Evidence map›Paper›PMID 38167056›Full record

ArticleBMC medical informatics and decision making2024

Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia.

Yujing Cheng, Xin Yang, Ying Wang, Qi Li, Wanlu Chen, Run Dai, Chan Zhang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
4.5field-weighted citation impact, top 5% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Characterization of SUSD3 as a novel prognostic biomarker and therapeutic target for breast cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
    Article
  5. Review
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

7 authors at 2 institutions in 1 country.

Yujing ChengDepartment of blood transfusion, The First People's Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, No.157 Jinbi Road, 650034, Kunming, Yunnan, China.
Xin YangDepartment of blood transfusion, The First People's Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, No.157 Jinbi Road, 650034, Kunming, Yunnan, China.
Ying WangDepartment of blood transfusion, The First People's Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, No.157 Jinbi Road, 650034, Kunming, Yunnan, China.
Qi LiDepartment of blood transfusion, The First People's Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, No.157 Jinbi Road, 650034, Kunming, Yunnan, China.
Wanlu ChenDepartment of blood transfusion, The First People's Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, No.157 Jinbi Road, 650034, Kunming, Yunnan, China.
Run DaiDepartment of blood transfusion, The First People's Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, No.157 Jinbi Road, 650034, Kunming, Yunnan, China.
Chan ZhangDepartment of blood transfusion, The First People's Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, No.157 Jinbi Road, 650034, Kunming, Yunnan, China. zhangchanyzt@163.com.
First People's Hospital of Yunnan Province · CNKunming University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute Myeloid Leukemia (AML) generally has a relatively low survival rate after treatment. There is an urgent need to find new biomarkers that may improve the survival prognosis of patients. Machine-learning tools are more and more widely used in the screening of biomarkers.

methodsLeast Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), lrFuncs, IdaProfile, caretFuncs, and nbFuncs models were used to screen key genes closely associated with AML. Then, based on the Cancer Genome Atlas (TCGA), pan-cancer analysis was performed to determine the correlation between important genes and AML or other cancers. Finally, the diagnostic value of important genes for AML was verified in different data sets.

resultsThe survival analysis results of the training set showed 26 genes with survival differences. After the intersection of the results of each machine learning method, DNM1, MEIS1, and SUSD3 were selected as key genes for subsequent analysis. The results of the pan-cancer analysis showed that MEIS1 and DNM1 were significantly highly expressed in AML; MEIS1 and SUSD3 are potential risk factors for the prognosis of AML, and DNM1 is a potential protective factor. Three key genes were significantly associated with AML immune subtypes and multiple immune checkpoints in AML. The results of the verification analysis show that DNM1, MEIS1, and SUSD3 have potential diagnostic value for AML.

conclusionMultiple machine learning methods identified DNM1, MEIS1, and SUSD3 can be regarded as prognostic biomarkers for AML.

Indexed as

Leukemia, Myeloid, AcuteHumansMachine LearningPrognosisRisk FactorsSupport Vector MachineAcute Myeloid LeukemiaeXtreme gradient boostingLASSOMachine learning methodsPan-cancerRFSVM-RFE

Identifiers

PMID38167056
PMCPMC10759623
OpenAlexW4390501740

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.