Multivariate transcriptome analysis identifies networks and key drivers of chronic lymphocytic leukemia relapse risk and patient survival

Abstract

Background Chronic lymphocytic leukemia (CLL) is an indolent heme malignancy characterized by the accumulation of CD5+ CD19+ B cells and episodes of relapse. The biological signaling that influence episodes of relapse in CLL are not fully described. Here, we identify gene networks associated with CLL relapse and survival risk. Methods Networks were investigated by using a novel weighted gene network co-expression analysis method and examining overrepresentation of upstream regulators and signaling pathways within co-expressed transcriptome modules across clinically annotated transcriptomes from CLL patients (N = 203). Gene Ontology analysis was used to identify biological functions overrepresented in each module. Differential Expression of modules and individual genes was assessed using an ANOVA (Binet Stage A and B relapsed patients) or T-test (SF3B1 mutations). The clinical relevance of biomarker candidates was evaluated using log-rank Kaplan Meier (survival and relapse interval) and ROC tests. Results Eight distinct modules (M2, M3, M4, M7, M9, M10, M11, M13) were significantly correlated with relapse and differentially expressed between relapsed and non-relapsed Binet Stage A CLL patients. The biological functions of modules positively correlated with relapse were carbohydrate and mRNA metabolism, whereas negatively correlated modules to relapse were protein translation associated. Additionally, M1, M3, M7, and M13 modules negatively correlated with overall survival. CLL biomarkers BTK, BCL2, and TP53 were co-expressed, while unmutated IGHV biomarker ZAP70 and cell survival-associated NOTCH1 were co-expressed in modules positively correlated with relapse and negatively correlated with survival days. Conclusions This study provides novel insights into CLL relapse biology and pathways associated with known and novel biomarkers for relapse and overall survival. The modules associated with relapse and overall survival represented both known and novel pathways associated with CLL pathogenesis and can be a resource for the CLL research community. The hub genes of these modules, e.g., ARHGAP27P2, C1S, CASC2, CLEC3B, CRY1, CXCR5, FUT5, MID1IP1, and URAHP, can be studied further as new therapeutic targets or clinical markers to predict CLL patient outcomes.

LINC00511 enhances LUAD malignancy by upregulating GCNT3 via miR-195-5p

“…Wound images were observed at the specified time points (0 h and 24 h). The scratch width (W) was determined, and the cell migration rate was calculated as (W0 h-W24 h)/W0 h × 100% [ 21 ].…”

B-1 derived anti-Thy-1 B cells in old aged mice develop lymphoma/leukemia with high expression of CD11b and Hamp2 that different from TCL1 transgenic mice

“…2 D) and ZAP70 + increased as human U-CLL than M-CLL. Fzd2 is β-catenin dependent, and human CLL and ZAP70 + U-CLL showed expression of Wnt10a and Wnt6, which generated Fzd2 [ 78 ]. Ki67 (NKI67) is a proliferation-related antigen.…”

Multiple omics levels of chronic lymphocytic leukemia

“…Other extensive transcriptome analyses have also been carried out. Griffen et al [ 82 ] conducted a multivariate transcriptome analysis on 203 CLL patients, focusing on relapses. Between Binet stage A patients with and without relapse, 1703 genes were downregulated and 1250 were upregulated.…”

“…Certain hub genes within these modules have consequently been proposed as potential novel therapeutic targets or clinical markers. These include: ARHGAP27P2, C1S, CASC2, CLEC3B, CRY1, CXCR5, FUT5, MID1IP1 , and URAHP [ 82 ]. Sbarrato et al [ 83 ] showed that CLL B cells hold a ribosome-related signature with reduced expression of factors that modify ribosomal rRNA, including DKC1 .…”