ArticleMolecular genetics & genomic medicine2020
Development of an immune-related prognostic model for pediatric acute lymphoblastic leukemia patients.
Article in Molecular genetics & genomic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- A novel prognostic nomogram for adult acute lymphoblastic leukemia: a comprehensive analysis of 321 patients.Annals of hematology · 2023Trial
- Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis.Molecular biotechnology · 2025Article
- Upregulation of the long noncoding RNA GJA9-MYCBP and PVT1 is a potential diagnostic biomarker for acute lymphoblastic leukemia.Cancer reports (Hoboken, N.J.) · 2024Article
- The ANXA2/S100A10 Complex-Regulation of the Oncogenic Plasminogen Receptor.Biomolecules · 2021Review
- The lncRNA TEX41 is upregulated in pediatric B-Cells Acute Lymphoblastic Leukemia and it is necessary for leukemic cell growth.Biomarker research · 2021Article
- Development of an immune-related prognostic model for pediatric acute lymphoblastic leukemia patients.Molecular genetics & genomic medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute lymphoblastic leukemia (ALL) is the most common hematological malignancy in pediatrics, and immune-related genes (IRGs) play crucial role in its development. Our study aimed to identify prognostic immune biomarkers of pediatric ALL and construct a risk assessment model.
methodsPediatric ALL patients' gene expression data were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. We screened differentially expressed IRGs (DEIRGs) between the relapse and non-relapse groups. Cox regression analysis was used to identify optimal prognostic genes, then, a risk model was constructed, and its accuracy was verified in different cohorts.
resultsWe screened 130 DEIRGs from 251 pediatric ALL samples. The top three pathways that DEIRGs may influence tumor progression are NABA matrisome-associated, chemotaxis, and antimicrobial humoral response. A set of 84 prognostic DEIRGs was identified by using univariate Cox analysis. Then, Lasso regression and multivariate Cox regression analysis screened four optimal genes (PRDX2, S100A10, RORB, and SDC1), which were used to construct the prognostic risk model. The risk score was calculated and the survival analysis results showed that high-risk score was associated with poor overall survival (OS) (p = 3.195 × 10
conclusionsOur prognostic risk model can effectively divide pediatric ALL patients into high-risk and low-risk groups, which may help predict clinical prognosis and optimize individualized treatment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.