ArticleAmerican journal of translational research2022
Immune signatures of CD4 and CD68 predicts disease progression in cutaneous T cell lymphoma.
Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Review
- Advances and clinical applications of immune checkpoint inhibitors in hematological malignancies.Cancer communications (London, England) · 2024Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundCutaneous T-cell lymphoma (CTCL) is highly heterogeneous, and its prognosis is closely related to the disease stage. The tumor microenvironment (TME) is an important component of tumor tissue, driving cancer cell growth, progression, and metastasis. However, the diagnostic value of TME in CTCL has not yet been studied in-depth. To date, no study has performed a comprehensive evaluation of the significance of the TME in CTCL.
methodsUsing xCell methods based on bulk RNA sequencing data, we inferred immune cell fraction in the TME in 126 patients and assessed the prognostic importance of immune cells. Consensus clustering was performed to determine the TME subtypes and characterize the transcriptome of each subtype. Based on the TME subtypes, we established the disease progression model using random forest algorithms and logistic regression. The efficacy of the model was examined using an additional 49-patient cohort. Finally, we validated our finding at the protein level using immunochemistry in a 16-patient cohort.
resultsPatients with advanced CTCL presented with a more active immunity overall than those with early stage. Random forest algorithms revealed that the immune cells CD4, macrophages, and dendritic cells (DCs) were the most effective prognosis predictors. Therefore, we constructed a risk model using logistic regression based on these immune cells. The TME score could be used to effectively predict disease conditions in three datasets with the AUC of 0.9414, 0.7912, and 0.7665, respectively. Immunochemistry at the protein level revealed that helper T cells and the macrophage markers CD4 and CD68 could successfully distinguish different CTCL stages in patients, whereas the DC marker langerin showed no change with disease progression.
conclusionWe found advanced-stage CTCL was associated with an active immune microenvironment, and the immune signatures CD4 and CD68 showed a relatively high accuracy in predicting CTCL disease progression.
Indexed as
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35702080PMC9185059W4285741747What OpenQuestion holds
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