Cracking the cytokine code in sickle cell disease: Immune‐informatics for precision inflammation mapping, risk prediction and therapeutic discovery
- 1Department of Biomedical and Laboratory Science Division of Haematology Africa University Harare Zimbabwe
- 2Department of Pathology and Biochemistry State University of Zanzibar Zanzibar Tanzania
Abstract
Background Sickle cell disease (SCD) is characterised by chronic inflammation involving coordinated interactions among cytokines, chemokines, endothelial cells, leukocytes, complement and coagulation pathways. However, conventional single‐biomarker approaches incompletely capture this dynamic inflammatory landscape. Immune‐informatics offers a framework for integrating multidimensional immune data to define inflammatory phenotypes, discover biomarkers, and identify therapeutic targets. Objective This narrative review examines cytokine‐network biology in SCD and evaluates how immune‐informatics, multi‐omics integration, and computational modelling may advance precision inflammation mapping, risk assessment, and therapeutic discovery. Methods Relevant literature addressing cytokine and chemokine biology, inflammatory profiling, SCD animal models, multiplex and proteomic technologies, transcriptomics, network analysis, and machine‐learning applications was critically synthesised. Particular attention was given to sampling standardisation, genotype‐dependent heterogeneity, longitudinal inflammatory trajectories, biomarker validation, and computational reproducibility. Results Cytokine dysregulation in SCD involves interconnected inflammatory pathways rather than isolated mediators. Interleukin (IL)‐1β, IL‐6, tumour necrosis factor‐α, IL‐8/CXCL8, IL‐10, IL‐17, interferons, transforming growth factor beta, chemokines and complement components may contribute to disease heterogeneity and complications. High‐dimensional profiling can reveal coordinated inflammatory signatures, while network and machine‐learning approaches may prioritise candidate biomarkers and druggable pathways. Nevertheless, robust, independently validated immune‐informatics models for routine prediction of SCD complications remain limited. Conclusion Immune‐informatics represents an emerging evidence‐informed framework for precision inflammation research in SCD. Standardised longitudinal sampling, genotype‐stratified multicentre cohorts, integrated multi‐omics, rigorous model validation, and prospective clinical studies are required to translate cytokine‐network discoveries into reliable biomarkers and individualised clinical therapeutic strategies.
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BibTeX
@article{obeagu2026cracking,
title = {{Cracking the cytokine code in sickle cell disease: Immune‐informatics for precision inflammation mapping, risk prediction and therapeutic discovery}},
author = {Emmanuel Ifeanyi Obeagu and Chukwuma J. Okafor},
journal = {Clinical and Translational Discovery},
year = {2026},
month = oct,
doi = {10.1002/ctd2.70221},
url = {https://doi.org/10.1002/ctd2.70221},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cracking the cytokine code in sickle cell disease: Immune‐informatics for precision inflammation mapping, risk prediction and therapeutic discovery
AU - Emmanuel Ifeanyi Obeagu
AU - Chukwuma J. Okafor
PY - 2026
DA - 2026/10/26
JO - Clinical and Translational Discovery
DO - 10.1002/ctd2.70221
UR - https://doi.org/10.1002/ctd2.70221
AB - Background Sickle cell disease (SCD) is characterised by chronic inflammation involving coordinated interactions among cytokines, chemokines, endothelial cells, leukocytes, complement and coagulation pathways. However, conventional single‐biomarker approaches incompletely capture this dynamic inflammatory landscape. Immune‐informatics offers a framework for integrating multidimensional immune data to define inflammatory phenotypes, discover biomarkers, and identify therapeutic targets. Objective This narrative review examines cytokine‐network biology in SCD and evaluates how immune‐informatics, multi‐omics integration, and computational modelling may advance precision inflammation mapping, risk assessment, and therapeutic discovery. Methods Relevant literature addressing cytokine and chemokine biology, inflammatory profiling, SCD animal models, multiplex and proteomic technologies, transcriptomics, network analysis, and machine‐learning applications was critically synthesised. Particular attention was given to sampling standardisation, genotype‐dependent heterogeneity, longitudinal inflammatory trajectories, biomarker validation, and computational reproducibility. Results Cytokine dysregulation in SCD involves interconnected inflammatory pathways rather than isolated mediators. Interleukin (IL)‐1β, IL‐6, tumour necrosis factor‐α, IL‐8/CXCL8, IL‐10, IL‐17, interferons, transforming growth factor beta, chemokines and complement components may contribute to disease heterogeneity and complications. High‐dimensional profiling can reveal coordinated inflammatory signatures, while network and machine‐learning approaches may prioritise candidate biomarkers and druggable pathways. Nevertheless, robust, independently validated immune‐informatics models for routine prediction of SCD complications remain limited. Conclusion Immune‐informatics represents an emerging evidence‐informed framework for precision inflammation research in SCD. Standardised longitudinal sampling, genotype‐stratified multicentre cohorts, integrated multi‐omics, rigorous model validation, and prospective clinical studies are required to translate cytokine‐network discoveries into reliable biomarkers and individualised clinical therapeutic strategies.
SN - 2768-0622
ER -