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Gene coexpression network analysis revealed biomarkers correlated with blast cells and survival in acute myeloid leukemia.

ABSTRACT: Acute myeloid leukemia (AML) is a hematological malignancy with a poorly understood pathogenesis, especially among patients with no known cytogenetic abnormalities. Furthermore, there is a lack of therapeutic gene targets and diagnostic biomarkers for the effective treatment of AML. The present study aimed to identify candidate biomarkers correlated with the clinical prognosis of patients with AML. Leukemic cells from 5 patients with AML exhibiting a normal karyotype, and hematopoietic cells from 5 healthy donors were processed for RNA sequencing (RNA-seq), and the obtained RNA expression profiles were subjected to weighted gene correlation network analysis. A novel group of genes (the red module) were identified to be significantly associated with AML, and this module contained a closely connected network with 147 nodes, which corresponded to 114 mRNAs. Analysis of the correlation between these mRNAs and blast cell percentage, overall survival (OS) and disease-free survival (DFS) using cases from The Cancer Genome Atlas (TCGA) database revealed that CSF3R, ALPL and LMTK2 were negatively associated with the percentage of blast cells, while high expression of these genes was associated with longer OS and DFS in patients with AML. The differential expression of these three genes between patients with AML and healthy control subjects was supported using the Genotype-Tissue Expression and TCGA databases and was further confirmed using reverse transcription-quantitative (RT-qPCR). These genes exhibited significantly lower expression in patients with AML compared with control subjects. The results indicated that CSF3R, ALPL and LMTK2 exhibit the potential to be prognostic biomarkers. However, the biological functions of these three candidate genes need to be assessed in further studies.

SUBMITTER: Pan Y

PROVIDER: S-EPMC7087465 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Gene coexpression network analysis revealed biomarkers correlated with blast cells and survival in acute myeloid leukemia.

Pan Yuming Y Zhang Qiaoxia Q Deng Xiaopeng X An Na N Du Xin X Liu Jiajun J

Molecular and clinical oncology 20200226 5

Acute myeloid leukemia (AML) is a hematological malignancy with a poorly understood pathogenesis, especially among patients with no known cytogenetic abnormalities. Furthermore, there is a lack of therapeutic gene targets and diagnostic biomarkers for the effective treatment of AML. The present study aimed to identify candidate biomarkers correlated with the clinical prognosis of patients with AML. Leukemic cells from 5 patients with AML exhibiting a normal karyotype, and hematopoietic cells fro ...[more]

PMID: 32257206

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Project description:BackgroundIncreasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC.MethodsHBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software.ResultA total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app.ConclusionThe present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC.

Dataset Information

Gene coexpression network analysis revealed biomarkers correlated with blast cells and survival in acute myeloid leukemia.

Publications

Gene coexpression network analysis revealed biomarkers correlated with blast cells and survival in acute myeloid leukemia.

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