MAPK14 over-expression is a transcriptomic feature of polycythemia vera and correlates with adverse clinical outcomes.

BACKGROUND: The transcriptomic signature has not been fully elucidated in PV, as well as mRNA markers for clinical variables (thrombosis, leukemic transformation, survival, etc.). We attempted to reveal and validate crucial co-expression modules and marker mRNAs correlating with polycythemia vera (PV) by weighted gene co-expression network analysis (WGCNA). MATERIAL AND METHODS: The GSE57793/26014/61629 datasets were downloaded from Gene Expression Omnibus (GEO) database and integrated into one fused dataset. By R software and 'WGCNA' package, the PV-specific co-expression module was identified, the pathway enrichment profile of which was obtained by over-representation analysis (ORA). Protein-protein interaction (PPI) network and hub gene analysis identified MAPK14 as our target gene. Then the distribution of MAPK14 expression in different disease/mutation types, were depicted based on external independent datasets. Genome-scale correlation analysis revealed the association of MAPK14 and JAK/STAT family genes. Then gene set enrichment analysis (GSEA) was performed to detect the activated and suppressed pathways associating with MAPK14 expression. Moreover, GSE47018 dataset was utilized to compare clinical variables (thrombosis, leukemic transformation, survival, etc.) between MAPK14-high and MAPK14-low groups. RESULTS: An integrated dataset including 177 samples (83 PV, 35 ET, 17 PMF and 42 normal donors) were inputted into WGCNA. The 'tan' module was identified as the PV-specific module (R2 = 0.56, p = 8e-16), the genes of which were dominantly enriched in pro-inflammatory pathways (Toll-like receptor (TLR)/TNF signaling, etc.). MAPK14 is identified as the top hub gene in PV-related PPI network with the highest betweenness. External datasets validated that the MAPK14 expression was significantly higher in PV than that of essential thrombocytosis (ET)/primary myelofibrosis (PMF) patients and normal donors. JAK2 homozygous mutation carriers have higher level of MAPK14 than that of other mutation types. The expression of JAK/STAT family genes significantly correlated with MAPK14, which also contributed to the activation of oxidated phosphorylation, interferon-alpha (IFNα) response and PI3K-Akt-mTOR signaling, etc. Moreover, MAPK14-high group have more adverse clinical outcomes (splenectomy, thrombosis, disease aggressiveness) and inferior survival than MAPK14-low group. CONCLUSION: MAPK14 over-expression was identified as a transcriptomic feature of PV, which was also related to inferior clinical outcomes. The results provided novel insights for biomarkers and therapeutic targets for PV.

The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML.

BACKGROUND: The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. METHODS: We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and 'WGCNA' package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by 'glmnet' package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. RESULTS: A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the 'lightyellow' module for NPM1 mutation, the 'saddlebrown' module for RUNX1 mutation, the 'lightgreen' module for TP53 mutation). ORA revealed that the 'lightyellow' module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The 'saddlebrown' module was enriched in immune response process. And the 'lightgreen' module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743-0.79). CONCLUSIONS: We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

HELQ and EGR3 expression correlate with IGHV mutation status and prognosis in chronic lymphocytic leukemia.

BACKGROUND: IGHV mutation status is a crucial prognostic biomarker for CLL. In the present study, we investigated the transcriptomic signatures associating with IGHV mutation status and CLL prognosis. METHODS: The co-expression modules and hub genes correlating with IGHV status, were identified using the GSE28654, by 'WGCNA' package and R software (version 4.0.2). The over-representation analysis was performed to reveal enriched cell pathways for genes of correlating modules. Then 9 external cohorts were used to validate the correlation of hub genes expression with IGHV status or clinical features (treatment response, transformation to Richter syndrome, etc.). Moreover, to elucidate the significance of hub genes on disease course and prognosis of CLL patients, the Kaplan-Meier analysis for the OS and TTFT of were performed between subgroups dichotomized by the median expression value of individual hub genes. RESULTS: 2 co-expression modules and 9 hub genes ((FCRL1/FCRL2/HELQ/EGR3LPL/LDOC1/ZNF667/SOWAHC/SEPTIN10) correlating with IGHV status were identified by WGCNA, and validated by external datasets. The modules were found to be enriched in NF-kappaB, HIF-1 and other important pathways, involving cell proliferation and apoptosis. The expression of hub genes was revealed to be significantly different, not only between CLL and normal B cell, but also between various types of lymphoid neoplasms. HELQ expression was found to be related with response of immunochemotherapy treatment significantly (p = 0.0413), while HELQ and ZNF667 were expressed differently between stable CLL and Richter syndrome patients (p < 0.0001 and p = 0.0278, respectively). By survival analysis of subgroups, EGR3 expression was indicated to be significantly associated with TTFT by 2 independent cohorts (GSE39671, p = 0.0311; GSE22762, p = 0.0135). While the expression of HELQ and EGR3 was found to be associated with OS (p = 0.0291 and 0.0114 respectively).The Kras, Hedgehog and IL6-JAK-STAT3 pathways were found to be associating with the expression of hub genes, resulting from GSEA. CONCLUSIONS: The expression of HELQ and EGR3 were correlated with IGHV mutation status in CLL patients. Additionally, the expression of HELQ/EGR3 were prognostic markers for CLL associating with targetable cell signaling pathways.

