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Last update 08 May 2025

IL1R2

Last update 08 May 2025

Basic Info

Synonyms

CD121 antigen-like family member B, CD121b, CDw121b

+ [18]

Introduction

Non-signaling receptor for IL1A, IL1B and IL1RN. Reduces IL1B activities. Serves as a decoy receptor by competitive binding to IL1B and preventing its binding to IL1R1. Also modulates cellular response through non-signaling association with IL1RAP after binding to IL1B. IL1R2 (membrane and secreted forms) preferentially binds IL1B and poorly IL1A and IL1RN. The secreted IL1R2 recruits secreted IL1RAP with high affinity; this complex formation may be the dominant mechanism for neutralization of IL1B by secreted/soluble receptors.

Drugs associated with IL1R2

WO2023115029

Patent Mining

Target

IL1R2

Mechanism-

Active Org.

Trex Bio, Inc.

Originator Org.

Trex Bio, Inc.

Active Indication

Neoplasms

Inactive Indication-

Drug Highest PhaseDiscovery

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

CN118105498

Patent Mining

Target

IL1R2

Mechanism-

Active Org.

Fudan University Cancer Hospital

Originator Org.

Fudan University Cancer Hospital

Active Indication

Neoplasms [+1]

Inactive Indication-

Drug Highest PhaseDiscovery

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

CN118440196

Patent Mining

Target

IL1R2

Mechanism-

Active Org.

Suzhou Institute of Systems Medicine

Originator Org.

Suzhou Institute of Systems Medicine

Active Indication

Neoplasms

Inactive Indication-

Drug Highest PhaseDiscovery

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with IL1R2

100 Translational Medicine associated with IL1R2

0 Patents (Medical) associated with IL1R2

560

Literatures (Medical) associated with IL1R2

01 Dec 2025·Immunologic Research

Tertiary lymphoid structure signatures are associated with survival and immunotherapy response in lung adenocarcinoma

Article

Author: Fan, Rongmei ; Han, Bing ; Deng, Jikun

The presence of tertiary lymphoid structures (TLSs) has been correlated with improved prognosis and clinical outcomes in response to immunotherapy in certain solid tumors. However, the precise role of TLSs in lung adenocarcinoma (LUAD) remains unclear. Four datasets of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TLSs model was constructed using a multivariate Cox proportional hazards model. GO and KEGG analyses were performed to explore the biological process associated with the TLSs model. The ESTIMATE and CIBERSORT algorithms were employed to quantify immune infiltration status. TLSs signature genes (TSGs) were identified, including chemokine signature genes, TFH cell markers, TH1 cell and B cell markers, and a plasma cell marker. Diagnostic evaluations identified key genes with high diagnostic value, particularly among chemokine signature genes and TFH cells markers. Furthermore, high expression of CCL20 and IL1R2 was correlated with poorer outcomes, while other TSGs indicated more favorable prognoses. A novel TLSs score model was constructed, integrating 4 TSGs (SGPP2, MS4A1, IL1R2 and CCL20), which accurately predicted patient survival and was independently associated with prognosis. Additionally, the TLSs score served as a robust indicator for LUAD survival prediction, outperforming traditional staging systems. Comprehensive analyses of enriched pathways and immune cell infiltration patterns revealed that this score involved in metabolic processes and immune cell regulation. Furthermore, the TLSs score showed potential as an indicator of response to immunotherapy, with higher scores associated with reduced expression of immune checkpoint genes and poorer response rates. The TLSs score may serve as a predictor of prognosis and immunotherapeutic response in LUAD. These findings may offer new insights on the study of malignancy and personalized immunotherapy for LUAD patients.

01 Feb 2025·Shock

COMPREHENSIVE CHARACTERIZATION OF CYTOKINES IN PATIENTS UNDER EXTRACORPOREAL MEMBRANE OXYGENATION: EVIDENCE FROM INTEGRATED BULK AND SINGLE-CELL RNA SEQUENCING DATA USING MULTIPLE MACHINE LEARNING APPROACHES

Article

Author: Chen, Zhen ; Zuo, Liuer ; Lu, Jianhai ; Wu, Shumin ; Su, Yongpeng ; Liu, Genglong ; Zhou, Xingliang ; Liu, Changzhi ; Xian, Lina

