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

ABSTRACT: 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.

SUBMITTER: Luo H

PROVIDER: S-EPMC8498117 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

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Project description:BackgroundAcute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. Although many studies on the treatment and detection of AML have been conducted, the molecular mechanism of AML development and progression has not been fully elucidated. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use.MethodsTo simplify the complicated regulatory networks, we performed the gene co-expression and PPI network based on WGCNA and STRING database using modularization design. Two machine learning methods, A least absolute shrinkage and selector operation (LASSO) algorithm and support vector machine-recursive feature elimination (SVM-RFE), were used to filter the common hub genes by five-fold cross-validation. The candidate hub genes were used to build the predictive model of AML by the cox-proportional hazards analysis, and validated in The Cancer Genome Atlas (TCGA) cohort and ohsu cohort, which were reliable in the experimental verification by qRT-PCR and western blotting in mRNA and protein levels.ResultsThree hub genes, FLT3, CD177 and TTPAL were used to build a clinically applicable model to predict the survival probability of AML patients and divided them into high and low groups. To compare the survival ability of the model with the classical clinical features, we generated the nomogram. The model displayed the most risk points contrast to other clinical characteristics, which was compatible with the data of cox multivariate regression.ConclusionThis study reveal the novel molecular mechanism of AML, and construct a clinical model significantly related to AML patient prognosis. We showed the integrated roles of critical pathways, hub genes associated, which provide potential targets and new research ideas for the treatment and early detection of AML.

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

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