scholarly journals Systematic Construction and Validation of an RNA-Binding Protein-Associated Prognostic Model for Acute Myeloid Leukemia

2021 ◽  
Vol 12 ◽  
Author(s):  
Hongwei Luo ◽  
Yingchun Zhang ◽  
Nan Hu ◽  
Yancheng He ◽  
Chengcheng He
Keyword(s):  
Regression Analysis ◽  
Myeloid Leukemia ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Net Benefit ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Acute Myeloid

Background: The abnormal expression of RNA-binding proteins (RBPs) in various malignant tumors is closely related to the occurrence and development of tumors. However, the role of RBPs in acute myeloid leukemia (AML) is unclear.Methods: We downloaded harmonized RNA-seq count data and clinical data for AML from UCSC Xena, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) cohorts. R package edgeR was used for differential expression analysis of 337 whole-blood data and 173 AML data. The prognostic value of these RBPs was systematically investigated by using univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO)–Cox regression analysis, and multivariate Cox regression analysis. C-index and calibration diagram were used to judge the accuracy of the model, and decision curve analysis (DCA) was used to judge the net benefit. The biological pathways involved were revealed by gene set enrichment analysis (GSEA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and the protein–protein interaction (PPI) network performed lateral verification on the selected gene set and LASSO results.Results: A prognostic model of 12-RBP signature was established. In addition, the net benefit and prediction accuracy of the prognostic model and the mixed model based on it were significantly higher than that of cytogenetics. It is verified in the TARGET cohort and shows good prediction effect. Both the selection of our gene set and the LASSO results have high credibility. Most of these pathways are involved in the development of the disease, and they also accumulate in leukemia and RNA-related pathways.Conclusion: The prognosis model of the 12-RBP signature found in this study is an optimized biomarker that can effectively stratify the risk of AML patients. Nomogram based on this prognostic model is a reliable method to predict the median survival time of patients. This study expands our current understanding of the role of RBPs in the occurrence of AML and may lay the foundation for future treatment of the disease.

scholarly journals Prognosis and Characterization of Immune Microenvironment in Acute Myeloid Leukemia Through Identification of an Autophagy-Related Signature

2021 ◽  
Vol 12 ◽  
Author(s):  
Denggang Fu ◽  
Biyu Zhang ◽  
Shiyong Wu ◽  
Yinghua Zhang ◽  
Jingwu Xie ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Regression Analysis ◽  
High Risk ◽  
Signaling Pathways ◽  
Myeloid Leukemia ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Prognostic Predictor ◽  
Risk Patients ◽  
Acute Myeloid

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has an unfavorable outcome and a high rate of relapse. Autophagy plays a vital role in the development of and therapeutic responses to leukemia. This study identifies a potential autophagy-related signature to monitor the prognoses of patients of AML. Transcriptomic profiles of AML patients (GSE37642) with the relevant clinical information were downloaded from Gene Expression Omnibus (GEO) as the training set while TCGA-AML and GSE12417 were used as validation cohorts. Univariate regression analyses and multivariate stepwise Cox regression analysis were respectively applied to identify the autophagy-related signature. The univariate Cox regression analysis identified 32 autophagy-related genes (ARGs) that were significantly associated with the overall survival (OS) of the patients, and were mainly rich in signaling pathways for autophagy, p53, AMPK, and TNF. A prognostic signature that comprised eight ARGs (BAG3, CALCOCO2, CAMKK2, CANX, DAPK1, P4HB, TSC2, and ULK1) and had good predictive capacity was established by LASSO–Cox stepwise regression analysis. High-risk patients were found to have significantly shorter OS than patients in low-risk group. The signature can be used as an independent prognostic predictor after adjusting for clinicopathological parameters, and was validated on two external AML sets. Differentially expressed genes analyzed in two groups were involved in inflammatory and immune signaling pathways. An analysis of tumor-infiltrating immune cells confirmed that high-risk patients had a strong immunosuppressive microenvironment. Potential druggable OS-related ARGs were then investigated through protein–drug interactions. This study provides a systematic analysis of ARGs and develops an OS-related prognostic predictor for AML patients. Further work is needed to verify its clinical utility and identify the underlying molecular mechanisms in AML.

scholarly journals ITGB2 Expression is Negatively Correlated with the Prognosis of Acute Myeloid Leukemia

2021 ◽  
Author(s):  
Jianan Zhou ◽  
Bobin Chen ◽  
Pei Li
Keyword(s):  
T Cells ◽  
Acute Myeloid Leukemia ◽  
Regression Analysis ◽  
Mast Cells ◽  
Myeloid Leukemia ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Cox Regression Analysis ◽  
Acute Myeloid