ABSTRACTBackground: Extracorporeal membrane oxygenation (ECMO) is an effective technique for providing short-term mechanical support to the heart, lungs, or both. During ECMO treatment, the inflammatory response, particularly involving cytokines, plays a crucial role in pathophysiology. However, the potential effects of cytokines on patients receiving ECMO are not comprehensively understood. Methods: We acquired three ECMO support datasets, namely two bulk and one single-cell RNA sequencing (RNA-seq), from the Gene Expression Omnibus (GEO) combined with hospital cohorts to investigate the expression pattern and potential biological processes of cytokine-related genes (CRGs) in patients under ECMO. Subsequently, machine learning approaches, including support vector machine (SVM), random forest (RF), modified Lasso penalized regression, extreme gradient boosting (XGBoost), and artificial neural network (ANN), were applied to identify hub CRGs, thus developing a prediction model called CRG classifier. The predictive and prognostic performance of the model was comprehensively evaluated in GEO and hospital cohorts. Finally, we mechanistically analyzed the relationship between hub cytokines, immune cells, and pivotal molecular pathways. Results: Analyzing bulk and single-cell RNA-seq data revealed that most CRGs were significantly differentially expressed; the enrichment scores of cytokine and cytokine-cytokine receptor (CCR) interaction were significantly higher during ECMO. Based on multiple machine learning algorithms, nine key CRGs (CCL2, CCL4, IFNG, IL1R2, IL20RB, IL31RA, IL4, IL7, and IL7R) were used to develop the CRG classifier. The CRG classifier exhibited excellent prognostic values (AUC > 0.85), serving as an independent risk factor. It performed better in predicting mortality and yielded a larger net benefit than other clinical features in GEO and hospital cohorts. Additionally, IL1R2, CCL4, and IL7R were predominantly expressed in monocytes, NK cells, and T cells, respectively. Their expression was significantly positively correlated with the relative abundance of corresponding immune cells. Gene set variation analysis (GSVA) revealed that para-inflammation, complement and coagulation cascades, and IL6/JAK/STAT3 signaling were significantly enriched in the subgroup that died after receiving ECMO. Spearman correlation analyses and Mantel tests revealed that the expression of hub cytokines (IL1R2, CCL4, and IL7R) and pivotal molecular pathways scores (complement and coagulation cascades, IL6/JAK/STAT3 signaling, and para-inflammation) were closely related. Conclusion: A predictive model (CRG classifier) comprising nine CRGs based on multiple machine learning algorithms was constructed, potentially assisting clinicians in guiding individualized ECMO treatment. Additionally, elucidating the underlying mechanistic pathways of cytokines during ECMO will provide new insights into its treatment.

01 Feb 2025·American Journal of Veterinary Research

Evaluating the feasibility of using whole blood gene expression profiles to identify novel targets in canine septic peritonitis

Article

Author: Moore, Raven ; Pivoda, Alexandra ; Stewart, Samuel D. ; Khanna, Chand ; Cawley, Jacob

AbstractOBJECTIVETo assess gene expression profiles in canine whole blood with and without septic peritonitis to assess workflow feasibility and identify potential blood biomarkers that could be further investigated in future studies.METHODSThis study enrolled 6 dogs with cytologically confirmed septic peritonitis of any cause and 6 healthy dogs. All dogs had a CBC and biochemistry performed. The dogs with septic peritonitis also had point-of-care lactate and blood oxygen saturation measured for acute patient physiologic and laboratory evaluation score calculation. All dogs then had 2.5 mL of whole blood collected and placed into an RNA stabilization tube, which was processed using a commercial assay based on the hybridization of fluorescent probes for transcript quantification. Quality control, normalization, and data visualization were performed. Raw counts were exported, and differential expression was performed.RESULTSThe evaluation of canine whole blood expression profiles was confirmed to be feasible. Differential expression analysis of septic and nonseptic dogs demonstrated distinct gene expression profile signatures. Five genes of interest were upregulated in septic whole blood including matrix metallopeptidase 9, IL-1 receptor type 2, proliferating cell nuclear antigen, phosphatidylinositol 3-kinase catalytic-γ, and cluster of differentiation 55.CONCLUSIONSThe study and associated workflow were feasible and can be scaled in confirmatory studies.CLINICAL RELEVANCEFuture studies are now proposed to further validate the increased expression of putative biomarkers in a larger cohort of canine septic peritonitis patients with more relevant comparator control cohorts.

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IL1R2

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