Evidence map›Paper›PMID 36708053›Full record

ArticleCancer medicine2023

High expression of LOC541471, GDAP1, SOD1, and STK25 is associated with poor overall survival of patients with acute myeloid leukemia.

Xibao Yu, Cunte Chen, Yanyun Hu, Kehan Li, Yikai Zhang, Zheng Chen, Dingrui Nie, Rili Gao, Youxue Huang, Mengjun Zhong and 5 more

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.4field-weighted citation impact, top 6% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  2. Bioinformatic Approach to Identify Positive PrognosticInternational journal of molecular sciences · 2025
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  5. Epigenetic regulation of the humanBiochemistry and biophysics reports · 2024
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  6. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 4 institutions in 1 country.

Xibao YuThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-0643-8372
Cunte ChenThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-3733-9174
Yanyun HuThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.
Kehan LiThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-4976-6202
Yikai ZhangThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-6851-6260
Zheng ChenThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-8069-481X
Dingrui NieThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0001-5185-344X
Rili GaoThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-4193-6433
Youxue HuangThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0003-2538-9860
Mengjun ZhongThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.
Caixia WangDepartment of Hematology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.ORCID 0000-0002-7895-1813
Shunqing WangDepartment of Hematology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Yixin ZengDepartment of Experimental Research, Sun Yat-sen University Cancer Center, State Key Laboratory Oncology in South China, Guangzhou, China.
Yangqiu LiThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-0974-4036
Chengwu ZengThe First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.ORCID 0000-0002-1333-3918
First Affiliated Hospital of Jinan University · CNGuangzhou First People's Hospital · CNSun Yat-sen University · CNBureau of Agriculture of Guangzhou Municipality · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute myeloid leukemia (AML) is an aggressive heterogeneous hematological malignancy with remarkably heterogeneous outcomes. This study aimed to identify potential biomarkers for AML risk stratification via analysis of gene expression profiles.

methodsRNA sequencing data from 167 adult AML patients in the Cancer Genome Atlas (TCGA) database were obtained for overall survival (OS) analysis, and 52 bone marrow (BM) samples from our clinical center were used for validation. Additionally, siRNA was used to investigate the role of prognostic genes in the apoptosis and proliferation of AML cells.

resultsCo-expression of 103 long non-coding RNAs (lncRNAs) and mRNAs in the red module that were positively correlated with European Leukemia Network (ELN) risk stratification and age was identified by weighted gene co-expression network analysis (WGCNA). After screening by uni- and multivariate Cox regression, Kaplan-Meier survival, and protein-protein interaction analysis, four genes including the lncRNA LOC541471, GDAP1, SOD1, and STK25 were incorporated into calculating a risk score from coefficients of the multivariate Cox regression model. Notably, GDAP1 expression was the greatest contributor to OS among the four genes. Interestingly, the risk score, ELN risk stratification, and age were independent prognostic factors for AML patients, and a nomogram model constructed with these factors could illustrate and personalize the 1-, 3-, and 5-year OS rates of AML patients. The calibration and time-dependent receiver operating characteristic curves (ROCs) suggested that the nomogram had a good predictive performance. Furthermore, new risk stratification was developed for AML patients based on the nomogram model. Importantly, knockdown of LOC541471, GDPA1, SOD1, or STK25 promoted apoptosis and inhibited the proliferation of THP-1 cells compared to controls.

conclusionsHigh expression of LOC541471, GDAP1, SOD1, and STK25 may be biomarkers for risk stratification of AML patients, which may provide novel insight into evaluating prognosis, monitoring progression, and designing combinational targeted therapies.

Indexed as

Leukemia, Myeloid, AcuteRNA, Long NoncodingAdultBiomarkers, TumorGene Expression ProfilingHippo KinasesHumansIntracellular Signaling Peptides and ProteinsPrognosisProtein Serine-Threonine KinasesSuperoxide Dismutase-1Biomarkers, TumorHippo KinasesIntracellular Signaling Peptides and ProteinsProtein Serine-Threonine KinasesRNA, Long NoncodingSOD1 protein, humanSTK25 protein, humanSuperoxide Dismutase-1acute myeloid leukemiaapoptosisbiomarkerlong non-coding RNAnomogramrisk stratification

Identifiers

PMID36708053
PMCPMC10134312
OpenAlexW4318323562

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.