Abstract Objective: Acute myeloid leukemia (AML) is a clonal malignant hematological neoplasm with a poor prognosis and high heterogeneity. Many studies have been conducted on the diagnosis and treatment of AML, but the immune microenvironmental mechanisms underlying AML disease progression have not been fully elucidated. The aim of this study was to find the potential genes in tumor microenvironmental mechanisms underlying the initiation and progression of AML through relevant biological informatics analysis, and investigate the potential influence of the gene in tumor microenvironment (TME).Methods: AML samples of genes were retrieved from The Cancer Genome Atlas (TCGA) databases. The number of tumor-infiltrating immune cells (TIC) as well as immune and stromal components in AML cases was calculated using the ESTIMATE and CIBERSORT algorithms. Two methods, COX regression analysis and protein-protein interaction (PPI) network, were applied to obtain related genes, and the intersection of related genes was taken to obtain differentially expressed genes (DEGs). Gene Set Enrichment Analysis (GSEA) was used for explore the biological signaling pathway. CIBERSORT analysis for the proportion of TICs was performed to reveal that TICs which are related of the target gene.Results: Cross-tabulation analysis of univariate COX regression analysis and PPI network known the β2 integrin factor (ITGB2) as a major predictor of AML prognosis. High expression of ITGB2 was correlated with low survival of AML patients. GSEA revealed that the higher the ITGB2 gene expression, the more active the immune-related activity. CIBERSORT analysis of the TICs ratio revealed that 9 kinds of TICs were negatively correlated with the expression of ITGB2, including CD4 memory resting T cells, CD8 T cells, naive B cells, resting NK cells, Plasma cells, follicular helper T cells, resting Mast cells, Eosinophils and activated mast cells. Only monocytes were positively correlated with ITGB2 expression. These results provided further evidence that ITGB2 levels may determine the prognosis of AML patients by modulating the immune status of TME, which provides an additional suggestion for the treatment of AML.

scholarly journals High EVI1 Expression Predicts Adverse Outcomes in Children With De Novo Acute Myeloid Leukemia

2021 ◽  
Vol 11 ◽  
Author(s):  
Yongzhi Zheng ◽  
Yan Huang ◽  
Shaohua Le ◽  
Hao Zheng ◽  
Xueling Hua ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Regression Analysis ◽  
Myeloid Leukemia ◽  
Pediatric Patients ◽  
Cox Regression ◽  
De Novo ◽  
Adverse Outcomes ◽  
Cox Regression Analysis ◽  
Pediatric Aml ◽  
Acute Myeloid

BackgroundA high ecotropic viral integration site 1 (EVI1) expression (EVI1high) is an independent prognostic factor in adult acute myeloid leukemia (AML). However, little is known of the prognostic value of EVI1high in pediatric AML. This study aimed to examine the biological and prognostic significance of EVI1high in uniformly treated pediatric patients with AML from a large cohort of seven centers in China.MethodsA diagnostic assay was developed to determine the relative EVI1 expression using a single real-time quantitative polymerase chain reaction in 421 newly diagnosed pediatric AML patients younger than 14 years from seven centers in southern China. All patients were treated with a uniform protocol, but only 383 patients were evaluated for their treatment response. The survival data were included in the subsequent analysis (n = 35 for EVI1high, n = 348 for EVI1low).ResultsEVI1high was found in 9.0% of all 421 pediatric patients with de novo AML. EVI1high was predominantly found in acute megakaryoblastic leukemia (FAB M7), MLL rearrangements, and unfavorable cytogenetic aberrance, whereas it was mutually exclusive with t (8; 21), inv (16)/t (16; 16), CEBPA, NPM1, or C-KIT mutations. In the univariate Cox regression analysis, EVI1high had a significantly adverse 5-year event-free survival (EFS) and overall survival (OS) [hazard ratio (HR) = 1.821 and 2.401, p = 0.036 and 0.005, respectively]. In the multivariate Cox regression analysis, EVI1high was an independent prognostic factor for the OS (HR = 2.447, p = 0.015) but not EFS (HR = 1.556, p = 0.174). Furthermore, EVI1high was an independent adverse predictor of the OS and EFS of patients with MLL rearrangements (univariate analysis: HR = 9.921 and 7.253, both p < 0.001; multivariate analysis: HR = 7.186 and 7.315, p = 0.005 and 0.001, respectively). Hematopoietic stem cell transplantation (HSCT) in first complete remission (CR1) provided EVI1high patients with a tendential survival benefit when compared with chemotherapy as a consolidation (5-year EFS: 68.4% vs. 50.8%, p = 0.26; 5-year OS: 65.9% vs. 54.8%, p = 0.45).ConclusionIt could be concluded that EVI1high can be detected in approximately 10% of pediatric AML cases. It is predominantly present in unfavorable cytogenetic subtypes and predicts adverse outcomes. Whether pediatric patients with EVI1high AML can benefit from HSCT in CR1 needs to be researched further.

