Hematology · Available online 31 Jul 2026 · In press · DOI 10.1080/16078454.2026.2708536
Wen-yuan Lin, Bei-li Chen, Zhi-mei Wu, Feng Liu, Wen-fang Du
Background Burkitt lymphoma (BL) is a highly aggressive malignancy with limited effective treatments due to toxicity/resistance. Identifying and prioritizing candidate biomarkers and potential therapeutic targets from complex transcriptomic data, ahead of functional validation, remains a major unmet need.Methods We integrated differential gene expression analysis across BL vs. control cohorts (Gene Expression Omnibus [GEO] datasets GSE43677/GSE12453) with Random Forest machine learning to prioritize candidates. Validated top genes via immunohistochemistry in an independent cohort (n = 10 BL, n = 10 reactive lymphoid hyperplasia [RLH] controls), followed by immune cell infiltration analysis.Results This approach identified four candidate genes; only Chromatin Assembly Factor 1 Subunit A (CHAF1A) showed profound protein-level overexpression in BL tumors vs. RLH. In a single-dataset CIBERSORT analysis, CHAF1A expression showed exploratory correlational associations with several immune-cell fractions, most strongly a positive association with M0 macrophages; these in silico associations are hypothesis-generating and do not by themselves establish a mechanistic role in the tumor immune microenvironment.Conclusion CHAF1A is identified as a candidate biomarker associated with BL that, in exploratory single-dataset analysis, also correlates with immune-cell infiltration; these findings nominate it as a potential therapeutic target that warrants functional validation in future studies.
Abstract from DOAJ. Public domain (CC0 1.0).
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Lin, W., Chen, B., Wu, Z., et al. (2026). CHAF1A identified as a candidate biomarker and potential therapeutic target in Burkitt lymphoma through integrated bioinformatics and histopathological analysis. Hematology. https://doi.org/10.1080/16078454.2026.2708536