analysis and Machine Learning. Molecular Immunology, 183, 44-55.
https://doi.org/10.1016/j.molimm.2025.04.009
[17] Huang, X., Yu, Z. and Zhuo, Z. (2025) S100A12 as a Key Biomarker in a Neutrophil-Associated Gene Prediction Model for Sepsis Diagnosis. Medicine, 104, e46140.
https://doi.org/10.1097/md.0000000000046140
[18] Qiao, J., Zou, L., Zhao, J., Cui, C., Huang, X. and Fan, Y. (2026) Systematic Analysis of Bacterial Lipopolysaccharide-Related Genes and Immune Cell Infiltration Characteristics in Pediatric Septic Shock Using Integrated Bioinformatics and Machine Learning Approaches. Frontiers in Bioinformatics, 6, Article 1729982.
https://doi.org/10.3389/fbinf.2026.1729982
[19] Li, L., Li, K., Qian, W., Jiang, H., Peng, H., Zhang, Y., et al. (2026) Multi-Cohort Transcriptomics Integration for Building and Validating a Diagnostic Model of Peripheral Blood Septic Shock. Frontiers in Immunology, 17, Article 1768866.
https://doi.org/10.3389/fimmu.2026.1768866
[20] Scicluna, B.P., Cano-Gamez, K., Burnham, K.L., Davenport, E.E., Moore, A.R., Khan, S., et al. (2025) A Consensus Blood Transcriptomic Framework for Sepsis. Nature Medicine, 31, 4119-4130.
https://doi.org/10.1038/s41591-025-03964-5
[21] Ma, J., Peng, L., Yu, H., Xie, J., Tang, Y., Su, S., et al. (2026) Single-Cell Transcriptomics Reveals Pathogen-Specific Monocyte Heterogeneity and Potential Biomarkers in Gram-Positive versus Gram-Negative Bloodstream Infections. World Journal of Emergency Medicine, 17, 162-171.
https://doi.org/10.5847/wjem.j.1920-8642.2026.035
[22] Ye, Q., Lai, X., Liu, Y., Zhang, Z., Fu, Y., Luo, J., et al. (2026) Single-Cell Multi-Omic Landscape Reveals Anatomical-Specific Immune Features in Adult and Pediatric Sepsis. Nature Immunology, 27, 150-165.
https://doi.org/10.1038/s41590-025-02345-x
[23] Wittkowski, H., Sturrock, A., van Zoelen, M.A.D., Viemann, D., van der Poll, T., Hoidal, J.R., et al. (2007) Neutrophil-Derived S100A12 in Acute Lung Injury and Respiratory Distress Syndrome. Critical Care Medicine, 35, 1369-1375.
https://doi.org/10.1097/01.ccm.0000262386.32287.29
[24] Kovach, M.A., Stringer, K.A., Bunting, R., Wu, X., San Mateo, L., Newstead, M.W., et al. (2015) Microarray Analysis Identifies IL-1 Receptor Type 2 as a Novel Candidate Biomarker in Patients with Acute Respiratory Distress Syndrome. Respiratory Research, 16, Article No. 29.
https://doi.org/10.1186/s12931-015-0190-x
[25] Jabaudon, M., Blondonnet, R., Roszyk, L., Pereira, B., Guérin, R., Perbet, S., et al. (2015) Soluble Forms and Ligands of the Receptor for Advanced Glycation End-Products in Patients with Acute Respiratory Distress Syndrome: An Observational Prospective Study. PLOS ONE, 10, e0135857.
https://doi.org/10.1371/journal.pone.0135857
[26] Achouiti, A., Föll, D., Vogl, T., van Till, J.W.O., Laterre, P., Dugernier, T., et al. (2013) S100A12 and Soluble Receptor for Advanced Glycation End Products Levels during Human Severe Sepsis. Shock, 40, 188-194.
https://doi.org/10.1097/shk.0b013e31829fbc38
[27] Takahashi, G., Hoshikawa, K., Matsumoto, N., Shozushima, T., Onodera, C., Kan, S., et al. (2011) Changes in Serum S100A12 and sRAGE Associated with Improvement of the PaO2/FiO2 Ratio Following PMX-DHP Therapy for Postoperative Septic Shock. European Surgical Research, 47, 135-140.