scholarly journals A Simple-to-Use Nomogram for Predicting Survival in Children with Acute Myeloid Leukemia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Feng Jiang ◽  
Xiang Yu ◽  
Chuyan Wu ◽  
Ming Wang ◽  
Ke Wei ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Prognostic Model ◽  
Cox Regression ◽  
Chromosomal Abnormalities ◽  
Web Based ◽  
Cox Regression Analysis ◽  
Predictive Variables ◽  
Target Database ◽  
Acute Myeloid

Background. The research analyzed a group of patients to develop a statistical nomogram and a web-based survival rate predictor for the comprehensive estimate of the overall survival (OS) of children with acute myeloid leukemia. Methods. Between 1999 to 2015, we used the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database to evaluate and randomly divide 440 children diagnosed with AML into the population of training ( n = 309 ) and validation ( n = 131 ). The analysis of Lasso Cox was used to identify separate predictive variables. We have used essential forecasting considerations to construct a nomogram and a web-based calculator focused on Cox regression analysis. Nomogram validation was tested through discrimination and calibration. Results. Compared to the multivariate training cohort models, a nomogram integrating gender, age of diagnose, WBC at diagnosis, bone marrow leukemic blast percentage, and chromosomal abnormalities [ t (8; 21), inv(16)] were designed for the prediction of OS. We also developed a predictive survival nomogram and a web-based calculator. C-indexes validated internally and checked externally were 0.747 and 0.716. The calibration curves have shown that the nomogram might accurately forecast 3-year and 5-year OS. Conclusions. A nomogram effectively predicts survival in children with AML. This prognostic model can be used in clinical practice.

scholarly journals Identification of Prognostic RBPs in Osteosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110049
Author(s):  
Bei Li ◽  
Long Fang ◽  
Baolong Wang ◽  
Zengkun Yang ◽  
Tingbao Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Differential Expression ◽  
Ribosome Biogenesis ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Malignant Tumors ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

scholarly journals Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

2020 ◽  
Vol 19 ◽  
pp. 153303382098417
Author(s):  
Ting-ting Liu ◽  
Shu-min Liu
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Small Gtpase ◽  
Functional Enrichment ◽  
Clinical Correlation ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Key Genes

Objective: The incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC). Methods: This study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes. Results: Of the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation. Conclusions: In this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Mutant DNMT3A: a marker of poor prognosis in acute myeloid leukemia

Blood ◽  
2012 ◽  
Vol 119 (24) ◽  
pp. 5824-5831 ◽  
Author(s):  
Ana Flávia Tibúrcio Ribeiro ◽  
Marta Pratcorona ◽  
Claudia Erpelinck-Verschueren ◽  
Veronika Rockova ◽  
Mathijs Sanders ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Cox Regression ◽  
Multivariable Analysis ◽  
Gene Expression Signature ◽  
Mutation Status ◽  
Cox Regression Analysis ◽  
Prognostic Effect ◽  
Or Gene ◽  
Acute Myeloid

Abstract The prevalence, the prognostic effect, and interaction with other molecular markers of DNMT3A mutations was studied in 415 patients with acute myeloid leukemia (AML) younger than 60 years. We show mutations in DNMT3A in 96 of 415 patients with newly diagnosed AML (23.1%). Univariate Cox regression analysis showed that patients with DNMT3Amutant AML show significantly worse overall survival (OS; P = .022; hazard ratio [HR], 1.38; 95% confidence interval [CI], 1.04-1.81), and relapse-free survival (RFS; P = .005; HR, 1.52; 95% CI, 1.13-2.05) than DNMT3Awild-type AMLs. In a multivariable analysis, DNMT3A mutations express independent unfavorable prognostic value for OS (P = .003; HR, 1.82; 95% CI, 1.2-2.7) and RFS (P < .001; HR, 2.2; 95% CI, 1.4-3.3). In a composite genotypic subset of cytogenetic intermediate-risk AML without FLT3-ITD and NPM1 mutations, this association is particularly evident (OS: P = .013; HR, 2.09; 95% CI, 1.16-3.77; RFS: P = .001; HR, 2.65; 95% CI, 1.48-4.89). The effect of DNMT3A mutations in human AML remains elusive, because DNMT3Amutant AMLs did not express a methylation or gene expression signature that discriminates them from patients with DNMT3Awild-type AML. We conclude that DNMT3A mutation status is an important factor to consider for risk stratification of patients with AML.

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