A prognostic survival model based on metabolism-related gene expression in plasma cell myeloma.

Accurate survival prediction of persons with plasma cell myeloma (PCM) is challenging. We interrogated clinical and laboratory co-variates and RNA matrices of 1040 subjects with PCM from public datasets in the Gene Expression Omnibus database in training (N = 1) and validation (N = 2) datasets. Genes regulating plasma cell metabolism correlated with survival were identified and seven used to build a metabolic risk score using Lasso Cox regression analyses. The score had robust predictive performance with 5-year survival area under the curve (AUCs): 0.71 (95% confidence interval, 0.65, 0.76), 0.88 (0.67, 1.00) and 0.64 (0.57, 0.70). Subjects in the high-risk training cohort (score > median) had worse 5-year survival compared with those in the low-risk cohort (62% [55, 68%] vs. 85% [80, 90%]; p < 0.001). This was also so for the validation cohorts. A nomogram combining metabolic risk score with Revised International Staging System (R-ISS) score increased survival prediction from an AUC = 0.63 [0.58, 0.69] to an AUC = 0.73 [0.66, 0.78]; p = 0.015. Modelling predictions were confirmed in in vitro tests with PCM cell lines. Our metabolic risk score increases survival prediction accuracy in PCM.

Systematic Construction and Validation of a Metabolic Risk Model for Prognostic Prediction in Acute Myelogenous Leukemia.

Background: Acute myelogenous leukemia (AML) is a heterogeneous disease with recurrent gene mutations and variations in disease-associated gene expression, which may be useful for prognostic prediction. Methods: RNA matrix and clinical data of AML were downloaded from GEO, TCGA, and TARGET databases. Prognostic metabolic genes were identified by LASSO analysis to establish a metabolic model. Prognostic accuracy of the model was quantified by time-dependent receiver operating characteristic curves and the area under the curve (AUC). Survival analysis was performed by log-rank tests. Enriched pathways in different metabolic risk statuses were evaluated by gene set enrichment analyses (GSEA). Results: We identified nine genes to construct a prognostic model of shorter survival in the high-risk vs. low-risk group. The prognostic model showed good predictive efficacy, with AUCs for 5-year overall survival of 0.78 (0.73-0.83), 0.76 (0.62-0.89), and 0.66 (0.57-0.75) in the training, adult external, and pediatric external cohorts, respectively. Multivariable analysis demonstrated that the metabolic signature had independent prognostic value with hazard ratios of 2.75 (2.06-3.66), 1.89 (1.09-3.29), and 1.96 (1.00-3.84) in the training, adult external, and pediatric external cohorts, respectively. Combining metabolic signatures and classic prognostic factors improved 5-year overall survival prediction compared to the prediction by classic prognostic factors (p < 0.05). GSEA revealed that most pathways were metabolism-related, indicating potential mechanisms. Conclusion: We identified dysregulated metabolic features in AML and constructed a prognostic model to predict the survival of patients with AML.

Identification of cancer stem cell characteristics in liver hepatocellular carcinoma by WGCNA analysis of transcriptome stemness index.

Cancer stem cells (CSCs) are characterized by self-renewal and -differential potential as compared to common cancer cells and play an important role in the development and therapeutic resistance of liver hepatocellular carcinoma (LIHC). However, the specific pathogenesis of LIHC stem cells is still unclear, and the genes involved in the stemness of LIHC stem cells are currently unknown. In this study, we investigated novel biomarkers associated with LIHC and explored the expression characteristics of stem cell-related genes in LIHC. We found that mRNA expression-based stemness index (mRNAsi) was significantly overexpressed in liver cancer tissues. Further, mRNAsi expression in LIHC increased with the tumor pathological grade, with grade 4 tumors harboring the greatest stem cell features. Upon establishing mRNAsi scores based on mRNA expression of every gene, we found an association with poor overall survival in LIHC. Moreover, modules of interest were determined based on weighted gene co-expression network analysis (WGCNA) inclusion criteria, and three significant modules (red, green, and brown) and 21 key genes (DCN, ECM1, HAND2, PTGIS, SFRP1, SRPX, COLEC10, GRP182, ADAMTS7, CD200, CDH11, COL8A1, FAP, LZTS1, MAP1B, NAV1, NOTCH3, OLFML2A, PRR16, TMEM119, and VCAN) were identified. Functional analysis of these 21 genes demonstrated their enrichment in pathways involved in angiogenesis, negative regulation of DNA-binding transcription factor activity, apoptosis, and autophagy. Causal relationship with proteins indicated that the Wnt, Notch, and Hypoxia pathways are closely related to LIHC tumorigenesis. To our knowledge, this is the first report of a novel CSC biomarker, mRNAsi, to predict the prognosis of LIHC. Further, we identified 21 key genes through mRNA expression network analysis, which could be potential therapeutic targets to inhibit the stemness of cancer cells in LIHC.

Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome.

Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814-7.630) and 2.047 (1.013-4.138) in the training cohort and validation cohort, respectively. The AUC of 3-year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high-risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.

[Imatinib in treatment of thrombocythemia and other myeloproliferative diseases].

Imatinib mesylate has been commonly used in the treatment of patients with chronic myeloid leukemia (CML). However, a significant number of CML patients treated with imatinib developed thrombocytopenia, oligocythemia, granulocytopenia. It has been confirmed that imatinib not only inhibits BCR-ABL mutations, but also suppresses other tyrosine kinase receptor genes such as PDGFR, JAK2V617F and C-KIT mutations, providing an important potential of targeted therapy for myeloproliferative disease. As the PDGFR, JAK2 and C-KIT play important roles in the regulation of hematopoiesis, suggesting that imatinib may block the phosphorylation of PDGFR, JAK2V617F and C-KIT receptors, interrupt the signal transduction cascades, disrupt cell differentiation and proliferation. In this review, the application and the potential molecular mechanism of imatinib in the treatment of thrombocythemia and other myeloproliferative diseases are discussed.

[Molecular mechanism of imatinib-induced thrombocytopenia in treatment of patients with CML].

Imatinib mesylate has been commonly used in the treatment of patients with chronic myeloid leukemia (CML). However, a significant number of CML patients treated with imatinib developed thrombocytopenia. Platelet-derived growth factor (PDGF)/platelet-derived growth factor receptor (PDGFR) plays a significant role in the regulation of thrombopoiesis. It is suggested that imatinib may block the PDGF/PDGFR and PI3-K/Akt pathway, then inducing the apoptosis of megakaryocytes and developing thrombocytopenia in these patients. In this review, the potential molecular mechanism of imatinib-induced thrombocytopenia in the treatment of CML patients is discussed, including imatinib and thrombocytopenia, PDGF/PDGFR and thrombopoiesis, potential mechanism of imatinib-induced thrombocytopenia in treatment of patients with CML and so on.

[The role of PDGF/PDGFR in the regulation of platelet formation].

Platelet-derived growth factor (PDGF), a potent chemotactic and mitogenic factor, is involved in the regulation of hematopoiesis and platelet production. Our studies demonstrate the presence of functional PDGF receptors (PDGFR) on human megakaryocytes/platelets and CD34(+) cells, and their ability to mediate a mitogenic response. PDGF promotes the ex vivo expansion of human hematopoietic stem (CD34(+)) and progenitor (CD41(+)) cells. More significantly, PDGF enhances the engraftment of human CD45(+) cells and their myeloid subsets (CD33(+), CD14(+) cells) in NOD/SCID mice. PDGF also stimulates in vitro megakaryocytopoiesis via PDGFR and/or the indirect effect on bone marrow microenvironment to produce TPO and other cytokines. It also shows a direct stimulatory effect of PDGF on c-Fos, GATA-1 and NF-E2 expressions in megakaryocytes. We speculate that these transcription factors may be involved in the signal transduction of PDGF on the regulation of megakaryocytopoiesis. PDGF also enhances platelet recovery in mouse model with radiation-induced thrombocytopenia. This radioprotective effect is likely to be mediated via PDGFR with subsequent activation of the PI3K/Akt pathway. It provides a possible explanation that blockage of PDGFR may reduce thrombopoiesis and play a role in imatinib mesylate-induced thrombocytopenia.