Figure 4.
Identification of proteins involved in increased IFN signaling in blood neutrophils during CLL. (A) GSEA of the proteome of blood neutrophils indicated a significant enrichment of IFNγ and IFNα response. The positive enrichment score (ES) indicates the contribution of those proteins that are overexpressed in blood neutrophils in CLL-bearing mice. Detailed expression values are shown in supplemental Tables 7 and 8. (B) Analysis of the enrichment score indicated 31 significantly enriched proteins of the pathway “IFNγ response” (Iγ) and 18 proteins of the pathway “IFNα response” (Iα). The heatmap indicates the expression intensities of proteins of both pathways, “IFNγ and -α response," in blood neutrophils of CLL-bearing mice. (C) A machine learning algorithm of the proteome of blood neutrophils in CLL indicates best features in according to logarithmic abundancies. A RFC was used to calculate the Gini decrease. RLM, ranked list metric. Non-CLL, n = 6; CLL, n = 12.

Identification of proteins involved in increased IFN signaling in blood neutrophils during CLL. (A) GSEA of the proteome of blood neutrophils indicated a significant enrichment of IFNγ and IFNα response. The positive enrichment score (ES) indicates the contribution of those proteins that are overexpressed in blood neutrophils in CLL-bearing mice. Detailed expression values are shown in supplemental Tables 7 and 8. (B) Analysis of the enrichment score indicated 31 significantly enriched proteins of the pathway “IFNγ response” (Iγ) and 18 proteins of the pathway “IFNα response” (Iα). The heatmap indicates the expression intensities of proteins of both pathways, “IFNγ and response," in blood neutrophils of CLL-bearing mice. (C) A machine learning algorithm of the proteome of blood neutrophils in CLL indicates best features in according to logarithmic abundancies. A RFC was used to calculate the Gini decrease. RLM, ranked list metric. Non-CLL, n = 6; CLL, n = 12.

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