https://doi.org/10.1159/000330448
[28] Nakashima, A., Carrero, J.J., Qureshi, A.R., Miyamoto, T., Anderstam, B., Bárány, P., et al. (2010) Effect of Circulating Soluble Receptor for Advanced Glycation End Products (sRAGE) and the Proinflammatory RAGE Ligand (EN-RAGE, S100A12) on Mortality in Hemodialysis Patients. Clinical Journal of the American Society of Nephrology, 5, 2213-2219.
https://doi.org/10.2215/cjn.03360410
[29] Bartáková, E., Štefan, M., Stráníková, A., Pospíšilová, L., Arientová, S., Beran, O., et al. (2019) Calprotectin and Calgranulin C Serum Levels in Bacterial Sepsis. Diagnostic Microbiology and Infectious Disease, 93, 219-226.
https://doi.org/10.1016/j.diagmicrobio.2018.10.006
[30] Tosson, A.M.S., Glaser, K., Weinhage, T., Foell, D., Aboualam, M.S., Edris, A.A., et al. (2020) Evaluation of the S100 Protein A12 as a Biomarker of Neonatal Sepsis. The Journal of Maternal-Fetal & Neonatal Medicine, 33, 2768-2774.
https://doi.org/10.1080/14767058.2018.1560411
[31] Wang, H., Li, D., Wang, Y., Mao, G., Lu, G., Lan, Q., et al. (2023) Clinical Significance of Serum S100 Calcium-Binding Protein A12 Concentrations in Patients with Community-Acquired Pneumonia. Journal of International Medical Research, 51, Article 3000605231191021.
https://doi.org/10.1177/03000605231191021
[32] Dubois, C., Marcé, D., Faivre, V., Lukaszewicz, A., Junot, C., Fenaille, F., et al. (2019) High Plasma Level of S100A8/ S100A9 and S100A12 at Admission Indicates a Higher Risk of Death in Septic Shock Patients. Scientific Reports, 9, Article No. 15660.
https://doi.org/10.1038/s41598-019-52184-8
[33] Wu, H.P., Chu, C.M., Liu, P.H., et al. (2022) Increased Production of Interleukin-10 and Tumor Necrosis Factor-Alpha in Stimulated Peripheral Blood Mononuclear Cells after Inhibition of S100A12. Current Issues in Molecular Biology, 44, 1701-1712.
https://doi.org/10.3390/cimb44040117
[34] Kikkawa, T., Sato, N., Kojika, M., Takahashi, G., Aoki, K., Hoshikawa, K., et al. (2010) Significance of Measuring S100A12 and sRAGE in the Serum of Sepsis Patients with Postoperative Acute Lung Injury. Digestive Surgery, 27, 307-312.
https://doi.org/10.1159/000313687
[35] Wu, F., Hong, H., Tian, Y. and Wang, X. (2025) Serum S100A12 in the Clinical Diagnosis of Sepsis-Induced Myocardial Dysfunction: An Integrated Bioinformatics and Clinical Data Analysis. Frontiers in Cardiovascular Medicine, 12, Article 1640788.
https://doi.org/10.3389/fcvm.2025.1640788
[36] Liu, H., Yu, M., Mao, Y., Xue, Q., Lv, Y., Qu, F., et al. (2026) Association between S100 Calcium-Binding Protein A12 and Sepsis Associated-Acute Kidney Injury: A Prospective Cohort Study. BMC Nephrology, 27, Article No. 118.
https://doi.org/10.1186/s12882-026-04760-0
[37] Mathews, J., Amaravadi, L., Eck, S., Stevenson, L., Wang, Y.C., Devanarayan, V., et al. (2022) Best Practices for the Development and Fit-for-Purpose Validation of Biomarker Methods: A Conference Report. AAPS Open, 8, Article No. 2.
https://doi.org/10.1186/s41120-021-00050-1
[38] Collins, G.S., Reitsma, J.B., Altman, D.G. and Moons, K.G.M. (2015) Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis: The TRIPOD Statement. British Medical Journal, 350, g7594.
https://doi.org/10.1136/bmj.g7594
[39] Vickers, A.J. and Elkin, E.B. (2006) Decision Curve Analysis: A Novel Method for Evaluating Prediction Models. Medical Decision Making, 26, 565-574.
https://doi.org/10.1177/0272989x06295361