LINC00649 underexpression is an adverse prognostic marker in acute myeloid leukemia.

BACKGROUND: Long noncoding RNAs (lncRNA) play a role in leukemogenesis, maintenance, development, and therapeutic resistance of AML. While few studies have focused on the prognostic significance of LINC00649 in AML, which we aim to investigate in this present study. METHODS: We compared the expression level of LINC00649 between AML patients and healthy controls. The Kaplan-Meier curves of AML patients expressing high versus low level of LINC00649 was performed. The LINC00649 correlated genes/miRNAs/lncRNAs and methylation CpG sites were screened by Pearson correlation analysis with R (version 3.6.0), using TCGA-LAML database. The LINC00649 associated ceRNA network was established using lncBase 2.0 and miRWalk 2.0 online tools, combining results from correlation analysis. Finally, a prediction model was constructed using LASSO-Cox regression. RESULTS: LINC00649 was underexpressed in bone marrow of AML group than that in healthy control group. The patients of LINC00649-low group have significantly inferior PFS and OS. A total of 154 mRNAs, 31 miRNAs, 28 lncRNAs and 1590 methylated CpG sites were identified to be significantly correlated with LINC00649. Furthermore, the network of ceRNA was established with 6 miRNAs and 122 mRNAs. The Lasso-Cox model fitted OS/PFS to novel prediction models, which integrated clinical factors, ELN risk stratification, mRNA/miRNA expression and methylation profiles. The analysis of time-dependent ROC for our model showed a superior AUC (AUC = 0.916 at 1 year, AUC = 0.916 at 3 years, and AUC = 0.891 at 5 years). CONCLUSIONS: Low expression of LINC00649 is a potential unfavorable prognostic marker for AML patients, which requires the further validation. The analysis by LASSO-COX regression identified a novel comprehensive model with a superior diagnostic utility, which integrated clinical and genetic variables.

Overexpression of HOXA10 is associated with unfavorable prognosis of acute myeloid leukemia.

BACKGROUND: HOXA family genes were crucial transcription factors involving cell proliferation and apoptosis. While few studies have focused on HOXA10 in AML. We aimed to investigate the prognostic significance of HOXA10. METHODS: We downloaded datasets from GEO and BeatAML database, to compare HOXA expression level between AML patients and controls. Kaplan-Meier curves were used to estimate the impact of HOXA10 expression on AML survival. The differentially expressed genes, miRNAs, lncRNAs and methylated regions between HOXA10-high and -low groups were obtained using R (version 3.6.0). Accordingly, the gene set enrichment analysis (GSEA) was accomplished using MSigDB database. Moreover, the regulatory TFs/microRNAs/lncRNAs of HOXA10 were identified. A LASSO-Cox model fitted OS to clinical and HOXA10-associated genetic variables by glmnet package. RESULTS: HOXA10 was overexpressed in AML patients than that in controls. The HOXA10-high group is significantly associated with shorter OS and DFS. A total of 1219 DEGs, 131 DEmiRs, 282 DElncRs were identified to be associated with HOXA10. GSEA revealed that 12 suppressed and 3 activated pathways in HOXA10-high group. Furthermore, the integrated regulatory network targeting HOXA10 was established. The LASSO-Cox model fitted OS to AML-survival risk scores, which included age, race, molecular risk, expression of IKZF2/LINC00649/LINC00839/FENDRR and has-miR-424-5p. The time dependent ROC indicated a satisfying AUC (1-year AUC 0.839, 3-year AUC 0.871 and 5-year AUC 0.813). CONCLUSIONS: Our study identified HOXA10 overexpression as an adverse prognostic factor for AML. The LASSO-COX regression analysis revealed novel prediction model of OS with superior diagnostic utility.