Evidence map›Paper›PMID 36660731›Full record

ArticleAnnals of translational medicine2022

The potential of glutamine metabolism-related long non-coding RNAs (lncRNAs) as prognostic biomarkers in multiple myeloma patients.

Yun Zhong, Shenghua Xu, Zhe Liu

Open access · diamondAbstract read
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Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 67% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 0 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Yun ZhongDepartment of Lymphohematology and Oncology, Jiangxi Cancer Hospital, Nanchang, China.
Shenghua XuDepartment of Lymphohematology and Oncology, Jiangxi Cancer Hospital, Nanchang, China.
Zhe LiuDepartment of Orthopedics, Jiangxi Cancer Hospital, Nanchang, China.
Jiangxi Provincial Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Glutamine (Gln) metabolism has been confirmed as an important fuel in cancer metabolism. This study aimed to uncover potential links of Gln with long non-coding RNAs (lncRNAs) and the prognostic value of Gln-associated lncRNAs in multiple myeloma (MM) patients. Methods: The RNA-seq expression profile and corresponding clinical data of gastric cancer obtained from Gene Expression Omnibus (GEO) database. Unsupervised consensus clustering was used to cluster MM samples based on Gln-associated lncRNAs. The overall survival (OS), biological pathways, and immune microenvironment were compared in different subtypes. Differential analysis was utilized to identify differentially expressed lncRNAs (DElncRNAs) in different subtypes. A risk model was constructed based on DElncRNAs by using Cox regression, least absolute shrinkage and selection operator (LASSO), and the stepAIC algorithm. Results: We screened 50 Gln-associated lncRNAs and identified 3 molecular subtypes (clust1, clust2, and clust3) based on lncRNA expression profiles. Clust3 subtype showed the worst prognosis and highest enrichment of Gln metabolism pathway. Angiogenesis, epithelial-mesenchymal transition (EMT), and cell cycle-related pathways were relatively activated in clust3. Then, we identified 11 prognostic DElncRNAs for constructing the risk model. The MM samples were divided into high- and low-risk groups with distinct prognosis according to the risk score. The risk score was significantly associated with cell cycle and infiltration of many immune cells. Conclusions: This study characterized the role of Gln-associated lncRNAs in Gln metabolism contributing for tumor-related pathways and immune microenvironment in MM patients. The 11 lncRNAs in the risk model may serve as potential targets for exploring the mechanism of Gln metabolism or serve as potential biomarkers for MM prognosis.

Indexed as

immune microenvironmentlong non-coding RNAs (lncRNAs)Multiple myeloma (MM)risk model

Identifiers

PMID36660731
PMCPMC9843343
OpenAlexW4313364124

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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.