ArticleJournal of the National Cancer Institute2018
Development and Validation of a Novel RNA Sequencing-Based Prognostic Score for Acute Myeloid Leukemia.
Article in Journal of the National Cancer Institute, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.Annals of hematology · 2026Review
- MALNC: a new mutant NPM1/IDH2Cancer gene therapy · 2025Article
- Transcriptome-based immune subtypes reveal the heterogeneity of tumor microenvironment in pediatric B-cell acute lymphocytic leukemia.Frontiers in oncology · 2025Article
- Bioinformatics-guided construction of a tumor microenvironment-derived prognostic model in acute myeloid leukemia.PloS one · 2025Article
- Identifying an AML Prognostic Model Using 10 Marker Genes from Single-Cell Transcriptome and Bulk Transcriptome Analysis.Biochemical genetics · 2024Article
- Improving risk stratification for 2022 European LeukemiaNet favorable-risk patients with acute myeloid leukemia.Innovation (Cambridge (Mass.)) · 2024Article
- Article
- A novel 5-gene prognostic signature to improve risk stratification of cytogenetically normal acute myeloid leukemia.Journal of cancer research and clinical oncology · 2023Article
- TP53-Mutated Myelodysplasia and Acute Myeloid Leukemia.Mediterranean journal of hematology and infectious diseases · 2023Review
- The transcriptomic landscape of elderly acute myeloid leukemia identifiesFrontiers in oncology · 2022Article
- The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia.BMC medicine · 2021Article
- Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing.Leukemia · 2021Article
- LINC00649 underexpression is an adverse prognostic marker in acute myeloid leukemia.BMC cancer · 2020Article
- Overexpression of HOXA10 is associated with unfavorable prognosis of acute myeloid leukemia.BMC cancer · 2020Article
- A novel RNA sequencing-based risk score model to predict papillary thyroid carcinoma recurrence.Clinical & experimental metastasis · 2020Article
- SOCS2 is part of a highly prognostic 4-gene signature in AML and promotes disease aggressiveness.Scientific reports · 2019Article
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7 authors.
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Abstract
Background: Recent progress in sequencing technologies allows us to explore comprehensive genomic and transcriptomic information to improve the current European LeukemiaNet (ELN) system of acute myeloid leukemia (AML). Methods: We compared the prognostic value of traditional demographic and cytogenetic risk factors, genomic data in the form of somatic aberrations of 25 AML-relevant genes, and whole-transcriptome expression profiling (RNA sequencing) in 267 intensively treated AML patients (Clinseq-AML). Multivariable penalized Cox models (overall survival [OS]) were developed for each data modality (clinical, genomic, transcriptomic), together with an associated prognostic risk score. Results: Of the three data modalities, transcriptomic data provided the best prognostic value, with an integrated area under the curve (iAUC) of a time-dependent receiver operating characteristic (ROC) curve of 0.73. We developed a prognostic risk score (Clinseq-G) from transcriptomic data, which was validated in the independent The Cancer Genome Atlas AML cohort (RNA sequencing, n = 142, iAUC = 0.73, comparing the high-risk group with the low-risk group, hazard ratio [HR]OS = 2.42, 95% confidence interval [CI] = 1.51 to 3.88). Comparison between Clinseq-G and ELN score iAUC estimates indicated strong evidence in favor of the Clinseq-G model (Bayes factor = 26.78). The proposed model remained statistically significant in multivariable analysis including the ELN and other well-known risk factors (HRos = 2.34, 95% CI = 1.30 to 4.22). We further validated the Clinseq-G model in a second independent data set (n = 458, iAUC = 0.66, adjusted HROS = 2.02, 95% CI = 1.33 to 3.08; adjusted HREFS = 2.10, 95% CI = 1.42 to 3.12). Conclusions: Our results indicate that the Clinseq-G prediction model, based on transcriptomic data from RNA sequencing, outperforms traditional clinical parameters and previously reported models based on genomic biomarkers.
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