Evidence map›Paper›PMID 35117360›Full record

ArticleTranslational cancer research2020

A comprehensive assessment of the prognostic role of cell adhesion molecules in acute myeloid leukemia.

Jing Cheng, Juan Han, Chunyan Lin

Open access · diamondAbstract read
In one paragraph

Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 21% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Jing Cheng *Department of Blood Transfusion, The First Affiliated Hospital of Soochow University, Suzhou, China.
Juan Han *Department of Laboratory, 904th Hospital of Joint Logistic Support Force of PLA, Suzhou, China.
Chunyan LinDepartment of Blood Transfusion, The First Affiliated Hospital of Soochow University, Suzhou, China.
Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe outcomes for patients with acute myeloid leukemia (AML) have been shown to vastly differ, predominantly due to genetic heterogeneity. Cell adhesion molecules (CAMs) concluding numerous genes play an important role in AML. We aimed to systematically assess the expression characteristics of adhesion molecules and their correlation to the outcomes of AML.

methodA total of 173 patients with AML were enrolled in this study. The genetic expressional information and clinical data sourced in previous studies were collected from the Cancer Genome Atlas (TCGA) database. The expression profiles of 141 CAMs were assessed, and the AML subgroups with specific patterns of expression were identified. The outcomes and clinical features of each AML subgroup were compared to detect the factors associated with prognosis. The differentially expressed genes (DEGs) between each subgroup were identified and the prognostic roles of those molecules were evaluated.

resultsAccording to subgroup clustering, both the primary cluster_1 and subcluster_1 showed a favorable prognosis compared to that of the other patients (26.3

conclusionsThis study showed a landscape of the expression of CAMs in AML and identified a distinct subgroup with a significantly favorable prognosis. We detected that CAMs can assist in distinguishing the cohort with long term survival and constructed two models to predict the prognosis. Those CAMs have the potential to be developed as therapy targets in the treatment of AML.

Indexed as

Acute myeloid leukemia (AML)cell adhesion molecules (CAMs)prognosissubgroup

Identifiers

PMID35117360
PMCPMC8798378
OpenAlexW3115529772

What OpenQuestion holds

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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.